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MCP.so

NBA Odds MCP Server

Created By
nikhilkichili6 months ago
Advanced NBA odds tracking, betting analysis, and trivia MCP server for Claude Desktop with line movement detection and AI/ML game simulations
# betting
# nba-analytics
OverviewContentToolsComments
Content

NBA Odds MCP Server

A Model Context Protocol (MCP) server that provides NBA basketball odds data from The Odds API.

Overview

This MCP server enables LLMs (Large Language Models) like Claude to access real-time NBA odds data. It provides information about:

  • Current NBA games with betting odds
  • NBA Championship winner odds
  • Team-specific game schedules
  • Basic NBA league information

The server acts as a bridge between LLMs and The Odds API, handling authentication, data formatting, and error handling.

Requirements

  • Python 3.10 or higher
  • The Odds API key (included in the server)
  • MCP-compatible client (Claude Desktop, etc.)
  • Matplotlib, NumPy, and Pillow for visualizations

Installation

  1. Set up Python 3.10 or higher

    # Using pyenv (already set up)
pyenv local 3.10.13

  2. Create and activate a virtual environment

    python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

  3. Install dependencies

    pip install "mcp[cli]" httpx

Usage

Running the Server

# Activate the virtual environment
source venv/bin/activate

# Run the server
python nba_odds_server.py

Automated Data Collection

To build a meaningful historical database for trend analysis, set up automated data collection:

  1. Create a data collection script (odds_collector.py) in your project directory that collects and stores odds data

  2. Set up cron jobs to run this script at regular intervals:

# Daily collection at 7:30 AM
30 7 * * * cd /Users/nikhil/Documents/MCP_test && /Users/nikhil/Documents/MCP_test/venv/bin/python odds_collector.py >> /Users/nikhil/Documents/MCP_test/collection_log.txt 2>&1

# Collection at 3:30 PM (for game days)
30 15 * * * cd /Users/nikhil/Documents/MCP_test && /Users/nikhil/Documents/MCP_test/venv/bin/python odds_collector.py >> /Users/nikhil/Documents/MCP_test/collection_log.txt 2>&1

# Collection at 8:30 PM (for game days and post-game)
30 20 * * * cd /Users/nikhil/Documents/MCP_test && /Users/nikhil/Documents/MCP_test/venv/bin/python odds_collector.py >> /Users/nikhil/Documents/MCP_test/collection_log.txt 2>&1
  1. Recommended collection frequency:
    • Daily baseline collection (morning)
    • Pre-game collection on game days (afternoon)
    • Post-game collection for market reactions (evening)

Configuring Claude Desktop

  1. Edit the Claude Desktop configuration file:

    ~/Library/Application Support/Claude/claude_desktop_config.json

  2. Add the following configuration:

    {
  "mcpServers": {
    "nba-odds": {
      "command": "/Users/nikhil/Documents/MCP_test/venv/bin/python",
      "args": [
        "/Users/nikhil/Documents/MCP_test/nba_odds_server.py"
      ]
    }
  }
}

  3. Restart Claude Desktop

Testing the Server

Ask questions like:

  • "What are the current odds for the NBA Championship?"
  • "Show me upcoming NBA games"
  • "What team is favored to win the NBA Championship?"
  • "Tell me about the NBA games happening today"
  • "Simulate a game between the Lakers and the Celtics"

Available Tools

The server provides the following tools:

Basic NBA Data Tools

  1. get_nba_info() - Provides basic information about the NBA basketball category

  2. get_nba_games(markets, regions) - Gets current NBA games with odds

    • Parameters:
      • markets: Comma-delimited list of markets (h2h, spreads, totals)
      • regions: Comma-delimited list of regions (us, uk, eu, au)

  3. get_nba_championship_odds() - Gets current NBA Championship winner odds

  4. get_nba_team_stats(team_name) - Gets upcoming games for a specific team

    • Parameters:
      • team_name: Name of the NBA team

Simulation Tools

  1. simulate_nba_game(home_team, away_team) - Simulate an NBA game using AI/ML models

    • Parameters:
      • home_team: Name of the home team
      • away_team: Name of the away team

  2. simulate_championship_odds() - Simulate NBA championship odds using 1000 season simulations

  3. simulate_season_remainder() - Simulate the remainder of the NBA season including playoffs

  4. get_simulation_accuracy() - Get the accuracy statistics of previous game simulations

Betting Analysis Tools

  1. get_betting_recommendations(max_recommendations) - Get smart betting recommendations based on odds movement and historical trends

    • Parameters:
      • max_recommendations: Maximum number of recommendations to return (default: 3)

  2. analyze_team_ats_performance(team_name) - Analyze a team's performance against the spread (ATS)

    • Parameters:
      • team_name: Name of the NBA team to analyze

  3. analyze_odds_movement(event_id, days) - Analyze how betting odds have moved for a specific game

