The UEFA Champions League seeding is not strategy-proof since the 2015/16 season

Csató, László

doi:10.1007/s10479-020-03637-1

Cited by 36 publications

(26 citation statements)

References 58 publications

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“…ELO-ratings were included as a measure of team strength, provided by the API of http://www.clubelo.com, accessed on 1 July 2020, which is freely available. This source provides ELO-ratings for each team of over 50 (inter)national leagues, including the UEFA Europa League and Champions League, and has previously been used for scientific purposes [22,23]. Each game, both national and international, results in an exchange in ELO-points (Equation ( 1)), where dr is the difference in ELO-rating between two clubs and R is the result (1 for win, 0.5 for draw and 0 for loss).…”

Section: Variablesmentioning

confidence: 99%

Machine Learning-Based Identification of the Strongest Predictive Variables of Winning and Losing in Belgian Professional Soccer

et al. 2021

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This study aimed to identify the strongest predictive variables of winning and losing in the highest Belgian soccer division. A predictive machine learning model based on a broad range of variables (n = 100) was constructed, using a dataset consisting of 576 games. To avoid multicollinearity and reduce dimensionality, Variance Inflation Factor (threshold of 5) and BorutaShap were respectively applied. A total of 13 variables remained and were used to predict winning or losing using Extreme Gradient Boosting. TreeExplainer was applied to determine feature importance on a global and local level. The model showed an accuracy of 89.6% ± 3.1% (precision: 88.9%; recall: 90.1%, f1-score: 89.5%), correctly classifying 516 out of 576 games. Shots on target from the attacking penalty box showed to be the best predictor. Several physical indicators are amongst the best predictors, as well as contextual variables such as ELO -ratings, added transfers value of the benched players and match location. The results show the added value of the inclusion of a broad spectrum of variables when predicting and evaluating game outcomes. Similar modelling approaches can be used by clubs to identify the strongest predictive variables for their leagues, and evaluate and improve their current quantitative analyses.

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Section: Variablesmentioning

confidence: 99%

Machine Learning-Based Identification of the Strongest Predictive Variables of Winning and Losing in Belgian Professional Soccer

et al. 2021

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“…The design of a hybrid tournament consisting of round-robin groups followed by a knock-out phase raises several interesting theoretical questions on various fields such as fairness (Cea et al, 2020;Guyon, 2018), or strategy-proofness (Csató, 2019b(Csató, , 2020bDagaev & Sonin, 2018;Pauly, 2014;Vong, 2017). The current paper has attempted to explore the potential effects of creating groups with different quality through the example of the EHF Champions League, the most prestigious men's handball club competition in Europe.…”

Section: Discussionmentioning

confidence: 99%

Optimal Tournament Design: Lessons From the Men’s Handball Champions League

Csató

2020

Journal of Sports Economics

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This paper challenges the traditional seeding regime of round-robin tournaments that aims to create balanced groups. In particular, the design of the most prestigious European men’s handball club competition is compared to two alternative formats with equally strong groups via simulations. We find that it is possible to increase the quality of all matches played together with raising the uncertainty of outcome, essentially without sacrificing fairness. Our results have useful implications for the governing bodies of major sports.

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“…Descriptive statistics show that, on average, in the English Premier League the home teams score 1.5 goals while the away ones 1.2. Hence, in a certain measure the home teams are more willing to win a match (probabily due to the so called home bias effect Goossens et al 2012;Csató 2020). Moreover, in almost the 50% of the matches both team scores and there are not less than 3 total goals.…”

Section: Data and Betting Strategymentioning

confidence: 99%

Forecasting binary outcomes in soccer

Mattera

2021

Ann Oper Res

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Several studies deal with the development of advanced statistical methods for predicting football match results. These predictions are then used to construct profitable betting strategies. Even if the most popular bets are based on whether one expects that a team will win, lose, or draw in the next game, nowadays a variety of other outcomes are available for betting purposes. While some of these events are binary in nature (e.g. the red cards occurrence), others can be seen as binary outcomes. In this paper we propose a simple framework, based on score-driven models, able to obtain accurate forecasts for binary outcomes in soccer matches. To show the usefulness of the proposed statistical approach, two experiments to the English Premier League and to the Italian Serie A are provided for predicting red cards occurrence, Under/Over and Goal/No Goal events.

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The UEFA Champions League seeding is not strategy-proof since the 2015/16 season

Cited by 36 publications

References 58 publications

Machine Learning-Based Identification of the Strongest Predictive Variables of Winning and Losing in Belgian Professional Soccer

Machine Learning-Based Identification of the Strongest Predictive Variables of Winning and Losing in Belgian Professional Soccer

Optimal Tournament Design: Lessons From the Men’s Handball Champions League

Forecasting binary outcomes in soccer

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