Personnel adjustment for home run park effects in Major League Baseball

In Major League Baseball, every ballpark is unique, with its own geometry and climate. Some ballparks may be more conducive to home runs than others. Quantifications of home run friendliness abound, but are often based on limited data and typically do not include uncertainty assessment. Further, personnel effects from individual players are rarely considered. We fit generalized linear models, taking as the observational unit the combination of game and handedness-matchup of the batter and pitcher, usually leading to four home run totals per game. The Poisson model provides a good fit for counts observed in the 2010-2024 season and generalizes well out-of-sample. We model personnel effects by constructing “elsewhere” measures of individual batter and pitcher home run tendency using data from parks other than the one in which the response is observed. All pairwise comparisons of ballparks are made, with multiplicity adjustment, using means adjusted to teams of average batters facing average pitchers in average handedness frequencies. Estimated standard errors for these means and differences are reported. We find that adjusted home run frequencies are substantially different from observed frequencies, leading to considerably different ballpark rankings than those based on unitless park factors appearing in baseball media.