Machine Learning and Data Mining for Sports Analytics
portes grátis
Machine Learning and Data Mining for Sports Analytics
9th International Workshop, MLSA 2022, Grenoble, France, September 19, 2022, Revised Selected Papers
Van Haaren, Jan; Brefeld, Ulf; Davis, Jesse; Zimmermann, Albrecht
Springer International Publishing AG
02/2023
127
Mole
Inglês
9783031275265
15 a 20 dias
Descrição não disponível.
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.
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artificial intelligence;computer vision;machine learning;human-computer interaction;software design;software engineering;neural networks;software architecture
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
