Abstract

Sports analytics (SA) incorporate machine learning (ML) techniques and models for performance prediction. Researchers have previously evaluated ML models applied on a variety of basketball statistics. This paper aims to benchmark the forecasting performance of 14 ML models, based on 18 advanced basketball statistics and key performance indicators (KPIs). The models were applied on a filtered pool of 90 high-performance players. This study developed individual forecasting scenarios per player and experimented using all 14 models. The models’ performance ranking was developed using a bespoke evaluation metric, called weighted average percentage error (WAPE), formulated from the weighted mean absolute percentage error (MAPE) evaluation results of each forecasted statistic and model. Moreover, we employed a comprehensive forecasting approach to improve KPI's results. Results showed that Tree-based models, namely Extra Trees, Random Forest, and Decision Tree, are the best performers in most of the forecasted performance indicators, with the best performance achieved by Extra Trees with a WAPE of 34.14%. In conclusion, we achieved a 3.6% MAPE improvement for the selected KPI with our approach on unseen data.

中文翻译：

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评估机器学习模型在篮球运动表现预测中的有效性：比较研究

摘要

体育分析 (SA) 结合了机器学习 (ML) 技术和模型来进行表现预测。研究人员之前评估了应用于各种篮球统计数据的机器学习模型。本文旨在基于 18 项高级篮球统计数据和关键绩效指标 (KPI)，对 14 种 ML 模型的预测性能进行基准测试。这些模型适用于经过过滤的 90 名高水平球员。这项研究为每个玩家开发了单独的预测场景，并使用所有 14 个模型进行了实验。模型的性能排名是使用定制的评估指标（称为加权平均百分比误差（WAPE））制定的，该指标是根据每个预测统计数据和模型的加权平均绝对百分比误差（MAPE）评估结果制定的。此外，我们采用了全面的预测方法来改善 KPI 的结果。结果表明，基于树的模型，即 Extra Trees、随机森林和决策树，在大多数预测性能指标中表现最好，其中 Extra Trees 的性能最好，WAPE 为 34.14%。总之，通过我们对未见数据的方法，我们将所选 KPI 的 MAPE 提高了 3.6%。