Kenpom

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

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:

{
  "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):

{
  "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:

# 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:

   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:

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 -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:

Get-Content logs\odds_fetch.log -Tail 50

Manage task:

# 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 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

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 for the complete documentation index.

Endpoint	Docs	Schema
Ratings	ratings.md	ratings.schema.json
Archive	ratings_archive.md	ratings_archive.schema.json
FanMatch	fanmatch.md	fanmatch.schema.json
Four Factors	four_factors.md	four_factors.schema.json
Height	height.md	height.schema.json
Misc Stats	misc_stats.md	misc_stats.schema.json
Point Dist	point_distribution.md	point_distribution.schema.json
Teams	teams.md	teams.schema.json
Conferences	conferences.md	conferences.schema.json

Workflows & Contracts:

Document	Description
ODDS_WORKFLOW.md	Automated odds fetching workflow
WORKFLOW_MONITORING.md	GitHub Actions workflow monitoring guide
DAILY_SLATE_API.md	Daily slate output contract
daily_slate_row.json	JSON Schema: single prediction
daily_slate_table.json	JSON Schema: prediction array

Development:

Document	Description
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 for complete documentation.

Manual Commands

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