EraScore
Executive Summary
Vision Statement
To become the definitive source for fair, transparent, and insightful cross-era sports performance analytics, fostering deeper understanding and appreciation of athletic achievement.
Problem Summary
Comparing athlete performance across different eras in sports like Formula 1 is fraught with inaccuracies due to evolving points systems, race formats, and season lengths. For example, rookie records are often misrepresented when commentators and fans cite raw points totals without accounting for changes in scoring rules, as seen in the debate between Antonelli's 2025 and Hamilton's 2007 rookie seasons. This leads to misleading narratives and diminishes the ability to fairly evaluate historical achievements.
Proposed Solution
EraScore is a sports analytics platform that ingests historical and current athlete performance data, then normalizes results across different scoring systems and formats. The platform utilizes rule-based conversion algorithms, context-aware adjustments (such as race length, number of events, and bonus point rules), and visualizations to present accurate, apples-to-apples comparisons. This empowers fans, commentators, analysts, and historians to make objective assessments of athlete performance, regardless of era.
Market Analysis
Target Audience
The ideal user is a sports analyst, commentator, or dedicated fan who regularly engages with historical and current sports data—especially in dynamic environments like Formula 1, where scoring systems and formats frequently change. Secondary audiences include sports historians, journalists, and content creators who require accurate context for storytelling and analysis.
Niche Validation
The Reddit thread demonstrates strong, organic demand for this solution. The original post and top-voted comments highlight widespread frustration with misleading comparisons based on raw points totals, and multiple users independently attempt manual normalization. The upvote count (3898), high upvote ratio (0.95), and depth of discussion (243 comments) all indicate a significant, underserved pain point among engaged F1 fans and analysts.
Google Trends Keywords
Market Size Estimation
Focusing on digitally engaged F1 fans, analysts, and content creators in English-speaking markets, the serviceable available market is estimated at 2–5 million users, including the top 5% most active fans and all professional commentators/journalists.
Assuming an initial penetration of 1% of the SAM (20,000–50,000 users) within the first 2 years, targeting early adopters via partnerships with sports media, analytics firms, and influencer channels.
The global market for sports analytics is projected to exceed $8.4 billion by 2027, with a CAGR of over 20% (source). Formula 1 alone boasts over 500 million fans worldwide, and similar needs exist in other sports with evolving scoring systems (e.g., NFL, NBA, cricket).
Competitive Landscape
There are no direct, comprehensive platforms that normalize historical sports data across scoring systems for public use. Current solutions are fragmented:
- Formula1Points.com and similar sites allow basic point system comparisons, but lack deep normalization, user-friendly visualizations, or cross-sport capabilities (Formula1Points.com).
- Stats Perform and Opta offer advanced analytics but do not provide consumer-facing, era-normalized comparisons.
- FanGraphs and Basketball Reference provide advanced stats for other sports, but their normalization is often limited to single-sport metrics and lacks customizable rulesets.
EraScore’s value lies in its cross-era, multi-sport normalization engine and intuitive, shareable analytics.
Product Requirements
User Stories
As a fan, I want to compare two drivers’ rookie seasons across different points systems, so I can understand who performed better.
As a commentator, I need to quickly generate era-adjusted stats for a broadcast segment.
As a journalist, I want to visualize normalized season results for historical context in my articles.
As a data scientist, I want to export normalized datasets for custom analysis.
MVP Feature Set
Input any athlete/season and normalize performance data across selectable scoring systems.
Automated conversion logic for major sports (starting with Formula 1).
Interactive, shareable visualizations (bar chart, line chart, etc).
Export comparisons as images or CSV.
Basic user authentication and dashboard.
Non-Functional Requirements
Fast response time for data queries (<2s for standard comparisons).
High availability (99.5% uptime).
Data accuracy and auditability (clear documentation of normalization rules).
Scalable backend to support spikes during major sporting events.
Key Performance Indicators
Number of normalized comparisons generated per month.
User retention rate (monthly active/registered users).
Conversion rate from free to paid tier.
Number of media mentions or B2B partnerships.
Average response time for analytics queries.
Data Visualizations
Visual Analysis Summary
The chart below visually demonstrates how raw points totals can be misleading by comparing rookie season performances for Hamilton (2007) and Antonelli (2025) under both original and normalized (2025 system) scoring. This highlights the need for normalization and the value of EraScore.
Loading Chart...
Go-to-Market Strategy
Core Marketing Message
Stop arguing over misleading stats—get the real story behind athlete performance with EraScore’s era-normalized analytics.
Initial Launch Channels
- Launch targeted posts in r/formula1, r/motorsports, and r/dataisbeautiful, highlighting viral comparisons and visualizations.
- Partner with F1-focused YouTube/Twitch creators for demo segments.
- Submit to Product Hunt and reach out to analytics-focused sports podcasts.
Strategic Metrics
Problem Urgency
High
Solution Complexity
Medium
Defensibility Moat
Proprietary normalization algorithms, comprehensive historical database, and potential network effects as the platform becomes the standard reference for media and fans.
Source Post Metrics
Business Strategy
Monetization Strategy
Freemium model:
- Free tier with limited comparisons and basic visualizations.
- Subscription tier ($10–$20/mo) unlocks advanced normalization, custom analytics, export features, and access to historical data.
- B2B licensing for media outlets, sports broadcasters, and research firms.
Financial Projections
If 2,000 users convert to paid at $15/month within 1 year, MRR would reach $30,000. Upside exists via B2B deals and expansion to other sports.
Tech Stack
Python with FastAPI for rapid development, data processing, and integration with sports data APIs.
PostgreSQL for structured, relational storage of historical results and normalization rules.
Next.js for its fast rendering, SEO benefits, and smooth integration with data visualization libraries like Recharts.
- Sports data providers (e.g., Ergast API for F1, Sportradar for multi-sport data)
- Stripe for payments
- AWS S3 for exportable reports and visualization storage
Risk Assessment
Identified Risks
- Incomplete or inconsistent historical data for certain sports or seasons could limit normalization accuracy.
- Potential user confusion or skepticism regarding normalization methodology.
Mitigation Strategy
- Start with sports/leagues with robust data (F1, NBA), and clearly indicate data confidence levels. Expand coverage as data sources improve.
- Provide transparent documentation, visual explanations, and allow users to customize normalization rules. Engage with the analytics community for feedback and credibility.