# Hitter Batted Ball Distance Correlations

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.

So in last year’s FG+ article, I devised an improved equation that accounted for the average absolute angle of a batter’s homers and flies and the standard deviation of the distances of those batted balls. The thinking was that the higher the absolute angle, the more the batter hits down the lines and the higher the HR/FB rate since fence distances are closer in those areas. Furthermore, the standard deviation tells us whether a hitter consistently hits 300 foot flies or alternates between 400 and 200 footers. The latter pair will yield a higher standard deviation and HR/FB rate. The r-squared jumped a meaningful amount and the range of possible outcomes fit reality much better than the initial equation. Unfortunately, those two additional metrics are not published on Baseball Heat Maps and the angle I am referencing is different than the “Angle” column on the leaderboard.

Oddly though, I never actually calculated the year-to-year correlations of any of these metrics. And a Google search and questioning of my FanGraphs colleagues yielded nothing either. It’s great to know how to estimate what a hitter’s HR/FB rate should be given those three components, but how stable are they from year to year?

For the batted ball distance, I took the data from the leaderboards linked to in the intro, which includes data back to 2007. That gave me 1,529 player season pairs to calculate a YoY correlation for. That correlation was 0.647, which is probably in a range we would all expect it to be. Because the effects of aging aren’t accounted for, it’s no surprise that the correlation isn’t higher. That correlation is about the same as Matt Klaassen found for wOBA, a little lower than SLG and a bit higher than OBP. That’s good news.

My data set for the other two variables was a bit smaller, coming in at 1,289 season pairs, since it dated back to 2008, not 2007. The average absolute angle was much less stable from year to year. Its correlation was just 0.157. It suggests that there is some persistence of hitting down the lines, but not much. Remember, since this is the absolute angle, a higher number doesn’t necessarily mean the batter is pull happy. He could also go the other way a lot. It just means the batter hits it more to left and right fields and stays away from center. The low correlation suggests to me that when a big change occurs one year, it’s much less likely to stick than when a change occurs in distance.

Lastly, we find that the standard deviation of distance is rather consistent, as it sports a YoY correlation of 0.500. Again this makes sense, as it’s a branch of a hitter’s power. Low power guys generally have low standard deviations because they lack the ability to ever hit it over 400 feet. On the other hand, the big power hitters will routinely hit balls over 400 feet and so their range of distances will be much larger.

Here are all the correlations again:

Distance Avg Abs Angle SD Dist
0.647 0.157 0.500

Knowing that the angle has a relatively low correlation, I could assume a return to career levels when projecting a hitter’s HR/FB rate. For example, I recently projected David Ortiz and noticed that his angle sat at the lowest mark since we have data for going back to 2008. That alone seemingly didn’t hurt his HR/FB rate since it sat at an identical mark to 2013. But knowing this information suggests the angle should rebound and I only have to be concerned with whether aging will ever rear its ugly head in Ortiz’s direction.

Tomorrow, I’ll move on to pitcher’s, making good on several requests I’ve had asking me to look into pitchers’ batted ball distance against.

Mike Podhorzer is the 2015 Fantasy Sports Writers Association Baseball Writer of the Year. He produces player projections using his own forecasting system and is the author of the eBook Projecting X 2.0: How to Forecast Baseball Player Performance, which teaches you how to project players yourself. His projections helped him win the inaugural 2013 Tout Wars mixed draft league. Follow Mike on Twitter @MikePodhorzer and contact him via email.

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