Saine, Noelle D.

Purpose: The purpose of the current study was to cross-validate previously developed RMR prediction equations using laboratory measurements of RMR as a criterion. Methods: National Collegiate Athletic Association (NCAA) Division I (n = 60) and III (n = 113) male wrestlers participated in the current study (n = 173; Age: 19.6 ± 1.5 yrs.; Height: 176.3 ± 6.3 cm; Weight: 76.6 ± 10.9 kg; Body Fat: 11.4 ± 3.0%). Indirect calorimetry was used to measure RMR and Skinfold thickness was used to estimate body fat percentage (BF%) during the preseason period under standardized dress (underwear only), fasting, and resting conditions. Results: Mean measured RMR was 2102±341 kcal·d −1 . All body mass-based RMR prediction equations significantly underestimated the measured RMR, with the exception of the Jagim and Schofield equations. The Tinsley fat-free mass (FFM) equation and the Cunningham equation had the lowest constant error (CE) values when compared to measured RMR (mean bias ± standard error: 80 ± 20 kcal and 126 ± 20 kcal·d −1 , respectively). Linear regression indicated the Tinsley FFM (R 2 = 0.422, RMSE = 279 kcals, %RMSE = 13.3%), and Cunningham FFM equations had the closest agreement to measured RMR (R 2 = 0.422, RMSE = 301 kcals, %RMSE = 14.3%), along with the Jagim equation for a body mass-based prediction equation (R 2 = 0.377, RMSE = 338 kcals, %RMSE = 16.1%). The standard error of the estimate (SEE) ranged from 260 to 272 kcal·day −1 across equations. Coefficients of determination (R 2 ) ranged from 37% to 42%. The new body mass and FFM-based equations exhibited good agreement based on the CE (26 and -13 kcal·day −1 ), RMSE (274 and 252 kcal·day −1 ), respectively. Conclusions: The newly developed equations demonstrated mean bias <2% of measured RMR.

Abstract Burke, AA, Saine, ND, King, EL, Fields, JB, Jagim, AR, Miller, AD, Green, BC, and Jones, MT. Longitudinal modeling of pitching workload and countermovement jump performance as determinants of fastball velocity and consistency in collegiate baseball pitchers. J Strength Cond Res XX(X): 000–000, 2026—Maintenance of four-seam fastball (FSFB) velocity across the baseball season is critical, yet the extent to which workload and neuromuscular status relate to velocity is unclear. Thus, aims of the current study were to assess the relationship between external pitching workload metrics and FSFB velocity; quantify within-season FSFB velocity; and examine how countermovement jump (CMJ) performance predicts FSFB velocity. Collegiate pitchers ( n = 8) completed weekly CMJ testing during a 15-week, 53-game season. Countermovement jump metrics included jump height (JH), reactive strength index–modified (RSImod), peak braking force, and peak propulsive force normalized to body mass. Workload variables (innings, total pitches, FSFB pitches) and FSFB velocity were evaluated via repeated measures correlation (RMCORR). A longitudinal heat map visualized velocity trends, and a hierarchical location–scale model assessed predictors of mean velocity and variance. Repeated measures correlation showed weak negative associations between FSFB velocity and innings ( r = −0.15, p = 0.11), total pitches ( r = −0.18, p = 0.05), and FSFB pitches ( r = −0.18, p = 0.05). Heat map analysis identified 3 distinct velocity clusters. Location model results indicated no predictors of mean velocity. The scale model showed JH ( γ = −0.060, p = 0.001) predicted reduced velocity variance, and RSImod ( γ = 3.800, p < 0.001) predicted increased variance. Findings suggest that velocity patterns varied across athletes, where greater workloads were negatively associated with FSFB pitch velocity. Further, CMJ performance related to velocity stability rather than mean velocity. Routine longitudinal monitoring and continued strength-power training are recommended to support FSFB velocity.