Time Series Modeling of Baseball Performance​

The 162 game long Major League Baseball season provides ample time for a player’s performance to vary and trend in different directions. Managers must set daily rosters for their teams, using past performance to help make decisions. But which prior performance periods tell us the most about upcoming performance? To answer this, it\u27s helpful to view a player’s future performance, for any given statistic, as a function of his performance in previous playing periods (e.g. previous game, previous week, previous year, etc.). In this on-going research project, we consider two approaches to predicting future performance from the past. In the first, we build a probability mass function for each of a set of discrete, disjoint past time periods and we use Expectation Maximization to learn the appropriate weights for each period to best predict future outcomes. In our second approach, we predict a player\u27s performance in the next game based on all previous history using a recurrent neural network