Machine Learning / Quantitative Research Consultant for Esports (LoL LCK) Betting Model
We’re building an esports betting/prediction system for League of Legends (LCK) and we’re looking for a senior Machine Learning / Quantitative Research consultant to help us validate and improve our model before scaling to production.
Our current pipeline trains on LCK-only historical match data (recent years, with time-decay weighting) and outputs win probabilities and expected value (EV/ROI). We need an expert who can audit the methodology, eliminate leakage, improve probability calibration, and help us implement evaluation that matches real betting conditions.
This is a consulting role: you’ll work closely with our technical team, review code/data approach, propose fixes, and help implement the highest-impact improvements.
What you’ll do
Audit the ML pipeline for data leakage, label leakage, and look-ahead bias (especially in rolling stats/ELO/time-based features).
Redesign evaluation to be production-realistic:
time-based splits / walk-forward validation
group splits by series/match (avoid map-level leakage)
proper reporting of accuracy + log loss / Brier score / calibration
Calibrate probabilities (e.g., Platt scaling / isotonic regression) and recommend confidence/uncertainty handling.
Review and improve EV / ROI calculations and ensure consistent definitions (EV per bet vs ROI on risk, no-vig odds handling, vig/hold modeling).
Help define bet selection rules and backtest methodology:
edge thresholds, stake sizing (flat risk vs “to win” vs fractional Kelly), drawdown controls.
Recommend feature and modeling improvements aligned with LoL reality:
recency weighting/patch awareness
matchup/counter and synergy features (duos/combos)
player–champion proficiency signals
series-state handling for BO series formats
Provide a clear “production readiness checklist” and a plan for ongoing monitoring (drift, calibration, stability).
Deliverables
A written audit report identifying issues, risks, and prioritized fixes.
A revised evaluation/backtest framework with reproducible methodology.
Calibration results + recommended probability output format.
Recommendations (and optionally implementation support) for model and feature improvements.
A “go/no-go” assessment for launch.
Required experience
5+ years in ML, quantitative research, or applied statistics (ideally in sports betting, trading, or forecasting).
Deep familiarity with:
leakage prevention, time-series / non-i.i.d. validation
probability calibration and proper scoring rules (log loss, Brier, reliability curves)
backtesting pitfalls (selection bias, survivorship bias, data snooping)
Strong Python skills (pandas, numpy, scikit-learn; bonus for PyTorch/XGBoost/LightGBM).
Comfortable reviewing code and giving actionable engineering guidance.
Nice to have
Experience with sports betting markets, odds → implied probability conversions, vig removal, CLV tracking.
Knowledge of esports / League of Legends (draft, patch effects, meta shifts).
Experience designing end-to-end ML systems in production (monitoring, drift, versioning).
Project details
Type: Consulting + code review + implementation support
Data: LCK match-level + map-level historical dataset
Output: win probability + EV/ROI suggestions per map/match
Start: ASAP
Duration: 2–6 weeks initial engagement (possible ongoing advisory)
To apply, please include
A brief summary of relevant ML + quantitative research experience (betting/trading/forecasting is a plus).
Examples of prior work around calibration, time-based validation, or backtesting.
Your recommended first steps to audit a model that shows unusually high accuracy/ROI (how you’d detect leakage quickly).
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Our current pipeline trains on LCK-only historical match data (recent years, with time-decay weighting) and outputs win probabilities and expected value (EV/ROI). We need an expert who can audit the methodology, eliminate leakage, improve probability calibration, and help us implement evaluation that matches real betting conditions.
This is a consulting role: you’ll work closely with our technical team, review code/data approach, propose fixes, and help implement the highest-impact improvements.
What you’ll do
Audit the ML pipeline for data leakage, label leakage, and look-ahead bias (especially in rolling stats/ELO/time-based features).
Redesign evaluation to be production-realistic:
time-based splits / walk-forward validation
group splits by series/match (avoid map-level leakage)
proper reporting of accuracy + log loss / Brier score / calibration
Calibrate probabilities (e.g., Platt scaling / isotonic regression) and recommend confidence/uncertainty handling.
Review and improve EV / ROI calculations and ensure consistent definitions (EV per bet vs ROI on risk, no-vig odds handling, vig/hold modeling).
Help define bet selection rules and backtest methodology:
edge thresholds, stake sizing (flat risk vs “to win” vs fractional Kelly), drawdown controls.
Recommend feature and modeling improvements aligned with LoL reality:
recency weighting/patch awareness
matchup/counter and synergy features (duos/combos)
player–champion proficiency signals
series-state handling for BO series formats
Provide a clear “production readiness checklist” and a plan for ongoing monitoring (drift, calibration, stability).
Deliverables
A written audit report identifying issues, risks, and prioritized fixes.
A revised evaluation/backtest framework with reproducible methodology.
Calibration results + recommended probability output format.
Recommendations (and optionally implementation support) for model and feature improvements.
A “go/no-go” assessment for launch.
Required experience
5+ years in ML, quantitative research, or applied statistics (ideally in sports betting, trading, or forecasting).
Deep familiarity with:
leakage prevention, time-series / non-i.i.d. validation
probability calibration and proper scoring rules (log loss, Brier, reliability curves)
backtesting pitfalls (selection bias, survivorship bias, data snooping)
Strong Python skills (pandas, numpy, scikit-learn; bonus for PyTorch/XGBoost/LightGBM).
Comfortable reviewing code and giving actionable engineering guidance.
Nice to have
Experience with sports betting markets, odds → implied probability conversions, vig removal, CLV tracking.
Knowledge of esports / League of Legends (draft, patch effects, meta shifts).
Experience designing end-to-end ML systems in production (monitoring, drift, versioning).
Project details
Type: Consulting + code review + implementation support
Data: LCK match-level + map-level historical dataset
Output: win probability + EV/ROI suggestions per map/match
Start: ASAP
Duration: 2–6 weeks initial engagement (possible ongoing advisory)
To apply, please include
A brief summary of relevant ML + quantitative research experience (betting/trading/forecasting is a plus).
Examples of prior work around calibration, time-based validation, or backtesting.
Your recommended first steps to audit a model that shows unusually high accuracy/ROI (how you’d detect leakage quickly).
Apply tot his job
Apply To this Job