Sharp Betting Engine: Build the Real-Time Prediction Core for TheSharpCircle
Budget: $1,500 – $3,000 USD
We’re building the v1 version of the Sharp Betting Engine — a real-time sports betting prediction system powering TheSharpCircle.com. This is the foundational backend that ingests volatile sports signals and converts them into actionable outputs like edgeScore, confidenceTier, and predictive tags (e.g., Smart Money, Trap Game, Steam Under).
This is a high-accountability, backend-only role for someone who thrives on building scalable, data-driven systems from the ground up. You’ll be responsible for constructing the API layer, signal ingestion logic, and scoring engine that forms the core of a sharp betting product used by real bettors.
No frontend, no CMS — just raw signal processing, structured prediction logic, and clean backend delivery.
We are not looking for a frontend developer, full-stack generalist, or someone focused on CMS or UI.
We are looking for a sharp, backend-focused engineer who can:
- Ingest and normalize multiple sports data APIs
- Build modular scoring logic (edgeScore, volatility flags, confidence tiers)
- Architect a system that outputs high-confidence picks and betting tags
- Write clean, well-documented APIs for use by our React frontend team
- Set up basic signal freshness QA and caching logic (Redis/PostgreSQL)
The ideal candidate has experience in real-time odds tracking, predictive signal processing, and knows what “steam movement” or “public fade” means in the context of sports betting. Bonus points if you’ve worked on a model or app in the betting, finance, or DFS (daily fantasy sports) space.
You’ll work alongside a structured team, but your sole focus is backend engineering — the engine, not the interface.
This is a foundational role. If done well, it will be the spine of a multi-sport, multi-model prediction system used by thousands of bettors.
Do NOT Apply If You...
- You specialize in frontend or full-stack development and want UI work
- You are unfamiliar with sports betting concepts like steam movement or sharp money
- You’ve never built a signal ingestion pipeline or scoring logic from scratch
- You rely heavily on CMS tools, page builders, or pre-built libraries for core logic
- You don’t test your APIs or track input volatility/freshness
You’re a Perfect Fit If You...
- You’ve built backend systems that use volatile data or real-time API feeds
- You understand predictive logic and enjoy data scoring and tag triggering
- You’re comfortable with Node.js or Python for structured backend builds
- You can ship a production-ready, testable backend engine within 3 weeks
- You want to contribute to a betting intelligence engine used by real bettors
Scope of Work (v1 Engine)
You’ll be responsible for architecting and delivering the production-ready v1 backend engine for EDGEFORCE, focused solely on signal ingestion, scoring, and predictive tag generation.
Ingest and normalize signals from six APIs:
• Odds Feed API (e.g., OddsJam) – Track real-time line movement to feed edgeScore calculation.
• Public % API (e.g., BettingPros) – Power Fade the Crowd logic and volatility confidence dampening.
• Injury API (e.g., SportsDataIO) – Update Player Impact Rating logic and Shorthanded Risk tags.
• Weather API (e.g., OpenWeather) – Trigger Weather Suppression tags and totals volatility
adjustments.
• Steam Movement API – Detect sharp money spikes and activate Smart Money or Steam Under tags.
• RefStats API – Trigger Ref Bias Alert tags based on officiating trends, especially in home/away
matchups.
BONUS is you can recommend other API feeds to enhance the prediction engine.
Build scoring engine:
• edgeScore – Primary signal scoring logic for pick strength.
• confidenceTier – User-facing trust tier output.
• volatilityFlag – Real-time volatility overlay flag for UI use.
Tag logic system:
• Trap Game, Fade the Crowd, Smart Money, Shorthanded Risk
• Steam Under (new), Ref Bias Alert (new), Weather Suppression
Setup backend API endpoints:
• /games – returns all active picks and associated tags
• /pick – detailed pick-level scoring and risk insight
• /tags – current tag logic, mappings, and thresholds
Integrate PostgreSQL (data store) and Redis (cache layer) for performant API response delivery.
Build basic test logging and QA layer to ensure signal freshness, tag trigger traceability, and model scoring consistency.
Deliverables
• Fully functional v1 backend engine (Postman testable)
• Signal ingestion logic and scoring engine
• REST or GraphQL API layer
• Minimal test coverage + signal freshness logging
• Clean codebase with documentation and versioning
Tech Stack (Recommended)
• Node.js or Python (FastAPI)
• PostgreSQL
• Redis
• Airflow (for batch jobs) or equivalent
• Kafka or WebSocket (for live odds/public %)
Budget & Timeline
Fixed Price: $1,800 USD
Milestones:
• - $600 – Signal ingestion + architecture setup
• - $600 – Scoring + tag engine delivery
• - $600 – Final API output + QA/monitoring layer
Timeline: ~3 weeks
You’re a Fit If You...
