jippy66/IPL-DATA-ANALYSIS

This project involves analyzing the Data Analysis of the IPL dataset of each year by Highest scores, WIn-Loss relation and different types of relationships are shown in this repo.

IPL-Data Analysis

The aim of the project is to analyze the data of the Indian Premier League (IPL) to gain insights into the game.

Here are some specific goals of an IPL dataset project in Python:

1. Identify the most successful teams and players in the IPL.
2. Determine the factors that contribute to a team's or player's success.
3. Analyze the impact of different playing conditions on the outcome of matches.
4. Identify trends in IPL data over time.
5. Generate insights that can be used to improve the performance of IPL teams and players.

IPL Dataset

Data Analysis with IPL match-by-match dataset of season 2008 - 2020.

The dataset contains two files:-

                          1. deliveries.csv
                              
                          2. matches.csv

Analysis

o Highest scorer batter in in overall IPL

o Lowest scorer batters in in overall IPL

o Bowler who takes the highest number of wickets overall IPL

o Bowler who takes zero wickets overall IPL

o Stats of top 5 bowlers

o Top fielder who takes catches

o Top fielder who takes run out

o Wickets stats between catches and run-out

o No. of toss won by each team with stats

o Match winner teams after winning the toss

o Are there any advantages in winning the match after winning the toss?

o The most successful IPL teams

o Most sixes and most fours by individual and teams

o Most likely decision after winning the toss

o Most likely a decision after winning the toss team-wise

o No. of matches hosted by different cities

o Lucky stadium for the topmost team

Prior Knowledge

Screenshots

o No. of matches played by the team.

o Maximum times won the player of the match.

o Total number of matches played in a session.

Challenges and Learnings

• Data Analysis: Extracting insights from data hones your analytical skills and gives you practical experience in drawing conclusions.
• Data Cleaning: Real-world datasets often have missing or inconsistent data. Cleaning and preparing the data for analysis can be time-consuming.
• Data Interpretation: Translating raw data into meaningful insights requires domain knowledge about cricket and IPL.
• Problem Solving: Addressing challenges boosts problem-solving abilities as you find ways to overcome hurdles.

Conclusions

1. Jos Buttler was the highest run scorer and Yuzvendra Chahal was the highest wicket-taker of IPL 2022.

2. Quinton de Kock was the highest run scorer in a single inning with 140 runs.

3. The highest team score was 222/2 by Rajasthan Royals.

4. CSK won by the highest run margin by defeating Delhi Capitals.

5. Jos Buttler was also the highest six hitter of IPL 2022

6. Most no. of tosses was won by Sunrisers Hyderabad and least by Rajasthan Royals.

7. Dinesh Kartik emerged as the best finisher by scoring the most runs in death overs.

8. Gujrat Titans scored the least no of sixes but still ended up winning the tournament.

9. Almost 90% of the time the toss winning team chose to field first which shows most teams preferred to chase in the tournament.

10. Out of 74 matches exactly 50% matches were won by chasing and the rest 50% by defending the score.