xg-model-building skillA
xg-model-building is agent-read markdown (skill) from puckapi/claude-sports-analytics: Builds expected goals (xG) models from NHL play-by-play shot event data using XGBoost or LightGBM. Use when user asks about expected goals, xG model, shot quality, building an xG model, xGF%, xGA, rebound detection, rush shot detection, or shot probability. Do not use for applying pre-built xG values to team or game analysis -- see team-analysis or hockey-analytics. Do not use for general game prediction models -- see model-building. Do not use for goalie evaluation using xGA -- see goalie-analy.
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# xG Model Building > **Important: PuckAPI does NOT have play-by-play data.** The SDH database contains game-level data (scores, teams, odds, goalie starts) but no event-level shot data, coordinates, or play-by-play events. > > **Primary data source for xG:** The NHL Stats API at `api-web.nhle.com` provides free play-by-play data with shot coordinates, event types, and strength state. No API key or credits required. > > **PuckAPI is useful for:** Validating your xG model output against team-level stats (`get_team_stats`, 5 credits) and goalie stats (`get_goalie_stats`, 5 credits). > > For user's own shot data CSV/JSON: skip external sources, work with the file directly. You are an expert in hockey expected goals modeling. Your goal is to build a shot-level xG model that estimates the probability any given shot results in a goal, controlling for shot quality rather than shot volume. ## When to Use - User asks "how do I build an xG model" - User wants to model shot probability or goal probability from play-by-play data - User wants to compute xGF%, xGA, or expected goals for teams or players - User asks about rebound detection, rush shot detection, or shot angle features …
Read the whole file at its exact version.
How to install
mdr add puckapi/claude-sports-analytics/xg-model-building@v1.1.0mdr add puckapi/claude-sports-analytics/xg-model-building@sha256:51496bee4f328421Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_eurwz2fgilesokf5)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.1.0 latest | 2026-05-06 | 3355ba8 | 12,439 B | A | view · diff |
| v1.1.0 | 2026-05-04 | c1ac62a | 12,481 B | A | view · diff |
| v1.0.0 | 2026-05-01 | df6b9a7 | 11,494 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (12439 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
puckapi/claude-sports-analytics · 3 stars · license MIT · pushed 2026-09-21 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_eurwz2fgilesokf5 GET https://markdownregistry.com/api/v1/resolve?ref=puckapi/claude-sports-analytics/xg-model-building GET https://markdownregistry.com/api/v1/blob/51496bee4f328421ccc0fea0d3797d8c91130d5b4623e8ea7ac3f32be77a4691
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