Graphs, Hitters, IceBat

Home Runs Follow Up

As I stated in my last post, predicting home runs from fly ball data could be another component of how we compare home run hitting abilities of players. As you can see from the graph above, I plotted the fitted values from the model split by each hitter. Most of the fitted values tend to be at the extremes, which coincide with logistic model properties. As for comparing hitters, it looks like Adam Dunn has the most predicted fly balls becoming home runs, while Jason Bay is on the other side of the fence.

In case you were wondering, here are each player’s mean HR prediction for fly balls: Adam Dunn (~50.4%), Manny Ramirez (~39.2%) and Jason Bay (~34.0%). No surprises there really. The order of these hitter’s HR predictions coincide with their career HR/FB rates and our general notion of their hitting style. Dunn swings for the fences or strikes out otherwise, while Manny and Bay display more use of the entire field (though that may be an optimistic statement about Manny’s capabilities now). While Hit Tracker does an excellent job telling us how far home runs really went, and what park/weather factors impacted the ball’s real projection, we don’t really have an idea of what those factors had on non-HR fly balls. Though I am speculating, maybe this topic of research will increase once the data from Field F/X (previewed in THT’s 2011 Annual) is published. Only time will tell…

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