kenpom
# KenPom Client
A Python API client and MCP server for KenPom basketball analytics. Get direct access to efficiency ratings, game predictions, and advanced stats through Claude or the command line.
## Features
- **MCP Server**: 11 tools for interactive analysis with Claude
- **Full API Coverage**: All 9 KenPom API endpoints
- **Smart Analytics**: Matchup comparisons, top team rankings
- **Resilience**: Rate limiting, retries, and caching built-in
- **Multi-Format Export**: CSV, JSON, and Parquet
## Quick Start
```bash
cd kenpom-client
uv venv && uv sync
cp .env.example .env # Add your KENPOM_API_KEY
```
## MCP Server Setup (Claude Code)
The MCP server lets Claude directly query KenPom data during conversations.
### Step 1: Project Configuration
The `.mcp.json` file is already included in this project:
```json
{
"mcpServers": {
"kenpom": {
"command": "uv",
"args": [
"--directory",
"C:/Users/omall/Documents/python_projects/kenpom-client",
"run",
"kenpom-mcp"
]
}
}
}
```
### Step 2: Enable Project MCP Servers
Add this to your Claude Code settings (`~/.claude/settings.json`):
```json
{
"enableAllProjectMcpServers": true
}
```
Or manually approve the server when prompted by Claude Code.
### Step 3: Restart Claude Code
Start a new session in the `kenpom-client` directory. The MCP server will load automatically.
### Available MCP Tools
| Tool | Description |
|------|-------------|
| `kenpom_ratings` | Current efficiency ratings (AdjOE, AdjDE, AdjEM) |
| `kenpom_predictions` | Game predictions with spreads and win probability |
| `kenpom_matchup` | Head-to-head comparison of two teams |
| `kenpom_top_teams` | Top N teams by any metric |
| `kenpom_fourfactors` | Four Factors analytics (eFG%, TO%, OR%, FT Rate) |
| `kenpom_pointdist` | Point distribution (% from FT, 2P, 3P) |
| `kenpom_height` | Height, experience, and continuity |
| `kenpom_miscstats` | Shooting %, blocks, steals, assists |
| `kenpom_teams` | Team rosters with coach and arena |
| `kenpom_conferences` | Conference list |
| `kenpom_archive` | Historical ratings from past dates |
### Example Queries
Once configured, ask Claude naturally:
- "What are Duke's efficiency ratings?"
- "Compare Auburn and Alabama head-to-head"
- "Show me the top 10 teams by AdjEM"
- "What games are predicted for today?"
- "Which teams have the best four factors on offense?"
## CLI Commands
For batch data collection and ML pipelines:
```bash
# Core data
uv run kenpom teams --y 2025
uv run kenpom conferences --y 2025
uv run kenpom ratings --y 2025 --date 2024-12-21
# Game predictions
uv run kenpom fanmatch --date 2024-12-21
# Advanced analytics
uv run kenpom fourfactors --y 2025
uv run kenpom pointdist --y 2025
uv run kenpom height --y 2025
uv run kenpom miscstats --y 2025
# Historical data
uv run kenpom archive --date 2024-12-21
# Real market odds (overtime.ag)
uv run fetch-odds
```
## Output File Naming
All files follow: `kenpom_{data_type}_{identifiers}.{ext}`
| Command | Example Output |
|---------|----------------|
| `teams` | `kenpom_teams_2025.csv` |
| `conferences` | `kenpom_conferences_2025.csv` |
| `ratings` | `kenpom_ratings_2025_2024-12-21.csv` |
| `fanmatch` | `kenpom_predictions_2024-12-21.csv` |
| `fourfactors` | `kenpom_fourfactors_2025.csv` |
| `pointdist` | `kenpom_pointdist_2025.csv` |
| `height` | `kenpom_height_2025.csv` |
| `miscstats` | `kenpom_miscstats_2025.csv` |
| `archive` | `kenpom_archive_2024-12-21.csv` |
Each command exports three formats: `.csv`, `.json`, and `.parquet`
## Configuration
Set in `.env`:
| Variable | Required | Default | Description |
|----------|----------|---------|-------------|
| `KENPOM_API_KEY` | Yes | - | Your KenPom API key |
| `KENPOM_RATE_LIMIT_RPS` | No | `2.0` | Requests per second |
| `KENPOM_CACHE_TTL_SECONDS` | No | `21600` | Cache TTL (6 hours) |
| `KENPOM_MAX_RETRIES` | No | `5` | Max retry attempts |
| `KENPOM_OUT_DIR` | No | `data` | Output directory |
| `OV_CUSTOMER_ID` | For odds | - | overtime.ag customer ID |
| `OV_PASSWORD` | For odds | - | overtime.ag password |
## Automated Odds Fetching
The project includes automated scraping of real market odds from overtime.ag for NCAA Basketball games.