• - Have built predictive or real-time scoring systems
• - Know how to cleanly structure APIs for frontend delivery
• - Are fluent in sports data APIs, odds logic, or betting tags
• - Work async and communicate clearly via GitHub or Slack
How to Apply
• - One similar project or code sample (sports/betting preferred)
• - Stack you're most comfortable using for this build
• - One idea to improve the tag logic system
This is a high-accountability, backend-only role for someone who thrives on building scalable, data-driven systems from the ground up. You’ll be responsible for constructing the API layer, signal ingestion logic, and scoring engine that forms the core of a sharp betting product used by real bettors.
No frontend, no CMS — just raw signal processing, structured prediction logic, and clean backend delivery.
We are not looking for a frontend developer, full-stack generalist, or someone focused on CMS or UI.
We are looking for a sharp, backend-focused engineer who can:
- Ingest and normalize multiple sports data APIs
- Build modular scoring logic (edgeScore, volatility flags, confidence tiers)
- Architect a system that outputs high-confidence picks and betting tags
- Write clean, well-documented APIs for use by our React frontend team
- Set up basic signal freshness QA and caching logic (Redis/PostgreSQL)
The ideal candidate has experience in real-time odds tracking, predictive signal processing, and knows what “steam movement” or “public fade” means in the context of sports betting. Bonus points if you’ve worked on a model or app in the betting, finance, or DFS (daily fantasy sports) space.
You’ll work alongside a structured team, but your sole focus is backend engineering — the engine, not the interface.
This is a foundational role. If done well, it will be the spine of a multi-sport, multi-model prediction system used by thousands of bettors.
Do NOT Apply If You...
- You specialize in frontend or full-stack development and want UI work
- You are unfamiliar with sports betting concepts like steam movement or sharp money
- You’ve never built a signal ingestion pipeline or scoring logic from scratch
- You rely heavily on CMS tools, page builders, or pre-built libraries for core logic
- You don’t test your APIs or track input volatility/freshness
You’re a Perfect Fit If You...
- You’ve built backend systems that use volatile data or real-time API feeds
- You understand predictive logic and enjoy data scoring and tag triggering
- You’re comfortable with Node.js or Python for structured backend builds
- You can ship a production-ready, testable backend engine within 3 weeks
- You want to contribute to a betting intelligence engine used by real bettors
Scope of Work (v1 Engine)
You’ll be responsible for architecting and delivering the production-ready v1 backend engine for EDGEFORCE, focused solely on signal ingestion, scoring, and predictive tag generation.
Ingest and normalize signals from six APIs:
• Odds Feed API (e.g., OddsJam) – Track real-time line movement to feed edgeScore calculation.
• Public % API (e.g., BettingPros) – Power Fade the Crowd logic and volatility confidence dampening.
• Injury API (e.g., SportsDataIO) – Update Player Impact Rating logic and Shorthanded Risk tags.
• Weather API (e.g., OpenWeather) – Trigger Weather Suppression tags and totals volatility
adjustments.
• Steam Movement API – Detect sharp money spikes and activate Smart Money or Steam Under tags.
• RefStats API – Trigger Ref Bias Alert tags based on officiating trends, especially in home/away
matchups.
BONUS is you can recommend other API feeds to enhance the prediction engine.
Build scoring engine:
• edgeScore – Primary signal scoring logic for pick strength.
• confidenceTier – User-facing trust tier output.
• volatilityFlag – Real-time volatility overlay flag for UI use.
Tag logic system:
• Trap Game, Fade the Crowd, Smart Money, Shorthanded Risk
• Steam Under (new), Ref Bias Alert (new), Weather Suppression
Setup backend API endpoints:
• /games – returns all active picks and associated tags
• /pick – detailed pick-level scoring and risk insight
• /tags – current tag logic, mappings, and thresholds
Integrate PostgreSQL (data store) and Redis (cache layer) for performant API response delivery.
Build basic test logging and QA layer to ensure signal freshness, tag trigger traceability, and model scoring consistency.
Deliverables
• Fully functional v1 backend engine (Postman testable)
• Signal ingestion logic and scoring engine
• REST or GraphQL API layer
• Minimal test coverage + signal freshness logging
• Clean codebase with documentation and versioning
Tech Stack (Recommended)
• Node.js or Python (FastAPI)
• PostgreSQL
• Redis
• Airflow (for batch jobs) or equivalent
• Kafka or WebSocket (for live odds/public %)
Budget & Timeline
Fixed Price: $1,800 USD
Milestones:
• - $600 – Signal ingestion + architecture setup
• - $600 – Scoring + tag engine delivery
• - $600 – Final API output + QA/monitoring layer
Timeline: ~3 weeks
You’re a Fit If You...
• - Have built predictive or real-time scoring systems
• - Know how to cleanly structure APIs for frontend delivery
• - Are fluent in sports data APIs, odds logic, or betting tags
• - Work async and communicate clearly via GitHub or Slack
How to Apply
• - One similar project or code sample (sports/betting preferred)
• - Stack you're most comfortable using for this build
• - One idea to improve the tag logic system