Graphs, Hitters, IceBat

Home Runs: They May Deceive You

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.

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Coolest Website

I discovered the most awesome baseball data website ever (besides Fangraphs), and I wanted to share with you.

Hit Tracker is a website that tracks every single home run hit in the majors. With every home run, it gives an analysis of the ball’s path, from hitter to fan. The main categories of stats to look for on a player’s profile of home runs are True Distance, Speed of the Ball off the bat, the angle of elevation and wind/temperature conditions in the ballpark. All these statistics will give you a rough estimate as to whether the player was lucky to have it land out of the park or not. Another added bonus to the site is that they categorize each home run into: “Lucky”, “Just Enough” or “No Doubts”. This allows you to gauge a hitter’s power beyond just the amount of home runs he has, or by a slugging percentage.

The variation in true distance is drastic, yet the same outcome occurs. Jimmy Rollins can get lucky by hitting a ball 380 feet out in Yankee Stadium that goes out, while Mark Reynolds can hit one 480 feet for a ‘no doubt’ trajectory, yet the return is the same. Seems a little unfair, no? Wouldn’t it be cool for a player to scout wind/temperature stats and know which field and at what vertical elevation to get it pass the fence? For football, kickers scout wind and trajectories of balls all the time, wouldn’t it be cool for hitters to do the same? I guess it’s different though, since the hitter doesn’t have full control of what the pitcher serves up.

Check it out sometime. They also have links to watch the home run clips.