The Orioles shocked the baseball world by making the American League playoffs last season, based largely on a 29-9 record in one-run games. This .763 winning percentage in one-run games was the best in baseball and had every analyst who knew how to calculate a Pythagorean record screaming, “Lucky!” Was the Orioles record in one-run games lucky? Or, the better question is, how much of it was luck?
“Luck” is a dangerous word in baseball analysis. If a hitter has a .450 BABIP or a pitcher has a 3.5% HR/FB, us saber-minded analysts usually chalk it up to luck and move on. To equate the difference of the rate from the league average is a disservice to the players. Oftentimes, some of that middle-ground can be explained. For example, a few years ago I looked at Matt Cain’s HR/FB rate and found that much of his “luck” can be attributed to inducing a lot of infield fly balls and out-of-the-zone contact.
By modeling a team’s record in one-run games, it becomes possible to find if there are certain types of teams that are just better equipped for tight games. The goal is to better determine how much of one-run record is skill, and how much is still unexplained.
I used a logit model, with the team’s win-loss record in one-run games as the dependent variable. I used 2007-2011, holding last year out of the sample to make projections. The independent variables were measures of the team’s skills for hitters, starters, and relievers and the team’s park effect. Included were measures of strike out rate, walk rate, power, fielding, and baserunning.
The resulting model showed that there are certain team attributes which lend themselves to better records in close games. However, the model had limited explanatory power. There were only three significant coefficients: isolated power for hitters, and strikeouts per nine and walks per nine of relievers. This means that those three variables are the most important for explaining a team’s one-run winning percentage. The reliever skill is not surprising, but it is unexpected that hitter power is more important in one-run games than contact rate or walk rate. It is also surprising that baserunning and fielding skill have almost no effect on one-run games.
I also ran the same model for greater-than-one-run games for comparison. The below graph shows the three significant variables’ effect on one-run games compared to their effect in greater-than-one-run games. Understandably a team’s relievers have more effect on one-run games than in all other games.
This model helps make sense of the 2012 Orioles and shows just how much we can’t explain. On one hand, the skills that this model identified were strengths for Baltimore. The model projects that the 2012 Orioles would have had a .647 winning percentage in one-run games, not bad considering they only had a .512 winning percentage in non-one-run games. On the other hand, the projection is still 116 points lower than Baltimore’s actual performance.
Is that gap luck? Certainly there are variables that my model does not include because they are difficult to measure; managerial skill, bench composition, clutch. Even with those and others included, a model would be unlikely to project any team for a .763 winning percentage in one-run games.
It would be hard to argue, at least from a stats perspective, that the 2012 Orioles weren’t lucky on some level. However, their magical season cannot just be dismissed. Baltimore had a perfect storm of team skills which led them to an excellent record in one-run games. Using a model, we can better explain how much of their performance was due to those skills and how much was, well, TWTW.
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