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PostPosted: Wed Oct 29, 2014 8:37 pm 
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http://www.sports-reference.com/blog/20 ... nus-bpm-2/
http://www.basketball-reference.com/about/bpm.html

Thanks Daniel. Thanks BR.

Shot defense disclosure made. Need to reason beyond it still on the user.


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PostPosted: Fri Oct 31, 2014 3:03 pm 
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I would encourage people to read that BPM "about" article. Tons of links and background information. I tried to really explain both the methodology and thought-process for BPM (which could be extended to other true SPM metrics).

For those who don't know, BPM (Box Plus/Minus) is the successor to my ASPM (Advanced Statistical Plus/Minus)

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PostPosted: Fri Oct 31, 2014 4:18 pm 
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Stuff to note about the stat:

-It includes MPG but does not include height? Thats weird.

-Nothing on it rewards player who make it to the FT line and make them

-Based on a cursory glance of the formula, a player who shoots 51 TS% on a 50 TS% team equals a players who shoots 55 TS% on a 54 TS% team


Suggestion to the stat:
The stat corrects players to the adjusted team margin differential. If it does that, why doesn't it only do that for the games the players played? Should a 60 game player be adjusted a 82 game team margin? It should be to the 60 games that he played. This rewards players whose teams played well in the games they played and sucked without them.

To piggyback on the last point, one great addition to the stat would be to do something like what J.E. did with the boxscore RAPM. Use the players MPG and whether he started and the teams margin in each quarter to determine how much of the credit he should get. Then add that onto the boxscore stat portion of BPM.


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PostPosted: Fri Oct 31, 2014 4:30 pm 
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colts18 wrote:
Stuff to note about the stat:

-It includes MPG but does not include height? Thats weird.


Thanks for your comments, colts18!

My rational for doing that is in the writeup. I recognize using MPG is a bit controversial, but 1. it helps the regression quite a bit (a lot more than height does) and 2. it ties more directly to value than height does.

colts18 wrote:
-Nothing on it rewards player who make it to the FT line and make them


TS% includes FTs, as does usage.

colts18 wrote:
-Based on a cursory glance of the formula, a player who shoots 51 TS% on a 50 TS% team equals a players who shoots 55 TS% on a 54 TS% team


Correct. That is a bit interesting, isn't it? But since we're doing a team adjustment and all the regression is doing is splitting credit, then that should be correct. A player that's 1% better than rest of team will be the same distance above team average BPM, no matter what that team average is or the TS% is.

Which makes sense. Also, it helps with the era adjustments.

Think about it--if you've got a GREAT team, which averages 60% TS, and a horrible team, which shoots 40%--is more usage better for a 50% TS player? It has to depend on the team.

colts18 wrote:
Suggestion to the stat:
The stat corrects players to the adjusted team margin differential. If it does that, why doesn't it only do that for the games the players played? Should a 60 game player be adjusted a 82 game team margin? It should be to the 60 games that he played. This rewards players whose teams played well in the games they played and sucked without them.


That would be ideal, but a relative pain to code, and may be difficult to do for other leagues (NCAA, Europe, etc.) It has been an issue in the NCAA when a player stacks up good stats in easy early season games and then gets hurt.

colts18 wrote:
To piggyback on the last point, one great addition to the stat would be to do something like what J.E. did with the boxscore RAPM. Use the players MPG and whether he started and the teams margin in each quarter to determine how much of the credit he should get. Then add that onto the boxscore stat portion of BPM.


Effectively, the MPG term is doing this empirically anyway.

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PostPosted: Sat Nov 01, 2014 6:02 am 
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Am I reading the stat correctly?

AST% is a massive negative according to the formula. Thats probably why it seems like the stat is not high on John Stockton. Its interesting that Stockton never had a positive in defensive BPM in his career despite having a ton of steals (which the stat loves) and playing on some really good defensive teams.

You would think AST% would be a positive because PG's rate pretty well in Offensive RAPM.

Also Personal fouls aren't in the stat even though they are a box score stat.


