EA-lect: an evolutionary algorithm for constructing logical rules to predict election into Cooperstown

While several papers exist focusing on the question of election into the Baseball Hall of Fame election, all of them use the same method for building a model: regression analysis. Problems with this include the fact that regressions are continuous functions, and thus have trouble modeling binary problems. While there are some methods that work reasonably well, a logical rule, which can be evaluated to only true or false, seems to be a better option for several reasons. In regression models, since the results are continuous, it is possible to get an answer other than just 1 or 0 for a binary problem. Because of this, an arbitrary cutoff to what is to be considered a positive example must be made. Instead, through the use of genetic algorithms, a logical rule, which can evaluate any example to either true or false (1 or 0) can be found. The rules found by this system are extremely accurate, with training accuracies around 99% and testing accuracies just lower at 97%.