Predicting NBA Playoffs using RAPM (updated with Finals)

[colts18]

I decided to try to predict the NBA playoffs using the xRAPM values that J.E. provided on ESPN because those are supposed to be predictive.

O rating, D rating, Total per 100 possessions
Spurs 6.6 6.9 13.4
Mavs 6.6 0.5 7.1

Thunder 6.1 4.1 10.2
Grizzlies 1.7 5.6 7.3

Clippers 10.0 2.4 12.4
Warriors 4.4 7.4 11.9

Rockets 5.8 3.7 9.5
Blazers 6.6 0.2 6.8

The Clippers look to have the best offense by this measure while the Warriors are the best defense even though I gave no minutes to Andrew Bogut. By RAPM the Spurs are the clear top team followed by Clippers, Warriors, and Thunder.


Here are the MPG values I used. Feel free to give me suggestions so that I can update these Minutes more accurately.

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Player	MPG	RAPM
Tony Parker	32	3.25
Tim Duncan	32	5.29
Kawhi Leonard	30	1.74
Marco Belinelli	25	-1.94
Boris Diaw	22	1.39
Danny Green	24	3.45
Manu Ginobili	24	5.27
Tiago Splitter	20	3.74
Patty Mills	16	3.51
Malcolm Thomas	0	-1.75
Cory Joseph	4	-3.6
Jeff Ayres	5	0.26
Nando De Colo	1	-2.86
Matt Bonner	5	2.48
Shannon Brown	0	
Damion James	0	
Aron Baynes	0	
Othyus Jeffers	0	
Austin Daye	0	

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Monta Ellis	40	2.09
Dirk Nowitzki	38	5.5
Shawn Marion	35	-0.47
Jose Calderon	32	-2.43
Vince Carter	28	3.57
Devin Harris	22	1.04
Samuel Dalembert	20	-0.33
Brandan Wright	15	1.81
Jae Crowder	5	2.04
DeJuan Blair	5	-2.21
Shane Larkin	0	
Gal Mekel	0	
Wayne Ellington	0	
Bernard James	0	
Ricky Ledo	0

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Kevin Durant	41	6.65
Serge Ibaka	33	3.21
Russell Westbrook	35	4.17
Reggie Jackson	18	1.74
Caron Butler	25	-4.29
Thabo Sefolosha	25	0.54
Jeremy Lamb	8	-1.69
Kendrick Perkins	15	-3.19
Derek Fisher	15	1.46
Nick Collison	15	6.34
Steven Adams	8	-3.17
Perry Jones	2	-1.11
Andre Roberson	0	
Hasheem Thabeet	0	
Ryan Gomes	0	
Reggie Williams	0	
Mustafa Shakur	0	
Royal Ivey	0	
		

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Zach Randolph	38	2.87
Mike Conley	38	4.57
Marc Gasol	38	3.41
Courtney Lee	22	-0.02
Tayshaun Prince	20	-1.27
Tony Allen	22	1.79
Jerryd Bayless	12	-2.63
Mike Miller	12	-1.67
James Johnson	6	-1.24
Quincy Pondexter	0	-0.69
Kosta Koufos	10	1.09
Nick Calathes	12	-0.56
Ed Davis	10	-2.07
Darius Morris	0	
Jon Leuer	0	
Jamaal Franklin	0	
Beno Udrih	0	
Seth Curry	0	
		

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Blake Griffin	41	3.98
DeAndre Jordan	35	4.12
Chris Paul	36	7.33
Jamal Crawford	25	-0.26
J.J. Redick	25	-0.32
Matt Barnes	23	3.18
Darren Collison	20	-1.54
Jared Dudley	10	-0.4
Danny Granger	10	1.24
Willie Green	0	
Glen Davis	10	-0.94
Stephen Jackson	0	
Antawn Jamison	0	
Hedo Turkoglu	5	-0.98
Reggie Bullock	0	
Ryan Hollins	0	
Byron Mullens	0	
Darius Morris	0	
Sasha Vujacic	0	
		

