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Record Nr. |
UNINA9910733728903321 |
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Autore |
Saikia Hemanta |
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Titolo |
Cricket Performance Management : Mathematical Formulation and Analytics / / by Hemanta Saikia, Dibyojyoti Bhattacharjee, Diganta Mukherjee |
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Pubbl/distr/stampa |
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Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (246 pages) |
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Collana |
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Indian Statistical Institute Series, , 2523-3114 |
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Disciplina |
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Soggetti |
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Statistics |
Statistics |
Popular Science in Statistics |
Statistics, general |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Chapter 1. Cricket, Statistics and Data Mining -- Chapter 2. Franchisee Cricket and Cricketer’s Auction -- Chapter 3. Quantifying Performance of Cricketers -- Chapter 4. Fielding Performance Measure: Issues, Concern and Solution -- Chapter 5. Performance Based Market Valuation of Players -- Chapter 6. Impact of Age on Performance of Cricketers -- Chapter 7. Performing Under Difficulty: The Magical Pressure Index -- Chapter 8. Decision Making Approaches to Optimum Team Selection. |
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Sommario/riassunto |
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This book focuses on the application of data mining techniques in cricket. It provides detailed examples of how data mining can be helpful for decision-making in sports with special reference to cricket, particularly the quantitative features related to Twenty20 cricket, the latest and the most popular format of the game. The book highlights the performance quantification of cricketers (batsmen, bowlers, all-rounders, and wicket keepers), determining the market valuation of cricketers based on their on-field performances and the effect of age on the performance of the cricketers. It also provides a comprehensive overview of the different aspects of the game where quantitative techniques are beneficial, and highlights the use of statistical and data |
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mining tools in analysing sports-related data and objective decision-making in sports. The book appeals to a wide readership, including postgraduate students of statistics/mathematics, data analysts, sports management bodies. It also offers data miners, such as researchers in statistics, mathematics, operations research, and computer science ideas for projects. |
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