🏏 IPL Performance Analytics

📌 Project Overview

IPL Performance Analytics is an Exploratory Data Analysis (EDA) project built using Python, NumPy, Pandas and Matplotlib.

The project analyzes IPL match-level data to identify meaningful patterns in:

• Team performance
• Season-wise match trends
• Toss decisions and toss impact
• Match-winning methods
• Player of the Match performance
• Venue activity

🔄 Analysis Workflow

Raw Data → Data Cleaning → Exploratory Analysis → Numerical Analysis → Visualization → Insights

This project demonstrates practical Data Analytics skills using Python.

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🎯 Objectives

• Analyze team-wise match wins
• Study IPL matches across seasons
• Analyze the impact of winning the toss
• Identify common toss decisions
• Find top Player of the Match award winners
• Compare wins by runs and wickets
• Analyze IPL venue activity

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🛠️ Technologies Used

• Python
• NumPy
• Pandas
• Matplotlib
• Jupyter Notebook

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📊 Dataset

• 950 IPL matches
• 20 features
• Match-level IPL data
• Includes teams, season, toss, venue, winner, win method and Player of the Match information

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📈 Visual Analysis

🏆 Top 10 Teams by Match Wins

📅 IPL Matches by Season

🪙 Toss Decision Distribution

🏏 Matches Won by Runs vs Wickets

⭐ Top Player of the Match Winners

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🔍 Key Insight

In this dataset, the team that won the toss also won the match in approximately 51.69% of matches with recorded toss and match winners.

This shows that winning the toss alone did not guarantee a match victory.

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🚀 Live Notebook Presentation

▶️ Open IPL Performance Analytics Notebook

The notebook contains:

• Dataset exploration
• Data cleaning
• Team performance analysis
• Season-wise analysis
• Toss impact analysis
• Player of the Match analysis
• Matplotlib visualizations
• Analytical insights

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📂 Project Files

File	Description
IPL_Performance_Analytics.ipynb	Complete Jupyter Notebook
ipl_analysis.py	Python analysis script
ipl_matches.csv	IPL match dataset
analysis_summary.txt	Analysis summary
top_10_team_wins.png	Team wins visualization
matches_by_season.png	Season-wise visualization
top_player_of_match.png	Player awards visualization
toss_decisions.png	Toss analysis visualization
win_type.png	Winning method visualization

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💡 Skills Demonstrated

• Data Cleaning
• Exploratory Data Analysis (EDA)
• Data Manipulation
• Data Aggregation
• GroupBy Analysis
• Value Counts
• Missing Value Analysis
• NumPy Numerical Analysis
• Pandas Data Analysis
• Matplotlib Visualization
• Insight Generation

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🚀 How to Run

git clone https://github.com/vaibhavtaydevit29-alt/IPL-Performance-Analytics.git

cd IPL-Performance-Analytics

pip install pandas NumPy matplotlib Jupyter

Jupyter notebook




👨‍💻** Author

Vaibhav Tayde

MTech – Artificial Intelligence

Data Analytics | Python | SQL | Power BI | Machine Learning