PSL Cricket Analytics

Repository Information

Repository Name: psl-cricket-analytics

Description: Comprehensive statistical analysis toolkit for Pakistan Super League cricket data with automated batting and bowling performance metrics, visualization generation, and detailed reporting capabilities.

Overview

This repository provides a complete suite of Python scripts for analyzing Pakistan Super League (PSL) cricket match data. The toolkit processes ball-by-ball JSON data to generate comprehensive statistical insights, professional visualizations, and detailed LaTeX reports for batting and bowling performance analysis.

Features

Core Functionality

Comprehensive Data Processing: Parse and analyze ball-by-ball PSL match data from JSON files
Batting Analysis: Detailed statistics including runs, averages, strike rates, boundaries, consistency metrics
Bowling Analysis: Complete bowling metrics including wickets, economy rates, dot ball percentages, strike rates
Pakistani Player Focus: Specialized analysis for Pakistani players with minimum match qualification filters
Automated Visualization: Generate publication-quality charts and graphs automatically
LaTeX Report Generation: Professional PDF reports with detailed statistical analysis

Analytical Capabilities

Player performance aggregation across multiple seasons
Strike rate vs average correlation analysis
Boundary hitting patterns and preferences
Consistency and reliability metrics
Match-winning performance identification
Comparative analysis across player cohorts
Milestone achievement tracking (50s, 100s)
Career trajectory projections

Visualization Suite

Multi-panel batting performance dashboards (12 charts)
Multi-panel bowling analysis dashboards (9 charts)
Top performance bar charts for individual players
Radar charts for multi-dimensional player comparison
Strike rate distribution analysis
Economy rate and wicket-taking visualizations
Boundary percentage analysis
Consistency and reliability metrics

Installation

Prerequisites

Python 3.7 or higher
pip package manager

Required Python Packages

pip install pandas matplotlib seaborn numpy

Data Format

Input Data

The scripts expect match data in JSON format following the standard cricket data schema:

{
  "info": {
    "teams": ["Team A", "Team B"],
    "dates": ["2016-02-04"],
    "outcome": { "winner": "Team A" }
  },
  "innings": [
    {
      "team": "Team A",
      "overs": [
        {
          "over": 0,
          "deliveries": [
            {
              "batter": "Player Name",
              "bowler": "Bowler Name",
              "runs": { "batter": 4, "total": 4 }
            }
          ]
        }
      ]
    }
  ]
}

Place all JSON files in the psl_json/ directory.

Usage

General Analysis

Run comprehensive batting and bowling analysis on all matches:

python scripts/psl_general_analysis.py

Output:

psl_batting_analysis.png - 9 batting visualizations
psl_bowling_analysis.png - 9 bowling visualizations
psl_batting_data.csv - Complete batting statistics
psl_bowling_data.csv - Complete bowling statistics
Console output with summary statistics

Pakistani Batters Deep Analysis

Generate detailed analysis for top Pakistani batters with minimum 20 matches:

python scripts/psl_pakistani_batters.py

Output:

psl_top10_pakistani_batters_analysis.png - 12-chart dashboard
psl_top_performances_pakistani_batters.png - Top 5 scores visualization
psl_radar_comparison_pakistani_batters.png - Multi-dimensional comparison
psl_pakistani_batters_detailed_stats.csv - Complete statistics export
Console output with detailed rankings and analysis

Generate LaTeX Report

After running the analysis scripts, compile the LaTeX report:

cd output/reports/
pdflatex psl_batting_deep_analysis.tex
pdflatex psl_batting_deep_analysis.tex  # Run twice for table of contents

Metrics Explained

Batting Metrics

Average: Total runs divided by number of dismissals
Strike Rate: (Total runs / Total balls faced) × 100
Boundary Percentage: ((Fours × 4) + (Sixes × 6)) / Total Runs × 100
Consistency: Standard deviation of individual scores (lower is better)
Runs per Match: Total runs divided by matches played
Dot Ball Percentage: Dot balls faced / Total balls faced × 100
Conversion Rate: Percentage of innings resulting in 50+ scores

