Predicting Baseball Hall of Fame Membership using a Radial Basis Function Network
This paper describes an objective way of predicting the likelihood of major league baseball players being elected to the Hall of Fame by members of the Baseball Writers Association of America. A radial basis function (RBF) network is used to build separate machine learning models for pitchers and non-pitchers. These models use simple player statistics such as number of wins and earned run average for pitchers and number of hits and home runs for non-pitchers. The models are trained on data representing players who played for at least 10 years and who retired between the years 1950 and 2002. In cross-validation tests, the models correctly identified 21 of 24 Hall of Fame pitchers, with 3 false positives, and 38 of 45 non-pitchers, with 7 false positives. When run over data representing active and recently retired players who played for at least 10 years, the models rate 12 pitchers and 22 non-pitchers to have approximately a 40% or better chance of election to the Hall of Fame.
neural networks, machine learning, Hall of Fame, radial basis function
Smith, Lloyd, and James Downey. "Predicting baseball hall of fame membership using a radial basis function network." Journal of Quantitative Analysis in Sports 5, no. 1 (2009).
Journal of Quantitative Analysis in Sports