What Has The Highest Correlation to Player Rater Score?

Jan 06

What Has The Highest Correlation to Player Rater Score?

My initial thought was to begin this article with the traditional sob story that every last place, self-absorbed fantasy owner sings; “Everyone on my team got hurt, all my stars underperformed, and I played every team in my league on their best week.”

While all of this may be true for my 12 team head-to-head categories league team, I pretense this article with the statement above, not to gain sympathy, but to give context to the research that resulted in this article.

In this article I show what metrics have the highest correlation with the fantasy basketball player rater score and propose a linear regression that looks at an expected player rater score based off of the variables with the highest correlations.

All of the references to player rater scores in this article refer to the player rater scores that are produced by ESPN Fantasy Basketball for 12 team head to head category leagues that use these categories: points, rebounds, three pointers, field goal percentage, free throw percentage, blocks, and steals.

The population for this study looks at player rater scores from the 2015 season so far. So while the sample size is very small, it gives us a general look at what metrics, outside of the metrics that are categories themselves, correlate highly to player rater scores. I was also unable to find a centralized database that archived historical player rater data, or any value metric in general, for fantasy basketball. I also didn’t have the time to go through each season from the last decade, create z scores for each category at each position, and create the player rater scores myself. But if anyone knows where I can get my hands on this information, I would be happy to do a more conclusive study.

We also don’t want to look at player rater scores specifically; we want to look at player rater score per game. This allows for us to give credit to players like Russell Westbrook and Chris Bosh, who have been absent from play for an appreciable duration of the season, but have played exceptionally well while they’ve been on the court.

Bellow are the metrics that I looked at and their correlation to player rater score per game:

MetricCorrelation to PR/GM
PTS0.813516556
FG0.779224727
FT0.731328749
FGA0.704824414
FTA0.683340911
MP0.672800305
MP/GM0.65573891
2P0.64840213
2PA0.600314706
TOV0.558714755
STL0.54331025
AST0.49912851
USG%0.461947653
DRB0.451675677
TRB0.356459697
BLK0.315271298
3P0.298185857
3PA0.295946302
FT%0.257915844
2P%0.204312922
PF0.169178919
FG%0.168585015
3P%0.150700146
ORB0.113957033

The two numbers that immediately jump out are MP (minutes played) and TOV (turnovers); all of the other metrics ahead of these statistics can be discounted because they are numbers that directly contribute to one of the eight categories used in ESPN head to head category leagues.

The .672 r for minutes played and the .558 r for turnovers show a strong positive relationship with player rater score per game and are statistically significant numbers.

A week ago, before I ran a correlation on all of the metrics above, I just looked at minutes played and usage rate as stats that might correlate highly with player rater scores. To me these numbers made the most sense; minutes per game shows that you are able to get on the court enough to get the opportunity to produce, and usage rate shows that when you get the opportunity, your team incorporates you in what they do from a schematic sense.

It may be because of the small sample of player seasons that was used for this study, but it looks like minutes played and turn overs are better proxies for opportunity and incorporation than minutes per game and usage rate.

I’m still not sure why minutes played correlates more highly with player rater score per game than minutes played per game (I’d love to hear what any readers might think), but my theory is that a player of lesser skill can artificially inflate his minutes played per game by playing big minutes in blow outs or games where players ahead of him on the depth chart are hurt or in foul trouble.

Turnovers make more sense. Think about it this way. Players that get a lot of turnovers are just like guys that get caught cheating on their girlfriends multiple times and don’t get broken up with; there has to be some aspect of who they are that makes them redeeming enough to keep the girlfriend around for more; that or their girlfriend has daddy issues, low self esteem, and is in her mid thirties and is afraid that she won’t be able to find another man to start a family with—either way. In order to get a large volume of turnovers, you have to be good enough to leave you in the game after you’ve made a mistake and continue to take risks. Just like Karen let Hank Moody back into her life again and again, after mistake upon mistake, Karen, just like the team, may have thought it was worth the risk.

Below is a linear regression of expected player rater score per game, with player rater score per game as the dependent variable and minutes played and turnovers as the independent variables.

