The Big Three: A Practical Framework for Designing Decision Support Systems in Sports and an Application for Basketball
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Author / Producer
Date
2024
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
In a world full of data, Decision Support Systems (DSS) based on ML models have significantly emerged. A paradigmatic case is the use of DSS in sports organisations, where a lot of decisions are based on intuition. If the DSS is not well designed, feelings of unusefulness or untrustworthiness can arise from the human decision-makers towards the DSS. We propose a design framework for DSS based on three components (ML model, explainability and interactivity) that overcomes these problems. To validate it, we also present the preliminary results for a DSS for rival team scouting in basketball. The model reaches state of the art performance in game outcome prediction. Explainability and interactivity of our solution also got excellent results in our survey. Finally, we propose some lines of research for DSS design using our framework and for team scouting in basketball.
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Publication status
published
External links
Book title
Machine Learning and Data Mining for Sports Analytics
Journal / series
Volume
2035
Pages / Article No.
103 - 116
Publisher
Springer
Event
10th Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA 2023)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Machine Learning; Explainability; Interactivity; Basketball; Game Outcome Prediction
Organisational unit
09822 - El-Assady, Mennatallah / El-Assady, Mennatallah