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PostPosted: Sat Nov 01, 2014 11:09 am 
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Ast% appears 3 times in the formula:
Quote:
Assist% is a linear term, but assists also figure in both of the interaction terms in the regression, so the specific coefficient for this linear term has no meaning by itself.
Quote:
i*AST% gives a positive value to assists multiplied by usage - basically, a player's assists are worth more if he is also finishing possessions.
Quote:
Finally, a positive interaction term between rebounding and assists is included.

Apparently guys who pass but do not rebound and/or score tend to be a liability on a team. Maybe they aren't very good on defense, in general.

Intuitively, this makes sense -- a well-rounded player is generally better than a specialist. And the 'interaction terms' may be so strongly positive that there's nothing left for the naked Ast% term.

Quote:
BPM is adjusted such that the minute-weighted sum of individual players' BPM ratings on a team equals the team's rating times 120%.
hmm .. I adjust player scoring rates by (TmPts/OpPts)^1.20

Now, if you would just account for home/away differential and starter/reserve strength, I could perhaps hang it up :)


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PostPosted: Sat Nov 01, 2014 1:44 pm 
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When the player WAR are calculated by the formula: WAR = VORP * 2.7, the league adds up to 810 WAR last year. That's just 66% of the the 1230 wins actually accumulated.
Who is winning the other 420 games? Sub-replacement players?
No, those players -- below -2.0 BPM -- sum to minus-128 WAR, while those above -2.0 sum to 938.

Maybe the sub-R players on opposing teams are giving 1/3 of the wins away?
[ It's hard to think about.]

If we want 1230 WAR to be the league total, a replacement level of -3.035 does the trick. Now we have 1293 WAR by R+ players, and -63 by R- players.
Those R- guys (< -3.035) played 11.2% of all NBA minutes last year.
With R at -2, we have twice as many R- minutes: 22.1% -- representing about 40% of all players who appeared.

For additional perspective, the 2013-14 league totals 1269.5 positive Win Shares vs -12.5 total from <.000 ws/48 players.
These don't sum to 1230, but negative-win players got just 2.7% of all minutes.


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PostPosted: Sat Nov 01, 2014 5:47 pm 
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League WAR isn't supposed to sum to league wins. A -2 player will produce about 0.034 wins per 48 minutes played. As such if an average player will produce about 0.1 wins per 48 minutes (as they must), then that player will produce 0.066 WAR per 48 minutes. Taking that to full league minutes ends up with about 812 wins, so pretty much exactly what you found.


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PostPosted: Sat Nov 01, 2014 7:46 pm 
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BPM_Team_Adjustment is a bit challenging to accept and not misunderstand for me. So I had a few questions. They too are perhaps a bit challenging to understand but I want to try.

The BPM_Team_Adjustment makes the results of this metric a ranking rather than a precise individual impact estimate because of the 120% inflation, if I am following?

Would there be an acceptable way to adjust this adjustment so that it reflected the actual or actual - adjusted team performance data of individual players when on the court vs their teammates when on court, instead of giving the same adjustment to all based on all minutes, including when not on the court?

Would there be an acceptable and separate way to account for just the performance change seen for when teams are leading or trailing (by some unspecified amount) so that it reflect the actual performance impact for that player instead of being team or league average change? The play by play data exists for recent years and I assume JE essentially has a player specific adjustment because it is based on number of actual player minutes meeting his critieria (correct?)

Even if one didn't redo the team adjustment, is there an understanding of how much of it is related to the blowout performance time issue versus other things about the team?

If one looked at BPM without the team adjustment and without blocks, would that be essentially equivalent in terms of what is covered / included to RPM (or RAPM) - defensive adjusted points per shot (more precise if it were for one year)? What is the R2 for defensive BPM and RPM (or RAPM) - defensive adjusted points per shot? Is it more impressive and, if so, shouldn't that be trumpeted to counter those who complain the r2 is too low to give it much weight?

I intend to compare defensive BPM to defensive RPM (or RAPM) - defensive adjusted points per shot. I wonder how close they are. If they are close on average I might start looking at defensive BPM + defensive adjusted points per shot. Is it correct to think that the "error" in RPM is present in the every component of BPM including the team adjustment? Is there any basis to suspect there is more error in the team adjustment quantity? Is there any basis to suggest that rather than remove col-linearity issues that they have just been shifted into the team adjustment? I am just asking, not actively presuming.

