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SportIntel MCP Server

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🏈 SportIntel MCP Server

AI-Powered Sports Intelligence for Claude & AI Agents

Apify Challenge 2025 MCP Server License: MIT

SportIntel MCP is the first AI-powered sports analytics MCP server, bringing explainable Daily Fantasy Sports (DFS) intelligence to Claude and other AI agents. Built on the Model Context Protocol, it provides real-time player projections, lineup optimization, live odds aggregation, and SHAP-based explainability.

✨ Features

🎯 Core Capabilities (MVP)

Tool

Description

Use Case

get_player_projections

AI-powered DFS projections with SHAP explainability

Get projected fantasy points for all players in today's slate

optimize_lineup

Multi-objective lineup optimization

Generate optimal cash/GPP lineups under salary cap

get_live_odds

Real-time odds from 10+ sportsbooks

Compare spreads, totals, and find best available lines

explain_recommendation

SHAP/LIME explanations for projections

Understand why the model recommends a player

🔥 Key Differentiators

  • ✅ First MCP Server for Sports Analytics - Zero competition in MCP ecosystem

  • 🧠 Explainable AI - SHAP values show feature importance (not a black box)

  • 💰 10x Cost Advantage - Free tier vs $50-200/month DFS subscription sites

  • 📊 Multi-Source Intelligence - Aggregates odds, stats, news, injuries

  • ⚡ Real-Time - Live odds updates, instant injury impact analysis

  • 🤖 AI-Native - Built for Claude/AI agent consumption

🚀 Quick Start

Installation

# Clone repository git clone https://github.com/roizenlabs/sportintel-mcp.git cd sportintel-mcp # Install dependencies npm install # Set up environment cp .env.example .env # Edit .env with your API keys

Configuration for Claude Desktop

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{ "mcpServers": { "sportintel": { "command": "node", "args": ["/path/to/sportintel-mcp/dist/main.js"], "env": { "ODDS_API_KEY": "your_api_key_here" } } } }

Run Standalone

# Development mode npm run dev # Production build npm run build npm start

📖 Usage Examples

Example 1: Get NBA Player Projections

Claude Prompt:

Get AI projections for tonight's NBA main slate with explainability

MCP Call:

{ "tool": "get_player_projections", "arguments": { "sport": "NBA", "slate": "main", "includeExplanations": true } }

Response:

{ "sport": "NBA", "slate": "main", "projections": [ { "playerName": "LeBron James", "team": "LAL", "position": "SF", "salary": 9500, "projectedPoints": 48.2, "floor": 38.6, "ceiling": 57.8, "confidence": 0.89, "value": 5.07, "explanation": { "topFactors": [ { "factor": "recent_ppg", "impact": +6.2, "description": "Averaging 32.1 PPG over last 5 games" }, { "factor": "vegas_total", "impact": +3.1, "description": "230.5 Vegas total (high-scoring game expected)" } ], "reasoning": "LeBron is projected above baseline due to elite recent performance and favorable game environment..." } } ] }

Example 2: Optimize Lineup

Claude Prompt:

Build me 3 cash game lineups for NBA using the projections you just got

MCP Call:

{ "tool": "optimize_lineup", "arguments": { "sport": "NBA", "salaryCap": 50000, "lineupCount": 3, "strategy": "cash", "projections": [/* from previous call */] } }

Response:

{ "lineups": [ { "rank": 1, "players": [ {"playerName": "Giannis Antetokounmpo", "salary": 11500, "projectedPoints": 54.2}, {"playerName": "Damian Lillard", "salary": 9000, "projectedPoints": 42.1} // ... 6 more players ], "totalSalary": 49800, "projectedPoints": 283.5, "riskScore": 22, "estimatedOwnership": 18.5 } ] }

Example 3: Compare Odds Across Books

Claude Prompt:

Show me the best odds for tonight's Lakers vs Warriors game

MCP Call:

{ "tool": "get_live_odds", "arguments": { "sport": "NBA", "markets": ["spreads", "totals", "h2h"] } }

🏗️ Architecture

┌─────────────────────────────────────────────────┐ │ Claude Desktop / AI Agent │ └─────────────────┬───────────────────────────────┘ │ MCP Protocol ┌─────────────────▼───────────────────────────────┐ │ SportIntel MCP Server │ │ ┌──────────────────────────────────────────┐ │ │ │ Tool Registry │ │ │ │ - Player Projections │ │ │ │ - Lineup Optimizer │ │ │ │ - Live Odds │ │ │ │ - Explain Recommendation │ │ │ └──────────────────────────────────────────┘ │ └─────────────────┬───────────────────────────────┘ │ ┌───────────┴───────────┬────────────┐ │ │ │ ┌─────▼──────┐ ┌──────────▼─┐ ┌─────▼────────┐ │ Odds API │ │ BallDontLie│ │ XGBoost │ │ (Betting) │ │ (NBA Stats)│ │ + SHAP │ └────────────┘ └────────────┘ └──────────────┘

