Angela D. Miller

Burke, AA, Saine, ND, King, EL, Fields, JB, Jagim, AR, Miller, AD, Green, BC, and Jones, MT. Assessment of countermovement jump performance in collegiate baseball athletes: A longitudinal multilevel modeling approach. J Strength Cond Res XX(X): 000–000, 2026—Given the length and demands of the collegiate baseball season, monitoring body composition and neuromuscular performance can help optimize load management and athlete readiness. Relationships between body composition and countermovement jump (CMJ) performance were examined during a competitive baseball season. Pitchers and position athletes ( n = 32) completed pre- and post-season dual-energy x-ray absorptiometry scans and weekly CMJ testing. Countermovement jump variables included jump height (JH), reactive strength index modified, peak braking force (PBF), and peak propulsive force (PPF), all normalized to body mass (BM). Repeated-measures multivariate analyses of variance assessed time and group (pitcher vs. position player) effects. Pearson correlations evaluated associations between CMJ and body composition metrics, and hierarchical linear models (HLMs) quantified individual performance trajectories. Preseason-to-postseason reductions were observed in BM, fat free mass (FFM), JH, and PPF ( p < 0.05). Peak braking force declined in pitchers, while PPF declined across both groups. Body composition showed negligible correlations with CMJ metrics, except for a moderately negative correlation between JH and body fat percentage ( r = −0.52, p < 0.001). Hierarchical linear models revealed curvilinear CMJ performance trajectories ( p < 0.001), with position players exhibiting lower PBF throughout the season ( β = −1.47, p = 0.03) and preseason FFM positively predicting PBF ( β = 0.118, p = 0.010). Findings suggest that reductions in lean mass and force output occur across the competitive season and vary by position. It is recommended that practitioners implement routine longitudinal monitoring when using CMJ and body composition assessments, as they may assist in detecting neuromuscular fatigue and guide individualized training interventions.

Brown, FSA, Fields, JB, Jagim, AR, Miller, A, Baker, RE, and Jones, MT. Relationship between individual workload, training impulse, and game performance in women collegiate basketball athletes. J Strength Cond Res XX(X): 000–000, 2026—The purpose was to quantify game external load (EL) demands and explore relationships between EL, internal load (IL), and measures of basketball performance across a competitive season in women basketball athletes ( n = 7). Data were collected by an inertial measurement unit microsensor and heart rate monitor for games ( n = 28) and practices ( n = 61). Playerload™ (PL), training impulse (TRIMP), and player efficiency (PE) were used to quantify EL, IL, and basketball performance, respectively. Descriptive statistics were used to quantify individual athlete PE, IL, and EL game measures, and game statistics for each game and stratified by position type. Repeated-measures correlations assessed relationships between PL, TRIMP, and PE. Two separate location scale models (PL, TRIMP) assessed the effect of session, position, PE, and previous day workload (PL lag, TRIMP lag). Results showed small to very large relationships ( r = 0.21–0.85) between PL, TRIMP, and PE. Previous day workloads (PL, TRIMP) positively affected next-day load. Playerload increased as PE increased. Guards experienced higher within person variability of PL than forward or center positions. Athletes experienced high within person variability of PL (25.50 AU) and TRIMP (20.90 AU) across the season. The average TRIMP remained consistent across the season (230.2 AU) but varied between athletes ( B = 0.90 [95% CI: 0.30–6.50]). Results suggest preceding day workload is associated with next-day workload and player performance, with higher EL being associated with higher PE. Furthermore, higher variability among athletes indicates other factors may affect workload across the season beyond sport training.

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.

External (EL) and internal (IL) load are commonly used methods used to quantify training load in team sports. Playing time and playing position may influence the training loads for specific athletes throughout a season. The purpose of the current study was to evaluate the effect of athlete playing status and individual in-season practices on EL and IL across a collegiate women’s basketball season. Female basketball athletes were classified as high-minute (HMA; ≥15 min/game) or low-minute (LMA; p < 0.001). A location-scale model indicated that the within-person variability of IL across all athletes was 3.29 AU but was not due to athlete playing status. It is recommended to base in-season training on individual loads and game demands to promote athlete readiness and improved sport performance.

Mathematics educators often know the widespread nature of mathematics anxiety and may even be aware of the debilitating effect it can have on their students. Although educators may develop tricks or tools that seem to help, a comprehensive understanding of what students are experiencing and how the classroom environment can be adjusted to address their struggles is missing. Control-value theory offers a theoretical framework for how the classroom environment and students’ appraisals influence mathematics anxiety (Pekrun, 2006). Mathematics anxious students are focused on not failing, rather than on mastering the material, and have a low sense of control over outcomes. To decrease mathematics anxiety, teachers can implement strategies that increase emphasis on learning over performance and support autonomy, thus decreasing fear of failure and avoidant goal orientations, and increasing self-efficacy and control appraisals. Suggested strategies for adjusting instruction focus on high school and college classrooms.

Although research shows that higher-achieving students report engaging in cheating behaviors less frequently than lower-achieving students, the cheating rates among this population are still startling. Certain aspects of the context of being a high-achieving student support academic dishonesty. We investigate integrity among the highest achievers using a motivational framework, first examining why these students feel the need to cheat. We discuss personal standards of performance, social comparison and competition, pressure to succeed, and these students’ ability to rationalize cheating behaviors. Finally, we suggest what can be done to combat cheating among high achieving students, including thinking about approaches to pedagogy and assessment, providing clarity and consequences for cheating, and considering the culture of high achievers.