Underdogs in MLB can offer significant value when identified correctly. This article explores the methodology behind constructing a predictive model to spot those opportunities.

Data Collection and Variables

The first step involves gathering historical game data, player stats, and situational variables that influence outcomes, including pitcher matchups and team fatigue.

Choosing the Right Algorithm

We tested multiple machine learning algorithms from logistic regression to random forest classifiers to determine the most accurate fit for predicting underdog wins.

Model Validation and Refinement

Cross-validation techniques were applied to measure the model’s performance, ensuring it avoids overfitting and remains robust across various MLB seasons.

Applying the Model for Betting Edges

When combined with live betting odds, our model indicates profitable underdog bets by identifying games where the public odds underestimate the true win probability.