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Mountain
Joined: 13 Mar 2007
Posts: 1527
PostPosted: Sat Jul 19, 2008 5:03 pm Post subject: Reply with quote
Thanks, I missed 2 NY lineups that were indeed the worse on raw team +/- because of number formatting and bad eyesight with small numbers.
Worst -Crawford Curry Marbury Randolph Richardson by almost double
Next- Crawford Curry Jones Randolph Richardson
both 22667- a combination of 226 and 67 with both ranking terrible on average.
then
Watson Durant Green Collison Petro- 13557
perimeters with 1's were weak and 57 is halfway between the 2 worst interiors (5.5 and 6,7) and third weakest on average here.
Felton - Mohammed- Okafor - Richardson- Wallace
23557
Boston's best lineup compromised of 22345 had the best perimeter type and interior type and set the overall best team raw +/-. Chicken-egg and
small samples and could of course different combinations can work for different teams based on talent but I think the information from top 50 lineups is interesting.
Boston was 37% Perimeter Scorer, 24% Scorer's Opposite, 20% Pure Interior, 12% Pure Perimeter and 7% Mixed. There was no player predominantly a Pure Scorer or Interior Scorer.
The average for the 5 champions David provided since 1986 was 35% Perimeter Scorer, 13% Scorer's Opposite, 13% Pure Interior, 8% Pure Perimeter 12% Mixed 6% Pure Scorer or 7% Interior Scorer. Small sample but SP led the way. Mixed Pure Scorer and Interior were also light.
Purely on minutes distribution by type the ten teams most similar to Celtics last season were
2. SAC
LAL
PHO
HOU
League average
MIA
ATL
PHI
CLE
NJN
the least similar were
21. CHI
DAL
DET
TOR
ORL
MEM
MIN
POR
SEA
30. NOH with the least similar type distribution
Last edited by Mountain on Sat Jul 19, 2008 8:45 pm; edited 1 time in total
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Mountain
Joined: 13 Mar 2007
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PostPosted: Sat Jul 19, 2008 8:13 pm Post subject: Reply with quote
(MEV and BXS are boxscore based so the similarity rankings have more to do with offense than defense.
Pairing BXS quick n dirty with the adjusted defensive +/- of players in each type would probably give better similarity scores if you went beyond minutes distribution and also looked at performance by type as well.
Kings had nice breadth of type contributions towards offense. I'll have to look into them a little more.)
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dsparks
Joined: 22 Feb 2008
Posts: 61
PostPosted: Fri Jul 25, 2008 11:11 am Post subject: Reply with quote
Alright, I broke down and did a regression at the game level, instead of the season level. For the DV, I used final scoring margin for team A (thus, it will be a positive number of points if they won, negative if they lost). The IVs are, for each of the seven style archetypes, the number of minutes played in the game for team A, less the number of minutes played in the game for team B. Thus, you get something like this actual game observation, where team A beat team B by 13:
Code:
MARGIN Perimeter Scorer Pure Scorer Scorer's Opposite Mixed Pure Perimeter Interior Scorer
13 29 39 -19 16 -34 -39
Here's the regression output:
Code:
Call:
lm(formula = WIDE[, 1] ~ WIDE[, -1] - 1)
Residuals:
Min 1Q Median 3Q Max
-55.706 -5.163 3.571 11.646 62.769
Coefficients:
Estimate Std. Error t value Pr(>|t|)
WIDE[, -1]MIN.Perimeter Scorer -0.028335 0.002797 -10.129 <2e-16 ***
WIDE[, -1]MIN.Pure Scorer -0.102925 0.003515 -29.282 <2e-16 ***
WIDE[, -1]MIN.Scorer's Opposite 0.065226 0.003277 19.904 <2e-16 ***
WIDE[, -1]MIN.Mixed -0.003964 0.002963 -1.338 0.181
WIDE[, -1]MIN.Pure Perimeter 0.075319 0.003164 23.808 <2e-16 ***
WIDE[, -1]MIN.Interior Scorer -0.056873 0.003333 -17.062 <2e-16 ***
WIDE[, -1]MIN.Pure Interior 0.028303 0.003349 8.452 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 12.99 on 24703 degrees of freedom
Multiple R-Squared: 0.08299, Adjusted R-squared: 0.08273
F-statistic: 319.4 on 7 and 24703 DF, p-value: < 2.2e-16
And, here's that output in a graphical format, with a line indicating zero, and tiny error bars:
And here's a graphical version using Type-Year minute differential as the DV:
The error bars are a little larger, as we are estimating more coefficients with the same amount of data.
