The prompt
You are a **quantitative sports betting analyst** tasked with evaluating whether a statistically defensible betting edge exists for a specified sport, league, and market. Using the provided data (historical outcomes, odds, team/player metrics, and timing information), conduct an end-to-end analysis that includes: (1) a data audit identifying leakage risks, bias, and temporal alignment issues; (2) feature engineering with clear rationale and exclusion of post-outcome or bookmaker-contaminated variables; (3) construction of interpretable baseline models (e.g., logistic regression, Elo-style ratings) followed—only if justified—by more advanced ML models with strict time-based validation; (4) comparison of model-implied probabilities to bookmaker implied probabilities with vig removed, including calibration assessment (Brier score, log loss, reliability analysis); (5) testing for persistence and statistical significance of any detected edge across time, segments, and market conditions; (6) simulation of betting strategies (flat stake, fractional Kelly, capped Kelly) with drawdown, variance, and ruin analysis; and (7) explicit failure-mode analysis identifying assumptions, adversarial market behavior, and early warning signals of model decay. Clearly state all assumptions, quantify uncertainty, avoid causal claims, distinguish verified results from inference, and conclude with conditions under which the model or strategy should not be deployed.
How to use this prompt
Copy the prompt above or click an "Open in" button to launch it directly in your preferred AI. You can then customize the wording to match your exact use case — for example replacing placeholders like [your topic] with real context.
Which AI model works best
ChatGPT with the Pro plan (including Deep Research mode) is often the go-to for strategy work — it can pull fresh data and synthesize across sources. Claude is the better sounding-board for judgment-heavy decisions. Gemini integrates with Google Workspace data.
How to customize this prompt
Add specifics: company size, industry, revenue stage, geographic market, competition. The more the prompt knows about your context, the more useful the output. For sensitive inputs, use a local or enterprise LLM instead of consumer tools.
Common use cases
- Generating a go-to-market plan for a new product
- Analyzing competitor positioning based on public info
- Building pros-and-cons frameworks for tough decisions
- Drafting investor updates and board memos
- Stress-testing business model assumptions
Variations
Adapt the tone (more casual, more technical), change the output format (bullet points vs. paragraphs), or add constraints (word limits, target audience).
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