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Stephen Curry	40	5.74
Klay Thompson	40	2.66
David Lee	38	0.59
Andre Iguodala	36	6.91
Harrison Barnes	20	-3.31
Andrew Bogut	0	2.03
Draymond Green	25	2.75
Steve Blake	12	-0.91
Jermaine O'Neal	24	-0.07
Jordan Crawford	0	
Marreese Speights	5	-5.6
Toney Douglas	0	
Hilton Armstrong	0	
Kent Bazemore	0	
Nemanja Nedovic	0	
Ognjen Kuzmic	0	
MarShon Brooks	0	
Dewayne Dedmon	0	
		

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James Harden	40	3.03
Chandler Parsons	40	1.75
Dwight Howard	38	4.12
Patrick Beverley	32	4.88
Jeremy Lin	20	0.32
Terrence Jones	26	-1.7
Omer Asik	12	2.85
Francisco Garcia	12	-1.51
Josh Powell	0	
Omri Casspi	10	-2.22
Aaron Brooks	0	
Jordan Hamilton	0	
Donatas Motiejunas	10	-0.32
Troy Daniels	0	
Isaiah Canaan	0	
Greg Smith	0	
Ronnie Brewer	0	
Robert Covington	0	
		

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Lamarcus Aldridge	40	5.16
Nicolas Batum	40	-0.13
Damian Lillard	40	2.54
Wesley Matthews	38	0.78
Robin Lopez	35	2.91
Mo Williams	24	-2.18
Dorell Wright	8	-2.23
Joel Freeland	5	-2.12
C.J. McCollum	5	-1.31
Thomas Robinson	5	-3.95
Will Barton	0	
Meyers Leonard	0	
Victor Claver	0	
Earl Watosn	0	
Allen Crabbe	0	

[colts18]

Eastern Conference playoff rankings

O rating, D rating, Total per 100 possessions
Pacers -0.9 7.0 6.1
Hawks -0.6 0.2 -0.4

Heat 7.4 2.9 10.3
Bobcats -1.6 -0.3 -1.8

Raptors 2.9 0.6 3.4
Nets 3.1 2.1 5.1

Bulls -0.8 5.1 4.3
Wizards 0.5 3.1 3.6


Miami is the favorite by a wide margin based on RAPM. The Bulls-Wizards series should be a good one

Top teams in the playoffs by RAPM

1 Spurs 13.4
2 Clippers 12.4
3 Warriors 11.9
4 Heat 10.3
5 Thunder 10.2
6 Rockets 9.5
7 Grizzlies 7.3
8 Mavs 7.1
9 Blazers 6.8
10 Pacers 6.1
11 Nets 5.1
12 Bulls 4.3
13 Wizards 3.6
14 Raptors 3.4
15 Hawks -0.4
16 Bobcats -1.8


MPG values for Eastern conference playoffs

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Player	MPG	RAPM
Paul George	40	2.61
Lance Stephenson	36	0.46
George Hill	34	2.81
David West	34	3.34
Roy Hibbert	30	2.24
Danny Granger	0	1.24
Evan Turner	20	-2.55
C.J. Watson	16	-0.08
Andrew Bynum	0	-1.12
Luis Scola	15	-2.97
Ian Mahinmi	15	-0.48
Orlando Johnson	0	
Donald Sloan	0	
Solomon Hill	0	
Lavoy Allen	0	
Rasual Butler	0	
Chris Copeland	0	
		

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Kyle Korver	35	2.36
Paul Millsap	36	2.21
Al Horford	0	1.74
Jeff Teague	32	-1.05
DeMarre Carroll	32	3.19
Louis Williams	15	-1.91
Shelvin Mack	15	-1.15
Elton Brand	18	-0.74
Pero Antic	15	0.83
Mike Scott	10	-4.86
Gustavo Ayon	0	1.46
Cartier Martin	15	-2.76
James Nunnally	0	-2.56
Dennis Schroder	9	-8.41
John Jenkins	0	
Mike Muscala	8	-4.43
Jared Cunningham	0	
Dexter Pittman	0	
		
		

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LeBron James	39	7.72
Dwyane Wade	33	2.18
Chris Bosh	36	3.61
Mario Chalmers	26	1.82
Ray Allen	25	-1.5
Norris Cole	20	-2.73
Shane Battier	15	1.76
Chris Andersen	18	4.53
Rashard Lewis	5	-2.73
Toney Douglas	5	-1.11
Michael Beasley	8	-5.02
Udonis Haslem	10	-1.28
James Jones	0	
Roger Mason	0	
Justin Hamilton	0	
Greg Oden	0	
Joel Anthony	0	
DeAndre Liggins	0	
		

