Using Count Data To Find Unsustainable Performances

In this project I attempted to find the counts in which hitters were most successful during the 2019 season, and then find the hitters that were ending their at-bats in these counts the most in an effort to identify which players could potentially be under- or overperforming both in the past and going forward.

The data for this project was gathered by scraping Baseball Savant, which I used to create a dashboard to assist me in my analysis. I could not analyze every individual outlier performance from 2019 in this post, but the visualization I created can be accessed here, and the Github Repository for my project can be found here so you can take a look for yourself!

As the chart above shows, MLB hitters performed their best in counts with one or no strikes and their worst in two-strike counts. Using this data, I then explored individual performances in each count on the dashboard I had built to attempt to find outliers and discover who was ending at-bats in each count the most. Once players were identified, I would investigate why their performances were outliers and if their performances were sustainable. This post will highlight two of the more interesting unsustainable cases in hitters I found: Paul DeJong and
Javier Báez.

Paul DeJong – .250/.280/.417, 23 at-bats in 2-0 counts

League-wide data shows that hitters have a slugging percentage of .700 in 2-0 counts. These are significant numbers because, ideally, hitters would be finishing plate appearances in hitter-friendly counts that lead to improved results. For Paul DeJong, 2-0 counts did exactly the opposite of league-wide trends. DeJong finished 23 at-bats in 2-0 counts in 2019, recording only six hits — four of which were singles. His swinging strike rate in 2-0 counts was more than six percentage points lower than his overall rate in 2019, which should have led to results that were at least in-line with league trends. Despite all of this, DeJong only managed to bat .250 in 2-0 counts, with an underwhelming .417 slugging percentage.

DeJong is putting himself in more counts where hitters thrive, which, for a hitter with a wRC+ of 100, should lead to league-average results. However, DeJong’s 2-0 stats show that his underperformance is driven by a .217 BABIP in 2-0 counts, well below the .310 league BABIP in 2-0 counts, as well as his personal BABIPs of .259 in 2019 and .292 for his career.

Why might this be happening? The first place to look would be the shift: In 2019, DeJong was shifted in 24.5% of his at-bats compared to just 8.3% the year before. But he actually performed better in these at-bats than when he was not being shifted at all, posting a .326 wOBA against shifts compared to a .323 wOBA when not being shifted.

Another possible explanation would be the actual types of pitches he faced in 2-0 counts, as DeJong performs significantly worse against offspeed pitches than fastballs. However, in 2-0 counts in 2019, he fastballs at a rate close to league-average. Here are his numbers:

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And those for the league. Note: Stats like shift percentage might be inflated a bit due to no IBB data being included.

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Pitch data shows that pitchers are not using a different pitch mix against DeJong compared to the average hitter. What it does show, however, is that DeJong is shifted more in 2-0 counts than the average hitter. Nonetheless, the 5% difference is insignificant given DeJong’s performance against the shift. Therefore, if DeJong continues to reach 2-0 counts at the rate he is, his results should improve in 2020 and beyond.

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Javier Báez – .258/.258/.500 in 62 ABs in 0-2 counts

Javier Báez carried an impressive .758 OPS in 0-2 counts in 2019, a mark that is nearly double the league-wide average. Given that Báez has an 86th-percentile sprint speed and barrels the ball extremely well, a higher-than-normal BABIP should be expected. This is shown to be true through his .339 BABIP in 2019. However, in 0-2 counts, Báez’s BABIP inflated to .500 and his swinging strike rate increased to 21.6% (up from his season-long mark of 18.4%).

Hitters should be aiming to finish their at-bats in as many favorable counts as possible. Last season saw 0-2 counts bring the worst results of any count in terms of average, slugging percentage, BABIP, and OPS, and it was the fifth-most common count for an at-bat to end in. In 2019, Báez saw 62 at-bats finish in 0-2 counts, his fourth-highest total; Báez was not only overperforming in 0-2 counts, but he was not avoiding them either. In a count which should be providing his worst results, Báez’s slugging percentage is in line with his 2019 season mark in a sizable sample. An 0-2 count also produces the lowest BABIP, signifying a decrease in quality of contact.

Báez’s results are in no way sustainable given the fact that 0-2 counts are pitcher-friendly, he swings and misses more, and his underlying metrics (such as his BABIP) point to luck more than skill in this count. I would be expecting considerably worse results in 0-2 counts for Báez going forward. (Editor’s note: Good prediction so far.)

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There are, of course, countless examples of hitters having outlier performances in specific counts in any season, but trying to make sense of them can help to paint a clearer picture of breakouts and slumps. This “unsustainability” of performances will only be increased in 2020 as the shorter season and unique circumstances lead to incredibly interesting stat lines and perceived performances in hitters.

This article was originally published at my blog, From the Dome, in July.

We hoped you liked reading Using Count Data To Find Unsustainable Performances by Dante Signorella!

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would love to see more on this – who else is unsustainable?


Hey dantesignorella, I like the piece (I always love a good under/overperformer analysis), but I’m a bit confused why we would use OPS here to show how well the hitters do on these counts. Unless I’m mistaken as to the reason you show this, wouldn’t we just want to use Statcast stats (like Hard Hit % and general launch angle) to show contact quality and how well the hitters do? Since most of the difference between AVG and OBP is due to walks, and (I apologize for the obvious) no one walks on plate appearances ending in 2-0 or 0-2… Read more »