I’m building a predictive model for MLB game outcomes, trained on season data and calibrated to assess win probabilities. Rather than just predicting who will win, I focus on finding +EV (expected value) opportunities where the model’s probability differs significantly from a sportsbook’s implied odds.
For now, I’m comparing my predictions against Fliff’s Moneyline (betting on the team to win) odds, converting them into implied probabilities to spot potential edges.
Here’s what to expect in each daily pick post:
- My personal picks for the day
- A table including the higher EV picks for each game of the day
- A table including my model’s favored team for each game of the day
I will also likely post daily accuracy comparisons for my picks, overall favored teams, and the sportsbook picks.
I would appreciate feedback, questions, or suggestions, whether you’re here to follow along, test your own edges, or see how well machine learning can predict the MLB.
Bayesball Picks 