2014 NBA Playoffs 1st Round Preview

This is my first attempt at building a simple model for estimating NBA playoff series.  The idea was inspired by colts18 on the APBR forums.  The model is based off of the xRAPM numbers on ESPN (where it's called "real plus minus"), which are supposedly the most predictive of the all-in-one metrics for NBA games.  I utilized my own minutes estimates for each series (subjectively based on regular season minutes, an increase  for each team's top players in the playoffs, and potential matchup adjustments I expect each coach to employ) to calculate each team's offensive and defensive rating (the league average points per possession is set to 104 by weighting each team's offensive/defensive rating by each team's pace).  Then, I assumed 100 possessions per game (which is most definitely not true) and a home court advantage of four points (divided evenly between offensive and defense, so 1 point per team per side of the ball) to calculate each team's Pythagorean win percentage at home and on the road, and used Bill James' Log 5 formula for estimating a matchup based on each team's win%.  Finally, assuming games are independent (which, again, is also not true), each permutation was considered to calculate a team's series win percentage.  All of the computation was done in R and the script I ran will be included as well.  I will then comment on any other subjective observations for each series.


Series Previews:

East: (1) Indiana vs. (8) Atlanta

	Model	Vegas*
Game 1 Probability	IND: 76% / ATL: 24%	IND: -370 / +285
Game 1 Spread	IND: -8.1 / ATL: +8.1	IND: -7.5 (-115) / ATL: +7.5 (-105)
Series Probability	IND: 81% / ATL: 19%	IND: -650 / ATL: +425

Atlanta is nowhere near as bad as the mainstream media thinks, as they are significantly better than Charlotte (they just experienced a lot more injuries during the regular season that caused them to lose more games).  Atlanta also likely presents a lot of matchup issues for Indiana.  They had a lot of success in their regular season meetings by bringing Hibbert out onto three-point shooters like Antic, and Indiana experienced similar problems against Phoenix and Channing Frye in two games; similarly, Atlanta is not very dependent on shots at the rim as Teague, Millsap, and Louis Williams utilize floaters and essentially everyone else is a shooter, and that further neutralizes Hibbert's effectiveness.  In addition, my model does not adjust for season trends, so even though Indiana still looks decent over the entire year, they've been frighteningly bad for a while.  As a result, I would likely adjust my model significantly, by about two to three points each game, which would increase the Hawks' Game 1 Probability to 34% and their series probability to around 39%.  Given that without adjustments my model essentially agrees with Vegas and all of the adjustments are signficantly in Atlanta's favor, I'd feel relatively confident betting on Atlanta.

East: (2) Miami vs. (7) Charlotte

	Model	Vegas*
Game 1 Probability	MIA: 89% / CHA: 11%	MIA: -650 / CHA: +425
Game 1 Spread	MIA: -16.0 / CHA: +16.0	MIA: -10 / CHA: +10
Series Probability	MIA: 98% / CHA: 2%	MIA: OFF / CHA: OFF

Charlotte is significantly worse than Miami in terms of talent, as Miami has four players (LeBron, Bosh, Andersen, Wade) with xRAPM values at or above Charlotte's highest player (Jefferson).  Jefferson, Walker, Henderson, and McRoberts will likely get their minutes (although it might not be a bad idea to play a better shooting and more mobile Toliver over McRoberts), but the question will be who defends LeBron.  Kidd-Gilchrist will probably get the first, second, and third opportunities, and while he's generally a good player, his lack of offense will be a killer against a roaming LeBron and he's just not strong enough to defend LeBron.  Personally, I would use Tolliver on LeBron as much as possible (as a Diaw/Bass facsimile) and just hand LeBron wide open jumpers, and I would ideally surround him and Jefferson with as many shooters as possible (three of Walker, Henderson, Neal, Douglas-Roberts).  Tolliver didn't play much near the end of the season, but my guess is that he'll play at least 20 mpg given just how few big wings the Bobcats have to defend LeBron.  Schematically, Charlotte has it's bigs (namely Jefferson) hang back on pick-and-rolls in a similar manner to Indiana, but Jefferson just isn't quick or big enough to challenge LeBron's drives when he gets a head of steam.  I would probably be wary of my model's estimates for enormous mismatches as it's unclear to me how well xRAPM performs at such extremes, but given that I believe this is a bad matchup for Charlotte, I would still be very confident betting on the Heat.

East: (3) Toronto vs. (6) Brooklyn

	Model	Vegas*
Game 1 Probability	TOR: 58% / BKN: 42%	TOR: -160 / BKN: +140
Game 1 Spread	TOR: -2.5 / BKN: +2.5	TOR: -3.5 / BKN: +3.5
Series Probability	TOR: 43% / BKN: 57%	TOR: +115 / BKN: -135

There are a number of injury questions in this series, and it would help significantly if I could provide more confident minutes estimates for Amir Johnson, Garnett, and Deron Williams.  Ideally, the main bigs for Toronto should be Johnson and Patterson (Patterson is a much better player than Valanciunas), but there is no way this actually happens so I still estimated Valanciunas to average 25 mpg to Patterson's 20.  One matchup issue with this series is that Brooklyn's big perimeter players will have a significant advantage in the post (while isolation postups are generally inefficient plays and any increased efficiency in postups is often counteracted by an increased dependence on postups, this is a huge portion of Brooklyn's offense anyways so the size advantage is mostly positive); on the other end, the similarity in size between Brooklyn's bigs and their smalls will likely allow them to liberally switch Toronto's pick-and-rolls.  As a result, I would likely adjust the model's numbers slightly in the Nets' favor, by about one point each game, which would increase the Nets' Game 1 Probability to 46% and their series probability to around 65%.  Even given the Nets' name recognition perhaps inflating their price, I'd feel relatively confident betting on Brooklyn.

East: (4) Chicago vs. (5) Washington

	Model	Vegas*
Game 1 Probability	CHI: 67% / WAS: 33%	CHI: -200 / WAS: +170
Game 1 Spread	CHI: -5.1 / WAS: +5.1	CHI: -5 (-105) / WAS: +5 (-115)
Series Probability	CHI: 63% / WAS: 37%	CHI: -210 / WAS: +175

This will be an interesting series because of the matchups between each team's strengths.  Wall's biggest skill is through attacking the rim off pick-and-rolls and finding teammates for open threes, but Chicago doesn't help much off of shooters, because their perimeter defenders (besides Augustin) are extremely good at staying attached to the ball handler and bothering them from behind with their length and their bigs are extremely good at containing the both the ball handler and the roller with their length and quickness.  Chicago always plays two true bigs and their bigs are usually either too big or too quick for opposing bigs to keep from the offensive glass, but two of Washington's best players are Gortat and Nene who might be the rare pair capable of matching up with Chicago's bigs.  Chicago needs Augustin on the floor as much as possible on offense, but there isn't really anyone for him to defend besides Webster.  The Bulls have essentially been playing a playoff rotation the entire season already,so in a sense, they've already reached their ceiling; Washington, on the other hand, has a clear top three of Wall, Gortat, and Nene, and minutes increases for the trio could make Washington better than they were in the regular season.  I've estimated Nene will play 28 mpg, but this series could depend on how healthy Nene is and how many minutes he can play.

