Please Value Efforts! UPVOTE would be appreciated!

Any Queries or requirements, Please feel free to share them with me!!

Context:

The Indian Premier League (IPL) has carved out a special place for itself in the hearts of cricket lovers from the very first season itself in 2008. It is a professional Twenty20 cricket league in India, organized by the Board of Control for Cricket in India (BCCI). Founded in 2007, the league features ten state or city-based franchise teams. The IPL is the most popular and richest cricket league in the world and is held between March and May.

IPL 2025 is the 18th edition of the tournament. This edition of the prestigious tournament commenced on March 22, 2025 and the Final got played on June 3, 2025. RCB finally won the trophy, after defeating PBKS in the final.

Content:

Primary file -> matches.csv: Contains detailed information for each match played.
Secondary files->
deliveries: Ball by ball data
orange_cap: Top batting performances
purple_cap: Top bowling performances

Acknowledgements:

The data source is Google and the ESPN official website. But the efforts are manual.

Inspiration:

You can use this data to analyze each team performances, create visualizations to explore tournament results and also predict outcomes of future ipl matches (for eg: fantasy prediction).

📊 Global Top Football Leagues – Player Performance Dataset 🏟️ Description: This dataset brings together detailed player statistics from 10 of the world’s top football leagues for the 2024–25 season. Each sheet in the Excel file represents a different league, and a combined sheet (AllCompDataset) merges them into a unified dataset for easy analysis.

The data is ideal for:

Sports analytics & scouting, Machine learning model training, Fantasy football insights, Comparative player analysis, Football dashboards & EDA projects.

📁 Included Leagues: Premier League (England), Serie A (Italy), La Liga (Spain), Bundesliga (Germany), Ligue 1 (France), Brasileirão (Brazil), Primeira Liga (Portugal), Belgian Pro League, MLS (USA), Argentine Primera División (Argentina).

📌 File Structure: AllCompDataset – Merged data from all leagues 1_EPL to 10_Argentina – League-specific sheets Columns may include: Player Name, Position, Club, Matches, Goals, Assists, Minutes, xG, xA, Cards, and more.

🔖 License: Distributed under CC BY 4.0 – You are free to use this dataset with proper attribution. Note: Data sourced and compiled for research and educational purposes. Original statistics adapted from public football resources.

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Thanks for showing interest in taking part in our Co-learning lounge activity. We assure you of the journey full of learning by solving the IPL 2020 Dream team prediction. To solve any problem the first and foremost requirement in domain knowledge.

"Let’s put theory into practice"

In this competition, we will aim at increasing the accuracy of dream team selection for IPL 2020 based on player's stats collected by our team.

IPL 2020 Fantasy Team Prediction 🏏

It has been an amazing journey of 3 months (1-month pre-IPL and 2 months during IPL) where for each and every IPL match our team predicted Fantasy XI players. Have a look at some important aspects of this project

📌 This project aimed at helping those who are not fantasy app pro and tend to lose money based on gut feel selection. 📌 Carrer and Recent statistics play a crucial role in any player's performance thus it eventually boils down to historical data and thus it was collected accordingly. 📌 Along with this head-to-head statistics of players are really important to capture as in fantasy game we have players from 2 teams so if in case one player has a high probability of taking the wicket of another then we need to consider one of them which would give more point. 📌 Each format of cricket is different and T20 is quite unpredictable as parameters to judge a player's strength changes from format to format. IPL tops this list in terms of unpredictability and thus features selection becomes a real challenge. 📌 Pitch, weather, and opposition play a crucial role for any player to score big so these parameters also need to be captured.

Want see our performance😍

✔️ Click to watch video

Competition Outline

This is a closed group competition where mentoring and timely discussion is also planned. So, to have a healthy competition and arrive at the end goal successfully, few prerequisites are listed below.

✔️ Passion about sports and analytics. ✔️ Knowledge of Data Science. ✔️ Domain (All formats of cricket) knowledge. ✔️ Working Knowledge of excel/python or any data analytics and statistical modeling tool.

Duration ⌚

This competition is planned for 1 month from the date of launch.

Prize 🏆

This is unlike other Kaggle competition as here during competition mentoring/discussion sessions would be conducted with the current team working on this project.

📊 Access to exclusive data collected for this project. 📖 Chance to build your personal portfolio in the sports analytics domain. 👥 Interact and get guidance from the core team of the project. 💯 Get a chance to work in the upcoming IPL 2021 project as a core team.

Task List 📝

No competition is successful until the agenda is set to reach the final goal.

Task to be performed 👇

✔️ Capture data insights about players from input data. ✔️ List down features considered in the models and reason for consideration. ✔️ Details to be mentioned about the model being used for final prediction. ✔️ Current model on average has predicted 8 players in Dream Team. Create a model to predict more than 8 players in the dream team using the output file for 26 matches of IPL 2020. Notebook with model and reason needs to be mentioned to arrive at that conclusion. Team needs to be formed based on a data-driven approach and also keeping Dream 11 team selection criteria in mind as below.

Dream 11 team selection criteria

✔️ Atleast 1 Keeper. ✔️ Atleast 3 Batsmen. ✔️ Atleast 1 All-Rounder. ✔️ Atleast 3 Bowlers. ❌ Not more than 7 players of 1 team. ✔️ Total credit points of all 11 players listed in the dream team should not be more than 💯.

🏏 IPL Ball-by-Ball Dataset with Pitch & Player Stats (2020–2025)

This dataset includes ball-by-ball delivery data from Indian Premier League (IPL) matches between 2020 and March 2025, enriched with pitch condition information, and match-level batting and bowling performance statistics.

It's ideal for building machine learning models, conducting sports analytics, training fantasy cricket prediction systems, and exploring performance trends in T20 cricket.

📁 Dataset Overview

Files Included:

• Ipl match data - enriched.xlsx – Full ball-by-ball data with pitch types
• all_matches_batting_stats.csv – Player-wise batting stats per match
• all_matches_bowling_stats.csv – Player-wise bowling stats per match

🔍 Key Features

• ⚾ Ball-by-ball records for every delivery across 5 IPL seasons
• 🌱 Pitch condition data: Spin-friendly, Batting-friendly, etc.
• 🧢 Structured performance stats for batting and bowling per match
• 🏟️ Match metadata: Venue, city, date, teams, overs
• ✅ Well-cleaned, analysis-ready Excel and CSV formats

🎯 Use Cases & Keywords

• Fantasy Cricket Prediction Model
• T20 Match Analytics
• IPL Data Visualization Projects
• Cricket Simulation Engine
• Player Form and Consistency Analysis
• Pitch Impact on Performance
• Machine Learning with Cricket Data
• Python/Excel-based IPL Stats Projects

📝 Example Columns

From Ball-by-Ball: - match_id, date, venue, batter, bowler, runs_batter, runs_extras, wicket_taken, dismissal_kind, pitch_type

From Batting Stats: - player, team, runs_scored, balls_faced, fours, sixes

From Bowling Stats: - player, team, overs_bowled, runs_conceded, wickets

📜 License

CC0 1.0 Universal (Public Domain Dedication)
You are free to use this dataset in personal, academic, or commercial projects with no restrictions.