论文标题
使用社交网络改善专业体育中的群体过渡预测
Using Social Networks to Improve Group Transition Prediction in Professional Sports
论文作者
论文摘要
我们研究了社会数据是否可以用于预测美国职业棒球大联盟（MLB）的成员以及国家篮球协会（NBA）的成员如何在职业生涯中之间的过渡。我们发现将社交数据纳入各种机器学习算法大大提高了算法正确确定这些过渡的能力。特别是，我们衡量球员绩效，团队健身和社交数据如何单独和集体地预测这些过渡。结合个人表现和团队健身都提高了我们算法的预测准确性。但是，当我们包括社交数据表明社会关系对MLB和NBA的球员过渡的影响相对较大的影响时，这种改进的改善使这种改善相形见war。
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.