West: (1) San Antonio vs. (8) Dallas

	Model	Vegas*
Game 1 Probability	SAS: 79% / DAL: 21%	SAS: -550 / DAL: +375
Game 1 Spread	SAS: -9.7 / DAL: +9.7	SAS: -9 / DAL: +9
Series Probability	SAS: 88% / DAL: 12%	SAS: -900 / DAL: +550

It took Rick Carlisle the whole season, but he finally began giving Harris and Wright more minutes at the expense of Calderon and Dalembert.  Wright's biggest issue has been that he often gets pushed around in post defense and defensive rebounding, so Carlisle might still depend a lot on Dalembert against Duncan.  With Leonard playing the majority of the Spurs' SF minutes, it will be difficult for Calderon and Ellis to share the floor much, as one of them will have to guard Parker and Carlisle can't hide one of them on Leonard with Marion guarding Parker.  As a result, Harris, Carter, and Marion will likely split most of the two remaining perimeter spots around Ellis.  On the Spurs side, the minutes distribution won't matter much.  Their only two negative players during the regular season were Belinelli and Joseph, and Joseph likely will not play much during this series.  Personally, I've always been of the opinion that if a team doesn't have the athleticism to scare the Spurs off their threes on closeouts, the easiest way to compete is to maximize offense in their lineups and simply surrender pullup Parker 18-foot jumpers by not overhelping and hoping to just bother him with length from his on-ball defender, so it'll certainly be interesting to see how the Mavs do when/if they play their Harris/Crowder, Ellis, Carter, Nowitzki, Wright lineups.

West: (2) Oklahoma City vs. (7) Memphis

	Model	Vegas*
Game 1 Probability	OKC: 69% / MEM: 31%	OKC: -350 / MEM: +275
Game 1 Spread	OKC: -6.2 / MEM: +6.2	OKC: -7 (-115) / MEM: +7 (-105)
Series Probability	OKC: 68% / MEM: 32%	OKC: -500 / MEM: +350

Calathes will likely not impact the series as much as some expect as he's been a negative player on the year and Prince/Lee are good enough ball handlers to not force Memphis to play Udrih too much.  The biggest variable in this series is Scott Brooks.  If he manages minutes correctly, Oklahoma City is a much bigger favorite.  Specifically, Perkins (and Adams) should not play much, and Durant should play PF whenever either Randolph or Gasol are out of the game.  Collison has historically defended Randolph extremely well (big surprise...) so his minutes should be maximized to the point that he fouls out every game.  Caron Butler should almost never play over Jackson, Sefolosha, Lamb, or Fisher (yes, even Fisher), especially given that none of Memphis' wings are big offensive creators.  In reality, I don't expect many (if any) of these things to happen, and as a result, Perkins, Adams, and Butler are estimated to average 20, 10, and 18 mpg respectively.  On the Grizzlies' side, either Allen or Prince should be on the floor anytime Durant is but ideally never together.  Anytime Gasol or Randolph are on the bench and Oklahoma City downsizes, Memphis should downsize as well instead of resorting to too much Koufos or any Ed Davis.  Overall, I have some confidence that Brooks will eventually realize the Thunder should play Durant at PF whenever they can, which means their series chances should likely be higher than 68%.  Still, I would bet on Memphis, especially early in the series.

West: (3) L.A. Clippers vs. (6) Golden State

	Model	Vegas*
Game 1 Probability	LAC: 66% / GSW: 34%	LAC: -330 / GSW: +265
Game 1 Spread	LAC: -5.6 / GSW: 5.6	LAC: -7 / GSW: +7
Series Probability	LAC: 62% / GSW: 38%	LAC: -390 / GSW: +295

Along with the Rockets-Blazers, this is the matchup with the second largest discrepancy between my model and Vegas.  Ideally the Warriors would play four perimeter players almost all of the time (with Harrison Barnes and Draymond Green counting as perimeter players), but I don't have much confidence that Mark Jackson will do so, and as a result I split the difference and estimated that Lee, O'Neal, and Speights will average a combined 63 mpg.  On the Clippers' side, both Redick and Jamal Crawford are coming off injuries and it's unclear exactly how much they'll play, but luckily, most of the Clippers' rotation players outside of their top four (anyone other than Paul, Griffin, DeAndre Jordan, and Matt Barnes) are very similar in overall ability.  The Clippers love to go small in their backcourt (with two of Paul, Collison, and Crawford), which could be an issue defensively in the post against Thompson, Iguodala, and Harrison Barnes, but none of those three players are great postup players, and this could trick Mark Jackson and the Warriors into forcing isolations against mismatches instead of running their highly efficient motion pick-and-roll sets.  Overall, the Bogut injury likely doesn't hurt the Warriors as much as the public expects, which is why I would have significant confidence betting on the Warriors.

West: (4) Houston vs. (5) Portland

	Model	Vegas*
Game 1 Probability	HOU: 72% / POR: 28%	HOU: -230 / POR: +190
Game 1 Spread	HOU: -6.9 / POR: +6.9	HOU: -5.5 / POR: +5.5
Series Probability	HOU: 74% / POR: 26%	HOU: -220 / POR: +180

There are a bunch of interesting matchups and playing time questions regarding this series.  First off, both Beverley and Howard have been injured recently, and Houston needs both to be healthy.  In Beverley's case, it's because he's by far their best option next to Harden, especially in a series against Lillard.  In Howard's case, it's not so much because he's a significantly better option than Asik but because the drop off after Howard and Asik is steep (although for this matchup specifically, Howard might have more of an impact because Lopez has had no success historically defending Howard, and it will be an issue if he gets into foul trouble given the limitations of the Blazers backup big men).  On the other side, Terrence Jones has had little success defending Aldridge.  Jones is an example of the type of player that many fans' eye tests judge incorrectly; in reality, his lack of defensive-help awareness and rim protection makes him just as poor of a defensive option as their actual small-ball power forward options like Casspi, and his still limited three-point shooting makes him a much worse offensive option.  During the last matchup, Houston was willing to let Aldridge postup one-on-one against any defender to limit three-point kickouts, even if the defender was someone like Casspi, Parsons, or Harden.  Howard has had success defending Aldridge's postups, but the tradeoff is that it keeps Howard away from the rim and limits his rim protection and Lopez is an extremely good offensive rebounder that could abuse any other matchup.  Personally, I would limit Jones' minutes in favor of small ball options like Garcia, Casspi, or Hamilton, and live with Aldridge postups or have Howard defend some of them.  If Aldridge is forced to settle for many two-point jumpers, Houston's three-point attack should be much more efficient.  This is especially true given that Portland might not have the personnel to deal with Houston's pick-and-roll.&nbsp Lopez likes to hang back on pick-and-rolls but he doesn't have the width of Hibbert or Bogut to bother drivers nor the lateral quickness to stay in front, and Harden is very skilled at either shooting the three or attacking the backpedaling big with his variety of change of direction moves.&nbsp Because of both the overall talent disparity and the matchup issues Houston offers Portland, I'd feel relatively confident betting on Houston.

