Effectiveness of Spatial-Temporal Data using GIS in America’s Professional Sports Leagues (MLS, MLB, and NFL)

Abstract

This dissertation explores how information systems can improve the understanding of the home field advantage (HFA) in professional sports leagues in the United States. The literature related to the HFA conceptual framework and the game location factor—which represents four major impacts on teams (crowd, learning, travel, and rules)—led to an investigation into whether a relationship exists between game results and spatial, temporal, and stadium attributes. These stadium attributes include field surface type, roof type (i.e., open, fixed, and retractable), time zone, and field orientation (e.g., N/S, E/W, NE/SW) for U.S. professional sports such as soccer, baseball, and football. Winning percentage, winning streak, and losing streak were examined for their effect on game outcome. Collectively, all league games were examined to assess the effects of spatial, and temporal stadium attributes. A logistic regression (LR) analysis of the National Football League (NFL), Major League Soccer (MLS), and Major League Baseball (MLB) shows evidence of a significant relationship between spatial orientation, temporal, and stadium attributes and the game results. The LR model consider as an improvement over the base model, and the results vary from one sport to another. An IT artifact (dashboard) operationalized the proposed model based on these results. The dashboard provides team decision-makers with information to help them understand their opponent’s in the next home or away game. The artifact could be an integral part of the decision-making process for coaches and managers in game preparation and management.