Ice Bat decided to leave his lair for a bit. He’s only checking in here once in a while. Actually, to be honest, he tends to like his new home. In other news, I’ve been really fortunate to have the opportunity to write for The Hardball Times, so that’s where I’ve been lately. Follow my baseball musings there, if you’ve got a moment. I’ll be sure to check around here though, don’t worry.
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…
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.
Hey folks. Sorry to keep you all at bay these past couple of weeks. IceBat was…sick.
Another final report I wrote was based on MLB Free Agent contracts, and how or if we can model their outcomes based on prior years’ performance. The contract terms I used as response variables were contract length (in years) and average salary per season. I also focused on hitters and how metrics like Batting Average, On-Base Percentage, Home Runs, or even advanced ones like Wins Above Replacement (WAR) can help us see what the market is favoring and at what price. The reason to use different sources of metrics is to see what MLB Executives are listening to: traditional statistics or those advanced ones used by the Sabermetric community? Using models like this can also have some predictive powers.
Crazy schedule for me these next few weeks. I’ll try to stay active, I promise. If not, IceBat will take over, but I’m guessing he won’t say much (he’s pretty shy and likes to chill in the corner of my room). Anyways, I thought I’d share a recent report I did for my times series class. It’s about the general shift of runs scored per game (by one team) over the years of MLB’s existence. If you have some time (and enjoy a few technical terms) I’ve uploaded a link below. Happy December holidays!
About two weeks ago, the Oakland Athletics won negotiating rights (through a $19 million bid) with Hisashi Iwakuma, who has played in the Japan Pacific League his entire career. Afterwards, GM Billy Beane made a couple of moves to suggest the A’s were at least 75% sure they would sign Iwakuma. Unfortunately, talks have stalled between the two sides. There are numerous reports suggesting Iwakuma wants Barry Zito (and we all know how well that went for the Giants) money or that the A’s are unwilling to negotiate beyond a $3-4 million average salary base. Either way, one of the sides has been castrated by the media as the demon.
But who’s right here? Is there enough past history of Japanese pitchers coming to the American market to justify a $15+ million average salary? Or can the A’s justify giving Iwakuma the same salary he received in Japan because of the high cost of the negotiating bid? I’ve listed some recent Japanese pitchers who made the move to the big leagues, and some meaningful figures.
Ever wonder the exact location, movement, speed, rotation, spin angle of a pitch? With Pitch F/X, every ball thrown in the majors is calculated to a science. It’s pretty awesome but even after spending hours looking at the data, it can be a bit confusing as to what the variables mean and how they are meaningful. I’ll try to explain most of the variables to the best of my abilities. I’ll be using F/X data from Dallas Braden’s perfect game on May 9, 2010 against the Tampa Bay Rays.