This review is primarily guided by the following three research questions: (1) What baseball analytics problems have been studied using machine learning? (2) What data repositories have been used? (3) What and how machine learning techniques have been employed for these studies? The findings of …

pitch statistics from monocular video. We introduce PitcherNet, a pioneering deep learning system that tackles low-resolution video limitations through efficient 3D human modeling for robust player alignment (left) and reliable pitch statistics analysis zing perfor-mance and minimizing runs.

We introduce PitcherNet, a pioneering deep learning system that tackles low-resolution video limitations through eficient 3D human modeling for robust player alignment (left) and reliable pitch statistics analysis from estimated kinematic data (right). a pitcher’s mechanics holds the key to …

Ball tracking systems are becoming ubiquitous in sport, creating an unprecedented opportunity for big data applications to optimize human health and performance. These applications are especially common in baseball, a sport known for analyzing ball flight data to quantify performance. However, few …

GitHub - jman4162/Baseball-Pitch-Sequence-Prediction: This GitHub repository is dedicated to the development and training of a Transformer-based deep learning model for predicting the next pitch type in a baseball game, utilizing historical data on ball-strike counts and pitch sequences to …

cting the outcome of MLB matches. The accuracy in predicting the results of baseball games is low. Therefore, deep learning and machine learning methods were used to build models for predicting the outcomes (win/loss of MLB matches and investigate the differences between the mod-els in terms of …