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Optimizing Baseball Batting Orders: A Data-Driven Approach to Lineup Efficiency
- Program: Computer Science
- Course: EN.601.513 Group Undergraduate Project
- Year: 2025
Project Description:
Our project focuses on optimizing baseball batting lineups using a novel statistic we created called Baserunner-dependent Run Production (BRP). Traditional lineup evaluations often overlook how the performance of one batter affects others in sequence. To address this, we analyze all possible 4-batter groupings (4-tuples) within a lineup and assign each a BRP value, which captures the expected run contribution based on player-specific probabilities and base-out states. We then use these values to evaluate complete 9-player lineups and select the one that maximizes total BRP. This approach shifts the focus from individual metrics like batting average or RBI to a more holistic, interaction-aware model. Our optimizer accurately identifies the most productive batting orders and can be applied to real player data to help teams make more strategic decisions. Initial results are promising, and we plan to further refine the model through pilot testing and performance validation.
Project Photo:
Optimize your baseball lineup with data-driven insights! Our lineup optimizer combines player stats and strategic analysis to build the most effective batting order, enhancing offensive potential and game performance.