2010 PrOPS Over- and Under-Performers (Through 07/01)
PrOPS updated through July 1 (minimum 240 PA). I report the top-30 over- and under-performers. Introduction to 2010 PrOPS. Introduction to PrOPS.
Top-30 Over-Performers Rank Player Team OPS PrOPS Diff PA 1 Andres Torres SFG 0.814 0.680 0.134 269 2 Ian Kinsler TEX 0.811 0.684 0.127 244 3 Carl Crawford TBR 0.869 0.742 0.127 322 4 Nick Markakis BAL 0.821 0.699 0.122 340 5 Justin Morneau MIN 1.059 0.938 0.121 327 6 David DeJesus KCR 0.875 0.756 0.119 330 7 Andrew McCutchen PIT 0.825 0.710 0.115 332 8 Josh Hamilton TEX 0.993 0.880 0.113 328 9 Jayson Werth PHI 0.919 0.813 0.106 308 10 Daric Barton OAK 0.798 0.692 0.106 352 11 Kevin Youkilis BOS 0.983 0.878 0.105 322 12 Ichiro Suzuki SEA 0.813 0.716 0.097 351 13 Ben Zobrist TBR 0.797 0.710 0.087 336 14 Franklin Gutierrez SEA 0.767 0.681 0.086 311 15 Lastings Milledge PIT 0.715 0.634 0.081 263 16 Jason Bay NYM 0.812 0.732 0.080 323 17 Fred Lewis TOR 0.774 0.695 0.079 272 18 Brandon Phillips CIN 0.841 0.766 0.075 357 19 Troy Tulowitzki COL 0.877 0.806 0.071 265 20 Evan Longoria TBR 0.870 0.803 0.067 342 21 Colby Rasmus STL 0.921 0.856 0.065 275 22 Miguel Cabrera DET 1.040 0.976 0.064 325 23 Brett Gardner NYY 0.811 0.747 0.064 278 24 Cliff Pennington OAK 0.704 0.644 0.060 296 25 Adam Dunn WSN 0.917 0.858 0.059 327 26 Johnny Damon DET 0.753 0.695 0.058 302 27 Elvis Andrus TEX 0.706 0.649 0.057 344 28 David Wright NYM 0.929 0.874 0.055 338 29 Martin Prado ATL 0.857 0.803 0.054 367 30 Albert Pujols STL 0.989 0.936 0.053 346
Top-30 Under-Performers Rank Player Team OPS PrOPS Diff PA 1 Hunter Pence HOU 0.730 0.876 -0.146 313 2 Ian Stewart COL 0.738 0.866 -0.128 270 3 Yadier Molina STL 0.615 0.742 -0.127 267 4 Carlos Lee HOU 0.669 0.796 -0.127 319 5 Jose Lopez SEA 0.603 0.726 -0.123 325 6 Adam Lind TOR 0.608 0.729 -0.121 322 7 Skip Schumaker STL 0.655 0.768 -0.113 288 8 Justin Smoak TEX 0.697 0.800 -0.103 250 9 Derek Jeter NYY 0.754 0.857 -0.103 361 10 Carlos Gonzalez COL 0.825 0.925 -0.100 301 11 Juan Rivera LAA 0.725 0.820 -0.095 258 12 Pedro Feliz HOU 0.572 0.664 -0.092 255 13 Todd Helton COL 0.657 0.749 -0.092 281 14 Aaron Hill TOR 0.642 0.719 -0.077 287 15 Carlos Pena TBR 0.728 0.804 -0.076 323 16 Clint Barmes COL 0.706 0.781 -0.075 257 17 Mike Napoli LAA 0.838 0.912 -0.074 262 18 Derrek Lee CHC 0.699 0.772 -0.073 334 19 Miguel Tejada BAL 0.695 0.768 -0.073 325 20 Jason Bartlett TBR 0.631 0.702 -0.071 258 21 Alcides Escobar MIL 0.640 0.710 -0.070 282 22 Orlando Cabrera CIN 0.625 0.692 -0.067 337 23 Russell Martin LAD 0.678 0.743 -0.065 300 24 Carlos Quentin CHW 0.784 0.848 -0.064 279 25 Shane Victorino PHI 0.767 0.829 -0.062 346 26 Melky Cabrera ATL 0.653 0.715 -0.062 265 27 Howie Kendrick LAA 0.718 0.779 -0.061 336 28 A.J. Pierzynski CHW 0.651 0.711 -0.060 250 29 Ty Wigginton BAL 0.808 0.865 -0.057 299 30 Mark Teixeira NYY 0.757 0.812 -0.055 354
What Edwin Jackson’s Pitch Count Hath Wrought
Edwin Jackson threw a bit of a lame no-hitter on Friday. I’m sorry if it offends you when I call such a hallowed feat lame, but eight walks in a game for a major-league pitcher is bad (see Pulling a Homer). But aside from this, one aspect of his performance has gotten a lot of attention: 149 pitches thrown. This is the highest pitch count allowed in a game since 2005 (see my previous post on how pitch counts have changed over the past two decades).