*All Vegas numbers from Bovada


Potential future areas of improvements:

1) Determine a more systematic method for estimating minutes, potentially involving a combination of regular season averages, past playoff minutes/minutes increases, and minutes during the regular season series.
2) Research home court advantage.  The four points adjustment is currently based on a quick google search, and could be inaccurate.  Furthermore, home court advantage could affect matchups differently, depending on the relative team strengths.  Using a constant point differential adjustment means that closely matched teams are affected significantly more in terms of win percentage, and using a constant win percentage adjustment means that closely matched teams are affected significantly less in terms of point differential.  The most accurate adjustment could be something in between.
3) Use an actual estimate for number of possessions that would likely involve the average possessions per game for each team.  Obviously, the prediction should be somewhere in between the two teams' averages, but slower teams could affect pace more than faster teams or vice versa.  This is similar to the idea that batter/pitcher pace might not just be an arithmetic mean of each member's averages.
4) Examine the dependency of individual games of a series.  Conceptually, injuries/adjustments during one game should affect the subsequent games in the series.
5) Backtest how the model would have done in predicting prior playoffs.  This would likely require xRAPM numbers for regular seasons only (which might not exist if Jeremias Engelmann updates previous season numbers to include the playoffs).  Furthermore, a non-subjective minutes estimate would also be needed first, as any subjective estimate would be influenced by hindsight.
6) Attempt a similar endeavor for estimating regular season win% prior to the season that uses Monte Carlo simulations for each team's entire schedule.


List of minutes estimates and xRAPM ratings:

Show/Hide

     Spurs:

player	minutes	orpm	drpm	rpm	onet	dnet	net
Boris Diaw	22	0.55	1.45	2.00	0.25	0.66	0.92
Cory Joseph	5	-0.83	-3.38	-4.21	-0.09	-0.35	-0.44
Danny Green	25	0.68	2.61	3.29	0.35	1.36	1.71
Kawhi Leonard	38	0.58	1.84	2.42	0.46	1.46	1.92
Manu Ginobili	30	4.66	-0.11	4.55	2.91	-0.07	2.84
Marco Belinelli	15	1.03	-2.94	-1.91	0.32	-0.92	-0.60
Matt Bonner	5	2.05	0.37	2.42	0.21	0.04	0.25
Patty Mills	10	2.70	0.50	3.20	0.56	0.10	0.67
Tiago Splitter	20	-0.92	4.59	3.67	-0.38	1.91	1.53
Tim Duncan	35	-0.10	5.37	5.27	-0.07	3.92	3.84
Tony Parker	35	3.16	-0.07	3.09	2.30	-0.05	2.25

     Mavericks:

player	minutes	orpm	drpm	rpm	onet	dnet	net
Brandan Wright	22	1.74	-0.48	1.26	0.80	-0.22	0.58
DeJuan Blair	5	-2.21	0.11	-2.10	-0.23	0.01	-0.22
Devin Harris	30	1.92	-0.38	1.54	1.20	-0.24	0.96
Dirk Nowitzki	38	4.48	2.01	6.49	3.55	1.59	5.14
Jae Crowder	10	-0.05	1.75	1.70	-0.01	0.36	0.35
Jose Calderon	18	1.43	-3.67	-2.24	0.54	-1.38	-0.84
Monta Ellis	42	3.10	-1.34	1.76	2.71	-1.17	1.54
Samuel Dalembert	15	-3.51	3.08	-0.43	-1.10	0.96	-0.13
Shawn Marion	30	-1.79	0.68	-1.11	-1.12	0.43	-0.69
Vince Carter	30	1.98	2.04	4.02	1.24	1.28	2.51

     Thunder:

player	minutes	orpm	drpm	rpm	onet	dnet	net
Caron Butler	18	-0.75	-3.61	-4.36	-0.28	-1.35	-1.64
Derek Fisher	22	-1.14	2.00	0.86	-0.52	0.92	0.39
Jeremy Lamb	5	-0.49	-0.79	-1.28	-0.05	-0.08	-0.13
Kendrick Perkins	20	-7.12	2.73	-4.39	-2.97	1.14	-1.83
Kevin Durant	45	6.36	0.17	6.53	5.96	0.16	6.12
Nick Collison	18	2.75	3.40	6.15	1.03	1.28	2.31
Reggie Jackson	20	0.81	0.50	1.31	0.34	0.21	0.55
Russell Westbrook	35	4.30	0.19	4.49	3.14	0.14	3.27
Serge Ibaka	32	0.88	3.03	3.91	0.59	2.02	2.61
Steven Adams	10	-2.68	-0.93	-3.61	-0.56	-0.19	-0.75
Thabo Sefolosha	15	-1.14	2.29	1.15	-0.36	0.72	0.36

     Grizzlies:

player	minutes	orpm	drpm	rpm	onet	dnet	net
Beno Udrih	5	0.38	-1.61	-1.23	0.04	-0.17	-0.13
Courtney Lee	25	0.42	-0.52	-0.10	0.22	-0.27	-0.05
James Johnson	10	-1.94	0.55	-1.39	-0.40	0.11	-0.29
Kosta Koufos	10	-1.75	2.63	0.88	-0.36	0.55	0.18
Marc Gasol	40	-1.53	5.39	3.86	-1.28	4.49	3.22
Mike Conley	42	4.43	0.45	4.88	3.88	0.39	4.27
Mike Miller	20	1.84	-3.10	-1.26	0.77	-1.29	-0.53
Tayshaun Prince	23	-1.51	0.21	-1.30	-0.72	0.10	-0.62
Tony Allen	25	-0.64	2.91	2.27	-0.33	1.52	1.18
Zach Randolph	40	1.45	0.74	2.19	1.21	0.62	1.83