The results here look pretty conclusive. Without doing interactions, each additional minute played by a Pure Perimeter or Scorer's Opposite player yields an increase of 0.075 (or 0.065) additional points on the final scoring margin, on average. Pure Scorers, on the other hand, dock your team a point of margin for every ten minutes they play. And yet, as our esteem'd colleague Dr. Berri would note, many Pure Scorers like Kevin Durant and Carmelo Anthony are lauded for their contributions...
I'm not sure what-all to make of this, but I know that some of you will have some really good ideas.
And, once you've digested all that, I've got a big puzzle for everyone to solve that will require a whole new topic!
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dsparks
Joined: 22 Feb 2008
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PostPosted: Fri Jul 25, 2008 11:54 am Post subject: Reply with quote
Ok, so this isn't the puzzle I promised, and I know it's not cool to double post, but this is new stuff:
I ran a couple of regressions similar to the above, except instead of using scoring margin, I used other margins, such as offensive rebounding, assists, etc. Some of which make more sense than others:
Offensive Rebounds:
Defensive Rebounds:
Total Rebounds:
My guess is that Pure Scorer increases offensive rebound margin because they take (and possibly miss) a lot of shots, and Scorer's Opposites reduce OR margin not because necessarily they fail to gather a lot of boards, but because they don't chuck. Just a theory though.
One surprise is that it appears that Pure Perimeters contribute more overall to rebounding margin than do Perimeter and Pure Scorers... No theory as to why just yet.
Assists:
No surprises here, really. It's possible that Pure Interiors help assist margin by blocking shots, which prevents assists...
Turnovers:
Again, no real surprises here. The top three don't handle the ball as much, and are more defensively-oriented.
Blocks:
No surprises.
Personal Fouls:
I'm not sure about this one... It seems as though Pure and Perimeter Scorers would get fouled more, in their scoring attempts. Also, I would have thought that Scorer's Opps and Pure Interiors, playing a more defensive game would give up more fouls... any ideas?
Missed field goals:
Largely unsurprising, except for the Pure Perimeter helping the most (in this case a positive coefficient is bad--a larger margin of missed fgs), given that Point guard-types are not typically good shooters nor well-known shot defenders. My hunch is that this is the assist effect--better passes lead to more made shots, and certainly assists and made shots are highly correlated.
Anyway, I'd love to hear what you actual knowledgeable people have to say about all this.
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Harold Almonte
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PostPosted: Fri Jul 25, 2008 2:36 pm Post subject: Reply with quote
A strong correlation between scoring margin (wins), assists (linked to teammates's FG. FG%), low FGX, and def. rebounds (shot defense?)-this is the 1rst. factor and some of 3rd., and punishable towards scoring (attemptors). Tend scorers (or are they called) to be more unidimensional than defenders? Anyway, being too unidimensional towards the defensive end is not good either, but it should be worse?. I would like to see this puzzle with playoffs games.
I won't talk about your Boxscore metric, but in your playing styles exemplars, a lot of "pures" and "scorers" could or might be qualified in a more balanced "mixed" cathegory.
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Mountain
Joined: 13 Mar 2007
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PostPosted: Fri Jul 25, 2008 7:21 pm Post subject: Reply with quote
There are a lot of different snapshots / levels of analysis being provided to keep in mind. Scorer's Opposites and Pure Perimeters certainly looking good on these graphs.
Interesting to review an earlier dataset and see teams with the rare Scorer Opposites (PI) often have several and that increases the impression that at least in some cases this is knowledgeable selection. Posey as 9th best in this group might help explain the degree of interest more than his adjusted +/- score does. Odom#2, Josh Smith #3 seems worth mentioning too.
If I follow the "Code box" information / explanation correctly, it looks a good way to collapse all lineup match-ups onto a single spectrum. It would be interesting to see what the per minute average results look like for a set of "bands" (minute distributions that are fairly similar) for teams and the league as a whole. For a public snapshot you could "collapse" to positive and negative minute differentials and get down to 128 lineup bands. If you were on the inside I'd think you might look at around 600-3,000 bands and see what useful clues can be found from it.
