The Big Kevin Gausman Breakout Has Arrived, A Review

Back in late August, about a month into the season, I proclaimed that the big Kevin Gausman breakout has arrived. At the time, he had thrown 31 innings and was sitting with an unattractive 4.65 ERA. How could a pitcher’s breakout have arrived if his ERA was actually worse than the 4.48 MLB average?! It’s easy really, it’s all about the peripherals, or underlying skills as I call them. ERA, especially over a small sample like 31 innings, is pretty meaningless and doesn’t tell you a whole lot about how a pitcher has actually pitched. So we dive deeper, look at those underlying skills, which ultimately drive SIERA, a much better indicator of a pitcher’s performance, again, especially over a small sample. We now know that since I posted my breakout article, Gausman posted a sterling 2.51 ERA the rest of the way over 28.2 inning. So yeah, the breakout arrived. Let’s see if anything actually changed though, or if it was merely the luck metrics (BABIP, HR/FB, LOB%) that reverted back toward the league average.

Kevin Gausman Results & Underlying Skills
Period ERA SIERA K% SwStk% BB% LD% GB% FB% IFFB%
Pre-Article 4.65 3.11 31.6% 14.8% 4.5% 27.4% 36.9% 35.7% 13.3%
Post-Article 2.51 3.44 33.0% 15.6% 8.9% 15.6% 48.4% 35.9% 8.7%
The better period for each relevant metric is highlighted

We start with the most surface level metrics and results. Our first surprise is that based solely on SIERA, Gausman actually pitched better during the pre-article period! While it wasn’t a significant difference, it does suggest that it wasn’t a major skills improvement that led to his much better post-article results. We find that Gausman did slightly raise his strikeout rate from what was already a career best by far (obviously it’s not really fair to compare a 31 inning strikeout rate to a lower strikeout rate over as many as six times the number of innings, but this is what we got over a short season). The strikeout rate increase was driven by a marginal increase in SwStk%. His pre-article SwStk% actually matched last year’s mark, but that was accomplished with a higher percentage of innings in relief. His post-article SwStk% was higher than any previous season (but again, perhaps not something he hasn’t done before over a 28 inning period).

A major difference between the two periods was his walk rate, which nearly doubled during the post-article period. That’s a significant increase, but it merely brought his season line right back toward his career average, as his pre-article mark was well below what he has posted through a full season. He simply couldn’t keep that pace up.

Lastly, we look at his batted ball distribution. Gausman has posted a pretty league average distribution throughout his career, and for the season, this year was no exception. But how he got there is a differerent story. He was line drived to death in the pre-article period, but research suggests the batter has a lot more control over LD% than the pitcher. So it would be unfair to fully blame Gausman for his inflated pre-article mark. Sure enough, it came right back down in the post-article period, resulting in a full season mark barely above the league average. Did he suddenly learn how to limit line drives overnight like magic? Doubtful. All those pre-article line drives seemingly became ground balls in the post-article period, which is a good thing, especially if you have a good infield defense. Lastly, his IFFB% rate fell, but since that’s the least frequent batted ball type, it’s expected that rate is going to jump around, especially over tiny samples.

Kevin Gausman “Luck” & Statcast Metrics
Period BABIP LOB% HR/FB EV Barrel% HardHit%
Pre-Article 0.363 66.7% 16.7% 87.8 10.6% 34.1%
Post-Article 0.210 82.6% 13.0% 88.5 3.1% 41.5%
The better period for each relevant metric is highlighted

We now move along to the so-called “luck” metrics that are greatly influenced by factors outside the pitcher’s control, along with the primary Statcast metrics now available on player pages. It was pretty clear during the pre-article period that Gausman’s ERA was inflated by poor fortune, or performance if you want to blame him, in the “luck” metrics, BABIP, LOB%, and HR/FB rate. His .363 BABIP was the most obvious driver of his SIERA underperformance, and that reduced his LOB%. If you recall the last table, you’ll remember that inflated LD%, which undoubtedly contributed to the high BABIP. So we might say the BABIP was deserved given the high LD%, but was the high LD% itself deserved? Perhaps some, but I doubt all of it. Either way, any established Major Leaguer, especially one who has enjoyed success, is not going to have a true talent level that allows such a high LD%. He wouldn’t last in the highest league very long if he did. So whether it was better luck or better pitch location, you had to assume the LD% would drop closer to Gausman’s career average or the MLB average. It did, and his BABIP dropped as well. In fact, it actually overcorrected, suggesting his post-article BABIP was just as lucky as his pre-article BABIP was lucky! That kind of thing happens a lot. His LOB% did the same thing because fewer hits means the rare ones he did allow would be less likely to score.

The Statcast metrics are interesting. On the one hand, his EV rose slightly in the post-article period, while his HardHit% surged considerably. That sounds bad, and it is, but yet his Barrel% plummeted to a very low mark. Because barrels require a certain launch angle range to go along with a minimum exit velocity, this suggests that the harder contact he allowed was more of the ground ball and pop-up variety, rather than fly balls that typically land within that ideal launch angle range. This is a great example of why relying on averages for these metrics that include all batted ball typed is extremely misleading. So the Barrel% is most meaningful, but the same size is small, so it’s no surprise the gap is large between the two periods. The lower Barrel% does correspond to a low HR/FB rate.

Kevin Gausman Pitch Type Metrics
Period Pitch Info FA% Pitch Info FAv Pitch Info FS% Pitch Info FSv Pitch Info CH% Pitch Info CHv Pitch Info SL% Pitch Info SLv
Pre-Article 51.7% 95.5 29.9% 84.3 10.4% 85.0 7.2% 82.3
Post-Article 50.4% 95.3 28.5% 84.4 15.6% 84.7 5.5% 82.4

Now we’ll look at his pitch mix. Did he change things up during the post-article period? Did his velocity change at all?

The quick answers are barely and no. He threw all his non-changeups slightly less during the post-article period, while upping the usage of his changeup. By career wOBA allowed on each pitch, the changeup is his second best, but trailing far behind his best pitch, the splitter. So the switch was a slight positive, but shouldn’t have moved the needle much. There was also barely a change in velocity. So besides the heavier reliance on his changeup (which, by the way, was completely due to 20%+ usage rates during his first two starts of the period), this is clearly the same pitcher.


So what do we find here? Aside from small differences here and there, this is clearly the same pitcher that generally followed the same process during each period. When the same process yields dramatically different results, there has to be an explanation. That explanation looks overwhelmingly like “the luck metrics”, which always sounds like a cop out, but is usually true. I already advise to use SIERA instead of ERA during a full 162-game season. When we’re only talking 31 innings, then man, ERA is near worthless, and that’s mostly because BABIP, HR/FB, and LOB% jump around so much, and are influenced so greatly by factors outside of the pitcher’s control. I couldn’t have known it ahead of time, but Gausman’s two periods ended up perfectly illustrating luck metric regression at work and the trickle down effect on his surface results such as ERA and WHIP.

So the big Kevin Gausman breakout finally came, but now a free agent, will the magic continue into 2021?

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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Lunch Angle
Lunch Angle

So Gausman throws a change up AND a split. That seems unusual, how many pitchers throw both?