Sports Analytics Using Probabilistic Model Checking and Deep Learning

Jin Song Dong, Kan Jiang, Zhaoyu Liu, Chen Dong, Zhe Hou, Rajdeep Singh Hundal, Jingyu Guo, Yun Lin

Abstract: Sports analytics encompasses the use of data science, AI, psychology, and IoT devices to improve sports performance, strategy, and decision-making. It involves collecting, processing, and interpreting data from various sources such as video recordings and scouting reports. The data is used to evaluate player and team performance, prevent injuries, and help coaches make informed decisions in game and training. We adopt Probabilistic Model Checking (PMC), a method commonly used in reliability analysis for complex safety systems, and explain how this method can be applied to sports strategy analytics to increase the chance of winning by taking into account the reliability of a player’s specific sub-skill sets. This paper describes how we have integrated PMC, machine learning, and computer vision to develop a new and complex system for sports strategy analytics. Finally, we discuss the vision of a new series of international sports analytics conferences (https://formal-analysis.com/isace/2023/).