Production-grade ML prediction systems for sports tech, betting platforms, and analytics teams. 89.2% win rate on Soccer Grade A. Three sports deployed in one month.
Four service areas covering the full sports analytics stack — from raw data to live production predictions.
Production-grade ML models trained on millions of historical records — calibrated for accuracy, not inflated for demo impressiveness.
The quality of prediction starts with the quality of features. We build the pipelines that turn raw sports data into predictive signals.
Daily automated pipelines that fetch data, generate predictions, store results, validate output, and settle grades — without manual intervention.
Confidence-based grading systems and financial performance analytics that tell you what is working and what needs recalibration.
Calibrated figures from live production grading — not backtests.
Ensemble ML at the core, automated pipelines end-to-end.
Prediction APIs and analytics engines as a product layer for sports tech platforms
ML-powered edge identification for sports trading and market-making operations
Performance analytics and player evaluation models built on historical match data
Predictive content, match previews, and analytical storytelling tools
We ship systems that run daily in production — not Jupyter notebooks. GitHub Actions automation, PostgreSQL output, live grading.
Models are calibrated for real-world betting value, not academic accuracy metrics. Grade A selections outperform across all sports.
Soccer, NBA, and NASCAR shipped in one month. We know how to parallelise sport-specific modelling without duplicating effort.
Data collection, feature engineering, model training, pipeline automation, and analytics — one team, no gaps.
We need historical match or event results with pre-match statistics available for both participants. The more history and the richer the feature set, the better the model performance. We can advise on the best data sources for your target sport.
Approximately 4–6 weeks per sport for a full pipeline including feature engineering, model training, validation, and automation. We shipped three sports in one month by running in parallel — so timeline depends on scope, not just complexity.
Our Soccer Grade A Over/Under model achieves 89.2% win rate. NBA Grade A moneyline achieves 72.3% win rate with +18.5% ROI. These are calibrated figures from live production grading — not backtest inflation.
Yes. Our PostgreSQL output can be wrapped in REST API endpoints, allowing your platform or application to query predictions programmatically. We can build this as part of the engagement.
Yes. Cricket, tennis, rugby, and esports are all viable given appropriate historical data with pre-event statistics. Contact us with your sport and we will advise on data requirements and expected model performance.
Tell us your sport, data sources, and use case. We'll scope a plan in 48 hours.
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