PLAYER2015 PRPR/GMRKxPR/GMRKDIFF
James Harden17.760.592020.32711
John Wall12.490.402970.31625
LeBron James12.270.423160.29833
Ty Lawson6.980.2252500.292446
Damian Lillard15.290.463350.29150
Eric Bledsoe11.080.3358150.28869
Brandon Knight10.650.3328160.28279
Kobe Bryant6.240.2152520.274844
Monta Ellis8.970.2718340.266925
Gordon Hayward9.920.3100240.2661014
Blake Griffin8.370.2616410.2661130
Stephen Curry17.450.581730.25512-9
Tobias Harris8.980.2721330.2461320
Kyle Lowry12.360.3863100.24214-4
Rudy Gay9.630.3321180.241153
Marc Gasol12.240.394880.23916-8
Tyreke Evans4.550.1517740.2381757
Chris Paul15.70.490640.23418-14
Jrue Holiday9.710.3132220.228193
Trevor Ariza5.570.1857600.2252040
Jimmy Butler11.10.3700110.22421-10
Kyrie Irving9.840.3393140.22122-8
Kemba Walker7.780.2431440.2202321
Carmelo Anthony7.820.2697370.2192413
Josh Smith2.030.06551380.21825113
Kevin Love8.40.2710360.2182610
Paul Millsap9.670.3119230.21727-4
Evan Fournier2.280.06711350.21728107
Mario Chalmers4.90.1531720.2162943
Rajon Rondo3.650.1304930.2153063
Mike Conley9.530.3074290.21331-2
Michael Carter-Williams0.830.03611660.20732134
Wesley Matthews8.030.2433430.2053310
Goran Dragic7.520.2426450.2023411
Darren Collison7.410.2646390.198354
Markieff Morris8.030.2433420.197366
Pau Gasol9.540.3290190.19637-18
Nikola Vucevic8.430.2907320.19538-6
Trey Burke2.80.08751240.1933985
Ben McLemore3.790.12231000.1934060
LaMarcus Aldridge9.410.3245200.19141-21
Solomon Hill2.440.07631300.1914288
Reggie Jackson5.570.1921580.1904315
Serge Ibaka9.850.3078280.19044-16
Deron Williams5.870.2024550.1894510
Joe Johnson5.920.2041540.189468
DeAndre Jordan9.610.3003310.18947-16
Jeff Teague9.530.3404130.18748-35
Draymond Green9.290.3097250.18649-24
Klay Thompson10.670.3679120.18250-38
Arron Afflalo2.610.08161270.1775176
Anthony Davis18.510.617010.17652-51
Wilson Chandler5.370.1678660.1755313
Victor Oladipo3.590.1381830.1755429
Kyle Korver9.580.3090270.17455-28
Chandler Parsons6.540.2044530.17256-3
Andrew Wiggins0.240.00801900.17057133
Channing Frye4.450.1309920.1685834
Jeremy Lin4.290.1341900.1665931
Dwyane Wade5.630.2346460.16460-14
Danny Green10.170.3178210.16461-40
Al Jefferson*6.110.1909590.16162-3
Luol Deng5.330.1777610.15763-2
Tim Duncan8.670.3096260.15764-38
Tyson Chandler9.710.3034300.15665-35
Jeff Green5.580.1993570.15666-9
Giannis Antetokounmpo4.450.1391820.1546715
Enes Kanter4.520.1413800.1516812
Andre Drummond3.830.1235990.1516930
Alec Burks*1.890.07001330.1477063
Tony Wroten1.380.06001450.1467174
Kentavious Caldwell-Pope1.060.03421680.1457296
Jarrett Jack4.260.1374850.1437312
Greg Monroe3.020.10411110.1417437
J.J. Redick5.110.1597690.14075-6
Nicolas Batum4.540.1681650.13976-11
Marcin Gortat7.090.2287480.13777-29
Nerlens Noel1.70.06301400.1367862
Brandon Jennings3.790.1354870.134798
Jordan Hill4.890.1528730.13380-7
Chris Bosh6.470.2696380.13281-43
O.J. Mayo1.990.06221430.1268261
Derrick Favors6.570.2266490.12683-34
Dirk Nowitzki8.40.2710350.12584-49
Donatas Motiejunas1.610.05371510.1258566
Mike Dunleavy3.410.10661080.1228622
Al Horford7.930.2643400.12187-47
Steven Adams1.560.04881560.1208868
Courtney Lee4.320.1543710.11989-18
Terrence Ross3.70.11561030.1199013
Gorgui Dieng6.910.2303470.11891-44
Corey Brewer3.590.1282950.118923
Jamal Crawford6.260.2019560.11793-37
Harrison Barnes3.730.1243970.117943
Manu Ginobili2.770.09891150.1179520
Timofey Mozgov4.190.1309910.11596-5
Wesley Johnson4.490.1403810.11597-16
Zach Randolph*3.510.1350880.11198-10
DeMarcus Cousins7.840.392090.10899-90
Ryan Anderson5.070.1635680.106100-32
