Did Exit Velocity Predict Second-Half Slumps, Rebounds?

While we don’t entirely understand the significance of exit velocity yet or how important that sort of data might be, here’s one aspect of it that does appear to be true: the higher the exit velocity, the greater the production to which it will lead.

Armed with that knowledge, I developed a theory — namely, that players who had recorded high exit velocities, but poor production numbers, could expect to see better results going forward. I suspected, conversely, that players who’d recorded low exit velocities and strong production numbers could expect to do worse. I first tested this theory in February, using 2015 data, and it mostly rang true. With 2016 in the books, we have another season’s worth of data to test.

Back in early August, I identified a collection of players with whom to test thistheory. The table below (from that post) features the players who outperformed their exit velocities over the first half of the season. As in the past, this is how I determined if a player was over- or under-performing:

I created IQ-type scores for exit velocity and wOBA from the first half of last season based on the averages of the 130 players in the sample. In each case, I assigned a figure of 100 to the sample’s average and then, for each standard deviation (SD) up or down, added or subtracted 15 points.

Once the IQ scores for both stats were calculated, I subtracted the IQ score for exit velocity from the IQ score for wOBA to find the players with the biggest disparities.

Here are the overperformers from the first half of 2016, with exit-velocity numbers from Baseball Savant.

So how did they do? All but Derek Dietrich and John Jaso recorded at least 100 batted balls in the second half, so we have a decent sample of players to compare from half to half. As expected, they weren’t as good in the second half as they were in the first half.

Only Charlie Blackmon was able to significantly improve from the first half to the second half, and two-thirds of the players from the original sample saw healthy dropoffs. Of the five players who maintained their production, three saw an exit velocity increase in the second half, which served to counter an expected loss in  production. This dropoff was not as significant as the one found last year, but a 25-point loss of wOBA is a pretty substantial fall.

One thing I did not do for 2015 that I’m correcting for this year is to include projections into the analysis. Many of the players above were probably going to see a hit to their production given how high it was in the first half. We wouldn’t want to confuse change prompted by average exit velocity with another sort of change that could have been detected by other statistical indicators. The chart below shows second-half projected wOBA and second-half wOBA for the players above.

As we can see above, the average production in the second half was fairly close to the numbers for which the projections called. We have a group of six players who produced wOBA figures at least 10 points below their projections, another group of six who produced wOBAs at least 10 points above their projections, and three players who stayed pretty close to the same. Based on these players for this past season, it seems as though the projections were reasonably accurate despite the relative strength of these players’ first-half performances.

To look at another group, here are the underperformers from the first half of last season.

Unfortunately, and perhaps as would be expected given performance, fewer players in this sample received sufficient playing time in the second half to redeem themselves. Some of that was just bad luck due to injury, but some of it was a lack of playing time due to poor production — poor production with which teams decided they could no longer live. Of those 25 players, just under half recorded at least 100 batted balls in the second half. Here are the numbers for those who did play.

We see a major production increase from the group above, with only Adeiny Hechavarria failing to increase his wOBA by at least 10 points. The 42-point average increase lines up with what we saw last year. So how did this group do compared to their second-half projections?

Here, we see a pretty big difference between the overperformer and underperformer groups. While the overperformers ended up with production pretty close to their second-half projections, the underperformer group recorded substantially higher second-half wOBAs than even their projections suggested they would. Two-thirds of the players in the sample saw their production in the second half outperform their projections by a decent margin. It’s possible that the projections did a better job accounting for a good half, instead relying on past production while potentially overreacting to a poor first half.

There are still a numbers of questions for which we don’t have the answers at this point. While we have now added another year to the pile, we are still dealing with a pretty small sample of players. The numbers for over-performers differed pretty greatly from 2015 to 2016. The under-performers had similar numbers both years and outperformed projections, but we have a considerable number of players who didn’t play much in the second half, which could be artificially boosting those numbers. In any event, it is still something worth exploring and taking notice of in future seasons.