Strikeout and walk rates are perhaps the most popular and widely used peripheral statistics, particularly for pitchers. However, with pitch level data, these statistics now have “peripherals” of their own. I was curious if I could create an accurate-yet-interpretable model using FanGraphs’ plate discipline metrics that could offer insight on what drives the differences in strikeout and walk rates between players.

While many have noted individual correlations between a single statistic and strikeout rate, I have not seen many unifying models that incorporate several plate discipline metrics. For the first part in this study, I will focus on hitter strikeout rate, but I intend on also looking at walk rate and, later on, pitchers’ strikeout and walk rates.

If you are not a fan of mathematical details, feel free to skim or skip these next few sections to get to my overall conclusions.

Methodology

Note: I used BIS discipline statistics rather than PITCHf/x. I do not think this made a significant difference, but I think it is important to keep in mind.

FanGraphs gives us nine plate discipline statistics to work with. However, several of them can be removed as they can be derived using the other statistics. In a regression setting, this phenomenon is called perfect multicollinearity, which is when an explanatory variable can be perfectly formulated by other explanatory variables. With a high degree of multicollinearity, it can be extremely difficult to tell which particular variable is responsible for a change in the response variable, which is problematic for inference. Using some basic dimensional analysis, I found formulas for all three of these: Read the rest of this entry »

With the advent of Statcast, statistics like exit velocity, spin rate, and launch angle have become easily accessible to baseball fans. Catcher pop time data has also become available. However, unlike some of the other Statcast metrics, catcher pop time data has existed for much longer, with scouts measuring pop times in the minor leagues years before Statcast entered the mix.

This sounds all well and dandy, right? Well, it would be, if the Statcast numbers were consistent with scouting pop time tool grades. Baseball Prospectus, for example, calls a pop time from 1.7-1.8 a 70 pop time, which sounds reasonable enough without any context. However, considering the best average Statcast pop time to second base from 2015 to 2019 was JT Realmuto’s 1.88 (minimum 10 throws to second), something seems amiss here. I decided to take a deeper look into Statcast’s pop time data to get a better idea of what’s going on.

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