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Kemba Walker	36	2.09
Al Jefferson	36	1.28
Gerald Henderson	33	-2.18
Josh McRoberts	30	0.44
Jeffery Taylor	0	
Michael Kidd-Gilchrist	25	0.67
Ramon Sessions	12	-1.5
Gary Neal	18	-4.19
Chris Douglas-Roberts	12	-0.66
Anthony Tolliver	10	2.34
Cody Zeller	10	-1.66
Luke Ridnour	10	-3.59
Ben Gordon	0	
Bismack Biyombo	8	-4.64
Jeff Adrien	0	
Jannero Pargo	0	
D.J. White	0	
Justin Hamilton	0	
James Southerland	0	
		

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DeMar DeRozan	38	-0.08
Kyle Lowry	38	3.41
Rudy Gay	0	1.33
Amir Johnson	28	5.12
Jonas Valanciunas	28	-1.75
Terrence Ross	25	-1.64
Patrick Patterson	18	1.37
Greivis Vasquez	16	0.5
John Salmons	10	-1.27
Tyler Hansbrough	12	-2.99
Chuck Hayes	10	0.24
Landry Fields	0	0.97
Dwight Buycks	0	-1.77
Steve Novak	12	1.08
Nando De Colo	5	-2.86
Quincy Acy	0	
D.J. Augustin	0	
Julyan Stone	0	
Aaron Gray	0	
Austin Daye		
		

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Joe Johnson	35	2.13
Deron Williams	35	2.75
Brook Lopez	0	2.08
Paul Pierce	30	3.2
Shaun Livingston	20	-0.33
Marcus Thornton	15	-1.42
Alan Anderson	10	-2.73
Andray Blatche	18	0.72
Kevin Garnett	23	3.09
Mirza Teletovic	12	-3.03
Andrei Kirilenko	25	1
Mason Plumlee	12	-2.24
Jason Terry	0	
Jorge Gutierrez	5	-2.14
Reggie Evans	0	
Tyshawn Taylor	0	
Marquis Teague	0	
Tornike Shengelia	0	
Jason Collins	0	
		

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Jimmy Butler	40	2.05
Luol Deng	0	2.36
Joakim Noah	40	4.19
Mike Dunleavy	35	3.36
Derrick Rose	0	2.22
D.J. Augustin	27	-4.18
Kirk Hinrich	27	0.31
Taj Gibson	28	4.5
Carlos Boozer	28	-3.67
Tony Snell	5	-5.81
Marquis Teague	0	-5.78
Cartier Martin	0	-2.76
Nazr Mohammed	0	-3.28
Mike James	0	
Jimmer Fredette	10	-5.04
Erik Murphy	0	
Ronnie Brewer	0	
Jarvis Varnado	0	
Tornike Shengelia	0	
Louis Amundson	0	

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John Wall	40	2.23
Trevor Ariza	35	0.37
Bradley Beal	36	-1.72
Marcin Gortat	35	3.58
Nene Hilario	30	2.71
Martell Webster	22	-1.2
Trevor Booker	14	-3.6
Drew Gooden	10	-2.04
Al Harrington	8	-0.14
Andre Miller	10	2.3
Jan Vesely	0	
Kevin Seraphin	0	
Chris Singleton	0	
Glen Rice	0	
Eric Maynor	0	
Otto Porter	0	
Garrett Temple	0	

[J.E.]

Speights has been killing GSW all season long. His minute forecast will have a significant impact on their overall strength forecast

The predicted score differences are probably a little too wide, given what we now know about the 'effect of being up X', which the RPM ratings are adjusted for. (Also, the ratings are per 200 possessions, but each game only has about ~190 on average)

I'd guess the only series where this differs from the regular forecast (using Sagarin, point differential, SRS) is Toronto v. Brooklyn. Should be interesting

[Mike G]

Nando de Colo won't be commuting between Tor and SA. He's a Raptor.

[J.E.]

Compared to the current vegas lines (pinnacle) for all first games of each series most of the predictions posted by colts18 are similar

The largest differences are:
Clippers by 7. Colts18's method says 4 when factoring in HCA
Heat by 9.5 (15)

Their BRO@TOR line almost exactly matches what colts18 has posted (Toronto by 2)

[Bobbofitos]

take it for whatever it's worth, but I think the Raptors are a great play. I do not understand the series price on that one at all, I have a much much different view on it. pretty excited for the postseason!

