A little more than a year ago, Al Melchior had the brilliant and beautifully straightforward idea of investigating how strongly pretty much ever Statcast metric correlated with various traditional power metrics and compiling them in one post. He asked me to help out, which I was more than glad to do.
Recently, I saw folks talking about this again, and someone asked specifically about the 2019 season. I figured I could refresh the values from the original post quickly enough (certainly a lot more quickly than I did last time), and it would also help bring pertinent information to the fore for folks neck-deep in draft prep.
Spoiler alert: the results barely changed. But! I do feel more confident in this particular set of values, as I nerded out with programming instead of pulling dozens of different queries from the Baseball Savant search function and constantly getting frazzled.
OK, here’s the goods. For 2019 hitters:
|Avg EV: FB & LD||0.81||0.72||0.53||0.84|
|Avg FB dist||0.81||0.75||0.58||0.83|
|Avg EV: all||0.66||0.65||0.59||0.79|
|Avg HR dist||0.51||0.44||0.30||0.56|
|95+ mph %||0.50||0.56||0.55||0.56|
|Avg EV: GB||0.36||0.35||0.38||0.37|
(Default sort: descending by xwOBA)
Minimum 300 plate appearances
HR/oFB = home runs on outfield fly balls
- HR/oFB = home runs per outfield fly ball. I calculated this manually using Statcast data, which provides the following two quirks: (1) Statcast codes fly balls differently from Baseball Info Solutions, which powers fly ball percentage (FB%) on FanGraphs; (2) HR/FB on FanGraphs includes pop-ups, whereas HR/oFB does not, theoretically making it a more-accurate metric as far as measuring power is concerned.
- The individual numbers are Pearson correlation coefficients, or ‘r.’ You may have heard of r-squared when folks talk about goodness of fit models. This is the same! Except not squared. And it ranges from -1 (perfectly negative relationship) to +1 (perfectly positive), with 0 indicating no relationship whatsoever. The relationship between barrels per batted ball event (Brl/BBE %) and expected weighted on-base average (xwOBA) of 0.93 is extremely strong!
And here’s how each of the metrics correlates with itself across years (year 1 to year 2, e.g., 2018 to 2019). Folks often refer to this as year-to-year “stickiness.” The sample is limited to hitter-seasons pairs that have at least 300 plate appearances in both year 1 and year 2, and spans the 2016 through 2019 seasons.
|Statcast Metric||y1 → y2|
|Avg EV: FB & LD||0.82|
|Avg EV: all||0.78|
|95+ mph %||0.73|
|Avg FB dist||0.64|
|Avg EV: GB||0.61|
|Avg HR dist||0.47|
It’s worth noting that reducing the minimum plate appearance threshold from 300 PA to 100 PA immaterially changes the results, even though the individual player-seasons are subject to greater noise due to smaller performance samples. In fact, they’re so similar, they’re indistinguishable. It suggests to me that all of these Statcast metrics, such as maximum exit velocity (EV), average fly ball distance, etc., stabilize pretty quickly, making them good indicators of talent early in the season. This claim is presently unsubstantiated, at least by me, but, again, the evidence is there, and I’m interpreting it optimistically! A project for another day, I suppose.
Ultimately, the “best” metric to use for power purposes appears to be barrels any form. And if you’re not comfortable with using a component metric – something that requires multiple inputs (barrels requires exit velocity and launch angle) – then it appears average exit velocity on fly balls and line drives might be next-best when accounting for both strength of relationship and year-to-year stickiness.