Data Driven football scouting assistance with simulated player performance extrapolation

Abstract

In club football, scouting is a crucial aspect of player recruitment, with elite football clubs investing millions of dollars in scouting and signing the best player for their team every year. Scouting requires great analytical and observational skills from the scout, to find the best player for any position in the team. A scout needs to analyze the player by watching his in-game actions, physical attributes and make a judgement on how the player might fit into the team. Every team has a formation, a style of play and a specific profile of player is required for a given position depending on the aforementioned factors. But scouts only watch a player play a few matches in person, and prepare their scouting report based on a player’s performance in those matches. This process is flawed as the scout is expected to watch a few games and make estimates of the player’s performance in a new team. The player statistics can help the scout in making better data-driven decisions. A player’s career statistics can provide a picture of how the player performs individually, but they fail to predict player chemistry alongside a team. Misjudgement in scouting can lead to losses of millions of dollars to a club. We propose to solve this problem by utilising vast amounts of quantitative and qualitative player statistics (from 3+ sources), and by incorporating data science and machine learning algorithms to simulate real world performances of the team after the addition of the newly scouted player. We take into account specific player requirements and classify a player into one of our specific 15 player types, and use the team’s formation and style of play to predict the players that will have the best chemistry with any given lineup, thereby facilitating scouts in making better decisions.