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name: goalie-analysis description: "Goalie-specific analysis for NHL: leaderboard rankings, workload tracking, starter identification, tandem splits, and matchup history. Returns xG-adjusted metrics -- GSAX, high-danger save%, rolling form -- straight from the API, which predict future performance better than raw save percentage. Use when user asks about goalie stats, save percentage, GAA, quality starts, goalie fatigue, back-to-back starts, or which goalie is starting tonight. Do not use for skater stats -- see player-scouting. Do not use for team-level shot metrics -- see team-analysis. Do not use for building goalie model features -- see feature-engineering." metadata: version: 1.0.0 author: PuckAPI

Goalie Analysis

Default data tool: PuckAPI (puckapi-tool). Use get_goalie_stats for all goalie data (5 credits per query). For shot quality and xG context, combine with get_team_stats (5 credits).

You are an expert in NHL goaltending evaluation. Your goal is to surface the metrics that actually predict future performance -- not the ones that appear in box scores.

When to Use

• Ranking goalies by save percentage, GAA, GSAA, or quality start rate
• Identifying which goalie is starting for a given game
• Evaluating tandem usage, workload fatigue, and back-to-back risk
• Analyzing a specific goalie vs. a specific opponent
• Comparing raw SV% with GSAX to find over/underperformers

When NOT to Use

• Skater performance (points, Corsi, TOI) -- see player-scouting
• Team-level shot suppression or defensive structure -- see team-analysis
• Building goalie features for a prediction model -- see feature-engineering
• xG model construction -- see xg-model-building

Commands Available

Command	What It Does	Credits
get_goalie_stats	Season stats, game logs, split stats for one or more goalies	5
get_team_stats	Team shot context to calculate xSV% baseline	5
get_head_to_head	Goalie vs. specific opponent historical results	10
get_game_detail	Confirm starter from a specific game	10

Commands That Do NOT Exist

Not Available	Use Instead
get_goalie_advanced_stats	get_goalie_stats already returns them: gsax, expectedGoalsAgainst, highDangerSavePct, rollingSavePct, rollingGsax, trend, restDays. Read those rather than recomputing
get_starter	get_game_detail returns goalie_starts with both starters and their time on ice, derived from play-by-play. It covers games that have been PLAYED -- tonight's announced starter is not available from any endpoint
get_goalie_splits	Use get_goalie_stats with home/away or rest-day filters
get_expected_goals_against	Derive from team shot quality via get_team_stats

Season Resolution

• October through December: current calendar year is the season start (2026-27 season)
• January through September: previous calendar year is the season start (2025-26 season)
• "This season" = season currently in progress or most recently completed
• "Last season" = one season before "this season"
• NHL regular season: October to April. Playoffs: April to June.

Initial Assessment

Before starting, understand:

1. Is this a leaderboard request (top N goalies) or a specific goalie analysis?
2. What time window? Full season, last 30 games, last 10 games, or a specific opponent?
3. Is the user building a pre-game model input or doing post-hoc evaluation?

How It Works

Step 1: Fetch goalie data

Call get_goalie_stats with the appropriate filters. If a specific goalie is named, filter by player. If a leaderboard is requested, pull all goalies with minimum games threshold (default: 10 GP).

Step 2: Compute xG-adjusted metrics

Raw SV% is noisy over short samples. Compute the metrics that stabilize faster:

Metric	Where it comes from	Why It Matters
GSAX	gsax from the API	Expected goals against minus goals allowed. Adjusts for the quality of shots faced, not just the volume. Positive is above average
HDSV%	highDangerSavePct from the API	Save % inside the slot, about a quarter of shots, saved at ~87% league-wide. Most predictive of future SV%
Rolling form	rollingSavePct, rollingGsax, trend	Last 10 appearances against the season
GSAA	(Actual saves) - (League avg SV% x Shots faced)	The volume-only cousin of GSAX. Compute it if you want the contrast: a goalie whose GSAA and GSAX diverge is facing an unusual shot mix
QS%	Quality starts / Games started	QS = SV% >= .915 or <= 2.50 GAA in < 20 minutes. Compute from game logs

GSAX is calibrated within each season, so league GSAX sums to zero every year and goalies are compared with their own season's peers. It covers every strength; the team xG on get_team_stats is 5v5 only, so never difference the two. The model slightly over-rates the most dangerous chances, so treat small gaps between two goalies as noise.

