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This project focuses on analysing IPL (Indian Premier League) cricket datasets using SQL to extract meaningful insights on player, team, and match performance. The goal is to demonstrate SQL proficiency in querying, cleaning, and analysing real-world sports data to support data-driven decision-making.

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SQL-Project

📌 Project Overview

This project focuses on analysing IPL (Indian Premier League) cricket datasets using SQL to extract meaningful insights on player, team, and match performance. The goal is to demonstrate SQL proficiency in querying, cleaning, and analysing real-world sports data to support data-driven decision-making.

🎯 Objectives

Analyse the batting and bowling performance of players across IPL seasons

Identify top performers (highest runs, wickets, strike rates, averages)

Discover all-rounders (≥30 runs and ≥3 wickets in a match)

Extract team-wise winning patterns and venue performance

Optimise SQL queries for faster data retrieval on large datasets

🗂️ Dataset

BattingStats Table → Player_Name, Runs, Balls_Faced, Strike_Rate, 4s, 6s, 100s, 50s

BowlingStats Table → Player_Name, Wickets, Overs_Bowled, Runs_Conceded, Economy

MatchStats Table (if available) → Match_Info, Player_Name, Runs_Scored, Wickets

(Data taken from IPL scorecards/statistics — structured into CSV and imported into MySQL for analysis.)

⚙️ Tools & Technologies

SQL (MySQL])

Data import via LOAD DATA INFILE / Workbench Import Wizard

Query optimisation with indexing and joins

About

This project focuses on analysing IPL (Indian Premier League) cricket datasets using SQL to extract meaningful insights on player, team, and match performance. The goal is to demonstrate SQL proficiency in querying, cleaning, and analysing real-world sports data to support data-driven decision-making.

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