| Value Bet Identification | Compiled | 2026-06-09 | Value bet identification involves finding bookmaker odds that are higher than the true probability warrants; the core skill in profitable sports betting |
| Sources of Betting Edge (Dispersion, Bias, Forecasting) | Compiled | 2026-06-17 | A betting edge can only come from a price being wrong in your favour, and there are three distinct sources of that — exploiting price dispersion between books (line-shopping), a systematic directional bias in the consensus, or a genuinely superior forecast — each with different mechanisms, requirements, and the same soft-book/account-limiting trap |
| Kelly Criterion | Compiled | 2026-06-09 | Formula for sizing bets to maximize long-term geometric growth rate of bankroll; Kelly bet = (bp - q) / b where b=odds-1, p=win probability, q=loss probability |
| Expected Value (EV) | Compiled | 2026-06-09 | Expected value in sports betting is the average amount won/lost per bet if the same wager were placed repeatedly; positive EV (+EV) bets are the foundation of profitable betting strategy |
| Closing Line Value (CLV) | Compiled | 2026-06-09 | Closing line value (CLV) measures the difference between the odds placed at and the closing odds before the event starts; key metric for validating predictive model quality |
| Market Efficiency (Sharp vs. Soft Bookmakers) | Compiled | 2026-06-09 | Market efficiency in sports betting refers to how accurately odds reflect true probabilities; sharp vs. soft bookmakers, favorite-longshot bias, and implications for finding value |
| De-vigging (Removing the Vigorish) | Compiled | 2026-06-09 | De-vigging is the process of removing the bookmaker's overround (vigorish) from betting odds to get fair implied probabilities; essential for accurate EV calculations |
| Beating the Bookies with Their Own Numbers (Kaunitz et al. 2017) | Compiled | 2026-06-17 | A landmark study showing football bookmakers can be beaten by exploiting the odds themselves — bet outcomes where one book deviates above the multi-book consensus — proving the market is inefficient; and how bookmakers neutralize winners by limiting and closing accounts |
| Half-Kelly | Compiled | 2026-06-09 | Half-Kelly staking uses 50% of the full Kelly fraction to reduce volatility and account for model uncertainty while still capturing most of the growth benefit |
| Line Movement & Steam Moves | Compiled | 2026-06-09 | Line movement and steam moves track how betting odds change over time; steam moves indicate sharp bettor action and can signal market efficiency shifts |
| World Cup Market Inefficiencies (Recreational Bias) | Compiled | 2026-06-17 | International tournament betting markets — especially the FIFA World Cup — are less efficient than top domestic leagues because recreational money saturates the pools, producing favorite over-betting and draw/longshot underpricing; the exploitability is real but noisy and tournament-dependent |
| Poisson-ELO Ensemble | Compiled | 2026-06-09 | Ensemble approach combining Poisson goal-scoring models with ELO rating systems via weighted average; captures both team quality and historical performance dynamics |
| Knockout & Tournament Modelling | Compiled | 2026-06-12 | Extending a per-match W/D/L model to knockout football: 90-minute vs to-qualify markets, extra time and penalty shootouts, the World Cup 2026 format, and Monte Carlo tournament simulation |
| Multi-Sport Prediction Stack | Compiled | 2026-06-09 | Unified machine learning architecture for predicting matches across football, NFL, and NBA using shared infrastructure with sport-specific model heads |
| Poisson Distribution | Compiled | 2026-06-09 | Discrete probability distribution modeling the number of goals scored in a football match given a known mean rate; foundational model for expected goals prediction |
| ELO to Match Probabilities (W/D/L) | Compiled | 2026-06-12 | Converting an ELO expected score into three-way win/draw/loss probabilities: the expected-score identity, Davidson's tie model, empirical draw-rate regression, and ELO-to-goal-expectancy mapping |
| Bayesian Inference in Sports Prediction | Compiled | 2026-06-09 | Method of statistical inference updating probabilities as new evidence is acquired; foundational to modern sports prediction models combining prior beliefs with observed match data |
| Concepts Index | Compiled | | |
| Dixon–Coles Correction | Compiled | 2026-06-09 | Statistical model extending independent Poisson distribution to better capture correlations in low-scoring football matches; applies correction to 0-0, 1-0, 0-1, 1-1 scorelines |
| Massey Ratings | Compiled | 2026-06-09 | Method for measuring past performance of sports teams using a system of linear equations; designed for order rather than prediction; basis for many computer rankings including BCS |
| Bradley–Terry Model | Compiled | 2026-06-09 | Probability model for pairwise comparisons between items/teams; foundational to ELO rating system; used to estimate team strength from head-to-head results |
| Fitting Poisson Match Models via MLE | Compiled | 2026-06-12 | The Maher-style joint maximum-likelihood recipe for estimating per-team attack/defense parameters, home advantage, and time-decay weights from historical match data; the core fitting procedure for a v1 Poisson model |