     Clippers:

player	minutes	orpm	drpm	rpm	onet	dnet	net
Blake Griffin	42	2.58	1.88	4.46	2.26	1.65	3.90
Chris Paul	40	5.44	1.84	7.28	4.53	1.53	6.07
Danny Granger	5	0.30	0.70	1.00	0.03	0.07	0.10
Darren Collison	15	1.48	-3.00	-1.52	0.46	-0.94	-0.48
DeAndre Jordan	36	0.68	3.60	4.28	0.51	2.70	3.21
Glen Davis	10	-3.08	2.00	-1.08	-0.64	0.42	-0.23
Hedo Turkoglu	5	-0.96	0.24	-0.72	-0.10	0.03	-0.08
J.J. Redick	22	2.98	-2.96	0.02	1.37	-1.36	0.01
Jamal Crawford	25	1.55	-2.31	-0.76	0.81	-1.20	-0.40
Jared Dudley	10	-0.76	0.24	-0.52	-0.16	0.05	-0.11
Matt Barnes	30	2.14	0.58	2.72	1.34	0.36	1.70

     Warriors:

player	minutes	orpm	drpm	rpm	onet	dnet	net
Andre Iguodala	36	1.94	5.01	6.95	1.46	3.76	5.21
David Lee	35	-0.12	1.17	1.05	-0.09	0.85	0.77
Draymond Green	27	-0.89	3.98	3.09	-0.50	2.24	1.74
Harrison Barnes	20	-2.73	-0.28	-3.01	-1.14	-0.12	-1.25
Jermaine O'Neal	20	-3.47	2.44	-1.03	-1.45	1.02	-0.43
Jordan Crawford	3	-0.29	-2.48	-2.77	-0.02	-0.16	-0.17
Klay Thompson	40	2.61	-0.03	2.58	2.18	-0.03	2.15
Marreese Speights	8	-5.00	-0.65	-5.65	-0.83	-0.11	-0.94
Stephen Curry	42	6.41	-0.38	6.03	5.61	-0.33	5.28
Steve Blake	9	-1.23	-0.16	-1.39	-0.23	-0.03	-0.26

     Rockets:

player	minutes	orpm	drpm	rpm	onet	dnet	net
Chandler Parsons	40	1.35	0.58	1.93	1.13	0.48	1.61
Donatas Motiejunas	10	-1.35	0.46	-0.89	-0.28	0.10	-0.19
Dwight Howard	36	-0.72	5.14	4.42	-0.54	3.86	3.32
Francisco Garcia	10	-2.50	0.44	-2.06	-0.52	0.09	-0.43
James Harden	40	5.90	-2.73	3.17	4.92	-2.28	2.64
Jeremy Lin	22	0.04	0.19	0.23	0.02	0.09	0.11
Jordan Hamilton	5	-1.79	-1.21	-3.00	-0.19	-0.13	-0.31
Omer Asik	11	-2.34	4.99	2.65	-0.54	1.14	0.61
Omri Casspi	15	-2.61	0.25	-2.36	-0.82	0.08	-0.74
Patrick Beverley	35	3.21	1.54	4.75	2.34	1.12	3.46
Terrence Jones	16	0.42	-2.07	-1.65	0.14	-0.69	-0.55

     Blazers:

player	minutes	orpm	drpm	rpm	onet	dnet	net
C.J. McCollum	5	0.03	-0.66	-0.63	0.00	-0.07	-0.07
Damian Lillard	40	4.31	-2.03	2.28	3.59	-1.69	1.90
Dorell Wright	10	-0.86	-0.94	-1.80	-0.18	-0.20	-0.38
LaMarcus Aldridge	40	1.73	3.17	4.90	1.44	2.64	4.08
Meyers Leonard	5	-4.22	-0.98	-5.20	-0.44	-0.10	-0.54
Mo Williams	25	0.28	-2.66	-2.38	0.15	-1.39	-1.24
Nicolas Batum	40	1.47	-1.19	0.28	1.23	-0.99	0.23
Robin Lopez	35	-0.29	3.38	3.09	-0.21	2.46	2.25
Thomas Robinson	2	-2.37	-2.03	-4.40	-0.10	-0.08	-0.18
Wesley Matthews	38	1.54	-0.89	0.65	1.22	-0.70	0.51

     Pacers:

player	minutes	orpm	drpm	rpm	onet	dnet	net
C.J. Watson	20	0.39	-0.40	-0.01	0.16	-0.17	0.00
David West	35	0.98	2.33	3.31	0.71	1.70	2.41
Evan Turner	16	-1.14	-1.12	-2.26	-0.38	-0.37	-0.75
George Hill	35	2.00	0.60	2.60	1.46	0.44	1.90
Ian Mahinmi	18	-4.85	4.05	-0.80	-1.82	1.52	-0.30
Lance Stephenson	36	0.79	-0.04	0.75	0.59	-0.03	0.56
Luis Scola	12	-3.27	0.76	-2.51	-0.82	0.19	-0.63
Paul George	40	0.03	2.49	2.52	0.03	2.08	2.10
Roy Hibbert	28	-0.94	3.68	2.74	-0.55	2.15	1.60

     Hawks:

player	minutes	orpm	drpm	rpm	onet	dnet	net
Cartier Martin	7	-2.33	-0.52	-2.85	-0.34	-0.08	-0.42
DeMarre Carroll	38	1.37	1.96	3.33	1.08	1.55	2.64
Elton Brand	12	-2.82	1.92	-0.90	-0.71	0.48	-0.23
Jeff Teague	35	0.08	-0.79	-0.71	0.06	-0.58	-0.52
Kyle Korver	36	2.46	0.50	2.96	1.85	0.38	2.22
Louis Williams	18	1.75	-3.72	-1.97	0.66	-1.40	-0.74
Mike Muscala	6	-3.27	-1.35	-4.62	-0.41	-0.17	-0.58
Mike Scott	15	-2.00	-2.65	-4.65	-0.63	-0.83	-1.45
Paul Millsap	40	0.20	2.00	2.20	0.17	1.67	1.83
Pero Antic	20	-1.21	1.70	0.49	-0.50	0.71	0.20
Shelvin Mack	13	0.51	-1.16	-0.65	0.14	-0.31	-0.18

     Heat:

player	minutes	orpm	drpm	rpm	onet	dnet	net
Chris Andersen	18	0.42	3.99	4.41	0.16	1.50	1.65
Chris Bosh	38	0.29	3.70	3.99	0.23	2.93	3.16
Dwyane Wade	32	1.23	0.87	2.10	0.82	0.58	1.40
LeBron James	40	8.72	-0.16	8.56	7.27	-0.13	7.13
Mario Chalmers	35	0.54	1.03	1.57	0.39	0.75	1.14
Michael Beasley	2	-2.94	-2.89	-5.83	-0.12	-0.12	-0.24
Norris Cole	12	-1.60	-1.87	-3.47	-0.40	-0.47	-0.87
Rashard Lewis	8	-2.34	-0.72	-3.06	-0.39	-0.12	-0.51
Ray Allen	20	2.03	-2.77	-0.74	0.85	-1.15	-0.31
Shane Battier	17	-0.29	1.36	1.07	-0.10	0.48	0.38
Toney Douglas	6	0.25	-1.62	-1.37	0.03	-0.20	-0.17
Udonis Haslem	12	-4.21	1.60	-2.61	-1.05	0.40	-0.65