I have been conducting a study of pitch counts with Sean Forman, and we will be presenting our findings at the upcoming SABR convention in Atlanta. But since it’s applicable to Jackson’s situation, I’ll reveal some of the findings. Our study uses past pitching performances to estimate the impact of pitch counts on future performance, controlling for numerous factors, using fractional polynomial regression analysis to capture potential non-linear relationships. The results indicate that the impact of the pitch count in a single game on the following game is real but small; and, the impact is linear, not increasing as some analysts have theorized.
On average, every pitch thrown raises a pitcher’s ERA by 0.007 in the following game. Jackson’s ERA was 5.05 going into Friday’s game averaging 104 pitches per game; thus, based on the historical response of pitchers to pitch counts Jackson’s expected performance in his next start is about 5.37. So, Jackson can be expected to pitch worse, but not that much worse. Really, it’s not that big of a deal as a one-time event. Should Jackson continue to average around 150 pitches a game, the impact will grow, but I doubt that is going to happen. As for the impact on injuries, we didn’t look into it in this study. However, I have previously found little correlation between pitching loads and injury.
My take: if you have a pitcher going for a no-hitter—not matter how bad he’s pitching—the benefit of the excitement and media coverage of letting a pitcher throw more pitches is probably worth the cost. Let’s stop freaking out about pitch counts until we understand their influence a little better.
Update: In response to Jackson’s high pitch count, the Diamondbacks will push back his next start a day or two. How much will this help him recover? No much. On average, each day of rest lowers a pitcher’s ERA by approximately 0.015. Thus, his expected ERA drops from 5.37 to 5.34 (with two days of extra rest). Why rest days matter so little is an interesting question. A few years ago, I saw an presentation on muscle recovery from exercise, and one of the interesting findings was that most of the healing happens within the first few days. Whether this explains the finding or not, I don’t know.
2010 PrOPS Over- and Under-Performers (Through 6/15)
I have updated PrOPS through June 15 (minimum 200 PA). I report the top-30 over- and under-performers. Here is my previous post on 2010 PrOPS.
