2010 PrOPS Over- and Under-Performers
Since The Hardball Times stopped carrying stats, I have received several requests for 2010 PrOPS. I’m happy that some people have an interest in the numbers, and PrOPS was in need of a serious update anyway, so I decided to calculate PrOPS for 2010 based on the past five seasons using data from Baseball-Reference. While I can’t list a full slate of PrOPS for all players, I will report the top over- and under-performances for this season.
If you are not familiar with PrOPS, it’s an estimate of player hitting performance (measured on the scale of OPS) according to how players hit the ball rather than using in-play outcomes (see the introductory essay). OPS is estimated from batted-ball types (line-drive percentage and groundball-flyball ratio), walk rate, strikeout rate, home run rate, hit batter rate, home ballpark, and season. The idea is to identify players who are hitting the ball well (or poorly), but the measured outcome differs from what is typical for players who hit the ball in the same way. Players who are over- or under-performing their PrOPS are more likely to decline or improve than players whose performances are in-line with their OPS. PrOPS does not remove all aspects of luck, it just highlights one common area where random bounces on the field can distort outcome-based metrics.
The tables below are based on performances through Friday, May 14 for players with more than 100 plate appearances. Here is a list of players who are playing over their heads and may be headed toward a fall.
Top-30 Over-Performers Rank Player Team OPS PrOPS Diff. PA 1 Jayson Werth PHI 1.089 0.836 0.253 142 2 Justin Morneau MIN 1.142 0.948 0.194 148 3 Colby Rasmus STL 0.958 0.795 0.163 127 4 Carl Crawford TBR 0.868 0.710 0.158 154 5 Elvis Andrus TEX 0.791 0.646 0.145 153 6 Ichiro Suzuki SEA 0.853 0.708 0.145 158 7 Troy Tulowitzki COL 0.823 0.680 0.143 139 8 Kevin Youkilis BOS 0.969 0.828 0.141 157 9 Miguel Cabrera DET 1.088 0.948 0.140 157 10 Andrew McCutchen PIT 0.913 0.779 0.134 147 11 Adam Dunn WSN 0.917 0.788 0.129 147 12 Andre Ethier LAD 1.201 1.085 0.116 140 13 Stephen Drew ARI 0.848 0.733 0.115 140 14 Franklin Gutierrez SEA 0.865 0.752 0.113 144 15 Fred Lewis TOR 0.828 0.716 0.112 111 16 Carlos Ruiz PHI 0.948 0.841 0.107 106 17 Ryan Braun MIL 1.007 0.906 0.101 153 18 Johnny Damon DET 0.814 0.716 0.098 153 19 Daric Barton OAK 0.791 0.694 0.097 158 20 Nick Markakis BAL 0.837 0.741 0.096 159 21 Josh Willingham WSN 0.909 0.816 0.093 137 22 Adam LaRoche ARI 0.802 0.709 0.093 133 23 Geovany Soto CHC 0.954 0.863 0.091 111 24 Blake DeWitt LAD 0.711 0.622 0.089 108 25 Shin-Soo Choo CLE 0.853 0.768 0.085 148 26 Evan Longoria TBR 1.007 0.923 0.084 153 27 Brett Gardner NYY 0.832 0.751 0.081 131 28 Casey McGehee MIL 0.935 0.855 0.080 147 29 Kosuke Fukudome CHC 0.960 0.880 0.080 118 30 Ben Zobrist TBR 0.725 0.646 0.079 151
The players in the table below have had some tough luck to start the year and are likely to improve as the season progresses. It’s interesting that the list includes Derek Jeter and Victor Martinez, who have both been singled out for their unexpected poor play. And David Ortiz just misses the cut (99 PAs), but his PrOPS is .906, a difference of -.140 from his actual OPS.
Top-30 Under-Performers Rank Player Team OPS PrOPS Diff. PA 1 Hunter Pence HOU 0.705 0.927 -0.222 128 2 Skip Schumaker STL 0.589 0.800 -0.211 146 3 Brandon Wood LAA 0.414 0.622 -0.208 107 4 Chris Coghlan FLA 0.517 0.723 -0.206 132 5 Akinori Iwamura PIT 0.489 0.683 -0.194 142 6 Kendry Morales LAA 0.780 0.974 -0.194 150 7 Casey Kotchman SEA 0.627 0.811 -0.184 128 8 Derek Jeter NYY 0.717 0.886 -0.169 161 9 Adam Rosales OAK 0.677 0.837 -0.160 117 10 Victor Martinez BOS 0.641 0.801 -0.160 138 11 Rod Barajas NYM 0.860 1.016 -0.156 110 12 Melky Cabrera ATL 0.523 0.676 -0.153 124 13 Mark DeRosa SFG 0.537 0.686 -0.149 104 14 Cesar Izturis BAL 0.486 0.626 -0.140 104 15 Jose Lopez SEA 0.530 0.667 -0.137 149 16 A.J. Pierzynski CHW 0.547 0.680 -0.133 115 17 Russell Martin LAD 0.741 0.872 -0.131 140 18 Juan Rivera LAA 0.677 0.800 -0.123 127 19 Aramis Ramirez CHC 0.500 0.623 -0.123 146 20 Carlos Lee HOU 0.514 0.636 -0.122 139 21 Brendan Ryan STL 0.480 0.594 -0.114 117 22 Luis Castillo NYM 0.646 0.760 -0.114 122 23 Scott Sizemore DET 0.591 0.700 -0.109 112 24 Placido Polanco PHI 0.789 0.897 -0.108 149 25 Ian Stewart COL 0.906 1.010 -0.104 128 26 Jhonny Peralta CLE 0.690 0.791 -0.101 127 27 Grady Sizemore CLE 0.558 0.656 -0.098 137 28 Alcides Escobar MIL 0.622 0.719 -0.097 125 29 Pedro Feliz HOU 0.531 0.625 -0.094 123 30 Mark Teixeira NYY 0.733 0.824 -0.091 160
I want to reiterate that over- and under-performing does not guarantee a reversion, but it is one tool to identify those who are likely to deviate from their performances so far this season.
6 Responses “2010 PrOPS Over- and Under-Performers”
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[...] This post was mentioned on Twitter by J.C. Bradbury. J.C. Bradbury said: 2010 PrOPS Over- and Under-Performers http://bit.ly/bqBsMV [...]
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[...] } For those of you familiar with Bradbury and his work at the Sabernomics blog, he posted his first PrOPS (“proper OPS”) listings for 2010. Basically it’s a method of regression that helps remove some of the noise from a [...]
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[...] or simply needs an extra night off, they need to keep playing him. JC Bradbury listed him as one of the “unluckiest” players so far, so expect him to [...]
What is a better predictor of a player’s OPS in a season? Last year’s OPS or PrOPS?
What about within season? Say, what predicts 2nd half OPS better, first half OPS or first half PrOPS?
In this incarnation, lagged OPS predicts OPS better than PrOPS; however, previously PrOPS predicted better. I don’t know why it’s changed, but roughly they explain about the same amount of variance. Adding the lag of the difference between PrOPS and OPS improves the predictive power of the model. Also, I don’t think that all of Houston’s poor play can be explained by luck. Their OPS is so historically low that it’s not surprising there is such a disparity.
Okay, thanks.