     Bobcats:

player	minutes	orpm	drpm	rpm	onet	dnet	net
Al Jefferson	38	1.15	1.00	2.15	0.91	0.79	1.70
Anthony Tolliver	22	-0.16	2.29	2.13	-0.07	1.05	0.98
Bismack Biyombo	5	-3.82	-0.29	-4.11	-0.40	-0.03	-0.43
Chris Douglas-Roberts	13	-0.77	0.20	-0.57	-0.21	0.05	-0.15
Cody Zeller	10	-2.95	1.09	-1.86	-0.61	0.23	-0.39
Gary Neal	17	0.64	-4.65	-4.01	0.23	-1.65	-1.42
Gerald Henderson	35	-1.54	-0.89	-2.43	-1.12	-0.65	-1.77
Josh McRoberts	32	-0.17	0.46	0.29	-0.11	0.31	0.19
Kemba Walker	38	0.76	0.86	1.62	0.60	0.68	1.28
Luke Ridnour	5	-1.24	-2.73	-3.97	-0.13	-0.28	-0.41
Michael Kidd-Gilchrist	25	-0.77	1.84	1.07	-0.40	0.96	0.56

     Raptors:

player	minutes	orpm	drpm	rpm	onet	dnet	net
Amir Johnson	27	1.75	3.42	5.17	0.98	1.92	2.91
Chuck Hayes	5	-1.71	2.26	0.55	-0.18	0.24	0.06
DeMar DeRozan	40	0.39	-0.48	-0.09	0.33	-0.40	-0.08
Greivis Vasquez	18	1.31	-0.82	0.49	0.49	-0.31	0.18
John Salmons	15	-1.45	0.07	-1.38	-0.45	0.02	-0.43
Jonas Valanciunas	25	-2.00	-0.11	-2.11	-1.04	-0.06	-1.10
Kyle Lowry	38	3.76	0.05	3.81	2.98	0.04	3.02
Nando de Colo	5	-1.69	-1.36	-3.05	-0.18	-0.14	-0.32
Patrick Patterson	20	0.31	0.90	1.21	0.13	0.38	0.50
Steve Novak	5	2.07	-0.44	1.63	0.22	-0.05	0.17
Terrence Ross	30	0.75	-2.09	-1.34	0.47	-1.31	-0.84
Tyler Hansbrough	12	-2.97	-0.04	-3.01	-0.74	-0.01	-0.75

     Nets:

player	minutes	orpm	drpm	rpm	onet	dnet	net
Alan Anderson	7	-0.88	-1.64	-2.52	-0.13	-0.24	-0.37
Andray Blatche	5	-0.54	-0.04	-0.58	-0.06	0.00	-0.06
Andrei Kirilenko	25	-0.72	1.16	0.44	-0.38	0.60	0.23
Deron Williams	35	4.69	-1.86	2.83	3.42	-1.36	2.06
Joe Johnson	38	3.39	-1.08	2.31	2.68	-0.86	1.83
Jorge Gutierrez	2	-2.11	-1.27	-3.38	-0.09	-0.05	-0.14
Kevin Garnett	25	-2.86	6.57	3.71	-1.49	3.42	1.93
Marcus Thornton	15	-0.12	-1.96	-2.08	-0.04	-0.61	-0.65
Mason Plumlee	18	-0.90	-1.34	-2.24	-0.34	-0.50	-0.84
Mirza Teletovic	15	-0.28	-2.16	-2.44	-0.09	-0.68	-0.76
Paul Pierce	30	-0.62	3.14	2.52	-0.39	1.96	1.58
Shaun Livingston	25	-0.18	0.27	0.09	-0.09	0.14	0.05

     Bulls:

player	minutes	orpm	drpm	rpm	onet	dnet	net
Carlos Boozer	25	-4.09	-0.04	-4.13	-2.13	-0.02	-2.15
D.J. Augustin	28	-0.06	-4.02	-4.08	-0.04	-2.35	-2.38
Jimmy Butler	40	0.93	1.37	2.30	0.78	1.14	1.92
Joakim Noah	42	0.68	3.88	4.56	0.60	3.40	3.99
Kirk Hinrich	30	-1.58	1.31	-0.27	-0.99	0.82	-0.17
Mike Dunleavy	35	1.42	2.07	3.49	1.04	1.51	2.54
Nazr Mohammed	5	-4.95	1.64	-3.31	-0.52	0.17	-0.34
Taj Gibson	32	0.43	3.57	4.00	0.29	2.38	2.67
Tony Snell	3	-3.95	-1.66	-5.61	-0.25	-0.10	-0.35

     Wizards:

player	minutes	orpm	drpm	rpm	onet	dnet	net
Al Harrington	6	-0.29	0.22	-0.07	-0.04	0.03	-0.01
Andre Miller	10	1.80	0.12	1.92	0.38	0.03	0.40
Bradley Beal	37	-0.86	-0.08	-0.94	-0.66	-0.06	-0.72
Drew Gooden	8	-0.97	-2.19	-3.16	-0.16	-0.37	-0.53
John Wall	42	2.54	-0.46	2.08	2.22	-0.40	1.82
Marcin Gortat	36	-0.16	3.85	3.69	-0.12	2.89	2.77
Martell Webster	20	2.19	-2.87	-0.68	0.91	-1.20	-0.28
Nene Hilario	28	-1.65	4.39	2.74	-0.96	2.56	1.60
Trevor Ariza	37	-0.26	0.94	0.68	-0.20	0.72	0.52
Trevor Booker	16	-0.12	-2.77	-2.89	-0.04	-0.92	-0.96

R Script:

