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.
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.
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.
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.
I was pretty successful at developing an equation to estimate what a hitter’s HR/FB rate should be given his fly ball + home run distance, along with the average absolute angle and standard deviation of the distance of those batted balls. My formula resulted in an R-squared mark of 0.649, which seems pretty darn good to me, especially when it completely ignores park factors, which we know play a significant role. On Tuesday, I found that the year-to-year correlation of a pitcher’s batted ball distance is less than half that of a hitter’s. Then yesterday, I discovered there was some correlation between a pitcher’s batted ball distance and his HR/FB rate and ISO mark.
Yesterday, I continued batted ball distance week with our first look into the pitching side of the equation. Up until then, we had learned a lot about what a hitter’s distance meant, but essentially nothing about a pitcher’s. We now know that a pitcher’s batted ball distance allowed has a year-to-year correlation less than half that of a hitter’s. But, the correlation was still high enough to be meaningful.
Yesterday, I shared the year-to-year correlations of the three variables that comprise my current xHR/FB rate formula. We now know that batted ball distance is fairly stable each season, which is excellent news for those using it to project future player performance. But what led me to gather the data and perform the calculations was actually a request to look into the pitcher side of the ledger.
Two years ago, Chad Young and I embarked on a journey to analyze a hitter’s average home run and fly ball distance and ultimately formulate an expected HR/FB rate, or xHR/FB. While it wasn’t earth shattering news, we discovered that batted ball distance was highly correlated with HR/FB rate. But that correlation wasn’t nearly high enough to be worth using the equation that included just batted ball distance.
Before the season began, I decided to have a little fun by constructing a fictional $9 pitching staff using the winning bids from the 15 team Tout Wars mixed auction league. I didn’t bother to consider any relievers as speculating on starting pitching breakouts in significantly more exciting. So without further ado, let’s take a look at how my selections panned out, with Zach Sanders’ dollar values indicated. It’s not perfect since his values are based on 12 team leagues, but good enough.
Back in late March of 2012, Matt Swartz helped me conclude that pitcher strikeout and walk rates in spring training actually do matter. We are so used to hearing that spring stats mean nothing, so it was exciting to learn that there was some meaning after all, if you knew where to look. So with that in mind, I identified four starting pitcher breakout candidates based on strikeout rate surges during the spring. Let’s review their performances.