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Mountain
Joined: 13 Mar 2007
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PostPosted: Sat Jul 26, 2008 5:37 pm Post subject: Reply with quote
I can see how a Pure Perimeter facing an opponent without one of the court might have an advantage but going back to an issue of some discussion in last year or two how well do teams perform with 2 Pure Perimeters on the court together? Not just combo guards in size or mainstream description but Pure Perimeter types?
Noticed A Miller had one of worst adjusted +/-s in the league last season, a sharp contrast to previous 3 seasons. What to make of it? Only notably good with T Young. Who might trade for him? I don't see an obvious playoff team wanting him unless maybe the Suns wanted him instead of one of the large, longer term contracts. Wait n see how it goes for Philly. If things go well they probably stick with him and let his salary go away but if they don't meet higher expectations he will probably be the first to go. For their sake I'd think they'd want an experienced 3 pt shooting PG if they could get one. Would they go for Billups? Maybe, if Joe D didn't overprice him. Perhaps they could get Mo Williams. Or possibly Farmar. I'd guess something will get done this season or next summer. Unless he and Brand really click. They didn't as Clippers in 2002-3 Miller had worst season of his career to and Brand slipped from previous year and did better after Miller left. Team slipped considerably. But of course many things could have been involved other than their direct playing interaction. Will be interesting what raw player pairs show next season and the various adjusted measures.
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gabefarkas
Joined: 31 Dec 2004
Posts: 1313
Location: Durham, NC
PostPosted: Tue Jul 29, 2008 11:16 am Post subject: Reply with quote
dsparks wrote:
Alright, I broke down and did a regression at the game level, instead of the season level. For the DV, I used final scoring margin for team A (thus, it will be a positive number of points if they won, negative if they lost). The IVs are, for each of the seven style archetypes, the number of minutes played in the game for team A, less the number of minutes played in the game for team B. Thus, you get something like this actual game observation, where team A beat team B by 13:
Code:
MARGIN Perimeter Scorer Pure Scorer Scorer's Opposite Mixed Pure Perimeter Interior Scorer
13 29 39 -19 16 -34 -39
This is really, really, really neat.
One thought:
I seem to remember when you initially rolled out the different designations that although you're categorizing players, their assignments seemed more fluid. In other words, if someone looked like they were 70% Perimeter Scorer, 20% Pure Perimeter, and 10% Pure Scorer, they got assigned as a Perimeter Scorer.
So, I was thinking it might be interesting to factor this into the analysis, so that instead of saying "Perimeter Scorer XYZ played 30 minutes", you count those 30 minutes as 70% of a Perimeter Scorer on the floor, 20% of a Pure Perimeter on the floor, and 10% of a Pure Scorer on the floor.
I'm assuming the totals will still add up to 10 guys on the floor (give or take) at any one time.
But, if Team A has two guys on the floor who are both tagged as Perimeter Scorers, but in reality may be 55% Perimeter Scorer and 45% Pure Perimeter, it's almost like having one of each out there, no?
PS - this was my 1000th post on the board. Yikes!
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Mountain
Joined: 13 Mar 2007
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PostPosted: Tue Jul 29, 2008 8:47 pm Post subject: Reply with quote
Gabe makes a good suggestion. It probably would be worthwhile to profile lineups both ways and using player type splits might be more accurate. Then again if distinct role fulfillment by individuals matters then the dominant descriptor may be the most important.
If one was afforded the time you could look at the type split detail for each type in the league and year to year league type split movement. And Player type split change in games where the team won or lost or performed x amount better or worse on offensive or defensive efficiency or net counterpart production. And you could look at player type splits in lineups that are successful or not and how player type changes in and outside of particular player pairings. And you could compute average type split detail for starters by type, on just teams that made playoff or didn't. Or by age, height, pay, PER, BXS, playoff / regular season ratio levels, or where adjusted +/- is more than x standards errors from mean, etc.
Or look at the type split change for players changing teams or starter vs sub or pace or across coaches / career. Which coaches / GMs are type purists based on who they play or pay / trade? Which teams "change" players how and to what effect? Looking league-wide over enough time can you saying anything useful about major conversion projects?
Or tabulate team 4 factor performance across a player's game to game type spectrum and ask does type behavior correlate with important team 4 factor trends in subtle ways in addition to obvious ones. Or correlate player type in game to performance and see whether the trends suggest a player seek to be that archetype or "shift" some. When does a player's positive or negative performance occur and what type are they expressing at those times? And look at changes in players faced by various splits.
