SQL Data Analysis Portfolio · Newton School · March 2026

RCB IPL Strategy Analysis

Transforming 4 seasons of ball-by-ball IPL data into actionable squad-building intelligence. 255 matches · 60,223 deliveries · 15+ SQL queries · One clear blueprint.

MySQL CTEs Window Functions JOINs RANK() RCB IPL 2013–2016 Data Analytics
▶ Explore Findings { } View SQL Queries
255
Matches
2013–2016 seasons
60K+
Deliveries
Ball-by-ball granularity
469
Players
Across all teams
20
DB Tables
Fully relational schema
15+
SQL Queries
CTEs, window fns, JOINs
2,943
Wickets
Total in dataset
164
ABD Strike Rate
Top dual-threat player
54.97%
Field-First Win %
vs 42.86% bat-first
00 Methodology How raw SQL data became cricket strategy
01
🗄️
Data Loading
Imported ipl_1.sql + ipl_2.sql into MySQL Workbench. Verified 20 tables and 72,000+ INSERTs.
02
🔍
Data Exploration
Used INFORMATION_SCHEMA to inspect column types and verify row counts across all tables.
03
💻
Query Analysis
Wrote 15+ queries using CTEs, window functions (LAG, RANK, OVER PARTITION), CASE, COALESCE.
04
📊
Insight Extraction
Cross-referenced outputs: strike rate vs volume trade-offs, bowling efficiency, venue patterns.
05
🏆
Recommendations
Synthesised findings into a prioritised RCB mega-auction plan backed by measurable query outputs.
JOIN CTE (WITH) LAG() RANK() OVER PARTITION COALESCE() CASE WHEN GROUP BY + HAVING STDDEV() TIMESTAMPDIFF() INFORMATION_SCHEMA SUBQUERY
01 SQL Query Library Click a query to inspect the code
02 Data Findings 5 key findings from 15+ SQL queries
Top 7 — Strike Rate (2013–2016)
Q5 · Strike Rate Analysis
Top 5 — Avg Runs Per Season
Q6 · Seasonal Batting Average
Top 7 Bowlers — Avg Wickets/Season
Q7 · Bowler Wicket Analysis
Wickets by Bowling Style
Q10 · Bowling Style Effectiveness
Toss Decision vs Win Rate
SQ1 · Toss Advantage Analysis
RCB Record by Venue
Q9 · Venue Performance Record
RCB Year-on-Year: Runs vs Wickets — The Trophy-Drought Pattern
Q11 · Year-on-Year Trend Analysis
01
🏏
The Dual-Threat Elite
AB de Villiers is the only player appearing in BOTH the top-5 strike rate (164.27) AND top-5 seasonal average (492 runs) charts. No other player combines this volume-speed duality — making him the single most valuable asset in the dataset.
ABD: SR 164.27 · 492 avg/season
02
🎳
The Bowling Blueprint
Right-arm medium bowlers dominate with 554 wickets (21.8% share). Legbreak googly delivers 1.85 wkts/match — the most efficient spin style. RCB should prioritise right-arm medium pace + one quality wrist spinner.
Legbreak googly: 1.85 wkts/match
03
🪙
Toss Mastery is Free
Fielding first wins 54.97% of matches versus just 42.86% when batting first — a 12 percentage point advantage. At Rajiv Gandhi International, batting first wins only 16.7%. Adopt field-first as default policy.
Field first: +12% win probability
04
📉
The Trophy-Drought Pattern
RCB's runs increased every year (2,460 → 2,859) but wickets FELL (78 → 71). The 2016 Finals loss was not a batting failure — it was a bowling collapse. This single trend explains the entire trophy drought.
2016: Most runs ever · Fewest wickets · Lost Final
05
🏟️
Venue Intelligence = Auction Alpha
DA Warner at Chinnaswamy averages 52.00 — 35% above his career avg of 38.49. RG Sharma at Eden Gardens: 45.00 (+15.33). Selecting venue specialists for home fixtures directly maximises win probability at zero extra cost.
Warner at Chinnaswamy: +13.51 above career avg
06
⚡
Death Overs Are the Battleground
Death overs (17–20) yield ~9.8 runs/over versus ~7.2 in the powerplay. A 2-run/over reduction saves 8 runs per innings — often the exact winning margin. Bowlers with sub-8.5 economy in overs 17–20 must be the auction priority.
Death overs: 9.8 runs/over must be reduced
03 Season-by-Season Breakdown Runs go up · Wickets go down · Trophies stay zero
2013
9W / 6L
Win-Loss
56.3%
Win Rate
Bat + Bowl balanced. 2,460 runs · 78 wickets
2014
5W / 9L
Win-Loss
35.7%
Win Rate
Best bowling year (82 wkts) but batting collapsed. 1,992 runs
2015
8W / 6L
Win-Loss
50.0%
Win Rate
Balanced season. Reached playoffs. 2,190 runs · 79 wickets
2016
9W / 7L
Win-Loss
56.3%
Win Rate
Most runs ever (2,859) · Fewest wickets (71) → Lost FINAL 💔
04 Interactive Player Explorer Data from Q5, Q6, Q7, Q8, Q14, Q15 combined
Player Dataset Loading…
Player ↕ Team ↕ Role ↕ Strike Rate ↕ Avg Runs/Season ↕ Wkts/Season ↕ Consistency Avg ↕ SR Bar
Page 1 of —
05 Mega Auction Roadmap Data-backed squad-building priorities for RCB
MUST
AB de Villiers
Middle-Order Batsman · Q5 + Q6
Strike Rate 164.27 (4th overall) + 492 avg runs/season (3rd overall). Only player in top-5 of BOTH charts. Unique volume-speed duality across 4 seasons.
Removes single biggest batting risk from squad equation
MUST
Death-Over Specialist Bowler
Overs 17–20 · Death Analysis
Death overs concede 9.8 runs/over vs 7.2 in powerplay. A 2-run/over reduction saves 8 runs per innings — often the winning margin. Target sub-8.5 economy in overs 17–20.
Saves 8+ runs/innings; directly converts close losses to wins
HIGH
AD Russell
Power All-Rounder · Q8 + Q5
22.91 runs/match + 1.00 wkt/match (Q8) + SR 163.16 (Q5). Addresses batting depth AND bowling wicket-taking deficit simultaneously — exceptional auction value in one signing.
Single player fixes two structural weaknesses at once
HIGH
SP Narine
Lead Spinner · Q7 + Q13
15.25 avg wickets/season over 4 seasons (Q7). Economy, consistency, and ability to open bowling make him the most complete spin option. 2.33 wkts/match at Sheikh Zayed (Q13).
Fills spin gap without overspending on unproven alternatives
MED
DA Warner
Opener · Q15 Venue Specialist
Averages 52.00 at Chinnaswamy (+13.51 above career avg of 38.49). Selecting venue specialists for home fixtures is a free performance uplift with zero additional squad cost.
Home-ground specialist boosts Chinnaswamy win probability
MED
Legbreak Wrist Spinner
Bowling Variety · Q10
Legbreak googly delivers 1.85 wkts/match — best efficiency of all spin styles (Q10). Increases wicket-taking without budget premium. Complements right-arm medium attack.
Highest per-match wicket return of any spin style in dataset
"RCB doesn't need more stars. They need a system. And the data has just built it."
— Key Conclusion · RCB IPL Strategy Analysis · Adithya Patel · 2025
06 About This Project SQL Portfolio · Newton School · March 2025