K.J. McDaniels3.480.12001020.1051011
Zach LaVine0.230.00851890.10310287
Thaddeus Young0.570.02281760.10110373
Gerald Green5.160.1564700.101104-34
Joakim Noah2.620.10481100.1011055
Kenneth Faried2.460.08481260.10110620
Amar'e Stoudemire*4.610.1646670.100107-40
Jonas Valanciunas5.470.1765620.100108-46
Derrick Rose1.590.07231320.09610923
Matt Barnes2.870.09571170.0941107
Tristan Thompson2.080.06711340.09411123
Carlos Boozer1.690.05451500.09311238
Paul Pierce4.050.1350890.092113-24
Andre Iguodala0.470.01571860.09211472
Cory Joseph3.160.10191130.091115-2
DeMarre Carroll2.860.10591090.091116-7
Aaron Brooks3.20.10001140.090117-3
Jabari Parker*1.40.05601470.09011829
Bradley Beal3.030.1377840.090119-35
Evan Turner2.480.08861220.0901202
Roy Hibbert3.80.1357860.087121-35
Kelly Olynyk4.870.1739640.087122-58
Jared Sullinger3.590.1282940.086123-29
Avery Bradley1.090.04041640.08612440
Steve Blake0.60.01821830.08312558
Amir Johnson3.130.10791050.082126-21
Russell Westbrook5.990.3328170.081127-110
P.J. Tucker3.20.11031040.078128-24
Tony Parker*2.260.10761060.077129-23
Dion Waiters1.310.04371580.07713028
Patrick Patterson4.650.1453770.075131-54
Louis Williams4.720.1475750.075132-57
Zaza Pachulia1.410.04861570.07313324
Cody Zeller0.760.02381740.07213440
Marcus Morris2.040.06181440.0701359
Donald Sloan0.140.00541940.06713658
Luis Scola1.350.04221610.06713724
Jameer Nelson0.560.02071790.06713841
Dwight Howard0.470.02611730.06713934
Kawhi Leonard*4.750.2159510.067140-89
Henry Sims1.980.06601370.067141-4
Jerryd Bayless2.530.07911290.065142-13
Miles Plumlee2.20.06671360.063143-7
Gerald Henderson0.90.03001690.06314425
Devin Harris2.880.09291190.062145-26
Greivis Vasquez0.250.00781920.06214646
Robin Lopez*3.090.1236980.060147-49
Marvin Williams0.690.02301750.06014827
Ronnie Price1.590.05131530.0601494
Khris Middleton2.310.07971280.059150-22
Chris Kaman1.840.05751460.057151-5
Mo Williams0.680.02961700.05715218
Rodney Stuckey0.030.00121950.05615342
Omer Asik0.190.00701930.05115439
Jason Terry1.430.05111540.051155-1
Isaiah Thomas3.040.12161010.048156-55
Shawne Williams2.790.09001210.047157-36
Mirza Teletovic0.810.02891710.04615813
Tony Allen1.570.06281410.043159-18
Shane Larkin0.850.02831720.04116012
Anderson Varejao*2.530.09731160.041161-45
Lavoy Allen2.730.08811230.040162-39
Taj Gibson1.980.08611250.037163-38
Shawn Marion0.480.01601850.03716421
Kyle Singler0.630.02031800.03616515
Shabazz Muhammad1.280.04271600.033166-6
Jared Dudley2.030.06341390.031167-28
Pablo Prigioni1.710.05521480.030168-20
Kris Humphries0.630.02101780.0281699
Jose Calderon0.310.01551870.02817017
Brook Lopez2.250.10711070.027171-64
Shaun Livingston0.580.01931810.0261729
James Johnson3.610.1245960.024173-77
Rudy Gobert4.580.1431780.023174-96
Alex Len30.09091200.021175-55
Samuel Dalembert1.130.03531670.019176-9
Kevin Garnett1.110.04271590.019177-18
Patrick Beverley0.720.04001650.017178-13
Larry Sanders*1.320.04891550.014179-24
Carl Landry0.590.01901820.0131802
Nikola Mirotic3.010.09411180.006181-63
Andrew Bogut*2.930.1465760.005182-106
Anthony Morrow1.050.04201620.005183-21
Rasual Butler20.07411310.005184-53
Beno Udrih0.640.02131770.005185-8
Marreese Speights2.970.10241120.002186-74
Robert Covington1.150.05481490.001187-38
Aron Baynes1.30.0406163-0.001188-25
Tyler Zeller3.960.141479-0.004189-110
Nick Young1.370.0623142-0.012190-48
Brandan Wright5.440.175563-0.015191-128
Jeremy Lamb0.40.0167184-0.020192-8
Kosta Koufos0.240.0080191-0.033193-2
Kyle O'Quinn1.070.0535152-0.050194-42
Kevin Durant*0.080.0089188-0.082195-7

