Full-Stack Developer + Sports Data Engineer Needed for Advanced Sports Betting Analytics Platform

I am looking for an experienced developer to build a private sports betting research and analytics platform.
This is not a basic odds-comparison website or picks app. I already have a detailed betting methodology/model and workflow. The goal is to turn that methodology into a reliable automated system that continuously gathers sportsbook markets, sports statistics, historical results, projections, and other relevant data, then evaluates betting markets according to defined rules.
The platform will initially be for private/personal use, not a public sportsbook.
Sports to Support
The architecture should eventually support:
NFL
MLB
NBA
WNBA
NHL
College Football
College Basketball
Tennis
Soccer
MMA/UFC
Golf
The system should be designed so additional sports and market types can be added without rebuilding the application.
Core Features
The developer will build a system capable of:
Pulling current sportsbook odds and markets, with FanDuel as the primary/execution sportsbook.
Comparing FanDuel pricing with other sportsbooks.
Capturing standard markets AND alternate markets/ladders.
Collecting historical player/team/game statistics.
Automatically retrieving final results and player statistics after events.
Maintaining a historical database of every evaluated play.
Calculating implied probability and removing sportsbook vig where appropriate.
Comparing sportsbook probability against model-estimated probability.
Evaluating alternate thresholds rather than simply using default sportsbook lines.
Tracking line movement.
Identifying potentially mispriced markets.
Running sport-specific statistical models.
Producing estimated probabilities rather than simply generating “picks.”
Maintaining configurable qualification rules.
Separating qualified plays, research/watch candidates, passed plays and placed bets.
Tracking results and model performance over time.
Providing a dashboard that can be used easily from both desktop and mobile.
Market Coverage
I want broad market coverage rather than just moneylines/spreads/totals.
Examples include:
MLB: pitcher strikeouts, pitcher outs, hitter props, alternate props, NRFI/YRFI, F3/F5, team totals, game totals, sides.
NFL: passing/rushing/receiving/receptions, alternate player lines, team markets, spreads, totals and alternate lines.
NBA/WNBA: points, rebounds, assists, threes, PRA and combination markets, alternate ladders, double-doubles, team/game markets.
Tennis: moneyline, player to win a set, set handicaps, game handicaps, total games, alternate totals, set betting and other available markets.
Soccer: match result, double chance, BTTS, team totals, game totals, player shots/SOT, goals and assists.
MMA/UFC: moneyline, method of victory, decision/finish, rounds, alternate rounds and related markets.
College sports: primarily team markets rather than player props.
Statistical / Model Requirements
I already have many of the decision rules and want them implemented programmatically.
The system should eventually be capable of evaluating factors such as:
Historical hit rates at the exact proposed threshold
L5/L10/L20 performance where appropriate
Larger historical samples
Opponent/matchup
Home/away and surface/context splits
Expected playing time or role
Starting lineups/starters
Injuries
Recent form
Market movement
Consensus sportsbook pricing
No-vig market probabilities
Alternate-line distributions
Correlated markets
Model probability vs sportsbook break-even probability
Expected value
Confidence/calibration
Closing-line value
Performance of previously rejected bets as well as placed bets
The system needs to preserve the underlying data so that the model can be backtested and improved, rather than functioning as a black-box picks generator.
Betting/Results Database
Every evaluated market should be capable of storing information such as:
Date/time
Sport/league/event
Team/player
Market
Exact threshold
Sportsbook
Odds when evaluated
Closing odds where available
Implied probability
No-vig probability
Model probability
Expected value
Model classification
Whether the bet was actually placed
Stake
Final result
Exact final player/team statistic
Win/loss/void
Margin relative to the betting threshold
This historical database is a major part of the project.
Dashboard
I want a clean dashboard showing things such as:
Qualified Plays | Watch/Research | Placed Bets | Passed/Tracked Plays | Future Events
It should support filtering by:
Sport, league, date, sportsbook, market, model probability, edge/EV, status and start time.
The interface should work well on a phone.
Automation
Eventually the system should automatically:
Odds/data ingestion → market normalization → statistical analysis → model scoring → candidate generation → dashboard → pregame monitoring → final-result retrieval → grading → model-performance database
The architecture should allow scheduled scans and alerts when:
A new qualifying opportunity appears
Odds materially change
A market reaches a target price
A lineup/injury change affects an existing candidate
Technology
I am open to the developer recommending the stack.
Experience with some combination of the following is preferred:
Python, FastAPI/Django, PostgreSQL, React/Next.js, sports APIs, sportsbook/odds APIs, statistical modeling, scheduled/background jobs, cloud deployment and API integrations.
Python experience is particularly important because the analytics/modeling layer will likely be Python-based.
Important
I do not want someone who will simply build a nice-looking frontend around a basic odds API.
The difficult part of this project is:
Obtaining and normalizing reliable sports/odds data.
Supporting many different market types.
Building a maintainable statistical/modeling engine.
Recording historical data correctly.
Automating result/stat retrieval.
Backtesting and validating the model.
Making it easy to add new models and rules later.
Data integrity is more important to me than flashy UI.
Ideal Freelancer
Please apply if you have experience with at least several of the following:
Sports betting analytics
Sports data APIs
Odds APIs
Python statistical modeling
PostgreSQL/database design
React/Next.js
Automated data pipelines
Probability/EV calculations
Backtesting systems
Cloud deployment
Background/scheduled jobs
Previous sports analytics or betting-platform work is a major plus.
When Applying, Please Answer These Questions
Have you built a sports betting, sports analytics, trading, or odds-comparison system before? Please provide examples.
Which sports/odds APIs have you personally integrated?
How would you obtain FanDuel markets, including alternate player/team lines, reliably?
How would you normalize the same market when different sportsbooks use different naming conventions?
How would you design the database so historical odds are retained instead of overwritten?
How would you obtain and automatically grade final player/team statistics?
What would you use for the modeling layer?
How would you calculate fair/no-vig probabilities from sportsbook prices?
How would you handle missing, stale, or conflicting sports data?
How would you backtest the model without introducing look-ahead bias?
What technology stack would you recommend and why?
Would you build this using milestones so I can test each stage before proceeding?
Please estimate the time and cost for an initial working version.
Are you personally doing the development, or will the project be subcontracted?
Screening instruction
Start your proposal with the words MARKET ENGINE.
Applications that do not begin with those words will not be considered.