Automated Player Selection for Sports Team using Competitive Neural Networks
| Autor: | Jamshed Memon, Furqan M. Khan, Rabah Al-Shboul, Tahir Q. Syed |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
021103 operations research
Basketball Knowledge management General Computer Science Operations research Computer science business.industry ComputingMilieux_PERSONALCOMPUTING 0211 other engineering and technologies 02 engineering and technology Football League Adversary Order (business) 0202 electrical engineering electronic engineering information engineering Sabermetrics 020201 artificial intelligence & image processing business Team management |
| Zdroj: | International Journal of Advanced Computer Science and Applications. 8 |
| ISSN: | 2156-5570 2158-107X |
| DOI: | 10.14569/ijacsa.2017.080859 |
| Popis: | The use of data analytics to constitute a winning team for the least cost has become the standard modus operandi in club leagues, beginning from Sabermetrics for the game of basketball. Our motivation is to implement this enomenon in other sports as well, and for the purpose of this work we present a model for football, for which to the best of our knowledge, previous work does not exist. The main objective is to pick the best possible squad from an available pool of players. This will help decide which team of 11 football players is best to play against a particular opponent, perform prediction of future matches and helps team management in preparing the team for the future. We argue in favour of a semi-supervised learning approach in order to quantify and predict player performance from team data with mutual influence among players, and report win accuracies of around 60%. |
| Databáze: | OpenAIRE |
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