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on Sports and Economics |
| By: | Ian Gregory-Smith; Alex Bryson; Rafael Gomez |
| Abstract: | This paper examines the role that racial discrimination plays in the decision to retain or release an employee and demonstrates the implications for estimating pay gaps. Our empirical setting, professional American football players (NFL), allows us to separate the retention decision from the wage decision. For the first four years of a player's career, wages are mechanically determined and players are under a restricted ‘rookie' contract, during which they can be released without cost. Players who survive in the league beyond four years receive a large uptick in their remuneration upon signing their first ‘free-agency' contract. Consequently, marginal decisions over employment retention during the rookie contract have substantial implications for earnings realised over a player's career. We find subtle but significant differences in retention rates between Black and White players (approximately 3 percentage points) that can't be explained by a comprehensive set of individual characteristics including their productivity. We also show that traditional wage gap estimates, which appear to show equal earnings between Black and White players conditional upon playing position and productivity, mask underlying disparities in career earnings that become apparent when adjusting for these unequal retention rates. |
| Keywords: | discrimination; wages; retention |
| JEL: | J71 J31 Z22 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:2554 |
| By: | Zweigle, Tanja; Hanig, Janis |
| Abstract: | This paper examines the influence of perceived stadium atmosphere on fan loyalty in German professional football (soccer). While previous research has primarily focused on team performance and identification as drivers of loyalty, the role of stadium atmosphere as an experiential and co-created factor has received limited empirical attention. Using a mixed-methods approach consisting of a literature review, a focus group, a Delphi study, and a quantitative online survey (n = 209), the study analyzes how perceived stadium atmosphere affects attitudinal and behavioral dimensions of loyalty. The results reveal a significant positive relationship between stadium atmosphere and overall fan loyalty. The effect is particularly strong for attitudinal loyalty, while behavioral loyalty is influenced to a lesser extent. Notably, the relationship proves stable across different fan segments, independent of age, membership status, stadium attendance frequency, or satisfaction with sporting success. The findings suggest that a fan-centered and authentic stadium atmosphere represents a strategic lever for strengthening long-term fan attachment and supporting the economic resilience of professional football clubs in an increasingly commercialized environment. |
| Keywords: | Profifußball, Fankultur, Fanloyalität, Stadionatmosphäre, co-creation |
| JEL: | M31 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:iubhma:340171 |
| By: | Bratanova, Alexandra; Evans, David B; Irons, Jessica |
| Abstract: | This paper examines the emerging applications of artificial intelligence (AI) across the Australian sports sector, focusing on how the technology is reshaping athlete performance, operations, fan engagement and inclusion. Drawing on international evidence, Australian case studies and stakeholder insights, the paper identifies a set of use cases that illustrate both current applications and near-term opportunities. In high-performance contexts, AI is being applied to enhance athlete performance through automated data capture and real-time analytics, support personalised training and coaching, and predict injury and health risks. Additional uses include improving officiating accuracy, expanding talent identification pathways, strengthening integrity through anti-doping and match-fixing detection, and monitoring online abuse directed at athletes. Across sporting organisations, AI is supporting operational efficiency by automating administrative tasks, improving scheduling and communication, enhancing access to information, and strengthening volunteer recruitment and retention. Data-driven tools are also being used to optimise facility utilisation, support sustainability goals and assist with compliance processes. In fan engagement, AI enables more accessible and cost-effective broadcasting, generates highlights and personalised content, and delivers real-time insights to audiences. It also supports participation pathways and enhances marketing and sponsorship activities. Finally, AI is contributing to greater inclusion and accessibility by reducing language barriers, improving experiences for people with disability, and increasing the visibility and participation of under-represented groups. Together, these use cases demonstrate the breadth of AI’s potential to transform the sports ecosystem, while highlighting the need for careful and context-aware implementation. |
| Keywords: | Artificial intelligence; sport; AI; governance; responsible AI |
| JEL: | H4 I18 O3 O33 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128742 |
| By: | Margherita Comola (RITM - Réseaux Innovation Territoires et Mondialisation - Université Paris-Saclay, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Agnieszka Rusinowska (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Marie Claire Villeval (GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - UL2 - Université Lumière - Lyon 2 - UJM - Université Jean Monnet - Saint-Étienne - UJM EPE - Université Jean Monnet (EPSCPE) - EM - EMLyon Business School - CNRS - Centre National de la Recherche Scientifique, IZA - Forschungsinstitut zur Zukunft der Arbeit - Institute of Labor Economics) |
| Abstract: | We experimentally investigate targeting decisions in a setting where a human player competes for influence in a network against a computerized opponent with opposing views, whose targeting choice is revealed before the player acts. By varying network structure, opponent influence, and nodes opinion heterogeneity, we find that players typically adopt best-response strategies based on relative influence. However, they sometimes deviate – for example, by erroneously targeting central nodes or by avoiding the opponent's target. Targeting is also affected by affinity and opposition biases, the strength of which depends on the initial opinion distribution. Targeting the center, avoiding the competitor's target, or selecting nodes based on their initial opinions when these are not best responses generates significant efficiency losses. |
| Keywords: | Network, Influence, Targeting, Competition, Experiment |
| Date: | 2026–04–12 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04706311 |
| By: | Yesiltepe, Demet; Fotios, Steve; Balela, Maan; Uttley, Jim |
| Abstract: | In this work we use Odds Ratios (ORs) to establish the impact of darkness on the number of cyclists across 117 locations in five cities (Arlington (VA, USA), Bergen (Norway), Berlin (Germany), Birmingham and Leeds (UK)) using data from automated counters. We considered four reasons to explain the variance in ORs between counter locations: lighting condition (whether lit or unlit), the proportion of recreational journeys, distance from the city centre and the number of cyclists. The results suggest that lighting condition had the greatest influence, followed by the proportion of recreational journeys and the number of cyclists. The model developed to predict ORs explained 65% of the variance. |
| Date: | 2026–04–22 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:jfkhc_v1 |