If someone (not necessarily you Daniel, given your stated positions) wanted to separately try to model the missing defensive component not in current BPM (I'll call it broadly shot defense), what to try? Minutes again, team opponent pts per shot, counterpart pts per shot, height, years of experience, what else? Could some of these terms be significant for this portion of the project when they weren't for the original BPM effort? What significance does sqrt(AST%*TRB%) have for this portion of the project? Is there any basis for assuming that BPM or BPM enhanced with shot defense (via use of defensive adjusted points per shot or regression based model or combination) has less "error" than RPM / RAPM?

Anyone interested in running a DBPM that is an exact mirror of OBPM?

Overall for BPM what is most different at the stat coefficient level when compare to the last version of ASPM or the last public version's of Neil Paine's SPM? (to OR/DR winshares too)? For comparison with Neil's http://www.basketball-reference.com/blog/?page_id=4122 (is this the most recent public version?) He has TSA/36 separate from assists, whereas you have usage. PFs included in the model here.

(Age and height are 2 differences in a previous version http://www.basketball-reference.com/blog/?p=2191 Versatility Index was the cube root of Pts/40*Ast/40*Reb/40, where you you a ast/reb interaction term)

If one laid out BPM, shot defense enhance BPM, XRAPM, would there be any appropriate use for machine learning to find optimal blend of these metrics for retrodiction or prediction or both? Or to find a new metric that is essentially based on this optimal blend?

Is there anything in this article http://www.basketball-reference.com/blog/?p=8339 that contributes to the discussion?

In the regression what exactly is Shot%? It didn't make it into final BPM in any fashion? Not significant or...?

Is there anything from old or new IPV that is different, discussable and potentially helpful for BPM or anything beyond it?

Is there different meaning / reason to compare BPM to play by play, game level or level level actual scoreboard instead of to RPM? (Isn't this one of Berri's main / old complaints? Is there any reason to address that further here and now?)

I know this jumps around and is probably behind the knowledge curve in some areas. Any clarifications or additional thinking about the topics will be appreciated.


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PostPosted: Sat Nov 01, 2014 10:53 pm 
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From the about/BPM article:
Quote:
... the "replacement player" is defined as a player on minimum salary or not a normal member of a team's rotation.

But at BPM = -2 we find Jeff Green. Garnett was barely above.
Below R are 31 guys who got 1000+ minutes; 7 were over 2000.

Adding .034 to everyone's WAR/48, Green has 2.0 'wins' on the year, KG has 0.8
Just about the same results are gotten by making -3.035 the 'replacement level'.

We already have an arbitrary zero (BPM) defining the average player. What is the utility of describing wins above yet another arbitrary zero (R) value?
And then another point on the continuum describes zero wins added.

Since R = -2.0 fails to meet the criteria in the quoted definition above -- instead defining 43% of the players in the NBA -- why not merge a couple of these zero values?

If -3.035 is R, then a team of such players have MOV worse than -15. No team has ever been quite that bad; and those teams which have been closest have had some key players who were decidedly better than a minimum salary or 'non rotation' player.


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PostPosted: Sat Nov 01, 2014 11:55 pm 
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I'm not actually a proponent of WAR, I was merely speaking to its intents. It's supposed to represent the value a player has over the nominal minimum salary replacement supposedly available in perpetual free agency.

Since my primary concern is not contract valuation (interesting though it is), I do not compute my own WAR for my own purposes.


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PostPosted: Sun Nov 02, 2014 6:15 am 
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was curious what this statement means:

Defense is only partially captured by the box score, so elite defenders based on position and communication, like Kevin Garnett and Tim Duncan, will not be properly represented. The regression mathematically accounts for that, pulling all of the estimates closer to average.

does this mean the regression somehow rates them better at defense than an unregressed defensive box plus/minus would?...

how do seasons like david robinson 91-92 or hakeem olajuwon 89-90 compare to the DBPM top 10 list that shows ben wallace with the top 5 spots? how about alvin robertson's 85-86 season? what are the top 10 DBPM by position?...


