kabaddiPy - Data collection and analysis tools for professional Kabaddi leagues Features • Installation • Usage • Contributing • License • Documentation

kabaddiPy is a Python module that provides tools for collecting and analyzing data from professional Kabaddi leagues. It uses web scraping techniques to gather information about teams, players, and match statistics from various online sources.

Please install the development version of kabaddiPy using pip:

pip install kabaddiPy

Deployed here: https://pypi.org/project/kabaddiPy/

Here's a quick minimal example of how to get started with the kabaddiPy package:

import kabaddiPy

pkl = PKL()

# Get roster for a specific team for a specific season
team_roster = pkl.build_team_roster(team_id=3, season=1)
print(team_roster)

For more detailed usage instructions and API documentation, please refer to our documentation page.

For more complicated examples, check out the examples directory.

KabaddiPy offers a comprehensive set of features for analyzing Pro Kabaddi League (PKL) data. Here are some of the key functionalities:

standings = pkl.get_standings(season=9, qualified=True)

Retrieve PKL standings for a specific season, with options to filter for qualified teams.

season_matches = pkl.get_season_matches(season=6)

Get detailed information about all matches in a specific season.

team_rank, team_value, team_per_match, team_raider_skills, team_defender_skills = pkl.get_team_info(season=6, team_id=29)

Access comprehensive team statistics, including rankings, absolute values for statistics, and per-match metrics. Additionally get a summary of the various Raider/Defender Skills employed throught the season by the team.

player_stats_rank, player_stats_value, player_stats_per_match = pkl.get_player_info(player_id=660, season=9)

Retrieve detailed player statistics, including rankings, performance metrics, and Raider vs. Defender (RVD) data.

match_detail_df, events_df, zones_df, team1_df, team2_df, breakdown_df = pkl.load_match_details(season=9, match_id='2895')

Access comprehensive match data, including events, team performances, and breakdown statistics.

pbp_events_data = pkl.load_pbp(season=9, match_id='2895')

Get detailed play-by-play data for specific matches.

team_roster_df = pkl.get_team_roster(season=9, team_id=3)

Generate a comprehensive roster for a specific team in a given season.

pkl.plot_player_zones(player_id=143, season=5, zone_type='strong')
pkl.plot_team_zones(team_id=4, season=5, zone_type='weak')
pkl.plot_point_progression(season=10, match_id=3163)

Create visual representations of player and team performance, including zone analysis and point progression charts.

pkl.plot_player_zones_grid([143, 12, 211, 160], season=5, zone_type='strong', max_cols=2)

Generate grid visualizations for comparing multiple players' performances.

These features provide a robust toolkit for analyzing PKL data, from high-level season statistics to detailed player and match analyses.

The package offers both data retrieval and visualization capabilities, making it a versatile resource for kabaddi enthusiasts, analysts, and researchers.

We welcome contributions to the Kabaddi Data Aggregator project! If you'd like to contribute, please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature/your-feature-name)
3. Make your changes
4. Commit your changes (git commit -am 'Add some feature')
5. Push to the branch (git push origin feature/your-feature-name)
6. Create a new Pull Request

This project is licensed under the GPL-2.0 License - see the LICENSE file for details.

• Thanks to the various website owners for providing the data source

If you have any questions, feel free to reach out to Aniruddha Mukherjee or Bhaskar Lalwani or open an issue in the GitHub repository.

Made with ❤️ for Kabaddi enthusiasts and data analysts

Please ⭐️ this repository if you found it helpful! Your support is greatly appreciated. :)