Map Deceptive Betting Movements
Краткое
Freelancer Client is hiring: Map Deceptive Betting Movements.
Location: Remote
I already have minute-by-minute spreadsheets that track price shifts from six hours out to five minutes before kick-off across every major soccer league. The files cover Over/Under goals, Moneyline and Half-Time/Full-Time markets at four leading sportsbooks, so the raw material is in place; what I need now is a sharp analytical framework that exposes when and where those odds are being steered.
Your core mission is to turn timing of odds changes into a clear map of potential deception. That means reading the sequences, separating routine line moves from suspicious ones and then presenting the evidence in a way that lets me focus instantly on the true signals.
A reproducible analysis pipeline (Python, R or similar) that ingests my Excel sheets, cleans them and aligns time stamps across books.
Rule-based or statistical logic that flags abnormal shifts relative to baseline volatility and cross-market consensus, with parameters I can tweak.
An interactive dashboard or report (Power BI, Tableau, Jupyter, etc.) that highlights red-flag periods, explains why they were tagged, and lets me drill down by league, provider and market.
Skills: Python, Software Architecture, Statistics, Machine Learning (ML), R Programming Language, Data Visualization, Data Analysis, Power BI
Budget: $750–$1500 USD
Source: Freelancer Client via Remote / Online. Apply on the source website.
Оригинал
I already have minute-by-minute spreadsheets that track price shifts from six hours out to five minutes before kick-off across every major soccer league. The files cover Over/Under goals, Moneyline and Half-Time/Full-Time markets at four leading sportsbooks, so the raw material is in place; what I need now is a sharp analytical framework that exposes when and where those odds are being steered.
Your core mission is to turn timing of odds changes into a clear map of potential deception. That means reading the sequences, separating routine line moves from suspicious ones and then presenting the evidence in a way that lets me focus instantly on the true signals.
Deliverables (all must be met for acceptance)
• A reproducible analysis pipeline (Python, R or similar) that ingests my Excel sheets, cleans them and aligns time stamps across books.
• Rule-based or statistical logic that flags abnormal shifts relative to baseline volatility and cross-market consensus, with parameters I can tweak.
• An interactive dashboard or report (Power BI, Tableau, Jupyter, etc.) that highlights red-flag periods, explains why they were tagged, and lets me drill down by league, provider and market.
• A short “corrective lens” methodology note showing how to recalibrate my own models once deceptive patterns are removed.
If this sounds routine, it probably isn’t—the value lies in the nuance of those last-minute moves. Show me you can separate noise from manipulation and we’ll be off to a flying start.
Локация & Details
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