Archive for June, 2010
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)
