Machine Learning and Data Mining for Sports Analytics. 9th International Workshop, MLSA 2022 Grenoble, France, September 19, 2022 Revised Selected Papers

Preface
Organization
Contents
Football
Towards Expected Counter - Using Comprehensible Features to Predict Counterattacks
	1 Introduction
	2 Framework for Understanding Complex Sequences
	3 Definition of Sequences of Interest and Success Criteria
		3.1 Rule-based Identification of Persistent Open-Play Turnovers
		3.2 Definition of Success Criteria for Counterattacks
		3.3 Emerging Dataset
	4 Comprehensible Features for Prediction
		4.1 Constructing Features from Domain-Specific Assumptions
		4.2 Influence of Ball Loss Location for Feature Assessment
		4.3 Prediction Capability of the Constructed Features
	5 Model-based Test of Features
	6 Conclusion
	References
Shot Analysis in Different Levels of German Football Using Expected Goals
	1 Introduction
	2 Related Work
	3 Methodology
		3.1 Data
		3.2 Statistical Analysis
		3.3 Expected Goals Models
	4 Results
		4.1 Statistical Analysis
		4.2 Expected Goals Models
	5 Conclusions
	A  Box plots of significantly different distributions
	References
Analyzing Passing Sequences for the Prediction of Goal-Scoring Opportunities
	1 Introduction
	2 Problem Definition
	3 Methodology
		3.1 Tracking Data
		3.2 Event Data
		3.3 Data Alignment
		3.4 Extraction of Goal Scoring Opportunities
		3.5 Pitch Partitioning
		3.6 Sequential Pattern Mining
	4 Experimental Study
	5 Style of Play for the Top-2 Teams
	6 Conclusions and Future Work
	References
Let\'s Penetrate the Defense: A Machine Learning Model for Prediction and Valuation of Penetrative Passes
	1 Introduction
	2 Related Work
	3 Penetrative Pass Prediction and Valuation
		3.1 Dataset and Preprocessing
		3.2 Potential Penetrative Pass Situation
		3.3 Penetrative Pass Label Generation
		3.4 Penetrative Pass Decomposed Model
	4 Experiments and Results
		4.1 Best Performing Prediction Model
		4.2 Does a Penetrative Pass Affect Goal Scoring or Conceding?
		4.3 Teams\' Penetrative Performance Analysis
		4.4 Field Section Analysis:
	5 Conclusion
	References
Evaluation of Creating Scoring Opportunities for Teammates in Soccer via Trajectory Prediction*-12pt
	1 Introduction
	2 Proposed Framework
		2.1 Potential Score Model in Modified OBSO
		2.2 C-OBSO with Trajectory Prediction
	3 Experiments
		3.1 Dataset
		3.2 Data Processing for Verification
		3.3 Our Model Verification
		3.4 C-OBSO Results
	4 Related Work
	5 Conclusion
	A  Overview of our Method
	B  Off-Ball Scoring Opportunity ch5Spearman18
	C  Variational Recurrent Neural Network ch5Chung15
	D  Graph Variational Recurrent Neural Network ch5Yeh2019
	E  Validation Results of Trajectory Prediction Model
	F  C-OBSO and OBSO Results Without the Potential Score Model
	G  Relationship Between Rating, C-OBSO, and Goal
	References
Cost-Efficient and Bias-Robust Sports Player Tracking by Integrating GPS and Video
	1 Introduction
	2 Related Work
		2.1 Optical Player Tracking
		2.2 GPS-Based Player Tracking
		2.3 GPS-OTS Integration Approach
	3 Main Contributions
		3.1 Anchor Starter Detection
		3.2 Anchor Segment Detection
		3.3 GPS-OTS Trajectory Matching per Anchor Segment
		3.4 Initial Estimation of GPS Biases
		3.5 Fine-Tuning GPS Biases
	4 Experiments
		4.1 Data Preparation
		4.2 Implementation Detail
		4.3 Model Evaluation
	5 Conclusion and Future Work
	References
Racket Sports
Predicting Tennis Serve Directions with Machine Learning
	1 Introduction
	2 Related Work
	3 Basic Information About Tennis Serves
	4 Data
	5 Feature Engineering
		5.1 Outcome of Previous Points
		5.2 Fatigue
		5.3 Performance Anxiety
		5.4 Other Features
	6 Machine Learning
	7 Discussion
	8 Conclusion and Future Work
	References
Discovering and Visualizing Tactics in a Table Tennis Game Based on Subgroup Discovery
	1 Introduction
	2 Methodology
		2.1 Dataset
		2.2 Tactics in Table Tennis
		2.3 Mining Frequent and Discriminant Sequential Pattern
		2.4 Summary of Assumptions
	3 Results
		3.1 Presentation of the Obtained Alternate Sequences
		3.2 Visualization of the Tactics
	4 Conclusion and Perspectives
	A  Appendix
	References
Cycling
Athlete Monitoring in Professional Road Cycling Using Similarity Search on Time Series Data
	1 Introduction
	2 Related Work
	3 Materials
		3.1 Materials
		3.2 Data Preprocessing
	4 Methodology
		4.1 Selection of Potential Matches
		4.2 Taylor-made Approach
		4.3 Dimensionality Reduction Approach
	5 Results
		5.1 Modeling Performance
		5.2 Athlete Monitoring
	6 Discussion
	7 Conclusion
	References
Author Index