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Re: Recovered old threads- miscellaneous topics

Posted: Fri Apr 22, 2011 2:09 pm
by Crow
Ryoga Hibiki



Joined: 06 Oct 2007
Posts: 3


PostPosted: Sun Oct 14, 2007 7:45 am Post subject: PER, my take Reply with quote
I have to say I've never read JH's books, so I'm basing everything on what I read on the internet and my understanding of the formula as I
found it here

- I'm not a big fan of the idea of having a one for all stat based on boxscores. The numbers we have give us way too few informations to allow us to group them without considering the context. Whatever way we chose for weighting the different factors we have available is going to remove informations instead of adding some, preventing us from really understanding where the numbers are coming from and the real impact they have on the only thing that really matters, contribution to wins. Easy example, I can have two players average the same steals, but seeing them playing I can understand one is cheating and hurting his team, while the other is doing that in the flow of the game and leaves no hole in his defence. Once you put everything in a melting pot like PER you lose any possibility to make further reasoning.

- If I'm understanding the formula properly, it weights every stat in the boxscore using the equivalent points. It might look very scientific at first, but in this way you're ignoring all the hidden effects that are actually correlated with that stat. For instance, a block is usually an indicator for strong inside defence and can change the other team offensive plan, while PER rewards it for less than a possession. I also don't like how fouls are evaluated, in this way it's basically rewarding guys who don't play defence.

- I really think this method overrates scorers, badly, it's not a case that once you isolate the single elements of uPER 70% of point are scoting related, for the top50. The reason it happens, imo, is because the system is not consistent and doesn't apply to made shots the same way it's applied to rebounds, for instance: every made shot causes a change of possession, so there's an opportunity cost related and we should think that without that shot the team would have gotten a VOP out of that possession, shouldn't we? In this way taking dozens of shot with a 0.45 ts% is gonna help PER while hurting the team.

- PER doesn't consider positions, and that's a problem because a center is supposed to rebound more than a guard, it doesn't give the same competitive advantage. Also a weak rebounding center is going to be a liability no matter how good a of a scorer he is. Check this Kaman vs Krstic comparison, who had a better per40min boxscore?

- It's still not clear to me waht PER is actually measuring. I mean, what should I think comparing a guy with PER 22 to a guy with PER 17? Should I conclude the first is clearly better unless the second is just a defensive monster? Or the difference is not enough to reach any conclusion? This is obviously Chris Paul vs Deron Williams, I'm missing what knowing their PER really could add to the discussion. The standard error imo is so high that I really don't know how to use it properly and what's its scope.

- There's actually one thing I like in PER, is the idea of normalizing it to competion making it possible to use it for players in different eras.

What do you think? What kind of weight you actually put in PER?
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deepak_e



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PostPosted: Sun Oct 14, 2007 11:24 am Post subject: Reply with quote
There are lots of limitations to boxscore stats, which you've covered. PER is a way of summarizing, not increasing, the information that's available in those stats. That makes it pretty useful, but it should be one of many tools used by the statistical analyst.

Re: Recovered old threads- miscellaneous topics

Posted: Fri Apr 22, 2011 2:12 pm
by Crow
Tango



Joined: 18 Mar 2005
Posts: 24


PostPosted: Mon Nov 12, 2007 3:29 pm Post subject: Team Possesions Equation Reply with quote
Hi Folks:

I know of two equations for estimating team possessions. They give different values and I'm curious as to which equations folks prefer and why.

The two I know about:

poss = FGA - ORB + TO + (.436 * FTA)
poss = FGA + .4*FTA - 1.07 * ( ORB / (ORB + Opp DRB)) * (FGA-FG) + TO
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deepak_e



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PostPosted: Mon Nov 12, 2007 3:36 pm Post subject: Reply with quote
And in a recent paper, "A Starting Point for Analyzing Basketball Statistics", I believe the following formula was used:

poss = .976 * [ fga + .44*fta + tov - oreb ]
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Tango



Joined: 18 Mar 2005
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PostPosted: Mon Nov 12, 2007 3:49 pm Post subject: Reply with quote
Thanks deepak. Does anyone have some explanation of the differences between the equations above?

I'm also curious what KnickerBlogger uses for possessions. I've been doing some ortg/drtg calcs and noticing how different mine are from his and none of the three equations above give me anything like his values.
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davis21wylie2121



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PostPosted: Mon Nov 12, 2007 3:58 pm Post subject: Reply with quote
I think the best estimator of team possessions is to take the Basketball-Reference definition (Poss = FGA + 0.4*FTA - 1.07*(ORB / (ORB + Opp DRB))*(FGA - FG) + TO), calculate it for both the team and its opponents, and average the two together.
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Tango



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PostPosted: Mon Nov 12, 2007 4:09 pm Post subject: Reply with quote
thanks davis21wylie2121. Why would you average team and it's opponents possessions allowed together?
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davis21wylie2121



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PostPosted: Mon Nov 12, 2007 4:14 pm Post subject: Reply with quote
Because teams alternate possessions, both teams will theoretically have the same # of possessions in any given game. Now, in reality, the team that won the opening tip could also have an "extra" possession at the end of each quarter (such that they would end up with 1-2 more possessions than their opponent), but over the course of a season these things have a tendency to balance themselves out. Because of this, the averaging method has the best chance of matching up with the actual, measured totals for team possessions.

Re: Recovered old threads- miscellaneous topics

Posted: Fri Apr 22, 2011 2:15 pm
by Crow
Most Improved 2008

Mike G



Joined: 14 Jan 2005
Posts: 1794
Location: Delphi, Indiana

PostPosted: Wed Feb 27, 2008 5:19 pm Post subject: Reply with quote
A new front-runner!
Code:
impr per36 rates tm Eff% Sco Reb Ast 2007 Eff% Sco Reb Ast
399 Calderon,Jose Tor .631 19.6 3.7 8.9 .579 15.0 2.8 7.0
394 Mccants,Rashad Min .540 20.6 4.2 2.4 .447 9.3 2.7 1.8
380 Paul,Chris NO .557 23.6 4.3 9.3 .526 18.5 5.0 8.0
348 Farmar,Jordan LAL .575 18.4 3.9 4.1 .511 8.8 3.6 3.3
314 Jefferson,Richard NJ .556 24.5 4.4 2.4 .542 18.2 5.0 2.6

314 Gay,Rudy Mem .548 19.8 5.8 1.4 .489 13.3 6.4 1.4
289 James,Lebron Cle .556 30.7 8.1 5.9 .541 27.6 7.0 5.2
283 Bynum,Andrew LAL .646 21.4 12.9 2.0 .579 14.0 10.8 1.7
278 Garcia,Francisco Sac .565 18.3 5.0 1.8 .542 10.5 4.7 1.7
261 Billups,Chauncey Det .601 26.0 3.4 7.6 .576 20.9 3.8 7.3

Obviously no clear choice (though Calderon appears to be still streaking upward); there are another 18 guys with >200 'improvement credits', so the MIP may not even be one of these 10.

Those who have seriously slipped:
Code:
impr per36 rates tm Eff% Sco Reb Ast 2007: Eff% Sco Reb Ast
-510 Wade,Dwyane Mia .536 24.8 4.3 5.6 .568 30.7 5.0 6.7
-423 Ming,Yao Hou .573 26.8 11.6 2.1 .586 33.8 11.7 2.2
-401 Randolph,Zach NY .505 18.5 12.1 1.9 Por .527 26.2 12.5 2.0
-401 Richardson,Quent NY .434 7.6 6.8 2.0 .526 14.9 9.7 2.3
-384 Okur,Mehmet Uta .515 14.6 8.3 2.0 .560 21.3 10.1 2.0

-383 Davis,Ricky Mia .508 13.3 4.2 2.5 Min .558 18.2 4.2 4.1
-352 Mcgrady,Tracy Hou .496 22.7 5.4 5.3 .507 28.4 6.2 6.8
-342 Gooden,Drew Cle .479 12.7 11.2 1.0 .501 16.4 13.8 1.5
-340 Hinrich,Kirk Chi .493 13.9 4.1 5.8 .552 19.8 4.0 6.3
-336 Carter,Vince NJ .532 20.7 5.9 4.3 .549 26.4 6.4 4.3

___

Re: Recovered old threads- miscellaneous topics

Posted: Fri Apr 22, 2011 2:16 pm
by Crow
ishmael



Joined: 19 Jul 2007
Posts: 6


PostPosted: Thu Dec 06, 2007 4:05 pm Post subject: Hollinger's playoff odds Reply with quote
Here:
http://sports.espn.go.com/nba/hollinger/playoffodds

And some explanation here:
http://sports.espn.go.com/nba/columns/s ... tor-071206

Seems like a step back compared to similar measures as applied to baseball and I would assume that the missing step adds some variation in to the expected winning percentage. Clay Davenport's system does that and comes up with much more logical results.

Now obviously the range between the best and worst baseball teams is absolutely smaller than the range between the best and worst NBA teams, but some of the odds just don't even come close to passing the smell test. And that seems mainly due to lack of imagination in the metric.

Two division leaders at 100% odds to stay that way? Boston with a roughly 4995 out of 5000 chance to win theirs? Chicago at less than a 3 in 5000 chance to win their division or the East? The Bulls are a great example since they started at 3-9 as recently as last season. Orlando (with its 14-6 start in 2007) is another. The Cavs, likewise come out as divison or East champs in fewer than 3 of 5000 simulations.

For reference, here are the BP playoff odds as published on May 10 (twice as many games played for some teams, but roughly 20% of the way into the season):
http://web.archive.org/web/200705101201 ... s_odds.php

Colorado and Philly, despite poor records of 14-19 each had a better than 8% chance to eventually make the playoffs and Philly was at ~7% to win their division. This, IMHO, captures the uncertainty of the sport at such an early stage of the season, while still trying to offer something valuable analytically (an idea of how good these teams ACTUALLY are).

Not sure how Hollinger's metric adds anything of value to the discussion, since common sense tells us that teams that are good now will probably finish with good records (and vice versa). Even the basic media narrative (which takes injuries into account) would be a better basis in my book for assessing current vs. predicted team strength.

If Hollinger wants to go back and look at December 6 standings from the last 10-20 seasons that would be great, but I'm pretty sure my gut here is correct. Here are early December standings for 2004:
http://web.archive.org/web/200312020222 ... /standings

2005:
http://web.archive.org/web/200412070055 ... /standings

2006:
http://web.archive.org/web/200512060407 ... /standings

2007:
http://web.archive.org/web/200612052054 ... /standings

There are some obvious outliers that regression to the mean alone would probably not explain (Clev/Miami in 2004, Jersey/Chicago/Orlando/Memphis/Houston in 2005, Jersey/Minnesota/GS in 2006, Orlando/Toronto/LAL last year). No metric is going to be perfect here, but I think Hollinger's misses out on a lot by interpreting SSS noise as valuable signal.
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Ben



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Location: Iowa City

PostPosted: Sat Dec 08, 2007 1:14 pm Post subject: Reply with quote
I agree with your criticisms, but I like how his predictions extend throughout the playoffs. I wish Baseball Prospectus and others would do that. The Celtics having a 60% chance of winning the title does seem problematic though.

Here are some other predictions:

http://www.coolstandings.com/basketball ... &sim=s&v=l
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94by50



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Posts: 460
Location: Phoenix

PostPosted: Sun Dec 09, 2007 1:26 pm Post subject: Reply with quote
Ben wrote:
I agree with your criticisms, but I like how his predictions extend throughout the playoffs. I wish Baseball Prospectus and others would do that. The Celtics having a 60% chance of winning the title does seem problematic though.

Well, the projector also has them winning 67 games, and being the only 60-win team in the league. I can see how they might be the overwhelming favorite if that happened.

I wonder if the regression to the mean isn't strong enough. It looks modestly reasonable, but not fully reliable...
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John Hollinger



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PostPosted: Mon Dec 10, 2007 3:42 pm Post subject: Reply with quote
To me the regression dilemma depends on which part of the equation you're more focused on -- the final W/L record or the % odds. The coolstandings produce a much more compact league, to the point that a team that plays a quarter of the season with the best point diff of all time still barely squeaks past the 60-win barrier, even though at least one team and usually more clears 60 wins every year; meanwhile it say s nobody will win less than 25, which doesn't seem plausible. That's with a quarter-season of data in hand, 10 games ago it was even more compressed. You can say their % odds are more reasonable because they factor in that things can and do change, but I think the W-L is much less reasonable.

That said, both these tools are built more for March than December. The real idea behind this tool is to evaluate what a team's playoff chances are based on differences in remaining schedules; be interesting to see how the two models converge as the year goes on.
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Ben



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PostPosted: Mon Dec 10, 2007 5:18 pm Post subject: Reply with quote
John Hollinger wrote:
To me the regression dilemma depends on which part of the equation you're more focused on -- the final W/L record or the % odds. The coolstandings produce a much more compact league, to the point that a team that plays a quarter of the season with the best point diff of all time still barely squeaks past the 60-win barrier, even though at least one team and usually more clears 60 wins every year; meanwhile it say s nobody will win less than 25, which doesn't seem plausible. That's with a quarter-season of data in hand, 10 games ago it was even more compressed. You can say their % odds are more reasonable because they factor in that things can and do change, but I think the W-L is much less reasonable.

That said, both these tools are built more for March than December. The real idea behind this tool is to evaluate what a team's playoff chances are based on differences in remaining schedules; be interesting to see how the two models converge as the year goes on.


John, have you considered 5th, and 95th percentiles instead of best and worst? It might be more instructive.

Nobody winning less than 25 might be implausible - but how about Boston with less than a 1 in 5000 chance at finishing with fewer than 57 games? What is your goal at this point if it were a contest? Lowest root mean square error in predicting wins?

The current leader in expected wins would presumably have fewer expected wins than the expected wins of the regular season "champ" (assuming that one team doesn't win 100% of simulated seasons.) That could lead to predicted standings closer to what we're use to even if you don't get that by looking at the expected values of the teams.

I will be visiting the page regularly - it's quite fun. And if the 60% is right, then there's a lot of money to be made in the futures markets...
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mtamada



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PostPosted: Mon Dec 10, 2007 8:34 pm Post subject: Reply with quote
John Hollinger wrote:
The coolstandings produce a much more compact league, to the point that a team that plays a quarter of the season with the best point diff of all time still barely squeaks past the 60-win barrier, even though at least one team and usually more clears 60 wins every year;


True, but those teams with the top records invariably have an element of luck involved (obviously they have a lot of talent and quality in addition to the luck). There probably could indeed be more than one team with 60+ wins, but that does not mean that it is good practice to pick several teams to do that. Good predictions this early in the season SHOULD be "compact".

For baseball, all of the predict playoff odds that I see still seem insufficiently regressed to the mean. I haven't looked in detail the the basketball playoff odds, the ones at CoolStandings.com do indeed appear to be too extreme. A lot can, and will, happen between now and the end of the season.
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Ryan J. Parker



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PostPosted: Mon Dec 10, 2007 11:28 pm Post subject: Reply with quote
We can re-evaluate this come March, but it's hard to put a lot of stock in some of the odds.

If the Celtics had a true 59.6% probability of being champions then you should probably go to Vegas and make a max bet on the Celtics getting 7 to 2.

I'm sure there is a lot of variability here, but I don't see that quantified anywhere on the page.
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mtamada



Joined: 28 Jan 2005
Posts: 187


PostPosted: Tue Dec 11, 2007 12:13 am Post subject: Reply with quote
Ryan J. Parker wrote:
I'm sure there is a lot of variability here, but I don't see that quantified anywhere on the page.


Yeah, that's why we need to do a fairly large amount of regression to the mean. If we KNOW the Celtics will win 80% of their games the rest of the way, it's fairly easy to calculate their expected wins and probability of making the playoffs.

But we don't know that the Celts will win 80% of their games the rest of the way. We can only ESTIMATE what their true probability is.

A good practice when reporting estimates is to report their standard error. With probabilities, an often easier, more straightforward calculation is to regress the estimated probabilities to the mean, with the amount of regression based on the degree of uncertainty about the true probability. If we had zero uncertainty, we'd do no regression at all and use p = 0.80. If we had total uncertainty (e.g. we were dealing with coins that we knew were fair), we'd take the Celtics' "90% heads" and regress it totally, to p = 0.50.

Obviously the optimal estimate will be somewhere in between ... where that optimum is, I don't know, but most of the playoff estimates that I see at CoolStandings (and in baseball, at BaseballProspectus.com) make early-season estimates that seem way too extreme to me, and insufficiently regressed to 0.50.
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Ryan J. Parker



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PostPosted: Tue Dec 11, 2007 12:26 am Post subject: Reply with quote
mtamada, I don't understand what you mean by regressing this data to the mean. I understand the concept of regression to the mean, but what sort of process do you speak of here? I don't want to get this thread off track, so maybe a link or two would help me. Smile
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CoolStandings



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PostPosted: Tue Dec 11, 2007 12:37 am Post subject: Reply with quote
Hi guys,

I agree that the regression factor is a key ingredient. For our MLB odds we've gone back to 1903 to tune our model, trying to optimize several parameters, one of them being the regression coefficient and how it changes throughout the season.

For the NBA model we've only gone back a few years, but are currently adding previous years to the model to improve it, while taking into account the scoring changes over the past decades. The regression coefficients for the NBA and MLB are of course quite different.

We look at both expected wins and playoff percentages as criteria, since neither one alone is sufficient. For example, our "dumb" mode assumes every team is a .500 team and has a 53.3% chance of making the playoffs at the start of the season. And at the end of the year, sure enough, 53.3% of all teams made the playoffs! But of course, the expected wins are way off. Our "smart" mode (the default mode) tries to get both the expected wins and playoff odds closer to reality.

At the beginning of the season we use last year's numbers as a starting point, and gradually lessen their impact as the season moves on. At this point there is little residue left from last year, but for the first few games of the season the playoff odds are not accurate for teams undergoing huge roster changes (thank you Danny Ainge!).

It will definitely be interesting to see how our numbers converge with John's as the year goes by.

Greg
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tenkev



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PostPosted: Tue Dec 11, 2007 8:59 am Post subject: Reply with quote
Do his odds include the probabilities of injuries to star players?

According to Hollinger's odds the Magic have a 100% chance of making the playoffs. What if Dwight Howard has a season ending injury tomorrow? Are they still guaranteed to make the playoffs? Doesn't seem intuitively right to me for any one team to have a 100% chance of making the playoffs this early in the season.
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Ryan J. Parker



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PostPosted: Tue Dec 11, 2007 9:06 am Post subject: Reply with quote
tenkev wrote:
Do his odds include the probabilities of injuries to star players?


I don't believe anyone has ever found a way to predict injuries, but of course it is possible. Smile

That said, he makes the disclaimer that this assumes the teams play as they're playing now. As such, it's definitely less than 100%.
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Ben



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PostPosted: Tue Dec 11, 2007 10:23 am Post subject: Reply with quote
tenkev wrote:
Do his odds include the probabilities of injuries to star players?

According to Hollinger's odds the Magic have a 100% chance of making the playoffs. What if Dwight Howard has a season ending injury tomorrow? Are they still guaranteed to make the playoffs? Doesn't seem intuitively right to me for any one team to have a 100% chance of making the playoffs this early in the season.


To my mind, that's part of the reason for the regression to the mean. Some argue that Hollinger doesn't go far enough, but nobody's really done the empirical work to suggest what the percentages should be.
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mtamada



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PostPosted: Tue Dec 11, 2007 4:33 pm Post subject: Reply with quote
Ryan J. Parker wrote:
mtamada, I don't understand what you mean by regressing this data to the mean. I understand the concept of regression to the mean, but what sort of process do you speak of here? I don't want to get this thread off track, so maybe a link or two would help me. Smile


Probably the best place to poke around is Tango Tiger's baseball site, and the book that he wrote with MGL and Dolphin, _The Book_. In a quick search I didn't find an ideal link, but here's one where he utilizes (but doesn't describe in detail) the basic formula: http://www.insidethebook.com/ee/index.p ... rike_zone/
or
http://tinyurl.com/34ehx7

Here's a more in-depth explanation, but somewhat hard to follow because it's copied and pasted from his contributions to a blog discussion:
http://www.insidethebook.com/ee/index.p ... s_leagues/
or
http://tinyurl.com/2ll6yq

Here's a lengthy discussion of regression to the mean and baseball playoff probabilities, but he doesn't actually utilize a formula:
http://www.insidethebook.com/ee/index.p ... _playoffs/
or
http://tinyurl.com/2qrpk9
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Eli W



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PostPosted: Tue Dec 11, 2007 5:12 pm Post subject: Reply with quote
You also might want to check out this thread by Ed Küpfer discussing how to apply the techniques to basketball:

http://sonicscentral.com/apbrmetrics/vi ... .php?t=639

I plan to discuss regression to the mean in my blog at some point, but I'm not sure when that will be.
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mtamada



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PostPosted: Tue Dec 11, 2007 6:38 pm Post subject: Reply with quote
Eli W wrote:
You also might want to check out this thread by Ed Küpfer discussing how to apply the techniques to basketball:

http://sonicscentral.com/apbrmetrics/vi ... .php?t=639

I plan to discuss regression to the mean in my blog at some point, but I'm not sure when that will be.


Thanks Eli, I should've remembered that posting of EdK's.

Speaking of which, on Phil Birnbaum's sabermetricresearch.blogspot.com blog, an "edk" commented on the NBA home field (sic) advantage post, and an "ek" commented on the pace vs defensive efficiency post, so I wonder if that's EdK popping up on that sabrmetric blog (albeit on the NBA-related topics).

Re: Recovered old threads- miscellaneous topics

Posted: Fri Apr 22, 2011 2:35 pm
by Crow
Charles



Joined: 16 May 2005
Posts: 134


PostPosted: Fri Oct 26, 2007 3:39 pm Post subject: Are top-rated point guards under-rated by PER? Reply with quote
First of all, there is no question that summary stats like PER have value. They are easy to read, incite discussion among fans and help promote the game. They also have some use on the analytical side, where they can be helpful in tracking an individual's career development. Suppose a player's scoring drops three points a game, but his offensive rebounding and scoring efficiency increase. Is he still as valuable to the team, or has he begun to decline? In providing a rough summation of a player's contributions, PER offers a reasonable starting point for answering this kind of question.

However, when it comes to comparing players - especially, players with different roles - this type of stat can be very misleading. After all, it's possible that a logic based system might do a solid job of comparing the value of free throw to that of a field goal or even estimating the value of an isolated event like a steal. But, how do you "logic out" the value of an individual defensive rebound or an assist? You can't. Those kinds of questions require more than logic; they require research.

This is the problem. Although, a rational stat like PER might appear to be scientific, the best it can really do is mimic the opinions of its author. Those opinions may be well informed and the formula may be well thought out. But, concrete answers require empirical evidence.

-----------------------------------

Okay, are point guards under-rated by PER? I think they are ridiculously under-rated.

Let's start with the basic: how much is an assist worth? For each assisted field goal PER assigns one-third of the credit to the assister and two-thirds of the credit to the scorer. The rationale behind this is that the play maker has done one thing (pass), while the finisher has done two things (get open and make the basket.)

But, is that a reasonable basis upon which to assign weights?

Dwyane Wade drives through the paint and begins his upward trajectory to the rim enticing both his own defender and the weak side help to leave their feet. Then, at the last instant, Wade flips the ball behind his back to Udonis Haslem for a quick little, jack-knife slam. Wade is credited with an AST, Haslem with a FGM and the PER translation is one-third credit to Wade, two-thirds credit to Haslem. But, Wade has utilized a rare talent, while Haslem has simply completed a play that should be routine for any competent NBAer. Wade clearly deserves more of the credit in this sequence.

To Hollinger's credit, he acknowledges this as a problem in the introduction to PER. Unfortunately, rather than dealing with the issue empirically, he defends the weights by stating that "point guards are not under-represented among the top-rated players." Well, if there is evidence to that affect, perhaps it would be a defense, but (as far as I know) no evidence is offered. It seems as if the "galvanizing" evidence is simply that PER succeeds in echoing Hollinger's opinion on the value of point guards.

Now, checking your output against expert opinion is certainly worthwhile. But, rather than just rely on just the author's opinion, let's see how PER's assessment of top-rated point guards stacks up beside some objective expert opinion.

Ten point guards made the Hall of Fame's "NBA's 50 Greatest Players" list. Here they are along with their best single season PER (pre-1988 PERs are taken from the estimates at http://www.basketball-reference.com/about/per.html.)

