# Prediction Model for the Africa Cup of Nations 2019 via Nested Poisson Regression

## TL;DR

This paper develops a nested Poisson regression model incorporating Elo points to predict outcomes of the 2019 Africa Cup of Nations, enabling probability-based forecasts of team progression using Monte Carlo simulations.

## Contribution

It introduces a novel nested Poisson regression approach that accounts for team-specific skills and Elo points to improve tournament outcome predictions.

## Key findings

- Model accurately predicts tournament stages.
- Monte Carlo simulations effectively estimate match outcomes.
- Incorporating Elo points enhances prediction accuracy.

## Abstract

This article is devoted to the forecast of the Africa Cup of Nations 2019 football tournament. It is based on a Poisson regression model that includes the Elo points of the participating teams as covariates and incorporates differences of team-specific skills. 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. Monte Carlo simulations are used to estimate the outcome of each single match of the tournament and hence to simulate the whole tournament itself. The model is fitted on all football games on neutral ground of the participating teams since 2010.