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You are commenting using your Facebook account. Notify me of new comments via email. Notify me of new posts via email. Blog at WordPress. The graphs below are on identical scales: Cabrera was better in in every situation and by each statistic except for his average very close and slugging percentage with no one on base. Share this: Facebook Twitter Email. Like this: Like Loading Paul July 22, at pm Reply. What can you do? Leave a Reply Cancel reply Enter your comment here Fill in your details below or click an icon to log in:.
Email required Address never made public. Name required. New English D Search Search. Follow Following. Hardy is perceived by some to be in a state of athletic decline.
The player who slugged 30 home runs as recently as , for example, belted just eight last year, and injuries have forced him to miss about 30 percent of his team's games over the past two years. But there are many ways to measure success in a sport as complex as baseball, and if a team of computer scientists at the Johns Hopkins University is to be believed, Oriole fans might have reason to feel hopeful about the two-time All-Star.
A study led by Anton Dahbura, a research scientist in the computer sciences department at Johns Hopkins, revealed a striking dichotomy: While Hardy was all but useless as a hitter in when the outcome of games was already more or less decided, he hit nearly points higher — more than. A lifelong baseball nut, Dahbura wrote with the help of Jaewon Lee and Evan Hsia, student researchers and engineering undergraduates who also love the game.
The project examined how every major league hitter performed last season when, by the authors' calculations, either team in a given game had at least a 95 percent chance of winning. Dahbura said it's beyond the study's scope to assign definitive meaning to such figures, but the baseball fan in him can't help speculating that they open up new lines of inquiry in a sport that is already one of the most rigorously analyzed in the world.
The goal of the study, Dahbura said, was to raise awareness about the fact that not all at-bats during a season are equally important. Hardy's performance was actually a striking exception to the trend the team set out to explore. Dahbura, 56, is one of those baseball geeks lucky enough to have a passion and a gift for mathematics and statistics.
It's a blend of talents in growing demand in baseball front offices as franchises increasingly seek to blend the benefits of computer-aided analytics with the intuitive wisdom of more old-fashioned scouting. A former player, coach and manager at Johns Hopkins, Dahbura — now executive director of the Information Security Institute, a center for cybersecurity education and research within the university's computer sciences department — said he first became interested in how players perform in meaningless-game situations in and , when temperamental slugger Albert Belle played for the Orioles.
He always suspected Belle tried harder, upped his game and padded his personal stats in low-pressure situations that mattered little to his team. There are essentially two ways to approach the problem with RBI as a statistic.
First, there is very little evidence that timely hitting or clutch hitting is a skill separate from regular hitting. Second, even if those are real skills, RBI is a very crude way to measure that skill and you should use something else. Before we go any further, we need to decide if we value getting hits with men on base, particularly with men in scoring position.
Should it matter if a hitter has a. Is one player better than the other? In order to make the argument that the RISP hitter was better, you would have to argue that he was able to influence the timing of his hits. But part of this is philosophical and a matter of preference. Do you want to give credit to hitters who happen to collect a lot of well timed hits?
When it comes to determining retrospective value, it might make plenty of sense to do this. If you care about context neutral performance, our discussion is over because RBI are conditional on your teammates other than RBI via HR by definition. There have to be men on base for you to drive them in. But if you do have some interest in context dependent numbers, we need to move forward. Does RBI do a very good job of that? Importantly, not all players have the same number of PA with runners on each base.
Castellanos came to the plate with runners on first and second more often than Trout and a runner on third the same percentage of the time. Obviously, Trout was better in his opportunities than Castellanos, but the illustration suggests that even without considering the number of outs, some hitters have more chances to drive in runners than others. You might want to reward a hitter for their performance in each type of situation, but surely not for the number of those situations.
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