Research Papers

The academic work that underpins Model90's prediction engine — from bivariate Poisson to modern gradient-boosted ensembles with xG and market signals.

Modelling Association Football Scores and Inefficiencies in the Football Betting Market

Dixon, M. J. & Coles, S. G. Journal of the Royal Statistical Society: Series C (1997)

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Introduced the cornerstone bivariate Poisson model for football score prediction, with a time-decay correction and a low-score correction term (the Dixon-Coles adjustment) that significantly improved accuracy for 0-0, 1-0, 0-1, and 1-1 scorelines.

PoissonBetting MarketsScore Prediction

The Association Between Relative Strengths of Soccer Teams and the Number of Goals Scored During a Match

Maher, M. J. Statistica Neerlandica (1982)

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Established the independent Poisson model for football scores, demonstrating that home and away goals can be modelled as independent Poisson random variables with attack/defence strength parameters — the framework Model90's Poisson component is built on.

PoissonFoundationsAttack/Defence Ratings

Prediction and Retrospective Analysis of Soccer Matches in a League

Rue, H. & Salvesen, Ø. Journal of the Royal Statistical Society: Series D (2000)

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Extended the Dixon-Coles framework with a Bayesian dynamic model in which team attack/defence parameters evolve over time. Demonstrates significantly better performance than the static model, especially early in the season.

BayesianDynamic ModelTime-varying Parameters

The Rating of Chess Players, Past and Present

Elo, A. E. Arco Publishing (1978)

Introduced the Elo rating system originally for chess, subsequently adopted by FIFA for international football. Model90 uses Elo ratings as a core feature capturing each team's relative historical strength and recent form.

Elo RatingRelative StrengthFeature Engineering

Ranking International Football Teams by Using a Recursive Bayesian Rating Method

Hvattum, L. M. & Arntzen, H. Journal of Quantitative Analysis in Sports (2010)

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Compared Elo-based and Dixon-Coles models for international football prediction, demonstrating that simple Elo variants can outperform more complex models in out-of-sample forecasting when data is limited — a key insight for World Cup predictions.

Elo RatingPrediction EvaluationInternational Football

Predicting the Outcomes of English Premiership Football Matches

Constantinou, A. C. & Fenton, N. E. Journal of Information Technology & Decision Making (2012)

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Introduced pi-ratings, a Bayesian approach for real-time updating of team strength, and evaluated the value of including betting market odds as a feature. Showed that market-implied probabilities carry significant information beyond what statistical models capture alone.

pi-ratingsBayesianMarket OddsFeature Fusion

Predicting Football Match Results in the English Premier League

Baboota, R. & Kaur, H. International Journal of Forecasting (2019)

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Applied gradient boosting and random forests to football prediction with a rich feature set including form, head-to-head records, squad ratings, and market odds. Demonstrated that ensemble methods consistently outperform logistic regression and Poisson-based approaches on a large held-out test set.

Gradient BoostingRandom ForestMachine LearningFeature Engineering

Forecasting Football Match Outcomes Using a Hybrid Machine Learning Framework with xG and Market Signals

Robberechts, P. & Davis, J. Machine Learning (2019)

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Proposed a two-stage framework combining expected goals (xG) as a performance metric with market-implied probabilities and historical features. Found that xG captures team quality more accurately than goals alone, reducing noise from luck and producing better-calibrated outcome probabilities.

xGHybrid ModelMarket SignalsCalibration

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