Identification of relevant performance indicators in round-robin tournaments

TL;DR

This paper evaluates various soccer performance indicators to identify which best predict team strength and match outcomes, introducing a statistical framework to quantify their forecasting effectiveness.

Contribution

It presents a novel statistical approach to assess and compare the predictive power of soccer performance indicators, highlighting the packing rate as the most effective.

Findings

Packing rate is the most predictive indicator.

The framework quantifies the intrinsic forecasting potential of indicators.

Match-specific observations offer new insights into match outcomes.

Abstract

A myriad of different data are generated to characterize a soccer match. Here we discuss which performance indicators are particularly helpful to forecast the future results of a team via an estimation of the underlying team strengths with minimum statistical uncertainty. We introduce an appropriate statistical framework and exemplify it for different performance indicators for the German premier soccer league. Two aspects are involved: (i) It is quantified how well the estimation process would work if no statistical noise due to finite information is present. The related score directly expresses to which degree the chosen performance indicator reflects the underlying team strength. (ii) Additionally, the reduction of the forecasting quality due to statistical noise is determined. From both pieces of information a normalized value can be constructed which is a direct measure of the overall forecasting quality. It turns out that the so-called packing rate works best. New perspectives of performance indicators enter when trying to understand the outcome of single matches based on match-specific observations from the same match. Implications for the purpose of forecasting as well as consequences for the interpretation of team strengths are discussed.