Or look at entire league's performance against say the champs and see what type or type split composite does best / worst against them by position to help guide the giant killer strategy.
Or look at the team archetype net minutes profile game by game vs the average type playing time profile of those teams to describe the coach vs coach clash and you could look at which patterns looked better / worse across the league when facing them regular season and especially in playoffs. Archetype net minutes profiles could help with team similarity statements or for that matter coaching similarity.
Plenty to keep one busy full-time. Or more.
Last edited by Mountain on Wed Jul 30, 2008 5:56 pm; edited 1 time in total
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dsparks
Joined: 22 Feb 2008
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PostPosted: Wed Jul 30, 2008 9:43 am Post subject: Reply with quote
Mountain: You could write a book using just your good ideas, and call it "Things We Should Look Into," by Mountain. Once I figure out a good framework for investigating some of those splits, I'm all over it.
This time, though, I used gabefarkas' idea. By looking at each player's proximity to each of six "ideal" archetype points, I calculated their percentage of similarity to that point. Each player's percentages add up to one, and so I've divided each player's minutes, in each game he played, according to these percentages. E.g. Player A played 40 minutes, and was classified as 20% pure scorer, 50% perimeter scorer, and 30% interior scorer: his minutes would be counted for his team as 8 SS, 20 SP, and 12 SI. One possible advantage is that we are getting rid of the "Mixed" type this way... I ran some regressions with very cool results, I'll report the output, then discuss each in turn:
Code:
(MARGIN) Predicting margin by difference in minutes played by split type
Estimate Std. Error t value Pr(>|t|)
dSS -0.302 0.004 -82.940 <2e-16 ***
dSP -0.118 0.003 -36.502 <2e-16 ***
dPP 0.129 0.004 35.196 <2e-16 ***
dPI 0.051 0.003 17.613 <2e-16 ***
dII -0.045 0.030 -1.506 0.132
dIS -0.187 0.003 -66.030 <2e-16 ***
(JOINT) Predicting own team scoring, model includes both own team and opponent minutes played by split type
Estimate Std. Error t value Pr(>|t|)
tSS 0.135 0.005 28.652 < 2e-16 ***
tSP 0.232 0.004 59.798 < 2e-16 ***
tPP 0.361 0.005 76.984 < 2e-16 ***
tPI 0.150 0.004 41.702 < 2e-16 ***
tII 0.336 0.041 8.150 3.74E-16 ***
tIS 0.080 0.003 23.207 < 2e-16 ***
oSS 0.436 0.005 92.909 < 2e-16 ***
oSP 0.350 0.004 90.076 < 2e-16 ***
oPP 0.232 0.005 49.419 < 2e-16 ***
oPI 0.100 0.004 27.669 < 2e-16 ***
oII 0.380 0.041 9.232 < 2e-16 ***
oIS 0.267 0.003 77.223 < 2e-16 ***
(OFFENSE) Predicting own team scoring, including only own team minutes played by split type
Estimate Std. Error t value Pr(>|t|)
tSS 0.410 0.004 93.823 < 2e-16 ***
tSP 0.521 0.003 170.220 < 2e-16 ***
tPP 0.687 0.004 160.293 < 2e-16 ***
tPI 0.444 0.003 149.168 < 2e-16 ***
tII 0.178 0.047 3.765 0.000167 ***
tIS 0.343 0.003 120.562 < 2e-16 ***
(DEFENSE) Predicting opponent scoring, including only own team minutes played by split type
Estimate Std. Error t value Pr(>|t|)
tSS 0.636 0.004 155.430 <2e-16 ***
tSP 0.561 0.003 195.660 <2e-16 ***
tPP 0.452 0.004 112.640 <2e-16 ***
tPI 0.278 0.003 99.700 <2e-16 ***
tII 0.486 0.044 11.010 <2e-16 ***
tIS 0.438 0.003 164.510 <2e-16 ***
The MARGIN results report the change in final margin for every minute played FOR a team by a type less every minute played AGAINST a team by a type. So, every minute you had a Pure Perimeter on the floor that your opponent did not (well, not literally, but on net), your team adds an average of 0.129 to the final margin.
