Lucas (Yifan) Wu

Title: Soccer Analytics
Date: November 15th, 2022
Time: 10:00AM PST
Location: Hybrid, over zoom and in Library Room 2020

Abstract

This thesis consists of a compilation of four projects all related to soccer. The first short chapter investigates how to obtain reliable speed measurements from player tracking data. The second chapter considers the problem of crossing the ball in soccer. In recent years, some research suggests that there exists a negative correlation between crossing and scoring. However, correlation does not imply causation. There are various factors that affect the decision of crossing. In the crossing problem, an experimenter can not assign whether a player crosses or does not cross the ball during a particular crossing opportunity due to the fact that matches are observational studies. For this reason, we use a causal inference framework to investigate the causal relationship of crossing on shots. Our findings suggest that crossing remains an effective tactic for increasing shot probabilities. The third chapter considers the evaluation of off-the-ball actions in soccer. There are numerous statistics and metrics that have been proposed to evaluate the performance of players in team sports based on actions involving the ball. In soccer, players typically don’t have the possession of the ball for even three minutes during a game. In this paper, we develop methods that analyze the activities of players that are “off-the-ball”. Then a defensive anticipation metric is developed based on the tenet that moving faster to the expected location is better than moving slower. The last chapter considers the problem of pitch control in soccer. With the availability of tracking data, one of the most intriguing ideas in soccer is to model how much space the player or the team owns at any given time, which is known as pitch control or field ownership in soccer analytics community. This project first conducts a literature review on various approaches for the determination of pitch control and introduces a new field ownership metric that takes into account associated movement dynamics, such as speed, acceleration and change of direction etc.

Keywords: Sports Analytics; Player Tracking Data; Causal Inference; Machine Learning; Pitch Control.