Model Accuracy
Backtested on 7,289 MLB games across 2023–2025. Every game simulated 10,000 times.
56.1%
Win Prediction
57–62%
O/U at 9.5
64–69%
O/U at 10.5
3.39–3.65
Score MAE
Win Prediction by Season
Markov, Elo, and ensemble blend accuracy across three full seasons.
Win Probability Calibration
When we say a team has X% chance of winning, how often do they actually win? Perfect calibration follows the dashed line.
Over/Under Accuracy by Line
Accuracy of over/under calls at different total run lines, averaged across all three seasons.
Score Prediction Error
Mean Absolute Error for total score predictions (lower is better). Our median-based predictions outperform mean-based.
Full Season Breakdown
| Season | Games | Markov | Elo | Blend | O/U 9.5 | O/U 10.5 | Score MAE |
|---|---|---|---|---|---|---|---|
| 2023 | 2,430 | 53.8% | 56.1% | 56.5% | 56.8% | 64.4% | 3.65 |
| 2024 | 2,429 | 54.4% | 56.2% | 56.2% | 61.7% | 68.5% | 3.39 |
| 2025 | 2,430 | 52.6% | 56.7% | 55.6% | 60.4% | 67.3% | 3.61 |
About these numbers
The theoretical accuracy ceiling for MLB game prediction is probably 58–62%. Baseball is inherently random — the best team in baseball loses 40% of its games. Our ensemble model approaches that ceiling by combining player-level Markov chain simulations with team-level Elo ratings. All results are out-of-sample: the model never sees future data when making predictions.