Data-driven decision-making is increasingly vital in the sports domain to enhance team performance and inform recruitment strategies. This paper introduces ChampionView, a player recommendation system designed to support selection committees during the athlete recruitment process. ChampionView applies machine learning techniques and domain-specific performance metrics to evaluate player skill sets, physical attributes, historical performance, and growth potential. The system provides objective, data-backed recommendations, thereby streamlining decision-making and promoting the selection of high-potential athletes. The study also addresses key challenges in sports data analytics, including the integration of heterogeneous data sources and the design of fair, transparent recommendation algorithms. Broader implications are considered in relation to team optimization and athlete career development. The proposed system includes interactive visualization capabilities via a Power BI dashboard to facilitate user interpretation and engagement. These contributions advance the field of sports analytics and talent identification by demonstrating the potential of intelligent systems in real-world recruitment contexts.