Dynasty Baseball

Jordan’s Research Ramblings: Expected Power Based on Fly Ball Distance – MLB Observations

My social distancing experience thus far is best summed up in two ways: increased hours of Fallout 4 (underrated!) and Outer Worlds on PlayStation, and ruminations on baseball power metrics. Let’s continue with the latter!

In recent weeks, I’ve published work on minor league fly ball distance and fly ball distance-derived power numbers, particularly HR/FB rate and BA/OBP/SLG. While fly ball distance research on minor leaguers has only become possible in recent years thanks to Prospects Live’s leaderboards, it has for a long time been well-covered at the MLB level. Throughout my minor league explorations, I’ve also looked at how fly ball distance impacts MLB power numbers—mostly to confirm the relationship between fly ball distance and power is similar at the minor league level to the major league level. As a byproduct of this, I’ve come across some interesting power observations for major leaguers—the subject of the remainder of this article.

Before getting into player-specific observations, some notes on the methodology:

  • I looked at the relationship between average fly ball distance (excluding pop-ups) and HR/FB rate in each season from 2016 to 2019, adjusted for differing league contexts each year. I generated a Podhorzer-ian xHR/FB (expected home run per fly ball) rate based on a batter’s average fly ball distance.
  • I found HR/FB rate is more of a quadratic function of fly ball distance than a linear one. The quadratic model also suffers less from biases against certain classes of players, e.g. at both the top-end and low-end of the power spectrum (heteroscedasticity). A quadratic model of HR/FB adds a squared term of fly ball distance, rewarding players more at the top of the power spectrum, and hurting players less at the bottom of the power spectrum. Adjusted R^2 was 60.9% for the quadratic model vs 59.6% for the linear model (n=439, minimum 80 fly balls, 2016-2019). Why does a quadratic model fit the data better? I welcome reader speculation in this area. Perhaps it’s because there’s not a significant difference between weaker, non-home run fly balls; most of them tend to be outs regardless of how far they’re hit.

  • I also looked at exit velocity’s relationship with HR/FB rate. It is a better predictor of next season HR/FB rate compared to fly ball distance (though both are sticky), but a worse descriptor of same season HR/FB rate. This article focuses on describing 2019 power rather than predicting 2020 power so it only considers fly ball distance. When considering someone with, for example, high fly ball distance, you should be more confident it’s indicative of true, repeatable power skill if said player also has high exit velocities—and less confident if their exit velocities are weaker. After all, distance is a function of exit velocity and launch angle (as well as spin rate and environment).
  • When adjusting statistics for expected power based on average fly ball distance, I awarded players additional home runs according to differences between their HR/FB rate and xHR/FB rate. I also applied a small penalty to account for the fact that a small minority portion of the new home runs would replace hits rather than outs.
  • A predictive model would guess players perform somewhere in between their 2019 xHR/FB % and actual HR/FB % in 2020. Actual HR/FB % captures important information beyond just fly ball distance, e.g. pull tendency, park, opponent quality, spin, different player-specific fly ball distributions.

Next, I present commentary on a few players who had fewer home runs than their average fly ball distance suggests they deserved; fewer home runs than could have been expected given their average fly ball distance. I suggested these fellas because I think they’re generally a good bet to improve on their 2019 performance in 2020, all things considered (not just average fly ball distance).

First, to establish context, league leaders in average fly ball distance and xHR/FB (expected home run per fly ball rate based on average fly ball distance): Miguel Sano & Joey Gallo

  • 367 feet for Sano, 42% xHR/FB versus 37% actual HR/FB.
  • 359 feet for Gallo, 36% xHR/FB versus 37% actual HR/FB.

