How we predict matches — and why we show our work
Most prediction sites show you a number — "CSK 58% to beat MI" — with no explanation of where that number comes from. No data sources, no methodology, no uncertainty bounds. You're expected to trust a black box.
Our approach is different. Every prediction on this site has three transparent layers, and every data point traces back to a verifiable source with an access date. If we're wrong, you can see exactly why we were wrong — and that makes us more useful than sites that are sometimes right by accident.
Each Intelligence-Tier prediction combines three independently computed factors:
The baseline win probability computed from the complete IPL head-to-head record between the two teams. We use the full historical record (not just recent seasons) as a Bayesian prior.
Source: ESPNcricinfo head-to-head records, verified manually against match-by-match data.
Limitation: H2H records don't account for squad changes between mega-auction cycles. A 37-match CSK vs MI record spans 17 years of completely different squads. This is why it's only 40% of the model.
The H2H record from the last 3 IPL seasons (2023-2025) is weighted at 60% of the overall prediction. This captures current squad strength, recent tactical trends, and the most relevant competitive data.
Why 60/40? Mega-auction cycles fundamentally reshape squads every 3 years. The 2025 mega auction (Jeddah, Nov 24-25, 2024) redistributed nearly every player. Recent performance is the strongest signal available.
When recent data is scarce (e.g., GT vs MI have only 7 total H2H matches), the model falls back toward 50/50 and the confidence interval widens significantly.
A venue-specific adjustment based on historical performance at the match venue. This accounts for pitch characteristics (spin-friendly Chepauk vs pace-friendly Wankhede), altitude, dew factor, and crowd advantage.
Computation: Home team gets a +3.5% adjustment for strong home advantages (Chepauk, Eden Gardens) and +2.0% for neutral/moderate venues. Away venues receive a -1.5% adjustment.
Source: ESPNcricinfo ground records, Cricbuzz pitch reports, and venue-specific first innings averages.
Every prediction includes a ± confidence bound. When we say "CSK 63.7% ± 15.5%", we mean:
Why some intervals are very wide: GT vs MI have only 7 H2H matches. With so few data points, the Wilson interval correctly expands to ±35-37%, essentially saying "we don't have enough data to be confident." That honesty is the point.
The 35-65% cap: No matter what the model computes, we never report a probability below 35% or above 65% for a T20 cricket match. This reflects the empirical reality that in T20, any team can beat any other team on a given day. Historical upset rates hover around 35-45%.
| Data Type | Source | Access Method |
|---|---|---|
| Head-to-Head Records | ESPNcricinfo | Manual verification against match-by-match data |
| Venue Statistics | ESPNcricinfo Ground Records, Cricbuzz | Average 1st innings score, pace/spin split, bat-first % |
| Squad Compositions | iplt20.com, BCCI | Post-2025 mega auction retained + bought players |
| Player Roles & Form | ESPNcricinfo Player Profiles | Last 5 innings/matches performance |
| Pitch Reports | Cricbuzz, ESPNcricinfo | Recent match-day pitch assessments |
| Match Schedule | iplt20.com (official) | BCCI-confirmed fixture list for IPL 2026 |
Cricket Lab serves three clearly separated page types:
Why this separation? Because publishing exact predictions for unconfirmed fixtures — while pretending they are scheduled — would be dishonest. We label uncertainty clearly so you always know what is verified and what is speculative.
bharath.ai Intelligence Analysis
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Built by bharath.ai — February 2026