Archive for Meta Analysis

Over the last couple of weeks, I have talked a lot about a hitter’s batted ball distance and the additional components included in my xHR/FB rate formula. The cool thing about learning about these metrics is now it’s a bit easier to understand what drove a particular hitter’s power surge or what led to a decline in home run output. As I am in the middle of my hitter projections, I have stumbled upon players with some interesting trends.

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This past season, I introduced a xBABIP equation which uses Inside Edge’s hard hit rate and player speed. Well, I have been a little slow on any updates. I have finally gotten around to getting all the 2014 values in one place.

When creating the xBABIP values, I found it correlated more to the next season’s BABIP then any other easily created formulas. Here are the 2014 values with some thoughts on some individual players.

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Yesterday, I shared with you the leaders and laggards of one of the components of my xHR/FB ratio, average absolute angle. Today, I check in on the leaders and laggards of the other non-distance variable of my equation, the standard deviation of distance (SDD). The SDD is much more stable year to year than the average absolute angle, coming in a bit below batted ball distance with a 0.50 correlation.

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So we learned that a hitter’s average absolute angle isn’t very stable from year to year, but that doesn’t make it any less interesting to dissect. What is this strange sounding metric you ask? Think of the field broken into equal segments, with center field at 0 and each side of the field a value increasing or decreasing from there. According to the definition at Baseball Heat Maps, -45 is the left field line and 45 is the right field line. Since I don’t care which line the ball is hit toward, I asked for the absolute value of the angle, and then the average of all those batted ball angles. So the higher the number, the more toward the lines the hitter’s batted balls were hit. And obviously since fence distances are closest down the lines, a higher absolute angle should yield more homers. Therefore, it follows that this variable has the highest coefficient in my xHR/FB rate regression equation.

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Imagine you own a $20 outfielder. It’s pre-draft, and he’s the only player on your roster. He costs $20, and he’s projected to provide $20 of value. You’re offered a $17 shortstop who is projected to be worth $17. The projections properly account for position-adjusted replacement level. Would you take the trade?

No, right? You wouldn’t give me a dollar for three quarters, so why should you trade $20 for $17. Who cares about the positions, that’s supposedly included in the price.

Dig down another layer and you might consider the swap. It’s a $3 loss, but perhaps you can make up the ground in-draft. Shortstop is such a scarce position that we’re probably talking about trading the 20th best outfielder for the third best shortstop. Is losing $3 in a trade worth solving the fantasy nightmare known as shortstop?

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Yesterday, I discussed the hitters who enjoyed the largest surges in batted ball distance compared to their 2013 campaigns. Today I take a look at the other side of the coin, the decliners. In the 114 player sample that lost at least 15 feet of distance in Chad Young’s study, the hitters gained back about a third of their lost distance in year three. So a distance loss is more permanent than a gain, but players generally bounce back toward their historical marks.

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Because I love examining how far batters hit their fly balls and homers, I’m going to continue the hop through the leaderboard for more analytical fun. Two years ago, Chad Young found that it is very difficult for a batter to sustain a surge in batted ball distance. Between aging and good old regression to the mean, batters who gained 15+ feet in distance gave half of that back in year three. The sample used in the study was certainly small, but I doubt the conclusion would change much with a larger data set. With that in mind, here are your 2014 distance surgers of at least 15 feet.

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I posit that some acronyms are ridiculous. For example, BATNA engenders images of the very worst of the Batman series. You know, like little inexplicable gremlins playing ice hockey with a giant diamond in a bank while Arnold Schwarzenegger rampages. Why doesn’t my bank have massive diamonds sitting out in the open?

However, BATNA can play a useful role in our ongoing discussion about spying and negotiating. It stands for Best Alternative To a Negotiated Agreement, which sort of defines itself. For fantasy baseball purposes, it means: “what will I do if I don’t make this trade.”

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Mat Latos began and ended his 2014 season by missing time because of injuries. Between the two layoffs, he seemed to produce like he did in the past with a 3.25 ERA (3.34 for his career). One difference between his previous couple of seasons and 2014 was a huge drop in strikeouts. They went from 8.0 K/9 in both 2012 and 2013 to 6.5 K/9 in 2014. The biggest reason for the decline in strikeouts was a near 2 mph drop in his fastball velocity. This off season he was traded from the Reds to the Marlins where he hopes regain some of the zip on his fastball, but I wouldn’t count on it.

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Yesterday, we discussed how to be a spymaster in your league. It’s a role that’s both fun and rewarding. As usual, we had a good chat session in the comments. I was also emailed a reminder of a previous life – an article titled “Negotiation is not the same as haggling.”

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