2020 Surgers

  • Josh Rojas, OF, ARI
    • 346 feet average fly ball distance (versus 323 feet MLB average). 89 MPH average exit velocity (versus 88.1 MPH MLB average).
    • 6% HR/FB rate versus 15% MLB average
    • 27% xHR/FB given very strong average fly ball distance. +7 HR
    • His average fly ball distance will likely regress next year given his solid but not nearly as elite exit velocity, as well as his minor league power reputation. Nonetheless, he deserved better than a 6% HR/FB rate in 2019
    • Old triple-slash: .217/.312/.312; new triple-slash: .260/.355/.471
    • Arizona has buried him with a bunch of signings this offseason, but his power-speed combo has many maintaining hope. His positional versatility should keep him in the lineup regularly anyway if he hits, and David Peralta is no beacon of perfect health, nor is Kole Calhoun a model of consistency.
  • Kevin Cron, 1B, ARI
    • Earth-shattering 382 average fly ball distance in brief MLB sample would have led the league. +3 HR. After incorporating additional home runs, .211/.269/.521 triple-slash jumps to .245/.304/.646. Likely selling out for power somewhat as his strikeouts were super high, both power and strikeouts should regress toward the mean in 2020.
    • 90.1 average exit velocity also quite strong, and he broke isolated power records in the Pacific Coast League in 2019 (even after adjusting for the new juiced ball context).
    • Optioned to Triple-A. He’ll get his chance eventually, whether it comes from Christian Walker struggling, or, if Walker keeps hitting, a different team taking a chance on him. He looks to me like the next Voit-Muncy-Cruz Quad-A success.
  • David Dahl, OF, COL
    • .302/.353/.524 triple-slash jumps to .324/.376/.606 after adding additional home runs from elite 347 average fly ball distance (note his exit velocity is less stellar, 88.2 MPH on average). Perhaps a full breakout is in store in 2020. His price still seems to be reasonable after a quietly strong 2019. His career wOBA is now .363 in 921 plate appearances, but no projection system has his him above .350.
  • Danny Jansen, C, TOR
    • 19% xHR/FB versus 12% HR/FB. Above-average fly ball distance and solid exit velocity (88.7 MPH) suggests he deserved better power-wise in 2019. Painful 2019 has him on discount in 2020, previous owners left traumatized. Elite defense gives him a chance to emerge as rare everyday backstop option in 2020.
    • Old triple-slash: .207/.279/.360; new triple-slash: .229/.301/.439

A couple more quick-hits:

  • Andrew Benintendi, OF, BOS
    • 15% xHR/FB rate (assuming merely a league average HR/FB % boosts his numbers significantly) versus 8% HR/FB rate
    • Old triple-slash: .266/.343/.431; new triple-slash: .285/.362/.502
    • +12 home runs
    • At the least, look for the return of 2018 Andrew in 2020
  • Rougned Odor, 2B, TEX
    • Strong, 343 average fly ball distance. 26% xHR/FB rate
    • Old triple-slash: .205/.283/.439; new triple-slash: .223/.302/.506. Should bounce back in 2020 to at least his 2019 xwOBA (.322)
  • Mookie Betts, OF, BOS LAD
    • Old-triple slash: .295/.391/.524; new triple-slash: .312/.408/.588
    • In the words of Grey Albright, Mookie Best, Mookie Ballgame!
  • Vladimir Guerrero, 3B, TOR
    o Old-triple slash: .272/.339/.433; new triple-slash: .285/.353/.484. +7 additional home runs.
    o The future is coming
  • Anthony Rendon, 3B, LAA
    • 22% xHR/FB rate versus 16% HR/FB rate. Above average exit velocity and fly ball distance
    • Old triple-slash: .319/.412/.598; new triple-slash: .339/.431/.671
    • I can’t remember Rendon ever making out last year, can you?
    • I doubt he’ll be better than his amazing 2019 this season, but simply repeating his 2019 would be quite alright with me.

Want to know someone else’s xHR/FB rate and new triple-slash? Let me know in the comments or on twitter @rosenjordanblum. Thanks for following along!

The Author

Jordan Rosenblum

Jordan Rosenblum

Jordan is an American living in Finland. In addition to writing for The Dynasty Guru, he's a doctoral candidate at Åbo Akademi researching explanations of income inequality, and a Workforce Strategist at OnWork Oy. His favorite baseball area is quantitative analysis of prospects.

Fun fact about Finland: they play pesäpallo here, which is like a soft-toss version of American baseball, except home runs are somehow outs.

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