fantabeto

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Author	SHA1	Message	Date
ramseshk	b0fab62a87	feat: add 10 new ML models for auction optimization (Phases 1-6) Phase 1 - Quick Wins: - QuantileEnsemble: P10/P50/P90 predictions for risk-aware bidding - MinutesSurvivalModel: Weibull AFT for minutes distribution modeling Phase 2 - Adaptive Auction: - BanditAuctionSolver: Thompson Sampling for live auction bids - OpponentBidModel: Predict competitor bids via LightGBM - BudgetOptimizer: Bayesian optimization for role-level allocation Phase 3 - Deep Learning: - RLAuctionPolicy: Double DQN agent for auction strategy - SetTransformer: Team composition valuation via set-based ML Phase 4 - Probabilistic: - BayesianPlayerModel: Hierarchical pooling for rookie uncertainty - ConformalPredictor: Calibrated prediction intervals Phase 5 - Chemistry & Form: - PlayerChemistryGAT: Graph attention network for player synergies - PlayerFormModel: Hawkes process for form momentum Phase 6 - Causal: - TransferCausalModel: Causal forest for transfer effects - AuctionEffectAnalyzer: Bid adjustment from causal analysis 81 tests passing	2026-08-11 17:56:03 +08:00
ramseshk	3b065775f5	Major refactor: Fantabeto 26/27 — modular package, GBM ensemble, MILP/MCTS optimization Phase 1: Data Engineering - Refactored notebooks into src/{scraper,features,models,optimization,bot} - FBref scraper with proxy rotation + Playwright Cloudflare bypass - Fantacalcio.it integrated scraper (authenticated API + HTML fallback) - api-football RapidAPI client for supplementary xG/xA/injuries - RAG news pipeline: Gazzetta, Sky Sport, Di Marzio → injury/suspension/tactical extraction - 26/27 season config: teams, scoring rules, name mappings, news sources Phase 2: SOTA ML Architecture - GBM Ensemble (LightGBM + CatBoost + XGBoost) with stacked blending - Bootstrap ensemble for uncertainty quantification - SinhArcsinh distribution head (ported from original TF Probability) - Card classifiers (yellow/red), penalty model, goal probability (Poisson) - Temporal GNN for player interaction modeling (crosses→goals, passes→assists) - Optuna hyperparameter tuning with time-series CV Phase 3: Operations Research - Auction solver: MILP knapsack with PuLP (budget + role constraints) - Grid Auction (Asta a Griglia): Minimax game theory bidding strategy - Weekly lineup optimizer: MCTS maximizing win probability vs opponent - Modificatore Difesa integration + captain selection - Transfer market analyzer: buy-low/sell-high via xG regression to mean - Opponent behavior modeling from historical lineage patterns Phase 4: Agentic Workflow - Telegram bot: auto-briefing (Friday + Sunday morning) - Tactical briefing generator with start/sit recommendations - GitHub Actions CI/CD: scheduled pipeline (scrape → predict → notify) Infrastructure: - 31 pytest unit tests (features, models, scraper, optimization) - requirements.txt (lightgbm, catboost, xgboost, optuna, pulp, playwright, langchain) - Makefile with install/test/lint/scrape/train/bot targets - Jupyter notebook: 26_27_strategy.ipynb demonstrating auction + matchday 1 mockup - Completely rewritten README.md with architecture diagram	2026-08-11 13:16:07 +08:00

Author

SHA1

Message

Date

ramseshk

b0fab62a87

feat: add 10 new ML models for auction optimization (Phases 1-6)

Phase 1 - Quick Wins:
- QuantileEnsemble: P10/P50/P90 predictions for risk-aware bidding
- MinutesSurvivalModel: Weibull AFT for minutes distribution modeling

Phase 2 - Adaptive Auction:
- BanditAuctionSolver: Thompson Sampling for live auction bids
- OpponentBidModel: Predict competitor bids via LightGBM
- BudgetOptimizer: Bayesian optimization for role-level allocation

Phase 3 - Deep Learning:
- RLAuctionPolicy: Double DQN agent for auction strategy
- SetTransformer: Team composition valuation via set-based ML

Phase 4 - Probabilistic:
- BayesianPlayerModel: Hierarchical pooling for rookie uncertainty
- ConformalPredictor: Calibrated prediction intervals

Phase 5 - Chemistry & Form:
- PlayerChemistryGAT: Graph attention network for player synergies
- PlayerFormModel: Hawkes process for form momentum

Phase 6 - Causal:
- TransferCausalModel: Causal forest for transfer effects
- AuctionEffectAnalyzer: Bid adjustment from causal analysis

81 tests passing

2026-08-11 17:56:03 +08:00

ramseshk

3b065775f5

Major refactor: Fantabeto 26/27 — modular package, GBM ensemble, MILP/MCTS optimization

Phase 1: Data Engineering
- Refactored notebooks into src/{scraper,features,models,optimization,bot}
- FBref scraper with proxy rotation + Playwright Cloudflare bypass
- Fantacalcio.it integrated scraper (authenticated API + HTML fallback)
- api-football RapidAPI client for supplementary xG/xA/injuries
- RAG news pipeline: Gazzetta, Sky Sport, Di Marzio → injury/suspension/tactical extraction
- 26/27 season config: teams, scoring rules, name mappings, news sources

Phase 2: SOTA ML Architecture
- GBM Ensemble (LightGBM + CatBoost + XGBoost) with stacked blending
- Bootstrap ensemble for uncertainty quantification
- SinhArcsinh distribution head (ported from original TF Probability)
- Card classifiers (yellow/red), penalty model, goal probability (Poisson)
- Temporal GNN for player interaction modeling (crosses→goals, passes→assists)
- Optuna hyperparameter tuning with time-series CV

Phase 3: Operations Research
- Auction solver: MILP knapsack with PuLP (budget + role constraints)
- Grid Auction (Asta a Griglia): Minimax game theory bidding strategy
- Weekly lineup optimizer: MCTS maximizing win probability vs opponent
- Modificatore Difesa integration + captain selection
- Transfer market analyzer: buy-low/sell-high via xG regression to mean
- Opponent behavior modeling from historical lineage patterns

Phase 4: Agentic Workflow
- Telegram bot: auto-briefing (Friday + Sunday morning)
- Tactical briefing generator with start/sit recommendations
- GitHub Actions CI/CD: scheduled pipeline (scrape → predict → notify)

Infrastructure:
- 31 pytest unit tests (features, models, scraper, optimization)
- requirements.txt (lightgbm, catboost, xgboost, optuna, pulp, playwright, langchain)
- Makefile with install/test/lint/scrape/train/bot targets
- Jupyter notebook: 26_27_strategy.ipynb demonstrating auction + matchday 1 mockup
- Completely rewritten README.md with architecture diagram

2026-08-11 13:16:07 +08:00

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