A Neural Network Model of the NBA Most Valued Player Selection Prediction
| Autor: | Changjiang Zhang, Yuefei Chen, Junyan Dai |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Basketball
Artificial neural network business.industry Computer science 02 engineering and technology 01 natural sciences 010104 statistics & probability Current season 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence 0101 mathematics business Selection (genetic algorithm) |
| Zdroj: | Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence. |
| DOI: | 10.1145/3357777.3357786 |
| Popis: | This study analyzed all the performance of the players in the National Basketball Association (NBA) during a particular season and predicted the most valued players (MVP) of that season. The NBA game is the most popular basketball game all over the world. Every game attracted hundreds of and thousands of audiences and fans. Some fans supported the specific teams and many of fans supported some specific players in these teams. When they want to observe the performance of their preferred basketball stars and determine whether they can be awarded as the most valued player in the current season. Our study can help answer this question. We developed a novel NBA MVP prediction system with the neural network. We trained and tested this neural network using each season performances of NBA players from 1997 to 2019. These features of inputs are specific and optimized with training results. Based on our model, we randomly chose testing dataset from season 2009-2010 and season 2016-2017, and successfully predicted that the most valued players of the chosen seasons are LeBron James(season 2009-2010) and Russell Westbrook(season 2016-2017). |
| Databáze: | OpenAIRE |
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