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

Commands That Do NOT Exist

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:

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

Anti-patterns

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