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PostPosted: Mon Nov 03, 2014 1:43 pm 
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colts18 wrote:
Am I reading the stat correctly?

AST% is a massive negative according to the formula. Thats probably why it seems like the stat is not high on John Stockton. Its interesting that Stockton never had a positive in defensive BPM in his career despite having a ton of steals (which the stat loves) and playing on some really good defensive teams.

You would think AST% would be a positive because PG's rate pretty well in Offensive RAPM.

Also Personal fouls aren't in the stat even though they are a box score stat.



Mike G is correct--the interaction terms dominate the AST% valuation. Steve Nash is an outlier in RAPM--most pass ONLY point guards aren't much of a positive on offense. I believe Nash's assists are more valuable than most (I think I've seen a study to that effect), but the regression can't pick up on that nuance. He's the rare offensive player that is really valuable despite not shooting a lot and not getting rebounds. BPM can't account for that.

Personal fouls might be a beneficial addition, though when I evaluated them a while back I found no benefit to the regression at the time. Might be different now; I didn't include them in the current variable selection process.

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PostPosted: Mon Nov 03, 2014 1:54 pm 
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Mike G wrote:
When the player WAR are calculated by the formula: WAR = VORP * 2.7, the league adds up to 810 WAR last year. That's just 66% of the the 1230 wins actually accumulated.
Who is winning the other 420 games? Sub-replacement players?
No, those players -- below -2.0 BPM -- sum to minus-128 WAR, while those above -2.0 sum to 938.

Maybe the sub-R players on opposing teams are giving 1/3 of the wins away?
[ It's hard to think about.]

If we want 1230 WAR to be the league total, a replacement level of -3.035 does the trick. Now we have 1293 WAR by R+ players, and -63 by R- players.
Those R- guys (< -3.035) played 11.2% of all NBA minutes last year.
With R at -2, we have twice as many R- minutes: 22.1% -- representing about 40% of all players who appeared.

For additional perspective, the 2013-14 league totals 1269.5 positive Win Shares vs -12.5 total from <.000 ws/48 players.
These don't sum to 1230, but negative players got just 2.7% of all minutes.


v-zero wrote:
League WAR isn't supposed to sum to league wins. A -2 player will produce about 0.034 wins per 48 minutes played. As such if an average player will produce about 0.1 wins per 48 minutes (as they must), then that player will produce 0.066 WAR per 48 minutes. Taking that to full league minutes ends up with about 812 wins, so pretty much exactly what you found.


A team of replacement level players will win about 15 games, as found by the research linked to in my writeup. The research found that replacement level players are about -2.0 on average--in other words, players not in a team's rotation at the beginning of the season that eventually end up playing some end up at about -2.0. A team of -2.0 players would win something like 15 games.

So for wins and value over replacement, those 15 or so are a given for any team.

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PostPosted: Mon Nov 03, 2014 2:06 pm 
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bchaikin wrote:
was curious what this statement means:

Defense is only partially captured by the box score, so elite defenders based on position and communication, like Kevin Garnett and Tim Duncan, will not be properly represented. The regression mathematically accounts for that, pulling all of the estimates closer to average.

does this mean the regression somehow rates them better at defense than an unregressed defensive box plus/minus would?...

how do seasons like david robinson 91-92 or hakeem olajuwon 89-90 compare to the DBPM top 10 list that shows ben wallace with the top 5 spots? how about alvin robertson's 85-86 season? what are the top 10 DBPM by position?...


Basically, the regression can't account for the wide spread in defensive value, so the estimates are closer to 0. The spread in the RAPM defensive basis is much wider than the spread in D-BPM, whereas on offense, the spreads are similar.

