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Found 6 papers, 2 papers with code
TeamTrack: A Dataset for Multi-Sport Multi-Object Tracking in Full-pitch Videos
Multi-object tracking (MOT) is a critical and challenging task in computer vision, particularly in situations involving objects with similar appearances but diverse movements, as seen in team sports.
Machine Learning for Soccer Match Result Prediction
The aim of this chapter is to give a broad overview of the current state and potential future developments in machine learning for soccer match results prediction, as a resource for those interested in conducting future studies in the area.
Evaluating Soccer Match Prediction Models: A Deep Learning Approach and Feature Optimization for Gradient-Boosted Trees
The original training set of matches and features, which was provided for the competition, was augmented with additional matches that were played between 4 April and 13 April 2023, representing the period after which the training set ended, but prior to the first matches that were to be predicted (upon which the performance was evaluated).
Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: an application to rugby union
Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent subsequences from different sequences or within the same sequence.
Performance Indicators Contributing To Success At The Group And Play-Off Stages Of The 2019 Rugby World Cup
Our results using RIPPER found that low ball carries and a low lineout success percentage jointly contributed to losing at the group stage, while winning a low number of rucks and carrying over the gain-line a sufficient number of times contributed to winning at the play-off stage of the tournament.
The Application of Machine Learning Techniques for Predicting Results in Team Sport: A Review
In this paper, we provide a review of studies that have used ML for predicting results in team sport, covering studies from 1996 to 2019.
