System Dashboard

Complete overview of the IPL Win Probability architecture.

v1.1.0
Model Version
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Test Accuracy
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ROC-AUC
25
Features
Dataset Snapshot
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Matches
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Deliveries
Model Architecture

Stacking ML Ensemble deployed via FastAPI. Combines Random Forest, Gradient Boosting, and XGBoost with a Logistic Meta-Learner.

Mode: ml_only Threshold: 0.34

Live Match Intelligence

Real-time ball-by-ball win probability powered by stacking ML.

Real-time Win Probability
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Required RR
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Current RR
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Resource Rem
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Match State Inputs

Simulation Lab

Counterfactual "What-If" Analysis to quantify strategic impact.

Base Match State
Scenario Intervention
Strategic Outcome Impact

Quantifies the exact shift in win probability caused by the scenario intervention.

Momentum Timeline

Ball-by-ball win probability and pressure index.

Win Probability
Pressure Index
Hover over points to see Runs/Wickets

Model Explainability (SHAP)

Understanding model predictions through feature attribution.

Global Importance
Local SHAP Analysis
Interpretability Insight

The model dynamically assigns importance based on match context. While Required Run Rate and Overs Remaining generally dominate global importance, local ball-by-ball analysis reveals that Momentum and Pressure Index strongly influence predictions during critical death-over chases.

MLOps Governance

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System Action
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Rolling AUC
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Features Drifted
Audit Log
TimeActionDrift FlagPerf Flag

Franchise Intelligence

Strategic insights and export tools for teams.

VS

Phase Dominance

Tactical Intelligence Brief

Run analysis to generate strategy insights...

Pressure Metrics

Collapse Risk Over Time