### Setup
1. **Install Playwright browser**:
```bash
uv run playwright install chromium
```
2. **Add credentials to `.env`**:
```
OV_CUSTOMER_ID=your_customer_id
OV_PASSWORD=your_password
KENPOM_API_KEY=your_kenpom_api_key
```
### Manual Usage
Fetch current odds and generate predictions:
```bash
uv run fetch-odds
```
This will:
1. Scrape NCAA Basketball odds from overtime.ag
2. Save odds to CSV in `data/` directory
3. Automatically generate game predictions using KenPom data
### Automated Workflows
#### Option 1: GitHub Actions (Recommended for CI/CD)
A GitHub Actions workflow is available at `.github/workflows/odds_workflow.yaml` that:
- Runs daily at 4:00 AM PST (12:00 PM UTC)
- Fetches odds from overtime.ag
- Generates KenPom predictions
- Calculates betting edge
- Uploads results as artifacts
**Setup**:
1. Add GitHub Secrets:
- `OV_CUSTOMER_ID` - overtime.ag customer ID
- `OV_PASSWORD` - overtime.ag password
- `KENPOM_API_KEY` - KenPom API key
2. The workflow runs automatically on schedule or can be triggered manually via `workflow_dispatch`
**View results**:
- Go to Actions tab in GitHub repository
- Download artifacts from completed workflow runs
#### Option 2: Windows Task Scheduler (Local)
For local Windows machines, set up Task Scheduler (runs daily at 4:00 AM PST):
```powershell
powershell -File setup_task_xml.ps1
```
The scheduled task runs with automatic retry logic:
- Retries every 10 minutes if odds not yet available
- Stops after 2 hours or successful fetch
- Logs all activity to `logs/odds_fetch.log`
**View logs**:
```powershell
Get-Content logs\odds_fetch.log -Tail 50
```
**Manage task**:
```powershell
# Check status
schtasks /query /tn "FetchOvertimeCollegeBasketballOdds" /fo LIST
# Run manually
Start-ScheduledTask -TaskName 'FetchOvertimeCollegeBasketballOdds'
# Stop task
Stop-ScheduledTask -TaskName 'FetchOvertimeCollegeBasketballOdds'
# Delete task
schtasks /delete /tn "FetchOvertimeCollegeBasketballOdds" /f
```
See [docs/ODDS_WORKFLOW.md](docs/ODDS_WORKFLOW.md) for complete documentation.
## Project Structure
```
kenpom-client/
├── src/kenpom_client/
│ ├── mcp_server.py # MCP server (11 tools)
│ ├── client.py # API wrapper
│ ├── cli.py # Command-line interface
│ ├── overtime_scraper.py # overtime.ag odds scraper
│ ├── models.py # Pydantic models
│ ├── config.py # Settings
│ ├── cache.py # File-based caching
│ ├── http.py # Rate limiting & retries
│ └── exceptions.py # Custom exceptions
├── docs/ # API documentation
│ ├── _index.md # Documentation index
│ ├── ratings.md # Ratings endpoint
│ ├── ratings_archive.md # Archive endpoint
│ ├── fanmatch.md # FanMatch endpoint
│ ├── four_factors.md # Four Factors endpoint
│ ├── height.md # Height endpoint
│ ├── misc_stats.md # Misc Stats endpoint
│ ├── point_distribution.md # Point Distribution endpoint
│ ├── teams.md # Teams endpoint
│ ├── conferences.md # Conferences endpoint
│ ├── ODDS_WORKFLOW.md # Automated odds fetching guide
│ └── DAILY_SLATE_API.md # Daily slate output contract
├── schemas/ # JSON Schemas
│ ├── ratings.schema.json
│ ├── ratings_archive.schema.json
│ ├── fanmatch.schema.json
│ ├── four_factors.schema.json
│ ├── height.schema.json
│ ├── misc_stats.schema.json
│ ├── point_distribution.schema.json
│ ├── teams.schema.json
│ ├── conferences.schema.json
│ ├── daily_slate_row.json
│ └── daily_slate_table.json
├── fetch_odds_scheduled.bat # Windows scheduled task script
├── setup_task_xml.ps1 # Task Scheduler setup
├── .mcp.json # MCP server configuration
├── data/ # Output directory (gitignored)
├── logs/ # Task logs (gitignored)
├── .cache/ # API cache (gitignored)
└── .env # API keys (gitignored)
```
## Programmatic Usage
```python
from kenpom_client.client import KenPomClient
from kenpom_client.config import Settings
settings = Settings.from_env()
client = KenPomClient(settings)
# Get ratings
ratings = client.ratings(y=2025)
for team in ratings[:5]:
print(f"{team.TeamName}: AdjEM {team.AdjEM}")
# Get predictions
games = client.fanmatch(d="2024-12-21")
for game in games:
spread = game.HomePred - game.VisitorPred
print(f"{game.Visitor} @ {game.Home}: {spread:+.1f}")
# Compare teams
four_factors = client.four_factors(y=2025)
height_data = client.height(y=2025)
misc_stats = client.misc_stats(y=2025)
client.close()
```
## API Endpoints Reference
| Endpoint | Method | Description |
|----------|--------|-------------|
| Ratings | `ratings(y, team_id, c)` | Current season efficiency ratings |
| Archive | `archive(d, preseason, y)` | Historical point-in-time ratings |
| Four Factors | `four_factors(y)` | eFG%, TO%, OR%, FT Rate |
| Point Dist | `point_distribution(y)` | Scoring breakdown by shot type |
| Height | `height(y)` | Height, experience, continuity |
| Misc Stats | `misc_stats(y)` | Shooting %, blocks, steals, assists |
| FanMatch | `fanmatch(d)` | Game predictions and spreads |
| Teams | `teams(y, c)` | Team rosters with arena info |
| Conferences | `conferences(y)` | Conference metadata |
## Documentation
Full API documentation and JSON schemas are available in the `docs/` and `schemas/` directories.
