Machine Learning and Data Mining for Sports Analytics

Football.- Towards expected counter - Using comprehensible features to predict counterattacks.- Shot analysis in different levels of German football using Expected Goals.- Analyzing passing sequences for the prediction of goal-scoring opportunities.- Let’s penetrate the defense: A machine learning model for prediction and valuation of penetrative passes.- Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction.- Cost-efficient and bias-robust sports player tracking by integrating GPS and video.- Racket sports.- Predicting tennis serve directions with machine learning.- Discovering and visualizing tactics in table tennis games based on subgroup discovery.- Cycling.- Athlete monitoring in professional road cycling using similarity search on time series data.