Forecasting basketball players’ performance using sparse functional data

Abstract

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.