Bowling Metrics

Bowling Average: Total runs conceded / Wickets taken
Economy Rate: (Total runs conceded / Balls bowled) × 6
Strike Rate: Balls bowled / Wickets taken
Dot Ball Percentage: Dot balls bowled / Total balls × 100

Customization

Modify Minimum Match Qualification

Edit the psl_pakistani_batters.py file:

num_matches = len(self.player_matches[player])
if num_matches < 20:  # Change this value
    continue

Add Additional Pakistani Players

Update the pakistani_players set in the analyzer class:

self.pakistani_players = {
    'Babar Azam', 'Mohammad Rizwan',
    # Add more player names here
}

Customize Visualizations

Modify chart parameters in the visualization functions:

plt.rcParams['figure.figsize'] = (14, 8)  # Adjust figure size
sns.set_palette("husl")  # Change color palette

Output Files Description

Visualizations

psl_batting_analysis.png: Contains 9 charts analyzing runs distribution, strike rates, top scorers, boundaries, and consistency metrics
psl_bowling_analysis.png: Contains 9 charts analyzing wickets, economy rates, dot balls, and bowling averages
psl_top10_pakistani_batters_analysis.png: Comprehensive 12-chart dashboard for top 10 Pakistani batters
psl_top_performances_pakistani_batters.png: Bar charts showing top 5 individual scores for each player
psl_radar_comparison_pakistani_batters.png: Multi-dimensional radar chart comparing top 5 batters

Data Files

CSV exports: Contain complete statistics for all players, sortable and filterable for custom analysis
LaTeX report: Professional publication-quality PDF report with detailed analysis and insights

Requirements

Create a requirements.txt file with:

pandas>=1.3.0
matplotlib>=3.4.0
seaborn>=0.11.0
numpy>=1.21.0

Install all requirements:

pip install -r requirements.txt

Contributing

Contributions are welcome. Please follow these guidelines:

Fork the repository
Create a feature branch (git checkout -b feature/new-analysis)
Commit your changes (git commit -am 'Add new analysis feature')
Push to the branch (git push origin feature/new-analysis)
Create a Pull Request

Areas for Contribution

Additional visualization types
More detailed bowling analysis
Team-level performance metrics
Match outcome prediction models
Player comparison algorithms
Web dashboard interface
Real-time data ingestion

Performance Considerations

Large Datasets: For datasets with 100+ match files, processing may take 2-5 minutes
Memory Usage: Typical memory usage is 200-500 MB depending on dataset size
Visualization Generation: PNG files are saved at 300 DPI for publication quality

Troubleshooting

Common Issues

Issue: FileNotFoundError: psl_json directory not found

Solution: Ensure the psl_json/ directory exists and contains JSON files

Issue: No Pakistani players found with minimum 20 matches

Solution: Reduce the minimum match threshold or verify player name matching logic

Issue: Module not found: matplotlib

Solution: Install required packages using pip install -r requirements.txt

Issue: LaTeX compilation errors

Solution: Ensure all visualization PNG files exist in the correct location before compiling

License

This project is licensed under the MIT License. See LICENSE file for details.

Acknowledgments

Pakistan Super League for providing the context for cricket analytics
Cricket data format standards maintained by the cricket data community
Python data science community for excellent analytical libraries

Contact

For questions, suggestions, or bug reports, please open an issue on the GitHub repository.

Citation

If you use this toolkit in your research or analysis, please cite:

PSL Cricket Analytics Toolkit
https://github.com/hasnain1241/psl-cricket-analytics

Data source

cricsheet.org: https://cricsheet.org/matches

Changelog

Version 1.0.0

Initial release with batting and bowling analysis
Pakistani batters deep analysis module
Comprehensive visualization suite
LaTeX report generation
CSV data export functionality

Last Updated: 2025

Name		Name	Last commit message	Last commit date
Code		Code
DataSet		DataSet
Report		Report
Visualizations		Visualizations
LICENSE		LICENSE
README.md		README.md

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