Cricket Analytics SQL Case Study – Match, Team & Player Performance Insights
This project is a SQL-based analytical case study focused on evaluating cricket match outcomes, team performance, and individual player contributions. The assignment simulates real business-style analytical questions commonly asked in data analyst interviews, requiring structured thinking, clean SQL logic, and performance-based evaluation.
The analysis answers 21 complex problem statements covering batting, bowling, fielding, match results, consistency, impact scoring, and ranking using SQL.
The objective of this assignment is to:
Analyze player and team performance across multiple matches
Identify top performers, consistent players, and high-impact contributors
Evaluate match competitiveness and winning margins
Demonstrate advanced SQL querying skills used in real-world analytics roles
The dataset includes structured cricket data with:
Match details (teams, date, location, results)
Player-level performance (runs, wickets, catches, stumpings, run-outs)
Team participation and outcomes
Player roles (batsman, bowler, all-rounder, etc.)
This case study answers questions such as:
Best batting average across all matches
Team with highest win percentage
Player with highest contribution to team runs in a match
Most consistent player using standard deviation logic
Closest match victories based on run margins
Players winning most Player of the Match awards
High-impact players using a custom impact score formula
Ranking players using window functions
Cumulative and running impact analysis by match date
Core SQL Skills
Complex JOINs (multi-table joins)
GROUP BY with aggregations
HAVING filters
Subqueries & CTEs
WINDOW FUNCTIONS (RANK, DENSE_RANK, cumulative sums)
Conditional logic using CASE WHEN
Analytical Concepts
Batting averages & win percentages
Contribution analysis
Consistency measurement using standard deviation
Impact score modeling
Ranking and comparative performance analysis
To evaluate overall player contribution, an Impact Score was calculated as:
Runs × 1.5
- Wickets × 25
- Catches × 10
- Stumpings × 15
- Run Outs × 10
Only players who participated in at least 3 matches were included to ensure fairness and statistical reliability.
Translated business-style analytical questions into optimized SQL queries
Designed logic to measure performance, consistency, and impact
Applied window functions for ranking and running totals
Ensured data accuracy and fairness using participation thresholds
Structured queries in a clear, readable, and interview-ready format
Focused on decision-making insights, not just query output
SQL (MySQL / SQL Server Management Studio 21 / PostgreSQL compatible syntax)
Google Colab Notebook (for structured query presentation)
Clone the repository
Review SQL queries question-wise
Run queries on a compatible SQL database
Interpret results for business insights
This project demonstrates structured data analysis using SQL to evaluate performance, consistency, and impact in a sports dataset. The approach emphasizes accuracy, clarity, and meaningful metric design to support data-driven insights.