The top 15 seasons at each position by DBPM, minimum 1000 minutes:
Code:
╔══════╦═════╦════╦════════════════════════╦═════╦════╦══════╦═══════╗
║ Year ║ Tm  ║ Rk ║         Player         ║ Age ║ G  ║  MP  ║ D-BPM ║
╠══════╬═════╬════╬════════════════════════╬═════╬════╬══════╬═══════╣
║ C    ║     ║    ║                        ║     ║    ║      ║       ║
║ 2007 ║ CHI ║ 1  ║ Ben Wallace            ║ 32  ║ 77 ║ 2697 ║ 6.4   ║
║ 2004 ║ DET ║ 2  ║ Ben Wallace            ║ 29  ║ 81 ║ 3050 ║ 6.3   ║
║ 2003 ║ DET ║ 3  ║ Ben Wallace            ║ 28  ║ 73 ║ 2873 ║ 6.3   ║
║ 2002 ║ DET ║ 4  ║ Ben Wallace            ║ 27  ║ 80 ║ 2921 ║ 5.9   ║
║ 2006 ║ DET ║ 5  ║ Ben Wallace            ║ 31  ║ 82 ║ 2890 ║ 5.9   ║
║ 1985 ║ UTA ║ 6  ║ Mark Eaton             ║ 28  ║ 82 ║ 2813 ║ 5.9   ║
║ 2008 ║ DEN ║ 7  ║ Marcus Camby           ║ 33  ║ 79 ║ 2758 ║ 5.8   ║
║ 2007 ║ DEN ║ 8  ║ Marcus Camby           ║ 32  ║ 70 ║ 2369 ║ 5.8   ║
║ 1986 ║ WSB ║ 9  ║ Manute Bol             ║ 23  ║ 80 ║ 2090 ║ 5.7   ║
║ 1987 ║ UTA ║ 10 ║ Mark Eaton             ║ 30  ║ 79 ║ 2505 ║ 5.6   ║
║ 1989 ║ UTA ║ 11 ║ Mark Eaton             ║ 32  ║ 82 ║ 2914 ║ 5.5   ║
║ 1992 ║ SAS ║ 12 ║ David Robinson         ║ 26  ║ 68 ║ 2564 ║ 5.5   ║
║ 1991 ║ HOU ║ 13 ║ Hakeem Olajuwon        ║ 28  ║ 56 ║ 2062 ║ 5.3   ║
║ 2006 ║ DEN ║ 14 ║ Marcus Camby           ║ 31  ║ 56 ║ 1857 ║ 5.3   ║
║ 1990 ║ HOU ║ 15 ║ Hakeem Olajuwon        ║ 27  ║ 82 ║ 3124 ║ 5.2   ║
║ PF   ║     ║    ║                        ║     ║    ║      ║       ║
║ Year ║ Tm  ║ Rk ║ Player                 ║ Age ║ G  ║ MP   ║ D-BPM ║
║ 2000 ║ ORL ║ 2  ║ Bo Outlaw              ║ 28  ║ 82 ║ 2326 ║ 5.1   ║
║ 2007 ║ SAS ║ 1  ║ Tim Duncan             ║ 30  ║ 80 ║ 2726 ║ 4.6   ║
║ 2004 ║ SAS ║ 3  ║ Tim Duncan             ║ 27  ║ 69 ║ 2527 ║ 4.4   ║
║ 2004 ║ MIN ║ 1  ║ Kevin Garnett          ║ 27  ║ 82 ║ 3231 ║ 4.4   ║
║ 1998 ║ ORL ║ 1  ║ Bo Outlaw              ║ 26  ║ 82 ║ 2953 ║ 4.3   ║
║ 2008 ║ BOS ║ 3  ║ Kevin Garnett          ║ 31  ║ 71 ║ 2328 ║ 4.2   ║
║ 1995 ║ LAC ║ 6  ║ Bo Outlaw              ║ 23  ║ 81 ║ 1655 ║ 4.2   ║
║ 2013 ║ SAS ║ 3  ║ Tim Duncan             ║ 36  ║ 69 ║ 2078 ║ 4.1   ║
