RADIO
For pitchers, K% minus BB% is generally recognized to be a superior metric to K/BB, and one of the better metrics out there for capturing pitcher talent. For batters, though, BB/K is still the dominant metric, widely referenced in baseball research.
BB% minus K%, while not new, is only referenced in a few sabermetric niche corners of the universe. This article argues on behalf of using BB% minus K% for batters more broadly, showing it is generally a more reliable, descriptive, and predictive statistic than its ratio counterpart – just as we see for pitcher K% minus BB%.
Below, we'll test the value of each metric and find standout hitters for plate discipline.
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Testing BB/K and BB% - K%
The two metrics will be compared in terms of their reliability, or their correlation with themselves in the future, giving an idea of how much stable skill is measured by each. They will be compared in terms of their descriptive power to see how much they explain present hitting success (wRC+). They will also be compared in terms of predictive power, to see how indicative they are of future hitting success (wRC+).
To run these tests, I looked at all American League hitters from 2012 to 2022, with a minimum of 300 plate appearances in adjacent seasons (n = 688 hitters).
The differences aren't huge, but BB% minus K% outperforms BB/K in terms of reliability, with a higher correlation to itself one year into the future, descriptive power, with a higher correlation with same season wRC+, and predictive power, with a higher correlation with wRC+ one year into the future. A similar story emerges if we look at all hitters instead of just American League hitters.
Again, BB% minus K% slightly outperforms BB/K across metrics.
As an additional robustness test, I conduct a weighted regression analysis for each metric pairing in the correlation matrices. Weighted regression allows me to capture data covering more hitters, weighting hitter impact on the regression by plate appearances (specifically, the lesser of plate appearances in two adjacent seasons) rather than use some arbitrary plate appearance cutoff (I still use a lower 90 PA cutoff for convenience, to filter out pitchers, etc.).
As seen with the raw correlations, the correlations generated from the weighted regression show BB% minus K% slightly outperforming BB/K across metrics.
Shifting Focus from BB/K to BB% - K%
Having established the reliability, and predictive and descriptive power of BB% - K%, we take a look at the leaders in projected BB% minus K% using Depth Charts Rest of Season projections from FanGraphs.
There are no surprises on the list above. It features the game's best players in terms of contact and plate discipline, ignoring contact quality. There is a tight correlation between the two measures of focus (.84 across the full sample of hitters with rest of season depth charts projections) and percentile ranks too (.85) – despite their differences, both metrics align more often than they don't. Notwithstanding, there are some big movers and shakers at the extreme ends of the sample. The table below shows the top 10 biggest percentile rank gainers (green) and fallers (red) when looking at BB% - K% instead of BB/K.
Generally, the low BB%, low K% guys are penalized more harshly by BB/K, whereas the differential looks upon them more favorably. The high BB%, high K% guys, on the other hand, are penalized more by the differential.
Conclusion
This article established the validity of BB% - K% for hitters, making the case that it is an underused metric that captures contact and plate discipline skills better than alternatives. It ignored power, though one of the nifty things about BB% minus K% is that you can add barrel % to it, and then you have an all-encompassing metric of hitter ability, similar to wRC+ or wOBA – an area for future research.
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