Applying Machine Learning Techniques to Baseball Pitch Prediction

Abstract

Major League Baseball, a professional baseball league in the US and Canada, is one of the most popular sports leagues in North America. Partially because of its popularity and the wide availability of data from games, baseball has become the subject of significant statistical and mathematical analysis. Pitch analysis is especially useful for helping a team better understand the pitch behavior it may face during a game, allowing the team to develop a corresponding batting strategy to combat the predicted pitch behavior. We apply several common machine learning classification methods to PITCH f/x data to classify pitches by type. We then extend the classification task to prediction by utilizing features only known before a pitch is thrown. By performing significant feature analysis and introducing a novel approach for feature selection, moderate improvement over former results is achieved. Count (number of balls and strikes in the current at bat) is often cited as a basis for decisive strategy.

Machine Learning Approach for Pitch Type Classification Based on Pelvis and Trunk Kinematics Captured with Wearable Sensors

“…Therefore, to explore the possibility of pitch type classification based on pitching mechanics, it makes sense to first investigate whether we can detect fastballs. Previous studies that used a binary approach for pitch type prediction focused on predicting whether the next pitch will be Fastball rather than detecting whether Fastball was thrown [15,19]. They used pre-pitch ball data as an input, which resulted in accuracies of 70% [15] and 77.45% [19].…”

“…Previous studies that used a binary approach for pitch type prediction focused on predicting whether the next pitch will be Fastball rather than detecting whether Fastball was thrown [15,19]. They used pre-pitch ball data as an input, which resulted in accuracies of 70% [15] and 77.45% [19]. Even though such approach offers benefits for choosing the right strategy, it does not contribute to the pitch tracking as part of the workload monitoring for an individual pitcher.…”

“…We provide a machine learning approach to the utilization of wearables data through pitch type classification based on the pelvis and trunk peak angular velocity and their separation time recorded using body-worn motion sensors. Machine learning methods showed promising results in pitch type classification investigated in similar contexts [14][15][16][17][18][19][20]. Opposed to predicting the next pitch thrown based on the information available prior to that pitch [14][15][16], our approach relies on inclusion of post-delivery features to detect which pitch was thrown purely based on pitching mechanics.…”