This project explored how markerless biomechanical data collected from a single MiLB player across repeated run-outs of the batter's box during baseball games over the course of one month could be transformed into an objective measure of player workload and daily readiness. I developed a biomechanics pipeline that estimated whole-body center-of-mass motion, extracted interpretable running metrics, and quantified cumulative mechanical demands across repeated run-outs from the batter's box (Figure 1). Rather than treating all workload equally, the proposed framework distinguished between movement strategies that achieved similar physical output with greater or lesser efficiency by incorporating measures of sprint economy and explosiveness into a composite workload score (Figure 2). The resulting readiness framework simulated how longitudinal biomechanical monitoring could inform pre-game lineup decisions using only information available before first pitch. Statistical analyses were performed to evaluate relationships between extracted biomechanical features and game outcomes, while visualization tools were developed to verify movement quality and monitor workload accumulation throughout individual games. I propose extending this framework beyond a single athlete into a population-level model capable of comparing individualized movement baselines with the biomechanical signatures of elite performers. The long-term objective is to combine markerless biomechanics, statistical modeling, and longitudinal monitoring to support performance optimization, workload management, and injury-risk assessment in professional athletes.