Top-30 Over-Performers Rk Player Team OPS PrOPS Diff PA 1 Andres Torres SFG 0.885 0.721 0.164 220 2 Justin Morneau MIN 1.079 0.925 0.154 266 3 Kevin Youkilis BOS 1.043 0.889 0.154 273 4 Ichiro Suzuki SEA 0.830 0.698 0.132 293 5 Jayson Werth PHI 0.904 0.776 0.128 243 6 Nick Markakis BAL 0.818 0.695 0.123 282 7 Andrew McCutchen PIT 0.861 0.741 0.120 271 8 David DeJesus KCR 0.873 0.761 0.112 276 9 Colby Rasmus STL 0.997 0.888 0.109 223 10 Daric Barton OAK 0.818 0.712 0.106 293 11 Evan Longoria TBR 0.964 0.861 0.103 279 12 Carl Crawford TBR 0.831 0.731 0.100 274 13 Adam Dunn WSN 0.951 0.852 0.099 265 14 Johnny Damon DET 0.808 0.711 0.097 262 15 Ben Zobrist TBR 0.824 0.728 0.096 275 16 Billy Butler KCR 0.890 0.798 0.092 278 17 Fred Lewis TOR 0.780 0.689 0.091 218 18 Franklin Gutierrez SEA 0.755 0.666 0.089 273 19 Robinson Cano NYY 1.022 0.936 0.086 278 20 Brett Gardner NYY 0.842 0.763 0.079 236 21 Elvis Andrus TEX 0.722 0.647 0.075 279 22 Aubrey Huff SFG 0.909 0.835 0.074 256 23 David Freese STL 0.809 0.735 0.074 234 24 Jason Bay NYM 0.790 0.719 0.071 272 25 Brandon Phillips CIN 0.849 0.780 0.069 287 26 Andre Ethier LAD 1.021 0.959 0.062 203 27 Josh Hamilton TEX 0.941 0.880 0.061 268 28 Drew Stubbs CIN 0.738 0.678 0.060 242 29 Erick Aybar LAA 0.688 0.629 0.059 292 30 Troy Tulowitzki COL 0.869 0.810 0.059 257
Top-30 Under-Performers Rk Player Team OPS PrOPS Diff PA 1 Casey Kotchman SEA 0.551 0.749 -0.198 200 2 Carlos Lee HOU 0.658 0.823 -0.165 259 3 Hunter Pence HOU 0.751 0.906 -0.155 251 4 Jose Lopez SEA 0.571 0.717 -0.146 271 5 Kendry Morales LAA 0.833 0.970 -0.137 211 6 Skip Schumaker STL 0.613 0.748 -0.135 247 7 Ian Stewart COL 0.758 0.877 -0.119 224 8 Derek Jeter NYY 0.780 0.896 -0.116 301 9 Mike Napoli LAA 0.798 0.912 -0.114 211 10 Juan Rivera LAA 0.746 0.853 -0.107 223 11 Adam Lind TOR 0.636 0.737 -0.101 268 12 Carlos Gonzalez COL 0.824 0.919 -0.095 250 13 Cameron Maybin FLA 0.631 0.726 -0.095 201 14 Clint Barmes COL 0.671 0.761 -0.090 202 15 Carlos Pena TBR 0.736 0.823 -0.087 261 16 Aaron Hill TOR 0.666 0.752 -0.086 230 17 A.J. Pierzynski CHW 0.649 0.734 -0.085 204 18 Pedro Feliz HOU 0.567 0.651 -0.084 224 19 Carlos Quentin CHW 0.681 0.764 -0.083 224 20 Melky Cabrera ATL 0.646 0.724 -0.078 212 21 Nate McLouth ATL 0.577 0.653 -0.076 205 22 Yadier Molina STL 0.666 0.739 -0.073 227 23 Matt Wieters BAL 0.629 0.697 -0.068 231 24 Howie Kendrick LAA 0.712 0.777 -0.065 276 25 Miguel Tejada BAL 0.676 0.741 -0.065 265 26 Alcides Escobar MIL 0.657 0.722 -0.065 233 27 Jerry Hairston SDP 0.618 0.682 -0.064 229 28 Derrek Lee CHC 0.688 0.750 -0.062 270 29 Juan Pierre CHW 0.584 0.645 -0.061 277 30 Brandon Inge DET 0.715 0.774 -0.059 248
A Lesson in Concentrated Benefits and Dispersed Costs
File under: Mancur Olson was right.
From San Francisco Chronicle:
The vote came after the football team spent an astonishing $4 million-plus on a campaign in a city with only 46,000 registered voters. Signs backing the 49ers sprang up in front yards across the community as the team carpet-bombed the city with TV spots, radio ads and campaign mailers.
It was a different story for opponents of the stadium, who managed to collect about $20,000, enough for some yards signs and some campaign handouts for the volunteers who knocked on doors.
