The Indian Premier League (IPL), founded in 2008, is a premier Twenty20 (T20) cricket league that has captivated audiences worldwide. This project offers an in-depth analysis of the IPL, spanning from its inception to the 2024 season. By leveraging the latest and most comprehensive IPL dataset, the analysis delves into match statistics, player performances, team comparisons, and much more.
This project provides a detailed exploration of IPL matches and player performances over the years. It aims to uncover trends, patterns, and insights that can help fans, analysts, and teams better understand the dynamics of the game. From the number of matches played each season to the most prolific players, this analysis covers it all.
The dataset used in this project consists of two CSV files: matches_2008-2024.csv and deliveries_2008-2024.csv. These files contain detailed information on match summaries and ball-by-ball data.
- Analysis of the number of matches played each season, revealing trends and changes over time.
- Examination of the number of matches played across different venues, highlighting the most popular and frequently used stadiums.
- Insights into the number of matches played by each team and their win-loss records across different seasons.
- Identification of top-performing players based on metrics like "Player of the Match" awards, runs scored, wickets taken, etc.
- Analyzing how winning the toss impacts match outcomes and exploring any correlation between toss results and match wins.
- Detailed analysis of batting performance, including the number of 4s, 6s, top run-scorers, and milestone achievements like half-centuries and centuries.
- In-depth examination of bowler performance, including the most common types of dismissals and leading wicket-takers.
- Python
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Plotly
- IPL Complete Dataset (2008-2024): The dataset used in this analysis is sourced from Kaggle and is up-to-date through the 2024 season. Available here.
This project was inspired by the desire to analyze and visualize cricket data, apply statistical methods to real-world scenarios, and gain insights into one of the most popular sports leagues globally. Through this analysis, we aim to contribute to the understanding and appreciation of the game of cricket.
Feel free to contribute to this project by forking the repository and submitting pull requests. Let's explore and enjoy the world of cricket through data!