2008 PrOPS-Stars, Overperformers, and Underperformers
Here are the top-three performers in terms of PrOPS by position and league (via THT).
If you are unfamiliar with PrOPS, it is a metric that estimates how players typically perform in terms of OPS based on how they hit the ball, along with a few other characteristics. You can read the primer here.
National League American League C Year Last First Tm PrOPS Year Last First Tm PrOPS 2008 McCann Brian M ATL 0.900 2008 Mauer Joe MIN 0.841 2008 Soto Geovany CHN 0.868 2008 Hernandez Ramon BAL 0.793 2008 Martin Russell LAN 0.861 2008 Rodriguez Ivan DET 0.727 1B Year Last First Tm PrOPS Year Last First Tm PrOPS 2008 Pujols Albert STL 1.054 2008 Giambi Jason NYA 1.017 2008 Berkman Lance HOU 0.961 2008 Youkilis Kevin BOS 0.853 2008 Howard Ryan J PHI 0.940 2008 Morneau Justin MIN 0.838 2B Year Last First Tm PrOPS Year Last First Tm PrOPS 2008 Utley Chase PHI 1.001 2008 Kinsler Ian M TEX 0.834 2008 Uggla Dan C FLA 0.894 2008 Ellis Mark OAK 0.803 2008 DeRosa Mark CHN 0.849 2008 Roberts Brian BAL 0.792 3B Year Last First Tm PrOPS Year Last First Tm PrOPS 2008 Jones Chipper ATL 1.030 2008 Rodriguez Alex NYA 0.946 2008 Ramirez Aramis CHN 0.922 2008 Lowell Mike BOS 0.870 2008 Wright David A NYN 0.910 2008 Longoria Evan TB 0.867 SS Year Last First Tm PrOPS Year Last First Tm PrOPS 2008 Ramirez Hanley FLA 0.921 2008 Peralta Jhonny CLE 0.797 2008 Reyes Jose NYN 0.820 2008 Scutaro Marco TOR 0.775 2008 Rollins Jimmy PHI 0.808 2008 Renteria Edgar DET 0.751 LF Year Last First Tm PrOPS Year Last First Tm PrOPS 2008 Dunn Adam CIN 1.057 2008 Cust Jack OAK 0.956 2008 Burrell Pat PHI 1.025 2008 Quentin Carlos CHA 0.926 2008 Lee Carlos HOU 0.912 2008 Ramirez Manny BOS 0.882 CF Year Last First Tm PrOPS Year Last First Tm PrOPS 2008 Ankiel Rick STL 0.898 2008 Sizemore Grady CLE 0.932 2008 Beltran Carlos NYN 0.878 2008 Hamilton Josh TEX 0.926 2008 McLouth Nate PIT 0.877 2008 Swisher Nick CHA 0.879 RF Year Last First Tm PrOPS Year Last First Tm PrOPS 2008 Ludwick Ryan STL 0.992 2008 Drew J.D. BOS 0.954 2008 Hawpe Brad COL 0.896 2008 Dye JermaineCHA 0.908 2008 Nady Xavier PIT 0.872 2008 Markakis Nick BAL 0.883 DH Year Last First Tm PrOPS 2008 Bradley Milton TEX 1.045 2008 Thome Jim CHA 0.941 2008 Huff Aubrey BAL 0.865
Also here is a list of the top-25 overperformers, which means their OPS exceed their PrOPS. I expect these players’ performances to decline.
Last First Tm PrOPS OPS OPS-PrOPS Berkman Lance HOU 0.961 1.096 0.135 Kinsler Ian TEX 0.834 0.945 0.111 Lewis Fred SF 0.701 0.798 0.097 Uggla Dan FLA 0.894 0.978 0.084 Damon Johnny NYA 0.774 0.856 0.082 Youkilis Kevin BOS 0.853 0.933 0.080 Holliday Matt COL 0.896 0.975 0.079 Jones Adam BAL 0.658 0.732 0.074 Roberts Brian BAL 0.792 0.864 0.072 Morneau Justin MIN 0.838 0.903 0.065 Granderson Curtis DET 0.782 0.838 0.056 Jones Chipper ATL 1.030 1.086 0.056 Rios Alex TOR 0.691 0.737 0.046 Durham Ray SF 0.754 0.799 0.045 Loney James LAN 0.752 0.796 0.045 Gomez Carlos MIN 0.594 0.638 0.044 Hudson Orlando ARI 0.773 0.816 0.044 Guzman CristianWAS 0.721 0.765 0.044 Rowand Aaron SF 0.764 0.804 0.041 McCann Brian ATL 0.900 0.940 0.040 Young Delmon MIN 0.677 0.716 0.039 Ramirez Hanley FLA 0.921 0.957 0.036 Reyes Jose NYN 0.820 0.854 0.035 Young Michael TEX 0.743 0.777 0.034 Hart Corey MIL 0.799 0.831 0.033
And here are the top-25 underperformers. I expect these players to improve.