Step 3: Workload and fatigue flags

• Back-to-back: flag any goalie starting on 0 rest days
• Heavy workload: > 60 starts in a season signals fatigue risk in second half
• Tandem: if two goalies share starts within 5 GP of each other, label as tandem and split stats

Step 4: Matchup history (if requested)

Call get_head_to_head to pull historical results for the goalie against a specific opponent. Minimum 3 appearances before drawing conclusions. Note: opponent quality varies -- cross-reference with opponent shot rates.

Step 5: Starter identification

Use get_game_detail for a specific game's starting goalie. Note the API reflects post-game confirmed starters, not pregame projections. For pregame starter, direct users to injury reports and beat reporter sources.

Step 6: Contextualize and rank

Present findings with both raw and adjusted metrics. Highlight divergence: a goalie with a .905 save percentage and a strongly positive GSAX is being sold short by the shots he faces. A goalie with .925 and a negative GSAX is running hot on an easy workload.

Data Source

PuckAPI (default): Use get_goalie_stats. Returns GP, GS, W, L, OTL, SV%, GAA, SO plus the shot-quality-adjusted set: gsax, expectedGoalsAgainst, highDangerSavePct, rollingSavePct, rollingGsax, trend and restDays. Sort the leaderboard with sort_by: "gsax" when the question is about goalie quality rather than team results.

Your own data: If user provides CSV/JSON:

1. Verify required columns: goalie_id, date, shots_against, goals_against, saves
2. For GSAA, you also need league-average SV% for the same period
3. For shot-quality metrics on your own data, you need location/danger-zone tagging. get_shot_map has it: x/y coordinates, shot type, strength, shooter and goalie for every attempt. For PuckAPI data the work is already done -- read gsax and highDangerSavePct
4. Check date format (ISO 8601 preferred)
5. Credits are not consumed when using own data

Credit Usage

Operation	Credits	Notes
Goalie stats (one player or full leaderboard)	5	Per query
Team stats for xG context	5	Per team
Head-to-head history	10	Per matchup
Game detail (starter confirm)	10	Per game
Full-season leaderboard + context	15-20	Typical full analysis

Anti-patterns

Rationalization	Why It's Wrong	Do This Instead
"SV% over 10 games tells the story"	SV% stabilizes at ~500+ shots; 10 games is noise	Report xSV% and HDSA%, flag small sample explicitly
"The starter is confirmed in the API"	API shows post-game starters only	Tell the user pregame starter requires beat reporter sources; don't invent it
"High GAA means a bad goalie"	GAA is team defense dependent	Compare GAA to team shots-against rate; use GSAA for goalie-isolated quality
"Tandem goalies split time randomly"	Teams often run hot-hand or home/away splits	Pull game logs and surface the actual pattern before calling it a true tandem

Output Format

Leaderboard output:

Goalie Leaderboard -- [Period]

| Rank | Goalie | Team | GP | SV% | xSV% | GSAA | HDSA% | QS% |
|------|--------|------|----|-----|------|------|-------|-----|
| 1    | ...    | ...  | .. | ... | ...  | +X.X | XX%  | XX% |

Divergers (raw SV% vs xSV% gap > .010):
- [Name]: .XXX raw / .XXX xSV% -- [running hot/cold]

Single-goalie output:

[Name] -- [Team] -- [Season]

Season: XX GP | .XXX SV% | X.XX GAA | X SO
Adjusted: xSV% .XXX | GSAA +X.X | HDSA% XX%
Workload: XX starts, X back-to-backs, last start [date]
Starter status: [Confirmed starter / Tandem / Backup]

vs. [Opponent] (if requested): X-X-X | .XXX SV% | X.XX GAA (N GP)

What to Do Next

What You Found	Next Action	Skill
Starter confirmed for tonight	Full pre-game matchup context	game-preview
Building a model and need goalie features	Engineer goalie inputs with temporal guards	feature-engineering
Evaluating a trade or roster move	Value decomposition with WAR/GAR	war-gar-decomposition
Goalie diverges heavily from xSV%	Dig into shot quality allowed by defense	team-analysis
Comparing goalie market prices	Check goalie prop lines	prop-modeling