Machine Learning and Data Mining for Sports Analytics

Name: Machine Learning and Data Mining for Sports Analytics | 5th International Workshop, MLSA 2018, Co-located with ECML/PKDD 2018, Dublin, Ireland, September 10, 2018, Proceedings
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5th International Workshop, MLSA 2018, Co-located with ECML/PKDD 2018, Dublin, Ireland, September 10, 2018, Proceedings

Ulf Brefeld Jesse Davis Jan Van Haaren Albrecht Zimmermann(Editor)

Springer (Publisher)

Published on 6. April 2019

X, 179 pages

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978-3-030-17274-9 (ISBN)

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Content

Intro
Preface
Organization
Contents
Soccer
Measuring Football Players' On-the-Ball Contributions from Passes During Games
1 Introduction
2 Dataset
3 Approach
3.1 Constructing Possession Sequences
3.2 Labeling Possession Sequences
3.3 Valuing Passes
3.4 Rating Players
4 Experimental Evaluation
4.1 Datasets
4.2 Methodology
4.3 Impact of the Parameters
4.4 Results
4.5 Application: Replacing Manuel Lanzini
5 Related Work
6 Conclusion
References
Forecasting the FIFA World Cup - Combining Result- and Goal-Based Team Ability Parameters
1 Introduction
2 Related Work
3 The Models
3.1 The Elo Rating System
3.2 Offense-Defense Ratings
3.3 Match Result Predictions
4 Evaluation Procedures
5 Validation on Previous World Cups
6 Validation on Domestic League Football
7 Conclusion
References
Distinguishing Between Roles of Football Players in Play-by-Play Match Event Data
1 Introduction
2 Dataset
3 Player Roles
4 Approach
4.1 Feature Engineering
4.2 Probabilistic Classification
5 Experimental Evaluation
5.1 Methodology
5.2 Discussion of Results
6 Related Work
7 Conclusion
References
Player Valuation in European Football
1 Introduction
2 Related Work
3 Data Collection and Preparation
3.1 Data Collection
3.2 Data Preparation
4 Feature Selection
4.1 Filter Method
4.2 Wrapper Method
4.3 Discussion
5 Prediction
5.1 Methods
5.2 Results
5.3 Discussion
6 Conclusion
References
Ranking the Teams in European Football Leagues with Agony
1 Introduction
1.1 Our Contributions
2 Related Work
3 A Partial Order for a Partial Season
4 Agony for Sport Leagues with Draws
4.1 Agony for Football Leagues
5 Evaluation
5.1 Case Study on European Football Leagues
6 Conclusion
References
US Team Sports
Interpreting Deep Sports Analytics: Valuing Actions and Players in the NHL
1 Introduction
2 Related Work
3 Play Dynamics in NHL
4 Q-Values and Action Impact
4.1 Compute Q Functions with Deep Reinforcement Learning
4.2 Evaluate Players with Impact Metric
5 Mimicking DRL with Regression Tree
6 Interpreting Q Functions and Impact with Mimic Tree
6.1 Compute Feature Importance
6.2 Draw Partial Dependence Plot
7 Highlighting Exceptional Players
8 Conclusion and Future Work
References
Player Pairs Valuation in Ice Hockey
1 Introduction
2 Related Work
3 Background
4 Player Pair Metrics
5 Data-Driven Analysis
5.1 Top Pairings
5.2 TOI-Based Analysis
5.3 Relative Ice Time Together
6 Conclusion
References
Model Trees for Identifying Exceptional Players in the NHL and NBA Drafts
1 Introduction
2 Related Work
3 Datasets
4 Methodology
4.1 Success Metrics
4.2 Model Trees
5 Results
5.1 Modelling Results of the NHL Draft
5.2 Modelling Results of the NBA Draft
6 Case Studies: Exceptional Players and Their Strong Points
6.1 Explaining the Rankings: Identify Strong Points
6.2 Case Studies
7 Conclusion
References
Evaluating NFL Plays: Expected Points Adjusted for Schedule
1 Introduction
2 Adjusting for Opponent Strength
2.1 What Is the Impact of the Value Adjustment?
3 Case Studies
4 Discussion and Conclusions
Appendix A Intuition Behind the WCS Expected Points Model
Appendix B Other Adjustment Approaches
Appendix C 33 Points Equal 1 Win in NFL
References
Individual Sports
Real-Time Power Performance Prediction in Tour de France
1 Introduction
2 Related Work
3 Dataset
4 Methodology
4.1 Feature Engineering
4.2 Regression Model
5 Result
5.1 Evaluation on Feature Engineering
5.2 Evaluation on Regression Models
5.3 Qualitative Analysis - Real Deployment in Tour de France 2017
6 Conclusion
References
Automatic Classification of Strike Techniques Using Limb Trajectory Data
1 Introduction
2 Methods
2.1 Data Collection
2.2 Trajectory Extraction and Labeling
2.3 Data Cleaning and Analysis
2.4 Classification Models
3 Results
3.1 Limb and Technique Classification
3.2 Minimal Required Training Set
3.3 Hierarchical Classification
3.4 Classification of Skill Level
4 Discussion
4.1 Applications
4.2 Limitations and Further Developments
References
Challenge Papers
Predicting Pass Receiver in Football Using Distance Based Features
1 Introduction
2 Dataset Presentation
2.1 Dataset Exploration
3 Model Description
3.1 General Approach
3.2 Learning Pass Probabilities
4 Results
4.1 Learning from Failures
4.2 Qualitative Analysis
5 Conclusion
References
Football Pass Prediction Using Player Locations
1 Introduction
2 Observations About the Data
3 The FPP Model
4 Experimental Evaluation
5 Conclusion
References
Deep Learning from Spatial Relations for Soccer Pass Prediction
1 Introduction
1.1 Related Work
1.2 Dataset
2 Predictive Model
2.1 Knowledge Representation
2.2 Neural Architecture
3 Experiments
3.1 Human-Level Performance
3.2 Discussion
4 Conclusion
References
Predicting the Receivers of Football Passes
1 Introduction
2 Data Exploration
3 Methodology for Predicting the Receivers of Football Passes
3.1 Feature Extraction
3.2 Modeling Approach
3.3 Baseline Models
3.4 Evaluation Approaches
3.5 Feature Importance
4 Results
4.1 RQ1: How Well Can We Model the Receiver of a Pass?
4.2 RQ2: What Are the Important Factors that Explain the Receiver of a Pass?
5 Conclusions
References
Author Index

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Machine Learning and Data Mining for Sports Analytics

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