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…

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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My New Year’s Resolution

So I feel bad for digressing from this blog…if you’ve been checking in from time to time, waiting for that one day to come for me to post…I’m sorry to you…you unkown reader, if there are any of you out there.

So a New Year’s Resolution (b/c everyone does it…) of mine: blog blog blog.

I thought it’d be fun to act like some of the other 30 GM’s of the major leagues.

New York Mets

Omar Minaya has been trying to fix his team so the 2010 version looks like absolute nothing like the 2009 version that collapsed on all sides of the field, and by the end of the season even a die hard Met fan couldn’t name half of the roster that came to be. Something the Mets have been known for is giving huge contracts to players over their prime and aging fast. So, for you Minaya, “I vow to not give out so much money to these aging ballplayers..*COUGH* except Jason Bay [5 year, $80 million]…” …Whoops, right?

Cincinnati Reds

This doesn’t have much to do with the Red’s GM, except that he recently signed 22 year old pitcher Aroldis Chapman [a phenom Cuban who signed for $30 million]. By doing this, he’s putting this fine prospect into the hands/care of Dusty Baker in a couple years when he’s up with the big club. Dusty Baker, you ask? He’s kinda been at the helm of top pitching prospects who have suffered numerous injuries in their careers [think Mark Prior, Kerry Wood]. Not saying it’s his fault, but he has been known to overwork these pitchers. So, Mr. Baker, your resolution entails to play it safe with Mr. Chap, as well as the rest of your pitching crew, especially Edinson Volquez who’s coming off season ending surgery.

New York Yankees

Brian Cashman: I will continue to flex my yankee blue power [AKA $$$] and keep the core players in tact for several years, so that by 2020, 8 more rings will be won. YANKEE POWER!

Oakland Athletics

Billy Beane: Yeah..I’ll keep ahead of most GM’s by going against the curve. Remember back when I liked fat catchers like Jeremy Brown eight years ago? While you guys are doing that now, you’ll be eaitng my left coast dust while I pick up suave-looking guys for my team. Hang-ten.

I tried to be funny at the end. Can’t you picture Billy Beane all relaxed and embracing his native San Diego vibe? [On the contrary, I’ve heard he yells a lot when he’s mad…and throws things..]

PS – It’s IceBat’s first New Years! He says hello to all you “loyal” readers.