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Statistics: Posted by rlee — Fri Jul 01, 2016 5:38 pm

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The formula I used to estimate blocks before the box stats:((((Sqrt(Calc. GP/TRB)-1.6)*TRB)*1.13888888888889)+(49))*0.76)))

The other stuff I don't understand, and extra parentheses?

Anyway, from anecdotal evidence, I arbitrarily bump Russell's Reb/36 up to 4.0

Otherwise, Jerry Lucas is about as good. Never heard of him blocking shots.

Meanwhile, Kareem thrived in an era of 2nd-string centers promoted to starters, during the hyper-inflationary early '70s. Almost half his opponents were expansion teams for a while. He put up his biggest numbers then.

Statistics: Posted by Mike G — Fri Jul 01, 2016 1:44 pm

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Bill Russell's career RPG is reduced from 22.5 to 12.11 in a neutral era. It's important to pool players in a neutral era, because now I can compare his rebound stats in a more fair and accurate way. By comparison, Dikembe Mutumbo's career RPG=12.18 but in 300 less games (RS & PS). I estimated Russell, Lucas, and Chamberlain ORB = 25-26% * TRB, possibly a bit too conservative for top rebounders (I may adjust the ORB levels without changing TRB). Russell finishes with 3.1 ORPG, compared to Mutumbo @ 4.07.

The formula I used to estimate blocks before the box stats:((((Sqrt(Calc. GP/TRB)-1.6)*TRB)*1.13888888888889)+(49))*0.76)))

Russell's top two shot-blocking seasons after the adjustments:

'62 = 205

'59 = 202

Career Blocks = 3186 10th all-time.

In a neutral era Russell is exposed as a subpar offensive player and a top 10 defensive player.

I'll give more of a response when I get some more time

Statistics: Posted by D-rell — Fri Jul 01, 2016 2:11 am

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Statistics: Posted by rlee — Thu Jun 30, 2016 4:23 pm

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Expected Pace = Team1Pace% * Team2Pace% * Average Pace

So a game between two teams that are 93/98 = 95% of average would be predicted to come in at 0.95*0.95*98 = 88.5

Some variation on this approach probably gets you a more exact fit.

Statistics: Posted by DSMok1 — Thu Jun 30, 2016 2:10 pm

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If not, might be worth a try to talk to them.

Statistics: Posted by Crow — Thu Jun 30, 2016 12:35 am

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http://www.nbaminer.com/four-point-plays-and-one/

http://nyloncalculus.com/stats/player-s ... reakdowns/

Stat to look for on nylon would be the 1/1, comparing with adding 4 point play + and 1 stats up. Sometimes it matches up exactly, sometimes it does not. Gut says to trust nyloncalc though...

Statistics: Posted by zhang8787 — Wed Jun 29, 2016 11:00 pm

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I'll suggest it's because the Cavs were more fatigued -- their top 5 guys were playing more minutes than usual.

Finals Min/G Min. rest

Cavs RS Fin RS Fin F/R

LeBron 35.6 41.7 12.8 6.3 .49

Irving 31.5 39.0 16.9 9.0 .53

Smith 30.7 37.3 17.7 10.7 .60

Thompson 27.7 32.3 20.7 15.7 .76

Love 31.5 26.3 16.9 21.7 1.28

total 157.0 176.6 85.0 63.4 .75

Love's minutes were down for various reasons; these others got barely (or not even) half as much rest as in the RS. And most of that came in blowout times.Cavs RS Fin RS Fin F/R

LeBron 35.6 41.7 12.8 6.3 .49

Irving 31.5 39.0 16.9 9.0 .53

Smith 30.7 37.3 17.7 10.7 .60

Thompson 27.7 32.3 20.7 15.7 .76

Love 31.5 26.3 16.9 21.7 1.28

total 157.0 176.6 85.0 63.4 .75

Statistics: Posted by Mike G — Wed Jun 29, 2016 9:00 pm

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In a game there is roughly 45.5 missed shots, and with missed 2nd free throws, let's estimate a game has 47 missed shots.

Offensive rebounding % for each player on the Cavs and the defensive rebounding % for each player on GSW is:

Kyrie Irving 2.3%; J.R. Smith 1.1%; LeBron James 6.1%; Kevin Love 6.5%; Tristan Thompson 16.3%

Steph Curry 14.9%; Klay Thompson 9.0%; Harrison Barnes 12.0%, Draymond Green 22.2%, Andrew Bogut 20.3%

The Cavs = 32.3%, GSW = 78.4%. Together they = 110.7%.

Whenever a player is on the floor, my thought is that they would get their percentage divide by 110.7% of the rebounds. So for Tristan Thompson, when he is offensive rebounding, he would get 16.3%/110.7% = 14.7% of the rebounds while he is on the floor.

To compare with average, I would then multiple that by all missed shots. 14.7% * 47 = 6.9 offensive rebounds for TT if he played the whole game. Adjusting for his minutes, (29.6/48) * 6.9 = 4.25. When compared to his average of 4.1, it is pretty close.

