Pro Gambling – Sports Forecasting Platform

Overview of the project

Pro Gambling came to us with a problem that sits at the hard end of sports betting infrastructure: not bet placement, but prediction. The product needed to ingest live match feeds, model outcome probabilities across 500+ data points per event and surface results to professional bettors and trading desks with sub-200ms latency. Built right, this is a platform processing 2TB of sports data per day at 99.95% uptime. Built wrong, it is a stale analytics dashboard that nobody opens past matchday one.

The challenge

Sports bettors make decisions in seconds during live events. Most analytics platforms deliver insights with 30-60 second delays – predictions that are worthless by the time they arrive. Pro Gambling needed a forecasting engine fast enough to stay actionable during Champions League matchdays with 40,000 concurrent users hitting the platform simultaneously. Every second of degradation during peak traffic is a trust event. The system also needed to retrain ML models continuously on incoming match results without taking prediction endpoints offline during retraining cycles.

Engineering approach

We built around a Java/Spring Boot backend streaming live match events through Apache Kafka into Apache Flink-based ML models that retrain automatically every 24 hours on the latest results and market movements. The inference layer handles 8,000+ prediction requests per second at sub-200ms latency – keeping forecasts actionable during the most pressure-intense moments of live events. Observability-first from the start: every prediction logs model version, input feed snapshot and decision context for audit and reproducibility.

Tech stack

• Backend: Java with Spring Boot for APIs, forecasting logic and data ingestion pipelines.
• Streaming: Apache Kafka for live match events and odds updates. Apache Flink for real-time stream processing, metric calculation and forecast triggers.
• Storage: Apache Cassandra for historical match data, user activity and prediction results at scale. Apache Pinot for real-time analytics dashboards. Redis and Ignite for low-latency caching of live data.
• Frontend: React and Next.js optimised for fast navigation and real-time probability updates. Clean interface for desktop and mobile.
• Infrastructure: Kubernetes with Istio for traffic management on AWS. Terraform for infrastructure-as-code. Elastic and fault-tolerant at matchday scale.

Key features delivered

Real-time sports forecasting

The forecasting engine analyses live match data and odds changes continuously. Users receive timely insights reflecting current game state rather than pre-match models frozen at kickoff. Forecasts process 500+ data points per match including player form, historical stats and real-time conditions – the same inputs a professional analyst would use, delivered automatically and in real time.

Unified analytics and insights

The platform integrates historical statistics, live data and model outputs into a single dashboard. Users see not just the prediction but the underlying data driving it – confidence intervals, model inputs, comparable historical outcomes. Traders understand why the platform is saying what it is saying, which is what builds the trust that drives retention.

Scalable APIs and data pipelines

Pro Gambling exposes APIs for external data access and partner integrations. The pipeline architecture – Kafka ingest, Flink processing, Cassandra storage – scales horizontally with event volume. No special configuration required for peak traffic; the system absorbs Champions League matchdays the same way it handles a Tuesday afternoon fixture.

Business results

• 67% directional prediction accuracy across major sports leagues. The algorithm processes 500+ data points per match, giving users an analytical edge that sustains engagement and return visits.
• 8,000+ prediction requests per second handled at sub-200ms model inference during live events without degradation.
• 40,000+ concurrent users sustained during Champions League matchdays. Peak traffic is now the platform’s strongest engagement moment, not its weakest.
• 2TB+ of sports data processed daily at 99.95% uptime. The pipeline runs without human intervention across normal and peak operating conditions.
• Automated model retraining every 24 hours incorporating latest match results and market movements. Predictions stay fresh without manual ML operations work.

Why it worked

Forecasting platforms fail at one of two points: the pipeline that cannot keep up with live data, or the model layer that cannot retrain without going dark. We resolved both with architecture chosen for the problem, not for familiarity. Kafka and Flink gave us a streaming backbone built for exactly this event volume. Cassandra gave us storage that does not degrade under concurrent writes at matchday scale. Pinot gave the analytics layer real-time query performance without batching delays. Every component earns its place. The trading desk got a system they could trust. The operator got a platform that scales with revenue instead of fighting it at exactly the moments that matter most.