Tennis Rankings Tracker
1. The High-Level Pitch (The "Elevator" Version)
The Tennis Rankings Tracker is a desktop application for dedicated tennis fans and analysts. It scrapes real-time data from across the web to provide a single, powerful dashboard that tracks player rankings, weekly point changes, and YTD performance. Unlike static websites, this tool allows you to track, visualize, and analyze player trends over time, providing color-coded insights that official sources don't offer.
2. Problem Statement
Following professional tennis is fragmented. Dedicated fans, analysts, and bettors have to jump between multiple websites (ATP, WTA, ITF, various live-score sites) to get a complete picture of a player's performance. It is very difficult to track historical weekly ranking changes and visualize a player's momentum (or slump) without manually compiling data in a spreadsheet. There is no simple, all-in-one tool that shows granular, color-coded performance data at a glance.
3. Product Overview (The Solution)
Tennis Rankings Tracker is a powerful Python application with a user-friendly graphical interface. It scrapes real-time data from sources like TennisExplorer.com to consolidate and present a detailed view of ATP and WTA player performances. It allows users to track weekly rankings, monitor Year-to-Date (YTD) point changes, and "expand" any player to see a detailed history of their performance trends over time.
4. Key Features
Unified Dashboard: Displays ATP & WTA rankings in a single, clean interface.
Historical Trend Analysis: Expand any player to see their weekly ranking and YTD point changes over time.
Visual Insights: Uses color-coding (e.g., green for gains, red for losses) to provide instant insights into a player's momentum.
Real-time Data: Scrapes data on launch to ensure you always have the latest rankings.
Cross-Platform: As a Python application, it can be run on Windows, macOS, and Linux.
5. Market Opportunity
The market for sports analytics is exploding, valued at over $5.7 billion in 2025 and growing at over 22% annually. This growth is driven by a massive shift in fan engagement; modern fans demand deeper, data-driven insights, not just live scores. With tennis participation in the US alone at over 25 million people, there is a large, underserved niche of "prosumer" fans, fantasy players, and analysts who want to go beyond what the official (and often clunky) ATP/WTA sites offer.
6. Competitive Advantage
Aggregation: Official ATP/WTA sites are the source of truth but make it difficult to compare or analyze historical data. This tool scrapes and aggregates it.
Trend Visualization: Most competitors (like TennisExplorer, Tennislive) are websites focused on current scores and betting odds. This app's unique advantage is its focus on historical trend analysis in a desktop GUI.
Speed & Convenience: As a native desktop app, it can be faster and more data-dense than a web page, with no ads or website clutter.
Open Source (Presumed): Being on GitHub allows for community contributions, building trust and enabling advanced users to customize it.
7. Target Market: Use Cases & Personas
Primary Use Case: A dedicated tennis analyst or fan wants to track the "rise and fall" of 20-30 specific players over the season, something that is nearly impossible to do without a custom spreadsheet.
Target Personas:
Persona 1: The Data-Driven Analyst/Bettor
Goals: Find inefficiencies and patterns in player performance to gain a competitive edge in analysis or betting.
Pain Points: "I waste hours cross-referencing websites to see how a player's YTD points really changed after that last tournament. I just want a dashboard."
Persona 2: The "Super Fan" / Content Creator
Goals: Follow their favorite players and create more insightful content for their blog, podcast, or social media.
Pain Points: "It's hard to explain why a player is slumping. I want to be able to show a graph of their ranking changes over the last 12 weeks."
8. Go-To-Market (GTM) Strategy
Initial Wedge: Target hyper-niche online communities. This is a 100% community-driven launch.
Traction Plan:
Post the GitHub link and screenshots on Reddit (in
r/tennis,r/dataisbeautiful,r/sports).Share in tennis-specific forums (e.g., Talk Tennis, Men's Tennis Forums).
Share on Twitter/X and tag prominent tennis analysts and data-journalists.
Acquisition Funnel: The GitHub "README" is the landing page. It must be excellent, with clear GIFs/screenshots of the app in action and simple, one-line installation instructions.
9. Business Model (Value Ladder)
As an open-source desktop application, traditional SaaS models don't fit. The primary goal is likely user adoption and project building, but monetization is possible.
Option A (Recommended): The "Open-Core" Model
Free Version (Current Git Project): The core application is 100% free and open-source.
Pro Version ($10 One-Time Purchase): A pre-compiled, easy-to-install version (no Python/Git needed) sold on Gumroad or a simple website. This "Pro" version could also include premium features like player-to-player comparisons, data export to CSV, or alerts for specific players.
Option B (Patronage Model):
Keep it 100% free.
Place a "Buy Me a Coffee" or "Sponsor on GitHub" link directly within the app's "About" page to fund development.
10. Roadmap & Next Steps (Internal)
Phase 1 (Current): Beta version is available on GitHub. Focus on gathering bug reports and feature requests from initial community feedback (Reddit, etc.).
Phase 2 (V1.0 Launch): Package the application into an easy-to-use installer (for Windows/Mac). Solidify the "Pro" feature set and set up the (Option A) payment-processing page.
Phase 3 (Expansion): Add more data sources. Integrate player stats (aces, double faults) in addition to rankings.
Phase 4 (Scale): If demand is high, explore building a web-based version of the tool.
