Ff Ml Modeling

Machine Learning & Feature Engineering for Fantasy Football

Overview

Provide expert guidance on building ML-based player projection models using research-backed feature engineering patterns, appropriate model selection, and sports-specific validation strategies. Apply domain expertise to help design features, choose models, avoid common pitfalls, and create interpretable predictions.

When to Use This Skill

Trigger this skill for queries involving:

• Feature engineering: "What features should I include?" "How do I create age curve features?" "What are good opportunity metrics?"
• Model selection: "Which ML model should I use?" "Random Forest or XGBoost?" "When to use regularized regression?"
• Validation strategies: "How do I validate sports models?" "What's wrong with standard cross-validation?" "How to avoid data leakage?"
• Sports-specific challenges: "How to handle small sample sizes?" "How to model position differences?" "Handling regime changes?"
• Feature selection: "How to reduce 109 stats to key features?" "Lasso vs Ridge?" "How to handle multicollinearity?"
• Model interpretability: "How to explain predictions?" "What features matter most?" "SHAP values for fantasy?"

Note: For dynasty strategy questions (player valuation, trade analysis, roster construction), use ff-dynasty-strategy. For statistical methods (regression types, simulations, GAMs), use ff-statistical-methods.

Core Capabilities

1. Feature Engineering

Core Principle: Feature engineering is more important than model selection for sports predictions.

Key Feature Categories:

Age Curves

• Marcel system: 3-year weighted average + age adjustment + regression to mean
• Position-specific peaks: RB 23-26, WR 26-28, QB 28-33, TE 26-29
• Implementation: age_factor = 1 - (age - peak_age) * 0.003 for decline phase

Opportunity Metrics

• Target share, snap share, weighted opportunities (carries + targets×1.5)
• Points per opportunity (efficiency measure)
• Volume is king: opportunity metrics predict better than TDs

Efficiency Statistics

• Yards per route run (YPRR), yards per carry (YPC)
• Yards after contact (YAC), catch rate
• Warning: Noisy with small samples, use rolling averages

Interaction Terms

• QB quality × target share (receiver production context)
• Opponent strength adjustments
• Game script (leading = rushing, trailing = passing)
• ~40% of team performance from synergy effects

Rolling Averages

• Last 3 games, last 5 games, season-long
• Trend features: recent form vs established baseline
• Lag features: last game, same opponent last season

Reference: references/feature_engineering.md for formulas, implementation patterns, and common mistakes.

2. Model Selection

Decision Framework:

Primary Goal?
├─ Interpretability → Linear/Ridge/Lasso Regression
└─ Performance
   ├─ Small (<1000) → Ridge/Lasso/Elastic Net
   ├─ Medium (1K-10K) → Random Forest or XGBoost
   └─ Large (>10K) → XGBoost/LightGBM or Ensemble

Model Types:

Linear Regression: Baseline, interpretability, small samples

Regularized Regression: High-dimensional data, multicollinearity, automatic feature selection (Lasso)

Random Forest: Medium data, robustness, feature importance

XGBoost/LightGBM: Best single-model performance, handles missing values

Ensemble: Combine Ridge + RF + XGBoost (weighted 1:2:2), often 2-5% improvement

Position-Specific Modeling: Train separate models per position (RB features ≠ WR features)

Reference: references/model_selection.md for detailed comparisons, hyperparameters, and implementation.

3. Validation Strategies

Critical Rule: NEVER use standard cross-validation with shuffle=True

❌ Wrong: KFold(n_splits=5, shuffle=True) → Data leakage!

✅ Correct: TimeSeriesSplit(n_splits=5) → Train on past, test on future

Time-Series Split: Always predict future from past data

Appropriate Metrics:

• MAE (Mean Absolute Error): Most interpretable
• RMSE: Penalizes large errors
• R²: Proportion of variance explained

Nested Cross-Validation: Outer loop for evaluation, inner loop for hyperparameter tuning

Reference: references/validation_strategies.md for detailed workflows and common mistakes.

4. Sports-Specific Challenges

Small Sample Sizes: NFL = 17 games/season → Use regularization (Ridge/Lasso)

Position-Specific Modeling: Separate models per position with different feature sets

Regime Changes: Weight recent seasons heavier, use sliding window validation

Data Leakage Prevention: Only use data available at prediction time, time-series validation

Reference: references/model_selection.md sections on sports-specific considerations.

Workflow: Building a Player Projection Model

Step 1: Feature Engineering

• Start with raw stats (yards, TDs, targets, snaps)
• Create opportunity metrics (target share, snap %)
• Add efficiency features (YPRR, YPC)
• Generate rolling averages (3-game, 5-game)
• Include age curves and interaction terms
• Use assets/player_projection_model_template.py as starting point

Step 2: Feature Selection

• Check correlation (remove highly correlated features)
• Use Lasso for automatic selection
• SHAP values for importance
• Domain knowledge: prioritize opportunity > efficiency > TDs

Step 3: Model Selection

• Establish baseline (linear regression or Marcel)
• Try regularized model (Elastic Net)
• Test tree-based (Random Forest, then XGBoost)
• Position-specific models
• Ensemble top 2-3 models

Step 4: Validation

• Hold out most recent season as final test
• TimeSeriesSplit on training data
• Nested CV for hyperparameter tuning
• Evaluate MAE by position

Step 5: Interpretability

• SHAP values for feature importance
• Partial dependence plots for age curves
• Validate on new season

Identifying Data Requirements

For Player Projection Models:

• Historical performance (3+ years for aging curves)
• Opportunity metrics (targets, snaps, routes run, carries)
• Efficiency stats (YPRR, YPC, catch rate)
• Contextual data (opponent strength, QB quality, game script)
• Position and age

For Feature Engineering:

• Player-level: Stats, age, position, career year
• Team-level: Total targets, snaps, carries (for share calculations)
• Game-level: Score differential, home/away, opponent defense rank
• Season-level: Rule changes, schedule strength

Integrating with Other Skills

Complement with ff-dynasty-strategy when:

• Need domain knowledge for feature selection (aging curves, TD regression)
• Interpreting model outputs (sell-high candidates)
• Understanding position-specific patterns

Complement with ff-statistical-methods when:

• Choosing regression type (OLS vs Lasso vs GAMs)
• Running Monte Carlo simulations using predictions
• Performing variance analysis

Best Practices

Feature Engineering Over Model Complexity - Well-engineered features make simple models outperform complex ones

Always Use Time-Series Validation - Standard CV inflates performance 15-20%

Position-Specific Models - RB features ≠ WR features ≠ QB features

Regularization for Small Samples - NFL has limited games (17/season)

Prioritize Interpretability - SHAP values for explainability, start simple

References

• references/feature_engineering.md - Age curves, opportunity metrics, efficiency stats, interaction terms
• references/model_selection.md - Decision framework, model types, hyperparameters
• references/validation_strategies.md - Time-series splits, nested CV, metrics

Assets

• assets/player_projection_model_template.py - Python template for building player projection models