Article Details

Title · Authors · Abstract · Keywords · DOI · References

CBA Player Position Prediction Based on Node2Vec-LGBM Model

HE Jiali, YANG Jun

(School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201209, China)

Section：Artificial Intelligence · Vol. 2025, Issue 8 · Pages：65-70 · 2096-4706（2025）08-0065-06 · CLC number：TP181 · Document code：A · DOI：10.19850/j.cnki.2096-4706.2025.08.013

Abstract

With the accumulation of sports data and the rapid development of Artificial Intelligence technology, it is particularly important to use Big Data and Machine Learning methods to optimize player position prediction. However, traditional methods often ignore the complex structural relationships between players, which are crucial for position prediction. Therefore, this paper proposes a player position prediction model based on Node2Vec and Light Gradient Boosting Machine (LGBM). Through data mining and analysis, the basic data of CBA players in three seasons are crawled, and the LGBM model is used to predict the position of players. Combined with hyper-parameter optimization and Node2Vec graph embedding algorithm, the accuracy of the model itself is further improved. The experimental results show that the model can not only effectively optimize the team's lineup and tactical arrangements, but also provide strong support for the team to enhance its competitiveness and overall performance.

Keywords

Machine Learning; Light Gradient Boosting Machine; Node2Vec; prediction model

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About the authors

何家丽（1999—），女，汉族，山西长治人，硕士在读，研究方向：机器学习；杨军（1986—），男，汉族，湖北荆州人，副教授，博士，研究方向：孤立子与可积系统。