| ELO Rating System | Compiled | 2026-06-09 | Method for calculating relative skill levels of players/teams in zero-sum games via logistic win-probability function; adapted for football, tennis, and American football |
| Expected Goals (xG) | Compiled | 2026-06-09 | Statistical metric assigning a probability to each shot in football that it results in a goal; sum of shot probabilities gives expected goals for a team/player |
| International Football Modelling | Compiled | 2026-06-12 | What changes when the model targets national teams instead of clubs: neutral venues, sparse and heterogeneous match data, match-importance weighting, and cross-confederation identifiability |
| Player-Level Team Strength | Compiled | 2026-06-12 | Estimating team-level attack/defense parameters by aggregating individual player contributions; roadmap for a v4 (post-tournament) player-level Poisson upgrade |
| Glicko-2 Rating System | Compiled | 2026-06-09 | Improved rating system over ELO that introduces rating deviation (RD) to measure uncertainty and rating volatility; used in chess, Dota 2, Counter-Strike, and other competitive games |
| International Results Datasets | Compiled | 2026-06-12 | The free GitHub datasets the World Cup model trains on: martj42/international_results (all internationals since 1872, with neutral-venue flag) and jfjelstul/worldcup (deep World Cup history); schemas, traps, and preprocessing decisions |
| Sportradar | Compiled | 2026-06-09 | Professional-grade sports data provider covering official statistics, live odds, player tracking, and event data for 80+ sports; enterprise pricing typically $1k+/month |
| OddsJam | Compiled | 2026-06-09 | Sports betting odds API focused on market efficiency and value identification; $199/mo plan with full historical odds, line movement, and sharp book comparisons |
| Football-Data.co.uk | Compiled | 2026-06-09 | Free historical football results, match statistics, and closing betting odds for 22+ European leagues spanning 25+ seasons; the gold standard for free backtesting data |
| Pinnacle API | Compiled | 2026-06-09 | Pinnacle Sports API provides sharp bookmaker odds for major sports; free API requires funded account and approval, historically the benchmark for market efficiency |
| The Odds API | Compiled | 2026-06-09 | Aggregated sports betting odds API covering 80+ bookmakers, multiple sports, free tier with 500 credits/month, paid plans from $99/mo |
| API-Football | Compiled | 2026-06-09 | Comprehensive football REST API covering 1,200+ competitions, fixtures, match statistics, lineups, player data, and odds; plans from $19/mo |
| Polymarket | Compiled | 2026-06-09 | Prediction market for sports, politics, and world events built on Polygon blockchain; public API for market prices, volumes, and settlement |
| Walk-Forward Validation | Compiled | 2026-06-09 | Walk-forward validation is a chronological train/test split where a model is trained on one period and tested on the next; mimics real deployment and prevents look-ahead bias |
| Brier Score | Compiled | 2026-06-09 | Brier score is a strictly proper scoring rule measuring mean squared error between predicted probabilities and actual outcomes; lower is better, 0 is perfect |
| Overfitting in Sports Models | Compiled | 2026-06-09 | Overfitting occurs when a sports prediction model learns noise in training data rather than true patterns; prevention requires walk-forward validation, regularization, and small parameter counts |
| Backtesting Framework | Compiled | 2026-06-09 | Backtesting framework for sports betting models including walk-forward validation, CLV tracking, ROI metrics, and bet-level simulation across historical World Cups |
| Log-Loss (Cross-Entropy) | Compiled | 2026-06-09 | Log-loss (cross-entropy) is a scoring rule that heavily penalizes overconfident wrong predictions; used for optimizing probabilistic classifiers |
| Calibration Plots | Compiled | 2026-06-09 | Calibration plots (reliability diagrams) visually compare predicted probabilities vs. actual outcomes; a well-calibrated model shows points on the diagonal |
| scikit-learn Probability Calibration Documentation | Sources | | Official scikit-learn documentation on probability calibration; covers calibration_curve function, CalibrationDisplay, and post-hoc calibration methods (Platt scaling, isotonic regression) |
| Poisson Distribution | Sources | | Discrete probability distribution modeling the number of events occurring in a fixed interval, widely used for expected goals prediction in football/soccer |
| Glicko-2 Original Paper (Mark Glickman) | Sources | | Mark Glickman's original Glicko-2 paper (PDF); complete mathematical specification including the iterative volatility (sigma) estimation algorithm |
| ML-KULeuven soccer_xg — Open-Source xG Implementation | Sources | | ML-KULeuven's Python package for training and analyzing xG models using SPADL event stream data; open-source implementation with ML pipelines and feature experimentation |
| Pinnacle No-Vig Price (NVP) — De-vigging Guide | Sources | | Pinnacle Odds Dropper guide on removing vig from Pinnacle odds; covers the NVP (No-Vig Price) concept and four de-vigging methods including the recommended Power method |
