Archive for Meta Analysis

Although we have done a pretty good job determining what underlying statistics can combine to formulate a strong expected strikeout percentage metric, unfortunately the same cannot be said for a pitcher’s walk percentage. Though one would assume the majority of a pitcher’s control skill would show up in his ability to throw strikes, whether by throwing first pitch strikes or throwing the ball inside the strike zone, that just isn’t the case. But of course that doesn’t mean that this data is useless.

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Yesterday, I unveiled another use of a pitcher’s BABIP, besides quickly identifying who has benefited from some good fortune and whose luck should improve. I focused on the K/9 rate and how a low or inflated BABIP could dramatically affect it as it increases or reduces the number of hitters a pitcher will face each inning, giving him additional or fewer opportunities to strike batters out.

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We all know by now how to best utilize a pitcher’s BABIP data during and after the season. For the most part, a pitcher with a BABIP significantly below the league average is probably going to see that metric rise, while a pitcher with a mark above the league average will likely post a better one moving forward. But that’s not the only use of BABIP. It also affects a pitcher’s strikeout and walk per nine innings rates. This is why many now prefer to use strikeout and walk percentage.

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Yesterday, I took a look at the potential pitcher strikeout percentage surgers based on the regression equation I developed and shared with all of you last week. Today, I will look at the opposite side of the coin — those pitchers whose expected strikeout percentages are significantly lower than their actual strikeout percentages.

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Last week, I published the results of my expected strikeout rate study. I used the looking, called, and foul strike rates data from Baseball-Reference.com. It is important to understand that the regression formula I concluded with is meant to estimate what the pitcher’s K% should be given his different strike rates. This is not meant to be predictive. Instead, in smaller sample sizes, it might be a more accurate picture of the pitcher’s true strikeout ability so far, which would then help shape your projection of him going forward.

With that said, here are the top 10 pitchers whose xK% is higher than his K%.

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There have been many attempts to develop an expected pitcher strikeout percentage (xK%) formula, usually involving one of my favorite metrics SwStk%, perhaps average fastball velocity, and maybe another statistic or two. All of the regression equations did a fairly decent job, but there were always outliers, and I was beginning to see a commonality between them.

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Batters always seem to heat once summer rolls around. While some players may like to play in the summer versus the cooler months, the warmer weather also increases hitter production. Over the the rest of the year, our authors will be giving our readers a heads up for any possible games which may be hitter or pitcher friendly.

Weather has a significant effect on the distance a ball travels. In his book, the Physics of Baseball, Robert K. Adair noted a 10 degree temperature increase leads to an extra four feet on a 400-foot fly ball and each one mile per hour following wind speed means an extra three feet. For example, a fly ball hit at Wrigley field will travel 38 feet further on a day with the wing blowing out five mph and at 80 degrees versus an early spring day when the wind is blowing in five mph and the temperature is 60 degrees. This information shouldn’t be used to start Wade Davis over King Felix, but if an owner is deciding between a couple of similar players, it could be used as a tie-breaker.

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Yesterday I discussed the leaders in average home run plus fly ball distance, some of which were surprises and validated the player’s early power surge. Instead of simply looking at the laggards today, which include such obvious names as Juan Pierre and Skip Schumaker, I’m just going to cherry pick some of the surprising names toward the bottom of the ranking list.

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Earlier this year, I finally derived an official equation that highlighted the value batted ball distance has and its strong correlation with home runs. While we still don’t have a lot of vital information, such as how quickly batted ball distance stabilizes, it is probably still worth taking a peek at the leader board. It might very well explain some of the early home run spikes. Whether or not the hitter will continue hitting balls that far on average is unknown, but it will at least validate what has already happened.

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Since I develop my own player forecasts, I am always looking for better ways to use the advanced metrics to project the more basic ones. You may remember the quest Chad Young and I engaged in earlier this year to predict HR/FB ratio using batted ball distance. That didn’t go as far as I had hoped, but it did highlight the value of the batted ball distance data. When I started doing the research for my Contact% articles last week, I figured there might be a combination of plate discipline metrics, including Contact%, that does a good job of estimating a hitter’s strikeout percentage. I was right. Of course, this is nothing earth shattering, as Jeff Zimmerman reminded me that he looked at that very same concept late last season, which clearly had already escaped my brain. Since I had the data and regressions done, I decided to do a version 2.0 of formulating a hitter’s expected K%.

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