# 🏈 SportIntel MCP Server
**AI-Powered Sports Intelligence for Claude & AI Agents**
[](https://apify.com/challenge)
[](https://modelcontextprotocol.io)
[](https://opensource.org/licenses/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
```bash
# 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):
```json
{
"mcpServers": {
"sportintel": {
"command": "node",
"args": ["/path/to/sportintel-mcp/dist/main.js"],
"env": {
"ODDS_API_KEY": "your_api_key_here"
}
}
}
}
```
### Run Standalone
```bash
# 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:**
```json
{
"tool": "get_player_projections",
"arguments": {
"sport": "NBA",
"slate": "main",
"includeExplanations": true
}
}
```
**Response:**
```json
{
"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:**
```json
{
"tool": "optimize_lineup",
"arguments": {
"sport": "NBA",
"salaryCap": 50000,
"lineupCount": 3,
"strategy": "cash",
"projections": [/* from previous call */]
}
}
```
**Response:**
```json
{
"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:**
```json
{
"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**:
- [the-odds-api.com](https://the-odds-api.com) - Real-time odds
- [balldontlie.io](https://balldontlie.io) - NBA stats
- ESPN scraping (via Apify Actor)
---
## 🎯 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
```bash
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:
```typescript
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) ✅
- [x] Core MCP server
- [x] Player projections tool
- [x] Lineup optimizer tool
- [x] Live odds tool
- [x] 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](./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](./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
- **Website**: [sportintel.ai](https://sportintel.ai)
- **GitHub**: [roizenlabs/sportintel-mcp](https://github.com/roizenlabs/sportintel-mcp)
- **Twitter**: [@SportIntelAI](https://twitter.com/SportIntelAI)
- **Discord**: [Join Community](https://discord.gg/sportintel)
---
## ⚡ Quick Links
- [API Reference](./docs/api-reference.md)
- [Claude Desktop Setup Guide](./docs/quickstart.md)
- [Examples](./docs/examples/)
- [Apify Actor Page](https://apify.com/roizenlabs/sportintel-mcp)
---
**Built with ❤️ by RoizenLabs** | From railroad diagnostics to AI-powered DFS intelligence
