论文标题
欧洲联赛篮球比赛的描述性和预测性分析和篮球人群的智慧
Descriptive and Predictive Analysis of Euroleague Basketball Games and the Wisdom of Basketball Crowds
论文作者
论文摘要
在这项研究中，我们专注于使用机器学习建模的欧洲联赛比赛中篮球比赛的预测。预测是一个二进制分类问题，可以预测比赛是1（主场胜利）还是2（视线）。数据是从欧洲联赛官方网站2016-2017、2017-2018和2018-2019的官方网站收集的，即在新格式时代。从匹配的数据中提取功能，并应用了现成的监督机器学习技术。我们校准和验证我们的模型。我们发现，简单的机器学习模型在测试集中的准确性不超过67％，比某些复杂的基准模型差。此外，这项研究的重要性在于“篮球人群的智慧”，我们演示了集体篮球爱好者的预测能力如何胜过本研究中讨论的机器学习模型。我们认为，为什么应将这组“专家”的准确性水平作为使用机器学习预测（欧洲）篮球游戏的未来研究的基准。
In this study we focus on the prediction of basketball games in the Euroleague competition using machine learning modelling. The prediction is a binary classification problem, predicting whether a match finishes 1 (home win) or 2 (away win). Data is collected from the Euroleague's official website for the seasons 2016-2017, 2017-2018 and 2018-2019, i.e. in the new format era. Features are extracted from matches' data and off-the-shelf supervised machine learning techniques are applied. We calibrate and validate our models. We find that simple machine learning models give accuracy not greater than 67% on the test set, worse than some sophisticated benchmark models. Additionally, the importance of this study lies in the "wisdom of the basketball crowd" and we demonstrate how the predicting power of a collective group of basketball enthusiasts can outperform machine learning models discussed in this study. We argue why the accuracy level of this group of "experts" should be set as the benchmark for future studies in the prediction of (European) basketball games using machine learning.