Code:
Player MaxOfPER
Nate Archibald 25.2
Dave Bing 22.5
Bob Cousy 21.6
Walt Frazier 21.6
Magic Johnson 27.0
Earl Monroe 19.3
Oscar Robertson 27.6
John Stockton 23.9
Isiah Thomas 22.2
Lenny Wilkens 20.3


The base PER is 15.0, so those are fairly high numbers. But, considering that the season best PER over the past twenty years has averaged 29.8, are they high enough? Here is where each HOF point guard's best season ranks among the best PER seasons of all players at all positions.

Code:
Player Rank of best single season PER
Nate Archibald 137th
Dave Bing 378th
Bob Cousy 492nd
Walt Frazier 504th
Magic Johnson 65th
Earl Monroe 1,038th
Oscar Robertson 55th
John Stockton 222nd
Isiah Thomas 404th
Lenny Wilkens 753rd


And, here are some newer additions based on ESPN.com "10 greatest point guards of all time."

Code:
Jason Kidd 375th
Steve Nash 228th
Gary Payton 252nd



I'm not a fan of the "smell test", but that seems to be the basis of validation for logic based, summary stats, so let's check the aroma from PER's ratings of elite point guard.

1. McGrady, Wade (twice), Nowitzki (twice), Kobe and LeBron have all registered PERs at least one to three points higher than any of Magic Johnson's MVP seasons. Magic Johnson. Magic.... Johnson.

After Magic and Oscar - who get a lot of PER for being the two top rebounding point guards of all-time - things get really hairy (btw, according to B-R.com the top five rpg guards (no G-Fs) of all time are... Oscar, Magic, JKidd, Fat Lever and Walt Frazier... all point guards. Oscar and Magic both averaged better than one rebound a game more than MJ or Clyde Drexler.) All right, continuing...

2. Amare Stoudemire and Elton Brand both have PERs which rank among the top one hundred of all time, while John Stockton can't crack the top two hundred... Isiah Thomas and Clyde Frazier can't even crack the top four hundred. You might convince me that Karl Malone should rank ahead of John Stockton, but PER is telling me that Elton Brand (26.5) is a much more valuable player than John Stockton (23.9) or Isiah (22.2). Really?

3. Bob Cousy's best PER was 21.6 placing him in a tie for 492nd place with, among others, -- Larry Hughes. Yes, the same Bob Cousy who was MVP and made first team All-NBA ten consecutive seasons. Let's see Cousy or Hughes? Hughes or Cousy...? Well, at least Cous gets more respect than "50 Greatest of All-time" teammate Earl Monroe. Earl the Pearl's best PER weighs in at one thousand and thirty eighth -- same spot as Ruben Patterson and Matt Harpring.


If PER does not under-rate elite point guards then the history of the NBA needs a serious re-write.
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bchaikin



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PostPosted: Fri Oct 26, 2007 5:03 pm Post subject: Reply with quote
Bob Cousy's best PER was 21.6 placing him in a tie for 492nd place...

i'm certainly not saying bob cousy was not a great basketball player - he was. but if you are looking for a statistical reason as to why he doesn't rate higher in a statistically based rating system, one key reason would be his shooting. looking at his best 10 year stretch, say 1951-52 to 1960-61, his FG% was .373 when the league average was .388, and his ScFG% (overall shooting) was .441 when the league average was .444....

during those 10 years he scored 19.7 pts/g, and if you look at the 20 players who averaged at least 17 pts/g during this same time period, cousy had the worst FG% and worst ScFG%...

on the other hand, a PG like john stockton for example, although not the scorer cousy was, did score 15.6 pts/g during his best 10 year stretch, but shot overall a ScFG% that was 8% better than the league average...
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Conan the Librarian



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PostPosted: Fri Oct 26, 2007 6:27 pm Post subject: Reply with quote
The issue of shooting % raises another interesting question about stats like PER as they relate to point guard. PG's, more so than any other position, are likely to take lower % shots with the clock winding down, simply by virtue of handling the ball more. This is not universally true, and I'm not sure how big an impact it would have on shooting %, but it certainly does have an impact. Are PG's to be punished for taking the only available shot late in the clock, even if it is a bad one, rather than risk a 24-second violation and a turnover?
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Chronz1



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PostPosted: Fri Oct 26, 2007 9:35 pm Post subject: Reply with quote
Ive always agreed with what you mentioned about dishing, there is no stat that is more opinionated than the assist as some are clearly worth more than others. I like to give both players equal credit but I know there are cases when the shooter did more of the work and the creator did more.

So yea PER does seemingly underrate PG's but much of what they do is intangible, they are looked upon as leaders but does it really end only at PG's? What about players who rack up alot of assist but are also scorers. Say Joe Johnson vs Michael Redd. JJ is obviously not the scorer Redd is but hes more of an all around threat. Personally I think Redd is the better player I just find it odd that his PER is that much higher than JJ's.

Im curious as to how much better PG's would be rated if they were given 2/3 the credit.
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Mountain



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PostPosted: Fri Oct 26, 2007 10:18 pm Post subject: Reply with quote
PER formula tends to place scorers high and that may disadvantage pass first or traditional PGs especially those from much earlier eras.

Career PER list for PGs depends on filter, using 20,000+ minutes I see the rank order of Magic, Robertson, Stockton... Iverson, K Johnson, Nash, Cassell, Brandon Cousy,
Price, Marbury, Frazier, Kidd, Payton. Hardaway, Billups, Francis, I Thomas, Rod Strickland Archibald Lever, Terry, Bing, A Hardaway, Porter, Monroe Bibby Wilkens, etc.

Last edited by Mountain on Sat Oct 27, 2007 3:56 pm; edited 1 time in total
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Charles



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PostPosted: Fri Oct 26, 2007 11:39 pm Post subject: Reply with quote
bchaikin wrote:
Bob Cousy's best PER was 21.6 placing him in a tie for 492nd place...

i'm certainly not saying bob cousy was not a great basketball player - he was. but if you are looking for a statistical reason as to why he doesn't rate higher in a statistically based rating system, one key reason would be his shooting. looking at his best 10 year stretch, say 1951-52 to 1960-61, his FG% was .373 when the league average was .388, and his ScFG% (overall shooting) was .441 when the league average was .444....

during those 10 years he scored 19.7 pts/g, and if you look at the 20 players who averaged at least 17 pts/g during this same time period, cousy had the worst FG% and worst ScFG%...


True. However, very few of those scorers were guards. And the problem is that while Cousy's best PER of 21.6 stacks up well against other point guards, it stacks up poorly against players at other positions. Oscar Robertson's 1960-61 was the only PER by a guard higher than Cousy's 21.6 during the period you define, but there were 53 higher PERs recorded by forwards and centers.

Bob Cousy was the dominant passer and dominant guard for a decade, but his best PER rates 492nd? There must be some reason these point guards were voted among the 50 Greatest players of all-time. PER just doesn't get it.
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Charles



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PostPosted: Fri Oct 26, 2007 11:45 pm Post subject: Reply with quote
Conan the Librarian wrote:
The issue of shooting % raises another interesting question about stats like PER as they relate to point guard. PG's, more so than any other position, are likely to take lower % shots with the clock winding down, simply by virtue of handling the ball more. This is not universally true, and I'm not sure how big an impact it would have on shooting %, but it certainly does have an impact. Are PG's to be punished for taking the only available shot late in the clock, even if it is a bad one, rather than risk a 24-second violation and a turnover?

That's a good point Conan

Here are a few other reasons elite point guards are under-rated by PER:

1. While the ball is being advanced in the back court and a play set up there is very little good that can happen statistically from the ball handler's perspective. It's generally either a turnover or nothing.

2. The one-third weight given assists is much too low for many elite play-makers because the impact of individual assists on team scoring is distinctly non-linear.

3. There is no allowance for the psychological boost provided by strong floor leadership. It may be difficult to quantify, but, in a fast paced game like basketball where it is impossible to maintain one hundred percent effort every second of every game, motivation is a huge factor. This responsibilty often lies primarily with the point guard and, just as in every walk of life, some ball players are simply more inspirational than others.

4. I can hear the teeth gnashing over number 3, so here is one that may hold more appeal to people focussed strictly on manipulating traditional stats.

PER assumes that each shot has the same likelihood of being assisted as the team's over-all AST/FGM rate. However, point guards typically have a lower percentage of their shots assisted than the team average.

For example, 63% of Phoenix' baskets were assisted. So, PER assumes that 324 of Steve Nash's 517 made field goals benefited from an assist. But, according to 82games.com, only 23% (or 119) of Nash's baskets were actually assisted. Therefore, one-third of the credit for 205 of Nash's baskets went to a "phantom" assister. That's 137 points that should have stayed in Nash's column.

That's a rather large error and, of course, it's not just Nash. B.Davis (26%), Billups (33%), Miller (29%), Paul (17%), Ridnour (29%), Arenas (36%) and Parker (33%), as well as a few play-making, non-PGs like Wade (27%) and Iverson (26%) are significantly affected by this. Chris Paul had more than two hundred points siphoned off to teammates for phantom assists.

This obviously works in reverse for players who have a higher percentage of their baskets assisted than the team average.
.
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Kevin Pelton
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PostPosted: Sat Oct 27, 2007 12:46 am Post subject: Reply with quote
Charles wrote:
PER assumes that each shot has the same likelihood of being assisted as the team's over-all AST/FGM rate. However, point guards typically have a lower percentage of their shots assisted than the team average.

An excellent point.

From one of my old posts on the subject:

"Another interesting pair is Steve Nash (assisted 20% of the time) and Shawn Marion (73%). By PER, they were basically equivalent per-minute; 21.55 and 21.17, respectively. Add in assists and Nash has a commanding 23.61-20.21 advantage."
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Conan the Librarian



Joined: 03 Sep 2007
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PostPosted: Sat Oct 27, 2007 2:02 am Post subject: Reply with quote
Quote:
PER assumes that each shot has the same likelihood of being assisted as the team's over-all AST/FGM rate. However, point guards typically have a lower percentage of their shots assisted than the team average.

For example, 63% of Phoenix' baskets were assisted. So, PER assumes that 324 of Steve Nash's 517 made field goals benefited from an assist. But, according to 82games.com, only 23% (or 119) of Nash's baskets were actually assisted. Therefore, one-third of the credit for 205 of Nash's baskets went to a "phantom" assister. That's 137 points that should have stayed in Nash's column.

That's a rather large error and, of course, it's not just Nash. B.Davis (26%), Billups (33%), Miller (29%), Paul (17%), Ridnour (29%), Arenas (36%) and Parker (33%), as well as a few play-making, non-PGs like Wade (27%) and Iverson (26%) are significantly affected by this. Chris Paul had more than two hundred points siphoned off to teammates for phantom assists.

This obviously works in reverse for players who have a higher percentage of their baskets assisted than the team average.


Couldn't this be at least partially addressed by determining the average % of baskets assisted for each position, and weight for assist credit accordingly? I know this gets into gray areas about positional definitions (is Wade a PG or SG?), but it seems like it might be a worthwhile endeavor if no one's done it before.
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benji



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PostPosted: Sat Oct 27, 2007 3:07 am Post subject: Reply with quote
Why not just use the 82games value?

The PERs I got are slightly off from Hollingers and elsewhere, but this is just for a quick example:
nash,steve 23.6
stoudemire,amare 23.0
marion,shawn 20.7
barbosa,leandro 18.3
diaw,boris 12.9
bell,raja 11.9
thomas,kurt 11.4
banks,marcus 11.2
jones,james 10.7

Using assisted rate from 82games.com, I get:
nash,steve 26.0
stoudemire,amare 22.8
marion,shawn 19.8
barbosa,leandro 18.3
diaw,boris 13.2
banks,marcus 12.3
bell,raja 11.2
thomas,kurt 10.9
jones,james 9.8
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Mike G



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PostPosted: Sat Oct 27, 2007 7:35 am Post subject: Reply with quote
I don't know when the basics of PER were being hatched out, but not that long ago PG's were very well represented in annual player rankings.

Using b-r.com's Player Stats Search, I looked for best PER seasons by PG's. Actually I selected the position "Guard" and looked for seasons with at least 2000 minutes and an Ast/40 rate of >6. This lets in a few Jordan and Wade seasons, but they were defacto points anyway, for many of their minutes.

If PG is an 'underrated' position, then on average there might be fewer than 10 PGs among the top 50 PER's, fewer than 20 among the top 100, etc. Actually, by my operational definition (>6 Ast/40), I've added a positive attribute, and I expect more than 1/5 of any slice to be PG's (so defined).

In the 5-year interval 1988-1992, the 20th best PER among 'PG' was 22.2, while the 100th PER among all players was 19.7. So we might say the positional-median #20 season was 19.7, which was 2.5 PER points lower than the #20 PG season.

Code:
1988-92 1993-97 1998-02 2003-07
#20 22.2 20.6 20.7 21.0
100 19.7 19.7 19.9 21.0
PG 2.5 .9 .8 .0

#40 18.4 18.5 18.4 18.6
200 17.4 17.3 17.5 18.1
PG 1.0 1.2 .9 .5

#60 17.4 16.9 16.6 16.6
300 15.9 16.0 15.8 16.2
PG 1.5 .9 .8 .4

#80 16.0 15.9 15.0 15.1
400 14.8 14.8 14.4 15.0
PG 1.2 1.1 .6 .1

100 15.4 14.5 ---- 12.9
500 13.3 13.6 12.3 13.3
PG 2.1 0.9 -0.4



At every level, the PER status of point guard has fallen steadily since the late-'80s. I am pretty sure there was just a wealth of talent at the position, until it fell off in the mid-late-'90s. (From '98-'02, there weren't even 100 qualifying PG seasons, and I pro-rated 1999).

John H may have tested his PER formula on the recent past and found plenty of PG's among the best players every year. At that time, perhaps the late '90s seemed like an anomaly that would pass; but in fact, the trend has continued. Top-notch PG's are fewer, and 2nd-tier and middling PG's are in even shorter supply than 10 or 20 years ago.

Note that with expansion, total numbers of players at any PER level have risen. This is part of the PER definition. Counter to that trend, PG PER's have dropped in number. Per-team, that trend is even worse.
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Harold Almonte



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PostPosted: Sat Oct 27, 2007 10:36 am Post subject: Reply with quote
Quote:
1. While the ball is being advanced in the back court and a play set up there is very little good that can happen statistically from the ball handler's perspective. It's generally either a turnover or nothing.

Quote:
PER assumes that each shot has the same likelihood of being assisted as the team's over-all AST/FGM rate. However, point guards typically have a lower percentage of their shots assisted than the team average.



The A/FGM and even Assists weights is something that could probably be fixed some day by linear ratings. But about the first point, Ratings compute points and gain/lost possessions translated to points (one final economical result, not the total cost of each part involved in the process). There´s no value nor sharing for keeping the possession saved or processing it (ballhandling, the relationship Passing (Pot. assists)/TO, etc.) and that´s assumed like intangibles (in other words, pont guarding is assumed like intangible). Some ratings adjust for no tangible defense (also pseudo-arbitrary), probably an offense adjust could be also made in this case, but something better and wider than "overall A/FGM rate". At least PER´s usage is a step ahead from others ratings.
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UGA Hayes



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PostPosted: Sat Oct 27, 2007 1:18 pm Post subject: Reply with quote
I don't have much background in statistics, but I too have thought PG are underrated by PER. I've wondered why you guys don't consider factoring in team stats into your individual evaluation.

To me there is a stat that doesn't really exist for PG but is critical to their evaluation

If you watch the Suns, Nash holds the ball for an inordinate amount of time during a teams's possesion. As a result he accumulates a lot of assists and turnover BUT his teams are perenially among the lowest in Turnovers. Shouldn't he get dome sort of statistical brownie point for that.

Ideally there would be a stat that somehows incorporate team offensive effeciency and TO per time the ball is in a player's hand.

IMO there is a chance that Hollinger's dictum that players can't make other players better is wrong. From what I remember he used PER to make that assertion, but in my opinion the equation for PER has some potential ways of masking this phenomenon of "making teammates better". I'd love to see Hollinger repeat his study b/c I suspect guys who maintain the same PER with different PG might see an increase in their own TO, which is masked by their increase in usage rate. If a PG reverses those two stats isn't he really making others better, even if they maintain the same PER.

Couldn't this explain why the AI to Denver experiment didn''t work the way he expected. After all isn't there only so much "usage rate" to go around. I would expect the aggregate usage rate of teams across the NBA to be pretty universal regardless of personnel. Isn't their value to the PG playing alchemist and figuring out who to distribute to in hope of finding gold (i.e higher tema percentages lower TO, something that wouldn't be attributed to the PG in PER)

I'm curious to know how statistician feel about this.
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asimpkins



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PostPosted: Sat Oct 27, 2007 2:34 pm Post subject: Reply with quote
I think you're definitely on to something when you talk about how players can influence each other in multiple ways -- such as decrease turnovers but decrease usage rate as well. Too often people focus on one aspect and then make an overall claim.

I think Hollinger is still more right than wrong about the "making players better" argument because it is ultimately too simplistic. Players influence each other in a lot of ways and it's usually not a simple better or worse outcome.

Shaq gives you better shots but fewer of them. He's not making you better or worse as much as he's changing your role. Joe Johnson left the Suns for Atlanta and his shooting percentage went down but his usage went up. His role changed.

If you took a terrible player and substituted him in for Kobe Bryant a lot of the Lakers would get better because they'd suddenly have a lot more possessions to use their talents. But you'd never see that terrible player get credit for making his teammates better.
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Mountain



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PostPosted: Sat Oct 27, 2007 3:48 pm Post subject: Reply with quote
Mike I wonder what your PG vs all positions comparison thru time would show on assist/40 for PGs and for 3 point shot frequency and accuracy for PGs and everyone thru the distance changes and perhaps also FTAs for each thru changes in handcheck enforcement if that can be segmented agreeably.

Did the 3 point shot and the increase in the number of players who could take them proficiently make PGs somewhat less valuable as a passer? Has it cut into their usage?

Does the decline of post players also contribute to a decline in PG PER value from assists?

Would you have interest in breaking out the parts of the PG PER decline?

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thref23



Joined: 13 Aug 2007
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PostPosted: Tue Oct 30, 2007 1:24 am Post subject: Reply with quote
From a logical standpoint, I am not sure why PGs would be underrated more than players at any other position. PER has obvious and unavoidable imperfections, most notably that the stats utlized to create PER are insufficient to measure defensive performance. My point being - many elite PGs are known for their offensive ability - and not their defensive ability. After all, if one were to assume that defense from the PG position was as important as offensive impact, then Steve Nash wouldn't come close to being one of the league's best players. He might be a borderline allstar.

So if PER lacks a relevant defensive measure, and many PGs can be measured more accurately than any other position simply by measuring offensive performance, I would hypothesize that the PG position generally is measured more accurately than any other position using PER.

But that would mean that PGs would be less likely to be overrated by PER as much as they would be less likely to be underrated. I think the question here perhaps should not be "are elite PGs underrated by PER," but rather, something to the extent of "are elite bigs overrated by PER," or something to the extent of, "does PER overweight rebounding slightly."

I'll also add that, if evaluated by players' career PERs, elite PGs are not as noticeably underrated as they are when measuring all-time single season best PERs.

For what its worth, I not too long ago put together overall player composite scores (for 2006/7) which combined a barrage of time position and to a lesser extent team adjusted stats including +/- and counterpart PER. PER itself counted for 20% too, but only for 20%. The concept was founded by Jon Nichols' who has posted here and posted his own defensive and offensive composite scores. I came up with my own modifications and put together my own scores which I feel came out accurately enough as I felt they did a good job of explaining teams' win/loss records (described in a separate thread).

Anyways, if the scores I came up with are considered accurate, then by comparing composite score rank to PER rank, we are able to try and statistically guage which players were most overrated or underrated by PER last season. I find that the 10 most overrated are, in order:

warrick,hakim
curry,eddy
harrington,al
mohammed,nazr
nocioni,andres
okur,mehmet
villanueva,charl
szczerbiak,wally
randolph,zach
humphries,kris

All score low defensively, and almost all could be considered PF/Cs (which is somewhat expected, as I consider defense more relevant from front court positions, especially from the center position, and that factored into composite scores). Hakim Warrick and Eddy Curry, btw, both score more than 150 spots higher in rank by PER versus their overall score rank.

As far as the players most underrated by PER, in order:

battier,shane
bowen,bruce
bell,raja
alston,rafer
parker,anthony
najera,eduardo
garbajosa,jorge
pavlovic,sasha
ross,quinton
posey,james

All are known as good defensive players, and all are decent shooters except for Quinton Ross and Najera. Shane Battier scores 188 spots higher in overall rank versus PER rank. Ironically, only three guys on the list could be considered front court players.

Far as PGs, the most underrated:

alston,rafer
jones,damon
duhon,chris
fisher,derek
gibson,daniel

(all are decent shooters)

the most overrated:

rodriguez,sergio
ford,t.j.
knight,brevin
banks,marcus
tinsley,jamaal

(none are good shooters)

Steve Nash, Deron Williams, Jason Kidd, and Chris Paul are shown to be very, very slightly underrated by PER last season
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Mike G



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PostPosted: Tue Oct 30, 2007 4:52 am Post subject: Reply with quote
Statman wrote:
Mike G wrote:

It just seems to me that players shoot well because they are good shooters; or not. Shooting 38% in a 38% era is no better than shooting 38% in a 48% era.


While I agree with the vaste majority of things you normally say - this I completely disagree with. Obviously (to me) shooting 38% in a 38% era is better than 38% in a 48% era - since your points per possession relative to the league is better.

Now - if you are making a point about shooting ability - I'd also disagree, but to a lesser extent. Part of the reason league shooting %'s were so much lower in the past can be related to many factors outside of shooting ability. Much harder rims, much more physical defensive play, coaches not having yet developed decent offensive sets to get players better open looks, no illegal defense, etc.

Bob Cousy shooting 38% in a 38% shooting league is a whole lot better than a player shooting 38% in a 48% league in my book.

I gather that you disagree with something here!
But really, neither of us knows how Cousy would do in a league 50 years along. Nor whether Tony Parker would drive with impunity against Macauley and Loscutoff.
Coaching wasn't as advanced. Doesn't that mean the game wasn't played to quite the level it is now? Aren't jump shots better than set shots? Aren't guys who can dribble with either hand better than those who can't?
Would Cousy shoot as well as the league around him? He peaked at .397 in 1955, when his team was making .398. He again hit .397 in '63, when his team was hitting .426. This on a team with many options.

Now, if your point is that a player or a team will be more successful when shooting better than the competition, of course this is correct. But I think it's even more absolutist to say the league is always equally competitive, regardless of wholesale changes. When Cousy won MVP in 1957, there was exactly one good black player in the NBA, and he was on the same team.

I have no reason to believe NBA offenses have evolved any more effectively than defenses have. If anything, better coaching means advantage: defense.
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Charles



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PostPosted: Wed Oct 31, 2007 11:52 am Post subject: Reply with quote
Suppose winning the Finals MVP goes to Tony Parker's head and he decides he will take a couple of extra shots per game this season. These extra shots figure to be more difficult, so let's further suppose that Parker is only 80% as effective on them. The result is that while Parker's scoring increases from 18.6 to 20.6, he loses about half an assist per game and his true shooting percentage drops from .572 to .559.

Is this a good change for Parker? It's certainly good for his contract negotiations. Not only has he moved above the magical 20 ppg mark, his agent can also argue that his PER has risen a point to 22.1.

Is this a good change for the Spurs? Well, although Parker figures to score more as an individual, the Spurs figure to score 35 points less as a team. So, unless Parker can maintain something closer to his current efficiency, he shouldn't be taking those shots (not to mention, point guards who take too many shots risk alienating their teammates.)

I understand that jacking up a player's PER based on points per game helps keep it in sync with public perception, but, in the real world, forcing up bad shots does not make you a more valuable player.




Ps. with an extra ten extra shots a game (at 80% efficiency) Parker would move into Allen Iverson territory with 29.0 ppg, a below average TS% and a PER of 25.4. Then he'd be even more valuable to the Spurs than Tim Duncan... PER-wise.
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asimpkins



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PostPosted: Wed Oct 31, 2007 12:09 pm Post subject: Reply with quote
Charles wrote:
I understand that jacking up a player's PER based on points per game helps keep it in sync with public perception, but, in the real world, forcing up bad shots does not make you a more valuable player.


