Fair Play Football Analytics is a sports data startup dedicated to bringing transparency and consistency to soccer officiating. Founder Theo Struthers used Baselight to merge and clean his existing datasets, enrich them with new match context, and eliminate time-consuming manual work. With Baselight’s structured data, custom queries, and merging tools, Fair Play transformed from a side project into a scalable analytics product ready for market. Today, Theo can focus on delivering referee fairness reports, powering prediction models, and expanding into new leagues with a reliable, repeatable data foundation.

TL;DR

Fair Play Football Analytics uses Baselight to power its consulting services for soccer refereeing organizations. By tapping into Baselight’s Ultimate Soccer Dataset, built-in query engine, and schema-merging capabilities, founder Theo Struthers was able to:

• Improve overall data quality and attribution accuracy across historical match data
• Enrich datasets with critical context like manager info, formations, and player-level metrics
• Publish consistent, scalable referee performance reports without building custom tooling
• Eliminate time-consuming manual data cleaning from his workflow
• Create a reliable foundation for monetization, reporting, and public data access at scale

Baselight isn’t just for performance analysts; it’s built for anyone turning structured sports data into edge.

From referee fairness scoring to predictive modeling and market forecasting, Baselight gives analysts, bettors, and traders the tools to cross-reference real-world and on-field data instantly.

The Problem

Fair Play Football Analytics was created to bring more transparency to how soccer matches are officiated. Founder Theo Struthers set out to build referee fairness metrics by analyzing how consistently referees call fouls, penalties, and cards across different teams and leagues. But while the vision was clear, executing it with publicly scraped data introduced major challenges.

Theo relied on multiple scraping tools and sources to build his initial datasets, but encountered widespread issues with data accuracy and structure. Players were being misattributed to the wrong teams, particularly in matches involving clubs with similar names like Manchester United and Manchester City, because of limitations in the source tables. On top of that, key match context like managers, formations, and player ratings was missing or inconsistently formatted, making deeper analysis difficult.

Most frustrating of all, every update required re-scraping, cleaning, and manually patching errors across a massive dataset of 100,000+ rows. Without reliable infrastructure, publishing referee reports or building a scalable product remained out of reach.

“I was spending way too much time trying to fix things instead of analyzing them.”
Theo Struthers, CEO

The Baselight Solution

When Theo discovered Baselight’s Ultimate Soccer Dataset, he reached out to see if it could help and quickly found more than just data. The Baselight team worked with him toward a complete solution that not only fixed the immediate issues but unlocked new potential for Fair Play.

By joining Baselight, Theo gained access to structured datasets containing referee assignments, match events, team formations, managers, odds, and player ratings, none of which were reliably available in his prior workflow.

One of the most impactful tools was Baselight’s Merger function, which let Theo combine his previously-sourced database with Baselight’s curated tables field-by-field. That allowed him to prioritize Baselight’s clean data where it mattered most (like team attribution) while preserving his existing workflows and calculations. As a result, he was able to:

• Fix over 2,000 player records across his 100,000+ row database
• Enrich his analysis with new dimensions like manager-referee relationships and advanced penalty data
• Query and download updated datasets in CSV or Parquet formats on demand

Baselight removed the complexity and friction from Theo’s data stack and provided a reliable foundation for growth.

The Outcome

With the help of Baselight, Fair Play Football Analytics is no longer just an idea; it’s a functioning analytics product ready for market. The data merger solved key quality issues, while the added match context opened the door to richer and more meaningful insights. What once took hours of manual cleaning can now be run as a simple query.

Theo is now gearing up to launch Fair Play’s public reports in time for the new Premier League season. He plans to publish league-wide referee fairness metrics, offer performance evaluations to clubs and referee associations, and eventually monetize the service through subscriptions or consulting.

• Baselight improved his data accuracy dramatically, correcting thousands of mislabeled records
• New features like formation tracking and duel data enriched the reports beyond what was previously possible
• Most importantly, Theo can now focus on scaling insights instead of fixing pipelines.

“The merger feature with Baselight didn’t just improve my data; it saved me weeks of manual cleanup and gave me the tools to finally build something real.”
Theo Struthers, CEO

What’s next

Theo is preparing to launch the public-facing version of Fair Play this August, just in time for the new Premier League season. Powered by Baselight, he plans to:

• Publish fairness scores for referees and teams
• Offer analytics to clubs and officiating bodies
• Explore monetization via subscriptions or direct consulting
• Expand to new leagues beyond the EPL

Why It Matters

Fair Play is proof that one person—equipped with structured data and the right tools—can launch a real analytics business. For sports tech teams, prediction modelers, and performance consultants, Baselight replaces weeks of manual cleaning and engineering with instant access to structured, clean, and remixable data.

And for prediction market users and bettors, Baselight opens a powerful new frontier:

• Rapidly prototype and test betting signals using real match context
• Merge odds with in-game referee behavior to quantify edge
• Build richer, data-backed hypotheses around bias, fatigue, or underdog variance
• Remove the guesswork from model validation and backtesting

If you’re building with data, Baselight gives you clean pipelines, flexible querying, and time back to focus on edge—not engineering.

Ready to Build with Sports Data?

Whether you’re analyzing referee trends, building predictive models, or powering fan-facing apps, Baselight gives you everything you need to work with structured sports data without the hassle. Join analysts, developers, and bettors who are already using Baselight to unlock insights from formations, match events, odds, and more. No scraping, no patching, no pipelines—just clean, powerful data you can query and scale.

Start exploring the Ultimate Soccer Dataset or the Ultimate Basketball Dataset today and turn your idea into impact.