Analysis of basketball through objective evidence
http://www.apbr.org/metrics/
If that is the case then surely the factor of 1.2 is simply overstating player impact? Regardless of whether you first divide by all player values by 1.2 before calculating lineups, or divide the summed lineups by 1.2 after the fact, you will reach the same result. Lineups which sum to zero will still sum to zero, and lineups which sum to anything else will sum to their correct(ed) values, so player impact will always be BPM/1.2, and BPM then is needlessly inflated.DSMok1 wrote:Try it out. If a lineup's BPM sums to 0, then the expected performance is 0. But if it sums to +10, then it's expected performance is only +8.333 actually.
The 1.2 translates from the team context (where winning teams perform worse than if they were in a close game, and vise versa) to a neutral context.
Actually, RPM and the 14 year RAPM has the exact same scale and setup, with effectively the same 1.2 factor (it's the "playing with the lead" adjustment).
Nevertheless, some box score stats are clear that Shaq was the uber-dominant player in the league for 5 or 6 years. PER and eWins say he's one of the top 4 players ever. BPM says he's 18th, not counting anyone's numbers before 1973-74. He ranks just below Kirilenko.colts18 wrote:A stat like RAPM will love Shaq because it picks all the things that make him a great player that the box score won't pick up (defensive attention, triple teams, hockey assists, fouls generated, etc.). BPM doesn't pick up those things so he gets underrated by the stat.
The 1.2 is important for estimating things when players change teams, helped the overall fit of the regression as well significantly, and helps compare players from good and bad teams more accurately--a player who, without the 120% adjustment would be +0 would move to perhaps +0.5 on a good team and -0.5 on a bad team. For instance, without the 120% adjustment last season, Thadeus Young was a +0.0. With the adjustment, he was -0.5. Conversely, Matt Bonner was +0.0 without the 120% adjustment and +0.4 with it. It's basically adjusting for context--Young's teams were always behind and Bonner's teams were always ahead.v-zero wrote:If that is the case then surely the factor of 1.2 is simply overstating player impact? Regardless of whether you first divide by all player values by 1.2 before calculating lineups, or divide the summed lineups by 1.2 after the fact, you will reach the same result. Lineups which sum to zero will still sum to zero, and lineups which sum to anything else will sum to their correct(ed) values, so player impact will always be BPM/1.2, and BPM then is needlessly inflated.DSMok1 wrote:Try it out. If a lineup's BPM sums to 0, then the expected performance is 0. But if it sums to +10, then it's expected performance is only +8.333 actually.
The 1.2 translates from the team context (where winning teams perform worse than if they were in a close game, and vise versa) to a neutral context.
Actually, RPM and the 14 year RAPM has the exact same scale and setup, with effectively the same 1.2 factor (it's the "playing with the lead" adjustment).
I would imagine the RAPM adjustment causes changes which cannot be captured by such a simple adjustment in a box score model.
This is true. Once there are a variety of box score metrics with similar out of sample performance (Only RAPM and WS are good of the list above), looking at a variety of similar-quality metrics that use different approaches will help to capture the potential error in a player's box score estimates.Crow wrote:Relative to the others, BPM will favor passers, WS shooters,PER volume scorers, WP rebounders, RAPM defenders. A five part weighted blend did well in Alex's old retrodiction and should again if included. All at 20% for simplicity or maybe 25% to BPM and RPM, 15% to WP and PER and 20% to Ws. I am really leaning on a blended approach as compromise resolution for all the headaches of perceived slants in the box score metrics. For prediction, RPM should get a bigger share per Alex's findings.
The thing is, if you tweak the weights of BPM so that it better appreciates Shaq and Moses, then you also have to live with the changes to other players' ratings wrought by that decision. (Changes that would most likely reduce its out-of-sample performance, if what you want is a more PER-like metric.) There are always going to be these "corner cases" in any metric, but you can't pick and choose which component stats to value more/less for each individual player on a case-by-case basis.Mike G wrote:Nevertheless, some box score stats are clear that Shaq was the uber-dominant player in the league for 5 or 6 years. PER and eWins say he's one of the top 4 players ever. BPM says he's 18th, not counting anyone's numbers before 1973-74. He ranks just below Kirilenko.
Absolutely. One must look at the overall performance of a model, not pick particular cases. I agree that BPM may be struggling with Moses, and Shaq could perhaps be underrepresented... but what about the other thousands of players?Neil Paine wrote:The thing is, if you tweak the weights of BPM so that it better appreciates Shaq and Moses, then you also have to live with the changes to other players' ratings wrought by that decision. (Changes that would most likely reduce its out-of-sample performance, if what you want is a more PER-like metric.) There are always going to be these "corner cases" in any metric, but you can't pick and choose which component stats to value more/less for each individual player on a case-by-case basis.Mike G wrote:Nevertheless, some box score stats are clear that Shaq was the uber-dominant player in the league for 5 or 6 years. PER and eWins say he's one of the top 4 players ever. BPM says he's 18th, not counting anyone's numbers before 1973-74. He ranks just below Kirilenko.
