Can Data Measure Football Excitement? Ranking FIFA World Cup 2026 Matches with Python
When fans remember a great football match, they usually think about unforgettable goals, dramatic comebacks and emotional moments. But from a Sports Analytics perspective, another question appears.
Can we measure how exciting a football match was using only data?
In this project I analyzed the FIFA World Cup 2026 using Python, Pandas and Matplotlib to create a simple metric called Excitement Score.
Dataset
The analysis uses the Football World Cup 2026 Dataset from Kaggle containing:
- Match events
- Expected Goals (xG)
- Possession
- Shots
- Shots on target
- Big Chances
- Player information
- Momentum
Dataset: Football World Cup 2026 Dataset
Building the Excitement Score
Goals alone do not tell the whole story. I combined multiple offensive statistics into a simple metric.
Excitement Score =
Shots
+ (Big Chances × 2)
+ (Goals × 3)
The weights are intentionally simple and can easily be adjusted for future experiments.
Top 15 Most Exciting Matches
Using the Excitement Score, every match was ranked according to its offensive production.
- X-axis: Shots Inside the Box
- Y-axis: Shots on Target
- Bubble Size: Goals
- Bubble Color: Big Chances
Does the Model Match My Favorite Games?
After generating the ranking, I compared it with the matches I personally enjoyed the most.
- 🇦🇷 Argentina vs 🇨🇻 Cape Verde
- 🇲🇽 Mexico vs 🏴 England
- 🇳🇱 Netherlands vs 🇯🇵 Japan
- 🇪🇸 Spain vs 🇦🇷 Argentina
- 🏴 England vs 🇫🇷 France
- 🇪🇸 Spain vs 🇧🇪 Belgium
- 🇪🇸 Spain vs 🇨🇻 Cape Verde
- 🇪🇬 Egypt vs 🇦🇷 Argentina
- 🇳🇴 Norway vs 🏴 England
- 🇳🇴 Norway vs 🇸🇳 Senegal
Several of my favorite matches also received high Excitement Scores, suggesting that offensive statistics capture an important part of what fans perceive as an exciting game.
Key Findings
- More goals do not always mean a more exciting match.
- Big Chances strongly influence match excitement.
- Simple analytics can identify memorable games.
- Sports Analytics complements, rather than replaces, the fan experience.
Technology Stack
- Python
- Pandas
- NumPy
- Matplotlib
- Jupyter Notebook
GitHub Repository
The complete project includes data preparation, feature engineering, Excitement Score calculation and visualization code.
GitHub: GITHUB_URL
Final Thoughts
This experiment shows that even a simple metric based on football statistics can identify many of the matches fans remember as the most entertaining.
Future versions could include xG, match momentum, cards, game state and win probability to build a more advanced model.
How would you build an Excitement Score? Which variables would you include?
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