Name
Title | ||
|---|---|---|
Basketball players' score prediction using artificial intelligence technology via the Internet of Things |
Abstract | ||
|---|---|---|
The National Basketball Association's (NBA) tournaments are watched by people all over the world via the Internet of Things (IoT). Players who score more points are regarded as star players and are part of the focus of competition for each team. The purpose of this research is to predict the score of NBA league players based on player data using deep learning methods. Various algorithms were compared, the best model for score predictions was obtained, and a comparative analysis of the team was provided to the players. The prediction effect and average MAPE index of limit gradient boosting (XGB) are better than that of other methods. It can be seen that the XGB method has a better fit to the regression problem and high interpretability. Thus, it has the ability to accurately predict and evaluate NBA score predictions. The results can support the team's decision-making in player management. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s11227-022-04573-6 | JOURNAL OF SUPERCOMPUTING |
Keywords | DocType | Volume |
Internet of Things (IoT), Basketball, Deep learning, Random forest, Backward neural network | Journal | 78 |
Issue | ISSN | Citations |
17 | 0920-8542 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fuzhi Su | 1 | 0 | 0.34 |
| Meihong Chen | 2 | 0 | 0.34 |