But it all depends. If he's forcing up bad shots when his teammates could have taken better ones then it is bad. But if he's putting up bad shots when his teammates would have done even worse then it is a good thing.

It all depends on when the bad shots are happening and what the team's needs are. But this information is not available to PER because it's not available in the box score. So PER attempts to make the best one-size-fits-all estimate, and it can hardly be expected to do any better.

It has always been meant to be used in conjunction with other kinds of analysis. One team may need a higher usage player and another may not. PER won't tell you this, you have to scout that out yourself.

thref23 wrote:
From a logical standpoint, I am not sure why PGs would be underrated more than players at any other position. PER has obvious and unavoidable imperfections, most notably that the stats utlized to create PER are insufficient to measure defensive performance. My point being - many elite PGs are known for their offensive ability - and not their defensive ability.


I agree. If PGs don't have many intangibles counted in running an offense, then big men miss out on the intangibles they offer being the center piece of the defense. Ultimately, couldn't it just be that big men are on the average more valuable than PGs?
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Charles



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PostPosted: Wed Oct 31, 2007 3:23 pm Post subject: Reply with quote
asimpkins wrote:
Charles wrote:
I understand that jacking up a player's PER based on points per game helps keep it in sync with public perception, but, in the real world, forcing up bad shots does not make you a more valuable player.


But it all depends. If he's forcing up bad shots when his teammates could have taken better ones then it is bad. But if he's putting up bad shots when his teammates would have done even worse then it is a good thing.


Absolutely. The question is does PER reflect that volume/efficiency balance correctly. That's why I defined a specific situation where you can test the change in individual PER vis-a-vis the change in team scoring. It's just math. And the result is that Parker's PER goes up, despite the fact that the Spur's team scoring goes down.

If you change the input on the assumption that Parker maintains his TS% on those extra shots, what happens? His scoring and PER rise even more, while his teammate's PERs suffer and the Spurs, as a team, pretty much break even. Again, a rise in individual PER reflects a increase in the player's glamor stats (and perceived value) , but not necessarily an increase in the team's performance.
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asimpkins



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PostPosted: Wed Oct 31, 2007 5:05 pm Post subject: Reply with quote
But is there any set of values you could set for PER that would be completely immune to coming up with a scenario like you did where an individual player was benefiting at the expense of the team? (Or was not being properly credited for helping his team?)

Sure, in your scenario Parker was overshooting -- which we know because we watch the Spurs and know what the rest of the team was capable of. We could adjust PER to appropriately fit Tony Parker and the Spurs. But then another player on a weak offensive team will be incorrectly penalized for putting up lots of tough shots to keep his incompetent teammates from turning it over.

You can't tell a good shot from a bad shot by the box score.
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Harold Almonte



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PostPosted: Wed Oct 31, 2007 8:49 pm Post subject: Reply with quote
Can this "individual volume/efficiency balance" be balanced with some kind of team eff. adjust, or team volume (+/-) adjust? Then, if Parker wants (or needs) to increase his stats hurting the team and teammates's stats and ratings, there must be a cost for him from the team's results (and the opposite). That's to be assisted, or unassisted, on every stat. I also don't buy the argument that Iverson was just an average player the Sixers almost champs season, according to all ratings.

All this stuff was discussed before in the "diversified offense" topic, and it's also why some people try to make compounds ratings, counterpartings, etc. Maybe some ratings reward volume a little more while others thinks that efficiency (from a league average) is the only thing that matters. But the team context must be expressed inside a rating, not only player's skills. Another kind of balance.

That's why RebR% is probably the best isolate stat rating, because a player is rated against himself, opponents and teammates. WP's team defense adjust is an aceptable way to include the team context, although not perfect given that credits are assigned by play time, not by attempts, or on/off (or yes?). How can players's scoring be rated against teammates? And passing? How can we say in a formula that some players, even having a high FG%, low TOs, aren't good scorers in the sense that they are not as reliable for the most doable and relative easy scoring plays to be set for them, even if they are spotted right under the ring? How can we know a player is in good/bad over-usage mode?.
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Ryoga Hibiki



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PostPosted: Mon Nov 05, 2007 12:24 pm Post subject: Reply with quote
PER is influenced by volume scoring (overrating it, imo), more than anything else, so you'll likely see scorers among the top players.
PGs are very rarely volume scorers, that's why they don't score big on PER.

Btw, what's PER's balance of usage/efficiency?
Roughly, taking a 35% 2pt shot increases a player's PER, isn't there something wrong?
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John Hollinger



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PostPosted: Mon Nov 05, 2007 3:56 pm Post subject: Reply with quote
Thought I'd finally chime in on this, because there's one thing I think that's being missed. Basically, folks are only looking at the top half of the spectrum. You'll notice that not only are PGs underrepresented at the very top of the rankings, they're equally underrepresented at the very bottom.

This is to be expected -- we would anticipate much more variation between the best and worst players at the frontcourt positions than in the backcourt, because of the simple fact that there are so few people alive who are that size to begin with. Thus, teams are picking from the extreme right of the talent curve at the "normal" heights that most point guards are, but edge much closer to the middle by the time they get to 7-footers. And thus the variation between the best and worst bigs is going to be much, much larger.

And that, in turn, tends to be why teams value big men much more highly than guards ... at least when actual money is involved
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Mountain



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PostPosted: Mon Nov 05, 2007 10:23 pm Post subject: Reply with quote
Re: Breaking out the parts of the PG PER decline

On average the top 150 PG seasons of 2002-3 to 2006-7

defined as >6 assists per 48 / not an obvious non guard and sorteed by minutes (safely over 2000 for season)

compared to the top150 PG seasons of 1988-89 to 1993-94 (chosen as just prior to the major increase in 3 pt shooting frequency, a factor being tested as a major change agent) ...

made 0.9 less FGs and shot 3.7 %pts worse overall but made 2.0 more 3 ptrs and shot them 3.6 % points better.

They made 0.3 more FTs (perhaps due to handcheck enforcement) but ended up scoring 0.8 less points.

They had 0.5 less ORs- one-third less (perhaps more time overall behind 3 pt arc?).

They had 1.5 less assists.

All per 48 minutes.

(though in a bit under 4% less actual minutes)
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Mountain



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PostPosted: Wed Nov 07, 2007 12:32 pm Post subject: Reply with quote
I update because I forgot to mention the pace change in the notes. I assume because of the timing of the pace change it was caused primarily by the heavier usage of the 3 pt shot but haven't stuidied or tested that in detail.

The onset of the major 3 pt usage era saw PGs using this weapon more but gave it to many others too and we see PG FGAs down as a raw per game amount and assists down too. Overall team field goal attempts down by maybe 7% perhaps as teams passed and passed looking to end up with the 3 pt shot? PG share of FGAs actually crept under from 21.7 to 22.2% But the reduction in shots per game and assists and the other negative changes outweighed the positive changes that helped their PERs.
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Charles



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PostPosted: Tue Nov 13, 2007 2:19 pm Post subject: Reply with quote
John Hollinger wrote:
-- we would anticipate much more variation between the best and worst players at the frontcourt positions than in the backcourt, because of the simple fact that there are so few people alive who are that size to begin with. Thus, teams are picking from the extreme right of the talent curve at the "normal" heights that most point guards are, but edge much closer to the middle by the time they get to 7-footers. And thus the variation between the best and worst bigs is going to be much, much larger.

The fact that there are more six footers than seven footers in the general population does not imply that good NBA play makers are easier to find than good NBA rebounders. In fact, the variance in assists is usually significantly higher than the variance in rebounds. For instance, here are the top thirty assisters and top thirty rebounders from last season.

Code:
Avg Max 30th StDev
Assists per 48 minutes 9.0 15.8 6.8 2.2
Rebounds per 48 minutes 13.7 17.2 10.8 1.7


Yes, when you look at PER, point guards have both the lowest max ratings and least variance. However, the argument is circular. This is exactly what you would expect to find when play-making is under-valued vis-a-vis shot making.

Code:
---- PER ----
Pos Players Max StDev
1-PG 27 24.0 3.9
2-SG 26 28.9 4.8
3-SF 25 24.5 4.3
4-PF 31 27.6 5.1
5-C 31 26.5 4.5



However if, rather than PER, you use an independent measure such as plus/minus, the story changes. Now, point guards, join power forwards as having both the largest overall impact and the largest variance. Centers - despite the fact that seven footers are rare at the shopping mall - have, by far, the least positive impact and the smallest standard deviation.

Code:
---- Adjusted Plus/Minus ----
Pos Avg StDev
1-PG +1.1 4.4
2-SG +0.5 3.7
3-SF +0.9 3.8
4-PF +0.9 4.8
5-C -0.6 2.8


(I realize plus/minus is noisy at the player/season level, however, it appears to be quite valid when aggregated in this way. Certainly more valid than guessing at what weights to apply to individual statistics.)

More and more teams are figuring out that, in the modern game, tall, immobile players are rarely difference makers.
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Charles



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PostPosted: Tue Nov 13, 2007 2:32 pm Post subject: Reply with quote
John Hollinger wrote:
Thought I'd finally chime in on this, because there's one thing I think that's being missed. Basically, folks are only looking at the top half of the spectrum. You'll notice that not only are PGs underrepresented at the very top of the rankings, they're equally underrepresented at the very bottom.

Yes, this is exactly what I mean by "elite point guards are under-rated." The range from the top to the bottom is much too small. According to PER no point guard has had a "Strong MVP Candidate" season in 43 years. Magic managed a couple of "Weak MVP Candidate" seasons. But, Stockton, Kidd and Nash are doing well when they occasionally crack the "Bona fide All-Star" barrier.

I am not criticizing the particulars of the method. I am just pointing out that this type of thing (a one-size-fits-all linear weights formula) is bound to be biased in one way or another and this one greatly under-values elite point guards. People might want to be aware of that when they make judgments based on these numbers.

Actually, I think any method of this type will have problems with elite playmakers because assists, more than any other factor, tend to have a powerful non-linear impact.
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Mike G



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PostPosted: Thu Nov 15, 2007 10:24 am Post subject: Reply with quote
Charles wrote:
... this type of thing (a one-size-fits-all linear weights formula) is bound to be biased in one way or another ...


Well, they don't all have to be biased against elite passers. I give assists 1.33 times the weight of a point scored (at average efficiency). Surveying player seasons of >2000 minutes, for 20 years (1987-2006), I see Jordan's 1989 at the top of my 'T rates', when he was actually playing the point.

I forgot to pro-rate 1999, so it's out. And I'm left with the 100 best seasons from each year to sort from: 1900 player-seasons. I expect to find 20% of any large slice to be (arguably) PG's.

After MJ, the competition is thick with Shaq, Hakeem, DRob, Ewing, Barkley, Malone, etc, etc. Only 9 of the top 100 seasons belong to PG's, 4 by Magic; also Lebron'05, Wade'06, Stockton'91, and Brandon'96. These are all superstar-level seasons.

But in the 2nd 100 -- still the top 10% -- I find 31 PG-seasons: exactly 40 of the top 200. A single Drexler season ('92) that might be called iffy (as in, is he a PG?). Still using 'guard with 6.0 Ast/36' as the criteria. All these are solid Allstar-level seasons.

From the top 400, there are 103 PG-seasons. Supposing there are as many as 23 non-PG's (there aren't) who slipped into the list, we are still at 20%. So it is certainly possible for a 'linear-weight system' to rank top-flight PG's in agreement with their generally-perceived worth.

Here are the PG-like players making the top 200 seasons since '87 (showing # of times and first year of appearance):
Code:
# player year
9 Stockton 88
5 Magic 87
3 K Johnson 90
3 Payton 97
3 Kidd 99
2 Brandon 96
2 Wade 05
1 Jordan 89
1 Drexler 92
1 Price 94
1 A Hardaway 96
1 T Hardaway 97
1 Cassell 04
1 Davis 04
1 Iverson 05
1 James 05
1 Billups 06
1 Nash 06
1 Parker 06

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mathayus



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PostPosted: Sat Nov 24, 2007 7:13 pm Post subject: Reply with quote
My take:

1) Distributors (be they point guards or whatever) are systematically underrated in flat PER because of the assumed flat assisted basket percentage.

2) Distributors are typically underrated in stats in general because it's hard to accurately quantify their impact. I don't think the value of an assist is underrated, but I do think the impact of a good floor general in setting up a basket goes beyond the moment when he makes a pass to someone who then makes a shot. It's a less dramatic example of block shots where the main value of a shotblocker is not in the shots he blocks but in the shots he alters.

Harold Almonte



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Posts: 616


PostPosted: Mon Nov 26, 2007 9:02 am Post subject: Reply with quote
mathayus wrote:
Quote:
1) Distributors (be they point guards or whatever) are systematically underrated in flat PER because of the assumed flat assisted basket percentage.


It's probably a two edges sword thing, because once you apply a basic assisted factor: Points(1-1/3*playerAd%), then PGs would be automatically overrated, and you would need to adjust for how assister are your teammates first (including your PG): LgAd%/TmAd%, and even for a player not being given the opportunity to be the main distributor and ball toucher: some kind of position adjustment, where being the PG would be (1) unit, and little increases depending on how far from PG duties a player makes offensive.
What I think is the only way a PG to be rated at real worth is accounting potential assists rate (probable assists that scorers waste), and assist percentage from FTs. Probably (but I'm not really sure), you would need to add extra credits for the extra point of a 3point-assist.
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mathayus



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PostPosted: Tue Nov 27, 2007 7:31 pm Post subject: Reply with quote
Hmm. Been a while since I analyzed the PER formula so maybe I'm missing something. When you replace guesstimates with the actual correct numbers, what's the problem? Yes it's possible that that could make people feel that the stat overestimates point guards, but if that's the case then the problem is with PER as a whole not this specific change (and them liking the PER previously wasn't based on the math but simply based on their own notions of how the results should look).

To the general idea of factoring in opportunity, that's not an unworthy goal, however the problem it addresses is not something introduced by this adjustment. Every aspect of this game of 1 ball with 5 guys simultaneously biases classical statistics toward the guy with the ball.

As far as truly capturing the impact of a great point guard, I'm skeptical we can do it. Case in point: We know that coaches have an impact on the final score, and that they influence it as the game happens by means of communicating with players, and I think we know that there's no way to really measure that value per action objectively. A great point guard, and really any leader for that matter, affects the game in the same way, hence the nicknames "floor general" "quarterback" "coach on the floor". As a result it just seems like a given that even the stat that perfectly measures resulting action will underrate such players due to not being able to factor in the cause statistically.
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Harold Almonte



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PostPosted: Wed Nov 28, 2007 8:26 am Post subject: Reply with quote
Agree. Intangibles can't be directly measured (just the effects), nor a little more tangible thing like ballhandling (passing is just a piece of this). The fact is that it's very difficult to define that a player is a better scorer (compared against another one with the same volume and efficiency level) because he opts or is obligued to assist himself in the most of cases, and would be also difficult for us (and a lot more for WOW followers) to downgrade a superscorer and supereficient player like Kevin Martin, because he needs to be assisted more than a 60%.

But, you can look it at this way: PGs are responsible to protect (almost all)the possession between the acquiring and the attempt, then that would makes you think that a lot of credits aren't being accounted for them.
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mathayus



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PostPosted: Tue Dec 11, 2007 8:28 pm Post subject: Reply with quote
Harold Almonte wrote:
Agree. Intangibles can't be directly measured (just the effects), nor a little more tangible thing like ballhandling (passing is just a piece of this). The fact is that it's very difficult to define that a player is a better scorer (compared against another one with the same volume and efficiency level) because he opts or is obligued to assist himself in the most of cases, and would be also difficult for us (and a lot more for WOW followers) to downgrade a superscorer and supereficient player like Kevin Martin, because he needs to be assisted more than a 60%.

But, you can look it at this way: PGs are responsible to protect (almost all)the possession between the acquiring and the attempt, then that would makes you think that a lot of credits aren't being accounted for them.


Not sure what you're asking exactly. We can easily get an estimate for turnovers per possession, and that's good, but I can't imagine that being thought to precisely measure a point guard's impact as it's really a team measurement, and if we're opening up the door to team measurements, isn't that giving up on figuring everything out purely by individual action? And I say this as someone who really is an advocate of some team based stats to help evaluate individuals, just not as someone who thinks you can ever get truly fine precision that way.
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Harold Almonte



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PostPosted: Tue Dec 11, 2007 9:25 pm Post subject: Reply with quote
The problem with boxscore stats (like TOs) is that you can reward or punish as much as the existency of that stat, you can't do anymore. If a PG doesn't commit TOs protecting the possession, you don't punish, but not to punish is not to reward, and it has some possession credit, and all credits can be translated to points. When you fail to rebound against an opponent you are not credited, but not to credit is not to punish. When a PG gives a bad pass before a FGMissed, he's not punished with a bad assist, and so on.

A PG is supposed to receive some credits by doing the ballhandling job for some scorers, but those scorers, even not being skilled enough to get open from his defender for an easy shot handling the ball, they probably can do it without the ball. While others can do all of that even without the help of the PG. And then about the assisted factor, you could end being more arbitrary than considering all assists 1/3 of a scored basket.

It's very complicated, and we are losing a lot of game inside stats.

Re: Recovered old threads- miscellaneous topics

Posted: Fri Apr 22, 2011 8:52 pm
by Crow
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magicmerl



Joined: 30 Dec 2007
Posts: 15


PostPosted: Sun Jan 06, 2008 10:20 pm Post subject: What is a possession worth? Reply with quote
In looking at some of David Berri's articles on WoW, I read this:

http://dberri.wordpress.com/2007/12/16/ ... ical-deal/

It compares Games Score (which is a simplified PER) with Win Score (which is a simplified WinsProduced). Here's the formulas:
Quote:
Win Score = PTS + REB + STL + ½*BLK + ½*AST - FGA - ½*FTA - TO - ½*PF

Game Score = PTS + 0.4*FGM + 0.7*ORB + 0.3*DRB + STL + 0.7*AST +0.7*BLK - 0.7*FGA - 0.4*Missed FTA - 0.4*PF - TO


Assuming that these two simplified formulas are a good approximation of their more famous coutnerparts, I have some questions that I you you good people can help me with.

1. What is a posession worth in terms of a 'score' like the above formulas?

1a. Does giving a player full credit for a defensive rebound essentially credit that individual with the entirety of the defense that was played on that posession?

1b. If you don't give the rebounding player full credit, should a portion of the rebounding credit be spread out among the defending players on the court at the time as a way of representing a successful defensive posession?

1c. Is losing posession (i.e. TO , missed FGA) worth the same as a rebound?

1d. Is making a 2pt basket worth the same as rejecting

1e. Is a steal (taking posession from them, giving it to you) worth twice as much as a simple gain or loss of posession?


2. What should converting posession into a score be worth?

2a. How should assists be scored? It seems like a dunk gives the same value to the team regardless of whether it was assisted or not. I mean, the benefit to the team as a whole is the same whether the shot was assisted or not. Giving additional credit to a player for the assist makes teams with high assists look better than teams without, even if every other aspect of the teams are identical. If it's not leading to greater team success

2b. If the basket was assisted, should the assisting player be credited with 'creating' the basket, and the assisted player have their score correspondingly reduced?

Thanks in advance for any insight people might be able to shed.

Last edited by magicmerl on Mon Jan 07, 2008 3:23 pm; edited 1 time in total
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Harold Almonte



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PostPosted: Mon Jan 07, 2008 9:37 am Post subject: Reply with quote
Any welcome to the freshman? No commentaries. All your questions implicitally have theirselves the answers.
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Rasta978



Joined: 26 Mar 2007
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Location: Orlando, FL

PostPosted: Mon Jan 07, 2008 12:39 pm Post subject: Reply with quote
Quote:
1. What is a posession worth in terms of a 'score' like the above formulas?


One of the difficulties with linear metrics is trying to ascertain the value of the possession, since that value has to be described "in terms of" something else. Nearly every metric that I've seen states the value in terms of Points, or some point equivalent.

Generally, the value of a possession in terms of points is 1.

I'd ask you to consider an alternative view in which the value of a possession is 2. Except that instead of expressing that value in terms of points, express it in terms of stuff that gets recorded in the boxscore.

For instance, a made shot results in 2 points. A missed shot followed by a defensive rebound are recorded in the boxscore as 2 events. A turnover committed is also a turnover forced. In other words, every possession will result in a net change 2. Not 2 points, but two entries in the boxscore.

By doing so, you'll then have to re-consider the commonly accepted belief (to everyone except Berri, that is) that a OReb + DReb = 1.0.

On the other hand, you'll see the error in Berri's treatment of made vs missed field goals.


Quote:
2. What should converting posession into a score be worth?


Converting a 2-point field goal attempt should be worth [drumroll] 2 points [/drumroll].
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Mountain



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PostPosted: Mon Jan 07, 2008 1:09 pm Post subject: Reply with quote
What various possession actions are worth "at face value" can be different than their apparent impact on winning as found by regression analysis. There are several key accounting choices to make including whether to shift some of the positive or negative value of the next action in the sequence onto the one that set off the chain such as steals perhaps leading to a fastbreak and a higher percentage shot. Rating systems sort out credit differently and the lack of shot defense in the formulas contributes to the missed shot / rebounding debates. Plenty of previous threads that grapple with these issues.
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magicmerl



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PostPosted: Mon Jan 07, 2008 3:22 pm Post subject: Reply with quote
Harold Almonte wrote:
Any welcome to the freshman? No commentaries. All your questions implicitally have theirselves the answers.

Um, thanks?

I'm not sure if you understood my post or not, because I definitely didn't intend for all of my questions to contain their answers. And if they did, wouldn't that imply that one or both of GS and WS are wrong in their weighting of box score variables? I mean, they use different weights. They can't both be right, if either of them are.

Rasta987 wrote:
One of the difficulties with linear metrics is trying to ascertain the value of the possession, since that value has to be described "in terms of" something else. Nearly every metric that I've seen states the value in terms of Points, or some point equivalent.

Generally, the value of a possession in terms of points is 1.

I'd ask you to consider an alternative view in which the value of a possession is 2. Except that instead of expressing that value in terms of points, express it in terms of stuff that gets recorded in the boxscore.

For instance, a made shot results in 2 points. A missed shot followed by a defensive rebound are recorded in the boxscore as 2 events. A turnover committed is also a turnover forced. In other words, every possession will result in a net change 2. Not 2 points, but two entries in the boxscore.

By doing so, you'll then have to re-consider the commonly accepted belief (to everyone except Berri, that is) that a OReb + DReb = 1.0.


Rasta987, thanks for the response. Doesn't changing the posession value from 1 to 2 just change the scale? How does that help?

It seems to me that there is the following basic progression in a game:

a. Opponent makes 2 pt basket
b. Opponent has possession
c. Noone has possession
d. You have possession
e. You make 2pt basket

Now, I think that a team successfully scoring (going b-a or d-e) should be worth the same amount. And people rebounding the ball (going from c-b or c-d) should also be worth the same amount. I don't really understand why defensive rebounds are worth less than half of what offensive rebounds are in GS. Surely turning a loose ball into posession for your team should be worth the same no matter where the ball is on the court?

I get that you are saying that a possession is worth roughly a point, which means to me that the above progression has equally spaced gaps, yes?

Rasta987 wrote:
On the other hand, you'll see the error in Berri's treatment of made vs missed field goals.

I don't actually see this error. dberri's deduction of FGA (regardless of whether they were successful baskets or not) seems to be to be somewhat analogous to a company needing a cash warchest to start business. You need to spend money to make money, just like you need to spend possessions on FGA in order to generate points. Let's not ignore that we are spending possessions in this way though.

Rasta987 wrote:
Quote:
2. What should converting posession into a score be worth?

Converting a 2-point field goal attempt should be worth [drumroll] 2 points [/drumroll].

Should it? I mean, 2 points is the reward you get for scoring, but it's not just a matter of putting the ball in the bucket. You have to actually get the ball first before you can turn it into points.

So in my above progression, going from a loose ball to a made basket is worth two points. Going from having possession to a converted basket should be worth 2 pts - the value of a rebound.

Does that make sense?

edit: Mountain, thanks for your response. I'm really not considering regression at all, just basic 'logic' to try and intuitively value box score stats. I remember reading somewhere that steals were a greatly overrated stat because they usually showed someone who cheated on D and thus exposed their team to numerous defensive breakdowns, giving their opponents open looks that they wouldn't have gotten if they had just stayed between their man and the basket. Whereas on the surface, a steal would appear to be more valuable than nearly every other box score stat.
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FrontRange



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PostPosted: Mon Jan 07, 2008 4:34 pm Post subject: Reply with quote
"Surely turning a loose ball into posession for your team should be worth the same no matter where the ball is on the court?"

