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.

First-Half Exit-Velocity Overperformers
wOBA wOBA IQ Exit Velo 1st Half 2016 Exit Velo IQ wOBA IQ-Exit Velo IQ
Brandon Belt .394 124.0 86.2 79.4 44.5
Derek Dietrich .365 113.1 86.2 79.1 34.0
Jose Altuve .400 126.2 88.7 94.5 31.7
Anthony Rizzo .419 133.3 90.3 104.5 28.9
John Jaso .327 98.9 85.0 72.1 26.8
Cameron Maybin .359 110.9 87.1 84.9 26.0
Ian Kinsler .358 110.5 87.3 85.9 24.6
Mike Trout .415 131.8 90.8 107.5 24.3
Jose Iglesias .281 81.6 82.7 58.0 23.7
Daniel Murphy .410 130.0 90.7 106.6 23.4
Didi Gregorius .339 103.4 86.4 80.2 23.1
Charlie Blackmon .371 115.4 88.4 92.6 22.8
Dexter Fowler .381 119.1 89.0 96.5 22.6
Lonnie Chisenhall .348 106.7 87.0 84.4 22.4
Stephen Piscotty .366 113.5 88.2 91.6 21.9
Matt Carpenter .414 131.5 91.3 110.6 20.8
Starling Marte .353 108.6 87.7 88.4 20.2
AVERAGE .371 115.2 87.8 89.2 26.0

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.

EV Overperformers, Projections Vs. Results
1st Half EV (MPH) 2nd Half EV (MPH) 1st Half wOBA 2nd half wOBA DIFFERENCE
Matt Carpenter 91.3 90.3 .414 .312 -.102
Anthony Rizzo 90.3 89.2 .419 .357 -.062
Lonnie Chisenhall 87.0 85.9 .348 .301 -.047
Stephen Piscotty 88.2 89.6 .366 .319 -.047
Brandon Belt 86.2 89.3 .394 .349 -.045
Didi Gregorius 86.4 86.8 .339 .295 -.044
Dexter Fowler 89.0 87.8 .381 .352 -.029
Jose Altuve 88.7 88.8 .400 .379 -.021
Cameron Maybin 87.1 90 .359 .343 -.016
Starling Marte 87.7 88.2 .353 .347 -.006
Daniel Murphy 90.7 92.3 .410 .405 -.005
Ian Kinsler 87.3 87.9 .358 .354 -.004
Jose Iglesias 82.7 85 .281 .286 .005
Mike Trout 90.8 93 .415 .422 .007
Charlie Blackmon 88.4 87.8 .371 .418 .047
AVERAGE 88.1 88.8 .374 .349 -.025

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.

Second-Half Results for First-Half Overperformers
2nd half wOBA 2nd Half Proj wOBA DIFFERENCE
Matt Carpenter .312 .364 -.052
Anthony Rizzo .357 .384 -.027
Lonnie Chisenhall .301 .320 -.019
Didi Gregorius .295 .309 -.014
Stephen Piscotty .319 .332 -.013
Jose Iglesias .286 .296 -.010
Brandon Belt .349 .355 -.006
Starling Marte .347 .345 .002
Mike Trout .422 .414 .008
Dexter Fowler .352 .339 .013
Jose Altuve .379 .358 .021
Cameron Maybin .343 .317 .026
Ian Kinsler .354 .326 .028
Daniel Murphy .405 .353 .052
Charlie Blackmon .418 .339 .079
AVERAGE .349 .343 .006

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.

First-Half Exit-Velocity Underperformers, 2016
wOBA wOBA IQ Exit Velo 1st Half 2016 Exit Velo IQ wOBA IQ-Exit Velo IQ
Avisail Garcia .281 81.6 89.9 101.8 -20.1
Mitch Moreland .300 88.8 91.2 109.6 -20.9
Juan Uribe .273 78.7 89.5 99.6 -21.0
Kendrys Morales .332 100.7 93.2 121.9 -21.2
Yasiel Puig .308 91.8 91.8 113.2 -21.4
Adonis Garcia .275 79.4 89.8 101.1 -21.7
Anthony Rendon .326 98.5 93.0 120.7 -22.2
Yasmany Tomas .314 94.0 92.3 116.3 -22.3
Justin Smoak .318 95.5 92.7 119.2 -23.7
Brad Miller .316 94.8 92.8 119.5 -24.7
Randal Grichuk .305 90.6 92.2 115.9 -25.2
Adeiny Hechavarria .258 73.0 89.4 98.7 -25.7
Trevor Plouffe .292 85.8 91.5 111.6 -25.8
Logan Morrison .301 89.1 92.1 115.3 -26.2
Nick Markakis .302 89.5 92.2 116.2 -26.7
Chris Coghlan .232 63.3 88.1 90.6 -27.3
Justin Upton .288 84.3 91.6 112.0 -27.8
Aaron Hicks .246 68.5 89.4 98.6 -30.0
Alex Rodriguez .274 79.0 91.3 110.3 -31.3
Yan Gomes .221 59.2 88.0 90.5 -31.3
Adam Lind .292 85.8 92.6 118.4 -32.6
Giancarlo Stanton .348 106.7 96.1 139.8 -33.0
Matt Holliday .328 99.3 95.5 135.9 -36.7
Ryan Zimmerman .293 86.1 94.4 129.1 -43.0
Ryan Howard .240 66.3 92.8 119.5 -53.2
AVERAGE .291 85.2 91.7 113.0 -27.8

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.

First-Half Exit-Velocity Underperformers Results
1st Half EV (MPH) 2nd Half EV (MPH) 1st Half wOBA 2nd half wOBA DIFFERENCE
Justin Upton 91.6 93.1 .288 .383 .095
Yasmany Tomas 92.3 90.4 .314 .381 .067
Adonis Garcia 89.8 89.9 .275 .337 .062
Aaron Hicks 89.4 90.7 .246 .301 .055
Avisail Garcia 89.9 92.8 .281 .334 .053
Nick Markakis 92.2 90.4 .302 .344 .042
Randal Grichuk 92.2 92.5 .305 .346 .041
Brad Miller 92.8 90.4 .316 .351 .035
Adam Lind 92.6 91.8 .292 .320 .028
Mitch Moreland 91.2 91.6 .300 .316 .016
Kendrys Morales 93.2 94.8 .332 .346 .014
Adeiny Hechavarria 89.4 87.5 .258 .253 -.005
AVERAGE 91.7 91.3 .292 .334 .042

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?

EV Underperformers, Projections Vs. Results
2nd half wOBA 2nd Half Proj wOBA DIFFERENCE
Yasmany Tomas .381 .316 .065
Justin Upton .383 .340 .043
Adonis Garcia .337 .297 .040
Brad Miller .351 .317 .034
Nick Markakis .344 .311 .033
Randal Grichuk .346 .313 .033
Avisail Garcia .334 .302 .032
Kendrys Morales .346 .331 .015
Aaron Hicks .301 .295 .006
Adam Lind .320 .316 .004
Mitch Moreland .316 .327 -.011
Adeiny Hechavarria .253 .282 -.029
AVERAGE .334 .316 .018

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.

We hoped you liked reading Did Exit Velocity Predict Second-Half Slumps, Rebounds? by Craig Edwards!

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Craig Edwards can be found on twitter @craigjedwards.

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HotSpinachDip
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HotSpinachDip

I’m curious about the decision to use wOBA as our outcome metric. wOBA incorporates factors that are unrelated to quality of contact (e.g., BBs, HBPs, etc.). Why not look at under/over-performers in batted ball outcomes (e.g., BABIP, HR/FB, etc.)?