CricSight Intelligence
We saw a gap in how cricket's hidden forces were measured. So we built a 5.2-million delivery proof-of-concept to test a new paradigm.
Traditional scorecards count runs and wickets, but they fail to capture the invisible, structural pressures of the game. CricSight is an experimental intelligence engine. Powered by a dedicated 64GB VM, we processed 50 years of international cricket history to computationally quantify leverage, behavioral collapse, and true match-altering momentum.
Cricket analytics are often broken by context. A strike rate of 150 means little if it happens in a dead rubber. An economy rate of 6.0 is misleading if bowled exclusively to the tail. We built this platform not as a flawless authority, but as a rigorous exploration. By running 5.2 million historical deliveries through predictive models, we set out to measure what fans inherently feel but scorecards ignore: win probability swings, behavioral responses under duress, and underlying delivery equity.
CricSight separates raw data retrieval from synthesized intelligence. We output two distinct surfaces. 'The Stat Bank' provides canonical facts: leaderboards, timed queries, and exact threshold measurements. But data is not narrative. For that, we built 'The Insight Engine.' Insights are deep, synthesized narrative cards combining multiple models (e.g., Action Value + Archetype + Form). These are passed through a strict 'Narrative Cage' to ensure broadcast-grade signal, completely devoid of generic hype.
Every insight card on this platform is tagged with the specific machine learning technique that generated it. Here is how to interpret our underlying models:
01 / Match-Defining Leverage
We trained XGBoost models to calculate dynamic Win Probability for every ball bowled since 1975. If you see this tag, we are not looking at runs; we are isolating the exact delivery that caused a massive WP delta, breaking the opposition's mathematical architecture.
02 / Behavioral Baselines
How does a batter react when 3 wickets fall in the powerplay? We build individual behavioral baselines, comparing a player's standard strike rate to their collapse-scenario strike rate to prove who counter-attacks and who shells up.
03 / Action Value Generation
Moving beyond descriptive stats, advanced predictive modeling tracks underlying equity. Action Value (VAEP) calculates the expected run/wicket value of a delivery, exposing the gap between bowlers who face easy conditions and those who genuinely disrupt an innings.
04 / Algorithmic Trend Reversals
Our flagship capability. The engine scans 5.2 million rows to find instances where a player's aggregated stats look mediocre, but their isolated phase-specific metrics reveal elite underlying performance. We hunt for hidden truths, not leaderboard echoes.
By the Numbers
5.2M
Deliveries Tagged
Phase + WP context
16,101
Player Profiles
R-CRAN enriched
10,413
Matches Processed
ODI, T20I, Test
50yrs
Historical Data
1975 to present
6
Player Archetypes
K-Means k=6 clusters
2.0%
Weather Coverage
Known data gap
This is a prototype. We are incredibly proud of the depth and intricacy of the setup, but we know there is work to do. Dive into our methodology to see exactly how we engineered history.