[colts18]

Does anyone know how to convert SRS into win% or odds so that can I create a simulation for the playoffs?

[DSMok1]

Does anyone know how to convert SRS into win% or odds so that can I create a simulation for the playoffs?

Pythagorean method: http://www.basketball-reference.com/about/glossary.html

W Pyth
Pythagorean Wins; the formula is G * (Tm PTS14 / (Tm PTS14 + Opp PTS14)). The formula was obtained by fitting a logistic regression model with log(Tm PTS / Opp PTS) as the explanatory variable. Using this formula for all BAA, NBA, and ABA seasons, the root mean-square error (rmse) is 3.14 wins. Using an exponent of 16.5 (a common choice), the rmse is 3.48 wins. (Note: An exponent of 10 is used for the WNBA.)

[mtamada]

Have you built lagged weights into the model? The Nets are almost certainly a stronger team now than they were for the first 1/3 or so of the season. That doesn't mean that we should ignore the first 1/3 of the season, but it probably would be appropriate and potentially more accurate to give less weight to the earlier games and more to the later games. A similar principle to setting Bogut's minutes to 0, i.e. update in accordance with new information.

[colts18]

I converted these values into a win% vs the opponent. This is odds on a neutral court. The Spurs would win 70.3% vs the Mavs on a neutral court. My numbers have the Clippers-Warriors series as a dead heat. The Clippers are expected to win 50.03% of neutral court games vs GSW. Thats 3 extra wins per 10,000 games


Spurs 0.703
Mavs

Thunder 0.586
Grizzlies

Clippers 0.500
Warriors

Rockets 0.590
Blazers


Pacers 0.706
Hawks

Heat 0.827
Bobcats

Raptors 0.444
Nets

Bulls 0.525
Wizards


Here are those values adjusted for HCA. I used 3.5 points for HCA. The values next to the team is their expected home win% vs the Opponent. For example the Spurs are expected to win 79.3% of the time at home vs the Mavs while the Mavs are only expected to win 40.5% of the time when they play the Spurs at home.

Spurs 0.793
Mavs 0.405

Thunder 0.698
Grizzlies 0.535

Clippers 0.618
Warriors 0.618

Rockets 0.699
Blazers 0.528


Pacers 0.798
Hawks 0.406

Heat 0.885
Bobcats 0.253

Raptors 0.564
Nets 0.669

Bulls 0.645
Wizards 0.597

[colts18]

Series odds based on 1,000 simulations.

Spurs 90.8%
Mavs

Thunder 73.9%
Grizzlies

Clippers 55.9%
Warriors

Rockets 74.9%
Blazers


Pacers 91.2%
Hawks

Heat 98.0%
Bobcats

Raptors 44.2%
Nets

Bulls 62.6%
Wizards

[ryannow]

A few thoughts:
-Your minutes projections for the Spurs' stars seem low. Last year, Duncan, Kawhi, and Parker each saw large increases in min/g in the postseason, ending at around 36 each. Some of that was due to the Spurs playing a total of 7 overtimes in their 21 games (adding 1.4 minutes per game, or 7 additional player-minutes) or due to them having slightly less depth last year, but I'd bump up the minutes of each of those three, as well as Ginobili. Popovich spoke about Kawhi in particular here, saying,

“We want to up his minutes,” Popovich said. “He’s going to play more minutes (in the playoffs) than Tim Duncan does probably, more minutes than Manu Ginobili probably. This is his stretch run and he needs to be in shape for it. He’s never really been able to do this because it was a lockout season or we had to limit his minutes last year. This is the first time he’s been able to lay it out there.”

I'd project Kawhi at no less than 37 min/g and probably more, especially considering that if seeds hold, the Spurs' road would go through James Harden, Kevin Durant, and LeBron James. The extra minutes for the stars would come from Belinelli, Diaw, Mills, and Splitter at the back end of the rotation, I'd expect. Good lord, the Spurs are deep.

-Jerryd Bayless is no longer with the Grizzlies, having been traded to Boston in the deal that acquired Courtney Lee. I'd expect most of his projected minutes to instead go Mike Miller's way.

