Document Type
Poster
Publication Date
Fall 2024
Abstract
This project utilizes artificial intelligence Agents to generate high-fidelity synthetic data for Major League Baseball hitters and their statistics over a single 162 game season. The AI evaluates each player based on a five-tool scale, comparing their abilities to one another. Finally, optimization techniques are applied to select an optimal set of thirteen hitters (half of a full MLB roster of twenty-six players), maximizing overall performance while adhering to a specified salary cap. The operating hypothesis is that artificial intelligence can effectively generate high-fidelity synthetic data for baseball hitters and evaluate player performance based on a five-tool scale, given a large influx of data. By applying optimization techniques, it is possible to construct a competitive baseball team that maximizes overall performance while staying within a given salary cap, mirroring real-world decision-making in sports management.
Recommended Citation
Gibbons, Parker and Chun, Jon, "Optimizing MLB Roster Selection with Moneyball AI Scouting Agents: Using High Fidelity Synthetic Data To Optimize Team Performance" (2024). IPHS 391: Interdisciplinary AI Frontiers. Paper 1.
https://digital.kenyon.edu/dh_iphs_391/1
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.