Productivity and efficiency at english football clubs: a random coefficient approach

Abstract

This paper analyzes productivity and efficiency of English professional football clubs from 1981–1982 to 2010–2011, using a random coefficient stochastic distance frontier (SDF) model. Our Bayes factor analysis indicates that this model is strongly favored over the commonly used fixed coefficient SDF model. Our empirical results show that clubs in our sample operate at different levels of technical efficiency and technical change. Our further analysis using ordered logistic regression suggests that technical efficiency is more important than technical change in predicting whether clubs in our sample are promoted or relegated.

Foreign players, team production, and technical efficiency: Evidence from European soccer

“…However, increases in technical efficiency reduce the likelihood of relegation by 2.0%. This result is in-line with Feng and Jewell (2021), who showed that technical efficiency is negatively associated with the likelihood of relegation. Accordingly, conditional on team quality and squad characteristics, teams that are more efficient in the management of their resources rank better and are more likely to achieve their season objectives.…”

“…As in soccer the average same technology. Although some related studies have relaxed this using latent class (Barros et al, 2009;Jewell, 2017) or random parameter (Feng & Jewell, 2021) SFA models, these models require large datasets for reliable parameter identification.…”

“…To this end, we first define the variable RANK as an ordered indicator that takes value 1 if the team ends in any of the positions that qualify for playing the Champions League in the following season (n = 190), value 2 for the Europa League (n = 90), value 3 in case the team does neither qualify for a European competition but remains in the first division (n = 469), and value 4 if the team is relegated to the second division (n = 207). 20 Similar to Feng and Jewell (2021), we estimate a panel ordered probit regression with teamspecific random effects in which RANK is regressed on TE. Note that although the ranking position is related to the number of points (our output), the same number of points can lead to very different positions depending on the competitive balance of each league in each season.…”

“…In this vein, whereas most studies focus on a single league, we study productive efficiency exploiting a larger sample size using information for the Big Five. Although some scholars have considered different leagues within the same country (Bridgewater et al., 2011; Feng & Jewell, 2021; Jewell, 2017), there are no studies on productive efficiency that use data for the top five European leagues. Accordingly, we consider our results to be more general than previous research.…”

Ambiente institucional, estrutura do futebol feminino e desempenho de 61 clubes das ligas mais fortes do mundo

“…Em sua maioria, os estudos prévios seguem os trabalhos clássicos de Rottenberg (1956) e Scully (1974) e veem o funcionamento dos clubes de futebol profissional como um processo de produção, que transforma insumos esportivos (trabalho e capital) em produtos (atendimento, receita e sucesso no campo de futebol). Neste estudo, a função objetiva "verdadeira" dos clubes está entre a maximização da vitória e a do lucro (Feng & Jewell, 2018). Neste sentido, a Visão Baseada em Recursos (RBV) pode ajudar a compreender de que forma a estrutura do futebol feminino pode refletir no desempenho dos clubes.…”