Author ORCID Identifier

https://orcid.org/0000-0002-2029-0645

Semester

Fall

Date of Graduation

2023

Document Type

Dissertation

Degree Type

PhD

College

College of Applied Human Sciences

Department

Athletic Coaching Education

Committee Chair

William Hornsby

Committee Member

Sean Bulger

Committee Member

Michael Ryan

Committee Member

Joshua Hagen

Committee Member

Scott Galster

Abstract

The expanding opportunities to implement sport science frameworks in elite-level basketball environments coincide with the sport’s increasing global prominence. Concomitant to these opportunities is the continual growth of the sport technology market (e.g., wearables, force plates) and computational power (e.g., data management tools, coding capabilities), which yields solutions and challenges for both athletes and practitioners. Due to the rapid influx of new sport technologies in high performance environments, particularly American Collegiate Men’s Basketball, more formal and ecologically valid research on how to effectively utilize data derived from them, particularly over long periods of time (i.e., multiple seasons) is needed. To address these gaps in the research, the primary aim of this line of research was to retrospectively analyze longitudinal athlete monitoring data in NCAA Division-I men’s basketball athletes with the intent of generating practical and actionable insights for coaches to implement into future training plans. A novel component of this research was embedding a sport scientist within NCAA Division-I Men’s basketball for several consecutive years, which ultimately enabled this dissertation and the retrospective analysis of longitudinal data. To achieve the primary aim, there were three sub-studies that each address separate research questions and contribute uniquely to an overall athlete monitoring framework. The first sub-study aimed to simplify retrospective external workload data (from inertial measurement units) obtained during competitions via principal component analysis (PCA) and subsequent logistic regression. The PCA revealed two principal components that explained 81.42% of the total variance in external workload demands, while the multinomial logistic regression was able to accurately predict positional groups (Overall model, p < 0.0001; AUCCenters = 0.93, AUCGuards = 0.88, AUCForwards = 0.80) based on external load variables. Of note, maximal speeds, as well as deceleration and jumping volumes were the most sensitive to positional demands during competitions, indicating that powerful, eccentric neuromuscular capacities are a crucial component to basketball. With that in mind, the second sub-study sought to assess the utility of no arm-swing countermovement (CMJ) and loaded (20 kg) countermovement (LCMJ) jumps for monitoring potential alterations in deceleration capacities in response to the external workloads imposed on the athletes during training and competition. At varying timepoints and strength training periodization blocks across multiple seasons, at least two maximal CMJs and LCMJs were performed on dual force plates that sampled at 1,000 Hz. Linear mixed modeling (LMM) was utilized to predict jump heights while controlling for random effects of individual athletes and fixed effects of loading conditions (LOAD; 2 levels: CMJ, LCMJ), strength blocks (BLOCK; 3 levels: In-Season, Off-Season, Post-Season), interactions between LOAD and BLOCK, and reliable braking strategy metrics from the force plates. The model revealed a significant random effect for athletes (i.e., individualized athlete monitoring is warranted), as well as significant fixed effects for LOAD, Braking Net Impulse, and Avg. Braking Velocity. These findings suggested that LOAD during LCMJs elicited altered neuromuscular deceleration strategies (i.e., lower jump heights with slower and longer albeit more forceful decelerations during LCMJ) and that greater jump heights across the different LOAD and BLOCK conditions were obtained via slightly decreased braking net impulses that


were mostly influenced by remarkably faster braking velocities. Lastly, the third sub-study aimed to integrate data from the IMU and force plate technologies leveraged in the first and second studies, respectively. More specifically, the final sub-study sought to establish relationships between the external workload demands of training and competitions across a basketball season and their outcomes on neuromuscular capacities and deceleration strategies. Several LMMs were constructed to predict jump heights and different deceleration strategy metrics from the volume and intensity demands of competitions and practices. Power output (i.e., force plate jump heights) was found to increase with increases in deceleration volumes and acceleration intensities but decrease with increases in deceleration intensities and acceleration volumes. This suggested that while performing considerable volumes of decelerations may help promote maintenance of capacities, it is important for practitioners to keep close watch of intensities of decelerations as to not overly fatigue the athletes such that power output (i.e., jump height) is not adversely affected. These findings elucidate the widespread contributions of high volumes of intense eccentric contractions performed during basketball training and competition, as well as the relevancy for monitoring deceleration strategies to enhance long-term power output development. Moreover, this line of research comprised the addition of a sport scientist to the daily operations of player development in American collegiate men’s basketball, which served to produce a general longitudinal framework for exploring research questions centered around individualized athlete monitoring in ecologically valid environments. The framework comprised three main elements, which include 1) Programmatic Buy-In, 2) Technology Evaluation and Data Management, and 3) Periodized Athlete Training and Recovery, all of which are underpinned by an iterative and routine program evaluation process (Evaluate, Assess, Enhance). Indeed, certain elements of technology evaluations, data management, and periodization planning will inevitably need to be considered simultaneous to establishing programmatic buy-in. Also, in some cases, outcomes and processes stemming from technology evaluation, data management, and periodization may help promote staff engagement. Then, at time points that are applicable to the scenario and environment, the sport scientist must work with other relevant stakeholders to evaluate, assess, and enhance the various subcomponents of the athlete monitoring framework, as well as the entire framework itself. This line of research may serve future sport scientists who aim to effectively immerse themselves into their respective team culture while concurrently striving to integrate various data sources, manage team-specific databases, individualize athlete monitoring, and promote longitudinal development.

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