Validate YouTube Prediction Market Edge
Budget / Salary$30–250
TypeFreelance project
LocationRemote
Posted1 hour ago
**Statistician / Quantitative Modelling Specialist — YouTube prediction markets — USD 100 initial budget**
We are looking for a statistician or quantitative modelling specialist to guide the statistical development and validation of an existing prediction-market betting system.
**Example market:**
https://polymarket.com/it/event/of-views-of-next-mrbeast-video-on-day-1-20261027
The system forecasts YouTube video view counts, estimates the probability of each settlement range and compares those probabilities with executable market prices.
**WHO WE ARE LOOKING FOR**
Please apply only if you are confident in your ability to:
1. Design rigorous tests, before committing betting capital, to assess whether a measurable market advantage exists—or conclude that it does not exist or that evidence is insufficient.
2. Define no-bet rules and risk controls that avoid exposure when no credible advantage is established and limit potential losses when positions are taken.
We are particularly interested in candidates who believe that modern, well-documented tools such as CatBoost, LightGBM and similar methods make accurate view-count forecasting readily achievable, and who are willing to test that belief against real data.
Accurate forecasts alone are insufficient: your work must establish whether their calibrated probabilities offer an advantage over market prices after costs.
We expect confidence in your methods and willingness to challenge assumptions. We do not expect a guarantee of future profits or zero losses.
**EXISTING PROJECT — NOT A BUILD FROM SCRATCH**
A substantial software implementation already exists, including data collection, historical datasets, forecasting models, uncertainty estimates, range probabilities, expected-value calculations and betting strategies.
A software developer is responsible for implementation. Your role is to audit and improve the statistical approach, provide formulas and specifications, and help the developer verify that the software implements them correctly.
**FIRST PRIORITY: DOES A MARKET ADVANTAGE STILL EXIST?**
Before further model or strategy development, help the developer test whether the available data contains an exploitable advantage over the market.
This includes:
- Comparing forecasts with market probabilities and meaningful simple baselines.
- Using chronological out-of-sample or walk-forward testing without future-data leakage.
- Accounting for fees, spread, liquidity, slippage and realistic execution.
- Treating videos as independent evaluation units where appropriate, rather than counting correlated observations as independent evidence.
- Reporting the uncertainty around estimated returns and the amount of additional data needed.
- Recommending whether to proceed, collect more data or stop.
A clear negative finding is useful. We do not want a model optimised to produce an attractive backtest.
**SECOND PRIORITY: IMPROVE PROBABILITIES AND NET EXPECTED VALUE**
If the evidence supports further development, guide the developer in building an optimised statistical model and evaluating advanced betting strategies.
Your contribution should include:
- Auditing forecast bias and uncertainty estimates.
- Calibrating probabilities for the market’s outcome ranges.
- Selecting appropriate methods, potentially including quantile regression, conformal prediction and probability recalibration.
- Evaluating calibration with reliability analysis, coverage, Brier score and log-loss.
- Using confidence intervals, hypothesis tests, p-values or Bayesian methods where justified.
- Translating probability uncertainty into uncertainty about net expected value.
- Providing formulas and decision rules for entries, position sizing, exits and abstention.
- Accounting for dependence and shared exposure across related contracts.
- Controlling overfitting and bias from testing multiple models or strategies.
The developer will implement the strategies under your statistical guidance. You should help verify both the formulas and their implementation.
**VALIDATION AND RISK CONTROLS**
Evaluation criteria must be agreed before examining final test results.
We want explicit rules for when the system should not bet, reduce exposure or stop operating. These rules should address uncertain estimates, poor calibration, insufficient liquidity and deterioration in performance.
Any live test involving real money requires separate approval of its budget and limits. A short profitable run will not be treated as proof of a lasting advantage.
**EXPECTED DELIVERABLES**
- An initial assessment of whether a market advantage can credibly be tested with the available data.
- A reproducible testing procedure and an assessment of the evidence.
- Statistical formulas and practical implementation instructions for the developer.
- Recommended calibration, expected-value and risk-control methods.
- A final conclusion: evidence supports an advantage, evidence does not support an advantage, or insufficient data.
- Clear limitations and next steps.
**BUDGET AND POTENTIAL CONTINUATION**
The initial fixed-price budget is **USD 100**.
It may increase to **USD 175**, by prior agreement, depending on the quality of your proposed ideas, the agreed additional scope and your interest in the project.
For someone showing strong interest in a longer-term collaboration, we may also discuss a performance-related fee associated with the betting activity. Its basis, calculation and conditions would require a separate agreement.
Please propose a realistic initial scope within USD 100. Distinguish what you can deliver immediately from work requiring additional data or an increased budget. Payment for agreed deliverables must not depend on reporting a favourable statistical conclusion.
**IN YOUR APPLICATION, PLEASE INCLUDE**
- Relevant experience with probabilistic forecasting, calibration or betting-market analysis.
- How you would first test whether this market can be beaten.
- Why you believe view-count forecasting is tractable with the available tools, and what could invalidate that belief.
- How you would define no-bet conditions and limit exposure.
- What you would deliver for USD 100, and any justified extension up to USD 175.
- Your availability and interest in an ongoing collaboration.
Code, documentation and available data will be shared with suitable candidates.