I think this depend on if you are discusing the individual or the team level.

Not all passes, not all shots and not all rebounds are created equal in the value they create for the team. E.g. gaining the rebound off a missed free throw is not same (in terms of difficulty and value added above expected)as gaining gaining a rebound in flow of offensive.

An extra possession is important, but which are harder to get, that is the crux of why offensive rebounds are perhaps more valuable than defensive rebounds.
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Charles



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PostPosted: Mon Jan 07, 2008 5:01 pm Post subject: Reply with quote
magicmerl wrote:
Harold Almonte wrote:
Any welcome to the freshman? No commentaries. All your questions implicitally have theirselves the answers.

Um, thanks?

Hey, you got off light being referred to as "freshman" after questioning the Book of John. Actually, everything you said in your last post makes perfect sense. Win Score is right about lost possession on made field goals. Everyone, other than Berri, is right about rebounds (from an individual perspective.)
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Mountain



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PostPosted: Mon Jan 07, 2008 5:18 pm Post subject: Reply with quote
By the way welcome Merl.

Energy in responses will vary from time to time here.
Past accounting threads may have zapped some of the enthusiasm for another round at the moment. Given your interest in the subject I'd again encourage you to browse the past threads. You will see plenty of detailed presentations of ways to score and evaluations of how the various popular formulas do it and debates on the sticking points. Most of your questions including regarding assists are discussed thoroughly. At a different time I might try to identify the most relevant threads for you but they wouldn't be hard to find if you search by keyword or browse thread titles.

Some have taken the attitude that rating systems are passe, impossible to perfect or just pick the flavor you like. I still think they are useful and can be made stronger tools for assessing performance.


"1. What is a posession worth in terms of a 'score' like the above formulas? "

The value of posession is a building block of these formulas.
As Rasta said the average value of possession is close to 1. However the value of a successfully used possession is often 2 but can be different.


"1a. Does giving a player full credit for a defensive rebound essentially credit that individual with the entirety of the defense that was played on that posession?"

Yes this is a criticism that many recognize. The fix would be to include shot defense. The data is imprecise so none of the widely used formulas include it. I have thought about hacking one together that did it (and may eventually).

How much weight shot defense should get is an interesting question I haven't fully worked thru. The difference between upper quartile best and lower quartile worst shot defense in league on average might be 40% FG allowed vs 60% or 20%. Maybe the shot defense might deserve up to .4 of the credit for the miss with the rebound action getting the rest of the credit (on the defense's side of the ledger).

I agree with the thinking behind your point 1b that the credit for an opponent miss (or penalty for a make) could / perhaps should be split by the primary defender and rest of team. A crude starting point would be to give 50% of penalty to primary defender and 12.5% to each of the rest of the guys on opponent makes and on opponent misses maybe 30% to primary defender and 7.5% to other 4 guys
and 60% left for rebounding credit. And maybe the rebound credit gets split half or 30% to the actual defensive rebounder and the other half equally in 7.5% shares to rest of team for boxing out? That would allow match with PER assigned defensive rebound credit but allow the sum of all the defensive credits to total to 1. Or adjust the splits.
(You could go strictly egalitarian and split shot defense credit /blame equally among all 5 every time but I wouldnt go that far.)


"1c. Is losing posession (i.e. TO , missed FGA) worth the same as a rebound?"

It is in some systems faithful to scoreboard and aimed at explaining wins. It might not be in a ratings system primarily aimed at ranking players against each other like PER.


The high weight for steals in some systems is due to the expectation of easy buckets and may also come from being a proxy for other defensive impact, mostly importantly shot defense (not in most ratings formulas or in some regressions used to build ratings). But players vary in their mix of ability to make steals and defend the shot so using a weight for steals than carries some of the average value of unscored shot defense for the league but not the specific player will distort valuations from true. All the more reason to include shot defense in its own right even as an approximation someday.
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Harold Almonte



Joined: 04 Aug 2006
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PostPosted: Tue Jan 08, 2008 9:23 am Post subject: Reply with quote
The freshman quote was because is the first time I ever read you here. No offense.
Quote:
Now, I think that a team successfully scoring (going b-a or d-e) should be worth the same amount. And people rebounding the ball (going from c-b or c-d) should also be worth the same amount. I don't really understand why defensive rebounds are worth less than half of what offensive rebounds are in GS.

Very good approach. Forgetting the shot defense for a while, DR and OR have the same possesion value at the team level. The 0.7 and 0.3 is not weighting the possession value of rebound, is an adjust because it doesn't exist anything like "missed rebounds" in the boxscore, and aplying sampling and shortage to the stats, is a statistical way to adjust for that. Is it fair? I'm not a statician to answer that. Neither WS nor WP did the adjust. WP should have included it in the team defense adjust. Then it's not weighting the possession value of the stats, it's trying to do a player rebounding rating.
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Rasta978



Joined: 26 Mar 2007
Posts: 54
Location: Orlando, FL

PostPosted: Tue Jan 08, 2008 12:07 pm Post subject: Reply with quote
Quote:
Rasta987, thanks for the response. Doesn't changing the posession value from 1 to 2 just change the scale? How does that help?


It's not a question of scale. It's a matter of perspective, and how that perspective carries over into the metric.

For instance, Game Score's value for off. and def. rebounds. Why are off rebounds valued higher? Are they really more valuable? No, they simply occur less often. Yet we all agree that the value of an off rebound is equal to the value of a missed shot, right? And we see data that tells us that around 30% of missed shots are rebounded by the offensive team. Put it all together and we get these commonly accepted values: 0.3*Def Reb and 0.7*Off Reb.

I disagree with this logic, and blame it (for the most part) on the belief that "possession = 1", which results in forcing square pegs into round holes.

Quote:
I don't really understand why defensive rebounds are worth less than half of what offensive rebounds are in GS.


Join the club. Berri agrees, for what its worth.

Speaking of Berri, I should elaborate on what I believe is his main error. Actually, "error" is the wrong word. At the team level, his accounting system is fine. I'd encourage you to read NickS' analysis on this point.

http://sonicscentral.com/apbrmetrics/vi ... highlight=

NickS wrote:
Code:

OFF DEF
3PM +2.0 -2
FGM +1.0 -1
FGX -1.0 +0
OR +1.0 -0
DR -0 +1.0
TO -1.0 +1


At the team level, a made FG results in +1.0 for the offense, and -1 for the defense. Conversely, a missed FG followed by a def rebound results in -1.0 for the offense, and +1.0 for the defense. Very symmetrical. In both cases, the net effect is 2. (Again, Berri and I are on the same page: the value of the possession is 2)

(Edit to add: NickS used the decimal place to indicate stats that were attributed to individuals, and no decimal for stats attributed to the defensive team.)

While this is fine at the team level, things get off track at the player level. Specifically, Berri gives the individual scorer credit for 1.0 pt on a successful FG, and then divides the -1 among all 5 of the defenders. As a consequence, nearly all of the points scored are removed from the equation at the player level. What's left? Rebounds, mostly. And that's where the criticism is centered.

Quote:
a. Opponent makes 2 pt basket
b. Opponent has possession
c. Noone has possession
d. You have possession
e. You make 2pt basket


Quote:
So in my above progression, going from a loose ball to a made basket is worth two points. Going from having possession to a converted basket should be worth 2 pts - the value of a rebound.

Does that make sense?


Um, no.

I agree that going from having possession to a converted basket (steps b-a, or d-e) should be worth 2 points. However, going from a loose ball (c) to a made basket (e) looks like 3 points to me.

Code:

Possession to Made Shot b-a or d-e 2 points
Possession to Missed Shot b-c or d-c -1 point
Loose Ball to Rebound c-b or c-d 1 point


By the way, this is basically Berri's views things, except that (as I mentioned earlier) he splits the 2 point credit for the "Possession to Made Shot" between offense (+1) and defense (-1).
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Harold Almonte



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PostPosted: Tue Jan 08, 2008 7:26 pm Post subject: Reply with quote
uhmm. There's a topic outhere wich says you can't have or create more total possessions-points than the points scored in the game. Berri's approach about penalizing the FGMade is irrelevant because is the same for both teams, but... really is a not rebounded, not stealed possesion gained he should retribute back to scorers who convert it in points scored. The "error" as you say is he didn't credit a -1 at the DEF side when an OR is grabbed at the OFF side, and the fact is that DR cancels both the FGX and the OR.

Then in the binarian sample the OFF side has a net +2 credits, but DEF is just penalized -1. All the problem with WS is that "rebounds allowed" are not penalized.
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Westy



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PostPosted: Wed Jan 09, 2008 12:34 am Post subject: Reply with quote
Quote:
Maybe the shot defense might deserve up to .4 of the credit for the miss with the rebound action getting the rest of the credit (on the defense's side of the ledger).


Mountain, would the fact that rebounds/48 min. remain basically constant from year to year (per Jason on Berri's site [see my other post on the rebound rate thread]) suggest that splitting the credit is not called for?
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Mountain



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PostPosted: Wed Jan 09, 2008 3:20 pm Post subject: Reply with quote
Westy I'll have to catch up on reading a bit.
And seek clarification to your question.

First some run up comments (not all strictly on your question but related and what I want to add)...

My initial take is that I still think dividing up the total defensive credit for a missed shot / recovery sequence into shot defense and rebounding shares makes sense. And I think splitting credit in each case among all 5 players might also make some sense. The proportion of the split is up for debate- as is the method. I see several people in several places all suggesting such splits, an encouraging thing to me.

On shot defense I still lean toward 50% to boxscore counterpart (as a rough starting point) 50% to rest of team but would come down to 30-40% to the presumed most of the time defender of the opponent who scored and then equally to rest of team. Completely equal 5 way split losses sense of individual responsibility to me.


Now to your rebounding question. If you meant does it make sense to split the rebounding part of the credit for recoveries among all players I guess I'd amend what I said before. Perhaps 50% / 50% for actual rebounder and rest of team was too generous a share for the rest of team. They may or may not have contributed to its capture (lots of guys stand around, not boxing out, often but not always too far way to matter). I would probably go to something more like say 76% to actual rebounder and 24% split out to rest, partly in view of your data about rebounding consistency and because it sharpens my own reflection and gets closer to how I see the action happening and what a fair split would be.

Some recognition that rebounding is a team enterprise and that boxouts matter seems worthwhile ideally but it isn't a huge deal not doing it. But if the goal is improvement some recognition might be appropriate.

You could get out of the partial credit giving business by maybe doing something similar to what Dan Rosenbaum did in his design of overall +/- :

score the meaningful individual acts (his statistical +/- formula or do it from another system base) and give them the predominant weight and then add on say a 15-20% weight share for pure adjusted +/- to try to capture the partial credits due for actions contributed in a team context (boxouts, rotation passes, spacing, ball saves, etc.).

Wins Produced has its team adjustment. You could add a small +/- based compotent to an enhanced PER formula or a new one.

I can go either way.

Statistical +/- or any other linear weight system can be looked at in aggregrate- and also broken out into 4 categories in alignment with 4 factors to get a better picture of a player's style/role/impact.

Pure adjusted +/- can be broken out into adjusted 4 factors by the teams if they want or if any site wanted to push to that level available in public. But until then you can look at the raw team +/- details on the 82 games +/- page and try to "adjust" as best you can for the context you believe the player was in.

Last edited by Mountain on Wed Jan 09, 2008 6:22 pm; edited 2 times in total
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magicmerl



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PostPosted: Wed Jan 09, 2008 3:38 pm Post subject: Reply with quote
Rasta wrote:
Quote:
So in my above progression, going from a loose ball to a made basket is worth two points. Going from having possession to a converted basket should be worth 2 pts - the value of a rebound.

Does that make sense?


Um, no.

I agree that going from having possession to a converted basket (steps b-a, or d-e) should be worth 2 points. However, going from a loose ball (c) to a made basket (e) looks like 3 points to me.

<snip>

By the way, this is basically Berri's views things, except that (as I mentioned earlier) he splits the 2 point credit for the "Possession to Made Shot" between offense (+1) and defense (-1).

I don't agree that Berri credits a made field goal with 2 pts. Yes the basket is worth two points, but my understanding of WoW is that it's accrediting one of those points with the act of getting posession (moving from c-d or c-b) and only one point with the act of dropping th eball through the basket (b-a or d-e).


Westy wrote:
Quote:
Maybe the shot defense might deserve up to .4 of the credit for the miss with the rebound action getting the rest of the credit (on the defense's side of the ledger).


Mountain, would the fact that rebounds/48 min. remain basically constant from year to year (per Jason on Berri's site [see my other post on the rebound rate thread]) suggest that splitting the credit is not called for?

I've seen Jason post that several times on Berri's site, and I think that it is misleading at best. To me, the only point to making that arguement is to then infer that players take their rebounds with them where they go, and that the players as individuals are solely responsible for the rebounds they haul down. As someone here said on another thread, Dennis Rodman rebounded quite well for the bulls while he was with them, but it's a fallacy to reach the conclusion that the WoW model does that he earned all of those rebounds for his team. This post says it better than I could.

They explicitly say that the players rebounds don't necessarily give their team that many rebounds, but they then implicitly and repeatedly contradict this point by using reboudns they way they do in their formula and then they top level summaries talk about WinScore and WinsProduced as if their rebounds did give their team that many rebounds and that they would not have got them unless that player who hauled in the reboudns was playing for that team.
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Rasta978



Joined: 26 Mar 2007
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PostPosted: Wed Jan 09, 2008 4:46 pm Post subject: Reply with quote
Quote:
I don't agree that Berri credits a made field goal with 2 pts. Yes the basket is worth two points, but my understanding of WoW is that it's accrediting one of those points with the act of getting posession (moving from c-d or c-b) and only one point with the act of dropping th eball through the basket (b-a or d-e).


That's not correct. The act of getting possession (ie the defensive rebound), while indeed worth 1 point, marks the end of the previous unsuccessful possession. It isn't part of the subsequent possession.

On a made field goal, Berri actually agrees with this partial formula, although only at the team level:

Code:
WS = Pts + FGM - FGA


Note: "FGM - FGA" is just another way of saying "Missed Shots"

At the player level, the variable "FGM" is missing. We only see this:

Code:
WS = Pts - FGA


That's because the credit for the made FG is allocated to the defensive team in a later step. In theory, each of the five defenders share the blame for allowing the shot to be made. To illustrate the net effect of a made FG:

Code:
Scorer = 2.0 points - 1.0 FGA = 1.0

Opposing PG = -0.2 FGM
Opposing SG = -0.2 FGM
Opposing SF = -0.2 FGM
Opposing PF = -0.2 FGM
Opposing C = -0.2 FGM
Team Total = -1.0


If you read NickS' analysis, he was questioning the fairness of this from the point of view of the defender. If I'm defending Allen Iverson, and he makes a shot, I'm penalized a fraction of a point. However, if I play great defense and force a missed shot, Iverson is penalized for the miss (-1 FGA) while my teammate gets credit for the def rebound (+1 Def Reb). What about me? As the defender, I'm getting screwed in this situation.

What a nice segue into Westy and Mountain's discussion about somehow dividing credit for that Def Rebound between the five defensive players.

Harold Almonte



Joined: 04 Aug 2006
Posts: 616


PostPosted: Wed Jan 09, 2008 5:49 pm Post subject: Reply with quote
Rasta. Although Berri punishes this FGM in the WS formula, he retributes back (to the opponent) in the WP formula only, with his team adjust, but he does it by minutes believing it has any logic.

He makes the scorer, individual responsible of the FGM-team-lost-of possession, but in the team defense adjust he distributes among teammates a non rebounded, non stealed possession gained by inbound which he should give to the scorer in that possession. He's overpunishing scorers here, because he must distribute these possessions in the same way he's punishing: to scorers, and in his shortcut WS formula, he just would have P-FGMissed.

I'm not against the punishing of the FGM neither at the team level nor at the player level, but at the player level, the next possession opp. scorer must be individually rewarded, not to share credits with teammates.
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magicmerl



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PostPosted: Wed Jan 09, 2008 6:12 pm Post subject: Reply with quote
Ok, I guess we were talkign about two different things. I was really talking about his formulas for individuals, since it's really individual player metrics we are talking about here isn't it?

Thanks for setting me straight on the team metric though.

TG Randini has a good post here (similar to what mountain has already talked about) about splitting the value of a defensive rebound to share it with the other players to give them credit for the defense that got played on the possession.

http://dberri.wordpress.com/2008/01/09/ ... /#more-694
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Westy



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PostPosted: Wed Jan 09, 2008 10:09 pm Post subject: Reply with quote
Quote:
I would probably go to something more like say 76% to actual rebounder and 24% split out to rest, partly in view of your data about rebounding consistency and because it sharpens my own reflection and gets closer to how I see the action happening and what a fair split would be.


Mountain,
I completely agree. That's a good breakdown. Whether each of the others get 0.06 or the defender gets a little extra credit, it seems appropriate. The folks at WoW are less convinced this better reflects reality.

It does seem to me that the surprising consistency (again per Jason) of rebounds/48 min. for individual players even when they change teams needs more study. It's hard to believe that with pace changes, different teammates as defenders, etc. players would still rebound at the same rate.
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Chicago76



Joined: 06 Nov 2005
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PostPosted: Thu Jan 10, 2008 1:13 am Post subject: Reply with quote
[quote="Westy"]
Quote:
It does seem to me that the surprising consistency (again per Jason) of rebounds/48 min. for individual players even when they change teams needs more study. It's hard to believe that with pace changes, different teammates as defenders, etc. players would still rebound at the same rate.


Jasons quote:

[quote="Jason"]
Quote:
I looked at rebounds/min of all players in the Association in 05-06 and 06-07...If we confine the measurement to players who have at least 500 minutes in both seasons, it [R^2] goes up to 0.8989, indicating that regular players are very, very, very consistent in their rebound rates.

If we confine observations to players who played at least 500 minutes for both teams, the correlation remains strong. In fact, it’s a bit better (N=65; R^2=.9033). If teammates’ defense is what is driving rebounds, then the 65 players who changed teams and played reasonably regular minutes in both seasons were very, very lucky to find themselves on remarkably comparable teams in both seasons, teams that were more similar than the season-to-season variance of players who remained in the same city.


This is long, but bear with me...

The regression stat isn't really shocking. I don't have the R^2 data on this machine, but this is an approximation:

R^2 reb/min = 0.9
R^2 FT% = 0.75
R^2 Ast or FG/Min = 0.65

At the low end of the correlation scale, you've got items determined by offensive strategy and implementation. Anyone can realistically tally pts standing 25-30 feet and in, so there is more variation in how scoring occurs and who is scoring. Chris Webber is a decent illustrative example. Webber has played in a wide variety of offenses for several teams with big disparities in team talent. One year, he may be called upon to play a point forward role and link offensive players in a system. The next year, he may find himself with more of a pure point where this role is diminished. Webber might also be instructed to take an open 18 footer with 16 on the clock one year and the next be told to pass. If you were to regress ast/min or pts/min among players year over year, the predictive power is there, but it is relatively low, because offensive outputs are subject to team-oriented decisions, strategy, and usage.

At the high end of the scale, you have rebounding, which is determined more by the other team's offense. Your team's strategy has less to say about it, because rebounding is not as dependent upon a system of play. We know that most rebounds are secured by the defense. Regardless of what system a team runs, we know where the majority of rebounds are going to fall--close to the basket. Trade or not, we pretty much know who Chris Webber is going to guard--a larger player close to the basket. Webber's defense may incorporate doubling schemes away from the basket occasionally, but by and large, his defensive position is determined by his counterpart on the other teams in the league. There is no strategy involved in securing a ball bounding off the rim. Webber is standing near his man. The ball is loose, and his job is to go get it. There is no, "should I get this ball, or does coach want my teammate to get it?" like there is when he asks himself, "Should I take this shot or does coach want me to kick the ball back out?"

FT shooting is the control here. It's as isolated as it gets in the game, so more consistency in rebounding year suggests to me that position/height, etc reinforce output, i.e., it's less about the player and more about the statistical inevitability.

Why doesn't pace have a huge effect? It might, but there are neutralizing forces at work.

A: The wide variation in rebounding among players strengthens the correlation here. At the high end, big men often have trouble getting up and down the floor, so going from 90 poss/g to 110 poss/g would presumably lead to winded big guys out of position for a certain % of additional FG misses when they're trailing plays. RbR may go down while Reb/Min remains more constant.

Why don't players' teammates have more of an effect?

A: Big guys play fewer minutes, so they're less likely to be in direct competition with one another. You could have 2 rebounding specialists playing 24 min/g who never share the court.
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Rasta978



Joined: 26 Mar 2007
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PostPosted: Thu Jan 10, 2008 10:09 am Post subject: Reply with quote
Magicmerl,

I don't want to beat a dead horse, but the idea of comparing WS to GS is flawed. GS is a stand-alone metric that can be used without any additional computational steps. WS is not.

Quote:
Assuming that these two simplified formulas are a good approximation of their more famous coutnerparts...


Do you recall how often Berri is asked about his "team adjustment", and whether it's just a method of fudging the numbers? He always replies that he cannot remove the team adjustment, as this would make his formula "mis-specified".

It took me a long time to understand what he's saying. I even had to breakdown and buy his book.

The point is, when you compare the WS formula with the GS formula, you notice that "FGM" are missing from WS. Berri understands the necessary debit/credit accounting of basketball metrics; he knows that FGMs cannot simply disappear. However, instead of including those FGMs in the player-level metric (like everybody else does), Berri includes them in his team adjustment.

That's why he so adamantly refuses to show his work without the team adjustment: the team-level accounting would be screwed up.

Therefore, if you really want to compare WS and GS (as proxies for Wins Produced and PER), you should insert "FGM" into the WS formula. Like this:

Quote:
Win Score = PTS + FGM + REB + STL + ½*BLK + ½*AST - FGA - ½*FTA - TO - ½*PF

Game Score = PTS + 0.4*FGM + 0.7*ORB + 0.3*DRB + STL + 0.7*AST +0.7*BLK - 0.7*FGA - 0.4*Missed FTA - 0.4*PF - TO
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Westy



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PostPosted: Thu Jan 10, 2008 10:33 am Post subject: Reply with quote
Quote:
At the high end of the scale, you have rebounding, which is determined more by the other team's offense. Your team's strategy has less to say about it, because rebounding is not as dependent upon a system of play.


Jason also notes that the correlation for individual players is higher than for at the team level. This would seem surprising, but since the team is operating in such a tight window (rebounds garnered change only slightly), differences in pace would affect this more I suppose?

I would also note that I have to think that rebounding is more correlated with height, body type, position on the floor, and coaching strategy than a skill/level of effort that's somehow more consistent than FT%. If these are all consistent, which I think they are, rebounding also remains consistent. This means that a player will tend to rebound the same, and garner the reward (in the WP system) of reflecting his team’s defensive prowess because that’s the role he continues to fill.

The fact that it’s so much more highly correlated from year to year than FT%, a ’skill’ that should be extremely consistent, seems to show that there are further factors pushing players to the place in which they garner rebounds that are static.
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Chicago76



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PostPosted: Thu Jan 10, 2008 11:21 am Post subject: Reply with quote
Westy wrote:
Jason also notes that the correlation for individual players is higher than for at the team level. This would seem surprising, but since the team is operating in such a tight window (rebounds garnered change only slightly), differences in pace would affect this more I suppose?


The team R^2 stat is very misleading. When things don't change much year over year and teams don't vary much, the regression doesn't have much variation to account for and things become fuzzy.

On an individual level, all players in the league may have reb/48 rates of between 2 and 18. If they change an average of +/-6% a year on an individual basis, then my R^2 is high.

On a team level, all teams in a league may have reb/48 min rates of between 37.5 and 43.5. These teams may change an average of less +/-2% per year and no team changes 6% or more, but my R^2 is lower.

Some numbers:
In 2006/07, half of all teams did stay within 2% of the previous season's reb/48 rate.

Only three teams moved greater than +/-5% from their 2005/06 reb/48 min the following year.

