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| Titolo: |
Machine learning and data mining for sports analytics : 7th International Workshop, MLSA 2020, co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, proceedings / / Ulf Brefeld [and three others editors]
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| Pubblicazione: | Cham, Switzerland : , : Springer, , [2020] |
| â2020 | |
| Edizione: | 1st ed. 2020. |
| Descrizione fisica: | 1 online resource (X, 141 p. 6 illus.) |
| Disciplina: | 006.31 |
| Soggetto topico: | Machine learning |
| Data mining | |
| Sports - Data processing | |
| Persona (resp. second.): | BrefeldUlf |
| Nota di contenuto: | Routine Inspection: A playbook for corner kicks -- How data availability aects the ability to learngood xG models -- Low-cost optical tracking of soccer players -- An Autoencoder Based Approach to SimulateSports Games -- Physical performance optimization in football -- Predicting Player Trajectoriesin Shot Situations in Soccer -- Stats Aren't Everything: Learning Strengths andWeaknesses of Cricket Players -- Prediction of tiers in the rankingof ice hockey players -- A Machine Learning Approach for Road CyclingRace Performance Prediction -- Mining Marathon Training Data to GenerateUseful User Proles -- Learning from partially labeled sequences forbehavioral signal annotation. |
| Sommario/riassunto: | This book constitutes the refereed post-conference proceedings of the 7th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2020, colocated with ECML/PKDD 2020, in Ghent, Belgium, in September 2020. Due to the COVID-19 pandemic the conference was held online. The 11 papers presented were carefully reviewed and selected from 22 submissions. The papers present a variety of topics within the area of sports analytics, including tactical analysis, outcome predictions, data acquisition, performance optimization, and player evaluation. |
| Titolo autorizzato: | Machine learning and data mining for sports analytics ![]() |
| ISBN: | 3-030-64912-1 |
| Formato: | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 996465449503316 |
| Lo trovi qui: | Univ. di Salerno |
| Opac: | Controlla la disponibilità qui |