prop-modeling

Name: prop-modeling
Availability: InStock
Author: PuckAPI

Project NHL player statistics and compare them to sportsbook lines.

2|1|Updated May 1, 2026

Search Tags:#sports analytics #fantasy sports #betting #nhl #player projections #odds comparison

Ready to run

npx skills add https://github.com/PuckAPI/claude-sports-analytics --skill prop-modeling

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prop-modeling
Source: https://github.com/PuckAPI/claude-sports-analytics/tree/main/skills/prop-modeling
Command: npx skills add https://github.com/PuckAPI/claude-sports-analytics --skill prop-modeling

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scipy, sklearn, numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables users to build accurate projections for NHL player statistics such as points, shots on goal, and saves, facilitating informed betting and DFS decisions.

Core Features & Use Cases

Player Prop Projection: Estimate player-specific stats for upcoming games based on historical data, matchup, and line factors.
Market Line Comparison: Compare projections with sportsbook lines to identify positive expected value bets.
Use Case: A user wants to project points for Sidney Crosby tonight and find value compared to the bookmaker's line; this Skill computes the projected points, converts it into probabilities, and assesses the betting edge.

Quick Start

Ask the AI to generate a projected points line for a specific player, then compare it to available betting odds to find potential advantages.

Frequently Asked Questions about prop-modeling

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

FAQPage Schema

How do I project NHL player props for tonight's games?▼

Project NHL player props by estimating player-specific stats like points, shots on goal, and saves based on historical data, matchup context, ice time, team quality, and goalie performance. The projection is then converted into probabilities to assess potential betting edges.

What is the best way to find positive expected value on NHL player props?▼

Find positive expected value by comparing generated player statistic projections against current sportsbook lines. Calculating the difference between your projected outcomes and the bookmaker's odds identifies specific betting edges for over/under bets.

Can I use Python with scipy and sklearn for NHL betting analysis?▼

Yes, you can use Python with scipy, sklearn, and numpy to run advanced NHL betting analysis. These dependencies support the underlying statistical modeling and machine learning processes needed to generate player projections and probabilities.

How do I calculate over/under probabilities for NHL shots on goal and saves?▼

Calculate over/under probabilities by taking the projected NHL shots on goal or saves and modeling the distribution of outcomes. This accounts for goalie performance and matchup context to determine the likelihood of a player going over or under the market line.

Does this approach work for NHL daily fantasy sports lineup decisions?▼

Yes, this approach works for NHL daily fantasy sports decisions by providing accurate player statistic projections. Estimating points, shots, and saves for upcoming games directly informs your DFS lineup construction against opposing defenses and goalies.