Johnny B. Goode by Greenlee March 4, 2013 Controlling the run game, pitcher fielding and ERA Run & Glove Johnny Cueto has been mocking his peripherals ever since his big league debut. For the most part FIP serves as a terrific gauge for pitcher performance, but in 2011 Cueto made FIP look like a heart monitor trying to explain the weather. On what most consider a separate note, base runners have a healthy and robust fear of Cueto’s pickoff move, which is one of the best in the show. FIP measures outcomes a pitcher can control (home runs, walks and strikeouts) and chalks the rest up to random variation. Studies have shown that stolen bases contribute relatively little to run creation and perhaps on that basis the ability to control the run game has generally been ignored or deemed overrated. It is difficult, however, to ignore the six runs Cueto saved the Reds via his contributions to controlling the run game in 2012. By contrast, A.J. Burnett’s inability to control runners cost the Pirates four runs. The typical scale is that 10 runs amount to one team win – and teams will pay about $5 million per win. Acknowledging run game control cannot fully explain how Cueto has routinely outperformed his peripherals, just as it cannot wholly explain Pittsburgh’s inability to keep pace with Cincinnati in the NL Central last season. It does, however, get us closer. Incorporating a pitcher’s fielding ability proved of comparative importance in explaining and predicting performance. Here we’ll turn to Mark Buehrle, whose glove has saved four runs per year since 2004, and among fellow hurlers the fast-working lefty has been one of the decade’s most steadily superb fielders. FIP underestimated Buehrle in eight of the past nine seasons, slighting his ERA by an average of .30 per year over that span. Numbers The numbers indicate that a pitcher’s defense and ability to control the run game should both be considered in assessing and forecasting the pitcher’s value. Focusing on seasons in which pitchers hurled 100 same-league (AL or NL) innings from 2003-2012 (n=1400), I ran a multiple linear regression to create a formula (“MBRA”) incorporating run control (rSB) and pitcher fielding (rPM) on top of line drive and infield fly ball percentages (credit to BABIP guru Steve Staude) and a regressed take on FIP. MBRA = (55.25*HR + 14.05*BB – 8.57*K)/TBF – .041*rPM – .056*rSB + (5.71*LD – 8.27*IFFB)/(LD+GB+FB) + 2.34 Correlation Mean Absolute Error MBRA .7750 .4570 FIP .7647 .4697 BERA .7477 .4922 tERA .7472 .5616 MBRAT .7216 .5394 xFIP .6451 .5649 SIERA .6290 .5768 MBRA is engineered to properly credit pitchers who can field and control the run game. When I subtracted MBRA from FIP to locate the pitcher-seasons that benifitted most from my formula, I was encouraged seeing Buehrle show up twice in the top ten, and five times in the top 100 (again, that is out of 1400). Next, I looked at seasons in which pitchers threw 100 same-league innings in consecutive seasons from 2003 to 2012 (n=791). This time I ran a regression to create a model suited to predict a pitcher’s ERA based on his previous year’s statistics. MBRAT = (20.12*HR + 7.13*BB – 6.7*K)/TBF -.025*rPM -.034*rSB + 2.37*ZC% + 2.22 Correlation Mean Absolute Error MBRAT .4526 .6498 SBERA .4398 .6582 BERA .4347 .6634 xFIP .4220 .6803 MBRA .4198 .6987 FIP .4162 .7024 ERA .3630 .7920 MBRAT stands tall on the lofty pinnacle of public forward-looking ERA estimators, and if you factor in the percentage at which pitchers throw over the edge of the plate (EDGE%) its correlation jumps even higher (.4621). Unfortunately, I only have Edge% data from 2008 to 2012 (n=362) and cannot yet justify its inclusion. On Deck I will create expectations for pitchers with fewer innings pitched and convert my findings to a WAR measure that may serve as a middle-ground between fWAR and rWAR. I also stumbled on a potentially significant relationship between pick-off attempts and strand rates that may work its way into future formulas.