Soccer Analysis based on Markov Chain and PCA
KangMin Kim, Sangwhan Cha · 2022 IEEE International Conference on Big Data (Big Data) · 2022
With the most professional leagues, professional players, and national competitions, soccer is perhaps the most popular sport in the world. One of the reasons why soccer is so popular is because it is simple: two teams trying to score in each other’s goal only using their feets and heads. However, when we go deeper inside, there are numerous factors that can subtly or significantly impact the entire result of the game. The factor that this paper focuses on is the ball distribution between each player. It is common sense that teams with equal contribution from every player indicate better teamwork, and thus they are more likely to be stronger than teams that rely on one or two key players. In order to qualify this idea and devise useful strategies accordingly, we employed Markov chain and Principal Component Analysis (PCA). Through the Markov chain, we modeled a soccer game into a system (team) of eleven sections (players) continuously transitioning (giving passes) to other sections until they score a goal. Through PCA, we compared the patterns we found from the Markov chain modeling to other soccer statistics to evaluate how related the pattern we found is to the victory of the soccer team compared to other well-known soccer statistics. The evaluation of our approach shows promising results in analyzing soccer and constructing the most ideal player formation to win the game.