Show/Hide

library(XML)
library(RCurl)
v.pages <- 1:11            #change if increase in number of pages on http://espn.go.com/nba/statistics/rpm/_/page/1/sort/RPM

v.column.names <- c("player","team","orpm","drpm","rpm")
v.teams.home <- c("sas","okc","lac","hou","ind","mia","tor","chi")
v.teams.away <- c("dal","mem","gsw","por","atl","cha","bkn","was")
v.teams <- c("sas","dal","okc","mem","lac","gsw","hou","por","ind","atl","mia","cha","tor","bkn","chi","was")

hca <- 4                    #difference between neutral court and home court; this is total hca adjustment, not the adjustment for each team
avg_eff <- 104            #average league points per 100 possessions

df.rpm <- data.frame(player=character(0),team=character(0),orpm=numeric(0),drpm=numeric(0),rpm=numeric(0))

for(page in v.pages)
{
    v.url.rpm <- paste("http://espn.go.com/nba/statistics/rpm/_/page/",page,"/sort/RPM",sep="")
    table <- readHTMLTable(v.url.rpm)[[1]]
    table <- table[as.character((table[,2]))!="NAME",]
    table2 <- cbind(as.data.frame(matrix(unlist(strsplit(as.character(table[,2]),", ")),ncol=2,byrow=TRUE),stringsAsFactors=FALSE)[,1], as.data.frame(matrix(unlist(table[,3]),ncol=1,byrow=TRUE),stringsAsFactors=FALSE), as.data.frame(matrix(unlist(table[,6]),ncol=1,byrow=TRUE),stringsAsFactors=FALSE),as.data.frame(matrix(unlist(table[,7]),ncol=1,byrow=TRUE),stringsAsFactors=FALSE),as.data.frame(matrix(unlist(table[,8]),ncol=1,byrow=TRUE),stringsAsFactors=FALSE))
    table2[,3] <- as.numeric(table2[,3])
    table2[,4] <- as.numeric(table2[,4])
    table2[,5] <- as.numeric(table2[,5])
    colnames(table2) <- v.column.names
    df.rpm <- rbind(df.rpm,table2)
}

#minutes distributions and individual xrapm:
df.sas.r1 <- data.frame(player=c("Tony Parker","Tim Duncan","Kawhi Leonard","Marco Belinelli","Boris Diaw","Danny Green","Manu Ginobili","Tiago Splitter","Patty Mills","Cory Joseph","Matt Bonner"),minutes=c(35,35,38,15,22,25,30,20,10,5,5))
df.sas.r1 <- merge(df.sas.r1, df.rpm[,c(1,3,4,5)], by="player", all.x=TRUE)
df.dal.r1 <- data.frame(player=c("Monta Ellis","Dirk Nowitzki","Shawn Marion","Jose Calderon","Vince Carter","Devin Harris","Samuel Dalembert","Brandan Wright","Jae Crowder","DeJuan Blair"),minutes=c(42,38,30,18,30,30,15,22,10,5))
df.dal.r1 <- merge(df.dal.r1, df.rpm[,c(1,3,4,5)], by="player", all.x=TRUE)
df.okc.r1 <- data.frame(player=c("Kevin Durant","Serge Ibaka","Russell Westbrook","Reggie Jackson","Caron Butler","Thabo Sefolosha","Jeremy Lamb","Kendrick Perkins","Derek Fisher","Nick Collison","Steven Adams"),minutes=c(45,32,35,20,18,15,5,20,22,18,10))
df.okc.r1 <- merge(df.okc.r1, df.rpm[,c(1,3,4,5)], by="player", all.x=TRUE)
df.mem.r1 <- data.frame(player=c("Zach Randolph","Mike Conley","Marc Gasol","Courtney Lee","Tayshaun Prince","Tony Allen","Mike Miller","James Johnson","Kosta Koufos","Beno Udrih"),minutes=c(40,42,40,25,23,25,20,10,10,5))
df.mem.r1 <- merge(df.mem.r1, df.rpm[,c(1,3,4,5)], by="player", all.x=TRUE)
df.lac.r1 <- data.frame(player=c("Blake Griffin","DeAndre Jordan","Chris Paul","Jamal Crawford","J.J. Redick","Matt Barnes","Darren Collison","Jared Dudley","Danny Granger","Glen Davis","Hedo Turkoglu"),minutes=c(42,36,40,25,22,30,15,10,5,10,5))
df.lac.r1 <- merge(df.lac.r1, df.rpm[,c(1,3,4,5)], by="player", all.x=TRUE)
df.gsw.r1 <- data.frame(player=c("Stephen Curry","Klay Thompson","David Lee","Andre Iguodala","Harrison Barnes","Draymond Green","Steve Blake","Jermaine O'Neal","Marreese Speights","Jordan Crawford"),minutes=c(42,40,35,36,20,27,9,20,8,3))
df.gsw.r1 <- merge(df.gsw.r1, df.rpm[,c(1,3,4,5)], by="player", all.x=TRUE)
df.hou.r1 <- data.frame(player=c("James Harden","Chandler Parsons","Dwight Howard","Patrick Beverley","Jeremy Lin","Terrence Jones","Omer Asik","Francisco Garcia","Omri Casspi","Donatas Motiejunas","Jordan Hamilton"),minutes=c(40,40,36,35,22,16,11,10,15,10,5))
df.hou.r1 <- merge(df.hou.r1, df.rpm[,c(1,3,4,5)], by="player", all.x=TRUE)
df.por.r1 <- data.frame(player=c("LaMarcus Aldridge","Nicolas Batum","Damian Lillard","Wesley Matthews","Robin Lopez","Mo Williams","Dorell Wright","C.J. McCollum","Thomas Robinson","Meyers Leonard"),minutes=c(40,40,40,38,35,25,10,5,2,5))
df.por.r1 <- merge(df.por.r1, df.rpm[,c(1,3,4,5)], by="player", all.x=TRUE)
df.ind.r1 <- data.frame(player=c("Paul George","Lance Stephenson","George Hill","David West","Roy Hibbert","Evan Turner","C.J. Watson","Luis Scola","Ian Mahinmi"),minutes=c(40,36,35,35,28,16,20,12,18))
df.ind.r1 <- merge(df.ind.r1, df.rpm[,c(1,3,4,5)], by="player", all.x=TRUE)
df.atl.r1 <- data.frame(player=c("Kyle Korver","Paul Millsap","Jeff Teague","DeMarre Carroll","Louis Williams","Shelvin Mack","Elton Brand","Pero Antic","Mike Scott","Cartier Martin","Mike Muscala"),minutes=c(36,40,35,38,18,13,12,20,15,7,6))
df.atl.r1 <- merge(df.atl.r1, df.rpm[,c(1,3,4,5)], by="player", all.x=TRUE)
df.mia.r1 <- data.frame(player=c("LeBron James","Dwyane Wade","Chris Bosh","Mario Chalmers","Ray Allen","Norris Cole","Shane Battier","Chris Andersen","Rashard Lewis","Toney Douglas","Michael Beasley","Udonis Haslem"),minutes=c(40,32,38,35,20,12,17,18,8,6,2,12))
df.mia.r1 <- merge(df.mia.r1, df.rpm[,c(1,3,4,5)], by="player", all.x=TRUE)
df.cha.r1 <- data.frame(player=c("Kemba Walker","Al Jefferson","Gerald Henderson","Josh McRoberts","Michael Kidd-Gilchrist","Gary Neal","Chris Douglas-Roberts","Anthony Tolliver","Cody Zeller","Luke Ridnour","Bismack Biyombo"),minutes=c(38,38,35,32,25,17,13,22,10,5,5))
df.cha.r1 <- merge(df.cha.r1, df.rpm[,c(1,3,4,5)], by="player", all.x=TRUE)
df.tor.r1 <- data.frame(player=c("DeMar DeRozan","Kyle Lowry","Amir Johnson","Jonas Valanciunas","Terrence Ross","Patrick Patterson","Greivis Vasquez","John Salmons","Tyler Hansbrough","Chuck Hayes","Nando de Colo", "Steve Novak"),minutes=c(40,38,27,25,30,20,18,15,12,5,5,5))
df.tor.r1 <- merge(df.tor.r1, df.rpm[,c(1,3,4,5)], by="player", all.x=TRUE)
df.bkn.r1 <- data.frame(player=c("Joe Johnson","Deron Williams","Paul Pierce","Shaun Livingston","Marcus Thornton","Alan Anderson","Andray Blatche","Kevin Garnett","Mirza Teletovic","Andrei Kirilenko","Mason Plumlee","Jorge Gutierrez"),minutes=c(38,35,30,25,15,7,5,25,15,25,18,2))
df.bkn.r1 <- merge(df.bkn.r1, df.rpm[,c(1,3,4,5)], by="player", all.x=TRUE)
df.chi.r1 <- data.frame(player=c("Jimmy Butler","Joakim Noah","Mike Dunleavy","D.J. Augustin","Kirk Hinrich","Taj Gibson","Carlos Boozer","Tony Snell","Nazr Mohammed"),minutes=c(40,42,35,28,30,32,25,3,5))
df.chi.r1 <- merge(df.chi.r1, df.rpm[,c(1,3,4,5)], by="player", all.x=TRUE)
df.was.r1 <- data.frame(player=c("John Wall","Trevor Ariza","Bradley Beal","Marcin Gortat","Nene Hilario","Martell Webster","Trevor Booker","Drew Gooden","Al Harrington","Andre Miller"),minutes=c(42,37,37,36,28,20,16,8,6,10))
df.was.r1 <- merge(df.was.r1, df.rpm[,c(1,3,4,5)], by="player", all.x=TRUE)

