Methodology and evaluation in sports analytics: challenges, approaches, and lessons learned

There has been an explosion of data collected about sports. Because such data is extremely rich and complex, machine learning is increasingly being used to extract actionable insights from it. Typically, machine learning is used to build models and indicators that capture the skills, capabilities, a...

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Vydané v:Machine learning Ročník 113; číslo 9; s. 6977 - 7010
Hlavní autori: Davis, Jesse, Bransen, Lotte, Devos, Laurens, Jaspers, Arne, Meert, Wannes, Robberechts, Pieter, Van Haaren, Jan, Van Roy, Maaike
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.09.2024
Springer Nature B.V
Predmet:
Artificial Intelligence
Athletic recruitment
Automation
Computer Science
Control
Data analysis
Data science
Decision making
Indicators
Machine Learning
Mechatronics
Movable bridges
Natural Language Processing (NLP)
Robotics
Simulation and Modeling
Special Issue on Machine Learning for Soccer
SWOT analysis
Indicator evaluation
Model evaluation
Sports analytics
Model verification
Challenges with evaluation
Reliability
ISSN:0885-6125, 1573-0565
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Popis
Shrnutí:There has been an explosion of data collected about sports. Because such data is extremely rich and complex, machine learning is increasingly being used to extract actionable insights from it. Typically, machine learning is used to build models and indicators that capture the skills, capabilities, and tendencies of athletes and teams. Such indicators and models are in turn used to inform decision-making at professional clubs. Designing these indicators requires paying careful attention to a number of subtle issues from a methodological and evaluation perspective. In this paper, we highlight these challenges in sports and discuss a variety of approaches for handling them. Methodologically, we highlight that dependencies affect how to perform data partitioning for evaluation as well as the need to consider contextual factors. From an evaluation perspective, we draw a distinction between evaluating the developed indicators themselves versus the underlying models that power them. We argue that both aspects must be considered, but that they require different approaches. We hope that this article helps bridge the gap between traditional sports expertise and modern data analytics by providing a structured framework with practical examples.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-024-06585-0