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year player BPM year player BPM year player BPM
2009 LeBron 12.2 2001 Francis 6.4 2006 Arenas 5.2
1989 Jordan 11.6 1976 Adams 6.3 1975 Frazier 5.2
2009 Paul 10.3 2014 Noah 6.3 1992 Nance 5.2
1994 Robinson 9.9 1977 Walton 6.3 2001 Allen 5.2
2009 Wade 9.9 1994 Kemp 6.3 2013 Westbrook 5.2
1977 Kareem 9.6 1986 A Robertson 6.2 2010 Smoove 5.2
1989 Barkley 9.3 1997 Webber 6.2 1976 McGinnis 5.1
2004 Garnett 9.2 1996 A Hardaway 6.1 1974 Barry 5.1
1990 Magic 9.1 2007 Nowitzki 6.0 1995 Stockton 5.1
2003 McGrady 8.9 2010 Ginobili 6.0 1986 Pressey 5.1
1986 Bird 8.8 2000 Outlaw 5.9 2007 Marion 5.1
2000 Shaq 8.8 2003 Kobe 5.9 2010 Dwight 5.1
2014 Durant 8.2 2011 Rose 5.8 1992 Manning 5.1
1992 Drexler 8.0 1988 Lever 5.7 1997 T Hardaway 5.0
1997 Malone 7.9 1991 T Porter 5.7 2010 P Gasol 5.0
1994 Pippen 7.9 2008 Billups 5.7 1989 R Harper 5.0
2014 Love 7.9 2002 Brand 5.7 2000 E Jones 5.0
1974 Lanier 7.7 2013 M Gasol 5.6 1985 Richardson 4.9
1981 Erving 7.6 1976 J Drew 5.6 1992 Rodman 4.9
1993 Olajuwon 7.6 1981 Marques J 5.6 1985 Isiah 4.9
2004 Kirilenko 7.6 2014 Lowry 5.6 1991 K Johnson 4.9
1997 Hill 7.5 2007 B Wallace 5.5 2013 Noah 4.9
2014 Curry 7.1 2004 B Davis 5.5 1987 McHale 4.8
2002 Duncan 7.0 2014 Harden 5.5 1995 Divac 4.8
1977 B Jones 7.0 1975 Unseld 5.4 1980 Rollins 4.8
1992 H Grant 6.9 1996 Brandon 5.4 1990 Hornacek 4.8
1997 Blaylock 6.9 2009 Roy 5.4 1993 Daugherty 4.8
2003 Kidd 6.7 1983 Moncrief 5.3 1976 Boerwinkle 4.8
2001 Carter 6.6 2002 B Barry 5.3 1997 K Anderson 4.7
2000 Payton 6.4 1980 C Maxwell 5.2 1976 Cowens 4.7
So true, but a lot of people just seem to "check" the performance of a metric based on whether particular players show up on top.DSMok1 wrote: Absolutely. One must look at the overall performance of a model, not pick particular cases.
And yet, a team composed of players who are good at BPM is more reliably a good team than a team composed of players good at PER, WS, etc. Perhaps that "weirdness" relative to those other metrics actually helps it predict team outcomes more effectively.Mike G wrote:It's not just a couple of 'corner' dwellers that make BPM in its present form look weird.
Neil, slightly OT, but why don't you do a RAPM like stat for your Randy Moss article. You used RANY, but why not use QB's, receivers, and age of those QB's/WR's (or TE's), then run a RAPM like regression to see which WR's had the most impact on QB's and vice versa?Neil Paine wrote:
And yet, a team composed of players who are good at BPM is more reliably a good team than a team composed of players good at PER, WS, etc. Perhaps that "weirdness" relative to those other metrics actually helps it predict team outcomes more effectively.
Btw, can you send me an eWins file? I'd love to test it in the same manner.
Last year, there were 40 players within 0.2 of average (zero) BPM. If I sort these 40 by eWins/minute, I can draw a "team" of players who are above-avg in my metric.Neil Paine wrote:... a team composed of players who are good at BPM is more reliably a good team than a team composed of players good at PER, WS, etc. Perhaps that "weirdness" relative to those other metrics actually helps it predict team outcomes more effectively.
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BPM avg players my team
Dorell Wright LaMarcus Aldridge
Mike Miller Nikola Pekovic
Dante Cunningham Nene Hilario
Jeff Withey Greg Monroe
Caron Butler Jared Sullinger
Chris Douglas-Roberts Monta Ellis
Martell Webster Rajon Rondo
Andrei Kirilenko Reggie Jackson
Wesley Johnson Kyle O'Quinn
Maurice Harkless Joe Johnson
Kyle Singler
Iman Shumpert
Rashard Lewis
Andre Roberson
Francisco Garcia