PS: this is the original post https://www.freelancer.com/projects/software-development/Validate-Prediction-Market-System/details
We are looking for a statistician or quantitative modelling specialist to guide the statistical development and validation of an existing prediction-market betting system.
**Example market:**
https://polymarket.com/it/event/of-views-of-next-mrbeast-video-on-day-1-20261027
The system forecasts YouTube video view counts, estimates the probability of each settlement range and compares those probabilities with executable market prices.
**WHO WE ARE LOOKING FOR**
Please apply only if you are confident in your ability to:
1. Design rigorous tests, before committing betting capital, to assess whether a measurable market advantage exists—or conclude that it does not exist or that evidence is insufficient.
2. Define no-bet rules and risk controls that avoid exposure when no credible advantage is established and limit potential losses when positions are taken.
We are particularly interested in candidates who believe that modern, well-documented tools such as CatBoost, LightGBM and similar methods make accurate view-count forecasting readily achievable, and who are willing to test that belief against real data.
Accurate forecasts alone are insufficient: your work must establish whether their calibrated probabilities offer an advantage over market prices after costs.
We expect confidence in your methods and willingness to challenge assumptions. We do not expect a guarantee of future profits or zero losses.
**EXISTING PROJECT — NOT A BUILD FROM SCRATCH**
A substantial software implementation already exists, including data collection, historical datasets, forecasting models, uncertainty estimates, range probabilities, expected-value calculations and betting strategies.
A software developer is responsible for implementation. Your role is to audit and improve the statistical approach, provide formulas and specifications, and help the developer verify that the software implements them correctly.
**FIRST PRIORITY: DOES A MARKET ADVANTAGE STILL EXIST?**
Before further model or strategy development, help the developer test whether the available data contains an exploitable advantage over the market.
This includes:
- Comparing forecasts with market probabilities and meaningful simple baselines.
- Using chronological out-of-sample or walk-forward testing without future-data leakage.
- Accounting for fees, spread, liquidity, slippage and realistic execution.
- Treating videos as independent evaluation units where appropriate, rather than counting correlated observations as independent evidence.
- Reporting the uncertainty around estimated returns and the amount of additional data needed.
- Recommending whether to proceed, collect more data or stop.
A clear negative finding is useful. We do not want a model optimised to produce an attractive backtest.
**SECOND PRIORITY: IMPROVE PROBABILITIES AND NET EXPECTED VALUE**
If the evidence supports further development, guide the developer in building an optimised statistical model and evaluating advanced betting strategies.
Your contribution should include:
- Auditing forecast bias and uncertainty estimates.
- Calibrating probabilities for the market’s outcome ranges.
- Selecting appropriate methods, potentially including quantile regression, conformal prediction and probability recalibration.
- Evaluating calibration with reliability analysis, coverage, Brier score and log-loss.
- Using confidence intervals, hypothesis tests, p-values or Bayesian methods where justified.
- Translating probability uncertainty into uncertainty about net expected value.
- Providing formulas and decision rules for entries, position sizing, exits and abstention.
- Accounting for dependence and shared exposure across related contracts.
- Controlling overfitting and bias from testing multiple models or strategies.
The developer will implement the strategies under your statistical guidance. You should help verify both the formulas and their implementation.
**VALIDATION AND RISK CONTROLS**
Evaluation criteria must be agreed before examining final test results.
We want explicit rules for when the system should not bet, reduce exposure or stop operating. These rules should address uncertain estimates, poor calibration, insufficient liquidity and deterioration in performance.
Any live test involving real money requires separate approval of its budget and limits. A short profitable run will not be treated as proof of a lasting advantage.
**EXPECTED DELIVERABLES**
- An initial assessment of whether a market advantage can credibly be tested with the available data.
- A reproducible testing procedure and an assessment of the evidence.
- Statistical formulas and practical implementation instructions for the developer.
- Recommended calibration, expected-value and risk-control methods.
- A final conclusion: evidence supports an advantage, evidence does not support an advantage, or insufficient data.
- Clear limitations and next steps.
**BUDGET AND POTENTIAL CONTINUATION**
The initial fixed-price budget is **USD 100**.
It may increase to **USD 175**, by prior agreement, depending on the quality of your proposed ideas, the agreed additional scope and your interest in the project.
For someone showing strong interest in a longer-term collaboration, we may also discuss a performance-related fee associated with the betting activity. Its basis, calculation and conditions would require a separate agreement.
Please propose a realistic initial scope within USD 100. Distinguish what you can deliver immediately from work requiring additional data or an increased budget. Payment for agreed deliverables must not depend on reporting a favourable statistical conclusion.
**IN YOUR APPLICATION, PLEASE INCLUDE**
- Relevant experience with probabilistic forecasting, calibration or betting-market analysis.
- How you would first test whether this market can be beaten.
- Why you believe view-count forecasting is tractable with the available tools, and what could invalidate that belief.
- How you would define no-bet conditions and limit exposure.
- What you would deliver for USD 100, and any justified extension up to USD 175.
- Your availability and interest in an ongoing collaboration.
Code, documentation and available data will be shared with suitable candidates.
PS: this is the original post https://www.freelancer.com/projects/software-development/Validate-Prediction-Market-System/details
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