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Bannister has stong season debut


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I observe it every April and every year it is simply a cruel tease, but scoring is way down so far.

And yes, I know it is so early in the season that 4th and 5th starters have yet to see action. Just let me enjoy my low-scoring baseball while it still lasts.

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Bannister is one of the more stats-aware pitchers out there.

He intrigued some people with his statements in this interview with MLB Trade Rumours


MLBTR: Are you familiar with the Batting Average on Balls In Play (BABIP) stat? It's been suggested that the percentage of batted balls that drop in for hits may be largely out of a pitcher's control. What are your thoughts on that?

Bannister: I think a lot of fans underestimate how much time I spend working with statistics to improve my performance on the field. For those that don't know, the typical BABIP for starting pitchers in Major League Baseball is around .300 give or take a few points. The common (and valid) argument is that over the course of a pitcher's career, he can not control his BABIP from year-to-year (because it is random), but over a period of time it will settle into the median range of roughly .300 (the peak of the bell curve). Therefore, pitchers that have a BABIP of under .300 are due to regress in subsequent years and pitchers with a BABIP above .300 should see some improvement (assuming they are a Major League Average pitcher).

Because I don't have enough of a sample size yet (service time), I don't claim to be able to beat the .300 average year in and year out at the Major League level. However, I also don't feel that every pitcher is hopelessly bound to that .300 number for his career if he takes some steps to improve his odds - which is what pitching is all about.

One thing that I work a lot with, and that is not factored into common statistical analysis, is what counts a pitcher pitches in most often - regardless of what type of "stuff" he has. Most stats only measure results, not the situations in which those results occurred. In the common box score, an RBI is an RBI, but it doesn't show the count, number of outs, and number of runners on base when it occurred. For me, the area where pitchers have the most opportunity to improve or be better than average is in their count leverage.

There's a lot more there, I recommend clicking through and reading the whole thing.

A Royals fan wrote a very interesting three part in-depth review of Bannister, partially in response to his BABIP comments.


Amazingly enough, Bannister actually responded in the comments to the article:


First off, I wanted to say thanks for taking the time to write these articles. There are a lot of other pitchers out there that you could have evaluated, but I know that people are interested in the way that I approach the game of baseball.

Normally, I refuse to comment outside of formal interview requests (and readers, don’t expect me to get into a discussion here), but this series was definitely worthy of a response because of its objectiveness and detail (and from a professed Royals fan).

With regards to the MLBtraderumors.com interview Mike, I didn’t mean to cause any confusion, but I was including strikeouts in my percentages (and they were intended to be batting averages only, not BABIP’s). My last line was also incorrect (I need an editor) with regards to balls in play, because they were simply overall batting averages only.

The point of answering that question was not to prove why I beat the league average, but how any pitcher could hypothetically do it. I don’t claim to be able to do it every year (or ever again), but it’s a challenge to find ways that it could be done by giving yourself statistical advantages over the league.

Moving on to this series, you have to come to many of the same conclusions that I have with regards to my own pitch repertoire. I have been evaluating PITCHf/x data since the middle of 2007 because I feel that it is a far superior system to watching video. Video is still important in learning hitters tendencies and body language, but for pitchers, PITCHf/x is the best. Even though an easier system for managing and graphing the data needs to be developed, the possibilities are endless.

Again, there's more there, I recommend reading all of it.

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