New research at the Penn State College of Information Sciences and Technology could make a similar impact on the sport. The team has developed a machine learning model that could better measure baseball players' and teams' short- and long-term performance, compared to existing statistical analysis methods for the sport. Drawing on recent advances in natural language processing and computer vision, their approach would completely change, and could enhance, the way the state of a game and a player's impact on the game is measured. According to Connor Heaton, doctoral candidate in the College of IST, the existing family of methods, known as sabermetrics, rely upon the number of times a player or team achieves a discrete event -- such as hitting a double or home run. However, it doesn't consider the surrounding context of each action.