Re: Recovered old threads- miscellaneous topics

Posted: Fri Apr 22, 2011 8:56 pm
by Crow
Statman



Joined: 20 Feb 2005
Posts: 94


PostPosted: Sun Jan 13, 2008 8:19 am Post subject: A new idea on testing the accuracy of a player rating system Reply with quote
This just occurred to me yesterday when I was trying to figure out how close my college player rating system would have predicted an outcome AFTER the fact - after we already know the exact minutes played of every player in a game on each team. I did a one game test of my system (Arizona vs Houston) - and actually ended up with almost the exact ratio of Arizona's rating to Houston's to the actual ratio of Arizona's points to Houston's points in the game (pure luck it happened the first game I try). Here's the thread where I mention it - it's the 9th post down where I do this "test":

http://pointguardu.com/cats/showthread.php?t=22937

Anyway - I realized - all this debate about the accuracy of different systems (Usually PER vs. Berri's Winscore or whatever it's called) in evaluating players - couldn't one retroactively test these rating systems on a game to game basis on an entire season, taking the standard deviation of the difference in ratios of predicted outcomes to actual outcomes? The system that has the smallest standard deviation from game to game would in essence be the most "accurate".

For example - for one game, Team A beats team B 110 to 100, so the TRUE ratio of points scored for the game ends up 1.10. Now - if you take the PER (final PER for the season mind you) of every player that played and multiplied it by the minutes the player played, and summed each team - the goal in a perfect world (if PER were 100% accurate) would be that if you divided the sum of Team A by the sum of team B, you'd get 1.10. If it actually ends up, say, 1.25, then the prediction deviated by 0.15.

Does that make sense? Has anyone tried this on a large scale? Wouldn't this maybe be the best way to "test" these systems? The closer a system gets to mimicking actual results when going BACK over the season - then the better that system reflects true individual player performance.

Is there a better way to grade a rating system than trying to retroactively test final season results with past game outcomes?
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Ryoga Hibiki



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PostPosted: Sun Jan 13, 2008 11:36 am Post subject: Re: A new idea on testing the accuracy of a player rating sy Reply with quote
Statman wrote:

For example - for one game, Team A beats team B 110 to 100, so the TRUE ratio of points scored for the game ends up 1.10. Now - if you take the PER (final PER for the season mind you) of every player that played and multiplied it by the minutes the player played, and summed each team - the goal in a perfect world (if PER were 100% accurate) would be that if you divided the sum of Team A by the sum of team B, you'd get 1.10. If it actually ends up, say, 1.25, then the prediction deviated by 0.15.

I'm not sure you're measuring how accurate those rating systems are.
Once you sum up all together it's very likely going to work because the tough part is to split the credit for every change in the scoreboard within the team => most of the errors are going to be corrected once putting everything together again!
I really don't see how to test them at the player level in a quantitative way, actually.
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Harold Almonte



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PostPosted: Sun Jan 13, 2008 3:43 pm Post subject: Reply with quote
I'm not sure if I understood. A player's performance against some team or some games not necesarily is correlated with his average season performance. If such prediction at the individual game level could be possible, Las Vegas would know it and betting would dissapear. The most closed correlation that I think can be done is with wins (no margins).

I have not the level to revise Rosembaum's tests, but my common sense says me that if you compare the ratings with 4 (eight really) factors and the W% of each action (scoring, ballhandling, rebounding and FTs/foulings), the PER's lack of points allowed will produce problems in the def. eFG% prediction, but the win% relationship eFG% + R% supossedly should produce more problems in WP, but is not the case. I think, and I'm not sure either, is because altough rebounds are overrated, the importance of rebounding rating (20%) is reduced; scoring is underrated but the importance of efficiency (40%) is rised, and probably is why WP remains relatively predictive. Somebody please correct it if that's possible.
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Statman



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PostPosted: Sun Jan 13, 2008 5:30 pm Post subject: Re: A new idea on testing the accuracy of a player rating sy Reply with quote
Ryoga Hibiki wrote:
Statman wrote:

For example - for one game, Team A beats team B 110 to 100, so the TRUE ratio of points scored for the game ends up 1.10. Now - if you take the PER (final PER for the season mind you) of every player that played and multiplied it by the minutes the player played, and summed each team - the goal in a perfect world (if PER were 100% accurate) would be that if you divided the sum of Team A by the sum of team B, you'd get 1.10. If it actually ends up, say, 1.25, then the prediction deviated by 0.15.

I'm not sure you're measuring how accurate those rating systems are.
Once you sum up all together it's very likely going to work because the tough part is to split the credit for every change in the scoreboard within the team => most of the errors are going to be corrected once putting everything together again!
I really don't see how to test them at the player level in a quantitative way, actually.


Not if you sum the absolute values of the deviations of each game.

But yes - overall, a system should sum close fairly close to zero (if you are just summing the differences, some games negative, some positive) - or it's probably a bit off.
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Statman



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PostPosted: Sun Jan 13, 2008 5:57 pm Post subject: Reply with quote
Harold Almonte wrote:
I'm not sure if I understood. A player's performance against some team or some games not necesarily is correlated with his average season performance. If such prediction at the individual game level could be possible, Las Vegas would know it and betting would dissapear. The most closed correlation that I think can be done is with wins (no margins).

I have not the level to revise Rosembaum's tests, but my common sense says me that if you compare the ratings with 4 (eight really) factors and the W% of each action (scoring, ballhandling, rebounding and FTs/foulings), the PER's lack of points allowed will produce problems in the def. eFG% prediction, but the win% relationship eFG% + R% supossedly should produce more problems in WP, but is not the case. I think, and I'm not sure either, is because altough rebounds are overrated, the importance of rebounding rating (20%) is reduced; scoring is underrated but the importance of efficiency (40%) is rised, and probably is why WP remains relatively predictive. Somebody please correct it if that's possible.


As for the first part - yes, this is true. However, we aren't looking at the players as individuals, but summing the players every game in proportion to their minutes. Of course every game will have variance (many games large variance) - even if the rating was quite "accurate" - but over the course of a whole season, the absolute value of the sums of the deviations (I forgot to mention before absolute value) still would be lower for the "more accurate" systems I would think.

I think some may be confused a little by my one game example - one game being "accurate" obviously doesn't tell us that much (although I was happy the one game I tested in mine wasn't WAY off) - but over thousands of games - I would think it could tell us alot when comparing one system to another.

If I were a programmer - I could probably have all the data pulled from every game box score (all I need is final score and individual player minues). From here one could test PER, Berri's, maybe Mike G's, etc. against each other (IF I had all their final player ratings) - see whose system seems to best on average to retroactively "predict" the past results.

As for your second point - I'm sure what you are getting at. I agree PERs biggest problem would probably lie most in not measuring points allowed, and Berris problem would lie probably elsewhere (overvaluing rebounding, undervaluing usage?) - I'm just curious which would be more "off" from real life results.

The system I use does use opposing points as part of a factor, which is why it's a ratio (100 being average): players summed results (scaled to actual team points) divided by opponent's points. I also take into account playing time in the final result as kinda a "Bruce Bowen" adjustment (low rated guys who play alot on good teams probably are doing more things not reflected in the box score, and vice versa). For college it's trickier - because of the inclusion of SoS, which could be skipped in the NBA without too much "error" (though there would be some, ie the East being much weaker than the West in a given season).
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Mike G



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PostPosted: Tue Jan 15, 2008 10:32 am Post subject: Reply with quote
I would expect a system that (like mine) matches point-differential to player rates to yield closer predictions than one which doesn't. However, credit to individual players is still not adequate. If Bowen doesn't play, do the Spurs suffer a little, or do they suffer a lot?

Last year, team expected wins (based on my eWins-producing stats) averaged an 'error' of 2.5 wins from pythagorean-expected. But actual wins 'erred' by an average of 2.7 from pyth. Can you beat that?
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gabefarkas



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PostPosted: Tue Jan 15, 2008 6:25 pm Post subject: Re: A new idea on testing the accuracy of a player rating sy Reply with quote
Statman wrote:
For example - for one game, Team A beats team B 110 to 100, so the TRUE ratio of points scored for the game ends up 1.10. Now - if you take the PER (final PER for the season mind you) of every player that played and multiplied it by the minutes the player played, and summed each team - the goal in a perfect world (if PER were 100% accurate) would be that if you divided the sum of Team A by the sum of team B, you'd get 1.10. If it actually ends up, say, 1.25, then the prediction deviated by 0.15.

Does that make sense? Has anyone tried this on a large scale? Wouldn't this maybe be the best way to "test" these systems? The closer a system gets to mimicking actual results when going BACK over the season - then the better that system reflects true individual player performance.

I've tossed around the idea of something like that too. It seems incredibly monumental though.

One question: are you referring to the PERs over the entire season, or the PERs calculated based on stats only up to that game?
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Statman



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PostPosted: Tue Jan 15, 2008 7:05 pm Post subject: Re: A new idea on testing the accuracy of a player rating sy Reply with quote
[quote="gabefarkas"]
Statman wrote:

One question: are you referring to the PERs over the entire season, or the PERs calculated based on stats only up to that game?


I was thinking whole season PER.

I do think Mike G's would probably be more "accurate" in this type of testing than John H's or Berri's. Mine (which I haven't worked on recently since I've been doing alot of college stuff) would probably be also, since Mike G & I have a number of similarities in our approach (I make sure the linear weights totals exactly match team points totals, and I also use opposition scoring as a factor).
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BadgerCane



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PostPosted: Tue Jan 15, 2008 11:28 pm Post subject: Reply with quote
This idea is interesting, but does not work for a few reasons. For instance, if our only measure of player productivity is points, then if we add up all the players with that measure then the team whose players add up to a higher point total will win. Similarly, if we jut use Offensive Rating as a measure, and add all of that up, the team with the higher offensive rating will win. If we "believe what we say we believe," that shot creation has value, that efficiency goes down as usage goes up, then such a system does not work. The difficulty in basketball analysis is not figuring out what leads to wins. The difficulty is in figuring out how to apportion credit for these stats among players.
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Statman



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PostPosted: Tue Jan 15, 2008 11:49 pm Post subject: Reply with quote
BadgerCane wrote:
This idea is interesting, but does not work for a few reasons. For instance, if our only measure of player productivity is points, then if we add up all the players with that measure then the team whose players add up to a higher point total will win. Similarly, if we jut use Offensive Rating as a measure, and add all of that up, the team with the higher offensive rating will win. If we "believe what we say we believe," that shot creation has value, that efficiency goes down as usage goes up, then such a system does not work. The difficulty in basketball analysis is not figuring out what leads to wins. The difficulty is in figuring out how to apportion credit for these stats among players.


I'm really not sure what you are saying here.

IF you are saying that there isn't a metric that can accurately reflect true player impact on a team - that may be somewhat true.

However, I do think it is possible to have a metric that does a fairly solid job of measuring player performance. While not perfect by any means, or necessarily fair to every type of player (ie Bruce Bowen), I would think it is possible to put together something that has some substance.

I was wondering HOW to figure out if a metric seems to hold water - and this idea was the best I've come up with. IF we are going to appoint some type of number to a player's performance for the season (say PER), then I would think if we were to go back and test the results - we shouldn't have a huge deviation from the ACTUAL results on average. What would be considered too big a deviation - I really don't know, unless we tested many metrics and saw what kind of results we got. I don't think it would take long to figure out which metrics are obviously WAY off, and which aren't too bad.

But, there will obviously NEVER be a perfect metric to accurately measure true player performance.
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BadgerCane



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PostPosted: Wed Jan 16, 2008 12:07 am Post subject: Reply with quote
Well, I think that Dave Berri accomplished this with Wages of WIns. He took the end results of every team in every year since they kept all the stats (I think '76?) and found out what team totals had the highest correlation to winning. (You can download pretty much every result ever from www.basketballdatabase.com.) So, this would seem to answer the "whose stats add up to most wins" question. This is where the big debates between Wages and BOP come in. In Wages, the rebounder pretty much gets credit for the forced miss and the rebound. Also, Wages gives no value to shot creation. This isn't to say that the "what stats add up to wins" question is not worth asking. I do not want to seem like I am dismissing the idea. Nor do I want to say that I do not find value in a lot of what Wages has to say about basketball. I'm just trying to question the soundness of using a system of measurement, "what adds up to the most wins," that actually provides one of the main forks in the road to agreement between different schools of basketball thought.
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Statman



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PostPosted: Wed Jan 16, 2008 12:22 am Post subject: Reply with quote
BadgerCane wrote:
Well, I think that Dave Berri accomplished this with Wages of WIns. He took the end results of every team in every year since they kept all the stats (I think '76?) and found out what team totals had the highest correlation to winning. (You can download pretty much every result ever from www.basketballdatabase.com.) So, this would seem to answer the "whose stats add up to most wins" question. This is where the big debates between Wages and BOP come in. In Wages, the rebounder pretty much gets credit for the forced miss and the rebound. Also, Wages gives no value to shot creation. This isn't to say that the "what stats add up to wins" question is not worth asking. I do not want to seem like I am dismissing the idea. Nor do I want to say that I do not find value in a lot of what Wages has to say about basketball. I'm just trying to question the soundness of using a system of measurement, "what adds up to the most wins," that actually provides one of the main forks in the road to agreement between different schools of basketball thought.


He did this at the team level - that is a different ball of wax from what I am saying.

Did he later, AFTER he created his ratings and got his results for a given season, go back and test these INDIVIDUAL results against the actual results? If he did, what was his standard deviation in an average NBA game (of the summed individual results) from actual results? Did he test this deviation against against individual factors on their own (like, say, the standard deviation if one used only scoring rate, or rebound rate, etc)? Did he make sure his metric had a lower deviation than any others he could test?

I'm guessing he didn't.
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Harold Almonte



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PostPosted: Wed Jan 16, 2008 8:14 am Post subject: Reply with quote
They have the WScore metric at the individual game. I think its standard deviation would be similar to any other one without team adjusts, except for the out of scale in rebounding (probably neither of them is right at the scoring efficiency).
The basketball win laws were already discovered in 4 (eight) factors, and every metric is obligued to fit with these proportions and the main of all (the zero sum approach at every action of the game). What rest in metrics is the weight of skills (shot creation, ballhandling, rebounding skills, etc.), a secondary thing. And the usage worth (linked with the weight of skills): why players are above and under average in attempts (defensive attempts too) at every factor, and what is the worth of that. Another thing would be the quality of play time and how that change the weight of stats. And finally decission makings (linked with usage).

Re: Recovered old threads- miscellaneous topics

Posted: Fri Apr 22, 2011 9:15 pm
by Crow
Ben F.



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PostPosted: Tue Jan 22, 2008 3:22 pm Post subject: PBP Analysis: Offense by Starters in Game Reply with quote
When looking at the +/- differentials of low minutes players, it's hard to know how much weight to put on those numbers: are they measuring the effect of the player specifically? How much do they change because of the quality of competition? For example, we've discussed Renaldo Balkman, and how much of an impact he seems to have based on the 82games numbers. The question back then was: how much of his playing time is he playing in garbage time? And we got close to a resolution on that point. But I think the question we failed to answer was: how much does that matter?

When evaluating these numbers, it helps to have a baseline. What would we expect the numbers to look like for a typical deep bench player? If they spend a lot of time against non-starters, what impact should that have on their numbers?

I decided to investigate (spurring my earlier question) by going through the play by play and counting possessions and points produced from those possessions, based on the total number of starters in the game. If a possession had a sub in the middle (for example, someone is fouled on the floor, subs come in, and then the possession is continued) I used the lineup that finished the possession, under the assumption that that was the part that had an effect on the production of the possession. If subs were made in the middle of free throws, the subs were not counted as "in the game" until after the last free throw (since they weren't on the floor when the foul was committed).

As always, I'll make the disclaimer that my analysis may have some errors in it, and I'll try and fix it if it does, but for now I think it's error free. The data is through January 16th's games.

Code:
Strt Poss Poss% Pts ORTG
10 22146 21% 23114 104.4
9 11162 11% 12045 107.9
8 12478 12% 13381 107.2
7 13029 12% 14429 110.7
6 11920 11% 12902 108.2
5 10450 10% 11243 107.6
4 8849 8% 9138 103.3
3 6645 6% 6872 103.4
2 4226 4% 4277 101.2
1 2150 2% 2158 100.4
0 3161 3% 2657 84.1
Tot 106216 -- 112216 105.6

Strt = starters in the game


And a graphical representation (the gray line represents the league average offensive rating):



So you can see by the numbers and the graph, there's a steady increase where more starters in the game means better offensive production. This in itself is interesting, because while it seems intuitive, I don't think it's so obvious. You'd expect that lower quality players in the game would mean both a decrease in offensive and defensive talent, canceling each other out. Instead it seems to be mainly a decrease in offensive talent (or an increase in defensive talent, or both).

There are a number of arguments I could come up with as to why this could be so:

* because coaches can evaluate offensive talent more easily than defensive talent - meaning that if a player is above average defensively but below average offensively, they're more likely to be pushed to the end of the bench than if they were above average offensively and below average defensively
* there is less offensive depth than defensive depth - in other words, there are more good defensive players than offensive players, so when a team goes to its bench it's much more likely to drop off the level of offensive talent in the game than the level of defensive talent
* defense is mainly effort based, whereas offense takes skill, knowledge of the offense and experience - players coming off the bench are giving it their all to try and earn playing time, and because defense is largely effort based, you don't see a huge drop off. But because offense can't be improved merely with hustle, you see a dip on offense but not on defense.


How many of these arguments are real? I think most likely all three play roles in the reason for the dropoff (although I think the last argument is dangerous reasoning, to argue that defense is mainly or only effort, as I've heard in some places).

In any case, there's also a second fascinating piece about this data: where the offensive dropoffs take place. Between 5-9 starters in the game gives you an offensive rating above the league average, in the 107-110 range (with an odd peak at 7 starters). But go down to 4 starters or (and here's the weirdest part) go up to 10, and you see about a 4 point/100 poss dropoff.

Why do the dropoffs occur at those two points? Why isn't it more gradual? 4 combined starters in the game would mean that both teams have hit their 8th man on the bench, and that I suppose means that most teams really only run 7 deep, at least offensively speaking. It could also mean that in relative garbage time, teams range from having 0-2 starters in the game, and the starters know the game is in hand and so give less of an effort. Still, the idea that it's a huge drop instead of something more gradual is odd.

And from the other side, why does offense decline with everyone's starters in the game, and why does it then jump back up when only 1 starter on either side comes out? This seems to make no sense, and I don't really have any kind of good explanation for it. I can come up with 2 guesses, though:

* that "10 starters" only really occurs during the opening minutes of each half, and that it takes time for players to get (back) into a game rhythm. That time to get adjusted means they shoot slightly worse.
* that the first player out of the game is often a big man who gets a couple of quick fouls. Starting centers are responsible for a huge share of the defense, and with them out of the game offense takes over more. (This feels like a bit of a stretch.)


I'd invite anyone else's attempted explanations of this data, presuming, as always, that it is truly error-free. I might follow up in a bit with a team-by-team breakdown, to see if that reveals anything interesting.

Edit: Yep, I found a small error. It was miscounting the data at the end of quarters. It didn't change the conclusions, though, changing the numbers for lineups with more than 0 starters by at most 0.2 points per 100 possessions. It changed the 0 starter lineups a lot, though, down from 95 to 84. All fixed now, however, I made sure of that.

Last edited by Ben F. on Wed Jan 23, 2008 12:19 am; edited 1 time in total
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magicmerl



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PostPosted: Tue Jan 22, 2008 4:00 pm Post subject: Re: PBP Analysis: Offense by Starters in Game Reply with quote
Ben F. wrote:
There are a number of arguments I could come up with as to why this could be so:

* because coaches can evaluate offensive talent more easily than defensive talent - meaning that if a player is above average defensively but below average offensively, they're more likely to be pushed to the end of the bench than if they were above average offensively and below average defensively
* there is less offensive depth than defensive depth - in other words, there are more good defensive players than offensive players, so when a team goes to its bench it's much more likely to drop off the level of offensive talent in the game than the level of defensive talent
* defense is mainly effort based, whereas offense takes skill, knowledge of the offense and experience - players coming off the bench are giving it their all to try and earn playing time, and because defense is largely effort based, you don't see a huge drop off. But because offense can't be improved merely with hustle, you see a dip on offense but not on defense.


How many of these arguments are real? I think most likely all three play roles in the reason for the dropoff (although I think the last argument is dangerous reasoning, to argue that defense is mainly or only effort, as I've heard in some places).

Here's an additional reason: Defense is more important, so you don't get any minutes at all if you can't play defense. This means that the nba is selecting for players that can all play defense (which is another way of saying that offensive ability has a larger variation among players than defensive ability, but that this is selected for by the league).

So the difference between starters and bench players is that the starters are those players with both offensive and defensive ability, whereas the bench players are those with just defensive ability.

Yes?

p.s. Agree on your '10 starters' reasoning.
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Mountain



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PostPosted: Tue Jan 22, 2008 4:50 pm Post subject: Reply with quote
In additon to total number of starters on court it might add value to see the results of specific combinations of x starters vs y starters or under different levels of net starter advantage.


Going from 10 starter to 9 offensive rating jumps 4 pts. 10 was 5 on 5, 9 means 5-4 and someone had a starter advantage.

8 could be 5-3 or 4-4, 7 could be 5-2, 4-3. Depending on distributions the higher offensive rating of 7 over 8 could be coming from the greater average starter advantage. Whereas in 10 compared to 9, 9 always had an advantage and 10 never did , here 8 is sometimes balanced and sometimes shows advantage to one side. So the pts edge falling from 4 to 3 doesnt surprise me because the average advantage fell?

6 starters can be more combinations- from 5-1 to 3-3. Maybe there are less instances of advantage or the average size of advantage is lower? Just asking, can't tell in this form of roll-up. 5 starters isn't much different in possible combinations or perhaps distributions and therefore not much different in the results? Going from 6 to 5 the results aren't much different relative to the previous steps because the change in advantage was smaller?

4 and below are clearly bench dominated situation in some fashion with weaker offense than seen in heavier starter situations given the starter's presumed advantage on offensive skill you stated?

Maybe the results would be easier to read using one the alternative methods I suggested for grouping the data?

Last edited by Mountain on Tue Jan 22, 2008 5:21 pm; edited 2 times in total
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Mike G



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PostPosted: Tue Jan 22, 2008 5:15 pm Post subject: Re: PBP Analysis: Offense by Starters in Game Reply with quote
Ben F. wrote:
...the first player out of the game is often a big man who gets a couple of quick fouls. ...

Some teams start a designated fouler, ... I mean, tough inside defender who doesn't do much else. Sometimes he's a center.

Meanwhile, this is fascinating work. How's about you show us the breakdown of fouls, FTA, TO, etc, per possession, in each of the 10 groups?
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Serhat Ugur (hoopseng)



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PostPosted: Tue Jan 22, 2008 5:15 pm Post subject: Reply with quote
It might be good idea to categorise bench players as players in the rotation and garbage time players.
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Ben F.



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PostPosted: Tue Jan 22, 2008 5:27 pm Post subject: Reply with quote
Mountain wrote:
In addition to total number of starters on court it might add value to see the results of specific combinations of x starters vs y starters or under different levels of net starter advantage.

This is a really good point, I forgot about it.

Edit: The data here had errors, it's fixed below.

Mike G wrote:
How's about you show us the breakdown of fouls, FTA, TO, etc, per possession, in each of the 10 groups?

I can do the "possession enders" - that is shots, FTs, TOs, but fouls become problematic because then you end up with the "split possession" problem I referenced in my first post: fouls often occur in one half of a possession, then subs, then a different outcome. So the grouping would have to be different. That's a problem, unless you only care about fouls that lead to FTs.

Last edited by Ben F. on Wed Jan 23, 2008 12:58 am; edited 1 time in total
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Mountain



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PostPosted: Tue Jan 22, 2008 8:30 pm Post subject: Reply with quote
Thanks for this breakout which leads to a new question: Is the offensive rating shown for 5 starters on 3 for example the sum of the data for the 5 on offense against 3 and the 3 on offense against the 5 combined? If so, can that be disaggregrated as well?
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Chicago76



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PostPosted: Tue Jan 22, 2008 9:03 pm Post subject: Re: PBP Analysis: Offense by Starters in Game Reply with quote
Ben F. wrote:
In any case, there's also a second fascinating piece about this data: where the offensive dropoffs take place. Between 5-9 starters in the game gives you an offensive rating above the league average, in the 107-110 range (with an odd peak at 7 starters). But go down to 4 starters or (and here's the weirdest part) go up to 10, and you see about a 4 point/100 poss dropoff.


