Archive for Meta Analysis

In 2006, ESPN Home Run Tracker (then known as Hit Tracker Online) launched to provide us stat fiends with data about every home run hit in baseball. It wasn’t enough anymore to know that Ryan Howard led all of baseball with 58 long balls. No, we craved more information, and more information we got, in the form of distance, speed off the bat and a classification into a specific bucket depending on how far past the fence the ball flew. For years, I have been referencing a hitter’s percentage of “Just Enough” or “No Doubt” home runs, operating under the assumption that they have some predictive value. Although I had done a small scale study several years ago which did seem to support this thesis, nothing exhaustive has been done since. Until now.

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Phew. After a nearly four and a half hour snake draft online, I have officially completed the earliest draft in my fantasy baseball career. LABR stands for League of Alternative Baseball Reality, and along with Tout Wars, is one of the two most publicized “expert” leagues. In the past, LABR has had only two leagues, an AL-Only and NL-Only, with both formats using a live auction in Arizona to select players. Last year, a mixed league with an online draft was formed and I participated in the inaugural season as well. With that background out of the way, let’s get into more league specifics.

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**For those of you who have listened to the Fantasy Baseball Roundtable radio show in the past, I wanted to share that we are officially back on the air! And if you have never listened, well here’s your chance to hear my manly voice talking about nerdy stats. Listen live every Wednesday night at 9-10 PM EST.

The quest continues! On Monday, Chad Young and I set out on a journey to try utilizing the fly ball and home run distances and angles found on Baseball Heat Maps in an attempt to answer several questions regarding a hitter’s HR/FB rate. As expected, we found a strong correlation between it and batted ball distance. But, distance alone wasn’t telling us the whole story. Chad decided to incorporate batted ball angle and the previous season HR/FB rate and that certainly improved our equation. Then yesterday I took that another step further and found that including the HR/FB rate from two seasons ago was even better. But, I wasn’t satisfied. A hitter’s HR/FB rate in 2012 should not be affected by how he performed in the metric in previous years. We pinpointed what we thought may be one of the major hindrances and that was the way the angle data was presented as an average.

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On Monday, I set the groundwork for a quest to try to predict a hitter’s HR/FB ratio utilizing the new data first made available last year that tells us the average distance of a hitter’s batted balls. Our goal was to answer several questions:

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Last week Bill Petti did battle with Marcel and came away bruised, if not beaten. Numerous mathematicians and would-be projectionists have done the same: few strategies, no matter how cunning, have been able to provide a long-term advantage over Tom Tango’s painfully simple forecasting system. But Marcel goes beyond taunting the experts; he bares his nasty little teeth at us all.

I began using projection systems in my fantasy preparation a decade ago; I lined up Marcel, CAIRO, and CHONE and let them help me break ties for the rankings I was unsure about. Many of you probably do the same. Even then, I mistrusted Marcel.  Metaphorically speaking, if the Bill James projections are the mother who pats your head and makes you hot chocolate when you make a mistake, Marcel is the crotchety old uncle who snarls at you to stop being such a screw-up. That guy hated everyone, but I could never bring myself to get rid of him.

He hasn’t changed. Here’s how Marcel felt about Mike Trout, blue-chip prospect and demigod-in-process, in 2012:
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As a forecaster of baseball player performance for usage in my fantasy leagues, I am always looking for new data to incorporate into my projections and new methods for predicting outcomes. Home runs are a result that excites all, even chicks dig ’em. But how can we better determine whether Chase Headley’s breakout, for example, was for real? The answer may lie in newly available data collected by our own research extraordinaire Jeff Zimmerman.

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It’s 2013. I just drafted Lucas Duda again.

I just can’t quit him I guess. No, I’m not a glutton for punishment. I can give you the caveats — it was pick #328 in a 14-team league with five outfielders — and we can talk about Lucas Duda specifically, but that might not be the point in the end.

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Most of the time, you can determine if pitchers were unlucky over the course of a season by looking at few factors like LOB%, BABIP or HR/FB%. Today, I am going to look a little further to find pitchers who may have been even more lucky or unlucky on batted balls by looking at their wBABIP.

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A couple of weeks ago, I was helping fellow writer, Chris Cwik, look for possible reasons James McDonald fell off the cliff in the second half of the 2012 season. As Cwik pointed out, the 27-year-old McDonald started falling apart after he threw 122 pitches for his only complete game (CG) of the season. I decided to see how young pitchers performed after throwing just one complete game during a season.

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The Arizona Fall League as long been considered prospect finishing school. Most recently uber-talented youngsters Mike Trout and Bryce Harper decimated the pitching-light league prior to ascending to Triple-A and then the Major Leagues. But the 2012 crop in Arizona doesn’t feel as talented as previous years had. Is the talent level in the league dropping?

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