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arXiv 2009.00550cs.SI
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利用社交网络改进职业体育群体转移预测

Using Social Networks to Improve Group Transition Prediction in Professional Sports

Emily J. Evans, Rebecca Jones, Joseph Leung, Benjamin Z. Webb

摘要:

我们研究了社交数据是否可以用来预测美国职业棒球大联盟(MLB)和国家篮球协会(NBA)的成员在职业生涯中如何在球队之间过渡。我们发现,将社交数据整合到各种机器学习算法中,可以大大提高算法正确确定这些转换的能力。特别是,我们衡量球员的表现,团队健身和社会数据如何单独和共同有助于预测这些转变。提高个人表现和团队适应度都提高了我们算法的预测准确性。然而,当我们包括社会数据时,这种改善就相形见绌了,这些数据表明,社会关系对MLB和NBA的球员过渡都有比较大的影响。

英文摘要:

We examine whether social data can be used to predict how members of Major League Baseball (MLB) and members of the National Basketball Association (NBA) transition between teams during their career. We find that incorporating social data into various machine learning algorithms substantially improves the algorithms' ability to correctly determine these transitions. In particular, we measure how player performance, team fitness, and social data individually and collectively contribute to predicting these transitions. Incorporating individual performance and team fitness both improve the predictive accuracy of our algorithms. However, this improvement is dwarfed by the improvement seen when we include social data suggesting that social relationships have a comparatively large effect on player transitions in both MLB and in the NBA.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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