AI Meets Sports Predictions at Scale
Benfica and Sporting are neck-and-neck and the title will be decided this Saturday. Could you have predicted that? We built an AI engine that does just that!
Project Overview
CRON STUDIO partnered with a forward-thinking client in the sports prediction space to build an AI-driven service that powers their platform with accurate, data-backed predictions across multiple prediction markets — 1x2, Goals, Corners, Cards, and more. The system is in-production, active, in continuous improvement, and already delivering value to end-users.
Alongside the prediction models, we created a statistics engine that computes detailed match metrics, streaks, and tendencies at scale. By re-using computations and optimising data storage, the system delivers rich, near-real-time insights for thousands of games in parallel.
From system design and model development to close collaboration with the client’s engineering team for integration and documentation, we ensured every component worked seamlessly and delivered tangible value.
Key Results
+3% average improvement over direct competitors in both accuracy and logarithmic loss for the 1x2 market — particularly in the major football leagues — when benchmarked against the leading prediction providers in the sector.
200+ competitions supported and continuously updated.
16M+ statistics and trends generated each season.
What We Built
AI-Driven Prediction Models
Developed models tailored to multiple prediction markets, ensuring high quality predictions.
The models were highly focused on explainability, so users can not only see predictions, but also understand the reasoning behind them.
Robust Data Pipeline
Ingested data from various sources, such as match stats, player trends, and historical records.
Cleaned and curated the data, addressing missing or strange patterns to ensure high-quality inputs.
Created an efficient, scalable system to process data and train models in parallel for thousands of games.
Feature Engineering
Spent significant time analysing and understanding the data.
Designed features that capture temporal perspectives, like form streaks and historical trends, to enrich the models.
Focused on identifying the most impactful features for each market, ensuring optimal model performance.
Insights and Extended Statistics System
We developed a scalable engine to generate detailed match statistics, including streaks and tendencies.
Optimised computations by reusing previously computed values and strategically storing data close to processing units, ensuring a near-real-time extended vision of each match.
Delivered a set of insights that empower users with deeper, context-rich information alongside the sports predictions.
Seamless Integration with the Client platform
Delivered detailed, easy-to-follow documentation to guide their team in integrating the AI service into their platform.
Provided close support during the integration process, ensuring our service fit seamlessly into their workflow and met all expectations.
Challenges and Solutions
Diverse Markets: eachmarket required tailored models and data perspectives, demanding robust feature engineering and modelling strategies.
Scalability: handling large datasets and supporting predictions for thousands of games required efficient parallel processing and a scalable system.
Integration and Usability: ensuring the service worked seamlessly with the existing client platform required strong collaboration, clear communication, and well-crafted documentation.
How did we solve it?
Adopted a modular design that allowed for the parallel development and integration of both prediction and statistics systems.
Leveraged cloud technologies and scalable infrastructure to ensure efficient data processing.
Optimised computation workflows by caching results and strategically placing data to reduce latency.
Close collaboration with the clients’ team to align our technical solutions with their operational needs.
Project Timeline and Key Milestones
Exploratory Research and Market Analysis (Month 1)
Reviewed existing AI-powered prediction models to understand the landscape.
Identified key gaps and opportunities for improvement in current solutions.
Defined a clear strategy to differentiate our approach and align with clients’ vision.
Proof of Concept Development (Month 2)
Built an initial prototype to test both prediction accuracy and the feasibility of real-time statistics generation.
Validated the models and statistics computations with historical data, demonstrating significant improvements.
Shared early results with the client to gather feedback and set priorities for subsequent development phases.
AI Model Optimisation and Statistics Engine (Month 3)
Fine-tuned the AI algorithm to handle edge cases and complex scenarios..
Integrated additional data sources to make the model even stronger.
Initiated the development of the statistics engine to generate detailed match insights, ensuring its computational feasibility.
Infrastructure and Scalability Development (Months 4-5)
Built a scalable infrastructure capable of processing thousands of matches every day, covering both predictions and statistics.
Optimised the statistics engine to reuse computations and store data efficiently, ensuring minimal latency.
Continued refining both systems, ensuring they could run in parallel.
Full Launch and Post-Launch Optimisation (Month 6)
Rolled out the complete solution, offering AI-powered predictions and insights at scale.
Monitored system performance in a live environment, making iterative adjustments based on user feedback and performance metrics.
Collaborated with the client on planning future updates and enhancements based on real-world usage data and evolving market needs.
Tech We Used
Python for core development
Django for building REST APIs
RQ and RQ Scheduler for asynchronous processing, enabling efficient task management and scheduling in our data pipeline.
AWS Infrastructure with IaC (Terraform).
SageMaker for model training and serverless data processing.
Machine Learning Models for classification and regression across prediction markets.
Feature engineering to capture temporal and contextual insights
Parallel processing frameworks to manage high volumes of data and computation.
Caching and Optimised Storage Solutions to ensure that statistical computations are reused efficiently and data retrieval is fast.
What We Learned
Our work on this project highlighted the power of collaboration in delivering cutting-edge AI solutions. By combining advanced technology with a user-focused approach, we actively cooperated to create a platform that goes beyond predictions to provide actionable insights.
It may sound clichéd, but it is undoubtedly true: this project wasn’t just about solving technical challenges — it was about building a partnership. With detailed documentation, close support and our agile approach, we ensured our service was easy for their team to integrate, use, and improve upon.
Article written by João Braz Simões
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