NBA Player Score Prediction Based on Machine Learning

Haoyu Chen

2023

Abstract

With the success of machine learning and data visualization in many fields, the NBA(National Basketball Association) has also benefited from its huge demand for data analysis. These analysis results have been extensively applied in player draft, player training and tactical decisions, playing a crucial role in management and coaching staff decisions. This article utilizes data visualization technology and machine learning to analyze the NBA dataset. Using random forest and multiple linear regression models to predict NBA player scoring performance, and evaluate the model using R-square scores and MAE(Mean Absolute Error). There are some significant relationships between Points and several features like Turnovers, FGM and Minutes Played. After a ten-fold validation experiment, it was found that both the multiple linear regression and random forest are greater than 0.98 in R-square scores. And according to the result of the comparison, the multiple linear regression model is more suitable as a score prediction model and has a better stability for this dataset.

Paper Citation

in Harvard Style

Chen H. (2023). NBA Player Score Prediction Based on Machine Learning. In Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-705-4, SciTePress, pages 291-296. DOI: 10.5220/0012801700003885

in Bibtex Style

@conference{daml23,
author={Haoyu Chen},
title={NBA Player Score Prediction Based on Machine Learning},
booktitle={Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2023},
pages={291-296},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012801700003885},
isbn={978-989-758-705-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - NBA Player Score Prediction Based on Machine Learning
SN - 978-989-758-705-4
AU - Chen H.
PY - 2023
SP - 291
EP - 296
DO - 10.5220/0012801700003885
PB - SciTePress