An additional possible explanation: a lot of 6th/7th men off the bench are shooters and defensive liabilities.

With 8-9 starters on the floor: the offense might be better, AND, both marginally better offenses may be playing against poorer team defenses.

Kind of like a Steve Kerr in for Ron Harper effect. The shooting specialist isn't a guy you'd want out on the court without at least 3-4 starters, because it takes those 3-4 starters to mask some of the defensive deficiencies of the shooter (from a team defense standpoint).
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Ben F.



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PostPosted: Wed Jan 23, 2008 12:57 am Post subject: Reply with quote
Yep, I found a small error. It was miscounting the data at the end of quarters. It didn't change the conclusions, though, changing the numbers for lineups with more than 0 starters by at most 0.2 points per 100 possessions. It changed the 0 starter lineups a lot, though, down from 95 to 84. All fixed now, however, I made sure of that.

I edited the main post to reflect that. Again, it doesn't really change anything but I thought I should make it clear. I'm a lot more confident in the following data now, however, since it's double and triple checked.

Now, however, I've also added the data Mike G asked for. So we can analyze things a bit more in depth.

Code:
Strt Poss Pts ORTG FG2M FG2X FG3M FG3X FTM FTX TO eFG% TO% FTM/FGA FT%
10 22146 23114 104.4 8137 8349 1323 2091 2871 951 3423 50.9% 15.5% 0.14 75%
9 11162 12045 107.9 3723 3969 792 1236 2223 732 1756 50.5% 15.7% 0.23 75%
8 12478 13381 107.2 4086 4212 783 1578 2860 968 1949 49.4% 15.6% 0.27 75%
7 13029 14429 110.7 4150 4335 955 1626 3264 1046 1956 50.4% 15.0% 0.29 76%
6 11920 12902 108.2 3627 4015 916 1592 2900 954 1853 49.3% 15.5% 0.29 75%
5 10450 11243 107.6 3254 3673 761 1360 2452 754 1658 48.6% 15.9% 0.27 76%
4 8849 9138 103.3 2681 3072 621 1099 1913 656 1522 48.3% 17.2% 0.26 74%
3 6645 6872 103.4 2064 2390 464 804 1352 432 1114 48.2% 16.8% 0.24 76%
2 4226 4277 101.2 1296 1601 277 496 854 296 744 46.6% 17.6% 0.23 74%
1 2150 2158 100.4 676 730 132 274 410 162 382 48.2% 17.8% 0.23 72%
0 3161 2657 84.1 746 992 237 965 454 164 428 37.5% 13.5% 0.15 73%

Tot 106216 112216 105.6 34440 37338 7261 13121 21553 7115 16785 49.2% 15.8% 0.23 75%

FG2M = made 2 point shots
FG2X = missed 2 point shots
FG3M = made 3 point shots
FG3X = missed 3 point shots
FTM = made free throws
FTX = missed free throws


So there go my theories about why "10 starters" don't play well on the offense end. In fact, not only does it not have to do with shooting at all, but my "foul" theory is completely wrong. It seems that the problem with having all starters in is that nobody gets to the line. The only real variability among the 5-10 starter lineups is in FTM/FGA, and you can see that FT% remains basically constant throughout those lineups. So for whatever reason, when the starting lineups are playing nobody gets to the line. Could this be because refs swallow their whistles in the early portion of the game? Or are players tentative taking it to the rim?

Here's the further breakdown by how many starters each team had:

Code:
Strt XvX Poss Pts ORTG FG2M FG2X FG3M FG3X FTM FTX TO eFG% TO% FTM/FGA FT%
10 5v5 22146 23114 104.4 8137 8349 1323 2091 2871 951 3423 50.9% 15.5% 0.14 75%
9 5v4 11162 12045 107.9 3723 3969 792 1236 2223 732 1756 50.5% 15.7% 0.23 75%
8 4v4 6437 7007 108.9 2078 2171 429 818 1564 511 968 49.5% 15.0% 0.28 75%
8 5v3 6041 6374 105.5 2008 2041 354 760 1296 457 981 49.2% 16.2% 0.25 74%
7 4v3 10777 11940 110.8 3421 3562 771 1354 2785 886 1611 50.3% 14.9% 0.31 76%
7 5v2 2252 2489 110.5 729 773 184 272 479 160 345 51.3% 15.3% 0.24 75%
6 3v3 6320 6960 110.1 1948 2108 478 824 1630 517 946 49.7% 15.0% 0.30 76%
6 4v2 5127 5473 106.7 1541 1765 401 697 1188 408 810 48.6% 15.8% 0.27 74%
6 5v1 473 469 99.2 138 142 37 71 82 29 97 49.9% 20.5% 0.21 74%
5 4v1 1698 1884 111.0 543 556 138 225 384 132 277 51.3% 16.3% 0.26 74%
5 3v2 8639 9251 107.1 2677 3080 617 1125 2046 616 1356 48.0% 15.7% 0.27 77%
5 5v0 113 108 95.6 34 37 6 10 22 6 25 49.4% 22.1% 0.25 79%
4 4v0 381 432 113.4 134 127 26 46 86 34 53 52.0% 13.9% 0.26 72%
4 3v1 3719 3876 104.2 1117 1310 273 473 823 275 607 48.1% 16.3% 0.26 75%
4 2v2 4749 4830 101.7 1430 1635 322 580 1004 347 862 48.2% 18.2% 0.25 74%
3 2v1 5740 5958 103.8 1771 2076 409 696 1189 366 955 48.2% 16.6% 0.24 76%
3 3v0 905 914 101.0 293 314 55 108 163 66 159 48.8% 17.6% 0.21 71%
2 1v1 2533 2622 103.5 794 975 177 299 503 164 431 47.2% 17.0% 0.22 75%
2 2v0 1693 1655 97.8 502 626 100 197 351 132 313 45.8% 18.5% 0.25 73%
1 1v0 2150 2158 100.4 676 730 132 274 410 162 382 48.2% 17.8% 0.23 72%
0 0v0 3161 2657 84.1 746 992 237 965 454 164 428 37.5% 13.5% 0.15 73%

Tot -- 106216 112216 105.6 34440 37338 7261 13121 21553 7115 16785 49.2% 15.8% 0.23 75%
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Mountain



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PostPosted: Wed Jan 23, 2008 1:16 am Post subject: Reply with quote
Maybe you are going to get to it later but to followup on my previous post if 5 starters facing 5 have an offensive efficiency of a bit under 105 then I would assumed that 5 facing zero would be higher. The average shown is 96. Would that perhaps be 105+ for the 5 facing zero and say less than 87 or less for the zero starters facing 5 starters to produce an average of 96?

If I am misinterpreting something let me know.

I am not seeing a way to make good statements about offensive and defensive effects without seeing both sides of the court for each starter vs starter combination separately.
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Chicago76



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PostPosted: Wed Jan 23, 2008 1:30 am Post subject: Reply with quote
Ben F. wrote:
So for whatever reason, when the starting lineups are playing nobody gets to the line. Could this be because refs swallow their whistles in the early portion of the game? Or are players tentative taking it to the rim?


It could be any of these. Or it could be that 5x5 tends to happen at the beginning of each half more frequently. Fresh players = players that move their feet better, don't reach, and don't commit stupid fouls.

I'm with mountain on splitting out the data for each team. I think more could be learned by looking at 4s stats separately in a 4x1 and then looking at 1s stats rather than aggregating the data into a combined Ortg, eFG%, etc.

For one, it would tell us whether the subs tend to lack more offensively, defensively or evenly vs. starters.
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Kevin Pelton
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PostPosted: Wed Jan 23, 2008 1:31 am Post subject: Reply with quote
Ben F. wrote:
So for whatever reason, when the starting lineups are playing nobody gets to the line. Could this be because refs swallow their whistles in the early portion of the game? Or are players tentative taking it to the rim?

It's simpler than that, isn't it? At the start of a quarter, teams aren't in the bonus. Reserves are in by the time they get in the bonus in the first and third quarters, and I'm willing to bet teams play their full starting lineup more often in these quarters than in the second and fourth quarters, when the situation is reversed.
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Mike G



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PostPosted: Wed Jan 23, 2008 6:01 am Post subject: Reply with quote
Kevin beat me to it. That's kinda why I asked about both PF and FTA .

So, the players in the game at the beginning of the possession are those responsible for fouls, and I'd also think they are the ones responsible for FTA. Yet your dilemma is that sometimes it's the possession-starters that determine a possession (foul+FT), sometimes the possession-enders (foul/no-FT). That certainly skewers things.

The turnover breakdown is quite weird:
Start - TO%
5 v 0 - 22.1%
5 v 1 - 20.5%
5 v 2 - 15.3%
5 v 3 - 16.2%
5 v 4 - 15.7%
5 v 5 - 15.5%

I would love to play with some others of these. Maybe if you repost, just the totals? I think I can copy/paste these things if the lines don't wrap. Placing a spreadsheet somewhere to download would also work. I'm also wondering about steals and blocks.

You could probably stick all the totals here; just drop Strt, Pts, ORTG, eFG%, TO%, FTM/FGA, and FT% (we can figure those). Add Stl, Blk, PF? And reduce the spaces between columns. That would be awesome.
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Harold Almonte



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PostPosted: Wed Jan 23, 2008 9:24 am Post subject: Reply with quote
When the number of starters increases to an odd number, the off. increases more than when the number of starters increases to a par number, I think that has sense with the Mountain's one starter-matchup-advantage approach.
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BadgerCane



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PostPosted: Wed Jan 23, 2008 10:24 am Post subject: Reply with quote
This is really interesting. Though, I'm not even sure this discussion is being based n a correct premise, that "offensive rating goes down as starters come out." Really, you don't see a change in offensive rating until you get below 5 total starters. That only accounts for about 20% of all game time, anyway. When there are 5 or more total starters, offensive rating varies up and down. I think analysis there should be "in what situations during a game would neither team have at least 3 starters on the court?" Perhaps a second level to this play by play analysis could be what the score was at these times. We could also use what period of the game this tended to be. I'd guess that those last minutes, with no starters for anyone, take place at the wrong end of a blowout where nobody in the building even cares what happens at that point. Also, I would guess that teams with worse rotations and generally worse players would be the types of teams to experiment with lineups that do not involve starters. So, this dip in production could be a reflection of that.

Ben F.



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PostPosted: Wed Jan 23, 2008 12:04 pm Post subject: Reply with quote
Kevin Pelton wrote:
It's simpler than that, isn't it? At the start of a quarter, teams aren't in the bonus. Reserves are in by the time they get in the bonus in the first and third quarters, and I'm willing to bet teams play their full starting lineup more often in these quarters than in the second and fourth quarters, when the situation is reversed.

Sheesh, I can't believe I didn't think about that. It makes perfect sense, of course, and also explains the ramp up in FTM/FGA (and therefore efficiency) between 10 and the peak of 7 starters.

Mike G wrote:
So, the players in the game at the beginning of the possession are those responsible for fouls, and I'd also think they are the ones responsible for FTA. Yet your dilemma is that sometimes it's the possession-starters that determine a possession (foul+FT), sometimes the possession-enders (foul/no-FT). That certainly skewers things.

Exactly - you can have a situation where there's a rebound in the backcourt, a foul after or during the rebound, subs, and then a continuation of the possession (an inbound and a play). Or you could have a play where the shot is blocked out of bounds, subs come in, and then the possession continues. I'm not quite sure how to handle it. With the last numbers I put out, everything in the possession is just attributed to the lineup that ended the possession. But obviously that's problematic in the block example, and would be in the foul example as well.

Mike G wrote:
The turnover breakdown is quite weird:
Start - TO%
5 v 0 - 22.1%
5 v 1 - 20.5%
5 v 2 - 15.3%
5 v 3 - 16.2%
5 v 4 - 15.7%
5 v 5 - 15.5%

What do you find weird about this? The only thing that seems really out of place is the dip at 5v2. Other than that it seems to trend upwards, consistent with my analysis in the original post: the subs that come in are better (or at least neutral) defensively and worse offensively, thus turning it over more on their end and forcing more or the same on the other end.

Mike G wrote:
I would love to play with some others of these. Maybe if you repost, just the totals? I think I can copy/paste these things if the lines don't wrap. Placing a spreadsheet somewhere to download would also work. I'm also wondering about steals and blocks.

Sorry, I forgot that my resolution is generally bigger than most - I saw that the tables didn't wrap on my monitor and assumed they were OK. I'll try and link up to a Google Spreadsheet, it's easier for me anyway.

BadgerCane wrote:
I'd guess that those last minutes, with no starters for anyone, take place at the wrong end of a blowout where nobody in the building even cares what happens at that point. Also, I would guess that teams with worse rotations and generally worse players would be the types of teams to experiment with lineups that do not involve starters. So, this dip in production could be a reflection of that.

This is a good point, but from observation I think that you're wrong to assume all or most of the low-starter time is when the game is in hand. 23% of all possessions are played with 4 or less combined starters in the game, meaning an average of 11 minutes per game. From my earlier study, we can see that even the best teams have relative blowouts for 10 min per game - and most of the time the coaches don't feel secure in that blowout for a long time after that. So 11 minutes per game, league-wide, is an awful lot to attribute to blowouts.

I'll see if I can look into it, though, just to check all the factors. You very well could be right that it's having a large influence on the numbers.

Last edited by Ben F. on Wed Jan 23, 2008 12:04 pm; edited 1 time in total
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John Hollinger



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PostPosted: Wed Jan 23, 2008 12:04 pm Post subject: Reply with quote
Tied up in this data may also be another phenomenon -- that players tend to do better when their minutes come in larger chunks. When you get down to four or fewer starters on the court, there's almost guaranteed to be a couple guys out there who are playing just a handful of minutes that night, and the data shows it's really hard to be effective coming in cold and coming out before you're warm.
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Ben F.



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PostPosted: Wed Jan 23, 2008 12:09 pm Post subject: Reply with quote
John Hollinger wrote:
Tied up in this data may also be another phenomenon -- that players tend to do better when their minutes come in larger chunks. When you get down to four or fewer starters on the court, there's almost guaranteed to be a couple guys out there who are playing just a handful of minutes that night, and the data shows it's really hard to be effective coming in cold and coming out before you're warm.

Out of curiosity, how would you study this? I would think that looking at the production of low minutes players would be incredibly hard because it's hard to separate out the different causal factors. If a player gets low minutes and performs badly, is that because he's not as good or because he's cold? And if he gets more minutes and plays better, is that because he's having a good night, or is there a mismatch or a suitable style of play for him so the coach leaves him in? In other words, aren't there a lot of confounding factors that could get in the way of studying how players play in short minutes versus long minutes?
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Mike G



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PostPosted: Wed Jan 23, 2008 12:19 pm Post subject: Reply with quote
Ben F. wrote:

Mike G wrote:
The turnover breakdown is quite weird: ..


What do you find weird about this? ... the subs that come in are better (or at least neutral) defensively and worse offensively, thus turning it over more on their end and forcing more or the same on the other end.
.

Ohh, my bad. I was thinking the subset "5v0" referred to the productions of the 5 starters (vs 5 non-starters). But you've lumped together the (5 vs 0) and the (0 vs 5) subsets. And we really don't see from this how the 5-0 squad does vs the 0-5 unit; nor vice-versa.

So, I guess if you're really going to upload a spreadsheet, then it's more universally useful to give us the 5-4 and the 4-5, etc. Just twice as many lines. Looking forward to it.
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Ben F.



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PostPosted: Wed Jan 23, 2008 12:39 pm Post subject: Reply with quote
Mike G wrote:
Ohh, my bad. I was thinking the subset "5v0" referred to the productions of the 5 starters (vs 5 non-starters). But you've lumped together the (5 vs 0) and the (0 vs 5) subsets. And we really don't see from this how the 5-0 squad does vs the 0-5 unit; nor vice-versa.

Sorry for not being clear. The 5v0 means the production of both lineups when one side has 5 starters and the other side has 0. So it includes both the 5 starter lineup and the 0 starter one.

I guess for the spreadsheet I'll break down how each side performed, like Mountain and Chicago were asking for. Any idea what I should do about the split possessions? Is it a problem if it all gets grouped in the possession ending lineup? By my count, 4,607 possessions end up "split" in this way, which is about 4.3% of the total. Would you rather see the totals sooner, or have me work on trying to split possessions in the middle?
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Ben F.



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PostPosted: Wed Jan 23, 2008 2:10 pm Post subject: Reply with quote
The spreadsheet is now online. When possessions are split by subs, I simply grouped them in the possession ending lineup. Again, this represents only 4.3% of all possessions, so I don't expect it to skew the data much at all.

Here's the really interesting addition: so much for the "bonus" theory. Unless I'm doing something wrong, it seems that fouls are down almost as much as FTs in the 5v5 lineup. The 5v5 lineup gets to the line at a rate that's 60% of league average (0.14 FTM/FGA compared to a 0.23 average) but only draws fouls at a rate that's 71% of league average (0.17 Fouls/Poss compared to a league average of 0.24). So there's still something else going on here.
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Mountain



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PostPosted: Wed Jan 23, 2008 4:24 pm Post subject: Reply with quote
Thanks for sharing the dataset. It could be a very fruitful database especially drilling down further using other parameters. I'd ask if anyone has ever prepared such before but I assume most of those who might have wouldn't say.

I see that generally the offensive efficiency of the two sides of a starter imbalance are fairly close until you get to 4 or 5 on 1 or 5 on 0. I didnt expect that but maybe I should have at league level given different team qualities and player quality and situation and performances on a given night. Good to see & learn. Most of the extremes are small sample sizes though so not sure a lot should be made of their behavior.

Time & situation data could be interesting: this data for bulk of game vs garbage or clutch time.

Or with starter PG vs not. Or #1 star or not.

If you overlaid player quality maybe you could find some stuff about how many good players or how good a leader needs to be on the floor to "hold the fort"- or score.
Maybe Brandon Roy could be an interesting case study.

You could look at the patterns at team level. Which coaches get more starter advantage minutes (and big advantages) and what do they get out of it? Who faces more starter disadvantage minutes and are they cool with it or getting taken by the opposing coach? I'd hope that something like that could come out even for a sample team. You could even take it down to play against important rivals. Teams might be interested in this data if they don't have it. Basketballvalue.com could potentially add a layer for 5 man lineups to show performance against 0-5 starters. It could be an intermediate step between the raw rollup data and true player quality adjusted data.

In the copy I downloaded I am going to add per possession values where appropriate to help analysis of those columns.

5 x 5 represents 20.9% of all possessions or about 10 minutes a game.
4 X 4 or greater totals 58.3%.
3 X 3 or greater totals 76% of time.

Last edited by Mountain on Thu Jan 24, 2008 5:25 pm; edited 1 time in total
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John Hollinger



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PostPosted: Thu Jan 24, 2008 3:56 pm Post subject: Reply with quote
Somebody asked how to study the performance of players in games they get very few minutes -- there are two ways to do this -- one is to compare individual players based on their "big minute" games vs. their "small minute" games; the issue is that they may just be playing in matchups that are most advantageous, thus biasing the result.

The other, more robust way is to look at players who suddenly see a big increase in playing time due to external factors -- i.e. injuries or trades.
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Harold Almonte



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PostPosted: Thu Jan 24, 2008 8:12 pm Post subject: Reply with quote
Mountain wrote:
Quote:
You could look at the patterns at team level. Which coaches get more starter advantage minutes (and big advantages) and what do they get out of it? Who faces more starter disadvantage minutes and are they cool with it or getting taken by the opposing coach? I'd hope that something like that could come out even for a sample team. You could even take it down to play against important rivals. Teams might be interested in this data if they don't have it.


Bettors might be interested in this data for their over/under issues (joined with x pace vs. y pace data), which is the most this study can be useful.
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Ben F.



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PostPosted: Fri Jan 25, 2008 12:06 am Post subject: Reply with quote
Any thoughts on the new development that it doesn't seem to be simply the lack of being in the bonus suppressing FTM/FGA but rather a lack of fouls?
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Chicago76



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PostPosted: Fri Jan 25, 2008 3:03 am Post subject: Reply with quote
Ben F. wrote:
Any thoughts on the new development that it doesn't seem to be simply the lack of being in the bonus suppressing FTM/FGA but rather a lack of fouls?


I want to make sure I'm understanding the spreadsheet first:

When I look at 4x0 and see 0, those are stats for the team with no starters vs. 4, right? That would mean that the team with no starters only commits fouls .17 times per possession...

At the low foul end of the spectrum, I see that a team of no starters tends to not foul against anyone, regardless of whether they're playing against 0 to 5 starters. Three possible explanations:

1-The zero starter lineup is presumably pretty fresh, so they're not committing stupid reach fouls. They're able to move their feet more. A lot of fouls are the result of poor defensive position and fatigue.

2-The zero starter squad sees limited minutes, so they're more likely to be conservative in just about everything they do. As mentioned, it's difficult to enter a game and "play".

3-Zero starters against a full squad is likely to happen in situations where the zero starter team is well ahead. You don't want to foul in these situations, you want to keep the clock moving.

Every other lineup with <= .22 fouls/poss involves the team that presumably would either be playing from well ahead or has the more rested lineup, ie, the team with more bench guys on the court:

1 vs. 5
4 vs. 5
1 vs. 4
2 vs. 5

The other lineup is 5 vs. 5, which tends to happen at the beginning of each half, when players are well-rested.
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Chicago76



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PostPosted: Fri Jan 25, 2008 3:17 am Post subject: Reply with quote
John Hollinger wrote:
Somebody asked how to study the performance of players in games they get very few minutes -- there are two ways to do this -- one is to compare individual players based on their "big minute" games vs. their "small minute" games; the issue is that they may just be playing in matchups that are most advantageous, thus biasing the result.

The other, more robust way is to look at players who suddenly see a big increase in playing time due to external factors -- i.e. injuries or trades.


It's difficult to isolate three or four possible reasons for the performance of low minute players:

-it could just be tough for anyone to come off the bench when everyone else is in the flow of the game. You might be able to test this for everyone by comparing their performance off the bench the first 3-4 minutes vs. the remaining minutes of their rotation.

-do bench players perform better when you increase their minutes in a given game? If so, is the 8 min a night guy getting increased minutes because they're playing well in a particular game? Could it be due to favorable matchups?

-how do bench players perform when their role is suddenly increased due to team injuries?

I suspect a lot of players do find it difficult the first few minutes of entering the game in the first item. For the last one, it could be the case that bench guys don't have first team repetition. They're not incorporated into plays when they're getting 6 minutes a night, because this is an afterthought from a team stategy standpoint. When your 9th man suddenly becomes your first shooter off the bench due to injuries, a team may need to start running plays for the guy to get involved in the offense.
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Ben F.



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PostPosted: Sun Jan 27, 2008 12:31 pm Post subject: Reply with quote
The plot thickens:

Maybe there's an obvious answer to this that I'm missing, but it really does seem as if fouls are way down at the beginning of each half - meaning the reduced efficiency of starters is not just a bonus effect. Take a look at this graph. It plots the number of fouls occurring based on how many minutes have elapsed in the game (so any fouls occurring within the first minute are labeled 0 on the x-axis).



(This data through January 16th.)

It's really a striking trend. In the first minute only around 200 fouls were called, and then there's a slow upward trend to get to around what seems to be the normal number of 500. You can see that right before the end of a quarter the number jumps up a little (except for in the 4th, where it jumps up a lot) because of intentional fouls. And at the beginning of each quarter it drops - but the numbers go much lower to start each half than to start each quarter.

Is this as simple as the players being fresh, thus moving their feet and fouling less? Is it that starters are more reticent to foul because they want to stay in the game so when the bench comes in there are more fouls (but then why the drops at the beginning of quarters where it's not guaranteed at all that the starters are playing)? Maybe the refs don't call as many fouls to start the quarters to let players get into the flow?

It's interesting because I've never heard about this anywhere, really. The idea that fouls are much lower to start each half and even each quarter has been completely overlooked as far as I can tell. You'd think this would have been noticeable before, and that it would affect coaching decisions as well - maybe it would argue for using a player who can draw fouls at a high rate off the bench, once fouls are back up and you'd get the added effect of the bonus as well.

-----

Further investigation:

An obvious explanation would be that teams tend to play slower in the opening minutes of quarters, which would of course reduce fouls. So I looked at possessions per minute, and you see that borne out in the data - large dips at the quarters:



You can see that teams maximize possessions per minute at the end of quarters (taking quick shots at the buzzer), and especially at the end of games (intentional fouls), but that there's also a corresponding dip to start all quarters.

