Evaluating the Effect of Simulation Model Realism on Softball Batting Lineup Performance

Abstract

Softball is a team sport played worldwide, and numerous factors determine which team outperforms another. While defense matters, determining an effective batting lineup is important for maximizing offensive performance. Previous work investigated how different batting orders affect average runs scored using a simplified Java-based softball simulation, but it did not account for many realistic aspects of softball. There is still a lack of research on the strategic aspect of this sport, and contradictions from coaches’ decisions. This study expands upon the previous model by incorporating more realistic game mechanics, including advanced player statistics (batting average, on-base percentage, slugging percentage, and on-base plus slugging), player speed, baserunning decisions, stolen bases, situational hitting, and pitcher abilities. Using the improved simulation, we were not only able to determine the most effective lineup within “OBP + Power,” “Ascending-Descending,” and “OPS-Descending Lineup,” but also the optimal player batting order. Thirty simulated games were conducted for each lineup, and the mean runs scored per game were compared with the results obtained using the original model. While the original simulator identified the optimal lineup to be “OBP + Power,” the enhanced model generated the highest average runs scored for OPS-descending. However, the newer model also produced higher total runs, indicating that further calibration is needed to represent real softball gameplay accurately. This improved simulation offers a more accurate framework for evaluating offensive strategy and shows how computational modeling can support evidence-based lineup decisions. Future research could incorporate defensive ability, fielder’s choices, and additional game strategies.