Search arXiv⌕ Search

arXiv · 2610.21296

On the fairness of single round-robin tournaments: Evidence from European football

Abstract

Single round-robin tournaments are widely regarded as unfair because of their home-away imbalance: some competitors play more home games than the others. Therefore, most sports leagues are played in a double round-robin format. However, some European football leagues contain a single round-robin (sub)tournament. To test whether empirical data can validate their unfairness, we construct a unique database and develop a strength of schedule metric based on Elo ratings with favourable theoretical properties. In contrast to the robust impact of relative team strength, the strength of schedule is found to have no significant effect on the number of points scored under the assumption of a league-specific home advantage. Consequently, the theoretical unfairness is not a serious issue in practice, or the standard approach of modelling home advantage in Elo ratings might need to be reconsidered.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

László Csató, Diána Ivett Fűrész, Zsolt Havran. 2026-10-15. On the fairness of single round-robin tournaments: Evidence from European football. https://arxiv.org/abs/2610.21296

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Identification and Inference for Algorithmic Frontiers with Selective Labels

This paper provides identification results to characterize a fairness-accuracy (FA) frontier, and statistical inference tools to test hypotheses and build a confidence set for the FA-frontier, when outcomes are observed only for selected individuals. When the selection process is unrestricted but loss is measured in specific ways, we provide a characterization of the sharp identification region of the FA-frontier. When the observed distribution rules out frontier membership for a binary status quo algorithm, we construct a single alternative algorithm that improves both groups' accuracy without worsening fairness, uniformly over all admissible distributions of missing labels. For any loss functions, under selection on observables and strict overlap assumptions, we obtain point identification and propose a debiased machine learning estimator, derive its asymptotic distribution, and show how this can be used to carry out inference for the FA-frontier.

econ.EM↗

A Structural Matrix Autoregression Framework for International Spillovers

Understanding how macroeconomic shocks propagate across countries requires structural models that can jointly identify country-specific shocks and their international transmission. Yet extending structural vector autoregressions (SVARs) to large multi-country systems is challenging due to rapidly increasing dimensionality, computational costs, and the proliferation of identifying restrictions. This paper develops a Bayesian Structural Matrix Autoregression (BSMAR) framework that exploits the natural matrix structure of international macroeconomic data. By separating dependence across economic variables from dependence across countries, the framework provides a parsimonious representation that substantially reduces the dimensionality of large structural systems. We develop a Bayesian sampling algorithm for posterior inference that accommodates zero, sign, and ranking (magnitude) restrictions, allowing established SVAR identification schemes to be combined with a novel approach to identifying contemporaneous international spillovers. Applying the model to quarterly data for 15 economies, we find substantial heterogeneity in international shock transmission, with demand shocks playing a more prominent role than supply shocks in generating cross-country spillovers.

econ.EM↗

Propensity score with factor loadings: evaluating the effect of the Paris Agreement on the returns of European stocks

Factor models for longitudinal data, where policy adoption is unconfounded with respect to a low-dimensional set of latent factor loadings, have become increasingly popular for causal inference. Most existing approaches, however, rely on a causal finite-sample approach or computationally intensive methods, limiting their applicability and external validity. In this paper, we propose a novel method for panel causal inference within a super-population framework, based on inverse propensity score weighting, where the propensity score is a function of latent factor loadings. The approach relaxes the traditional restrictive assumptions of causal panel methods, while offering advantages in terms of causal interpretability, policy relevance, and computational efficiency. Under standard assumptions, we develop a three-step estimation procedure for the ATT and derive its large-sample properties using M-estimation theory. Simulation results show that the proposed estimator remains reliable across a variety of designs, including settings with misspecified factor structure and weakly dependent errors. We apply the method to assess the causal effect of the Paris Agreement - a policy aimed at fostering the transition to a low-carbon economy - on European stock returns. Our empirical results suggest a statistically significant and negative short-run effect on the stock returns of firms that demonstrate a strong commitment to sustainability.

econ.EM↗