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After the 2010 season, Jeff Zimmerman posted a series of articles on FanGraphs using regression models to try and predict the probability that a starting pitcher would get injured. It makes a lot of sense that he’d focus on pitchers. After all, the saying is “pitchers break.” Yet for the 2016 Mets, “everyone breaks” may be more appropriate. Every Opening Day starter except Curtis Granderson and Michael Conforto spent time on the disabled list. Can we predict how likely hitters are to get injured in 2017? To try and answer this question I combined injury data from Baseball Prospectus’ transaction tracker with Retrosheet’s play-by-play data from 2010-2016. I’m going to do a lot with this data set, but I will start with the yes/no question of whether someone goes on the disabled list because it is the easiest to answer. Zimmerman used a logistic regression model, which is specifically designed for outcomes that either occur or do not occur. I’ll do the same, and explain a few of the most important technical details as we go, but save as much of the #gorymath specifics for the footnotes at the end as I can. Zimmerman’s model for pitchers, based on 2002-2010 data, is remarkably straightforward. He found that the older a starting pitcher is, the more likely that pitcher gets injured. Pitchers who had been injured more of the previous three years are also more likely to get hurt this season. Controlling for these variables, pitchers who made more starts over the last three seasons were more durable than their peers. I started out trying something similar for hitters. The only change I made was loosening the minimum standards for games played / years of track record to get in the regression model, because higher standards didn’t make the models any more predictive. My minimum is two seasons and at least 100 games played in my dataset. Setting this more inclusive standard makes sure I don’t pre-emptively exclude a player like David Wright because of his career-threatening injuries. My initial results were very different than Zimmerman’s: (full model below) Prior years with injury (out of the last two): Increase probability of injury this year around 6.5 percent per prior year with an injury. Logistic regression is based on multiplying the effect of each independent variable instead of adding, so any number like 6.5 percent is a rough estimate. Age at midpoint of season: Each additional year of age increases a hitter’s probability of injury by around 0.36 percent. This is much smaller than we’d expect and not close to statistically significant (p = 0.236) Games played, last two years: Each additional game played increases a player’s chance of getting injured by 0.048 percent. It’s tiny, but considering the wide range of games played this was still statistically significant at the 0.001 level. This is when I started backing away from the computer very slowly. Is there a bug somewhere? Did I make some sort of programming error? It’s almost impossible to