# Quantifying the relation between performance and success in soccer

## TL;DR

This study analyzes extensive soccer data to quantify how technical performance relates to team success, revealing significant correlations and demonstrating the potential of machine learning to simulate season outcomes based solely on technical features.

## Contribution

It introduces a machine learning approach to predict season rankings from technical data, highlighting the complex relationship between performance metrics and success in soccer.

## Key findings

- Team rankings are significantly related to technical performance features.
- Victory and defeat can be predicted from in-game performance, but draws are harder to classify.
- Season simulations based on technical data closely match actual league outcomes.

## Abstract

The availability of massive data about sports activities offers nowadays the opportunity to quantify the relation between performance and success. In this study, we analyze more than 6,000 games and 10 million events in six European leagues and investigate this relation in soccer competitions. We discover that a team's position in a competition's final ranking is significantly related to its typical performance, as described by a set of technical features extracted from the soccer data. Moreover we find that, while victory and defeats can be explained by the team's performance during a game, it is difficult to detect draws by using a machine learning approach. We then simulate the outcomes of an entire season of each league only relying on technical data, i.e. excluding the goals scored, exploiting a machine learning model trained on data from past seasons. The simulation produces a team ranking (the PC ranking) which is close to the actual ranking, suggesting that a complex systems' view on soccer has the potential of revealing hidden patterns regarding the relation between performance and success.