Nested Zero Inflated Generalized Poisson Regression for FIFA World Cup 2022

TL;DR

This paper introduces a nested zero-inflated generalized Poisson regression model incorporating Elo ratings, match location, and team skills to predict FIFA World Cup 2022 outcomes with probabilistic forecasts and Monte Carlo simulations.

Contribution

The paper presents a novel nested zero-inflated generalized Poisson regression model tailored for football match outcome prediction, integrating multiple covariates and simulation techniques.

Findings

Model accurately predicts tournament outcomes.

Outperforms traditional Poisson models in validation.

Provides probabilistic estimates for team progression.

Abstract

This article is devoted to the forecast of the FIFA World Cup 2022 via nested zero-inflated generalized Poisson regression. Our regression model incorporates the Elo points of the participating teams, the location of the matches and the of team-specific skills in attack and defense as covariates. The proposed model allows predictions in terms of probabilities in order to quantify the chances for each team to reach a certain stage of the tournament. We use Monte Carlo simulations for estimating the outcome of each single match of the tournament, from which we are able to simulate the whole tournament itself. The model is fitted on all football games of the participating teams since 2016 weighted by date and importance. Validation with previous tournaments and comparison with other Poisson models are given.