The JOINT results try to estimate offensive and defensive production of each type. The DV is team points scored. The t** coefficients indicate the average increase in points per minute on the floor by that type, larger is better. The o** coefficients indicate the same, but since it's opponent type-minutes, larger is bad. Note that the difference in each type's t** and o** coefficients gives the coefficients we find in the MARGIN model. For example, tSS-oSS = 0.135 - 0.436 = -0.302 = dSS.
The OFFENSE results try to estimate offensive production, but without controlling for opponent type-minutes. Higher is better. The DEFENSE results try to estimate defensive production, without controlling for opponent type-minutes. Lower is better (indicating fewer points given up per additional minute played). I'm not sure which model is the most useful to look at, although I suspect that the JOINT model is more useful than the OFFENSE and DEFENSE models combined, but I haven't thought too much about it.
Incidentally, here's a breakdown of my dataset by minutes logged by each split type:
Code:
Number of minutes played by each split type
teamSS teamSP teamPP teamPI teamII teamIS
1511996 2728679 1646662 2157942 312689 2176822
Percentage of total minutes played by each split type
teamSS teamSP teamPP teamPI teamII teamIS
0.144 0.259 0.156 0.205 0.030 0.207
As you can see, Pure Interior play is very rare. I'm not sure exactly why this is. It could be that there are lots of minutes played by players who we would classify generally as Pure Interior players, but they actually swing back and fourth between Scorer's Opposite and Perimeter Scorer, and rarely play games in which they fit largely into the Pure Interior type. It could also be that such players exist, but don't get a lot of minutes...The regressions seem to indicate that II is not the least productive type.
Mountain, I've got a spreadsheet for you: It's each team, and their distribution of minutes at each split type.
http://spreadsheets.google.com/pub?key= ... HRx1PMkaPwIt's sorted by season, and wins within season. Someday, perhaps, Google Docs will let viewers sort by column, but until then, I guess you'll have to copy and paste to glean much more from that.
One final bit: Correlations of each team's type percentages with each other and with Pythagorean win projection:
Code:
teamSS teamSP teamPP teamPI teamII teamIS teampyth
teamSS 1.00000000 -0.04100399 -0.26441698 -0.4555426 0.24006872 -0.10362708 -0.28279211
teamSP -0.04100399 1.00000000 -0.56163292 -0.0974909 -0.03042045 -0.41776926 -0.09560936
teamPP -0.26441698 -0.56163292 1.00000000 -0.1557434 0.27530468 0.02720873 0.26684871
teamPI -0.45554255 -0.09749091 -0.15574338 1.0000000 -0.21398326 -0.40869420 0.31039440
teamII 0.24006872 -0.03042045 0.27530468 -0.2139833 1.00000000 -0.29709183 0.07201173
teamIS -0.10362708 -0.41776926 0.02720873 -0.4086942 -0.29709183 1.00000000 -0.24893058
teampyth -0.28279211 -0.09560936 0.26684871 0.3103944 0.07201173 -0.24893058 1.00000000
This (all of the above results, actually) backs up the Berri thesis (the one with which I agree), that scorers, especially pure scorers, are overrated.
Incidentally, I think Ron Artest is a much better fit for the Rockets than he would have been for the Lakers, especially given how much Houston had to give up. Morey is a genius--if he's reading this, I'd like him to know that I am available to run regressions and make graphs all day for the Rockets. He managed, essentially, to turn Bonzi Wells (for whom he got Bobby Jackson) plus a rookie who, like all rookies, is characterized by a high degree of uncertainty, into Ron Artest. Whenever I read about Morey's doings, I wonder what sort of statistics and models they're employing, because he manages to swing what appear to be really good deals almost all of the time.
I have a hard time believing that given the huge economic incentives involved, more teams haven't caught up to some of the leaders, in terms of statistical analysis. Is there truly that great of a gulf between the sophisticated and "unsophisticated" General Managers? Anyway, it's exciting to think that somewhere in Houston, someone is doing something like what we have going on here, and making things happen.
Update: Here's an SPI plot of last year's Rockets, plus Barry and Artest:
If anything, Artest is a more valuable version of Bobby Jackson, and is redundant for McGrady, not Battier. As many have pointed out, though, McGrady might not have to carry the load so much, and this would probably be a good thing for his back.
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dsparks
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PostPosted: Wed Jul 30, 2008 12:44 pm Post subject: Reply with quote
Please forgive me for monopolizing the conversation.