Last First Tm PrOPS OPS OPS-PrOPS Sanchez Freddy PIT 0.721 0.556 -0.165 Cust Jack OAK 0.956 0.815 -0.140 Dunn Adam CIN 1.057 0.918 -0.138 Hernandez Ramon BAL 0.793 0.664 -0.128 Renteria Edgar DET 0.751 0.627 -0.124 Swisher Nick CHA 0.879 0.754 -0.124 Mora Melvin BAL 0.802 0.688 -0.114 Howard Ryan PHI 0.940 0.832 -0.108 Greene Khalil SD 0.701 0.593 -0.107 Cabrera Melky NYA 0.751 0.648 -0.103 Giambi Jason NYA 1.017 0.915 -0.103 Millar Kevin BAL 0.828 0.730 -0.098 Garko Ryan CLE 0.765 0.668 -0.098 Griffey, Jr. Ken CIN 0.843 0.748 -0.096 Scutaro Marco TOR 0.775 0.680 -0.095 Cano RobinsonNYA 0.737 0.643 -0.094 Beltre Adrian SEA 0.863 0.769 -0.094 Delgado Carlos NYN 0.876 0.784 -0.092 Kent Jeff LAN 0.801 0.711 -0.090 Francoeur Jeff ATL 0.746 0.659 -0.087 Ellis Mark OAK 0.803 0.716 -0.087 Helton Todd COL 0.866 0.783 -0.083 Barton Daric OAK 0.718 0.639 -0.080 Pierre Juan LAN 0.716 0.644 -0.072
A quick look shows the underperformers to underpeform much more than the overperformers overperform. First place is higher. Last place (25) is higher. At spot 10 the overpeformance approximately equals the under performance at spot 25.
At an earlier rendering of PrOPS I seem to remember you suggesting that “big, slow guys” might underperform at a higher clip than they should (and with above random frequency). Just wondering how the mean, mode and median sort out.
Also I would suppose that you looked at “qualifying” batters only. If so, wondering how many that is. I would expect 180 to 200. (AL teams at 6 to 7 per team and NL teams at 5 to 6)
This is the result of offense being down this year. It is based on past data, so there is some bias in this direction.
This was suggested by others, but I did not find much evidence of this.
I did look at qualified batters only, but I’m not sure how many that is off the top of my head.
I just remembered that I received an e-mail from a reader who had looked into over/underperformance of PrOPS. He found some evidence that pull hitters underperformed, and he hypothesized that this was a result of defenders being able to shift to take away more hits on balls in play than hitters who spread the ball around the field.
I was just about to comment on the shift affecting pull hitters. I think Howard is a great example of this. How many times have you see him line it hard, but straight into the 2B who’s playing a very shallow RF? And “big, slow guys” tend to correlate with pull hitters who require shifts so that makes sense too.
I would imagine that there is probably a good deal of persistence to which players over- and underperform their PrOPS. If you try to separate individual skills that you think would cause this (pull hitting, speed, etc.), you might not blur the results with an imperfectly measured dependent variable, resulting in attenuation bias.
What about including a variable in the regression for the previous year’s {OPS-PrOPS}? Wouldn’t that capture most of the omitted variables bias? After all, if there is a characteristic that leads to over- or underperformance, it would probably be present in prevoius years.
JC–do you have league averages (DH excluded)? Among the leaders, there are several positions for which the NL props is greater than the AL props. E.g., the third place NL catcher tops the first place AL catcher. I’m wondering if there might be the beginnings of a shift back toward the NL after several years of AL domination (something like 9-10 all star games, a majority of recent WS, etc.).
It might also be interesting to see props by position (for all players not just top 3) b/c one often hears debates over players such as “so and so is below avg overall but above average for his position.”
I do not have the raw data anymore. What is presented is what is available through THT.