║ 2005 ║ SAS ║ 3  ║ Tim Duncan             ║ 28  ║ 66 ║ 2203 ║ 4.1   ║
║ 2006 ║ SAS ║ 1  ║ Tim Duncan             ║ 29  ║ 80 ║ 2784 ║ 4.0   ║
║ 2001 ║ ORL ║ 3  ║ Bo Outlaw              ║ 29  ║ 80 ║ 2534 ║ 4.0   ║
║ 1990 ║ DET ║ 8  ║ John Salley            ║ 25  ║ 82 ║ 1914 ║ 4.0   ║
║ 2010 ║ ATL ║ 2  ║ Josh Smith             ║ 24  ║ 81 ║ 2871 ║ 3.9   ║
║ 1994 ║ SEA ║ 3  ║ Shawn Kemp             ║ 24  ║ 79 ║ 2597 ║ 3.9   ║
║ 1993 ║ NYK ║ 4  ║ Charles Oakley         ║ 29  ║ 82 ║ 2230 ║ 3.9   ║
║ SF   ║     ║    ║                        ║     ║    ║      ║       ║
║ Year ║ Tm  ║ Rk ║ Player                 ║ Age ║ G  ║ MP   ║ D-BPM ║
║ 2005 ║ UTA ║ 8  ║ Andrei Kirilenko       ║ 23  ║ 41 ║ 1349 ║ 4.6   ║
║ 2006 ║ UTA ║ 2  ║ Andrei Kirilenko       ║ 24  ║ 69 ║ 2604 ║ 4.4   ║
║ 2004 ║ UTA ║ 1  ║ Andrei Kirilenko       ║ 22  ║ 78 ║ 2895 ║ 4.2   ║
║ 2006 ║ CHA ║ 5  ║ Gerald Wallace         ║ 23  ║ 55 ║ 1895 ║ 4.1   ║
║ 2014 ║ GSW ║ 6  ║ Draymond Green         ║ 23  ║ 82 ║ 1797 ║ 4.0   ║
║ 2007 ║ UTA ║ 5  ║ Andrei Kirilenko       ║ 25  ║ 70 ║ 2049 ║ 4.0   ║
║ 1995 ║ CHI ║ 1  ║ Scottie Pippen         ║ 29  ║ 79 ║ 3014 ║ 3.7   ║
║ 2003 ║ UTA ║ 5  ║ Andrei Kirilenko       ║ 21  ║ 80 ║ 2213 ║ 3.6   ║
║ 1994 ║ CHI ║ 2  ║ Scottie Pippen         ║ 28  ║ 72 ║ 2759 ║ 3.4   ║
║ 1986 ║ UTA ║ 8  ║ Carey Scurry           ║ 23  ║ 78 ║ 1168 ║ 3.4   ║
║ 2000 ║ SAS ║ 8  ║ Jerome Kersey          ║ 37  ║ 72 ║ 1310 ║ 3.3   ║
║ 2014 ║ SAS ║ 5  ║ Kawhi Leonard          ║ 22  ║ 66 ║ 1923 ║ 3.2   ║
║ 2008 ║ TOR ║ 4  ║ Jamario Moon           ║ 27  ║ 78 ║ 2166 ║ 3.1   ║
║ 1997 ║ LAL ║ 5  ║ Jerome Kersey          ║ 34  ║ 70 ║ 1766 ║ 3.1   ║
║ 2012 ║ TOR ║ 3  ║ James Johnson          ║ 24  ║ 62 ║ 1561 ║ 3.1   ║
║ SG   ║     ║    ║                        ║     ║    ║      ║       ║
║ Year ║ Tm  ║ Rk ║ Player                 ║ Age ║ G  ║ MP   ║ D-BPM ║
║ 1986 ║ SAS ║ 2  ║ Alvin Robertson        ║ 23  ║ 82 ║ 2878 ║ 3.2   ║
║ 2011 ║ CHI ║ 4  ║ Ronnie Brewer          ║ 25  ║ 81 ║ 1781 ║ 3.0   ║
║ 1991 ║ MIL ║ 2  ║ Alvin Robertson        ║ 28  ║ 81 ║ 2598 ║ 3.0   ║
║ 1986 ║ DEN ║ 3  ║ T.R. Dunn              ║ 30  ║ 82 ║ 2401 ║ 2.9   ║
║ 1988 ║ DEN ║ 7  ║ T.R. Dunn              ║ 32  ║ 82 ║ 1534 ║ 2.8   ║
