Team members: Ben Buman , Kiran Shay, Jake Rasmussen

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. 

Department: Computer Science