One of the stultifying but ultimately true maxims of the analytics movement in sports says that most narratives around player performance are lies. Each player has a "true talent level" based on their abilities, but the actual results are mostly up to variance and luck. If a player has, say, the true talent to hit thirty-one home runs in a season, the timing of those home runs is mostly random. If someone hits a third of those in April, that doesn't really mean he's a "hot starter" who is "building off a great spring"--it just means that if you take thirty-one home runs and toss them up in the air to land randomly on a time line, sometimes ten of them float over to April. What does matter, the analytics guys say, are plate appearances: you have to clock in enough opportunities to realize your true talent level.

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.