Every metric traces to a specific ball, a specific match, a specific window. Sample-size floors are enforced and visible. Canonical URLs don't change. Your citations hold up.
Batting SR / average — minimum 30 balls faced
Bowling economy / SR — minimum 15 balls bowled
Phase splits — minimum 15 deliveries in phase
Trend insights — minimum 3 matches
2,500+
Atomic claims
26
Leaderboards
~23K
Sitemap URLs
3
Leagues · 20 seasons
League coverage
League
Matches
Seasons
Ball-by-ball
Players
Status
IPL 2026
74
1 (current)
✓
256+
Complete · RCB champions
IPL Historical
1,169
18 (2007–2025)
✓
767
Cricsheet CC BY 3.0
MLC
64+
2023–2026
✓
167
Cricsheet CC BY 3.0
Choose your starting point
Cricketer → Analyst
You see patterns. Now prove them.
Your intuition is the hypothesis. The data is the test.
Turn a match observation into a testable question with an explicit window and sample size
Access 312,309 deliveries to find whether your read holds across enough evidence to publish
Link your finding to a stable canonical URL — cite it, share it, revisit next season
Start this path →
Aspiring Analyst
Build with data that won't let you cheat.
Sample-size discipline is enforced, not optional.
Sub-floor claims don't ship — you can't accidentally cite a 3-ball sample as a trend
Browse player, team, venue, and H2H pages — each is one entity × one dimension × one window
Build a source-backed portfolio: link directly to provenance, not just the number you found
Start this path →
Working Analyst
Citation-grade data under deadline pressure.
Skip the fundamentals — take the desk challenge.
Every metric carries source, window, delivery count, and timestamp — full provenance on one page
57 MCP tools — add CricketStudio to Claude, ChatGPT, or Gemini and query in plain language
Canonical URLs are permanent — citations don't rot when data updates