df.sas.r1$onet <- df.sas.r1$minutes * df.sas.r1$orpm / 48
df.dal.r1$onet <- df.dal.r1$minutes * df.dal.r1$orpm / 48
df.okc.r1$onet <- df.okc.r1$minutes * df.okc.r1$orpm / 48
df.mem.r1$onet <- df.mem.r1$minutes * df.mem.r1$orpm / 48
df.lac.r1$onet <- df.lac.r1$minutes * df.lac.r1$orpm / 48
df.gsw.r1$onet <- df.gsw.r1$minutes * df.gsw.r1$orpm / 48
df.hou.r1$onet <- df.hou.r1$minutes * df.hou.r1$orpm / 48
df.por.r1$onet <- df.por.r1$minutes * df.por.r1$orpm / 48
df.ind.r1$onet <- df.ind.r1$minutes * df.ind.r1$orpm / 48
df.atl.r1$onet <- df.atl.r1$minutes * df.atl.r1$orpm / 48
df.mia.r1$onet <- df.mia.r1$minutes * df.mia.r1$orpm / 48
df.cha.r1$onet <- df.cha.r1$minutes * df.cha.r1$orpm / 48
df.tor.r1$onet <- df.tor.r1$minutes * df.tor.r1$orpm / 48
df.bkn.r1$onet <- df.bkn.r1$minutes * df.bkn.r1$orpm / 48
df.chi.r1$onet <- df.chi.r1$minutes * df.chi.r1$orpm / 48
df.was.r1$onet <- df.was.r1$minutes * df.was.r1$orpm / 48

df.sas.r1$dnet <- df.sas.r1$minutes * df.sas.r1$drpm / 48
df.dal.r1$dnet <- df.dal.r1$minutes * df.dal.r1$drpm / 48
df.okc.r1$dnet <- df.okc.r1$minutes * df.okc.r1$drpm / 48
df.mem.r1$dnet <- df.mem.r1$minutes * df.mem.r1$drpm / 48
df.lac.r1$dnet <- df.lac.r1$minutes * df.lac.r1$drpm / 48
df.gsw.r1$dnet <- df.gsw.r1$minutes * df.gsw.r1$drpm / 48
df.hou.r1$dnet <- df.hou.r1$minutes * df.hou.r1$drpm / 48
df.por.r1$dnet <- df.por.r1$minutes * df.por.r1$drpm / 48
df.ind.r1$dnet <- df.ind.r1$minutes * df.ind.r1$drpm / 48
df.atl.r1$dnet <- df.atl.r1$minutes * df.atl.r1$drpm / 48
df.mia.r1$dnet <- df.mia.r1$minutes * df.mia.r1$drpm / 48
df.cha.r1$dnet <- df.cha.r1$minutes * df.cha.r1$drpm / 48
df.tor.r1$dnet <- df.tor.r1$minutes * df.tor.r1$drpm / 48
df.bkn.r1$dnet <- df.bkn.r1$minutes * df.bkn.r1$drpm / 48
df.chi.r1$dnet <- df.chi.r1$minutes * df.chi.r1$drpm / 48
df.was.r1$dnet <- df.was.r1$minutes * df.was.r1$drpm / 48

df.sas.r1$net <- df.sas.r1$minutes * df.sas.r1$rpm / 48
df.dal.r1$net <- df.dal.r1$minutes * df.dal.r1$rpm / 48
df.okc.r1$net <- df.okc.r1$minutes * df.okc.r1$rpm / 48
df.mem.r1$net <- df.mem.r1$minutes * df.mem.r1$rpm / 48
df.lac.r1$net <- df.lac.r1$minutes * df.lac.r1$rpm / 48
df.gsw.r1$net <- df.gsw.r1$minutes * df.gsw.r1$rpm / 48
df.hou.r1$net <- df.hou.r1$minutes * df.hou.r1$rpm / 48
df.por.r1$net <- df.por.r1$minutes * df.por.r1$rpm / 48
df.ind.r1$net <- df.ind.r1$minutes * df.ind.r1$rpm / 48
df.atl.r1$net <- df.atl.r1$minutes * df.atl.r1$rpm / 48
df.mia.r1$net <- df.mia.r1$minutes * df.mia.r1$rpm / 48
df.cha.r1$net <- df.cha.r1$minutes * df.cha.r1$rpm / 48
df.tor.r1$net <- df.tor.r1$minutes * df.tor.r1$rpm / 48
df.bkn.r1$net <- df.bkn.r1$minutes * df.bkn.r1$rpm / 48
df.chi.r1$net <- df.chi.r1$minutes * df.chi.r1$rpm / 48
df.was.r1$net <- df.was.r1$minutes * df.was.r1$rpm / 48

