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Machine Learning and Data Mining for Sports Analytics: 9th International Workshop, MLSA 2022, Grenoble, France, September 19, 2022, Revised Selected Papers 1st ed. 2023 [Pehme köide]

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  • Formaat: Paperback / softback, 127 pages, kõrgus x laius: 235x155 mm, kaal: 226 g, 38 Illustrations, color; 9 Illustrations, black and white; X, 127 p. 47 illus., 38 illus. in color., 1 Paperback / softback
  • Sari: Communications in Computer and Information Science 1783
  • Ilmumisaeg: 25-Feb-2023
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031275268
  • ISBN-13: 9783031275265
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Machine Learning and Data Mining for Sports Analytics: 9th International Workshop, MLSA 2022, Grenoble, France, September 19, 2022, Revised Selected Papers 1st ed. 2023Suurem pilt
  • Formaat: Paperback / softback, 127 pages, kõrgus x laius: 235x155 mm, kaal: 226 g, 38 Illustrations, color; 9 Illustrations, black and white; X, 127 p. 47 illus., 38 illus. in color., 1 Paperback / softback
  • Sari: Communications in Computer and Information Science 1783
  • Ilmumisaeg: 25-Feb-2023
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031275268
  • ISBN-13: 9783031275265
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783031275265.html
Märksõnad:
This book constitutes the refereed proceedings of the 9th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2022, held in Grenoble, France, during September 19, 2022. 

The 10 full papers included in this book were carefully reviewed and selected from 18 submissions. They were organized in topical sections as follows: Football, Racket sports, Cycling.
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.- Lets 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.