Soccer Match Analytics & Insights
The aim of this project is to give soccer analytics of matches being played. The goal is to build an AI tool that takes as input a video of a soccer match and then analyses each player and extract different analytics. This included: 1. Player and ball detection, 2. Player and ball tracking, 3. Player re-identification, 4. Field detection, 5. Event detection including passes, intercepts, goals etc.
The Challenge
Soccer matches are dynamic environments with variable lighting, camera movement, and frequent occlusions. MiniStats required a system that could not only detect players and the ball but also maintain their identities across the entire match, even when players exit and re-enter the frame in low-quality footage where facial recognition is impossible.
Our Solution
We used a multi-stage approach as follows:
Player and Ball Detection
For player and ball detection we annotated about 10 videos and trained a YOLOv8 model.
Player and Ball Tracking
For player and ball tracking we used NorFair tracker.
Team Identification
We also detect which player belongs to which team. To accomplish this, we experimented with several methods, including using DBSCAN and k-Means for clustering. In our clustering approach, we utilised different features such as color histograms, shirt images (extracting the upper region of the bounding box analytically), and the complete bounding box. We found that the combination of k-means clustering and the complete bounding box image of a person yielded the best results.
Event Detection Logic
For event detection we detected the position of the ball and the person closest to the ball and detected events based on the ball’s motion. For example, if the ball moved from near one person to another person of the same team, then this was considered a pass. Alternatively, if the ball moved from near one person to another person of another team then this was considered as an intercept.
The Impact
The tool automatically derives insights that help players and coaches identify specific areas for improvement. By quantifying metrics like ball possession duration, successful pass rates, and defensive intercepts, MiniStats provides a scientific basis for player development and tactical analysis.
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