FootStats: Soccer Analytics Dashboard

The Problem

I play Football Manager and always wondered: what stats actually matter? The game shows dozens of metrics per player, but which ones actually predict match performance?

I couldn't find a simple tool that lets me:

• Generate realistic player data
• Test correlations between stats
• Build a predictive model for player ratings
• Visualize everything interactively

So I built one.

The Approach

Data Generation: Instead of using real data (hard to get), I wrote a generator that creates realistic synthetic data. Each position has different distributions:

• Forwards: More goals, fewer tackles
• Midfielders: High pass counts, moderate goals
• Defenders: High tackles, low shots
• Goalkeepers: Specialized stats

Analysis Pipeline:

1. Descriptive stats: Means, distributions by position
2. Correlation analysis: Which stats relate to each other?
3. Regression: Can we predict player rating from other stats?
4. Hypothesis testing: Do forwards actually score more than midfielders? (spoiler: yes)

Dashboard: Built with Plotly Dash + Bootstrap. Interactive filtering by position, player, stat type.

The Result

A fully interactive dashboard showing:

• Player performance trends over 20 games
• Position comparisons (box plots, bar charts)
• Statistical analysis (correlations, regression, hypothesis tests)
• Player categorization (star/good/average/needs improvement)

Key Findings from the Data:

• Pass accuracy correlates with rating (r ≈ 0.4)
• Forwards score significantly more than other positions (p < 0.001, obviously)
• A simple linear regression can predict rating with R² ≈ 0.6 from just 5 stats

Run It

# Setup
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

# Launch dashboard
python my_football_dashboard.py

# Or run statistical analysis only
python stats_stuff.py

Then open http://127.0.0.1:8050 in your browser.

Project Structure

FootStats/
├── my_football_dashboard.py    # Interactive Dash dashboard (main entry point)
├── stats_stuff.py             # Statistical analysis module
├── requirements.txt           # Dependencies
└── README.md                 # This file

What I Learned

Statistics:

• T-tests for comparing groups (forwards vs midfielders)
• ANOVA for comparing multiple groups
• Pearson correlation for linear relationships
• Linear regression for prediction
• Effect size matters, not just p-values

Dash/Plotly:

• Callbacks for interactivity
• Layout management with Bootstrap
• Multi-axis charts for different scales

Data Generation:

• Poisson distribution for count data (goals, assists)
• Normal distribution for continuous stats (pass %, km run)
• Position-specific parameters make data realistic

What I'd Do Next

1. Real Data: Integrate with an API like Football-data.org or scrape real stats
2. More Models: Try Random Forest or XGBoost for better prediction
3. Time Series: Add trend analysis (players improving/declining over season)
4. Team Analysis: Not just individual players, but team-level patterns
5. Deployment: Deploy to Render or similar for public access

Screenshots

Requirements

• Python 3.8+
• Dash, Plotly, scikit-learn, scipy

See requirements.txt for pinned versions.

License

Student project - built for learning data science and visualization.