-Adding xRAPM values up like that may make sense, but calling them SRS values isn't passing the smell test. The Spurs, Clippers, and Warriors all would come in as the #1 overall team of all time were they to keep those values up for an entire season, and we'd have 10 teams with an SRS north of +6. If each value was multiplied by .6, the magnitudes would seem more reasonable, but it seems like the "effect of being up X" variable is having quite a large effect. Maybe there's some function we can apply to these totals, so that we know that when the Spurs are given a score of 13.4, due to the extra difficulty in adding each additional point of efficiency differential, that translates out to the difference between 0 and +8, or +9 or +7 or whatever it is.

-It also seems strange that Warriors-Clippers ended up that close in your model. How did you set that up? If you used the 12.4 and 11.9 values from before, a half-point difference is not insignificant, and should result in a swing of more than 3 games per 10,000, right? Am I missing something there? It may be that the model just minimizes differences as these scores grow. Strange behavior with large inputs like that wouldn't surprise me much considering how extreme of an outlier these are when interpreted as SRS scores, but I don't see how that would have that effect if you just generated predicted PPG for each team and plugged it into Pythagorean W/L. I might just have misunderstood what your inputs are, though.

[colts18]

A few thoughts:
-Your minutes projections for the Spurs' stars seem low. Last year, Duncan, Kawhi, and Parker each saw large increases in min/g in the postseason, ending at around 36 each. Some of that was due to the Spurs playing a total of 7 overtimes in their 21 games (adding 1.4 minutes per game, or 7 additional player-minutes) or due to them having slightly less depth last year, but I'd bump up the minutes of each of those three, as well as Ginobili. Popovich spoke about Kawhi in particular

My MPG projections were based on regular season MPG, adding minutes for starters, and looking at last years 1st round MPG. The Spurs are hard to project because no one played 30 MPG for them this season.

-Adding xRAPM values up like that may make sense, but calling them SRS values isn't passing the smell test. The Spurs, Clippers, and Warriors all would come in as the #1 overall team of all time were they to keep those values up for an entire season, and we'd have 10 teams with an SRS north of +6. If each value was multiplied by .6, the magnitudes would seem more reasonable, but it seems like the "effect of being up X" variable is having quite a large effect. Maybe there's some function we can apply to these totals, so that we know that when the Spurs are given a score of 13.4, due to the extra difficulty in adding each additional point of efficiency differential, that translates out to the difference between 0 and +8, or +9 or +7 or whatever it is.

That's because I gave a lot of minutes to star players and less to scrubs. Imagine a hypothetical world where Miami is healthy every game With no game to game fatigue and they play all out in that game without using scrubs, they are probably a +9 or +10 team in that scenario. The Spurs +13.4 xRAPM value is similar to the 96 Bulls +13.4 efficiency differential. If they played healthy and all out every game, they get around 70+ wins and challenge the 96 Bulls.

-It also seems strange that Warriors-Clippers ended up that close in your model. How did you set that up? If you used the 12.4 and 11.9 values from before, a half-point difference is not insignificant, and should result in a swing of more than 3 games per 10,000, right? Am I missing something there? It may be that the model just minimizes differences as these scores grow. Strange behavior with large inputs like that wouldn't surprise me much considering how extreme of an outlier these are when interpreted as SRS scores, but I don't see how that would have that effect if you just generated predicted PPG for each team and plugged it into Pythagorean W/L. I might just have misunderstood what your inputs are, though.

I converted SRS into a Pythagorean W-L%. While the Clippers had a higher SRS, they are virtually tied in the pythag% because Pythagorean tends to value better defenses if two teams are the of the same quality. The Clippers have a much better offense so they get punished a little by it. A team that wins 95-85 gets a higher pythagorean value than a team that wins 105-95 even though they both have a +10 PPG differential.

[ryannow]

My MPG projections were based on regular season MPG, adding minutes for starters, and looking at last years 1st round MPG. The Spurs are hard to project because no one played 30 MPG for them this season.

True, and if it's just the first round, that makes more sense, as I was reading these as being for the entirety of the playoffs. My comment wasn't meant as a criticism (and I don't think it would change the values much since Kawhi comes out surprisingly low in xRAPM), I was just responding to "feel free to give me suggestions so that I can update these Minutes more accurately." I'd be very surprised if Kawhi remained at 30 mpg.

