Senior ML / AI Sports Betting Prediction Expert — WNBA Moneyline & Spreads

We are building a sports betting prediction system focused initially on the **WNBA**, with the goal of later expanding into the NBA. This is not a generic data science or dashboard role. We need a senior-level ML / AI / quantitative betting specialist who understands how to build probability-based prediction models that are useful for real betting decisions — including odds, market efficiency, calibration, expected value, closing-line validation, injury/news handling, and realistic backtesting. Our initial markets are: * WNBA moneyline * WNBA point spreads The goal is to build a clean, explainable, trustworthy system that produces realistic win probabilities and spread projections, identifies potential betting edge correctly, and avoids common modeling mistakes such as leakage, inflated backtests, broken probability blending, unrealistic ROI assumptions, or misuse of sportsbook odds. ## What we are building We want a WNBA prediction pipeline that can produce: * Pregame win probabilities for moneyline markets * Predicted point margins and spread-cover probabilities * Clear explanations for why the model prefers one side * Properly calibrated probabilities * A disciplined framework for deciding when a bet should or should not be taken * Comparison against sportsbook odds, including vig/no-vig market probabilities * Reliable backtesting and ongoing model monitoring We expect to eventually expand this framework to the NBA, so experience with NBA basketball modeling is highly valuable. ## What you will do * Audit our current or planned prediction pipeline and methodology * Help define the correct architecture for WNBA moneyline and spread prediction * Review feature engineering, target definitions, assumptions, and validation methodology * Advise whether moneyline and spread models should be separate, connected, or derived from a shared margin-distribution framework * Build or recommend a robust pregame prediction framework * Help determine how team strength, player availability, injuries, rotations, travel, rest, back-to-backs, home court, recent form, pace, offensive/defensive efficiency, lineup data, and matchup factors should influence predictions * Review and improve LightGBM model setup, hyperparameters, feature importance, and training methodology * Identify leakage, survivorship bias, target leakage through closing lines, bad train/test splits, or unrealistic historical assumptions * Improve probability calibration using appropriate methods such as isotonic regression, Platt scaling, beta calibration, or other suitable approaches * Help us evaluate predictions using proper metrics, including log loss, Brier score, calibration curves, reliability plots, CLV, ROI, hit rate, and expected value * Review how sportsbook odds are used in the system and ensure we correctly calculate implied probability, vig removal, no-vig probability, expected value, and edge * Help establish realistic thresholds for betting decisions, including when the model should stay out of a market * Build explainability and visibility into each prediction using SHAP, feature contribution analysis, confidence ranges, and model diagnostics * Help us detect when the model is behaving irrationally or when one component is overpowering the rest of the system * Optionally provide implementation support in Python ## Examples of problems we need you to catch We need someone who can quickly identify issues such as: * Incorrect probability blending or ensemble weighting * A model showing unrealistic historical ROI due to leakage or incorrect odds timing * A high-accuracy model that is poorly calibrated and therefore unusable for betting * A model that performs well against stale lines but fails against realistic market-close conditions * Improper use of closing odds as a feature or benchmark * A recent-form feature that double-counts team strength already captured by ELO or ratings * An injury or player-status variable that would not have been known at actual betting time * A model that gives a team a high win probability but produces a contradictory spread estimate * Incorrect no-vig conversion, EV calculation, or spread-cover probability logic * A system taking bets with apparent edge that disappears once vig, line movement, and uncertainty are accounted for For example, if one component implies a 64% win probability and a neutral component implies 50%, a proper weighted blend should move toward the weighted average. We need someone who can audit the actual math, identify where the system is breaking, and explain the correction clearly. ## Required experience * Strong hands-on experience with machine learning prediction systems * Strong Python experience * Strong experience with LightGBM or comparable gradient-boosted tree models * Strong understanding of probability modeling, calibration, uncertainty, and forecasting evaluation * Proven experience with sports betting, quantitative betting, trading, market modeling, or forecasting systems * Strong understanding of sportsbook odds, implied probability, vig, no-vig probabilities, EV, ROI, and closing-line value * Experience auditing models for leakage, bad assumptions, unrealistic validation, broken ensembles, and inflated backtests * Ability to explain complex model behavior in practical, non-academic terms * Experience working with historical sports data, odds data, and time-aware validation ## Highly preferred * Direct WNBA betting, analytics, modeling, or data experience * NBA modeling or basketball analytics experience * Understanding of WNBA-specific context, including roster depth, player availability, travel, scheduling, rest, back-to-backs, pace, rotations, coaching, and lineup effects * Experience modeling moneyline and spread markets * Experience with player-level, lineup-level, and team-level basketball data * Experience using ELO, Bayesian ratings, adjusted net rating, RAPM-style signals, player impact metrics, or hierarchical models * Experience with SHAP, calibration plots, reliability curves, feature contribution analysis, and model monitoring * Familiarity with betting exchanges, Pinnacle-style markets, market movement, and closing-line benchmarks ## How we expect the system to work We are open to your recommendations, but we expect the person hired to help us define a strong framework such as: ### Pregame team-strength model A model or rating system using long-term team strength, player quality, roster changes, injuries, recent form, home court, rest, travel, pace, offensive/defensive performance, and schedule context. ### WNBA game prediction model A model that produces: * Win probability * Predicted point margin * Spread-cover probability * Confidence and uncertainty indicators * Clear feature-level explanations ### Odds and betting decision layer A framework that: * Converts sportsbook odds accurately * Removes vig appropriately * Compares model probabilities to market probabilities * Calculates realistic EV * Accounts for uncertainty, market liquidity, and line movement * Defines minimum edge requirements before recommending a bet * Tracks whether edge is confirmed or contradicted by closing-line movement ## What success looks like By the end of the initial engagement, we want: * A clear and defensible WNBA model architecture * Trustworthy moneyline probabilities * Sensible spread and margin predictions * Proper calibration and evaluation methodology * Better separation between predictive performance and betting profitability * A realistic backtesting framework using only information available at the time of each bet * Clear explanations for each model prediction * A better framework for comparing model output against sportsbook prices * Identification of weak assumptions, broken math, or unreliable parts of the pipeline * A practical roadmap for expanding from WNBA into NBA ## Engagement details * Sport: Basketball * Initial league: WNBA * Future expansion: NBA * Markets: Moneyline and point spreads * Type: Consulting, model audit, architecture design, possible implementation support * Engagement: Initial short-term project, with potential for ongoing work * Hours: Flexible * Level: Senior / expert only ## To apply, please include 1. Your experience with sports betting models, basketball forecasting, quantitative betting, trading, or market prediction systems. 2. Your hands-on experience with LightGBM, probability models, calibration, and time-series or event-based validation. 3. Any direct experience with WNBA, NBA, basketball analytics, odds data, or sportsbook markets. 4. How you would approach building: * A WNBA moneyline model * A spread/margin prediction model * A framework connecting margin predictions to spread-cover probabilities * A betting-decision layer using model probability, market odds, vig, and uncertainty 5. How you would audit a model that reports suspiciously high accuracy or ROI. 6. A brief example of how you think about calibration and why accuracy alone is not enough for betting. 7. How you would detect broken blending, weighting, feature leakage, or incorrect EV calculations in a prediction pipeline. 8. What historical data, odds data, and timestamps you would need before trusting a WNBA betting backtest. ## Recommended skills to tag * Machine Learning * Artificial Intelligence * Python * LightGBM * Sports Betting * Basketball Analytics * WNBA * NBA * Quantitative Research * Statistics * Probability * Forecasting * Predictive Modeling * Feature Engineering * Data Science * Time Series Analysis * Model Calibration * Sports Analytics