2021 Review — Surprise! You Believed Their BABIPs, But Shouldn’t Have – The Improvers

Today, we continue our exploration of my new hitter xBABIP equation by identifying hitters whose 2021 BABIPs were around the non-pitcher league average of .293, but whose xBABIPs were significantly different. When you see a BABIP of .380 or .220, that clearly raises red flags, with immediate reactions of decline, in the case of the former, or improvement, in the case of the latter, in the upcoming season. But no such reaction is triggered when you see a BABIP around the league average, right? However, just being around the league average doesn’t necessarily mean it’s legit. So today, let’s begin by discussing those hitters who posted BABIPs marks within .010 of league average (between .283 and .303), but xBABIP marks significantly higher. If your leaguemates are using 2021 BABIP to shape their 2022 hitter forecasts, these hitters’ batting average contributions could be undervalued.

Player BABIP xBABIP Diff Statcast xBABIP Sprint Speed PSIFAGB R%* PSIFAGB L%* Opposite GB%
Nick Senzel 0.284 0.362 -0.078 0.352 29.1 2.1% 0.0% 7.4%
Alex Kirilloff 0.295 0.339 -0.044 0.348 27.4 0.0% 16.7% 7.1%
Yan Gomes 0.287 0.328 -0.041 0.324 26.3 1.9% 0.0% 3.9%
Tomas Nido 0.292 0.327 -0.035 0.321 25.6 0.9% 0.0% 6.6%
DJ LeMahieu 0.301 0.334 -0.033 0.318 26.5 0.0% 0.0% 12.4%
David Fletcher 0.287 0.317 -0.031 0.297 27.7 0.0% 0.0% 13.5%
Taylor Ward 0.301 0.331 -0.030 0.314 28.3 0.7% 0.0% 11.0%
Gavin Lux 0.300 0.324 -0.024 0.324 29.1 0.0% 10.9% 5.3%
Jo Adell 0.298 0.322 -0.024 0.309 29.9 1.1% 0.0% 5.3%
Jake Cronenworth 0.283 0.307 -0.023 0.300 28.5 0.0% 6.5% 6.5%
Juan Lagares 0.294 0.317 -0.023 0.303 28.2 0.4% 0.0% 7.9%
Avisail Garcia 0.291 0.313 -0.022 0.314 28.7 12.0% 0.0% 6.3%
Manny Machado 0.290 0.311 -0.021 0.310 26.4 5.4% 0.0% 4.7%
Dataset Avg 0.293 0.295 -0.001 0.291 27.0 2.2% 4.4% 6.1%
*Pull Shift IF Alignment GB As R%/L%

Nick Senzel! Remember him? The former top prospect, who as recently as 2019 was ranked seventh overall, has battled injuries and accumulated just 616 plate appearances since his 2019 debut. And while those PAs were decent enough from a fantasy perspective, they came with just a .302 wOBA. As a result of his current health and slow offensive start, he’s only projected to be a role player this season. But my xBABIP (and even Statcast’s xwOBA) suggest his results should have been significantly better over a small sample last year. The improved strikeout rate is encouraging as well and you have to figure that at least some of that collapse in power was injury-related. I wouldn’t give up on him yet, but obviously health and playing time will be key.

Rookie Alex Kirilloff made my xHR/FB rate underperformers list, even having posted an actual HR/FB rate well above anything he had in the minors. Obviously, small sample size, and his appearance on this list is also from a small sample size. But it just goes to show that he enjoyed a far more impressive first 231 MLB PAs than his results give him credit for. I still don’t know what to think given pedestrian minor league skills and results though, so he’s a challenge to forecast.

It was a disappointing season for DJ LeMahieu, whose .301 BABIP fell significantly below his .340 career average (which was undoubtedly boosted by his many seasons with the Rockies). But xBABIP suggests he deserved far better results, more in line with his previous couple of seasons. I don’t think he’ll ever sniff a 20%+ HR/FB rate, but he doesn’t need to in order to deliver decent enough value given his spot atop a strong lineup. His cost should be way down now as well, so he becomes a potential profit maker.

Since 2018, Gavin Lux had been a monster in the minors, posting high BABIP marks and seeing his power take off at Triple-A in 2019. Fantasy owners were rightfully excited, but all he’s done in 532 MLB PAs is post a weak .297 wOBA, with a .291 BABIP and .135 ISO. Talk about disappointing! His xBABIP was a fair bit better though and although that doesn’t mean a power rebound, it could help boost a lowly .242 batting average. With solid skills around, he looks like an excellent post-hype target.

Speaking of post-hype targets, Jo Adell was the ninth best overall prospect back in 2020, but he flopped during his debut that season due to a boatload of strikeouts. That rate was much improved in 2021, but he underperformed his xBABIP, giving hope that his massive minor league BABIPs aren’t as far away as they seem. We’re still waiting on the power, but I think he’s another solid post-hyper to buy cheaply with big profit potential.

Amazingly, all it took was a .346 wOBA to give Avisail Garcia the second best offensive performance of his career. He even did that despite a BABIP well below his career average. But xBABIP thought his BABIP should have been fairly similar to his 2020 mark, which would have made him even more valuable. That said, he’s quite the risk moving into a more pitcher friendly home park and a career best HR/FB rate.

Manny Machado has rarely posted a strong BABIP. His career high sits at just .322 and his career average is barely better than the league at .297. A heavy fly ball tendency, combined with lots of pop-ups and lower than average line drive rate are some of the big culprits. And while he wasn’t that much different in 2021 in those regards, he did underperform his xBABIP. It’s insane that he’s just 29 right now, so it’s not like he’s on the downside of his career yet. With his strikeout rate, you’d expect a couple of more .300 seasons in his future.

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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9 months ago

I’m a little confused. Normally, a high BABIP means regression is coming, but a higher xBABIP doesn’t? If I understand you correctly, you are saying if the BABIP was much lower than xBABIP; then the player performed worse than expected so their perceived value may be lower. Have I got it right?

Joe Wilkeymember
9 months ago
Reply to  jamwelch

Correct. BABIP isn’t as much “luck” as originally thought. Guys who hit a lot of FB will have lower BABIPs, because FB are generally either HR or outs. BABIP was .124 on all batted balls classified as FB by StatCast last year. Shifting also has an effect, all pulled GB with a “traditional” shift on had a BABIP of .136 last year. Ground balls pulled into the shift accounted for 14% of all ground balls last year, so it’s not a lot, but it’s not nothing either.

Joey Gallo is a prime example. Look at his .247 BABIP and you think “oh, that’ll increase”. In reality, he had 45 line drives, 102 fly balls, 35 pop ups, 55 pulled GB, and 49 non-pulled GB (all categories per StatCast). If we just give him league average BABIP for line drives (.622), fly balls (.124 from before), pop ups (.017), LHB pulled GB (.156), and LHB non-pulled GB (.295), his BABIP would be .225, so he may have actually gotten lucky with that .247 BABIP. This is an oversimplification of an extreme case, but it gives you the idea that BABIP shouldn’t just be assumed to regress to league average in all cases.