| Sportradar | Sources | | Professional-grade sports data provider covering official statistics, live odds, player tracking, and event data for 80+ sports; enterprise pricing typically $1k+/month |
| Walk-Forward Validation | Sources | | Walk-forward validation is a chronological train/test split where a model is trained on one period and tested on the next; mimics real deployment and prevents look-ahead bias |
| Brier Score | Sources | | Brier score is a strictly proper scoring rule measuring mean squared error between predicted probabilities and actual outcomes; lower is better, 0 is perfect |
| OddsJam | Sources | | Sports betting odds API focused on market efficiency and value identification; $199/mo plan with full historical odds, line movement, and sharp book comparisons |
| Value Bet Identification | Sources | | Value bet identification involves finding bookmaker odds that are higher than the true probability warrants; the core skill in profitable sports betting |
| FiveThirtyEight NBA Elo Methodology | Sources | | FiveThirtyEight's detailed methodology for their NBA Elo ratings; documents K-factor, home advantage, margin of victory adjustments, and CARMELO extension |
| Overfitting in Sports Models | Sources | | Overfitting occurs when a sports prediction model learns noise in training data rather than true patterns; prevention requires walk-forward validation, regularization, and small parameter counts |
| Bayesian Inference in Sports Prediction | Sources | | Method of statistical inference updating probabilities as new evidence is acquired; foundational to modern sports prediction models combining prior beliefs with observed match data |
| Football-Data.co.uk | Sources | | Free historical football results, match statistics, and closing betting odds for 22+ European leagues spanning 25+ seasons; the gold standard for free backtesting data |
| Why Most AI Betting Models Fail in Football | Sources | | Analysis of why most AI betting models fail in football; focuses on overfitting to historical patterns that don't persist in live markets |
| Pinnacle API | Sources | | Pinnacle Sports API provides sharp bookmaker odds for major sports; free API requires funded account and approval, historically the benchmark for market efficiency |
| Kelly Criterion | Sources | | Formula for sizing bets to maximize long-term geometric growth rate of bankroll; Kelly bet = (bp - q) / b where b=odds-1, p=win probability, q=loss probability |
| Poisson-ELO Ensemble | Sources | | Ensemble approach combining Poisson goal-scoring models with ELO rating systems via weighted average; captures both team quality and historical performance |
| Expected Value (EV) | Sources | | Expected value in sports betting is the average amount won/lost per bet if the same wager were placed repeatedly; positive EV (+EV) bets are the foundation of profitable betting strategy |
| Backtesting Framework | Sources | | Backtesting framework for sports betting models including walk-forward validation, CLV tracking, ROI metrics, and bet-level simulation across historical World Cups |
| Dixon–Coles Correction | Sources | | Statistical model extending independent Poisson distribution to better capture correlations in low-scoring football matches; applies correction to 0-0, 1-0, 0-1, 1-1 scorelines |
| Unabated CLV Calculator& Education | Sources | | Unabated's CLV calculator and educational content; explains CLV as the expected value yardstick and why positive CLV is the fastest way to validate a betting model |
| scipy.stats.poisson — Official Documentation | Sources | | Official SciPy documentation for scipy.stats.poisson — the Python PMF/CDF functions used in all Poisson-based sports models |
| Pinnacle CLV — Official Educational Article | Sources | | Pinnacle's official educational article on closing line value (CLV); explains why the closing line is the market's most efficient price and how CLV measures predictive edge |
| Massey Ratings | Sources | | Method for measuring past performance of sports teams using a system of linear equations; designed for order rather than prediction; basis for many computer rankings including BCS |
| Market Efficiency (Sharp vs. Soft Bookmakers) | Sources | | Market efficiency in sports betting refers to how accurately odds reflect true probabilities; sharp vs. soft bookmakers, favorite-longshot bias, and market efficiency implications for modeling |
| De-vigging (Removing the Vigorish) | Sources | | De-vigging is the process of removing the bookmaker's overround (vigorish) from betting odds to get fair implied probabilities; essential for accurate EV calculations |
| Maher (1982) — Modelling Association Football Scores | Sources | | Original 1982 paper introducing the independent Poisson model for football goal prediction; foundational academic reference for the BP (Birnbaum–Poisson) model |
| Log-Loss (Cross-Entropy) | Sources | | Log-loss (cross-entropy) is a scoring rule that heavily penalizes overconfident wrong predictions; used for optimizing probabilistic classifiers |
| The Odds API | Sources | | Aggregated sports betting odds API covering 80+ bookmakers, multiple sports, free tier with 500 credits/month, paid plans from $99/mo |