It has occurred to me, in thinking about the Rocket's trade, that what the Rockets really need, and what this trade may allow them to do, is put someone in a more Pure Perimeter situation. If you look at the graphic in the last post, you'll see that their starting point, Alston, is somewhere between Perimeter Scorer and Pure Perimeter. I don't know that they'll be able to make such a trade, but perhaps they could ask Alston to focus more on the facilitating aspects of his position than the shooting. I wondered whether or not teams could shift players in a meaningful way and find success.
Compare the following two SPI plots. The first features the roster of the 2008 Celtics, but uses their 2007 statistics. The second plots their actual 2008 positions.
Note that both Rondo and Pierce move much more toward the Pure Perimeter position. Ray Allen (and Garnett) allowed Pierce to carry less of the scoring load, and work more on the facilitating. Posey's presence may have allowed Rondo to focus less on the defensive aspects of perimeter play and move more toward a Pure Perimeter facilitator. Certainly, the addition of two all-star players made the difference for the Celtics in 2008, but perhaps some of the improvement came via the ability to repurpose players they already had...
Incidentally, notice how neither of these teams has anyone remotely like a Pure Scorer or an Interior Scorer, which were the two worst types according to the MARGIN regression above.
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Harold Almonte
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PostPosted: Wed Jul 30, 2008 5:29 pm Post subject: Reply with quote
A pair of less FGA (more comfortable zone of the usage-eff. curve), a better defensive frontcourt (more increasing return on defensive stats), less carrying scoring load, and less carrying defense's attention (more comfortable scoring creation), and suddenly they are away from pure and more mixed. And more wins.
What I think is that being "tended to pure" at scoring a lot of times is not a scorer's option, but a sign of team's diversity weakness. Scorers are not "overrated", but called to be pure or unidimensional without any other coaching option.
PD: Not to mention that WOW underrates scoring.
Last edited by Harold Almonte on Wed Jul 30, 2008 8:53 pm; edited 2 times in total
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Mountain
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PostPosted: Wed Jul 30, 2008 6:26 pm Post subject: Reply with quote
David I threw out some leads, run with any you wish. I am not sure what if any I will get to anytime soon.
Thanks for the new spreadsheet, I'll look at it.
And eventually "the puzzle".
Could you remind me have you published a spreadsheet with the type splits for every player yet?
I thought you might have but didn't find it in a quick look backwards.It would be of interest for some of these newer research directions.
Last edited by Mountain on Wed Jul 30, 2008 7:36 pm; edited 3 times in total
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QMcCall3
Joined: 17 Jul 2008
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PostPosted: Wed Jul 30, 2008 6:28 pm Post subject: Reply with quote
Mountain wrote:
Or look at the type split change for players changing teams or starter vs sub or pace or across coaches / career. Which coaches /GMs are type purists based on who they play or pay / trade? Which teams "change" players how and to what effect? Looking leaguewide over enough time can you saying anything useful about major conversion projects?...Or look at entire league's performance against say the champs and see what type or type split composite does best / worst against them by position to help guide the giant killer strategy.
This line of thinking made me think about the Golden State Warriors -- and makes me wonder if Don Nelson runs through a mental model of these questions before making his decisions. In fact, I think he gets so caught up in trying to exploit mismatches in player styles that he puts together imbalanced rosters that can't win consistently or make deep runs in the playoffs.
Anyway, I'd be willing to bet that a lot could be learned from looking at the Warriors regarding shifting playing styles from game to game just because of the way Nelson plays with lineups. And looking at the Warriors roster and their offseason changes brought up a few additional questions too.
Here's the Warriors current roster, split into what I imagine their rotation might be (with some guesses as to the styles rookies might grow into):
Biedrins: Pure interior
Harrington: mixed
Maggette: perimeter scorer
Jackson: perimeter scorer
Ellis: perimeter scorer
Turiaf: scorer's opposite
Wright: pure interior
Azu: interior scorer
Williams: perimeter scorer
Beli: pure scorer
Watson: perimeter scorer
Morrow: (pure scorer)
Randolph: (perimeter scorer)
Hendrix: scorer's opposite
Perovic: pure interior
So a few questions:
First, Dave Berri often uses win score to analyze the impact of transactions in terms of wins. I wonder if boxscores can be used in the same way with any validity since they take a team's wins into account. For example, this new roster has a total box score of 40.19 based on last year's numbers compared to last season's 47.26 (minus a few bit players). Is it fair to say the Warriors will be 7 wins worse this year or is that not possible to say with Boxscores?
