A comprehensive collection of tools, datasets, and methodologies designed to identify value in Major League Baseball over/under betting markets through rigorous quantitative analysis and predictive modeling.
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The definitive repository for historical MLB statistics, providing extensive data on player performance, team metrics, and game outcomes essential for building long-term predictive models.
Offers advanced sabermetric statistics like wOBA, FIP, and WAR, along with projection systems that are critical for identifying discrepancies between model probabilities and market odds.
MLB's official Statcast data platform providing tracking data on exit velocity, launch angle, and barrel rates, enabling granular analysis of pitcher and hitter performance beyond traditional stats.
A comprehensive free database of MLB history containing over 100,000 rows of data, ideal for researchers and data scientists constructing robust historical training datasets for machine learning applications.
A Python package that simplifies retrieving data from Baseball-Reference and FanGraphs, allowing analysts to quickly scrape and clean data for statistical modeling and backtesting betting strategies.
Provides free play-by-play data for every Major League Baseball game since 1954, offering the granular detail necessary for building sophisticated event-based predictive models.
Visual tools provided by Baseball Savant that allow analysts to compare specific pitcher and batter matchups using exit velocity and launch angle data to predict run expectancy.
A statistical framework that calculates the average number of runs expected to score in a specific base-out state, helping bettors assess if the current line accurately reflects game context.
A table-based model used to evaluate the value of every possible game state, crucial for determining how specific player performances impact the total runs scored in a game.
Statistical adjustments that account for the impact of specific stadiums on scoring, allowing models to normalize performance data and improve the accuracy of over/under predictions.
A specialized analytics firm that provides custom modeling services and public insights focused on finding inefficiencies in baseball betting markets using advanced sabermetrics.
While no longer actively updated, their archived SPI ratings and game projections remain a benchmark for understanding how probabilistic forecasting models interpret team strength and schedule difficulty.
Community-contributed datasets on Kaggle often contain cleaned and merged data from multiple sources, providing ready-to-use resources for training classification and regression models.
Provides structured data extraction services for MLB stats, useful for analysts who need specific, custom-scraped data points that are not readily available through standard APIs.
Offers specialized calculators and simulation tools designed to help bettors visualize how different variables impact the probability of hitting the over or under in baseball games.
Publishes in-depth articles on statistical methodologies and forecasting techniques, providing theoretical frameworks that experienced analysts can adapt for personal betting models.
A stat that measures a pitcher's value compared to a replacement-level player, useful for evaluating pitching matchups and predicting total runs allowed in specific starting pitcher scenarios.
A predictive statistic that estimates home run totals based on fly ball distance and launch angle, helping to adjust for luck in pitcher and hitter performance over small sample sizes.
Books and papers on this topic explore how statistical models can identify mispriced odds across different bookmakers, a strategy often applied to MLB totals markets due to their liquidity.
Educational resources and GitHub repositories focusing on applying algorithms like Random Forests or Gradient Boosting to baseball data to forecast game totals with higher accuracy than simple linear regression.