<![CDATA[The development of sports analytics has transformed basketball into a data-driven discipline. While professional leagues fully utilize advanced metrics like the Player Efficiency Rating (PER), collegiate sports environments in Indonesia, including Universitas Advent Indonesia (UNAI), still rely on manual data tracking, leading to information loss and a lack of predictive foresight. This research bridges this gap by developing a web-based basketball statistical tracking and analytics system tailored for the UNAI Varsity Team. The platform integrates a comparative machine learning engine evaluating traditional Linear Regression against XGBoost (Extreme Gradient Boosting) to forecast future player efficiency based on box-score metrics. Developed using a Svelte frontend and FastAPI backend, the system incorporates automated median and sequential imputation to resolve collegiate "cold-start" data sparsity. Empirical evaluation demonstrates that XGBoost significantly outperforms the linear baseline, achieving a Residual Sum of Squares (RSS) of 0.0847 and an R² score of 96.42%, representing a 44.4% error reduction over Linear Regression (0.1523 RSS, 88.10% R²). The resulting web application equips UNAI athletic decision-makers with data-supported tactical insights and prospective roster optimizations.]]>
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