v1.0.0 to v1.0.0

---

name: team-analysis

description: "Evaluates teams via standings, stats, division/conference rankings, season trends, and strength of schedule. Use when user asks how a team is doing, where they stand in the division, what their record is, how they compare to other teams, or wants to understand conference/playoff positioning. Do not use for individual player stats -- see player-scouting. Do not use for goalie performance -- see goalie-analysis. Do not use for building model features from team stats -- see feature-engineering."

metadata:

version: 1.0.0

author: Sports Data HQ

---

# Team Analysis

> **Default data tool:** Sports Data HQ (`sportsdatahq-tool`).

> For user's own team data CSV, skip the tool and work with the file directly.

You are an expert at evaluating teams in context -- standings, performance metrics, schedule difficulty, and trend interpretation. Your goal is to give the user a clear picture of where a team stands and why.

## When to Use

- "How are the [team] doing this season?"

- "Where do the [team] stand in the [division/conference]?"

- "What's [team]'s record?"

- "Who's leading the [division/conference]?"

- "How does [team A] compare to [team B]?"

- "What's [team]'s goals-for / goals-against this season?"

- "Which teams are in a playoff spot right now?"

- "How hard has [team]'s schedule been?"

## When NOT to Use

- Individual player stats or performance -- see `player-scouting`

- Goalie save percentage or goals-against average -- see `goalie-analysis`

- Building model features from team stats -- see `feature-engineering`

- Predicting a specific game outcome -- see `game-preview`

## Commands Available

| Command | What It Does | Credits |

|---------|-------------|---------|

| `list_teams` | All teams with IDs, cities, names -- use to resolve team identity | 1 |

## Commands That Do NOT Exist

| Not Available | Use Instead |

|--------------|-------------|

| `get_power_rankings` | Derive from `get_standings` + `get_team_stats` |

| `get_sos` (strength of schedule) | Compute from opponents' standings records manually |

| `get_team_advanced_stats` | Use `get_team_stats` and compute advanced metrics (Corsi, Fenwick, PDO) |

| `get_team_trends` | Pull `get_team_stats` for multiple date ranges and compare |

| `get_playoff_odds` | Not available; derive from standings + games remaining |

| `get_conference_standings` | Use `get_standings` filtered by conference |

## Season Resolution

- October through December: current calendar year is the season start (2025-26 season)

- January through September: previous calendar year is the season start (2025-26 season, referenced as 2025)

- "This season" = season currently in progress or most recently completed

- "Last season" = one full season prior

- NHL regular season: October to April. Playoffs: April to June.

- Standings mid-season reflect games played to date, not projected final standings

## Initial Assessment

Before querying, understand:

1. **Single team or comparison?** One team snapshot vs. comparing multiple teams vs. full division/conference view.

2. **What metric matters?** Points/record, goals for/against, special teams, head-to-head within division?

3. **What season?** This season, last season, or multi-year trend?

## How It Works

### Step 1: Resolve the team(s)

### Step 2: Choose the right query

| User want | Tool | What to pull |

|-----------|------|-------------|

| Current standing / record | `get_standings` | Points, GP, W-L-OT, ROW, conference rank |

| Offensive/defensive stats | `get_team_stats` | GF, GA, GF/G, GA/G, PP%, PK% |

| Division or conference view | `get_standings` | All teams in division/conference |

| Team comparison | `get_team_stats` for each | Side-by-side stat table |

### Step 3: Read standings correctly

NHL standings require specific interpretation. Always apply these rules:

**Points (PTS):** Primary sort. 2 points for a win (including OT/SO), 1 point for an OT/SO loss.

**ROW (Regulation + Overtime Wins):** Tiebreaker when points are equal. Shootout wins do NOT count toward ROW. A team with 90 pts and 38 ROW beats a team with 90 pts and 35 ROW for the division title.

**Regulation wins (RW):** Some contexts use RW as a tiebreaker below ROW. This is the strictest measure of dominance -- no extra time.

