Overview

This project introduces Intent Quotient (IQ), a custom analytical metric designed to quantify batting intent in T20 cricket. Using IPL ball-by-ball data, the framework captures how aggressively a batter is playing relative to match context, game phase, and scoring outcomes.

Unlike traditional strike-rate-based measures, IQ explicitly incorporates situational awareness, separating the decision to attack from the result of that decision.

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The IQ Metric

Intent Quotient is a composite, context-aware metric derived from ball-level data that captures aggressive batting behavior relative to situation:

• Shot outcome signals — Runs scored, boundary frequency
• Risk indicators — Dot ball rate, dismissal events
• Match context — Over number, innings phase, pressure state (required rate, wickets in hand)
• Normalization — Scores are normalized across comparable match situations for fair cross-player comparison

The metric operates at multiple aggregation levels: ball, over, innings, player, and match phase.

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What IQ Enables

• Identify batters who consistently signal high intent independent of outcome
• Compare players with similar strike rates but fundamentally different tactical approaches
• Observe how intent shifts under scoreboard or wicket pressure
• Separate tactical aggression from execution quality
• Perform role identification (anchors vs. aggressors) grounded in quantitative evidence

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Project Structure

The codebase includes a full analytical pipeline:

• Data Processing — Ingestion and cleaning of IPL ball-by-ball data (Cricsheet format)
• Metric Development — Notebooks for IQ formula iteration and validation
• Analysis & Visualization — Phase-based comparisons, player profiles, and pressure-response analysis
• Scraping — Match index builders for expanding the data coverage

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Tech Stack

• Python, Pandas, NumPy
• Matplotlib, Seaborn, Plotly
• Jupyter Notebooks
• IPL ball-by-ball data (Cricsheet)