Pre-match player-form index
Illustrative index combining fictional recent-form inputs.
Cricket intelligence with AI agents
Turn match data into pre-match context, live signals, and post-match stories.
A cricket-analysis workflow can combine structured match events, historical player context, situational models, and narrative generation to support analysts, media teams, fantasy products, and fan experiences.
All example teams, players, probabilities, and scores on this page are synthetic demonstrations.
Agent hub
Organize specialized agents around the match lifecycle.
Each phase can be evaluated independently, which helps teams distinguish predictive-model errors from narrative or editorial errors.
Conditions analyst
Combines venue, pitch, weather, format, and match context.
Player matchup analyst
Compares role, recent form, phase performance, and opponent history.
Scenario modeler
Simulates lineup and toss/conditions scenarios with explicit uncertainty.
Win probability
Updates calibrated match-state probability from score, wickets, overs, and contextual features.
Momentum tracker
Identifies event clusters and phase-level changes without treating momentum as a substitute for probability.
Live analyst
Surfaces tactical questions, matchup context, and events worth editorial attention.
Impact scorer
Measures contribution relative to match state and role, not only raw runs or wickets.
Turning-point detector
Identifies events associated with the largest changes in win probability or match state.
Narrative agent
Builds summaries from verified scorecard and event facts with editorial style controls.
Illustrative match
Titans vs Vipers — synthetic T20 example.
The visualizations demonstrate possible outputs. They do not represent actual players, teams, or a historical match.
Live win-probability path
Illustrative probability values. A production model should be calibrated and backtested.
Opener A
Illustrative: 84* from 45 balls, with the largest batting contribution in the final five overs.
Fast Bowler B
Illustrative: four wickets with strong middle-over control.
16th over
Illustrative: a high-scoring over causes the largest modeled swing in win probability.
Validation
Sports intelligence needs calibration, not only compelling visuals.
Prediction, ranking, turning-point detection, and narrative generation require different validation methods.
Prediction
Out-of-sample accuracy, log loss, Brier score, and calibration by match phase.
Player projections
Error by role, batting position, venue, opposition, and uncertainty band.
Event intelligence
Agreement with analyst-labeled turning points and false-alert burden.
Narrative
Scorecard factuality, event coverage, chronology, terminology, and editorial quality.
KREATE + KONTROLS + KNOBS
Make the sports workflow reusable and governable.
KREATE
Build the match/event data product, analytics services, dashboards, fan experiences, and commentary agents.
KONTROLS
Govern data licensing, source provenance, editorial rules, factuality checks, model versions, and human publishing approval.
KNOBS
Tune lookback windows, feature weighting, probability thresholds, alert frequency, narrative style, detail level, and publishing autonomy.
FAQ
Cricket AI questions.
Is the Titans vs Vipers example a real match?
No. Team names, player names, scores, and probabilities are synthetic and included only to demonstrate how a cricket-intelligence workflow could present outputs.
What data can a cricket-analysis agent use?
Depending on licensing and availability, a workflow may use scorecards, ball-by-ball events, player history, lineups, venue and pitch context, weather, match situation, and editorial rules.
How should live win probability be validated?
Use historical out-of-sample matches, calibration tests, event-level error analysis, reliability plots, and performance broken down by match phase, format, venue, and game state.
Build a cricket workflow around your licensed data and editorial goals.
DataKnobs can help structure match data, model evaluation, agent responsibilities, factuality checks, and publishing controls for a sports-intelligence product.
Discuss a Cricket Use Case