{"id":28380907,"url":"https://github.com/wizardoftrap/football-team-analytics","last_synced_at":"2026-05-05T11:31:36.031Z","repository":{"id":295182362,"uuid":"989406847","full_name":"wizardoftrap/football-team-analytics","owner":"wizardoftrap","description":"This Jupyter notebook, created on Kaggle, analyzes football player and team statistics for the 2024-2025 season. 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It provides insights into player performance, team metrics, and playing styles across major European leagues using data from the dataset `players_data-2024_2025.csv`.\n\n## Dataset\nThe analysis is based on the dataset available at:\n- **Kaggle Dataset**: [Football Players Stats 2024-2025](https://www.kaggle.com/datasets/6586397/players_data-2024_2025)\n- Contains 2,840 entries with 267 columns, including player statistics like goals (Gls), expected goals (xG), assists (Ast), expected assists (xAG), tackles (Tkl), and more.\n\n## Notebook Contents\nThe notebook performs the following analyses:\n1. **Data Preprocessing**:\n   - Loads the dataset and handles missing values by filling with zeros.\n   - Displays dataset information and a preview of the data.\n\n2. **Player Analysis**:\n   - Extracts specific performance metrics (e.g., Gls, xG, Ast, xAG, G+A) for individual players, such as Pedri.\n   - Identifies players with similar playing styles using cosine similarity on normalized features like goals, tackles, progressive passes, and carries.\n\n3. **Team Analysis**:\n   - Aggregates team statistics by summing metrics like goals, assists, and defensive actions.\n   - Calculates derived metrics:\n     - `Goals_per_xG`: Ratio of actual goals to expected goals.\n     - `Defensive_Actions`: Sum of tackles, blocks, and clearances.\n     - `Progression_Rate`: Progressive passes per touch.\n     - `Turnover_Rate`: Turnovers (miscontrols + dispossessions) per touch.\n   - Categorizes team performance as \"Over,\" \"Under,\" or \"Partial\" based on goals and assists compared to expected values.\n\n4. **League Analysis**:\n   - Determines dominant playing styles by league (e.g., Bundesliga: Cautious Pass and Move, Premier League: Aggressive Long Ball).\n\n## Example Outputs\n- **Player Similarity**:\n  - For Lamine Yamal, the notebook identifies players like Iñaki Williams and Son Heung-min as similar based on metrics like goals, assists, and progressive carries.\n- **Team Performance**:\n  - Top teams by G+A (Goals + Assists): Barcelona (167), Bayern Munich (162), Paris S-G (158).\n- **League Styles**:\n  - Example: Premier League teams favor \"Aggressive Long Ball Football.\"\n\n## Notes\n- The notebook assumes the dataset is in the `/kaggle/input/football-players-stats-2024-2025/` directory, as per Kaggle's default structure. Update the file path if running locally.\n- Visualizations (e.g., plots) may require additional code to display results, as the provided notebook snippet focuses on data processing and analysis.\n- The dataset includes goalkeeper-specific metrics (e.g., Saves, CS), which are primarily relevant for goalkeeper analysis.\n\n## License\nThis notebook is intended for educational and analytical purposes. The dataset is sourced from Kaggle, and usage should comply with Kaggle's terms and the dataset's licensing.\n\n## Acknowledgments\n- **Kaggle**: For hosting the dataset and providing the environment to develop this notebook.\n- **Data Source**: The `players_data-2024_2025.csv` dataset, which compiles comprehensive football statistics.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwizardoftrap%2Ffootball-team-analytics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwizardoftrap%2Ffootball-team-analytics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwizardoftrap%2Ffootball-team-analytics/lists"}