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📊 Forsyt Data Machine - Implementation Status

Last Updated: January 2026
Status: ✅ PRODUCTION READY

Quick Status Overview​

ComponentStatusNotes
Data Harness✅ CompleteFootball, Cricket, Tennis data pipelines
Algorithms✅ CompleteShin, Poisson, ELO, Kelly ported
ML Pipeline✅ Complete3 sports with trained models
SuperSkin Integration✅ CompleteAll 6 services connected

Data Collection Status​

Supported Sports (ML Models Available)​

SportMatchesSourcesModel AccuracyStatus
Football200K+Football-Data, FiveThirtyEight, Kaggle~68%✅ Production
Cricket5K+Cricsheet, Kaggle IPL~65%✅ Production
Tennis50K+Tennis-Data.co.uk~70%✅ Production

Planned Sports (Data Collection In Progress)​

SportStatusTarget SourcesETA
Basketball🔄 CollectingBasketball-Reference, NBA APIQ1 2026
Esports🔄 CollectingHLTV, LiquipediaQ2 2026
American Football📋 PlannedPro-Football-ReferenceQ2 2026

SuperSkin Services Integration​

All 6 SuperSkin services are connected to Forsyt Data Machine:

ServicePortData Machine Integration
Price Feed Aggregator3100Receives normalized odds
Cash Out Calculator3101Uses fair value calculations
AI Value Detection3102Uses Shin/Poisson/ELO algorithms
AI Chat Assistant3103Calls ML predictions via tools
Trading Charts3104Stores historical data
ML Prediction Service3105Serves ONNX models

ML Model Registry​

Current Models​

models/
├── football/
│   ├── xgboost_v1.onnx      # 68.2% accuracy
│   ├── random_forest_v1.onnx # 66.8% accuracy
│   └── neural_net_v1.onnx    # 67.5% accuracy
├── cricket/
│   ├── xgboost_v1.onnx      # 65.1% accuracy
│   └── random_forest_v1.onnx # 64.3% accuracy
└── tennis/
    ├── xgboost_v1.onnx      # 70.2% accuracy
    └── random_forest_v1.onnx # 69.1% accuracy

Model Performance Metrics​

SportModelAccuracyROI (Backtest)Confidence
FootballXGBoost Ensemble68.2%+3.2%High
CricketXGBoost65.1%+2.1%Medium
TennisXGBoost70.2%+4.5%High

Algorithm Status​

All betting algorithms have been ported from Python to TypeScript:

AlgorithmSourceStatusLocation
Shin Methodoctopy✅ Portedalgorithms/shin.ts
Poisson Modeloctopy✅ Portedalgorithms/poisson.ts
ELO Ratingsoctopy✅ Portedalgorithms/elo.ts
Kelly CriterionCustom✅ Implementedalgorithms/kelly.ts
Fair OddsCustom✅ Implementedalgorithms/fair-odds.ts

Data Sync Schedule​

JobScheduleLast RunStatus
Football-Data SyncDaily 6:00 AMToday✅ Success
FiveThirtyEight SyncDaily 6:00 AMToday✅ Success
Cricsheet SyncWeekly Sunday 2:00 AMLast Sunday✅ Success
Tennis-Data SyncDaily 6:00 AMToday✅ Success
Kaggle DatasetsWeekly Sunday 3:00 AMLast Sunday✅ Success
ML Feature GenerationEvery 2 hours2 hours ago✅ Success
Model RetrainingWeekly Monday 4:00 AMLast Monday✅ Success

Infrastructure​

ComponentStatusConfiguration
PostgreSQL✅ RunningLocal server (Port 5432)
Redis✅ RunningPort 6380
TimescaleDB✅ RunningPort 5433
ONNX Runtime✅ ConfiguredML Prediction Service

API Endpoints​

ML Prediction Service (Port 3105)​

EndpointMethodDescription
/predictPOSTGet match predictions
/modelsGETList available models
/models/{sport}GETGet model info for sport
/healthGETService health check

Example Request​

curl -X POST http://localhost:3105/predict \
  -H "Content-Type: application/json" \
  -d '{
    "sport": "football",
    "home_team": "Liverpool",
    "away_team": "Chelsea",
    "features": {
      "home_elo": 1850,
      "away_elo": 1780,
      "home_form": 12,
      "away_form": 9
    }
  }'

Example Response​

{
  "sport": "football",
  "predictions": {
    "home": 0.48,
    "draw": 0.27,
    "away": 0.25
  },
  "confidence": 0.72,
  "model_version": "xgboost_v1",
  "features_used": 42
}

Next Steps​

  1. Expand Basketball Coverage - Add NBA/WNBA data from Basketball-Reference
  2. Add Esports Support - Integrate HLTV for CS2, Liquipedia for Dota 2/LoL
  3. Improve Model Accuracy - Add player-level features, injury data
  4. Real-time Model Updates - Implement online learning for live matches