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edge-detection

Compare model probabilities against market odds to identify positive expected value bets.

2|1|Updated May 1, 2026
Search Tags:#market analysis#betting#odds#CLV#expected value#kelley
One-click install
npx skills add https://github.com/PuckAPI/claude-sports-analytics --skill edge-detection-puckapi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: edge-detection
Source: https://github.com/PuckAPI/claude-sports-analytics/tree/main/skills/edge-detection
Command: npx skills add https://github.com/PuckAPI/claude-sports-analytics --skill edge-detection-puckapi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires puckapi-tool, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps users determine whether a sports betting opportunity offers a positive expected value by analyzing the gap between a calibrated model's predicted probabilities and current market odds.

Core Features & Use Cases

  • EV Calculation and Bet Selection: Computes the expected value for bets based on model probabilities and odds, guiding users on which to place.
  • Line Shopping and Market Comparison: Finds the best available odds across multiple bookmakers to maximize value.
  • Bankroll Management: Suggests bet sizes using Kelly criteria and manages risk with fractional Kelly adjustments.
  • Performance Tracking: Monitors closing line value (CLV) and evaluates model effectiveness over time to refine strategies.
  • Use Case: A user inputs model probabilities and current odds to identify small but consistent edge opportunities, then sizes bets appropriately to maximize long-term growth.

Quick Start

Use the edge-detection skill to analyze current odds for upcoming NHL games and evaluate which bets yield a positive expected value based on your model probabilities.

Frequently Asked Questions about edge-detection

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate expected value for sports betting odds using model probabilities?▼

To calculate expected value, compare your model's predicted probabilities against current market odds to identify gaps where positive EV exists, then input both values to compute the potential return and validate the edge.

What is closing line value and how does it track betting model performance?▼

Closing line value (CLV) measures the gap between the odds you bet at and the final market odds, serving as a key performance indicator to evaluate and refine your betting model's effectiveness over time.

How do I determine optimal bet size using Kelly criteria for positive EV bets?▼

Determine optimal bet sizes by applying the Kelly criteria to your calculated edge, using fractional Kelly adjustments to manage risk and maximize long-term bankroll growth on positive expected value wagers.

Can I compare odds across multiple bookmakers to find the best betting lines?▼

Yes, you can perform line shopping by comparing current odds across multiple bookmakers to find the best available prices, maximizing the value and expected return of your selected bets.

Do I need the puckapi-tool dependency to analyze NHL betting edges?▼

Yes, the puckapi-tool dependency is required to supply the market data and odds feeds necessary to analyze upcoming NHL games and identify positive expected value betting opportunities.

Why does my betting model show positive EV but still lose money consistently?▼

Positive EV bets can still lose consistently short-term due to variance; tracking closing line value (CLV) helps confirm if your model genuinely beats the market and whether your edge is real.

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