#team-level xrapm
df.all.r1 <- data.frame(team=v.teams)
df.all.r1$onet <- as.vector(t(data.frame(sas=sum(df.sas.r1$onet),dal=sum(df.dal.r1$onet),okc=sum(df.okc.r1$onet),mem=sum(df.mem.r1$onet),lac=sum(df.lac.r1$onet),gsw=sum(df.gsw.r1$onet),hou=sum(df.hou.r1$onet),por=sum(df.por.r1$onet),ind=sum(df.ind.r1$onet),atl=sum(df.atl.r1$onet),mia=sum(df.mia.r1$onet),cha=sum(df.cha.r1$onet),tor=sum(df.tor.r1$onet),bkn=sum(df.bkn.r1$onet),chi=sum(df.chi.r1$onet),was=sum(df.was.r1$onet))))
df.all.r1$dnet <- as.vector(t(data.frame(sas=sum(df.sas.r1$dnet),dal=sum(df.dal.r1$dnet),okc=sum(df.okc.r1$dnet),mem=sum(df.mem.r1$dnet),lac=sum(df.lac.r1$dnet),gsw=sum(df.gsw.r1$dnet),hou=sum(df.hou.r1$dnet),por=sum(df.por.r1$dnet),ind=sum(df.ind.r1$dnet),atl=sum(df.atl.r1$dnet),mia=sum(df.mia.r1$dnet),cha=sum(df.cha.r1$dnet),tor=sum(df.tor.r1$dnet),bkn=sum(df.bkn.r1$dnet),chi=sum(df.chi.r1$dnet),was=sum(df.was.r1$dnet))))
df.all.r1$net <- as.vector(t(data.frame(sas=sum(df.sas.r1$net),dal=sum(df.dal.r1$net),okc=sum(df.okc.r1$net),mem=sum(df.mem.r1$net),lac=sum(df.lac.r1$net),gsw=sum(df.gsw.r1$net),hou=sum(df.hou.r1$net),por=sum(df.por.r1$net),ind=sum(df.ind.r1$net),atl=sum(df.atl.r1$net),mia=sum(df.mia.r1$net),cha=sum(df.cha.r1$net),tor=sum(df.tor.r1$net),bkn=sum(df.bkn.r1$net),chi=sum(df.chi.r1$net),was=sum(df.was.r1$net))))
df.all.r1$home_perc <- (avg_eff+df.all.r1$onet+hca/4)^14/((avg_eff+df.all.r1$onet+hca/4)^14+(avg_eff-df.all.r1$dnet-hca/4)^14)
df.all.r1$away_perc <- (avg_eff+df.all.r1$onet-hca/4)^14/((avg_eff+df.all.r1$onet-hca/4)^14+(avg_eff-df.all.r1$dnet+hca/4)^14)

#specific round 1 matchups
df.r1.matchups.1 <- data.frame(team=v.teams.home, opponent=v.teams.away, stringsAsFactors = FALSE)
df.r1.matchups.1$team_home_perc <- merge(data.frame(team=df.r1.matchups.1$team),df.all.r1[,c(1,5)], by="team", all.x=TRUE, sort=FALSE)[,2]
df.r1.matchups.1$team_away_perc <- merge(data.frame(team=df.r1.matchups.1$team),df.all.r1[,c(1,6)], by="team", all.x=TRUE, sort=FALSE)[,2]
df.r1.matchups.1$opponent_home_perc <- merge(data.frame(team=df.r1.matchups.1$opponent),df.all.r1[,c(1,5)], by="team", all.x=TRUE, sort=FALSE)[,2]
df.r1.matchups.1$opponent_away_perc <- merge(data.frame(team=df.r1.matchups.1$opponent),df.all.r1[,c(1,6)], by="team", all.x=TRUE, sort=FALSE)[,2]
df.r1.matchups.1$team_matchup_home_perc <- (df.r1.matchups.1$team_home_perc - (df.r1.matchups.1$team_home_perc*df.r1.matchups.1$opponent_away_perc))/(df.r1.matchups.1$team_home_perc + df.r1.matchups.1$opponent_away_perc - (2*df.r1.matchups.1$team_home_perc*df.r1.matchups.1$opponent_away_perc))
df.r1.matchups.1$team_matchup_away_perc <- (df.r1.matchups.1$team_away_perc - (df.r1.matchups.1$team_away_perc*df.r1.matchups.1$opponent_home_perc))/(df.r1.matchups.1$team_away_perc + df.r1.matchups.1$opponent_home_perc - (2*df.r1.matchups.1$team_away_perc*df.r1.matchups.1$opponent_home_perc))
df.r1.matchups.1$series_perc <- df.r1.matchups.1$team_matchup_home_perc^4 + 4*df.r1.matchups.1$team_matchup_home_perc^3*(1-df.r1.matchups.1$team_matchup_home_perc)*(1-(1-df.r1.matchups.1$team_matchup_away_perc)^3) + 6*df.r1.matchups.1$team_matchup_home_perc^2*(1-df.r1.matchups.1$team_matchup_home_perc)^2*((3*df.r1.matchups.1$team_matchup_away_perc^2*(1-df.r1.matchups.1$team_matchup_away_perc))+df.r1.matchups.1$team_matchup_away_perc^3)+4*
df.r1.matchups.1$team_matchup_home_perc*(1-df.r1.matchups.1$team_matchup_home_perc)^3*df.r1.matchups.1$team_matchup_away_perc^3
df.r1.matchups.2 <- df.r1.matchups.1[,c(2,1)]
colnames(df.r1.matchups.2) <- c("team","opponent")
df.r1.matchups.2$team_home_perc <- df.r1.matchups.1$opponent_home_perc
df.r1.matchups.2$team_away_perc <- df.r1.matchups.1$opponent_away_perc
df.r1.matchups.2$opponent_home_perc <- df.r1.matchups.1$team_home_perc
df.r1.matchups.2$opponent_away_perc <- df.r1.matchups.1$team_away_perc
df.r1.matchups.2$team_matchup_home_perc <- 1-df.r1.matchups.1$team_matchup_away_perc
df.r1.matchups.2$team_matchup_away_perc <- 1-df.r1.matchups.1$team_matchup_home_perc
df.r1.matchups.2$series_perc <- 1-df.r1.matchups.1$series_perc
df.r1.matchups <- rbind(df.r1.matchups.1,df.r1.matchups.2)
df.r1.matchups <- df.r1.matchups[order(df.r1.matchups$series_perc, decreasing=TRUE),]



#print results
df.r1.matchups
df.all.r1[order(df.all.r1$net,decreasing=TRUE),c(1:4)]
df.sas.r1
df.dal.r1
df.okc.r1
df.mem.r1
df.lac.r1
df.gsw.r1
df.hou.r1
df.por.r1
df.ind.r1
df.atl.r1
df.mia.r1
df.cha.r1
df.tor.r1
df.bkn.r1
df.chi.r1
df.was.r1