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Statcast Review: Giancarlo Stanton, Francisco Lindor, Ryan Yarbrough (2021 Fantasy Baseball)

by Mike Maher | @mikeMaher | Featured Writer
May 5, 2021
Francisco Lindor

We’re now more than a month into the season, which means baseball nerds like me now have a decent sample size of data to evaluate. A month is still a relatively small sample in terms of a baseball season, but it’s our first substantial split and our first real opportunity to take a step back and look at some early trends. And we aren’t going to focus on the traditional statistics in this space. Anyone can look at box scores and find those. Instead, we’re interested in Statcast data and what it tells us about certain players.

In this series, we’ll look at different Statcast metrics for batters and pitchers each week. We’ll talk numbers and what they mean, and I’ll provide some player-specific notes after each section. The metrics themselves will change on a weekly basis, and we’ll circle back to some of our favorites every few weeks to see what trends we can identify.

Have something you want me to cover in this space or just want to talk baseball? Feel free to reach out on Twitter @mikeMaher with questions or feedback anytime.

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Hard Hit Numbers

Since we started getting access to Statcast data, the Hard Hit metrics have been among the most popular. We want to know how hard players are hitting the ball, how often they are hitting the ball hard, their average and maximum exit velocity, and how often they are making hard contact when they swing. Below is every batter in Major League Baseball with a Hard Hit % of 40 or above, presented in a chart that also shows the number of hard-hit balls (95 MPH+ EV) and how often these batters are making hard contact when they swing (Hard Hit % per Swing). Statcast defines a “hard-hit ball” as one hit with an exit velocity of 95 mph or higher.

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Notes

  • Giancarlo Stanton got off to a slow start, but his 65.8 Hard Hit % is absurd. He and Judge are the only two players with numbers above 60%. Those numbers aren’t sustainable, but they point to the damage those two are doing to baseballs whenever they connect on a swing. Byron Buxton would also be above 60% on this chart but didn’t qualify for this specific dataset. Over the last week, Stanton is slugging .857.
  • Francisco Lindor is near the bottom of our chart here, but this is a list of the Top 75 hitters in terms of Hard Hit %. This is an example of Hard Hit % not telling the whole story. Even though Lindor’s Hard Hit % is slightly above league average (the bottom of this chart is 51st percentile), the rest of his numbers (other than his walks and strikeouts) are very poor. His expected statistics match up (to an extent, though not as severe) with his earned statistics, which isn’t a great sign for a guy batting .163 with one home run for his new team.
  • Pavin Smith is somewhat quietly having a very solid start to the 2021 season. His .260 batting average and .762 OPS don’t jump off the page, but he is consistently making hard contact and has been batting atop the Arizona lineup for the last two weeks. As you can see above, his 27.7% Hard Hit % per Swing number is the best in the league among qualified batters.

Batters aren’t the only ones who have Hard Hit data we can evaluate. Statcast tracks that information for pitchers, as well. With pitchers, we want the numbers to be lower since that means they are limiting the hard contact against them. Below is every qualified pitcher in baseball with a Hard Hit % of less than 40%, with the number of hard hits allowed and Hard Hit % per Swing numbers on either side.

Note: This table is two pages (see the button on the top right) and is sortable and searchable, so feel free to look around!

Notes

  • The pitcher currently allowing the least amount of hard contact is…Ryan Yarbrough? Yarbrough hasn’t exactly had a stellar start to the season, but his FIP (3.14) and xERA (3.87) are significantly better than his actual ERA of 4.86. And it appears that limiting hard contact is helping him stay above water, to an extent. His main issue is that he doesn’t strike out a ton of batters, and pitchers with poor strikeout numbers are prone to bad luck, even really good pitchers who limit hard contact. Pitch mix is important here too, and Yarbrough has an odd trend. Batters hit .315 against his cutter last season, which was his primary pitch that he threw 36% of the time. For some reason, he is throwing that cutter more this season, up to 46.5%, and batters are hitting .288 against it.

  • It shouldn’t be surprising to anyone to see Corbin Burnes near the top of this list. His 26.8% Hard Hit rate is good for third, and his 6.9% Hard Hit % per Swing number is the second-lowest, behind only Freddy Peralta’s 6.8%. Peralta is off to an incredible start to the year, with 45 strikeouts in 28 innings to go with a 2.25 ERA, 3.00 FIP, and 2.31 xERA. His Hard Hit %, xwOBA, xERA, xBA, xSLG, K%, and Whiff% are all 90th percentile or better.
  • Sandy Alcantara has allowed the most hard hits on this list, and his 39.5% Hard Hit % is behind only Joe Ross’s 39.7%. That still puts Alcantara in the 47% percentile for hard hits, however, and nearly all of his other underlying numbers are good-to-excellent.
  • Aaron Sanchez has the highest Hard Hit % per Swing of the qualifiers for this list, but his 39.3% Hard Hit % is surprisingly the same as Shane Bieber. Sanchez is having a strong start to what looks like a bounce-back campaign with the Giants. His numbers definitely warrant a deeper dive because he is throwing his curveball significantly more, has cut his four-seam fastball usage way down, and the velocity of both his sinker and his fastball are both significantly lower than when we last saw Sanchez pitch in 2019.

Expectations vs. Reality

One of my favorite things to review when evaluating players, especially as we start to get larger sample sizes, is expected statistics vs. actual statistics. While they don’t always tell the whole story, these expected stats generally show you who is outperforming or underperforming, who may be getting lucky or unlucky, and who is likely due for some eventual positive or negative regression.

For pitchers, the quickest and easiest way to compare expected vs. actual statistics is by juxtaposing ERA (earned run average) to FIP (fielding independent pitching) and xERA (expected ERA). There are, of course, other aspects to evaluate for every pitcher, but these expected metrics are excellent aggregates that incorporate a number of criteria.

Most weeks, I’ll close with this section so we can see how the leaderboard changes on a weekly basis and get a good idea of what we can expect from some of these pitchers going forward.

That’s all for this week, friends. See someone above you’d like to talk more about, or just have a general question? Feel free to reach out on Twitter @mikeMaher anytime.

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Mike Maher is an editor and featured writer at FantasyPros and BettingPros. For more from Mike, check out his archive, follow him on Twitter @MikeMaherand visit his Philadelphia Eagles blogThe Birds Blitz.