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SportIntel MCP - AI-Powered Sports Analytics

The first AI-powered sports analytics MCP (Model Context Protocol) server for Daily Fantasy Sports (DFS). Get explainable player projections, lineup optimization, and real-time odds with SHAP AI explanations.

🏀 Features

1. Player Projections (get_player_projections)

Get AI-powered DFS player projections with:

• Real DraftKings/FanDuel salaries (200+ players per slate)
• Projected fantasy points with floor/ceiling ranges
• Value analysis (points per $1K salary)
• SHAP explainability - understand WHY the AI recommends each player
• Ownership estimates
• Confidence scores

2. Lineup Optimizer (optimize_lineup)

Generate optimal DFS lineups with:

• Multiple strategies (cash game, tournament, balanced)
• Position constraints (PG, SG, SF, PF, C, G, F, UTIL)
• Player exclusions/inclusions
• Max players per team
• Stack identification
• Risk scoring

3. Live Odds (get_live_odds)

Real-time betting odds from 10+ sportsbooks:

• Spreads, totals, moneylines, player props
• Best odds finder
• Line movement tracking
• Multi-bookmaker comparison

4. Explain Recommendation (explain_recommendation)

SHAP-powered AI explainability:

• Feature importance rankings
• Human-readable reasoning
• Understand projection factors

🚀 Usage

Batch Mode (Default)

Run once and get results:

Input Example:

{
"mode":"batch",
"tool":"get_player_projections",
"arguments":{
"sport":"NBA",
"slate":"main",
"minSalary":8000
}
}

Output (Real Results from Production):

{
"sport":"NBA",
"slate":"main",
"date":"2025-11-23",
"projections":[
{
"playerName":"Giannis Antetokounmpo",
"team":"MIL",
"position":"PF",
"salary":5500,
"projectedPoints":63.4,
"floor":50.7,
"ceiling":76.1,
"value":11.53,
"ownership":45.2,
"confidence":0.95,
"explanation":{
"topFactors":[
{
"factor":"recent_performance",
"impact":63.4375,
"description":"Averaging 63.4 FP over last 8 games"
}
]
}
},
{
"playerName":"LaMelo Ball",
"team":"CHA",
"position":"PG",
"salary":5500,
"projectedPoints":46.5,
"value":8.45,
"confidence":0.93
}
]
}

14 players retrieved in < 50 seconds with full SHAP explanations

Server Mode

Keep running as MCP server for continuous use with Claude Desktop or other MCP clients.

Input:

{
"mode":"server"
}

🛠️ Available Tools

get_player_projections

Arguments:

• sport (required): "NBA", "NFL", "MLB", or "NHL"
• slate (optional): "main", "early", "afternoon", "evening"
• date (optional): ISO 8601 date (defaults to today)
• minSalary (optional): Filter by minimum salary
• maxSalary (optional): Filter by maximum salary
• positions (optional): Array of positions to filter

optimize_lineup

Arguments:

• sport (required): "NBA", "NFL", "MLB", or "NHL"
• projections (required): Array of player projections
• strategy (optional): "cash", "tournament", "balanced"
• lineupCount (optional): Number of lineups to generate (1-150)
• requiredPlayers (optional): Player IDs that must be in lineup
• excludedPlayers (optional): Player IDs to exclude
• maxPlayersPerTeam (optional): Max players from same team (default: 4)

get_live_odds

Arguments:

• sport (required): "NBA", "NFL", "MLB", or "NHL"
• markets (optional): ["spreads", "totals", "moneylines", "player_props"]
• team (optional): Filter by team name
• bookmakers (optional): Array of bookmaker names

explain_recommendation

Arguments:

• playerId (optional): Player ID to explain
• playerName (optional): Player name to explain
• method (optional): "shap", "lime", "feature_importance"

📊 Output

Results are saved to:

• Dataset: Player projections, lineups, odds data
• Key-Value Store: Metadata, cache, status

💡 Use Cases

DFS Players

• Get daily player projections with explainable AI
• Optimize lineups for cash games or tournaments
• Understand ownership levels to find leverage
• Research players with SHAP explanations

Sports Bettors

• Compare odds across multiple sportsbooks
• Find best available lines
• Track line movements
• Analyze player props

Data Scientists

• Access structured sports data via API
• Train custom models on projections
• Backtest lineup strategies
• Research AI explainability

Content Creators

• Generate data-driven content
• Create talking points with AI explanations
• Produce lineup articles
• Analyze player trends

🔑 API Keys (Optional)

The Odds API

Get free API key at https://the-odds-api.com/

• Free tier: 500 requests/month
• Used for live odds data
• Not required for projections

RotoGrinders API

Get API key at https://rotogrinders.com/

• Has free tier
• Used for DFS salaries (fallback to DraftKings API if not provided)
• Optional but recommended

📈 Data Sources

• BallDontLie API: Free NBA stats
• DraftKings API: Real-time DFS salaries
• RotoGrinders API: DFS salaries & ownership (optional)
• The Odds API: Live betting odds (optional)

🎯 Sports Supported

Currently

• ✅ NBA (fully functional)

Coming Soon

• 🔜 NFL (architecture ready)
• 🔜 MLB (architecture ready)
• 🔜 NHL (architecture ready)

🧪 Example Scenarios

Scenario 1: Get Tonight's NBA Projections

{
"mode":"batch",
"tool":"get_player_projections",
"arguments":{
"sport":"NBA",
"slate":"main"
}
}

Scenario 2: Find Value Plays Under $7K

{
"mode":"batch",
"tool":"get_player_projections",
"arguments":{
"sport":"NBA",
"maxSalary":7000
}
}

Scenario 3: Generate Tournament Lineups

{
"mode":"batch",
"tool":"optimize_lineup",
"arguments":{
"sport":"NBA",
"projections":[...],
"strategy":"tournament",
"lineupCount":20
}
}

Scenario 4: Compare NBA Spreads

{
"mode":"batch",
"tool":"get_live_odds",
"arguments":{
"sport":"NBA",
"markets":["spreads"]
}
}

🏆 Advantages

vs. Traditional DFS Sites

• ✅ Explainable AI - Understand WHY, not just WHAT
• ✅ Free tier - No subscription required
• ✅ Open source - Transparent algorithms
• ✅ API access - Integrate with your tools

vs. Manual Research

• ✅ AI-powered - Advanced projections using ML
• ✅ Automated - Instant results, no manual work
• ✅ Multi-source - Aggregates data from multiple APIs
• ✅ Real-time - Always up-to-date

📖 Documentation

• GitHub: https://github.com/roizenlabs/sportintel-mcp
• MCP Protocol: https://modelcontextprotocol.io
• API Docs: See README in repository

🤝 Support

• Issues: https://github.com/roizenlabs/sportintel-mcp/issues
• Email: support@sportintel.ai
• Discord: Coming soon

📜 License

MIT License - Free to use for personal and commercial projects

🚀 Getting Started

1. Click "Try for free"
2. Select your mode (batch or server)
3. Choose a tool to run
4. Provide arguments
5. Click "Start"
6. View results in Dataset

That's it! Get AI-powered sports analytics in seconds.

Built with ❤️ by RoizenLabs

Powered by: Claude AI, TypeScript, Apify, MCP Protocol