Improvement in Fielding: Personal Evidence
In my previous post, I present some data that indicates fielding has improved over time. Here is a partial explanation.
My grandfather’s glove (circa 1910s)
My father’s glove (circa 1950s)
My glove (circa 1980s)
Do the Falcons Need a New Publicly-Funded Stadium?
In today’s AJC, Jeff Schultz lays a real egg in making the case for a new publicly-funded stadium for the Atlanta Falcons.
We obviously have more important needs in Atlanta than a new football stadium. The Georgia Dome is not falling apart. But if Blank wants to fund this project by himself, nobody should have a problem with that. If taxpayers are willing to pass an initiative for a special hotel-motel tax to help partially fund the project, nobody should have a problem with that, either. Yes, it would be wonderful if voters could be moved to vote for a hotel tax to help raise money for education and prevent 1,500 teachers from losing jobs. But realistically, that’s not going to happen.
We need other stuff that is more justified but, oh well, it’s going to happen anyway? Wow, how’s that for complacent apathy. Schultz is usually better than this. My guess is that a popular vote between schools and a new stadium would yield drastically different results. As of this moment, 78% of participants in an online AJC poll oppose a new stadium.
Luckily, his fellow columnist Mark Bradley picks him up with some help from the former head of the Georgia Dome Khalil Johnson.
“I love football and I love the Falcons,” Johnson said. “If they need and desire a new stadium, let the owner build it himself. In this current situation, to use tax dollars isn’t viable.”
Also this: “They’re having discussions of whether [an open-air stadium would cost] less than half a billion or more than half a billion. At the same time we’re closing schools, we’ve got transportation issues and we need to figure out Grady [Hospital] … It’s not a sports question. It’s an economic issue. There are a lot more pressing needs.”
Arthur Blank bought the Falcons in 2002, a decade after the Dome opened, and has been persistent in his desire that the building be updated. Johnson worked to placate the owner but knew the day would come when Blank would want a new building.
Said Johnson, who now works out of Douglasville as a consultant regarding events and venues: “What’s the pressing need? More money for the ownership. I don’t know how that lines up with what the public wants … I just question whether the public needs to give more when most of the benefits will go to a private owner.”
While many readers may be unfamiliar with Johnson, he is a big player in Atlanta sports. Kudos to Khalil, whom I had the pleasure to meet a few years ago, for standing up to politicians who are all too willing to dole out welfare to a billionaire.
Also, if you have been following the issue lately, have you noticed the new “open air” ruse being used to justify a new stadium? “Oh, we can’t use the Georgia Dome, we need something different.” I hope the people of Atlanta won’t fall for this.
What Is the Probability of Winning Back-to-Back Baseball Games?
At Decision Science News, Dan Goldstein asks the question, looks at the data, and finds a surprising result.
If a team wins on one day, what’s the probability they’ll win against the same opponent when they play the very next day?
We asked two colleagues knowledgeable in baseball and the mathematics of forecasting. The answers came in between 65% and 70%.
The true answer: 51.3%, a little better than a coin toss.
That’s right. When you win in baseball, there’s only a 51% chance you’ll win again in more or less identical circumstances. The careful reader might notice that the answer is visible in the already mentioned chart. The reversals of size 0, (meaning no reversal, meaning the same team won twice) occur 51,296 times per 100,000 pairs of consecutive games.
Statistician Andrew Gelman is not surprised and explains why.
I have to say, I’m surprised his colleagues gave such extreme guesses. I was guessing something like 50%, myself, based on the following very crude reasoning:
Suppose two unequal teams are playing, and the chance of team A beating team B is 55%. (This seems like a reasonable average of all matchups, which will include some more extreme disparities but also many more equal contests.) Then the chance of the same team winning both games is .55^2 + .45^2 = .505. Even .6^2 + .4^2 is only .52.
Two interesting posts, worth reading. Thanks to Jonathan for the pointer.
(Forthcoming October 2010)
