Simulating MLB Seasons using Bayesian Inference and Random Walks
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912366652817408 |
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| author | Cha, Simon |
| author_facet | Cha, Simon |
| contents | As a dedicated follower of sports statistics and with the MLB season beginning in late March, I set out to predict how many wins each team would accumulate by the end of the 162 game season. The goal was to build a simulation framework capable of forecasting the remainder of the season, starting from a 20 game burn-in period to establish initial estimates of team strength. My approach used a Bayesian inference model incorporating team win percentage, batting average, and pitching ERA to construct a posterior distribution of win probability for each matchup. For each game, I sampled from the posterior and simulated the outcome using a Bernoulli trial. Because future matchup inputs were unobserved, I forecasted batting averages using random walks and modeled pitching ERA with Kalman filters. After simulating many seasons, the model produced a distribution of win totals for all 30 teams and can also be used to estimate each team's probability of making the postseason. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_05120 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Simulating MLB Seasons using Bayesian Inference and Random Walks Cha, Simon Applications As a dedicated follower of sports statistics and with the MLB season beginning in late March, I set out to predict how many wins each team would accumulate by the end of the 162 game season. The goal was to build a simulation framework capable of forecasting the remainder of the season, starting from a 20 game burn-in period to establish initial estimates of team strength. My approach used a Bayesian inference model incorporating team win percentage, batting average, and pitching ERA to construct a posterior distribution of win probability for each matchup. For each game, I sampled from the posterior and simulated the outcome using a Bernoulli trial. Because future matchup inputs were unobserved, I forecasted batting averages using random walks and modeled pitching ERA with Kalman filters. After simulating many seasons, the model produced a distribution of win totals for all 30 teams and can also be used to estimate each team's probability of making the postseason. |
| title | Simulating MLB Seasons using Bayesian Inference and Random Walks |
| topic | Applications |
| url | https://arxiv.org/abs/2505.05120 |