<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Data mining is a practice that employs mathematical algorithms to search for hidden information in a large amount of data to analyse the underlying pattern and law, and this practice is also known as knowledge discovery in data. The National Basketball Association (NBA) is the professional basketball game at the highest level in the world, and many events in an NBA game are used for statistical analysis. In this paper, data mining technology was applied based on event statistics to quantify the ability of basketball players and teams, the aim of the exercise being to predict basketball results. According to the NBA (2013–2018) season competition data, the quantitative evaluation method was firstly used to establish a player ability evaluation model, and the feature variable selection history game data weighting method was selected to construct a team player ability evaluation feature system. Secondly, machine learning algorithms such as linear regression, <italic>XGBoost</italic> and neural network models were used to predict the player performance. Considering the randomness and uncertainty of sports competitions, this paper deploys a combination of data mining algorithms and statistical simulation methods to predict the uncertainty of events, and the results indicate a good prediction effect. Therefore, this combined method is worthy of being applied in the evaluation system of team players and in game prediction.</p>
</abstract>

Data mining is a practice that employs mathematical algorithms to search for hidden information in a large amount of data to analyse the underlying pattern and law, and this practice is also known as knowledge discovery in data. The National Basketball Association (NBA) is the professional basketball game at the highest level in the world, and many events in an NBA game are used for statistical analysis. In this paper, data mining technology was applied based on event statistics to quantify the ability of basketball players and teams, the aim of the exercise being to predict basketball results. According to the NBA (2013–2018) season competition data, the quantitative evaluation method was firstly used to establish a player ability evaluation model, and the feature variable selection history game data weighting method was selected to construct a team player ability evaluation feature system. Secondly, machine learning algorithms such as linear regression, XGBoost and neural network models were used to predict the player performance. Considering the randomness and uncertainty of sports competitions, this paper deploys a combination of data mining algorithms and statistical simulation methods to predict the uncertainty of events, and the results indicate a good prediction effect. Therefore, this combined method is worthy of being applied in the evaluation system of team players and in game prediction.

Application of data mining in basketball statistics

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Application of data mining in basketball statistics"}

Data mining is a practice that employs mathematical algorithms to search for hidden information in a large amount of data to analyse the underlying pattern and...

Application of data mining in basketball statistics

Online veröffentlicht: 29. Apr. 2022

Seitenbereich: 2179 - 2188

Eingereicht: 05. Jan. 2022

Akzeptiert: 23. Feb. 2022

DOI: https://doi.org/10.2478/amns.2021.2.00182

Schlüsselwörter
Data Mining, Linear Regression, Neural Network, Prediction

© 2023 Bin Ma et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Application of data mining in basketball statistics

Online veröffentlicht: 29. Apr. 2022

Seitenbereich: 2179 - 2188

Eingereicht: 05. Jan. 2022

Akzeptiert: 23. Feb. 2022

DOI: https://doi.org/10.2478/amns.2021.2.00182

SchlüsselwörterData Mining, Linear Regression, Neural Network, Prediction

© 2023 Bin Ma et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Data Mining, Linear Regression, Neural Network, Prediction