xPR/GM produces an r of .714 with this formula: xPR/GM =- 0.2063 + 0.0003 * MP + 0.0016 * TOV.

While I was able to produce a linear regression with minutes played per game and usage rate that yielded a correlation of .687, it appears that minutes played and turnovers are much better proxies for our formula.

I’m not sure there is much to xPR/GM other than that the concept provokes you to take a longer look at a player who steps into a meaningful role after another player on his team gets hurt, but what we can take away from this study is that minutes played and turnovers are the secondary metrics that have the highest correlation with player rater scores this year.

Follow Me on TwitterDevin Jordan is obsessed with statistical analysis, non-fiction literature, and electronic music. If you enjoyed reading him, follow him on Twitter

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6 comments

  1. Interesting article, but I’m not sure how to make this information actionable for my fantasy basketball team. How can I put this awesome info you compiled into good use? Thanks

    • Devin Jordan /

      Great question.

      I tried to summarize the takeaway in the last paragraph, but I could have done a better job of it.

      The take away is that minutes are one of the most important statistics in fantasy basketball.

      For example, Ed Davis’ minutes spiked a couple of days before Christmas in a game against the Warriors; as a result, because of the increase in minutes, the rest of his numbers–points, rebounds, etc.–spiked as well.

      If a player’s numbers spike, and this spike happens without an increase in minutes, it could be perceived as noise and a fluctuation of a small sample; but, if a players numbers increase, and there is an increase in minutes to go along with this increase, like Ed Davis, this increase can be perceived as a signal of consistent production, that is.

      • Thanks for the reply. It makes sense. I was just trying to figure out how to use your chart to target players to sell/buy. When I look at the DIFF, is it better to target players with a high/low number? Sorry, I shoulda payed more attention in Stats class! 🙂

        • Devin Jordan /

          Don’t pay attention to the players that have a negative DIFF; i still need to make an additional adjustment to compensate for injured players (e.g. Cousins show up with a negative DIFF, because he was sick for a while)

          You can find some good inefficiencies when you look at the players with positive DIFFs: Wiggins and Trey Burke.

          Just remember that these numbers are meant for people that play in category leagues.

          I’m going to post an article within the next couple of days that look at minute risers; that’s really what you want to look at for waiver wire pickups.

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