Time Series Analysis of National League Slugging Percentage (Major League Baseball)

Abstract

Time Series Analysis of National League Slugging Percentage (Major League Baseball) Major League Baseball records yearly National League slugging percentage values, and for this project the data was examined using a time series with 103 data points, from 1901-2003. The analyses performed include exploratory data analysis such as the plot of the time series, a histogram, a normal QQ-plot, and the Autocorrelation Function and Partial Autocorrelation function of the series. Numerous general choices for which model to select for the data such as Autoregressive (AR) and Moving Average (MA) models were considered, as well as possible transformations or differencing of the data in order to obtain stationarity and constant variance of the series. Once these possible models were chosen, the next step was fitting the models to the series and analyzing the coefficients through parameter estimation, as well as performing a diagnostic check of the residuals for each model. Information Criterion values were also used to interpret the results of the model fitting. To confirm any hypotheses made to that point, predictions were done to evaluate the success of the model in terms of forecasting future values. In this case, the time series including the years from 1901-2003 was used to predict the slugging percentage values for the next ten years, from 2004-2013. Clearly, these results have already been obtained, so the effectiveness of the forecasting was determined decisively by comparing the predicted values and actual values for each model being tested. A convincing choice of the optimal model for this time series was attained.

Discipline: Statistics

Faculty Mentor: Dr. Cristina Anton