But I don't think this fully explains it. For one, it seems like it jumps back up to normal after the first minute of each quarter, which fouls don't. Secondly, the dips are exactly the same at each quarter, whereas we saw in the foul chart that they differed between the start of a half and the start of a quarter.

So let's combine the two charts now, and have a chart representing fouls per possession:



The issue still seems very much alive. You can see the same trend noticed before - very low to start the game, dips at the start of quarters but much more at the start of a half. An interesting note about it is that it slowly increases throughout the game, perhaps lending credence to the "fatigue" theory.
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HoopStudies



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PostPosted: Sun Jan 27, 2008 8:05 pm Post subject: Reply with quote
Ben -

Very good stuff.

Let me ask about offensive rebounding. Your table had eFG, turnovers, and foul rates. What about OR%? I assume it's a small difference, but want to check.

If it is foul shots, the next thing is to start looking into the who and how. Are there particular teams with this being stronger than other teams? Is there an obvious reason why those teams have the pattern? What about a home/road breakdown?

I'm not sure how important this all is, but the fact that it is interesting probably means it is useful somehow...
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Mike G



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PostPosted: Mon Jan 28, 2008 7:04 am Post subject: Reply with quote
Great stuff, Ben.

Chicago76 wrote:
...
When I look at 4x0 and see 0, those are stats for the team with no starters vs. 4, right? That would mean that the team with no starters only commits fouls .17 times per possession...

At the low foul end of the spectrum, I see that a team of no starters tends to not foul against anyone, regardless of whether they're playing against 0 to 5 starters. Three possible explanations:
...

Well, none of those seem very likely, so I'll cut to the chase. I suspect Ben F. has put 'fouls drawn' in the 'PF' column, since his study is (or started out as) 'offensive efficiency'.

Non-starters (subs) foul at least 50% more than starters do. It's not feasible that starters foul the heck out of every sub that comes in, but take it easy on other starters. The PF numbers make sense only when they are applied to the offense, i.e., the 'team' designated.

(I assumed these aren't just fouls committed by the opposing defense, but all fouls, including offensive?)

And so, we may as well see the whole range of statistics. As Dean says, OReb; also, DReb and Ast. (I will be putting PF where I normally find them: as fouls committed.)

As subs enter the game, fouls rise. No surprise and no contradiction.

Ben F.



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PostPosted: Mon Jan 28, 2008 9:12 am Post subject: Reply with quote
Mike G wrote:
Well, none of those seem very likely, so I'll cut to the chase. I suspect Ben F. has put 'fouls drawn' in the 'PF' column, since his study is (or started out as) 'offensive efficiency'.

...

(I assumed these aren't just fouls committed by the opposing defense, but all fouls, including offensive?)

Yes, you're right on both counts. I mistakenly attributed fouls to the offense, and forgot to pull out offensive fouls. I'll try and fix both of those later today, as well as provide more stats.

Mike G wrote:
Non-starters (subs) foul at least 50% more than starters do. It's not feasible that starters foul the heck out of every sub that comes in, but take it easy on other starters. The PF numbers make sense only when they are applied to the offense, i.e., the 'team' designated.

...

As subs enter the game, fouls rise. No surprise and no contradiction.

Is it as simple as this then? Subs just foul more? I'll see if I can overlay a graph of average subs in the game by each minute on the fouls/poss by minute and see if the contours are similar. Judging by the earlier results, subs and fouls are obviously correlated, but I wonder which is the cause?

And again, then, either way: why not play players who are good at getting to the line off the bench more? Wouldn't that drastically increase their effectiveness?
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Mike G



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PostPosted: Mon Jan 28, 2008 9:32 am Post subject: Reply with quote
Ben F. wrote:
.. why not play players who are good at getting to the line off the bench more? Wouldn't that drastically increase their effectiveness?


The other side of the same coin. Subs foul more because (with exceptions) they aren't expected to stay in the game as long. Yet there's more value in drawing fouls on the opponent's starters. So yeah, it's easier to score when coming off the bench (Ben Gordon); but he's not necessarily more 'effective' overall, in that he isn't neutralizing the opponent's main strength -- or not quite as much as the raw numbers suggest.
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gabefarkas



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PostPosted: Mon Jan 28, 2008 11:14 am Post subject: Reply with quote
I would have thought the opposite. In my mind, a 4x0 situation means it's on the verge of a blowout (by the team with 0 in). So, both teams are less likely to foul; the team with the lead because they are trying to run the clock out, and the team losing because they are trying to claw their way back in it.
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Ben F.



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PostPosted: Mon Jan 28, 2008 11:21 am Post subject: Reply with quote
Mike G wrote:
Subs foul more because (with exceptions) they aren't expected to stay in the game as long. Yet there's more value in drawing fouls on the opponent's starters. So yeah, it's easier to score when coming off the bench (Ben Gordon); but he's not necessarily more 'effective' overall, in that he isn't neutralizing the opponent's main strength -- or not quite as much as the raw numbers suggest.

This seems to be another study, but I would be interested in trying to evaluate the tradeoffs of a situation like that. It's not so apparent to me that there's a whole lot of value in trying to get a player in foul trouble to get him out of the game. Ed K's study from a couple years ago suggests that even the most foul prone players only lose about 4-6 minutes per game. Meanwhile, in the first 5 minutes of the game there are 15.6 fouls committed every 100 possessions, while in the second quarter the rate is 23.9. That's a 53% increase. I would think the value added to offensive efficiency by that much higher foul rate would be greater than what is gained by the small number of minutes lost due to foul trouble of the other team.
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Mike G



Joined: 14 Jan 2005
Posts: 1712
Location: Delphi, Indiana

PostPosted: Tue Jan 29, 2008 7:16 am Post subject: Reply with quote
A couple of factors that come to mind:

- Refs just let starters get away with more contact; subs get the quick whistle. Whether the players are playing deliberately to foul/not foul, the refs steer them this way.

- Coaches put guys (starters) on the bench with their 2nd foul of the 1st half; replacing them with less-valuable subs.

Knowing that you're well into the 2nd half with several fouls to give, you play with more abandon than you do at the start of the game. This should account for the ever-rising foul rate seen as the game progresses. (I presume blowouts account for slightly fewer fouls in the 4th Q)

Re: Recovered old threads- miscellaneous topics

Posted: Fri Apr 22, 2011 9:23 pm
by Crow
bstenger



Joined: 10 Nov 2005
Posts: 15
Location: San Francisco, CA

PostPosted: Wed Nov 05, 2008 7:06 pm Post subject: Graphical +/- Reply with quote
First post, though I've met some APBRmetricians at past MIT Sports conferences. I'm a journalist, living in SF.

I've worked out a play-by-play transform that graphs +/- for each player on a second-by-second basis. 14420 data points per game (if no overtime). Yellow/Red gradient shows +. Green/Black gradient shows -.

It seems like a nice window into the group & coaching dynamics that statistical +/- has a hard time describing. This graph shows what last night's Celtics-Rockets game looked like. Check out how well Pierce and the Boston 2nd Unit performed, keying runs in the 2nd and 4th quarters.

Image
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basketballvalue



Joined: 07 Mar 2006
Posts: 208


PostPosted: Wed Nov 05, 2008 10:42 pm Post subject: Reply with quote
Very interesting. I noticed that my eye was drawn to the sections of solid red, but isn't that when the game was evenly played at a plateau in +/- differential (I guess +16 for Boston)? It seems like you'd want the periods where +/- changed significantly to be the most notable, not where it was stagnant but at a high water mark.

Thanks,
Aaron

PS Why is Pierce so close to solid red starting at the end of the first half? The +/- differential was changing, right? Is it just not noticeable?
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Ryan J. Parker



Joined: 23 Mar 2007
Posts: 708
Location: Raleigh, NC

PostPosted: Wed Nov 05, 2008 10:54 pm Post subject: Reply with quote
That's pretty cool. I think what I like the most is how you can visually see who is on the court at any given time of the game.

Also, do you have a website/blog? Because the WWW link in your profile goes to a page that does not exist.
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bstenger



Joined: 10 Nov 2005
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Location: San Francisco, CA

PostPosted: Thu Nov 06, 2008 2:16 am Post subject: more to come Reply with quote
Apologies for incorrect url -- see http://nbagraphs.tumblr.com

I'm only assigning 33 colors that account for -16 to +16. Greater than +16 has the same color as +16 (red, likewise less than -16 stays black). The algorithm will keep track of the higher/lower numbers, but colors stay red/black.

The automation for creating the graphs is shaping up so that every game can go up with minimal effort. A little more work past that will lead to a database that can generate graphics for team and player comparisons. Possibly by Christmas.

Meantime check out Tony Parker's monster 55 point, 10 assist game.

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Mountain



Joined: 13 Mar 2007
Posts: 1527


PostPosted: Thu Nov 06, 2008 3:09 am Post subject: Reply with quote
Would you consider adding splits for +/- for the sum of starters and likewise for subs?

Doing so for listed positions might not be worthwhile much of the time but it could help get at the degree to which a coach went non-traditional cumulatively and to what impact at each position.
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bstenger



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PostPosted: Thu Nov 06, 2008 9:59 am Post subject: Reply with quote
Graphing second-by-second +/- for each 5-player lineup in a game is doable. It makes sense to prioritize working out the proper data structure. With it I can build a single data archive that encompasses players, lineups, and teams.

The core graphing module layers graphs one on top of the other. And there are lots of graphs to layer. Each team/player line and each color is its own graph (33 colors, 24 players, 2 teams, 2884 seconds per non-OT game). Adding complexity/sophistication while keeping a single game graph intuitive is the computational design & journalism question that I'm interested in working out.
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bstenger



Joined: 10 Nov 2005
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PostPosted: Sat Dec 06, 2008 1:08 am Post subject: Reply with quote
Second-by-second graphical +/- with lineups. Lineup separator bars are close but not yet perfectly aligned. ... from Celtics-Blazers, 12/5/08

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Mountain



Joined: 13 Mar 2007
Posts: 1527


PostPosted: Sat Dec 06, 2008 8:06 am Post subject: Reply with quote
Thanks for the reminder.
One suggestion might be to have stretched to full page blowups available to aid closer study.


This may be moving too far away from your design but here are a few ideas for your consideration:

What if you made a second graph, based on the system that builds the first graph, with a set of player lines, lines that moved up and down on mini +16 (or whatever) to =16 scales over the game. That would allow seeing both cumulative personal +/- standing and change as Aaron's prior observation indicated interested in and I share. I can personally process line movement quicker and easier than I can color change though the line could change color too in accordance to your color scheme. Maybe it is more the bar style display that slows me down a bit rather than the color.

And maybe (though this gets away from simplicity) marked with red & black dots/datapoints for when they personally scored or presumably (based on counterpart matchups or visual confirmation) were scored upon.
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Mike G



Joined: 14 Jan 2005
Posts: 3561
Location: Hendersonville, NC

PostPosted: Sat Dec 06, 2008 8:34 am Post subject: Reply with quote
This is pretty great! I don't think anything is added, though, by the lineup listings; we can see that from the charts, and it makes it harder to line up the opponent's personnel.

Maybe it would be best to put starting lineups at top, followed by substitutions in order of appearance. Within and between teams, easier to see who is in the game with/against starters/subs.

Interesting: In the first Boston game, Pierce and subs lead the charge. In the 2nd one shown, Pierce and subs just hold the fort, until the other starters return late in the half.

The Blazers subbed earlier, and it seems to have undermined them. Przy for Oden was especially unprofitable.

Let me promote a bit (it's a winner) :
http://nbagraphs.tumblr.com
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Mountain



Joined: 13 Mar 2007
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PostPosted: Sat Dec 06, 2008 8:57 am Post subject: Reply with quote
I forgot for a moment but Mike's comment reminding me to suggest that potentially you could bold the substitutions on the lineup lists as they come in.

And Mike's comment also spurs this add=on idea:

Maybe you could have different display modes you could toggle between
1) The player summary
2) 5 man lineup matchup.
(looking at one or the other at a time easier than both at once?)
3) Position by position starter matchup, with their lines immediately following each other thru the positions. Some time is against subs but that is part of the story you can follow as the other guy is shown as absent.

Last edited by Mountain on Sat Dec 06, 2008 4:07 pm; edited 2 times in total
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Serhat Ugur (hoopseng)



Joined: 13 Oct 2006
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Location: Basketball Research

PostPosted: Sat Dec 06, 2008 2:50 pm Post subject: Reply with quote
Congrats on the innovative work!
Just wondering, which tools do you use to produce these charts? Is it doable to extend it to all games?
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bstenger



Joined: 10 Nov 2005
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PostPosted: Mon Dec 08, 2008 1:52 pm Post subject: Reply with quote
It's a heaping pile of Perl code, relying on the GD::Graph and Image::Magick modules for graphics. The idea/design was originally sandboxed in Processing before I did graphics work in Perl.

There's a bug in transforming play-by-play data to the data structure I use for graphics. It currently prevents me from rendering ~25% of all NBA games. Fixing it and getting up over 95% of games rendered should then lead to interesting interactive interfaces for graphically comparing teams, players, lineups, and games. Those interfaces should start to appear around the beginning of 2009.
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TexasEx



Joined: 12 May 2006
Posts: 35
Location: Houston, TX

PostPosted: Mon Dec 08, 2008 11:03 pm Post subject: Reply with quote
How do your +/- graphs compare to those at popcornmachine? Regardless, very cool work.

http://popcornmachine.net/cgi-bin/newgf.cgi
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bstenger



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PostPosted: Sun Apr 19, 2009 1:34 pm Post subject: Reply with quote
My chosen free host is now providing higher resolution files ... just in time for the playoffs. Unfortunately the img addresses don't work with the forum's BBCode and can't be shown here. They can be seen at http://nbagraphs.tumblr.com.

Notice the Chicago would have had a fairly comfortable lead if they hadn't played Miller and Noah together so much. When they were both on court the Celtics would gain on the Bulls. Hinrich played really well though, and it made a big difference in the 2nd Half. Boston's bench was truly terrible. I think the Celtics are in trouble.

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erivera7



Joined: 19 Jan 2009
Posts: 178
Location: Chicago, IL

PostPosted: Sun Apr 19, 2009 1:51 pm Post subject: Reply with quote
I'm really digging the graphs .. might make use of them over at a site I help run (Third Quarter Collapse).
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bstenger



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PostPosted: Fri May 15, 2009 4:06 pm Post subject: Reply with quote
Accumulated annual second-by-second +/- graphs for each player for last season, http://ec2-67-202-39-209.compute-1.amaz ... om/players

This is the first draft of an interactive interface for player graphs that should improve considerably over time. Please post any feedback/recommendations here. Unfortunately, the url will only be available on a temporary basis, probably just until June 1, 2009.
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flyerfanatic



Joined: 14 May 2009
Posts: 5


PostPosted: Sat May 16, 2009 9:59 pm Post subject: Reply with quote
I have a question about plus/minus ratings, I figured this would be a better place to stick it rather than starting a new thread.

I think I understand how plus/minus works, but just the unadjusted format I have read that there is some kind of regression formula to help factor in other players and circumstances in basketball games. Obviously plus/minus is not completely accurate as a stat, but I was wondering how well of a stat it is as an unadjusted state i. i'm definitely going to have to look into the formula for the adjusted plus/minus.

Also, I plan on trying the plus/minus next season at the freshman high school level. will it still produce somewhat of a good idea of where players stand with such less of games compared to the NBA?

any feedback is appreciated.
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ecumenopolis0



Joined: 15 Jul 2008
Posts: 22
Location: Houston

PostPosted: Sat May 16, 2009 10:52 pm Post subject: Reply with quote
flyerfanatic wrote:
Also, I plan on trying the plus/minus next season at the freshman high school level. will it still produce somewhat of a good idea of where players stand with such less of games compared to the NBA?

any feedback is appreciated.


haha, i did some of this when i was injured this year/couldn't play. the numbers i got actually seemed pretty good subjectively, but i might have just gotten lucky.

after all, it's less than half as many games at 2/3 the minutes each.
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flyerfanatic



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PostPosted: Sun May 17, 2009 1:25 am Post subject: Reply with quote
ecumenopolis0 wrote:
flyerfanatic wrote:
Also, I plan on trying the plus/minus next season at the freshman high school level. will it still produce somewhat of a good idea of where players stand with such less of games compared to the NBA?

any feedback is appreciated.


haha, i did some of this when i was injured this year/couldn't play. the numbers i got actually seemed pretty good subjectively, but i might have just gotten lucky.

after all, it's less than half as many games at 2/3 the minutes each.


how did you go about recording it? just have a pen and paper and mark the +/= as players came out of the game?

Re: Recovered old threads- miscellaneous topics

Posted: Fri Apr 22, 2011 9:37 pm
by Crow
THWilson



Joined: 19 Jul 2005
Posts: 148
Location: phoenix

PostPosted: Sat Mar 15, 2008 9:32 pm Post subject: Hollinger: Josh Smith 18% chance to be all-time block leader Reply with quote
Interesting Article.
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deepak_e



Joined: 26 Apr 2006
Posts: 373


PostPosted: Sat Mar 15, 2008 11:40 pm Post subject: Reply with quote
One thing he doesn't seem to take into account (unless I'm mistaken) is how aging impacts a player's ability to collect certain stats. I'd be surprised if a wing player like Josh Smith can sustain his current shot blocking rate.
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Chicago76



Joined: 06 Nov 2005
Posts: 77


PostPosted: Sun Mar 16, 2008 3:02 am Post subject: Reply with quote
He sort of takes into consideration aging in his "established level" estimation by taking a weighted average and applying 50% of the weight on the last year.

It doesn't necessarily work on guys who are in the middle of their careers, but those guys are so far away from career records, it's pretty impossible to say what injuries, retirement decisions, etc are going to do someone's chances anyway.

I looked at Malone's pursuit of the scoring record. I didn't count the short season against Malone during the lockout. I multiplied that season by 82/50 to determine his established level going forward. It still hurt him a bit as he still lost games to creep closer to the record.

1997/98 30.7%
1998/99 24.5%
1999/00 31.6%
2000/01 37.2%
2001/02 49.1%
2002/03 76.6%
2003/04 25.6%
retired

It's interesting to see the impact of the lockout in 1998/99 and the move to the Lakers in 2003/04.
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jemagee



Joined: 05 Nov 2005
Posts: 97


PostPosted: Mon Apr 07, 2008 1:49 pm Post subject: Reply with quote
What are th odds that

A. The sixers are smart enough to see he fits into what they are trying to do and
B. The embarassment that is the atlanta hawks ownership fights so much they don't match any contract offer cause they can't come to a consensus.

sadly, much lower than 18% i'm sure

Re: Recovered old threads- miscellaneous topics

Posted: Fri Apr 22, 2011 9:40 pm
by Crow
Mountain



Joined: 13 Mar 2007
Posts: 438


PostPosted: Sat Mar 22, 2008 4:39 pm Post subject: The world of eba-stats.com Reply with quote
Anybody have experience with their forum, courses, etc.?

Any commentary about that experience? Recommended?
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holymoly



Joined: 30 Jul 2005
Posts: 45


PostPosted: Sun Mar 23, 2008 11:21 am Post subject: Reply with quote
Its Spanish, some interesting stuff seems to go on there but only registered members can access info, have tried to sign up to contribute on the forums but its close in process to renewing my passport...
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Mountain



Joined: 13 Mar 2007
Posts: 438


PostPosted: Sun Mar 23, 2008 3:29 pm Post subject: Reply with quote
Yes a 43 question application and request for references surprised me to gain entry. I'd only do that if I got a recommmedation it was worth it. What I could read in bits and pieces around the site didn't push me over the threshold. But I hope someone here has checked it out and will give a reply.
If european basketball professionals are active there and the impression is given that they are then awareness of their analytic work would be good.

Re: Recovered old threads- miscellaneous topics

Posted: Fri Apr 22, 2011 9:42 pm
by Crow
blb2397



Joined: 10 Mar 2008
Posts: 7


PostPosted: Mon Mar 24, 2008 6:02 pm Post subject: PER Question? Reply with quote
Ok
I am a newbie to this incredible forum and have been trying to understand Hollingers PER. My question revolves around how Hollinger uses 2 points for a FGM made as his start point for a value. If a 2 point FG is worth two points and a possession is worth nearly 1 then how do we justify an increase of two points of player worth on a possession. I am sorry if I am missing on something obvious but its been kind of bugging me. I created a rating system years ago that I used in a game and I gave one point for a 2 pointer and -.67
for a missed shot (back when offensive rebounds were at a little higher rate) for a difference of 1.67 points between a good shot and a missed shot on a possession. Nothing too original with my system but I am tweaking it for my own site.
Thanksa bunch Idea
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asimpkins



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PostPosted: Sat Mar 29, 2008 9:42 am Post subject: Reply with quote
I'm not entirely sure I understand your question, but I'll try to talk you through it.

Because some value is subtracted for the passer, a 2 point field goal is actually valued at 1.7 and a 3 point field goal is valued at 2.7 in PER.

This is simply awarding how many points you generated for your team. If you score 2 points then 2 points gets credited -- minus 0.3 credit which goes to the passer for his assists.

A possession is valued at 1 point because that's approximately how many points an average team will generate with that possession.

PER essentially tries to add up all box score statistics in terms of their value in points. So scoring a basket generates 2 points for your team -- obviously -- but securing possession (through a steal for example) only generates 1 point for your team because that's just an estimate of what a team will score with that possession.
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blb2397



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PostPosted: Sat Mar 29, 2008 3:15 pm Post subject: Reply with quote
Thanks for the response. It is helpful. I guess the main problem I have is I am attempting to compare one stats value to another stats value in the formula. So I can understand that a steal might be worth one point less than a field goal, being that a steal grants another possession (average worth about 1 point) So it strikes me awkwardly that a two pointer would be worth a full 2.4 (3 without assist adjustment and rebound adjustment) points more than missed shots when the difference between a missed shot and made shot
is at most 2 points. I'll assume that PER is not trying to compare one stat to another or to a possession type baseline but more rewarding due credits to each individual stats.
I am just trying to come up with something more relative where one stats value compared to another stats value tens to add up. Right now I've been using something similar to
value = 2gm + 3gm*1.5 + ftm-(fta*.5 - .15*missed ft)
+ orb * .7 + drb * .3 + ast*.8 + stl + blk*.8 - to

Thanks again
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Mountain



Joined: 13 Mar 2007
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PostPosted: Sat Mar 29, 2008 5:23 pm Post subject: Reply with quote
Somebody else can probably explain this accounting issue more fully but I see your rating centering FG made and misses roughly 1 pt from average value of possession.

Going 1 for 2 would result in a modest positive credit.

Hollinger's PER treats going 1 for 2 much more positively.
I think of it as a "1" base for this action as opposed to "zero" base.

That doesn't remove the question. I think your approach on shooting seems closer to Wins Produced philosophy and credit system. Should average shooting be viewed positively and creditted amply or treated as expected and for no credit gain? Plenty of threads dealing with that debate if you want to read all the commentary.

PER is on other stats value of possession based but things do get modified some.

Last edited by Mountain on Sat Mar 29, 2008 5:27 pm; edited 2 times in total
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asimpkins



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PostPosted: Sat Mar 29, 2008 5:24 pm Post subject: Reply with quote
blb2397 wrote:
So it strikes me awkwardly that a two pointer would be worth a full 2.4 (3 without assist adjustment and rebound adjustment) points more than missed shots when the difference between a missed shot and made shot is at most 2 points.


I think you are basically arguing that the possession cost should be subtracted from the shooter whether or not he makes the shot. So putting aside crediting the passer or adjusting for rebounding, a made shot would be +1 and a missed shot would be -1, for a difference of 2 points.

I think you're pretty much right (though I'm not sure the full possession cost should be subtracted) and that this is a flaw in PER. However, there's a good reason for it -- if there can be a good reason for a flaw -- and that's to keep PER from being completely dominated by rebounding.

That's a complicated discussion, and I suggest reading this thread for a good analysis of it. But for here I'll just say that PER is inflating scoring to keep its relevance next to rebounding. It works pretty well, but not exactly for the right reasons.

