Page rendered in 0.2835 seconds
59 querie(s) executed
— Where BTF's Members Investigate the Grand Old Game
Monday, August 19, 2002
Win Values: A New Method to Evaluate Starting Pitchers
Your opinion of starting pitchers will not be the same.
I have developed a new system to evaluate the contribution a starting pitcher makes to his team.? Let me contrast my approach to the approach reflected in a pitcher?s Wins Above Average (WAA) figure.? WAA looks at a pitcher?s ERA for a season and the number of innings he pitched, and together with information on the pitcher?s home park derives the number of runs that the pitcher saved his team over and above what a league average pitcher would have allowed.? The final step of converting these saved runs into a number of wins is done by estimating how many additional runs, on average, lead to an additional team win.
WAA is a very compelling stat and has been the backbone of pitcher evaluations for many years.? My system takes into account two additional factors in evaluating a starting pitcher.? First, I evaluate a starting pitcher?s contributions on a game-by-game basis rather than simply evaluating his end-of-season stats.? After all, an average such as ERA can obscure as well as reveal information.? A team for whom the starting pitcher gives up 0, 1, and 17 runs in three starts is likely to win more games than if the pitcher gives up 6 runs in each game, even though the average runs allowed is the same in the two cases.
Evaluation schemes for hitters are almost always performed using seasonal data rather than game-by-game (play-by-play) data.? The reason is that hitters come up to bat 600-700 times over the course of a season.? This represents a large enough sample for things to generally ?even out? over the course of a season.? Evaluations of hitters based on seasonal stats are quite consistent with more detailed evaluations based on play-by-play data.? Therefore, it is not worth the extra effort to utilize more detailed evaluation methods for hitters.
Pitchers, on the other hand, start only about 30 games a season in today?s era.? 30 games is not enough for things to ?even out? over the course of a season.? We will see that, contrary to hitters, the evaluation of pitchers using game-by-game data can often be significantly different than the evaluation using seasonal stats.
Doing the evaluation on a game-by-game basis requires a great deal of detailed data as well as an entirely new set of machinery.? The rules of thumb that apply to seasonal averages (such as the number of runs needed for an additional win) no longer apply on a game-by-game basis.? In addition, depending upon what elements of the game you include in the evaluation, probabilities may need to enter the fray.? For example, if a starting pitcher gives up 3 runs in a game, how should we evaluate this outing?? If you choose to abstract from his team?s actual offensive run support, you would try to estimate how often the pitcher?s team would have won a game allowing 3 runs based upon the league average distribution for its own runs scored.? Clearly, the fewer runs allowed, the more likely the team would have won the game with average run support.? While I think Michael Wolverton?s game-by-game Support-Neutral Win (SNW) system is an improvement to the seasonal-based WAA system, I don?t think he goes far enough.?
Second, my system takes into account how many runs the pitcher?s own team actually scored in the game.? Clearly WAA or SNW do not take into account a pitcher?s run support.? Those systems purposefully abstract run support so as to evaluate a pitcher solely on what he has control over.?
While this sentiment is laudable, it does not necessarily lead to the most accurate evaluation of a pitcher?s actual contribution to his team?s actual winning of baseball games.? One or two examples will suffice.? A pitcher who gives up 2 runs in a 3-2 win contributed significantly more to his team winning the game than a pitcher who gives up 2 runs in a 14-2 win.? In the first game, the team that scored only 3 runs could easily have lost the game with league average pitching, whereas in the second game the team that scored 14 runs would very likely have won the game even with league average pitching.
Consider the flip side of the coin.? Suppose a team loses a game 12-0.? The starting pitcher should not shoulder a large portion of the blame for losing the game, despite giving up 12 runs.? Even with league average pitching (say allowing 5 runs), the team would not have come close to winning since it did not manage to score any runs.
Each of the evaluation methods described above, WAA, SNW, and my new Win Value stat, attempts to estimate how many extra games a pitcher?s team won due to his contributions over and above the contributions of a league average pitcher.? Acknowledging that run support can affect the importance of a pitcher?s runs allowed seems a definite step in the right direction.?
The confluence of personal computers, the internet, and the electronic availability of baseball data allows more accurate formulas to be developed.? WAA uses a player?s seasonal data, and therefore is necessarily a more general formula.? SNW and Win Values both depend upon game-by-game data, and are therefore more specific and more accurate in what they measure.
Stats such as WAA and SNW are good stats and are very good predictors of future success.? The reason is that they abstract from the pitcher?s run support which is notoriously variable from season to season.? However, this aspect that makes these stats good predictors (looking forward) is the reason that they may not be very good descriptors (looking backward).? For only by considering a team?s run support can you accurately evaluate a pitcher?s actual contribution to his team actually winning the game.
Win Values is the only stat that properly integrates run prevention information with win-loss information.? Win Values attempts to reflect the strengths of both types of information in a single stat.? By considering what actually happened in each game, Win Values is a very good descriptive stat.? When I look in a Baseball Encyclopedia and see that Sandy Koufax is deemed to have contributed 6.0 wins to the 1966 Dodgers, I want that figure to be the best possible estimate.? I have designed Win Values to be the best possible estimate.
Part 1: Introduction
? This is essentially what Michael Wolverton does in his Support-Neutral Wins.? I should also say that my system is similar to Doug Drinen?s Win Probability Added stat that appeared in the Big Bad Baseball Annual.
? After all, a starting pitcher is often told to ?keep his team in the game? or to ?give his team a chance to win?.
You must be logged in to view your Bookmarks.
Loser Scores 2015
(12 - 2:28pm, Nov 17)
Loser Scores 2014
(8 - 2:36pm, Nov 15)
Winning Pitcher: Bumgarner....er, Affeldt
(43 - 8:29am, Nov 05)
Last: ERROR---Jolly Old St. Nick
What do you do with Deacon White?
(17 - 12:12pm, Dec 23)
Last: Alex King
(15 - 12:05am, Oct 18)
Nine (Year) Men Out: Free El Duque!
(67 - 10:46am, May 09)
Who is Shyam Das?
(4 - 7:52pm, Feb 23)
Last: RoyalsRetro (AG#1F)
Greg Spira, RIP
(45 - 9:22pm, Jan 09)
Last: Jonathan Spira
Northern California Symposium on Statistics and Operations Research in Sports, October 16, 2010
(5 - 12:50am, Sep 18)
Mike Morgan, the Nexus of the Baseball Universe?
(37 - 12:33pm, Jun 23)
Last: The Keith Law Blog Blah Blah (battlekow)
Sabermetrics, Scouting, and the Science of Baseball – May 21 and 22, 2011
(2 - 8:03pm, May 16)
Last: Diamond Research
Retrosheet Semi-Annual Site Update!
(4 - 3:07pm, Nov 18)
What Might Work in the World Series, 2010 Edition
(5 - 2:27pm, Nov 12)
Last: fra paolo
Predicting the 2010 Playoffs
(11 - 5:21pm, Oct 20)
SABR 40: Impressions of a First-Time Attendee
(5 - 11:12pm, Aug 19)
Last: Joe Bivens, Floundering Pumpkin