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International Journal of Sport Finance

Issue 19.1 – February, 2024

By February 1, 2024No Comments


Spectators or Influencers? The Crowd Effect Upon Winning in the NFL: A Natural Experiment

Authors: Justin Ehrlich, Joel Potter, Shane Sanders, and Rodney Paul
Abstract: Previous research has determined that home field advantage (HFA) is positively related to crowd density. Isolating this effect is a substantial empirical challenge as crowd density is endogenous with home win-likelihood via fan interest. We consider a natural-experimental setting that introduces exogenous crowd-density variation into National Football League (NFL) games. COVID-19 safety protocols allow us to disentangle crowd-presence, crowd-density, and built-environment effects upon HFA. We find strong evidence that crowd presence is a significant, substantial source of HFA, but crowd density is not. No-crowd games in 2020 featured no measurable HFA conditional upon team strengths, whereas partially fan-restricted games featured no significant decline in HFA relative to games without crowd restrictions (2016‒2019 and 2021). Results suggest HFA is fully attributable to crowd presence, with no evidence of stadium familiarity or travel distance effects. Betting markets efficiently predicted the partial-crowd effect from season’s outset but adjusted incrementally (behaviorally) to incorporate the true no-crowd effect only by season’s end.
Keywords: home advantage, crowd density, sports betting, National Football League

Sports League Profit and Market Sizes

Authors: Jason Winfree, Connor Allen, and Stefan Szymanski
Abstract: It is often assumed that sports leagues should have teams in the largest markets. However, a very basic model of a sports league shows that, depending on talent investment’s role in team revenue, this assumption is not necessarily true. Heterogeneity in markets sizes can not only decrease costs but also increase expected league revenue. Having a smaller market leads to more wins for the larger market team and less competition for playing talent. Therefore, leagues may prefer smaller markets for expansion teams or team relocation. Given that leagues typically want larger markets, the paper investigates how the basic model of a sports league may be lacking. If leagues want competitive balance or more absolute talent, it may cause leagues to pursue larger markets.
Keywords: contest success function, sports leagues
JEL classification: C72, L10, L83

The Appraisal of Players’ Transfer Market Values: Empirical Evidence From Italian Serie A

Authors: Marco Di Domizio, Raul Caruso, and Bernd Frick
Abstract: This paper focuses on the determinants of transfer market valuations of Italian top division football players over the period 2007‒2017. We use data provided by to estimate the association between players’ characteristics and their transfer valuation. Additionally, by applying panel regression techniques, we separate team and season effects from individual attributes. We find an inverted U-shaped association with age and a positive association with goals, assists, and minutes played in national and international competitions. Moreover, the financial sustainability of the clubs appears to be a key factor since potential accountability turmoil may cause a reduction in the bargaining power of clubs and on the players’ market values consequently. Finally, season fixed effects show a statistically significant and strong negative trend of players’ market appraisals. That result should be taken seriously by representatives of Serie A League as well as the Italian Football Federation because the recently pursued strategy of adjusting the decrease of conventional revenues (e.g., ticket sales, TV rights and sponsorship) with the capital gains coming from player transfers proves to be rather risky.
Keywords: football, players’ transfer market, swarm intelligence, wisdom of crowds, asset management
JEL codes: Z20, Z22, L83

A Long Run Look at FBS Football Attendance

Authors: Paul A. Natke, Gregory A. Falls, and Linlan Xiao
Abstract: A balanced panel (99 teams over 40 years) is used to estimate three regression models: average attendance via fixed and random effects plus Tobit estimation of percent of capacity. Variables are either stationary or cointegrated. Estimation makes adjustments for serial correlation and endogeneity between several variables. Independent variables measure economic conditions, demographic characteristics, and team performance. Attendance is a normal good. Travel cost is insignificant in two models but positive in one. Weak evidence suggests undergraduate enrollment and county population exert a positive impact. Three team performance measures and power ranking scores exert consistent positive impacts. Stadium renovations that increase capacity also increase attendance while those that reduce capacity are insignificant except in the percent of capacity equation. Attendance responses vary, either in sign or significance, across Power Five and Group of Five teams for eight of 10 independent variables. Specification tests suggest that random effects estimation is preferred.
Keywords: college football, attendance, stadium capacity, time series, panel regression
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