The Effect of Seeding on Tournament Outcomes: Evidence From a Regression-Discontinuity Design

Engist, Oliver; Merkus, Erik; Schafmeister, Felix

doi:10.1177/1527002520955212

Cited by 21 publications

(13 citation statements)

References 19 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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“…Dagaev and Rudyak (2019) have also obtained numerical estimates for competitiveness changes in the UEFA tournaments caused by the 2015-16 seeding reform. Note that Engist et al (2021) find no evidence that seeding itself contributes positively to a team's success in the UEFA Champions League.…”

Section: Importance Of the Seeding Rulesmentioning

confidence: 89%

“Choose your opponent”: A new knockout design for hybrid tournaments†

Guyon

2022

JSA

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We present a new, simple knockout format for sports tournaments, that we call “Choose Your Opponent”, where the teams that have performed best during a preliminary group stage can choose their opponents during the subsequent knockout stage. The main benefit of this format is that it essentially solves a recently identified incentive compatibility problem when more than one teams from a group advance to the knockout stage, by effectively canceling the risk of tanking. This new design also makes the group stage more exciting, by giving teams a strong incentive to perform at their best level, and more fair, by limiting the risk of collusion and making sure that the best group winners are fairly rewarded in the knockout round. The choosing procedure would add a new, exciting strategic component to the competition. Advancing teams would choose their opponent during new, much anticipated TV shows which would attract a lot of media attention. We illustrate how this new format would work for the round of 16 of the UEFA Champions League, the most popular soccer club competition in the world.

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“…The seeding rules are discussed in Csató (2020) and Csató (2021, Chapter 2.3). Engist et al (2021) estimate the effect of seeding on tournament outcomes in European club football.…”

Section: Seeding In the Uefa Champions Leaguementioning

confidence: 99%