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Forecasting basketball players' performance using sparse functional data*
Statistical Analysis and Data Mining ( IF 1.3 ) Pub Date : 2019-09-09 , DOI: 10.1002/sam.11436
Guillermo Vinué 1 , Irene Epifanio 2
Affiliation  

Statistics and analytic methods are becoming increasingly important in basketball. In particular, predicting players' performance using past observations is a considerable challenge. The purpose of this study is to forecast the future behavior of basketball players. The available data are sparse functional data, which are very common in sports. So far, however, no forecasting method designed for sparse functional data has been used in sports. A methodology based on two methods to handle sparse and irregular data, together with the analogous method and functional archetypoid analysis is proposed. Results in comparison with traditional methods show that our approach is competitive and additionally provides prediction intervals. The methodology can also be used in other sports when sparse longitudinal data are available.

中文翻译:

使用稀疏功能数据预测篮球运动员的表现*

统计和分析方法在篮球中变得越来越重要。特别地,使用过去的观察来预测玩家的表现是一个巨大的挑战。这项研究的目的是预测篮球运动员的未来行为。可用数据是稀疏的功能数据,这在体育运动中非常常见。但是,到目前为止,体育中还没有使用为稀疏功能数据设计的预测方法。提出了一种基于稀疏数据和不规则数据两种处理方法,以及类似方法和功能原型分析方法。与传统方法相比,结果表明我们的方法具有竞争力,并提供了预测间隔。当可获得稀疏的纵向数据时,该方法还可以用于其他运动。
更新日期:2019-09-09
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