A data-driven approach to predict the most valuable player in a game - Romero, F. P.; Lozano-Murcia, J.; Lopez-Gomez, J. A.; Angulo, E.; Sanchez-Lopez, E. (Computational and Mathematical Methods, 2021)
The detection of outstanding behaviors is an issue of essential importance in sports analytics contexts. However, analyzing how human experts select each match's most valuable player according to objective and subjective factors, is a great challenge. This paper proposes a data-driven approach for sports team performance based on the weighted aggregation of statistical indicators. The proposal is divided into two approaches: The first one conducts a principal component analysis to examine the relationship between each game's statistical indicators. The other one addresses a metaheuristic analysis to weight the attributes and choose the most valuable players optimally. Finally, we apply the proposed approach to the 2018 European Men's Handball Championship and take the "Player of the match" of each game as an example to illustrate its usefulness and efficacy. We carry out multiple analyses that show that the data-driven approach can predict the "Player of the Match" in most matches.