Comparing the other players to their average:

Kyrie Irving = .76, actual average .7

JR Smith = .34, actual .3

LeBron James = 2.12, actual 2.0

Kevin Love = 1.77, actual 1.7

Tristan Thompson = 4.25, actual 4.1

Steph Curry = 4.52, actual 4.7

Klay Thompson = 2.84, actual 3.0

Harrison Barnes = 3.31, actual 3.5

Draymond Green = 7.55, actual 8.3

Andrew Bogut = 3.00, actual 3.5

Pretty similar, not perfect of course, as each player's rebound percentage will change based on who's out there. Does suggest Cleveland is a better offensive rebounding team than GSW is defensive, and that seems to check out. Could this be good enough to accurately project final scores? This method at least adjusts for different types of players. Perhaps it could be weighted a little based on lineup.

Statistics: Posted by zhang8787 — Wed Jun 29, 2016 8:00 pm

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I am trying to figure out a basketball situation. In a regular length basketball game, the average possessions (or PACE) each team has is 97.5. In any given game, because each team trades possessions, they will usually have the same PACE. When looking at the NBA, the range I have found is from 93 to 102 possessions (slowest to fastest PACE).

I have found when the slowest and fastest teams (93 and 102 PACE) face each other, they will average 97.5, the overall average. This seems logical.

However, I have found when teams of 102 PACE face each other, they average 104. When two teams of 91 PACE face each other, they average 89. It is logical for two teams of 102 to exeed their average PACE, as it is logical for two teams of 93 to fall below their average. However, is there a mathematical way to solve for the difference from their respective averages? Also is there a way to solve the average PACE of any two given teams (102 vs 95, 95 vs 99, etc)?

I've come up with a crude table in an attempt, but it doesn't seem like it's very practical, and I would like to know the concept behind this.

I also put this into golf terms when asking a friend to see if it made more sense.

Let me create the situation. There is 11 golfers and 11 golf courses. Actually the number of each is irrelevant.

When the #1 golfer plays every golf course, he shoots 70 on average. When the #11 golfer plays every golf course, he shoots 80 on average. The #6 golfer is an average golfer, and he shoots 75 on average.

So basically when each plays every course, #1player averages 70, #2player averages 71, #3player averages 72, ... #11player averages 80.

Now something else is the golf course difficulty. When everyone plays on the toughest golf course, they average 80. On the easiest course, they average 65.

Here is a table that could explain it better visually:

Inline image 2

As you can see, when the best golfer plays the easiest course he shoots 65. When the worst golfer plays the hardest course, he shoots 85.

A common misconception is to average - when an 70 golfer plays on a 75 course, many would assume he would shoot 72.5. When in fact, a 70 golfer on average will shoot 70 on an average course.

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I'm not sure if this makes sense to you. If it does, my question is this:

The best golfer who shoots 70 playing on a course that allows 70 on average, those 2 factors combined makes his average lower than 70. When the worst golfer who shoots 80 plays on a course that allows 80 on average, those 2 factors combined makes his average higher than 80. This is one of the basic, most important ideas. So my question is, is there a math model that calculates how much lower than 70 and how much higher than 80 in this situation?

Also, is there a way to calculate if a 77 golfer plays on a 72 golf course, without using the table? If I am given a random player and a random golf course, how can I calculate his average score?

I think this could provide some value to this discussion, and other things in regards to vs. average stats. I'm just curious if there is a mathematical basis behind it, if range of best/best vs worse/worse is needed. What I mean by this is when a 102 pace team plays vs 102 pace team, their pace is 104, and when 93 plays vs 93, their pace is 91.

Another question would be how would you calculate a starting 5 of players with different paces? One weird thing I noticed was sometimes a starting 5 would have a pace of 99 individually (not necessarily together), however when combined, their lineup PACE is lower. Which is weird when 99 is higher than the average, and goes against the ideas above (which a decent sample size agrees with)

Statistics: Posted by zhang8787 — Wed Jun 29, 2016 7:28 pm

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Statistics: Posted by rlee — Wed Jun 29, 2016 4:38 pm

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Here are the top 12 career rebounders in chronological order. Showing official (RS+PO) rebounds, the D-rell adjusted total, and the ratio of the 2.

rebounds years actual D-r D/ac

Russell 57 69 25724 16545 .64

Wilt 60 73 27837 19491 .70

Hayes 69 84 17523 16466 .94

Kareem 70 89 19921 20437 1.03

Gilmore 72 88 17597 15323 .87

Moses 75 95 19234 18511 .96

Parish 77 97 16480 16867 1.02

Hakeem 85 02 15369 15589 1.01

KMalone 86 04 17030 16910 .99

Shaq 93 11 15607 16016 1.03

Garnett 96 16 16196 16187 1.00

Duncan 98 16 17950 18955 1.06

Gilmore gets dinged by his ABA time, I suppose. How does Kareem do so well? Did you get a satisfactory estimate of opponent rebounds before 1971?Russell 57 69 25724 16545 .64

Wilt 60 73 27837 19491 .70

Hayes 69 84 17523 16466 .94

Kareem 70 89 19921 20437 1.03

Gilmore 72 88 17597 15323 .87

Moses 75 95 19234 18511 .96

Parish 77 97 16480 16867 1.02

Hakeem 85 02 15369 15589 1.01

KMalone 86 04 17030 16910 .99

Shaq 93 11 15607 16016 1.03

Garnett 96 16 16196 16187 1.00

Duncan 98 16 17950 18955 1.06

Statistics: Posted by Mike G — Wed Jun 29, 2016 11:45 am

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