**API Endpoints**: See [docs/_index.md](docs/_index.md) for the complete documentation index.
| Endpoint | Docs | Schema |
|----------|------|--------|
| Ratings | [ratings.md](docs/ratings.md) | [ratings.schema.json](schemas/ratings.schema.json) |
| Archive | [ratings_archive.md](docs/ratings_archive.md) | [ratings_archive.schema.json](schemas/ratings_archive.schema.json) |
| FanMatch | [fanmatch.md](docs/fanmatch.md) | [fanmatch.schema.json](schemas/fanmatch.schema.json) |
| Four Factors | [four_factors.md](docs/four_factors.md) | [four_factors.schema.json](schemas/four_factors.schema.json) |
| Height | [height.md](docs/height.md) | [height.schema.json](schemas/height.schema.json) |
| Misc Stats | [misc_stats.md](docs/misc_stats.md) | [misc_stats.schema.json](schemas/misc_stats.schema.json) |
| Point Dist | [point_distribution.md](docs/point_distribution.md) | [point_distribution.schema.json](schemas/point_distribution.schema.json) |
| Teams | [teams.md](docs/teams.md) | [teams.schema.json](schemas/teams.schema.json) |
| Conferences | [conferences.md](docs/conferences.md) | [conferences.schema.json](schemas/conferences.schema.json) |
**Workflows & Contracts**:
| Document | Description |
|----------|-------------|
| [ODDS_WORKFLOW.md](docs/ODDS_WORKFLOW.md) | Automated odds fetching workflow |
| [WORKFLOW_MONITORING.md](docs/WORKFLOW_MONITORING.md) | GitHub Actions workflow monitoring guide |
| [DAILY_SLATE_API.md](docs/DAILY_SLATE_API.md) | Daily slate output contract |
| [daily_slate_row.json](schemas/daily_slate_row.json) | JSON Schema: single prediction |
| [daily_slate_table.json](schemas/daily_slate_table.json) | JSON Schema: prediction array |
**Development**:
| Document | Description |
|----------|-------------|
| [RUN_TESTS.md](RUN_TESTS.md) | Guide for running the test suite |
## Development
### Automated Validation Hooks
This project uses automated hooks for quality assurance:
- **Pre-commit hook** - Validates code before commits (format, lint, type check, tests)
- **Post-edit hook** - Type checks after Claude edits files
- **Session start hook** - Syncs dependencies on session start
See [HOOKS.md](HOOKS.md) for complete documentation.
### Manual Commands
```bash
uv run ruff format . # Format
uv run ruff check . # Lint
pyrefly check # Type check
uv run pytest # Test
# Full validation (what pre-commit runs)
powershell -ExecutionPolicy Bypass -File scripts/hooks/validate-all.ps1
```
TDQS
Scored across 15 tools
Most tools have distinct purposes like ratings, predictions, or team rosters. However, 'kenpom_predictions' and 'kenpom_project' and 'kenpom_slate' overlap slightly in providing game forecasts, and the two effort-classification tools are unrelated to basketball data, causing potential confusion.
Thirteen tools follow the 'kenpom_' prefix with noun-based names, but 'classify_effort' and 'get_tool_effort' break the pattern. Some compound names lack underscores (e.g., 'fourfactors', 'miscstats') while others use them, creating minor inconsistency.
With 15 tools covering ratings, four factors, predictions, rosters, and more, the count is well-scoped for a college basketball analytics server. Each tool serves a clear function without redundancy.
The tool set covers core KenPom data like efficiency ratings, four factors, and game predictions. Missing are raw game results or player-level stats, but for team-level analysis the surface is nearly complete.