AI sports prediction that ships to production
Production-grade ML prediction systems for sports tech, betting platforms, and analytics teams. Three sports deployed in one month.
- Soccer Grade A Over/Under win rate
- 89.2%
- NBA Grade A moneyline ROI
- +18.5%
- Accuracy across Soccer, NBA and NASCAR
- 65%
- Concept to production
- 1 month
In short
What does Agility build for sports analytics?
Want to see our sports-prediction work as a live product? Explore Agility Picks, our AI match-prediction app for the FIFA World Cup 2026.
Services
What we build
Four service areas covering the full sports analytics stack, from raw data to live production predictions.
Multi-sport prediction platforms
Production-grade ML models trained on millions of historical records, calibrated for accuracy, not inflated for demo impressiveness.
- Soccer ensemble models (70K+ matches, 10 leagues)
- NBA XGBoost points and moneyline prediction models
- NASCAR track-type-specific model selection
- Custom sport modelling for new domains
Feature engineering and data integration
The quality of prediction starts with the quality of features. We build the pipelines that turn raw sports data into predictive signals.
- Historical data pipelines: FootyStats, Sportradar, SportsDataIO
- Pre-match feature engineering at scale
- Real-time odds integration and movement tracking
- Multi-source data fusion with deduplication
Automated prediction pipelines
Daily automated pipelines that fetch data, generate predictions, store results, validate output, and settle grades, without manual intervention.
- GitHub Actions automation: Fetch, Predict, Store, Validate
- Prediction storage and automated grading settlement
- Automated result collection and performance tracking
- Model performance monitoring and alerting
ROI and performance analytics
Confidence-based grading systems and financial performance analytics that tell you what is working and what needs recalibration.
- Confidence-based grading systems (A+ through D)
- Sharpe ratio, variance, and risk analytics
- Break-even analysis and ROI tracking by grade
- Model interpretability and feature importance reporting
Results
Live production results
Calibrated figures from live production grading, not backtests.
Soccer: 89.2%
Grade A Over/Under win rate
+0.28 units per bet
NBA: +18.5%
Grade A moneyline ROI
72.3% win rate
NASCAR: 58.7%
Top 10 accuracy
Track-specific models
All sports: 1 month
Time to production
Three sports shipped
Technology
Technology stack
Ensemble ML at the core, automated pipelines end to end.
- XGBoost
- LightGBM
- RandomForest
- Neural Networks
- FootyStats
- Sportradar
- SportsDataIO
- The Odds API
- Python
- GitHub Actions
- Azure PostgreSQL
- Power BI
- Pandas
- Platt Scaling
Who we build for
Teams that use our prediction systems
Sports technology companies
Prediction APIs and analytics engines as a product layer for sports tech platforms.
Betting and trading platforms
ML-powered edge identification for sports trading and market-making operations.
Sports organisations and scouts
Performance analytics and player evaluation models built on historical match data.
Media and content platforms
Predictive content, match previews, and analytical storytelling tools.
Why Agility
Why sports teams choose Agility
Production, not research
We ship systems that run daily in production, not Jupyter notebooks. GitHub Actions automation, PostgreSQL output, live grading.
ROI-first model design
Models are calibrated for real-world betting value, not academic accuracy metrics. Grade A selections outperform across all sports.
Multi-sport architecture experience
Soccer, NBA, and NASCAR shipped in one month. We know how to parallelise sport-specific modelling without duplicating effort.
End-to-end ownership
Data collection, feature engineering, model training, pipeline automation, and analytics. One team, no gaps.
FAQ
Sports analytics AI: common questions
Data needs, time to production, accuracy, APIs and other sports.
What data do you need to start building?
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.
How long does it take from data to a production pipeline?
Approximately 4 to 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.
What accuracy should I expect?
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.
Can you expose predictions via API?
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.
Can you build for sports not listed?
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.
Ready to build your sports prediction system?
Tell us your sport, data sources, and use case. We'll scope a plan in 48 hours.