    • Parameters:
      • event_id: Event identifier
      • days: Number of days to look back (default: 7)

NBA Trivia Game Tools

  1. start_nba_quiz(category, difficulty, length) - Start a new NBA trivia quiz game

    • Parameters:
      • category: Category of questions (random, nba_history, current_nba, nba_stats, nba_rules, championships)
      • difficulty: Difficulty level (rookie, veteran, all-star, hall-of-fame, mixed)
      • length: Number of questions (default: 5)

  2. answer_quiz_question(quiz_id, answer) - Answer a question in an active trivia quiz

    • Parameters:
      • quiz_id: ID of the active quiz
      • answer: The answer selected by the user

  3. get_daily_nba_question() - Get the daily NBA trivia question

  4. answer_daily_nba_question(answer) - Answer the daily NBA trivia question

    • Parameters:
      • answer: The answer selected by the user

  5. get_nba_quiz_stats() - Get statistics about NBA trivia quiz performance

Line Movement Analysis Tools

  1. analyze_reverse_line_movement(days) - Analyze reverse line movement patterns from historical odds data

    • Parameters:
      • days: Number of days of historical data to analyze (default: 30)

  2. analyze_sharp_money_action(days, threshold) - Identify games with potential sharp money action

    • Parameters:
      • days: Number of days to analyze (default: 30)
      • threshold: Minimum probability change to consider (default: 3%)

  3. calculate_betting_strategy_roi(movement_type, days) - Calculate historical ROI for betting strategies based on line movements

    • Parameters:
      • movement_type: Type of movement to analyze (reverse, sharp)
      • days: Number of days to analyze (default: 90)

  4. create_line_movement_chart(game_id, team_name, days) - Create a chart showing probability impact of line movements

    • Parameters:
      • game_id: Optional specific game to analyze (use "help" to see available games)
      • team_name: Optional team name to filter by
      • days: Number of days to analyze if no specific game/team

Odds Tracking & Analysis Tools

  1. analyze_championship_odds_trend(team_name, days) - Analyzes how a team's championship odds have changed over time

    • Parameters:
      • team_name: Name of the NBA team to analyze
      • days: Number of days to look back (default: 30)

  2. get_team_odds_history(team_name, days) - Gets historical championship odds data for a specific team

    • Parameters:
      • team_name: Name of the NBA team
      • days: Number of days of historical data to retrieve (default: 30)

  3. get_odds_tracking_stats() - Gets statistics about the tracked odds data in the database

Visualization Tools

  1. generate_team_odds_chart(team_name, days) - Creates a chart showing championship odds movement for a team

    • Parameters:
      • team_name: Name of the NBA team to chart
      • days: Number of days to include in the chart (default: 30)

  2. generate_teams_comparison_chart(team_names, days) - Creates a chart comparing championship odds for multiple teams

    • Parameters:
      • team_names: Comma-separated list of team names to compare
      • days: Number of days to include in the chart (default: 30)

  3. generate_game_odds_chart(event_id) - Creates a chart showing odds movement for a specific game

    • Parameters:
      • event_id: ID of the game/event to chart

  4. generate_bookmaker_comparison_chart(team_name, days) - Creates a chart comparing odds from different bookmakers

    • Parameters:
      • team_name: Name of the NBA team to analyze
      • days: Number of days to include in the chart (default: 30)

Data Storage and Visualization

SQLite Database

The server uses SQLite to store historical odds data, enabling trend analysis:

  • Championship Odds Table: Tracks team championship odds over time
  • Game Odds Table: Tracks individual game odds and line movements
  • Automatic Storage: All API requests automatically store data in the database

Visualization System

The server includes a robust visualization system powered by Matplotlib:

  • Charts Directory: All generated charts are saved to a charts folder
  • Base64 Embedding: Charts are returned as base64-encoded images for display in LLM responses
  • Chart Types:
    • Team odds trend charts
    • Multi-team comparison charts
    • Game odds movement charts
    • Bookmaker comparison charts

Data Analysis

The system provides automated analysis of odds data:

  • Trend detection (improvement, decline, stability)
  • Bookmaker discrepancy analysis
  • Historical odds movement patterns
  • Visual indicators of significant odds shifts

API Information

This server uses The Odds API (https://the-odds-api.com/) with the following endpoints:

  • GET /sports - List all sports categories
  • GET /sports/{sport_key}/odds - Get odds for a specific sport
  • GET /sports/{sport_key}/events/{event_id}/odds - Get odds for a specific event

API key is included in the server configuration.

Troubleshooting

  • Server not starting: Ensure Python 3.10+ is installed and the virtual environment is activated
  • MCP client not connecting: Check the configuration file path and format
  • API errors: Verify API key validity and check for rate limiting

License

This project is for educational purposes.

Acknowledgements

  • The Odds API for providing sports betting data
  • Model Context Protocol (MCP) for the server framework
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