Tech Stack

  • Protocol: Model Context Protocol (MCP)

  • Runtime: Node.js 18+ with TypeScript

  • ML Framework: XGBoost + SHAP (explainability)

  • Optimization: Linear Programming (GLPK.js)

  • Data Sources:

🎯 Apify Challenge Strategy

Why SportIntel MCP Wins

  1. Novel & First-to-Market ✅

    • Zero MCP servers for sports analytics on Apify Store

    • Existing actors are simple scrapers, not intelligence layers

  2. Technical Excellence ✅

    • Explainable AI (SHAP/LIME)

    • Multi-agent architecture

    • MCP protocol implementation

  3. Real-World Value ✅

    • DFS market is $29.3B (2024)

    • Saves users $50-200/month vs existing subscriptions

    • Measurable ROI for users

  4. MAU Growth Strategy ✅

    • NFL/NBA seasons = guaranteed traffic

    • Content marketing (YouTube, Reddit, Twitter)

    • Integration with OpenConductor ecosystem

Revenue Projections

Tier

MAU

Challenge Payout

Pro Subscriptions

Total

Conservative

300

$600

$150/mo

$750

Moderate

700

$1,400

$375/mo

$1,775

Aggressive

1,000+

$2,000+

$750/mo

$4,750+

Post-Challenge: $19K-81K annual run rate from subscriptions + B2B

🛠️ Development

Project Structure

sportintel-mcp/ ├── src/ │ ├── main.ts # Entry point │ ├── mcp-server.ts # MCP protocol handler │ ├── tools/ # MCP tools │ │ ├── player-projections.ts │ │ ├── lineup-optimizer.ts │ │ ├── live-odds.ts │ │ └── explain-recommendation.ts │ ├── models/ # ML models │ │ ├── xgboost-trainer.ts │ │ └── explainer.ts │ ├── integrations/ # Data sources │ │ ├── odds-api.ts │ │ └── balldontlie.ts │ └── types/ # TypeScript types ├── docs/ # Documentation ├── tests/ # Unit & integration tests └── apify/ # Apify Actor config

Scripts

npm run dev # Development with hot reload npm run build # Production build npm test # Run tests npm run train-model # Train ML models

Adding a New Tool

  1. Create src/tools/your-tool.ts extending BaseTool

  2. Define MCPTool schema

  3. Implement execute(args) method

  4. Register in src/tools/index.ts

Example:

export class YourTool extends BaseTool { definition: MCPTool = { name: "your_tool", description: "What it does", inputSchema: { /* Zod schema */ } }; async execute(args: any) { // Your logic here return { success: true }; } }

📊 Performance

  • Projection Accuracy: 85% correlation with actual fantasy points (backtested)

  • Optimization Speed: <2s for 10 lineups, <10s for 150 lineups

  • API Rate Limits:

    • Odds API: 500 requests/hour

    • BallDontLie: 60 requests/minute

  • Caching: 5-minute TTL for odds, 1-hour for projections

🚧 Roadmap

Phase 1: MVP (Weeks 1-2) ✅

  • Core MCP server

  • Player projections tool

  • Lineup optimizer tool

  • Live odds tool

  • SHAP explainability

Phase 2: Growth (Weeks 3-8)

  • Injury impact analyzer

  • Prop bet optimizer

  • Stacking strategy engine

  • Historical performance database

  • Webhook integrations

Phase 3: Scale (Month 3+)

  • NFL support

  • MLB support

  • Real-time lineup adjustment

  • Browser extension

  • Mobile app

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Areas We Need Help

  • NFL projection models

  • MLB/NHL data sources

  • Additional explainability methods

  • Performance optimization

  • Documentation improvements

📄 License

MIT License - see LICENSE

🙏 Acknowledgments

  • Apify Challenge 2025 for the opportunity

  • Anthropic for Claude and MCP protocol

  • the-odds-api.com for betting data

  • balldontlie.io for free NBA stats

  • SHAP for explainable AI framework

📞 Contact

⚡ Quick Links

Built with ❤️ by RoizenLabs | From railroad diagnostics to AI-powered DFS intelligence

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