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; AUC[subscript Centers] = 0.93, AUC[subscript Guards] = 0.88, AUC[subscript Forwards]= 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. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]