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on Sports and Economics |
| By: | Kunz-Kaltenhäuser, Philipp |
| Abstract: | The sport economic literature relies on the city size to proxy for the size of the home market of sports teams. This paper seeks to clarify whether the commonly used definition for home market size in sports economics is actually a valid measure for revenue potential in the modern digital age. Specifically, in this empirical exercise the interest is to investigate to what extent social media following is adding to our understanding of home markets. In doing so, it closely connects to the literature on outcome uncertainty, by considering the compounded season uncertainty for home games, and the literature on superstars in sport as a determinant for demand. The econometric analysis uses NFL stadium attendance data between 2009 and 2019 to examine the question of the relationship of social media and stadium attendance. It applies censored tobit models to estimate the effects. The results suggest a significant relationship between social media following and stadium attendance, even when controlling for the metropolitan area where the stadium is located. It argues that our commonly used definition of home market size is built on the outdated concept of localized markets and should be revisited. |
| Keywords: | home markets, national football league, social media, uncertainty of outcome, franchise reallocation, econometrics |
| JEL: | Z20 L83 D47 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:tuiedp:175&r=spo |
| By: | Pascal Flurin Meier (Department of Business Administration, University of Zurich); Raphael Flepp (Department of Business Administration, University of Zurich); Egon Franck (Department of Business Administration, University of Zurich) |
| Abstract: | Consistent with outcome bias, we replicate the finding of Lefgren et al. (2015) showing that professional basketball coaches in the NBA discontinuously change their starting lineup more often after narrow losses than after narrow wins, even though this outcome is conditionally uninformative. As our paper shows, this pattern is not restricted to the NBA; we find evidence of outcome bias in the top women’s professional basketball league and college basketball. Finally, we show that outcome bias in coaching decisions generalizes to the National Football League (NFL). We conclude that outcome bias is credible and robust, although it has weakened over time. |
| Keywords: | Outcome bias; Strategy revision; Regression discontinuity design; Replication |
| JEL: | D81 D83 D91 Z20 |
| Date: | 2023–02 |
| URL: | http://d.repec.org/n?u=RePEc:zrh:wpaper:400&r=spo |
| By: | Shanglyu Deng; Hanming Fang; Qiang Fu; Zenan Wu |
| Abstract: | Two potentially asymmetric players compete for a prize of common value, which is initially unknown, by exerting efforts. A designer has two instruments for contest design. First, she decides whether and how to disclose an informative signal of the prize value to players. Second, she sets the scoring rule of the contest, which varies the relative competitiveness of the players. We show that the optimum depends on the designer’s objective. A bilateral symmetric contest—in which information is symmetrically distributed and the scoring bias is set to offset the initial asymmetry between players—always maximizes the expected total effort. However, the optimal contest may deliberately create bilateral asymmetry—which discloses the signal privately to one player, while favoring the other in terms of the scoring rule—when the designer is concerned about the expected winner’s effort. The two instruments thus exhibit complementarity, in that the optimum can be made asymmetric in both dimensions even if the players are ex ante symmetric. Our results are qualitatively robust to (i) affiliated signals and (ii) endogenous information structure. We show that information favoritism can play a useful role in addressing affirmative action objectives. |
| JEL: | C72 D44 D82 |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31036&r=spo |
| By: | Minkyu Shin; Jin Kim; Bas van Opheusden; Thomas L. Griffiths |
| Abstract: | How will superhuman artificial intelligence (AI) affect human decision making? And what will be the mechanisms behind this effect? We address these questions in a domain where AI already exceeds human performance, analyzing more than 5.8 million move decisions made by professional Go players over the past 71 years (1950-2021). To address the first question, we use a superhuman AI program to estimate the quality of human decisions across time, generating 58 billion counterfactual game patterns and comparing the win rates of actual human decisions with those of counterfactual AI decisions. We find that humans began to make significantly better decisions following the advent of superhuman AI. We then examine human players' strategies across time and find that novel decisions (i.e., previously unobserved moves) occurred more frequently and became associated with higher decision quality after the advent of superhuman AI. Our findings suggest that the development of superhuman AI programs may have prompted human players to break away from traditional strategies and induced them to explore novel moves, which in turn may have improved their decision-making. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.07462&r=spo |