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Machine Learning and Data Mining for Sports Analytics : 10th International Workshop, MLSA 2023, Turin, Italy, September 18, 2023, Revised Selected Papers
- Available as
- Online
Details
- Creator
- Ulf Brefeld [and three others], editors
- Format
- Books
- Language
- English
- Contributors
- Publication
-
- First edition
- Cham, Switzerland : Springer, [2024]
- ©2024
- Physical Details
-
- 1 online resource (206 pages)
- ISBNs
- 9783031538339, 3031538331, 3031538323
- Includes bibliographical references and index.
- Intro -- Preface -- Organization -- Contents -- Keynote -- Sports Data Analytics: An Art and a Science -- 1 Definition of Sports and Its Consequencies for Sports Data Analytics -- 1.1 It's All About Skill -- 1.2 What Contributes to Complexity? -- 2 How Do We Develop Meaningful Skill Metrics in Complex Sports? -- 2.1 How We Deal with Complexity? -- 2.2 Skills on the Individual Level -- 2.3 Skills on the Team Level -- 3 Conclusion -- References -- Football/Soccer -- ETSY: A Rule-Based Approach to Event and Tracking Data SYnchronization -- 1 Introduction -- 2 Problem Statement and Existing Approaches -- 3 ETSY -- 3.1 Preprocessing -- 3.2 Synchronization -- 3.3 Action-Specific Filters -- 4 Evaluation -- 4.1 Experimental Setup -- 4.2 Experimental Comparison -- 4.3 Missed Events Analysis -- 4.4 Example Synchronization -- 4.5 Conceptual Comparison -- 5 Conclusion -- References -- Masked Autoencoder Pretraining for Event Classification in Elite Soccer -- 1 Introduction -- 2 Preliminaries and Problem Formulation -- 3 Trajectory Masked Autoencoder -- 3.1 Masked Autoencoder for Multiagent Trajectories -- 3.2 Factorized Transformer Encoder (FTE) -- 4 Event Classification in Elite Soccer -- 5 Conclusion -- References -- Quantification of Turnover Danger with xCounter -- 1 Introduction -- 2 Dataset -- 3 Application of the Framework for Understanding Complex Sequences -- 3.1 Sequences of Interest -- 3.2 Success Criteria -- 3.3 Comprehensible Features -- 3.4 Feature-Specific Prediction Capability -- 3.5 Predictive Model -- 3.6 Design Choices -- 4 Results and Discussion -- 4.1 Prediction Capability of Features -- 4.2 Predictive Models for Expected Counter -- 5 Related Work -- 6 Application -- 7 Limitations and Conclusion -- A Appendix -- References -- Pass Receiver and Outcome Prediction in Soccer Using Temporal Graph Networks -- 1 Introduction -- 2 Related Work
- 3 Decomposing the Pass Success Probability -- 4 Constructing Temporal Graph Networks -- 4.1 Model Definition -- 4.2 TGN Architecture -- 4.3 Training RSP and RPP Models -- 5 Experiments -- 5.1 Dataset -- 5.2 Evaluating Model Performance -- 5.3 Pass Difficulty in Different Areas of the Pitch -- 6 Conclusion -- References -- Field Depth Matters: Comparing the Valuation of Passes in Football -- 1 Introduction -- 2 Methodology -- 2.1 Event Data -- 2.2 VAEP -- 2.3 Groups and Tests -- 3 Results -- 3.1 Comparing Same Third Groups -- 3.2 Comparing 1-G and 3-NG -- 3.3 Notable Cases -- 4 Conclusion and Future Work -- 4.1 Conclusion -- 4.2 Future Work -- References -- Basketball -- Momentum Matters: Investigating High-Pressure Situations in the NBA Through Scoring Probability -- 1 Introduction -- 2 Pre-game Pressure -- 3 Identifying High-Pressure Scenarios -- 4 Evaluation of Players' Performance in High-Pressure Situations -- 5 Conclusion and Future Work -- References -- Are Sports Awards About Sports? Using AI to Find the Answer -- 1 Introduction -- 2 Background -- 3 Experimental Overview -- 3.1 Data Processing -- 3.2 Implementation Details -- 4 Results and Analysis -- 4.1 Predicting the Most Valuable Player -- 5 Conclusion and Future Work -- References -- The Big Three: A Practical Framework for Designing Decision Support Systems in Sports and an Application for Basketball -- 1 Introduction -- 2 Literature Overview -- 3 The Big Three: Model, Explanation and Interactivity -- 4 BasketXplainer: A DSS for Basketball -- 5 Results and Analysis -- 5.1 Model -- 5.2 Explainability and Interactivity -- 6 Conclusion and Future Work -- 6.1 Research Opportunities -- References -- Other Team Sports -- What Data Should Be Collected for a Good Handball Expected Goal model? -- 1 Introduction -- 2 State of the Art -- 2.1 The xG Metric -- 2.2 Use of xG -- 3 Handball Data
- 3.1 Data Acquisition -- 3.2 Qatar 2015 Data -- 3.3 Dataset Enrichment -- 4 Method for Calculating xG -- 4.1 Sample Calculation -- 4.2 Model Calculation -- 4.3 Wrapper: Attribute Selection Approach -- 5 Results -- 5.1 Sample Calculation -- 5.2 Wrapper Approach -- 6 Conclusion -- References -- Identifying Player Roles in Ice Hockey -- 1 Introduction -- 2 Data -- 3 Method -- 3.1 Preprocessing -- 3.2 Principal Component Analysis -- 3.3 Clustering -- 4 Results -- 4.1 Principal Component Analysis -- 4.2 Fuzzy Clustering -- 5 Comparing Player Salary -- 6 Team Composition -- 7 Conclusion -- References -- Elite Rugby League Players' Signature Movement Patterns and Position Prediction -- 1 Introduction -- 2 Method -- 2.1 GPS Data, Collection and Processing -- 2.2 Signature Movement Patterns Mining -- 2.3 Playing Position Prediction and Feature Contribution -- 3 Results and Discussion -- 3.1 Signature Movement Patterns -- 3.2 Playing Position Prediction -- 4 Conclusion and Future Works -- References -- Boat Speed Prediction in SailGP -- 1 Introduction -- 2 Background -- 3 Data Set -- 4 Boat Speed Analysis -- 4.1 Unveiling the Key Component -- 4.2 Random Forest Model -- 4.3 Gradient Boosted Model -- 4.4 Benchmark -- 4.5 Meta Model -- 5 Conclusion -- References -- Individual Sports -- Exploring Table Tennis Analytics: Domination, Expected Score and Shot Diversity -- 1 Introduction -- 2 Domination Analysis in Table Tennis -- 3 Expected Score (XScore) in Table Tennis -- 4 Shots Diversity in Table Tennis -- 5 Discussion, Limits and Perspectives -- A Appendix -- A.1 Definition of the Winning Probability Pa,b -- A.2 Definition of the Three Factors of Physical Domination -- A.3 Definition of the Three Factors of Mental Domination -- References -- Performance Measurement 2.0: Towards a Data-Driven Cyclist Specialization Evaluation -- 1 Introduction
- 2 Literature Review -- 3 Methodology -- 3.1 Data -- 3.2 Clustering -- 3.3 Performance Ranking -- 4 Results -- 5 Conclusion -- References -- Exploiting Clustering for Sports Data Analysis: A Study of Public and Real-World Datasets -- 1 Introduction -- 2 Related Work -- 3 Clustering Comparison -- 4 Evaluation on Real-World Data Set -- 5 Conclusion -- References -- Author Index