First of all, Happy New Years everyone! Hope you had a fun time doing whatever you do on these holidays. IceBat was a party pooper and decided to sleep all night in his freezer bed.
As you may remember, back in December I had to complete a couple of final projects. One idea that I didn’t use dealt with the concept that home runs are not always equal in displaying a player’s power or batting skills. We equate overpowering shots to right-center field by Prince Fielder with balls that graze the more-than-generous right field wall of Yankee Stadium. What I mean is, there are more variables than just pure distance that go in to determining whether or not a fly ball becomes a home run. With this in mind, I can run a regression model to compute the probability that a flyball will turn in to a home run. I received a large data set (many thanks to Greg Rybarczyk at Hit Tracker) that spans the 2006-2008 seasons for three players (Adam Dunn, Manny Ramirez and Jason Bay). The data includes observational and calculated data (in the similar ways of Hit Tracker’s data – i.e. True Distance or Elevation Angle, etc.) on every long fly ball the players hit, totaling a tad over 700 observations. Included are variables such as what ballpark the ball was hit in, date & time, and the outcome of the play (single, double, home run, out, etc.)
As you can tell from the graph above, the outcome of the play isn’t so clear when only given the elevation angle and distance traveled summary of the ball. All the outcomes are generally scattered so that we cannot conclude any real correlation. I superimposed two boxes to easily show how similar balls can have different outcomes. In the case of the right-side box, a slightly different elevation angle could mean the difference between a home run and a fly ball.