Social Sports Prediction Platform for Skill-Based Forecasting
Overview of Our Client
Our client is a digital sports product company operating in the sports analytics and entertainment space. The company wanted to create an application that would engage sports fans beyond passive viewing, allowing them to actively predict match outcomes and evaluate their accuracy over time. The product needed to feel competitive and exciting, yet, remain compliant and risk-free, avoiding direct wagering.
Existing fan engagement tools lacked real-time feedback, analytical depth, and structured performance evaluation. The client sought a platform that could simulate the analytical aspects of sports betting and function as a standalone learning and engagement product.
Challenge
The client aimed to build a prediction platform capable of supporting live sports events and thousands of concurrent users, but at the same time maintaining data integrity and fair competition. Thus, the main challenges included:
- Processing and broadcasting live in-game events, shifting odds, and community prediction updates with consistently low latency.
- Making sure leaderboards and crowd sentiment charts update equally for all users, even during traffic spikes, without infrastructure costs getting out of control.
- Maintaining accurate, tamper-resistant leaderboards by preventing prediction spam, fraud, and artificial score inflation through rate limiting and anomaly detection.
- Securing and validating third-party sports data feeds to preserve the integrity of analytical insights.
- Balancing a risk-free, skill-building experience with a compliant pathway to real-world social betting systems without compromising analytical neutrality.
- Retaining users long enough to develop measurable forecasting competence through gamification and proof of analytical edge.
Main Goals
To overcome all these challenges, the project set the following goals:
- Let users make predictions that instantly update as the match is happening.
- Visualize collective sentiment and “wisdom of the crowd” dynamics.
- Rank users based on long-term accuracy rather than short-term luck.
- Provide transparent performance metrics that encourage skill development.
- Build a technically decent foundation suitable for high-traffic sports events.
- Position the platform as an analytical gateway to social betting ecosystems.
Project Overview
We developed a real-time social prediction platform that allowed users to submit match predictions, track outcomes, and compare their performance against the broader community. The system unceasingly updated sentiment indicators, leaderboards, and accuracy scores as events unfolded.
The platform was made as a skill-based environment rather than a betting product. It emphasized learning, analysis, and competition through data visibility and performance tracking. Besides, the solution was created in a way to support traffic spikes during huge sports events.
- Region: the US
- Industry: Sports Analytics / Social Betting
- Timeline: Continuous delivery with iterative feature releases
Solution
We set up a real-time, cloud-based system architecture that could handle live sports data, user predictions, and leaderboard calculations with very short time intervals. Individual forecasts on the platform were transformed into collective sentiment indicators that helped users understand how opinions moved after major in-game moments, such as goals, penalties, or injuries.
As a measure for data integrity, the platform offered features like validation rules, rate limiting, anomaly detection, and interaction flow control. To make the competition more fascinating, we incorporated some gaming features (like leaderboards, accuracy streaks, and performance history) that functioned as a means of skill-based rivalry rather than impulsive behavior.
Key Platform Features
- Live prediction submission and result evaluation.
- Live sentiment tracking reflecting community consensus.
- Merit-based leaderboards where the main criterion was long-term prediction accuracy.
- Comprehensive performance metrics and prediction history for each user.
- Security mechanisms to prevent manipulation and spam.
- Real-time communication adapted to traffic related to live sporting events.
Results
The platform successfully launched as a high-engagement, analytics-driven sports prediction product. Users showed a significant improvement in their predictive skills as they had access to their own performance metrics and real-time crowd sentiment, which they learned from. The real-time, cloud-based infrastructure was able to operate even during the heavy traffic that usually occurred around huge sports events; thus, the selected architecture proved to be reliable.
- Stable low-latency performance during high-traffic live matches.
- Intense user interaction brought on by real-time leaderboards and sentiment insights.
- Clear signals of skill development among users who were consistently top-ranked.
- A lasting community that revolved around analysis and learning rather than financial risk.
- A behavioral data layer that was validated and could be utilized by or be in sync with real-world social betting systems.
Technology Stack
We chose the technology stack below to fulfill all the requirements of the project:
Backend
- Node.js
Frontend
- React
Databases
- Amazon Aurora/DynamoDB
Cloud Infrastructure
- AWS
Monitoring & Scaling
- Auto-scaling groups, logging, and alerting
Related Cases
Core Team
Project Manager: Ensured smooth delivery, planning of the roadmap, and communication with the stakeholders.
- Solution Architect: Created the design of the architecture, data flows, and strategy for scalability.
- Backend Developers: Carried out the implementation of prediction logic, live messaging, and analytics pipelines.
- Frontend Developers: Developed the interactive dashboards, leaderboards, and visualization components.
- QA Engineers: Verified data accuracy, system stability, and fair competition mechanisms.