I have a new toy: a dataset of “balls hit in play” that consists of the types of batted balls—infield ground balls, outfield flies, infield popups, etc.—that I coaxed out of the hieroglyphics that constitute Retrosheet play-by-play event codes, using a program I created for interpreting the same. There is a very high correlation between my results and the “Project Scoresheet” Retrosheet companion codes, which I had been steering clear of because of Sean Smith’s bad experience with them. But his troubles likely had to do with field-position coding, which I’m less focused on for now. Anyway, the new data is useful for assessing the perpetually disputed Voros McCracken postulate that pitchers have no influence over the fieldability of balls hit...

It had to be done. Well, not really. But I did it because I’m still collecting examples of season-by-season trends in performance standard-deviations for the purpose of testing the Gould conjecture. In this case, “it” was the compiling of standardized strikeouts per 9IP. That was essentially a natural byproduct of the aforementioned testing, which required computing season-by-season K/9IP weighted means and weighted standard deviations, something I did based on the performance of every AL/NL pitcher who threw ≥ 100 innings from 1900-2024. With that data I calculated each pitcher’s season-specific K/9IP z-score, which reflects his performance relative to the mean pitcher’s performance that year. Placed on this common, standardized scale, we can...

So I thought it would be fun to start the year with another standardized “all-time best seasons,” this one focusing on fielding-independent pitching or FIP. Before I get to the results, let me quickly review the motivation for, and mechanics of, this sort of analysis. If one wants to compare historical “best seasons” rate statistics in baseball, it’s usually a mistake to look at raw numbers. Changed game conditions, unrelated to player skills, bend and twist statistics like batting average, on-base percentage, and OPS, obscuring the quality of the performances behind them. Standardization dispels this variability fog. It transforms values from a normal distribution into units that reflect the number of standard deviations they are from the...

Well, with reluctance and trepidation, I’ve pretty much concluded that Stephen Jay Gould’s (r)evolutionary conjecture on the variability of sports performances is wrong. You might know (or if you have been following my own journey learned) that Gould famously conjectured that athletic performances can be expected to become less variable as a sport matures. This dynamic reflects selection forces akin to those in evolution. Just as members of a species can be expected to converge on a fitness-promoting gene mutation, so athletes can be expected to converge on a trait that promotes successful performance, thereby reducing variability in that trait. Gould applied that insight to batting averages and used it to explain the extinction of the .400...

I noticed this pretty much by accident as I was examining something else, and I thought it was pretty interesting: Baseball Reference and FanGraphs obviously disagree about the value of the five best single-season pitching performances of baseball’s most high-profile Johnsons. I think I know what’s going on here. FanGraphs bases its preferred pitcher WAR calculations on fielding-independent pitching (FIP), albeit with some adjustments, whereas Baseball Reference uses an “expected/actual run differential” formula. FIP is a metric that favors pitchers from the last three decades or so, the period in which strikeout rates have surged. FanGraph’s choice makes sense for pitchers of the “Great Transformation” era (the period in which the game has...

Okay, so here’s another crazy one. Look! See it? It’s another Gould-defying trend in a baseball-performance standard deviations! I suppose it makes sense to start with what it’s the standard deviation of. Basically, it’s a regression-derived fielding-independent pitching (FIP) metric.  FIP is an index comprising a pitcher’s strike out rate, home-run allowed rate, and hit-by-pitch rate. These are all elements of run-avoidance that don’t depend on the quality of the defense that backs the pitcher up. To calculate a metric of this sort, I regressed runs allowed per game for every pitcher for every season (≥ 100 IP) of AL and NL history against his (season-specific) strikeouts per 9/IP, HR/IP & HBP/IP. The resulting equation generates an expected...