-Adding xRAPM values up like that may make sense, but calling them SRS values isn't passing the smell test. The Spurs, Clippers, and Warriors all would come in as the #1 overall team of all time were they to keep those values up for an entire season, and we'd have 10 teams with an SRS north of +6. If each value was multiplied by .6, the magnitudes would seem more reasonable, but it seems like the "effect of being up X" variable is having quite a large effect. Maybe there's some function we can apply to these totals, so that we know that when the Spurs are given a score of 13.4, due to the extra difficulty in adding each additional point of efficiency differential, that translates out to the difference between 0 and +8, or +9 or +7 or whatever it is.

That's because I gave a lot of minutes to star players and less to scrubs. Imagine a hypothetical world where Miami is healthy every game With no game to game fatigue and they play all out in that game without using scrubs, they are probably a +9 or +10 team in that scenario. The Spurs +13.4 xRAPM value is similar to the 96 Bulls +13.4 efficiency differential. If they played healthy and all out every game, they get around 70+ wins and challenge the 96 Bulls.

The numbers still just don't add up. I ran the exact same equation, but with the actual total minutes played for this year's Spurs, and I came out with a value of 10.2. Did it for the Warriors and got 7.2. Both of those are more than 2 points higher than the team's actual SRS in that time period. It makes sense why this would happen: if you add one +4 player to a team at 0, you can expect them to go to around +4, but that doesn't mean a second one would lift the team to +8, or a third one to +12. That's what the "effect of being up x" term is attempting to capture; it's harder to take a team that's already very efficient and boost it by n points than it is to take an inefficient team and boost it by n points. As a result, you can take +10 worth of players, and end up with a +8 team, although I'm not sure exactly what the shape of this curve is in J.E.'s model.

I converted SRS into a Pythagorean W-L%. While the Clippers had a higher SRS, they are virtually tied in the pythag% because Pythagorean tends to value better defenses if two teams are the of the same quality. The Clippers have a much better offense so they get punished a little by it. A team that wins 95-85 gets a higher pythagorean value than a team that wins 105-95 even though they both have a +10 PPG differential.

That makes sense, but I'm still not following 100%. Did you generate a separate pythagorean winning percentage for each team by setting up Team PTS and Opp PTS as (average + sum of O) and (average - sum of D), respectively? In that case, I'm not sure how one combines the two winning percentages to find win% against each other. If you generated one win% for each series by setting, say, LAC pts= average + sum of LAC O - sum of GSW D, then it seems like offensive and defensive strength would cancel out and the Clippers would still be projected a half-point ahead per-game. I'm not sure which approach is better.

[colts18]

The numbers still just don't add up. I ran the exact same equation, but with the actual total minutes played for this year's Spurs, and I came out with a value of 10.2. Did it for the Warriors and got 7.2. Both of those are more than 2 points higher than the team's actual SRS in that time period. It makes sense why this would happen: if you add one +4 player to a team at 0, you can expect them to go to around +4, but that doesn't mean a second one would lift the team to +8, or a third one to +12. That's what the "effect of being up x" term is attempting to capture; it's harder to take a team that's already very efficient and boost it by n points than it is to take an inefficient team and boost it by n points. As a result, you can take +10 worth of players, and end up with a +8 team, although I'm not sure exactly what the shape of this curve is in J.E.'s model.

You ran the projection and got +10.2 for the Spurs because though are actual minutes. I'm using playoff strength. The Spurs aren't playing many scrubs in the playoffs so their strength is higher than +10.2. Plus J.E. already said that his xRAPM takes into account the leading by X effect.

That makes sense, but I'm still not following 100%. Did you generate a separate pythagorean winning percentage for each team by setting up Team PTS and Opp PTS as (average + sum of O) and (average - sum of D), respectively? In that case, I'm not sure how one combines the two winning percentages to find win% against each other. If you generated one win% for each series by setting, say, LAC pts= average + sum of LAC O - sum of GSW D, then it seems like offensive and defensive strength would cancel out and the Clippers would still be projected a half-point ahead per-game. I'm not sure which approach is better.

I used the pythagorean win% from DSmok1's posts for both teams. Then I used the log 5 method to generate an expected win%. Log 5 formula:

(home win%/Home loss%)/(home win%/home loss%+road win%/road loss%)