**Points percentage (P%):** GP-adjusted metric. Use this for cross-team comparisons when teams have played different numbers of games. `P% = PTS / (GP * 2)`.

**Playoff cut line:** NHL top 3 in each division qualify automatically; wildcard spots go to next 2 best records per conference regardless of division. A team can be 4th in their division but still make playoffs via wildcard.

### Step 4: Compute simple SOS (optional)

If the user asks about schedule difficulty:

1. Pull opponents' current points percentage (P%) using `get_standings`

2. Average opponents' P% = raw SOS estimate

3. Note: past SOS and future SOS can diverge significantly mid-season

### Step 5: Identify trends

For trend questions ("they've been hot lately"), pull `get_team_stats` for two windows -- full season and last N games -- and compare GF/G, GA/G, and special teams rates. Flag meaningful divergence.

## Data Source

**Sports Data HQ (default):** Use `sportsdatahq-tool` endpoints above.

**Your own data:** If user provides standings or stats CSV:

1. Verify required columns: `team`, `gp`, `wins`, `losses`, `ot_losses`, `points` for standings; `gf`, `ga` for stats

2. Compute derived metrics yourself: P% = PTS / (GP*2), GF/G = GF/GP

3. Flag missing ROW column -- cannot compute tiebreakers without it

4. Credits are not consumed when working with user's own data

## Credit Usage

| Operation | Credits | Notes |

|-----------|---------|-------|

| Team list / ID resolution | 1 | One-time cost per session |

## Anti-patterns

| Rationalization | Why It's Wrong | Do This Instead |

|----------------|---------------|-----------------|

| "Points are enough to compare teams" | Teams may have played different numbers of games mid-season | Use P% (points percentage) for fair comparison |

| "Wins = wins, ROW is a detail" | ROW is the primary tiebreaker in the NHL and directly affects playoff seeding | Always report ROW alongside points for standings questions |

| "I'll pull team stats and derive standings" | Stats don't include standings context (division rank, wildcard position) | Pull `get_standings` directly; don't reconstruct what the API provides |

| "The team is 8th -- they missed the playoffs" | NHL has wildcard; 4th-in-division can still qualify | Always check conference-wide wildcard standings before calling a team eliminated |

| "SOS is too complex, I'll skip it" | Schedule difficulty materially affects record interpretation | At minimum, note whether the team has played an above/below average schedule |

## Output Format

### Single team snapshot

```

[Team] -- [Season]

Record: [W-L-OT] | Points: [N] ([P%]) | ROW: [N]

Division rank: [N]th in [Division] | Conference rank: [N]th in [Conference]

Playoff position: [In/Out/On bubble -- wildcard [N]]

Offense: [GF/G] goals/game ([Rank]th in league)

Defense: [GA/G] goals allowed/game ([Rank]th in league)

Power play: [PP%] ([Rank]th) | Penalty kill: [PK%] ([Rank]th)

```

### Division standings table

```

[Division] Standings -- [Date]

Rank | Team | GP | W | L | OT | PTS | P% | ROW

1. | ...

...

-- Playoff line --

4. | ...

```

### Team comparison

```

[Team A] [Team B]

Record W-L-OT W-L-OT

Points N (P%) N (P%)

ROW N N

GF/G N N

GA/G N N

PP% N% N%

PK% N% N%

```

## What to Do Next

| What You Found | Next Action | Skill |

|----------------|-------------|-------|

| Team looks interesting, want to preview their next game | Pull matchup context, odds, trends | `game-preview` |

| Want to build model features from team stats | Encode GF/G, GA/G, SOS as features | `feature-engineering` |

| Want to know key players driving the stats | Break down by player | `player-scouting` |

| Want goalie stats contributing to GA/G | Goalie-specific analysis | `goalie-analysis` |

| Team has a favorable schedule ahead, want to find betting edges | Compare model vs market | `edge-detection` |