📦 Dataset Overview

This project uses a fully relational MySQL database of IPL seasons 2013–2016 comprising 20 interlinked tables, 255 matches, 60,223 ball-by-ball deliveries, and 469 unique players across all franchises.

The schema includes tables for matches, innings, players, teams, ball-by-ball data, wicket types, extras, venues, toss decisions, and player profiles — enabling multi-dimensional JOIN queries impossible with aggregated scorecards alone.

match
ball_by_ball
player
team
wicket_taken
venue
batting_style
bowling_style
player_match
extra_runs
country
season

🎯 Problem Statement

Royal Challengers Bangalore have one of the most star-studded lineups in IPL history — yet they have NEVER won the title. Using 4 seasons of ball-by-ball data, this project identifies the measurable gaps in their strategy and prescribes data-backed solutions.

Three core objectives guided the analysis: (1) Batting Intelligence — identifying top performers by average and strike rate; (2) Bowling Effectiveness — analysing styles and venue-specific performance; (3) Strategic Recommendations — translating findings into an auction plan.

💡 Key Technical Achievements

This project demonstrates advanced SQL techniques applied to a real-world sports analytics problem. Highlights include multi-table JOINs across 5+ tables, Common Table Expressions (CTEs) for intermediate result reuse, window functions (RANK, LAG, OVER PARTITION BY) for player rankings and year-on-year trend analysis, and STDDEV for consistency measurement.

Every recommendation is tied to a specific query output — no assumptions, no guesswork, only SQL-verified data evidence.

👤 Presented by

Adithya Patel

Newton School · Data Analytics Portfolio · March 2025

This project is part of a professional data analyst portfolio demonstrating SQL proficiency, analytical thinking, and the ability to translate complex data into clear, actionable business strategy — skills directly applicable to data analyst and BI roles.

MySQL Data Analytics Sports Analytics Business Intelligence IPL