| Bradley–Terry Model | Sources | | Probability model for pairwise comparisons between items/teams; foundational to Elo rating system; used to estimate team strength from head-to-head results |
| Forecasting: Principles and Practice — Time Series Cross-Validation | Sources | | Authoritative textbook chapter on time series cross-validation from Forecasting: Principles and Practice (3rd ed); the definitive reference on walk-forward vs. k-fold for temporal data |
| Closing Line Value (CLV) | Sources | | Closing line value (CLV) measures the difference between the odds placed at and the closing odds before the event starts; key metric for validating predictive model quality |
| Dixon–Coles Python Walkthrough (dashee87) | Sources | | Python walkthrough implementing the Dixon–Coles model from scratch with time-weighting; blog post by dashee87 with complete code and data examples |
| Upwork Intake: Sports Prediction MVP | Sources | | Project intake for a World Cup sports prediction MVP combining Poisson expected-goals modeling with live odds feeds, Kelly staking, and Telegram delivery |
| Half-Kelly | Sources | | Half-Kelly staking uses 50% of the full Kelly fraction to reduce volatility and account for model uncertainty while still capturing most of the growth benefit |
| Kelly (1956) — A New Interpretation of Information Rate | Sources | | John Kelly's original 1956 Bell System Technical Journal paper introducing the Kelly criterion; connects information theory to gambling — 'A New Interpretation of Information Rate |
| Arpad Elo — The Rating of Chessplayers, Past& Present | Sources | | Arpad Elo's own book 'The Rating of Chessplayers, Past& Present' (1978); the original authoritative source on the Elo rating system with full mathematical derivation |
| Elo Rating System | Sources | | Method for calculating relative skill levels of players/teams in zero-sum games; adapted for sports prediction including football, tennis, and American football |
| Line Movement & Steam Moves | Sources | | Line movement and steam moves track how betting odds change over time; steam moves indicate sharp bettor action and can signal market efficiency shifts |
| Kaggle Walk-Forward Validation Notebook | Sources | | Kaggle notebook on walk-forward validation for time series; practical Python implementation with expanding/rolling windows and financial time series examples |
| Expected Goals (xG) | Sources | | Statistical metric assigning a probability to each shot in football/soccer that it results in a goal; sum of shot probabilities gives expected goals for a team/player |
| All Pages | Sources | 2026-06-19 | 96 pages across 2 sections — machine-generated index |
| API-Football | Sources | | Comprehensive football REST API covering 1,200+ competitions, fixtures, match statistics, lineups, player data, and odds; plans from $19/mo |
| Multi-Sport Prediction Stack | Sources | | Unified machine learning architecture for predicting matches across football, NFL, and NBA using shared infrastructure with sport-specific model heads |
| Glicko-2 Rating System | Sources | | Improved rating system over Elo that introduces rating deviation (RD) to measure uncertainty and rating volatility; used in chess, Dota 2, Counter-Strike, and other competitive games |
| Calibration Plots | Sources | | Calibration plots (reliability diagrams) visually compare predicted probabilities vs. actual outcomes; a well-calibrated model shows points on the diagonal |
| About | Sources | 2026-01-01 | Compiled knowledge base for sports betting models — Poisson distributions, Elo ratings, Kelly criterion, Pinnacle closing lines, and more. |
| Kelly Criterion Blog (Stanford) | Sources | | Stanford blog walking through Kelly criterion with illuminating examples; explains the relationship to information theory, Bernoulli, and why log-wealth maximization is optimal |
| Building Reproducible xG Models from StatsBomb Open Data | Sources | | Academic paper using StatsBomb open data to build reproducible xG models; compares logistic regression and mixed-effects models, evaluates feature importance |
| Polymarket | Sources | | Prediction market for sports, politics, and world events built on Polygon blockchain; public API for market prices, volumes, and settlement |
| Brier (1950) — Original Paper Reference | Sources | | Glenn Brier's original 1950 paper introducing the Brier score as a proper scoring rule for probabilistic forecasts; published in Monthly Weather Review |
| Dixon& Coles (1997) — Original Paper | Sources | | Original 1997 Dixon–Coles paper introducing the DC correction for low-scoring football matches; published in Journal of the Royal Statistical Society Series C |
| Why CLV Is the Sharps' Preferred Metric (Pinnacle Odds Dropper) | Sources | | Pinnacle Odds Dropper article on why CLV is the sharps' preferred metric; explains that the closing line is the sharpest pre-match line and how positive CLV validates a model's edge |
| FIFA Elo Ranking Methodology | Sources | | FIFA's official Elo-based ranking system (SUM method) for international football; adapted from standard Elo with weighted match importance, home advantage removed, and larger scale divisor |