1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@kenpom Show me the top 10 teams by adjusted efficiency margin." That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

kenpom

by omalleyandy

Overview Schema Related Servers Score Discussions

Python

Hybrid

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

Related MCP server: College Basketball Stats MCP Server

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

Install Playwright browser:
```
uv run playwright install chromium
```

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:

Scrape NCAA Basketball odds from overtime.ag
Save odds to CSV in data/ directory
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:

Add GitHub Secrets:
- OV_CUSTOMER_ID - overtime.ag customer ID
- OV_PASSWORD - overtime.ag password
- KENPOM_API_KEY - KenPom API key
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

Install Server

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quality

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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