Computer Vision in Soccer: YOLOv11 Analytics Engine for Quantifying Game Strategy

Author

Connor S. Maurer, University of Arkansas, FayettevilleFollow

Date of Graduation

5-2025

Document Type

Thesis

Degree Name

Bachelor of Science in Data Science

Degree Level

Undergraduate

Department

Data Science

Advisor/Mentor

Schubert, Karl

Committee Member

Cothren, Jackson

Second Committee Member

Brown, Jamelle

Abstract

Single-shot object detection capabilities significantly reduce computational overhead for real-time computer vision in sports analytics at 60 FPS. YOLO11’s lightweight CNN gives promising accuracy while meeting the low-latency demand of dynamic soccer matches. As data-driven approaches take over the sport of soccer, efficient player tracking systems become critical for informing coach’s strategies. I prototype the ETL (Extract, Transform, Load) process of data collected from a single- shot detection program and evaluate its viability for estimating player fatigue. YOLO11 detects players, the ball, and other characteristics, with the output transformed by homography to estimate the positions in the real world. These frame-to-frame position estimates provide a data basis for further analytical metrics such as distance, speed, formation, and possession estimates. Experimental results demonstrate a 70% player detection rate using a single-camera setup and models trained on public datasets, validating the approach for cost-effective, near-real-time analytics.

Keywords

Computer Vision; Machine Learning; Data Visualization; Soccer; Artificial Intelligence; Sports Analytics

Citation

Maurer, C. S. (2025). Computer Vision in Soccer: YOLOv11 Analytics Engine for Quantifying Game Strategy. Data Science Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/dtscuht/25