Strength, speed, and anthropometric predictors of in-game batting performance in baseball.

A key focus of sports science research is the identification of quantitative assessments that can predict players' on-field performance and developmental potential. Despite efforts to establish predictive models, there are few validated measures that show reliable associations and large gaps in understanding. Here, we test a multidimensional battery of assessments developed through the USA Baseball, Prospect Development Pipeline that capture strength and functional movement abilities, and anthropometric characteristics, in a two-year cohort of collegiate baseball players from the Appalachian League. Swing propensity metrics for Zone Contact Percentage (ZCP: proportion pitches in strike zone swung at and hit) and Hard-Hit Percentage (HHP: proportion in-play balls with exit velocity ≥ 95 mph) were calculated on 189 players. Models testing hierarchical combinations of anthropometric and anthropometric plus assessment data were implemented using nested cross-validation with random forest and elastic net regression. Results indicate that anthropometric features account for 29% of variance in ZCP and 50-55% of HHP, while the addition of assessment contributed an additional 1-3% to ZCP and 5-12% to HHP, with top predictors coming from PDP strength and power assessments. These findings delineate contributions of andromorphic and physical abilities to in-game baseball performance using a validated assessment battery and advanced game statistics.

Predictors of batting and pitching performance in the USA baseball prospect development pipeline.

This study examined the relationship between psychomotor abilities and baseball performance by analysing data from 379 athletes who participated in the USA Baseball, Prospect Development Pipeline (PDP). Hit and pitch metrics were generated during practice sessions using the RapsodoTM System. Data were compared through exploratory factor analysis and hierarchical regression. Factor analysis grouped batter's PDP evaluations into four latent variables accounting for 63% of variance. Pitcher performance grouped into three factors accounting for 51% of variance. Regression on batter data revealed a significant demographic/anthropometric base model with height, weight, and age that accounted for 58% of the batted ball speed (R2 = 0.581). Player position explained 2% of the variance (R2 = 0.604), and PDP evaluation scores contributed an additional 3% (R2 = 0.631). Regression of pitcher data showed a significant base demographic/anthropometric model accounting for 36% of fastball pitch speeds (R2 = 0.363), with the PDP evaluation scores adding 6% additional variance (R2 = 0.424). Uniformly, assessments of lower body strength added the greatest predictive information. Hand grip strength did not correlate with pitch metrics. While demographics/anthropometrics are major contributors to batted and pitched ball speed, position and psychomotor variables add statistically significant contributions and may be of practical value for player selection.