Welcome to the Baseball Machine Learning Workbench
The Baseball Machine Learning Workbench is an interactive web application. Explore various analytics, decision intelligence & Machine Intelligence techniques using historical baseball data.
Machine Learning Probability Statement characteristics:
- Baseball data used: MLB batter data aggregated at the season level from 1876 to 2019. (Note: Only players that were predominatly position players are included, pitchers data has been omitted.)
- The prediction of Hall of Fame Ballot or Induction is surfaced as a probability percentage between 0% and 100%.
- Hall of Fame Ballot defined as the presence of the candidate batter on any of the yearly vote total for the Hall of Fame.
- Hall of Fame Induction defined as the candidate achieving 75% of the necessary vote by the BWAA electors or special BWAA sessions. Note: This explicitly excludes candidates in the Hall of Fame sent in by other means (i.e. veteran's comittee). More info: https://baseballhall.org/hall-of-famers/rules/bbwaa-rules-for-election
- The machine learning models have been built using the Generalized Additive Models (GAM) algorithm using ML.NET.
- The following batting features were used to build the ML models: Years Played, At Bats, Runs, Hits, Doubles, Triples, Home Runs, RBIs, Stolen Bases, Batting Average, Slugging Percentage, All-Star Appearances, MVPs, Triple Crowns, Gold Gloves, Total Bases, Total Player Awards.