Project 19 · Soccer Analytics

Champions League 2026/27 Predictions

⚠️ in developmentdata:2026

The project develops a probabilistic model to track the 2026/27 Champions League. It combines a Poisson goal model with Monte Carlo simulation to turn teams’ attacking and defensive strength into match probabilities and qualification scenarios.

Python
Pandas
Soccer
Data science
Data analysis
Simulation

CONTEXT AND OBJECTIVE

The project aims to estimate how the 2026/27 Champions League may evolve using the available historical information and the results added as the competition progresses.

The workflow will be updated throughout the tournament so the model can revise match probabilities and qualification scenarios without changing the overall analytical structure.

MODEL AND SIMULATION

  • Historical UEFA competition data will be prepared and quality-checked before being used for modelling.
  • The Poisson model will estimate each team’s attacking and defensive strength and translate those differences into expected goals for each matchup.
  • Those expectations will then be converted into probabilities for the possible outcomes of the remaining matches.
  • Completed results will remain fixed as observed outcomes, while the rest of the competition will be projected through Monte Carlo simulation.
  • The simulations will produce distributions of positions, points and qualification probabilities that will be updated as the season progresses.

OUTPUTS AND NEXT STEPS

The outputs will focus on comparing teams, interpreting match probabilities and following the evolution of qualification scenarios through visualisations and probabilistic summaries.

As new results are added, the process will re-estimate team strengths, recalculate probabilities and rerun the simulation to show how the competition outlook changes.