Contents for IJSF Issue 9:4
|Entire issue of IJSF 9:4
Authors: All editors in this section
|Abstract:This is the entire issue in PDF format that you can download
|Editors’ Note: A Fond Farewell, p. 283
Authors: Robert Simmons, Bernd Frick, and Brad R. Humphreys
|Abstract:This issue of the International Journal of Sport Finance (IJSF) is the final one under the stewardship of myself, Bernd Frick, and Brad Humphreys. It has been a pleasure and a privilege for us to manage the editorial process of IJSF for the last six years, succeeding inaugural editor Dennis Howard.
|If You Get Knocked Down, How Long Before You Get up Again?, pp. 284-304
Authors: Augusto Cerqua
|Abstract:This paper addresses an important question on the consequences of relegation from some of Europe’s top football leagues: What is the team’s performance following relegation, compared to the situation without relegation? We compare the performance of relegated and non-relegated teams that battled until the last match to escape relegation in four large European leagues. We find that, on average, up to six seasons are necessary to completely reabsorb the negative relegation shock in sports outcomes. Additionally, we exploit current information on future TV rights revenues to forecast the extra cost of an ‘unlucky’ relegation in the 2013-14 season. The results show that, on average, relegation will cause an extra cost of about €135m to ‘combative’ teams relegated from the English Premier League. Smaller extra losses apply to the Italian Serie A (€60m), the Spanish La Liga (€38m), and the French Ligue 1 (€32m).
|Leadership and Efficiency in Professional Cycling, pp. 315-330
Authors: César Rodríguez-Gutiérrez
|Abstract:The aim of this paper is to assess the determinants of cyclists’ performance over the season, particularly the effect of being a team leader. Several efficiency indicators are used, the most common being the number of Cycling Quotient (CQ) points accumulated by riders divided by the number of kilometers of competition. The results show that efficiency depends mainly on individual features (such as age) and the calendar of competition chosen by riders. However, the most decisive feature in enhancing rider efficiency is team status. Specifically, the estimates show that being team leader significantly increases efficiency.
|More Surf, Less Bias: The Influence of Advertising in Two-Sided Sport Markets, pp. 331-345
Authors: Jürgen Rösch
|Abstract: This paper analyzes the influence of advertisers on the results of the ASP World Tour in surfing. The close connection of the event with its sponsor, the interest of the sponsor in the outcome of the event, and the observability of the results allow to test the existence of a profit-orientated bias. In contrast to the theoretical and empirical predictions no significant influence from the sponsor over the outcome of the contest can be found. The high frequency of exposure and the observability of the decisions are the main reason for that result.
|Quantile Regression for Sports Economics, pp. 346-359
Authors: Michael A. Leeds
|Abstract:Quantile regression provides sports economists with a powerful research tool. Unlike least squares, it is not tied to restrictive assumptions about the distribution of the error term, which makes it particularly valuable in settings with highly skewed distributions, like sports labor markets. It allows investigators to check for heteroskedasticity and to avoid censored variable bias. Researchers can use it simulate the distribution of incomes or profits, not just their mean values. Still, few sports economists use quantile regression, and, when used, it is frequently misinterpreted. This article provides a user-friendly introduction to quantile regression that will stimulate its use in the sports economics literature.
|Stochastic Frontier Models in Sports Economics, pp. 360-374
Authors: Young Hoon Lee
|Abstract:This paper is intended to introduce a variety of stochastic frontier econometric models to applied economists working in the field of sports economics. First, it discusses the characteristics and assumptions of individual stochastic frontier models that should be used in empirical studies. For example, it distinguishes “preferred” from “not-preferred” models based on the size of the panel data sample or the research purposes. Second, it discusses the characteristics of the sports industry and differentiates it from manufacturing industry, which is necessary when using stochastic frontier models that have been developed for the study of general industries such as manufacturing. The third purpose of the paper is to introduce ready-made program codes for various stochastic frontier models. Information about the available FRONTIER4.1, STATA commands and GAUSS codes may help widen the domain from which empirical sports economists can select their regression models for use in efficiency studies.