This project performs a statistical analysis of Premier League teams and forwards using Z-scores and T-scores to normalize and compare performance across key metrics. It includes a Forward Profile Comparison Dashboard and a Team Stats Chart built in Excel, allowing objective evaluation of both players and clubs.
📥 Download — Microsoft Excel (charts and graphs) database.xlsx
Visual analysis of Goals vs xG for all Premier League clubs, presented through an Excel scatter plot with club badges.
📊 Key Conclusions:
- 🏆 Manchester City stand out as clear outliers — highest goals (94) and highest xG (79), confirming their dominance both in chance creation and finishing
- 🎯 Arsenal had a high xG (72) but scored fewer goals (88 vs City's 94), suggesting slightly less clinical finishing despite creating similar quality chances
- ⚽ Manchester United show an interesting pattern — high xG (67.7) but only 58 goals, indicating they underperformed their expected goals significantly
- 📈 Tottenham had 57 xG but scored 70 goals — a case of overperforming their xG, likely due to individual brilliance (Harry Kane)
- 📉 Chelsea and Everton sit in the lower half despite being big clubs, reflecting their poor attacking seasons
- 🐺 Wolves were the least threatening team — lowest goals and lowest xG, showing a very defensive and passive style
📥 Download — Dashboard João Almeida.xlsx
Statistical analysis of Premier League forwards using Z-scores and T-scores, with a final comparison dashboard.
The workbook contains 3 sheets:
- Folha1 — Raw Premier League forward statistics
- Folha2 — Z-score and T-score statistical normalization
- Dashboard Final — Forward profile comparison dashboard
- 📋 Raw dataset of Premier League forwards and teams
- 📐 Z-score normalization to standardize player metrics
- 📊 T-score conversion for easier comparison across different scales
- 🎯 Forward Profile Comparison Dashboard
- 📈 Team performance charts (goals vs xG)
- ⚽ Covers key metrics: minutes played, goals, xG, shots, shots on target
| Metric | Description |
|---|---|
| MP | Matches played |
| Min | Minutes played |
| Gls | Goals scored |
| xG | Expected goals |
| Shots | Total shots |
| Shots on Target | Shots on target |
- Data Collection — Premier League statistics sourced and compiled
- Z-score Normalization — Each metric standardized relative to the mean and standard deviation
- T-score Conversion — Z-scores converted to T-scores (mean=50, SD=10) for easier interpretation
- Dashboard — Final scores visualized in a comparison dashboard
A T-score above 50 means the player performs above average in that metric. Below 50 means below average.
This analysis helps to:
- Compare forwards on a level playing field regardless of minutes played
- Identify elite performers vs average players statistically
- Analyze team tendencies in chance creation and finishing efficiency
- Support scouting decisions with objective data
- Add more positions (midfielders, defenders)
- Include more seasons for trend analysis
- Add weighted composite score for overall rating
- Build an interactive Power BI version
- Include defensive and creative metrics (xA, key passes)
- Microsoft Excel
- Statistical Analysis (Z-scores, T-scores)
- Data Visualization (Excel Charts & Dashboard)
- Data sourced from FBref / Premier League statistics