Stacking-based deep neural network for player scouting in football 1

TL;DR

This paper introduces a stacking-based deep neural network model for football player scouting, which outperforms traditional statistical methods in identifying high-potential players using open-source data.

Contribution

The paper presents a novel stacking-based deep learning approach specifically designed for football player scouting, demonstrating improved detection accuracy over classical methods.

Findings

Model achieves higher accuracy than statistical methods

Effective in identifying high-potential players

Applicable to open-source football data

Abstract

Datascouting is one of the most known data applications in professional sport, and specifically football. Its objective is to analyze huge database of players in order to detect high potentials that can be then individually considered by human scouts. In this paper, we propose a stacking-based deep learning model to detect high potential football players. Applied on open-source database, our model obtains significantly better results that classical statistical methods.