Second, the Warriors seem to be following a theory that pure distributors are not necessary (though Marcus Williams may take on that role in this system). It brings up a question for me about whether it would be worth better defining a sub-spectrum, especially for point guards. For example, Ellis (the expected 08-09 point), Marcus Williams, and Baron Davis are all "perimeter scorers", but Davis is statistically more of a distributor and Ellis more of a scorer.
Within the SPI spectrum, I tend to think of point guards in terms of facilitators (lead guards who are able to create scoring opportunities for others), creators (lead guards who are able to create scoring opportunities for themselves) and utility guards (the bigger guards who might rebound more and score less). A "combo guard" is therefore any guard who does something other than just facilitate. All of those fall somewhere in the range from perimeter scorer to pure perimeter.
I wonder if there would be a way to tease out which specific type of point guard fits best with a given team and how they need to function/shift to most effectively run a given offense.
Third, Chris Mullin has said the Warriors intend to run more this year and we see that they have put together a roster where they can come at you in waves of the same types of players, constantly keeping the pressure on.
I haven’t really looked across multiple teams, but I wonder how many teams employ this "redundancy" strategy (maintaining a consistent style of play) vs a “diversity” strategy (having different styles of play and keeping opponents off-balance). It would be interesting to see if one or the other was more/less effective and with what combinations. For whatever reason, I tend to favor the redundancy strategy, especially if there is a coherent strategy around it. But I could see where it might be beneficial to have a backup who can do some other things, in the Warriors’ case – a post scorer or pure distributor could be useful in terms of making adjustments in the face of different matchups.
Fourth: The warriors also have a number of young players and it would be interesting to try to project style development (as I’ve mentioned before) much in the way the Kevin Broom’s diamond rating projects diamonds in the rough by looking at productivity. Perhaps using the diamond rating with the S-P-I scales would help in that regard?
Sorry for the long post, but this work is getting more and more interesting as people are digging into it further.
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Mountain
Joined: 13 Mar 2007
Posts: 1527
PostPosted: Wed Jul 30, 2008 6:34 pm Post subject: Reply with quote
Rockets PG staffing remains their biggest issue I think as I discussed some last season, though a corollary question might be how much should McGrady play with the ball. Morey (and Ed K. and Eli W.) probably have data on Rockets offensive efficiency when McGrady has ball in first x seconds of play or y # elapsed seconds of play vs when he doesn't. Are the Rockets more or less efficient when he is the "play-maker"? I don't know but they should and that information is key to next steps though under any circumstances a more Pure Perimeter would seem to be worthwhile supplemental option. I'll note that McGrady was estimated as only a +1.5 on offensive +/- this past season. Alston was barely under neutral. Not much difference. But this is for all their time on court. With great resources though you could run offensive adjusted +/- for splits of any kind- being the primary play-maker on the play or for an offensive set or a specific play or option call or whatever with useful sample sizes. Barry was almost +5 on offensive +/- but that was in a particular role. Still if you did a split for adjusted offensive +/- when he served as a play-maker last season or during past seasons you could get some read (in other team contexts) on using him for that- if they had any interest in him playing PG and felt they could live with him guarding PGs or felt comfortable with some type of cross-match in specific lineups / times in the game. Brooks was only -1 on offensive, not bad for a rookie PG, but was very weak on adjusted defensive +/-, clearly the worst on team. (Another small PG with this weakness.) How much does he improve on either next season? Adelman and McGrady are helpful resources reducing pressures on PG to be traditional and options are good but Rockets PG situation still looks not that strong- especially compare to many elite teams. Morey says he is staying with Yao and McGrady and Barry and Artest are nice changes but is he staying with Alston or open to or looking for the right opportunity for change from outside his assembled options? You can say some good things about Alston but I focus on the 17th ranked offense and think that PG decisionmaking is a big part of that.I can't see a non op ten on offensive efficiency team making it to conference finals much less to title winner. Barry is probably just a bit player and Artest was just neutral on adjusted offensive +/- so I don't expect his addition to vault the Rockets offense forward.
Pure Perimeter and Scorer's Opposite continue to look very good. Some analysis within the types to see where within the spectrum the team results are best would also be interesting or confirmation of guesses from the type scores. Do the best results within a type clearly lean toward one type line or the other or it is pretty scattered?
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