Replay Ratings: Spoiler-Free Football Match Ratings
Summary
Replay Ratings is a football data visualisation project designed around a simple user problem: helping fans identify the most entertaining matches to watch after they have aired without spoiling the result. Using third-party football data APIs, the platform will analyse match statistics and apply a custom scoring algorithm to generate an entertainment rating out of 100. Users can quickly determine whether a fixture is worth watching in full, watching highlights, or skipping entirely, with scores hidden by default until manually revealed.
The Challenge
The challenge is translating raw football statistics into a genuinely useful entertainment metric rather than simply displaying existing data. This requires designing an algorithm that meaningfully weights factors such as goals, shots, possession swings, cards, xG, momentum, and other indicators to reflect perceived match excitement. A secondary challenge is building prediction tooling for future fixtures and tracking forecast accuracy over time to create an engaging repeat-use data product rather than a static dashboard.
Product Rationale
Problem-led data visualisation
Rather than building a generic stats dashboard, the project focuses on solving a specific user need by transforming complex football data into a simple and actionable watchability score.
Custom scoring algorithm
The core product differentiator is a proprietary weighting model designed to interpret multiple data points into a single entertainment-focused rating, creating a more valuable output than raw statistics alone.
Spoiler-free UX design
Scores and match outcomes remain hidden unless revealed, aligning the product experience directly with the intended use case of preserving surprise for replay viewers.
Recurring automated content
Scheduled API pulls and automated data refreshes support continuously updating fixtures, predictions, and ratings without manual intervention.
Tech Stack
Key Decisions
Algorithm over raw statistics: Prioritised interpretation and scoring logic over displaying raw stat tables so the platform provides opinionated value rather than simply repackaging API data.
Prediction engine extension: Included future match prediction functionality to expand repeat engagement and create an additional performance-tracking data layer beyond retrospective analysis.
Database-backed history tracking: Historical fixture and prediction data will be stored to allow long-term performance analysis, trend tracking, and algorithm refinement over time.
Ad-friendly platform model: The content-driven structure creates optional future monetisation opportunities through display advertising without requiring core product changes.
Project Notes
No two projects solve the same problem, so each case study emphasises different aspects of delivery depending on what was most relevant to the challenge. Supporting visuals and implementation details are included here to provide additional context behind the final outcome.
Visuals
