The MVP Batter Through the First Month

In one of the later chapters of The MVP Machine, the authors describe a working relationship between an unnamed position player and a writer at an “analytically inclined” baseball website. The player felt that his club’s advanced scouting data wasn’t granular enough and asked the writer to supplement the information he was given by the club with additional detail. The writer was eventually performing scouting reports on the player himself, opposing pitchers, as well as the home plate umpires’ strike zones. In terms of evaluating his own performance, the writer summarized that the player was basically looking at three things: “Am I squaring up the ball? Am I swinging and missing? Am I swinging at strikes?”

With the first month of the season in the books, who would be some of the best performing hitters in the league according to this particular player’s criteria? Thanks to Statcast, we have the tools at our disposal to try and figure out just that. Note that the dataset I used for this exercise was all qualified batters as of the morning of April 30th, 2021.

First, we need to decide which parameters to use to represent each of the three questions posed by the player. Two of the three are pretty easy. “Am I swinging and missing?” We can look up a player’s whiff percentage on Statcast. “Am I swinging at strikes?” That information is represented in a player’s chase percentage. “Am I squaring up the ball?” The natural candidates here would be, if we’re using just one number, the average exit velocity, hard hit percentage, and barrel percentage. I decided to go with the average exit velocity because it takes into account every batted ball put in play by the batter. Let me explain.

The hard hit percentage — defined as the percentage of balls hit with an exit velocity of 95 mph or harder — is binary. If a batter puts six balls in play at 96 mph and four at 94 mph, his hard hit percentage will be 60%. Similarly, if a batter puts six balls in play at 96 mph and four balls leave his bat at 85 mph, his hard hit percentage will be the same 60% even though the first batter makes more consistent hard contact. Barrel percentage has the same binary issue, and I couldn’t find anywhere on Statcast what that “perfect combination” of exit velocity and launch angle is.

Now that we have our three parameters – average exit velocity, chase percentage, whiff percentage – I normalized all three to vary between 0 and 1 using the formula:

X_normalized = (x_true_value – dataset_min) / (dataset_max – dataset_min)

X_true_value is the actual value of the parameter, dataset_max is the highest value of that parameter in the dataset, and dataset_min is the lowest value in the dataset. Let’s look at an example. Shohei Ohtani’s average exit velocity – as of the morning of April 30th – was 91.1 mph. The highest average exit velocity in the dataset was 98.7 mph and the lowest 81.6. Ohtani’s normalized average exit velocity would then be:

(91.1 – 81.6) / (98.7 – 81.6) = 0.5555

Finally, I computed the weighted average with different weights for the normalized parameters. For the normalized average exit velocity, the higher the number the “better,” while for the chase and whiff raters, the higher the normalized value the “worse.” To account for this, I’m actually multiplying the respective weights by (1 – normalized_whiff_rate) and by (1 – normalized_chase_rate). Let’s call the final output “MVP value.”

Base Case

MVP = (1/3) * normalized_exit_velocity + (1/3) * (1 – normalized_chase_rate) + (1/3) * (1-normalized_whiff_rate)

In the first instance, I assigned equal weights to the exit velocity, whiff rates, and chase rates. This is a “baseline” run, if you wish. The top 10 is as follows (excuse some rounding errors):

Ronald Acuña Jr. has been spectacular at the plate so far this year, and he is head and shoulders above everybody else in this edition of the made-up metric. Otherwise, we see a lot of players who have been tearing the cover off of the ball early in the season. Besides Acuña, this would include the usual likes of Mike Trout, Aaron Judge, Justin Turner, José Ramírez etc.

Where it gets interesting is that we have two guys with a sub-70 wRC+ on the list: Tommy Pham and Myles Straw. Let’s take a look at each one’s April in turn.

I’m not going to lie, I had to look up who Straw was. He is making an appearance courtesy of a 15.3% chase rate (MLB average in April 2021 was 28.4%) and a 13.4% whiff rate (MLB average 24.4%). Unfortunately, his 86 mph exit velocity with a 6.1 launch angle led to a .325 xSLG, in line with his brief major league career. Unless Straw can start making harder contact, his elite contact ability and plate discipline alone might not keep him in the big leagues.

Pham, on the other hand, is primed for a hot streak here soon. The underlying metrics are solid across the board, as he’s just been unlucky so far. The average exit velocity is at 91.6 mph, and Pham’s wOBA sat at .256 vs. a .380 xwOBA. Similarly, Pham slugged .203 compared to a .483 xSLG, a 280 (!!) point difference.

Let’s change the weights on the normalized values a bit. Maybe we’d like to emphasize not chasing and staying within the strike zone. We’ll bump up the weight on the normalized chase rate to 50% and decrease the weights on exit velocity and whiff rates both to 25%.

No Chase

MVP = (1/4) * normalized_exit_velocity + (1/2) * (1 – normalized_chase_rate) + (1/4) * (1-normalized_whiff_rate)

We see a lot of the same names from the table above. Lowrie has been another recipient of tough luck. His average exit velocity is up over 90 mph for the first time in his career, yet his slugging percentage sits at .406 compared to a .546 xSLG.

Max Muncy is one of two players to make an appearance in this table after not being in the top 10 in the Base Case version. His average exit velocity of 86.8 mph is at its lowest level since 2016, and his 49.2% ground ball rate is at its highest level since 2016 as well. Yet he looked ready to “heat up” in the near future, as his .365 SLG vs .460 xSLG would suggest. Muncy’s chase rate of 11.9% was the lowest in the majors and a major contributor to his 24.1% walk rate and .422 on-base percentage. No need to press, Max.

Let’s do one more iteration, this time emphasizing hard contact; the weights in this iteration will be 50% on the normalized exit velocity and 25% on the whiff rates and chase rates, respectively.

Hit it Hard Somewhere

MVP = (1/2) * normalized_exit_velocity + (1/4) * (1 – normalized_chase_rate) + (1/4) * (1-normalized_whiff_rate)

That would be 3-for-3 for Ronald Acuña Jr. as the top dog. Besides him, some of baseball’s most powerful sluggers make an appearance on the list. I’d like to touch on two of them, Pete Alonso and Giancarlo Stanton.

Alonso’s average exit velocity jumped to 97.3 mph in April 2021 from about 91 mph in 2019 and 2020. He is doing more damage in the zone — his whiff and chase rates are in line with his career averages, but his zone swing rate jumped 10% from mid to high 60s in 2019/2020 to 75.8% so far in 2021. Alonso swings more in the zone than before and makes more contact in the zone as well; his zone contact rate is at a career-high 85%. That’s a recipe for success, and Statcast seems to agree; Alonso sported a healthy .500 SLG compared to a .597 xSLG.

Giancarlo Stanton is hitting the ball as hard as anyone as usual, but there might be trouble lurking on the horizon. Stanton had the lowest zone contact percentage of his career, along with a 30%+ chase rate for the first time since 2016, as well as his highest whiff percentage since 2015. Moreover, he has been destroying four-seam fastballs this year, posting an xSLG of .773 against the pitch. Looking at breaking balls and off-speed stuff, his xSLG against sliders was .438, and against curveballs it was .515, and against changeups .224. So far, Stanton has been seeing about 60% fastballs. It will be interesting to see if he starts seeing more breaking balls going forward and ends up closer to a 50-50 split for fastballs vs. non-fastballs for the year.

Besides learning that Acuña is an alien, looking at the three underlying “MVP” metrics combined seems like a good starting point to dig deeper into a player’s offensive profile. Adjusting the weights on the individual parameters emphasizes different skillsets of the batter and allows us to identify candidates for regression or improvement in some of the surface stats.