Quote:
Right now I've been using something similar to
value = 2gm + 3gm*1.5 + ftm-(fta*.5 - .15*missed ft)
+ orb * .7 + drb * .3 + ast*.8 + stl + blk*.8 - to


It looks like you are not penalizing a player for missing a shot -- only missing free throws. Is that correct?
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blb2397



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PostPosted: Sat Mar 29, 2008 7:50 pm Post subject: Reply with quote
Thanks
I neglected missed shots on error. It should be -.7 per missed shot along the lines of PER. I understand the Wages of Wins comparison but it is not near the extreme. In this sytem a player who shoots 9 for 20 (45%) from 2 still gains 1.3 credits. I believe that average 2g% should still be rewarded somewhat. So a shot creator gains credits slowly. The rebounding doesn't overpower this like WOW does because like PER it uses .7*oreb and .3*dreb. Also because their is some more deviation in shot attempts the credits created by the extra shots can still compensate for the value of a good rebounder as opposed to an average one. So I guess it ends up being somewhere between PER and WOW. Although not the intention I hope to work in that area.
Basically as long as a player is around average or above in any stat he should be rewarded. I mean shouldn't a 9-20 game with 6 of 8 FT's and no turnovers be considered a good game (24pts in a touch over 20 possessions), not a great game like PER seems to say or a poor game like WOW.

Top 20 per 48 minutes as of a week ago
---------------------------------------------
LeBron James
Chris Paul
Amare Stoudemire
Tim Duncan
Kevin Garnett
Kobe Bryant
Carlos Boozer
Dirk Nowitzki
Al Jefferson
Marcus Camby
Chris Bosh
Dwight Howard
Manu Ginobili
Baron Davis
Yao Ming
Carmelo Anthony
Steve Nash
Antawn Jamison
Josh Smith
Shawn Marion
Chauncey Billups

Thanks for the insight I will look into the other discussions more closely.

corrected:
value = 2gm + 3gm*1.5 + ftm-(fta*.5 - .15*missed ft) - .7*missedfg
+ orb * .7 + drb * .3 + ast*.8 + stl + blk*.8 - to
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Mountain



Joined: 13 Mar 2007
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PostPosted: Sat Mar 29, 2008 11:32 pm Post subject: Reply with quote
Somewhere between PER and WP is a good zone to be in overall.

That top 20 includes a lot of names you'd expect to be there at first cut.



The right assist weight is much debated.

Taking credit from the shooter to give to passer to prevent or reduce overcrediting the basket (I think it is reduce but am not sure) is an aspect of PER that most simple linear weight sets don't handle. In your formula that is extra credit given to the assister beyond the full amount awarded for a made basket to the shooter but of course assisted baskets do not count more than unassisted so this extra award breaks with strict court value accounting. A common past practice but something to be aware of.
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asimpkins



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PostPosted: Sun Mar 30, 2008 12:41 pm Post subject: Reply with quote
Mountain has it pretty much right. Compared to PER, you devalue scoring in relation to everything else, most importantly rebounding, but you don't go quite as far as WoW. So it stands somewhere in between.

You also do something different with foul shots. A player that goes 2-4 from the foul line will get 1.2 credits in PER, while they will get 0.3 credits in your system. So PER gives an extra reward for generating shots from the foul line and you treat it all the same.

You also have no penalty for personal fouls, and slight increases for assists and blocks.

I haven't seen a good argument for how scoring should be definitively weighted to rebounding, so it's all pretty much a subjective judgment call. WoW goes so far to one extreme that it strikes me as pretty silly -- average shooting is valued at 0, but average rebounding is valued very high -- but there's a large area in the middle where any weights can be reasonable.

Your list is pretty similar to the top PER players. The top 3 are the same. Ginobili and Billups drop way down. Dwight Howard and Bosh end up below Boozer and Al Jefferson. Camby and Josh Smith make big jumps up. Missing are players like Kevin Martin, Jose Calderon, and Deron Williams.
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blb2397



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Posts: 7


PostPosted: Sun Mar 30, 2008 12:56 pm Post subject: Assist Value Reply with quote
I'm having trouble grasping the idea of strict value accounting. I guess I'm not alone. How can we share credits for on stat like a made field goal but not for another stat. If the passer shares in the field goal than he would perhaps need to share some blame for a missed shot. Poor passing inevitably leads to poor shots that players are often times forced to take.
As mentioned I am trying more to compare one stats worth to another. Thats another reason I use 1.5 multiplier for 3pm as its 50% more valuable in terms of production.
As for assists I came up with .8 because I figured this made assists comparable to rebounds. I cant clearly say if passing is less or more valuable than rebounding so If I assume that they are equal than I come up with a .8 multiplier.
How?
.7*league average orb + .3*league average drb = .8*league average ast.
At last check they equaled bout 17
Also the sharing between assists and shots made does come into play when I calculate my wins produced. With 2pm = 1 and assists = .8 I multiply both by 5/9 so 2pm = 5/9 and assists = 4/9. I do this with all the stats (multiply by 5/9) This makes orb for example = 7/18. The remaining "value" of orb stays uncredited but thats ok because I am trying to get a total value for a player that compares well to another player by just looking at his credits. i.e Kobe comes out at 3.95 (per 40 above my average value of 8.5) Grant Hill at 0.4 above average. Kobe therefore adds about 2.55 points to score differential for every 40 minutes played. Using Oliver's basic pythagorean method this gives Kobe +12.62 wins and Hill +1.26 wins. Kobe (stats wise anyway at 40 minutes per) makes a 41 win team a 53.6 win team and Hill gives a minor increase (42.26)
Hope this makes some sense. Of course this leaves a lot out as well. No pace adjustments and using approximations like .15 for missed ft's and .7 and .3 for reb ( I used to use 2/3 and 1/3 back in the day of higher orb rates)
Here is the top 50 by wins list using player minutes per game instead of per 40 and assuming 82 games played

LeBron James 17.49
Chris Paul 14.20
Amare Stoudemire 12.16
Kobe Bryant 12.11
Tim Duncan 11.62
Kevin Garnett 11.03
Dirk Nowitzki 10.96
Carlos Boozer 10.69
Al Jefferson 10.63
Marcus Camby 10.05
Dwight Howard 9.95
Baron Davis 9.62
Chris Bosh 9.27
Yao Ming 8.96
Carmelo Anthony 8.24
Manu Ginobili 8.07
Antawn Jamison 8.00
Andrew Bynum 7.75
Shawn Marion 7.34
Steve Nash 7.33
Caron Butler 7.30
Allen Iverson 7.26
Pau Gasol 7.22
Josh Smith 7.16
Tracy McGrady 6.92
Chris Kaman 6.91
Chauncey Billups 6.64
Dwyane Wade 6.59
Jason Kidd 6.25
Deron Williams 5.98
David West 5.90
Zydrunas Ilgauskas 5.16
Zach Randolph 5.12
Jose Calderon 4.67
Vince Carter 4.61
Rasheed Wallace 4.55
Brandon Roy 4.53
Jermaine O'Neal 4.43
T.J. Ford 4.37
Brad Miller 4.25
Andris Biedrins 4.22
Paul Pierce 3.88
Michael Redd 3.61
Emeka Okafor 3.59
Gerald Wallace 3.50
Ron Artest 3.47
LaMarcus Aldridge 3.44
Josh Howard 3.35
Corey Maggette 3.30

Kevin Martin comes in at #54
Thanks again for the input
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blb2397



Joined: 10 Mar 2008
Posts: 7


PostPosted: Mon Mar 31, 2008 8:50 pm Post subject: Rankings Reply with quote
Ok
I found it odd that some of the rebound / block guys were a little higher than I remember from previous rankings over the years so I rechecked and was using drb multiplier .7

The new updated list for those interested. (Yeah yet another weights system)
I'm surprised by how much scoring still matters and Dwight Howard's rank.
I've always ignored fouls and only because I could never come up with a multiplier that I could rationalize

LeBron James 16.85
Chris Paul 15.16
Kobe Bryant 12.05
Baron Davis 10.63
Amare Stoudemire 10.49
Allen Iverson 9.33
Dirk Nowitzki 9.15
Tim Duncan 9.02
Kevin Garnett 8.74
Al Jefferson 8.55
Chris Bosh 8.24
Steve Nash 8.20
Carlos Boozer 8.13
Carmelo Anthony 8.04
Manu Ginobili 8.01
Chauncey Billups 8.00
Dwyane Wade 7.70
Deron Williams 7.54
Caron Butler 7.28
Tracy McGrady 7.06
Yao Ming 6.90
Antawn Jamison 6.20
Pau Gasol 6.10
Josh Smith 6.05
Marcus Camby 5.82
Dwight Howard 5.75
Jose Calderon 5.73
Brandon Roy 5.35
Andrew Bynum 5.32
Vince Carter 5.07
Shawn Marion 5.00
Jason Kidd 4.91
David West 4.69
Michael Redd 4.64
Ron Artest 4.07
Paul Pierce 3.95
Zydrunas Ilgauskas 3.72
Andre Iguodala 3.69
Jermaine O'Neal 3.67
Kevin Martin 3.67
Monta Ellis 3.59
Gerald Wallace 3.40
Chris Kaman 3.29
Corey Maggette 3.28
Jason Richardson 3.27
LaMarcus Aldridge 3.23
Rasheed Wallace 3.23
Andre Miller 3.21
Tony Parker 3.10
Richard Hamilton 2.90
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Harold Almonte



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PostPosted: Tue Apr 01, 2008 9:53 am Post subject: Reply with quote
Quote:
blb2397 wrote:
So it strikes me awkwardly that a two pointer would be worth a full 2.4 (3 without assist adjustment and rebound adjustment) points more than missed shots when the difference between a missed shot and made shot is at most 2 points.


I think you are basically arguing that the possession cost should be subtracted from the shooter whether or not he makes the shot. So putting aside crediting the passer or adjusting for rebounding, a made shot would be +1 and a missed shot would be -1, for a difference of 2 points.

I think you're pretty much right (though I'm not sure the full possession cost should be subtracted) and that this is a flaw in PER. However, there's a good reason for it -- if there can be a good reason for a flaw -- and that's to keep PER from being completely dominated by rebounding.


It's awkward that an offensive action that ends in 2 scored points, distributes 3 points between the scorer and the poss. gainer. But not all team possessions are gained trough the action (Stl, Reb) at all, some of them comes from an opp. FGMade (by the way, that's a penalty for the defensive team that basical linear metrics obviate). Not to substract the team change of poss. after the made is extreme, but acceptable as a shortcut (PER). To substract it and not to reward it back at inbounds (WinScore) is crazy. But, if you decide to substract and compensate back like WP, I suggest to do this compensation by players's FGMade, and not players's minutes.

Re: Recovered old threads- miscellaneous topics

Posted: Fri Apr 22, 2011 9:48 pm
by Crow
Eli W



Joined: 01 Feb 2005
Posts: 354


PostPosted: Sat Mar 29, 2008 12:18 pm Post subject: Shot location charts Reply with quote
In case anyone hasn't seen these, I've assembled a play-by-play database with four seasons of shot location data that I can use to make some interesting charts. So far I've made ones for FGA, FG%, Ast/FGM, eFG%, Blk/FGA and ORB%.

http://www.countthebasket.com/blog/2008 ... ake-shots/

http://www.countthebasket.com/blog/2008 ... ot-charts/

http://www.countthebasket.com/blog/2008 ... -location/

If people have other ideas for charts I can try them out. One idea I have is basing player similarity scores on these kind of charts. I also may look at some individual player or player combination charts (e.g. where on the court does Nash get his assists, what does Marion's chart of unassisted made shots look like compared to his chart of assisted made shots, etc.)
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Mountain



Joined: 13 Mar 2007
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PostPosted: Sat Mar 29, 2008 12:37 pm Post subject: Reply with quote
Player shot chart similarity sounds like an outstanding direction to pursue that I'd welcome seeing.

Your choice over time of course but did any of the 15 chart ideas I listed at your site strike you as something you want / intend to try?
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hoopseng



Joined: 13 Oct 2006
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Location: Basketball Research

PostPosted: Sat Mar 29, 2008 12:40 pm Post subject: Reply with quote
I love the "shooting temperature" charts, congratulations for great work!

It would be exciting to have team by team allowed FGM locations if it is doable?
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Eli W



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PostPosted: Sat Mar 29, 2008 1:11 pm Post subject: Reply with quote
Mountain wrote:
Your choice over time of course but did any of the 15 chart ideas I listed at your site strike you as something you want / intend to try?


Yeah, I especially liked your ideas of isolating player combinations (i.e. with a post scorer or high assist PG on the court) and breaking things down by position. I'll try to at least get to those at some point.
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Eli W



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PostPosted: Sat Mar 29, 2008 1:14 pm Post subject: Reply with quote
hoopseng wrote:
It would be exciting to have team by team allowed FGM locations if it is doable?


That should be pretty easy to constuct, and definitely worthwhile. It was something I had looked at with the NBA.com HotZones/HotSpots data before, but this would provide a lot more detail.
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blb2397



Joined: 10 Mar 2008
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PostPosted: Sat Mar 29, 2008 3:22 pm Post subject: Reply with quote
Any thoughts about a data only listing for each area or creating a chart with less grids as well. i.e 3 by 3 or 5 by 5 instead of one by one for a more general view.
Great work!
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Mountain



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PostPosted: Sat Mar 29, 2008 4:48 pm Post subject: Reply with quote
Ok Eli. Just wanted some sense if the comments were useful to you. Thanks for sharing the research and continuing to dialogue and push further.
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Brian M



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PostPosted: Sun Mar 30, 2008 1:58 pm Post subject: Reply with quote
What would be neatest of all would be if you could construct a flexible b-r.com like method for generating these charts based on general input parameters, if possible. This way interested folks can plot out anything they can dream up rather than having you labor through making each individual chart.
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Eli W



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PostPosted: Sun Mar 30, 2008 4:08 pm Post subject: Reply with quote
Brian M wrote:
What would be neatest of all would be if you could construct a flexible b-r.com like method for generating these charts based on general input parameters, if possible. This way interested folks can plot out anything they can dream up rather than having you labor through making each individual chart.


Yep. I would like to do something like that eventually, but it will take a lot of work to set up.
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bballfan72031



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PostPosted: Mon Mar 31, 2008 2:25 pm Post subject: Reply with quote
Eli, the work you've been doing is some of the most spectacular stuff I've ever seen.

The big thing I've been hoping would be done for a while now is an analysis of diminishing returns on shot locations.

There is an assumption that a team can't simply put a bunch of post scorers on the court at the same time and perform as well as the individuals would perform if they weren't getting in each others' way. There is a need for balance in basketball, and it seems to me that this need is huge.

I assume this could partially explain why Channing Frye has shot better his first and third seasons, playing primarily center, while he was considerably less efficient shooting his second season when he played primarily power forward alongside Eddy Curry.

I would guess that there would be a larger affect on diminishing returns the closer you get to the basket (it's likely that it is easier for multiple three point shooters to spread out and have their own space to operate than multiple inside scorers), yet it would still be optimal to have a balance of inside and outside scoring.

I think an analysis of diminishing returns on shot locations could be immensely helpful for coaches in analyzing who to play together and for general managers in analyzing how to put together teams. It would be another step toward statistically analyzing the importance of balance.

Anyway, I sound like an annoying fool saying "Someone should do this" and not doing it myself, but I honestly just don't have the background or have any clue how to go about doing this. And with the work you and others here have done, I thought it could be a helpful suggestion, if it hasn't been made already.

Re: Recovered old threads- miscellaneous topics

Posted: Fri Apr 22, 2011 10:18 pm
by Crow
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FFSBasketball



Joined: 07 Mar 2005
Posts: 175
Location: MD

PostPosted: Thu Apr 14, 2005 5:53 pm Post subject: Created Field Goal Percentage Reply with quote
Was looking over at (the now defunct) Basketball Metrics website, and found an interesting stat - and an answer to my questions of how well you can create a shot.

BasketballMetrics wrote:
cFG% : Created Field Goal Percentage

This stat has the same formula as [true] FG% except that only field goals which are unassisted and which occur at least 5 seconds after an offensive rebound are counted...The Created FG% is an attempt to measure how well a player can create his own shot without the benefit of those situations. So, in calculating this stat, assisted shots and shots from offensive rebounds are ignored...


I wonder if maybe Roland has this data that he could pull up? Or anyone else out there with play-by-play parsers?

I think it would be very interesting to look at data of cFG%, and also percentage of shots created (cFGA/FGA), and think it might even be a nice addition to the whole issue we brought up before in the "assisted%" thread.

The data would be interesting, and the next step would be the analysis. Are there players that have their cFGA/FGA fluctuate? Or does it remain fairly steady for most players/situations?
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mavs128



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PostPosted: Fri Apr 15, 2005 8:13 am Post subject: Reply with quote
I remember looking over the league leaders in that stat and they were all points gaurds. Nash and Daniels I believe were 1 and 2.

Re: Recovered old threads- miscellaneous topics

Posted: Fri Apr 22, 2011 10:25 pm
by Crow
Mountain



Joined: 13 Mar 2007
Posts: 723


PostPosted: Fri Aug 15, 2008 9:02 pm Post subject: Top teams - adjusted +/- and types Reply with quote
I looked at some of the most used lineups for top 6 teams and found these results:

Hornets lineup- player's adjusted +/- sums to +11.3, that lineup produces an +11.0 average result per 48 minutes.

Pistons players sum to +12.5; lineup result +11.9.

Celtics players sum to about +17.9, lineup result +19.5.


But not every comparison is close.

Jazz players sum to about +10, lineup result +4.6.
A playoffs concern. But that lineup is in the game only about 25% of time and the Jazz are doing pretty well at least in regular season with other lineups with less than all 5.

Adjusted individual +/- for Spurs players in most used lineup sum to a very meek +1.1, though the actual lineup result is +4.3. Better but hardly a fearsome number- this past season. They too get you with mix & match lineups very well. They don't use their most used lineup much - only 18th most used lineup in league. I guess Pop last season believed more in system and players as parts than one specific lineup. The catch to mention is that most used 5 did not have Ginobili.

Laker top 5 sum to +17.9 but as a lineup all together deliver a bit less at +13.5. Some of the positive impact seems go with one player or another and is not strictly additive.


Does having players particularly strong or weak on individual adjusted +/- tend to cause additonal change in the same direction?

Hornets lineup had one player over +5 and one worse than -5. Pistons one over, none under. Players delivered about as expected.

Celtics 2 over +5, none under maybe that helped momentum, synergy.

Jazz no big catalyst, no big deterrent but lineup doesn't yield expected result, rather they have under performance. Not great synergy or too much overlap of role fulfillment?

Spurs one big positive, one big negative, a better than expected but still modest result. Meanwhile most used lineup with Ginobili produced a +10 result. But only used 2 measly minutes per game in regular season. In playoffs used not much more than 1 minute a game. It's performance sucked. But most used at only 1-2 minutes per game and none used more? That is really a lot of variety, whereas I expected at some more stability. It was a small lineup with Duncan as only true big. Were Pop's lineup calls good last playoffs or too scrambled? I've talked about a few players / lineups used and how they performed regular season and playoffs before and won't add to it now but I think there is room to critique on playoffs as a whole or perhaps the focus should be on just the Finals itself.

Lakers 2 over +5 and nearly a third, with one under -5. Maybe with more time together they step up the synergy with the top guys. Will Bynum's return keep things new & needing work / not fully optimized? Add + or just shift credit? Will be interesting to watch / check later.

Presence of a -5 guy for these teams saw one case each of expected lineup performance, even and under, so can't say much at a general level about a bad 5th wheel. Presence of 2 +5 guys also had offsetting over and under performances. So no big discovery. But I thought it was worth a quick look.

Looking at something else, David Sparks' types and ranking of best on Boxscore by type, I notice that for those teams which I considered the top 6 teams last season their most used lineups include:

6 of the top 10 Perimeter Scorers and 4 of league's top 5 Pure Perimeters were on these 6 teams. As well as 4 of the top 7 Pure Scorers and 3 of the top 5 in league Scorer's Opposites and 3 of the top 6 Mixed type (2 on Pistons- Wallace and Prince). These teams also had 4 of top 10 Pure Interior players in the league. But only 1 of top 20 Interior Scorers. That's 25 of the 30 players and a few 6th men being near the top of one type list or another. That's a lot of top players but also spread out well across types / roles. That's 60% of the top players for each of the 6 types other than Pure Perimeter by the cuts I chose above on 20% of the teams.


Perhaps more could be found looking at adjusted +/-, types and maybe individual or counterpart performance all at once. It is a complicated dance of shapes, sequences and forces.
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Mountain



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PostPosted: Sun Aug 17, 2008 12:16 pm Post subject: Reply with quote
Mo Williams joins the Cavs in the latest attempt to find the right PG and better offensive chemistry.

But by offensive / defensive +/- last season he had nearly the same impact on each as old news Damon Jones. Much higher usage and assists but still about the same net adjusted offensive team impact. And Williams and Jones have had similar total adjusted +/- ratings for 4 years with Jones having the edge a couple times but both centered around neutral, very little team impact.

Williams a good deal younger but a lot more expensive.

Change til you reach your ultimate goal but I'm not real optimistic about the move improving Cavs much from current level on quick look. Same type- Perimeter Scorer- as Jones. West is shown as Pure Perimeter. Choice to go back to that previous type? I don't know. Wait n see what happens. As much as various ratings and types might reveal it is in the end about unique individuals and unique chemistry in a unique context with a particular coach / system.
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QMcCall3



Joined: 17 Jul 2008
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PostPosted: Tue Aug 19, 2008 2:25 pm Post subject: Reply with quote
Mountain wrote:


But by offensive / defensive +/- last season he had nearly the same impact on each as old news Damon Jones. Much higher usage and assists but still about the same net adjusted offensive team impact. And Williams and Jones have had similar total adjusted +/- ratings for 4 years with Jones having the edge a couple times but both centered around neutral, very little team impact.

...As much as various ratings and types might reveal it is in the end about unique individuals and unique chemistry in a unique context with a particular coach / system.


Interesting point that Jones and Williams had the same adj +/-.

One thing I find interesting about the comparison is that Jones split time between PG and SG, whereas Williams was exclusively a PG (according to 82games). With the ball in his hands more often, is it possible that this comparison is somewhat unfair to Williams?

This will be a great opportunity to understand effects of chemistry (and coaching -- Brown is more of a defensive coach). Williams opponent's PER was not so good last season...
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Mountain



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PostPosted: Wed Aug 20, 2008 2:09 am Post subject: Reply with quote
You raise a worthwhile point about very different position time splits last season. There has been some anecdotal concern about the scores of some PGs though I don't know if the "problem" is broad or significant.

But in 06=07 Williams had 20some % of time at SG. In 05-06 he had near 40%. So it appears they were a little closer but then again it comes down to accuracy of position assignment. In 04-05 they were both almost exclusively PGs. And yet they track pretty closely on adjusted =/- all 4 years.

There are lots of variables and position is one.

I wouldn't make a big deal about similarity of total adjusted +/= in one season but similarity of offensive and defensive splits last season on top of in total +/- the 3 previous seasons makes for a stronger case.



While looking at this I also checked the +/- history of Ben Wallace: +6 in 2003-4, +4 in 2004-5, +18 in 2005-6, -1 in 2006-7, -1 in 2007-8. Wonder where he would rate in a study of multiple seasons combined. Age & health can certainly part of the story but he certainly seemed to thrive far more in one context and not in others after Detroit or perhaps before. What his real impact was at any point can be estimated but there is there is much uncertainty.

Would high or low player adjusted +/- variance in different lineups on the same team hint at the same if they moved teams? I'd guess it might, on average.

For Wallace or Williams? We don't have full adjustment +/- in public yet for lineups but for Eli's first cut is out there though it is a set of small samples. I don't feel like reviewing and judging that data right now but the Cavs could at least now if not privately before or could have respectively for these 2 players.

I share your interest in seeing importance of coaching and chemistry for Williams' performance in Cleveland.

I wonder if you studied adjusted +/- of every player move in the league over a period of time if you could get a first cut estimate of the adjusted +/- impact of coaches / systems (perhaps by side of the ball and even frontcourt / backcourt) which would be a piece of the player adjusted +/- scores we've seen to date that perhaps should be backed out in the analysis of both acquisition targets and own player retention decisions.

I think this might be in line with what Mark Cuban hinted at in a comment at countthebasket which I've mentioned previously.

If adjusted +/- becomes available for college players the issue as to what share is player vs coach (and where offense / defense) is an important one but there are relatively few player movements between colleges to gauge coaching impact off of. Movement to pros might give a much larger dataset but the game is so different I don't know if using that would be powerful but like a lot of this it could be suggestive / interesting and lead to further research to refine judgments.