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Sam Chinitz dives into quality of contact metrics using zxwoBACON and Statcast data to search for fantasy baseball bargains and draft values heading into 2021 drafts.
Of the 1,015 hitters sampled(1) between 2015 and 2019, there were only 14 instances of hitters making more productive contact against pitches outside of the strike zone than against pitches inside of the strike zone based on xwOBAcon. Based on the same sample, the average hitter was a whopping .116 points better against pitches inside of the strike zone than outside of the strike zone in terms of xwOBAcon between 2015 and 2019.
One implication of that split is that plate discipline and contact skills can have an impact on a hitter’s contact quality. But is that impact significant enough to make a difference for fantasy managers? And if changes in plate discipline and contact skills don’t drive contact quality changes, what does?
This article is part one of a three-part series that will detail findings related to those questions. Part one will introduce the research and discuss the findings, part two will identify potential fantasy values for 2021 based on the findings, and part three will discuss the longer-term applications of the findings.
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The Math
xwOBAcon can be broken down into four variables:
- zxwOBAcon: xwOBAcon against pitches inside of the strike zone.
- oxwOBAcon: xwOBAcon against pitches outside of the strike zone.
- zBBE%: Percent of batted balls against pitches inside of the strike zone.
- oBBE%: Percent of batted balls against pitches outside of the strike zone. Since oBBE% + zBBE% represents all of a hitter’s batted balls, oBBE% can be expressed as 1-zBBE%.
With those variables in mind, the following equation represents xwOBAcon:
xwOBAcon = (zxwOBAcon * zBBE%) + (oxwOBAcon * (1-zBBE%))
One way to determine how much influence each of those variables has on changes in xwOBAcon is to hold all other variables constant while applying a one standard deviation increase to the variable in question. The graph below shows both the scale of a one-standard-deviation change in each variable as well as the average effect of that change on xwOBAcon for the sampled hitters.
Although the standard deviation of season-to-season changes in zxwOBAcon is the smallest of the three variables, its effects on xwOBAcon are the most significant by far. Using 2020 weights for wOBA(2), a one-standard-deviation increase in zxwOBAcon would be the equivalent of a hitter adding more than six home runs to their home run total at the expense of outs(3), while a one-standard-deviation increase in oxwOBAcon is worth about two home runs, and a one-standard-deviation increase in zBBE% is worth about one home run.
That zBBE% is by far the least significant of the three variables in this regard does not mean that plate discipline and contact skills never drive changes in contact quality, but those skills are likely not worth focusing on when searching for potential xwOBAcon risers. Instead, fantasy managers should look for likely zxwOBAcon risers to spot contact quality based draft values.
Spotting zxwOBAcon Risers
Since season-to-season changes in zxwOBAcon (and each of the other variables) can be approximated by a normal distribution, around 16% of hitters will improve their zxwOBAcon by more than one standard deviation each season. Identifying the hitters likely to be a part of that top 16% will allow fantasy managers to find undervalued hitters in drafts.
One aspect of hitters who tend to find themselves in the top 16% of zxwOBAcon risers each season is that they are overwhelmingly rebound candidates(4). Rebound candidates account for 80% of hitters who increased their zxwOBAcon by at least one standard deviation in season x+2, despite making up just under half of the sampled hitters. And rebound candidates in general tend to see their zxwOBAcon increase season-to-season, especially compared to non-rebound candidates.
To some extent, that breakdown should be expected. That hitters don’t maintain all of their declines (or gains) in zxwOBAcon each season -- although valuable information -- is not particularly notable.
What is notable, though, is the extent to which rebound candidates bounce back season to season. The average rebound candidate posts a higher zxwOBAcon in season x+2 than they did in season x, and nearly two-thirds of rebound candidates post a zxwOBAcon in season x+2 that’s at least 95% of their zxwOBAcon in season x.
Still, not every rebound candidate is a lock to recoup most of their zxwOBAcon losses in season x+2. It’s not clear what separates hitters who bounce back from those who don’t -- the size of a hitter’s drop in zxOBAcon had only a weak relationship to the size of their rebound -- but fantasy managers should probably expect less robust bouncebacks (or a continued decline) from hitters who saw their zxwOBAcon decline in two consecutive seasons(5).
Even so, with 60% of rebound candidates recovering all of their season x+1 zxwOBAcon losses (and then some) and three-quarters of rebound candidates posting a season x+2 zxwOBAcon that’s more than 90% of their zxwOBAcon in season x, rebound candidates available for steep discounts make for attractive draft picks.
What This Means For 2021
In part two, I’ll go into more detail on players who suffered most from drops in zxwOBAcon in 2020 and establish target draft pick ranges for those players. For now, here are the five hitters who saw their zxwOBAcon drop most between 2019 and 2020 (min. 100 batted ball events against pitches inside of the strike zone):
| Player | 2019 zxwOBAcon | 2020 zxwOBAcon | Difference |
| Joey Gallo | 0.693 | 0.416 | -0.277 |
| Carlos Correa | 0.549 | 0.368 | -0.181 |
| Yoan Moncada | 0.505 | 0.359 | -0.146 |
| Josh Bell | 0.513 | 0.369 | -0.144 |
| Cody Bellinger | 0.532 | 0.398 | -0.134 |
Joey Gallo jumps out as the biggest faller in zxwOBAcon by a wide margin. Based on the research outlined above, fantasy managers should not be concerned about the scale of Gallo’s decline in zxwOBAcon, and the relative likelihood of a bounceback season (in terms of contact quality) makes Gallo an attractive buy-low candidate if he falls in drafts.
Yoan Moncada is another player who is a good bet to rebound in 2021. Coronavirus-related drags on Moncada’s 2020 performance should (hopefully) be non-issues in 2021, and the data supports a zxwOBAcon rebound under normal circumstances.
The last player I’ll touch on here is Cody Bellinger. Fantasy managers who may be worried about Bellinger’s steep performance drop off between 2019 and 2020 should prepare for him to rebound in a big way next season, and the 25-year-old represents a potential bargain in drafts because of his relatively poor 2020.
Notes:
- Players sampled were those who hit at least 200 batted balls against pitches inside of the strike zone and 50 batted balls against pitches outside of the strike zone. Each hitter/year combination counts as one hitter in the sample, so players may be counted multiple times if they qualify in multiple seasons.
- It’s worth noting that xwOBAcon is not the ideal metric for this exercise because the weights for each hit type change each season. xSLG is likely a more fitting metric for that reason, but xSLG was not available and xwOBAcon is still a serviceable metric.
- Or around 14 singles or several other combinations of improved batted ball production.
- For this article, rebound candidates are hitters who saw their zxwOBAcon decrease from season x to season x+1.
- Rebound candidates whose zxwOBAcon losses were mostly the result of launch angle struggles were no more or less likely to bounce back than those whose losses were mostly the result of exit velocity decreases. There were only 36 sampled instances of hitters posting zxwOBAcon losses in consecutive seasons with a third consecutive season of data, and that small sample size makes those impacts unclear.
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