║ 2013 ║ MEM ║ 4  ║ Tony Allen             ║ 31  ║ 79 ║ 2109 ║ 2.8   ║
║ 2003 ║ SAC ║ 1  ║ Doug Christie          ║ 32  ║ 80 ║ 2710 ║ 2.8   ║
║ 1983 ║ MIL ║ 6  ║ Paul Pressey           ║ 24  ║ 79 ║ 1528 ║ 2.7   ║
║ 1990 ║ MIL ║ 2  ║ Alvin Robertson        ║ 27  ║ 81 ║ 2599 ║ 2.7   ║
║ 2011 ║ MEM ║ 8  ║ Tony Allen             ║ 29  ║ 72 ║ 1494 ║ 2.6   ║
║ 2010 ║ OKC ║ 4  ║ Thabo Sefolosha        ║ 25  ║ 82 ║ 2348 ║ 2.6   ║
║ 1984 ║ DEN ║ 3  ║ T.R. Dunn              ║ 28  ║ 80 ║ 2705 ║ 2.5   ║
║ 2004 ║ SAS ║ 5  ║ Manu Ginobili          ║ 26  ║ 77 ║ 2260 ║ 2.5   ║
║ 2012 ║ MEM ║ 5  ║ Tony Allen             ║ 30  ║ 58 ║ 1525 ║ 2.4   ║
║ 1986 ║ MIL ║ 1  ║ Paul Pressey           ║ 27  ║ 80 ║ 2704 ║ 2.4   ║
║ PG   ║     ║    ║                        ║     ║    ║      ║       ║
║ Year ║ Tm  ║ Rk ║ Player                 ║ Age ║ G  ║ MP   ║ D-BPM ║
║ 1994 ║ SEA ║ 6  ║ Nate McMillan          ║ 29  ║ 73 ║ 1887 ║ 4.1   ║
║ 1996 ║ SEA ║ 8  ║ Nate McMillan          ║ 31  ║ 55 ║ 1261 ║ 3.3   ║
║ 1993 ║ SEA ║ 6  ║ Nate McMillan          ║ 28  ║ 73 ║ 1977 ║ 3.2   ║
║ 1981 ║ LAL ║ 6  ║ Magic Johnson          ║ 21  ║ 37 ║ 1371 ║ 3.1   ║
║ 1995 ║ SEA ║ 6  ║ Nate McMillan          ║ 30  ║ 80 ║ 2070 ║ 3.0   ║
║ 1983 ║ PHO ║ 7  ║ Johnny High            ║ 25  ║ 82 ║ 1155 ║ 2.9   ║
║ 1982 ║ LAL ║ 2  ║ Magic Johnson          ║ 22  ║ 78 ║ 2991 ║ 2.9   ║
║ 1983 ║ NJN ║ 8  ║ Micheal Ray Richardson ║ 27  ║ 31 ║ 1002 ║ 2.7   ║
║ 2008 ║ BOS ║ 4  ║ Rajon Rondo            ║ 21  ║ 77 ║ 2306 ║ 2.6   ║
║ 1995 ║ SAC ║ 8  ║ Randy Brown            ║ 26  ║ 67 ║ 1086 ║ 2.6   ║
║ 1981 ║ NYK ║ 1  ║ Micheal Ray Richardson ║ 25  ║ 79 ║ 3175 ║ 2.5   ║
║ 1989 ║ WSB ║ 1  ║ Darrell Walker         ║ 27  ║ 79 ║ 2565 ║ 2.5   ║
║ 2013 ║ LAC ║ 8  ║ Eric Bledsoe           ║ 23  ║ 76 ║ 1553 ║ 2.5   ║
║ 2006 ║ NJN ║ 2  ║ Jason Kidd             ║ 32  ║ 80 ║ 2975 ║ 2.5   ║
║ 2008 ║ DAL ║ 8  ║ Jason Kidd             ║ 34  ║ 29 ║ 1011 ║ 2.5   ║
╚══════╩═════╩════╩════════════════════════╩═════╩════╩══════╩═══════╝


Looks like the seasons you asked about all showed up! That's nice to see.

_________________
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