Tennis superstars: The relationship between star status and demand for tickets

Chmait, Nader; Robertson, Sam; Westerbeek, Hans; Eime, Rochelle; Sellitto, Carmine; Reid, Machar

doi:10.1016/j.smr.2019.03.006

Cited by 14 publications

(26 citation statements)

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“…In Table 3, we present a concise overview of all competitions in our data set. In sum, we note 195 manuscripts featuring a total of 235 studies on competitions in 13 different sports, including Hockey (e.g., Coates & Humphreys, 2012), Ultimate Fighting (e.g., Watanabe, 2015), Handball (Storm et al, 2018), NASCAR racing (Berkowitz et al, 2011) and Tennis (Chmait et al, 2020), amongst others. Despite this apparent diversity, it is, however, interesting that there seems still to exist no manuscript exploring the spectator demand for such otherwise popular sports as Athletics, Badminton, Boxing, Cycling, Golf, Field hockey, Formula 1 racing, Gymnastics, Skiing, Snooker, Swimming, and Volleyball.…”

Section: Resultsmentioning

confidence: 99%

“…In fact, somewhat surprisingly, the authors of only about 30 manuscripts discussed, often briefly, the potential limitations that may arise from the use of such data generated across both matchday ticket holders and season ticket holders (e.g., Barajas et al, 2019; Buraimo et al, 2018; Paul et al, 2019). As such, it is perhaps not surprising that the authors of only about a handful of manuscripts explicitly explored the behavioral intentions of matchday ticket holders (e.g., Allan & Roy, 2008; Benz et al, 2009; Bond & Addesa, 2020), that is, by analyzing attendance data after having subtracted season ticket holder data, or added such season ticket holder data as an explanatory variable (e.g., Chmait et al, 2020). 27 Perhaps one potential reason for this is that most authors still wrongly consider season ticket holders to be behavioral loyal (e.g., Schreyer et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

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Stadium Attendance Demand Research: A Scoping Review

Schreyer

Ansari²

2021

Journal of Sports Economics

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Because maximizing stadium attendance demand is of utmost importance, for both sports economists and sport management researchers, understanding the potential determinants of such demand better has become a priority in the last decades. Here, conducting a systematic scoping review, we map this previous research in terms of its characteristics, its nature, and its volume, thus offering a concise perspective on what has been previously explored, and, more importantly, what remains to be analyzed in the future. Intriguingly, we observe a lack of studies exploring data generated in both niche and women’s sports, as well as in most emerging markets. Further, the field has not yet established the use of disaggregated stadium attendance data, despite notable potential methodological pitfalls.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Stadium Attendance Demand Research: A Scoping Review

Schreyer

Ansari²

2021

Journal of Sports Economics

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“…• In Major League Baseball (Ormiston, 2014;Lewis and Yoon, 2016) • In the National Basketball Association (Berri et al, 2004;Jane, 2016) • In tennis: superstar player effect in demand for tennis tournament attendance (Chmait et al, 2020a), the presence of a stardom effect in social media (Chmait et al, 2020b), 1 For conferences and published articles on AI and sports analytics see Swartz (2020).…”

Section: Research On Ai and ML In Sportsmentioning

confidence: 99%

“…This is often achieved by the use of statistical modelling as well as other machine learning solutions, for example, to understand the value of players from an economic perspective. As shown in Chmait et al ( 2020a ), investigating the relationship between the talent and success of athletes (to determine the existence of what is referred to as superstardom phenomenon or star power) is becoming an important angle to explore value created in sport. To provide an idea of the extent of such work, we note some sports in which the relationship between famous players/teams and their effect on audience attendance or sport consumption has been studied:…”

Section: Research On Ai and ML In Sportsmentioning

confidence: 99%

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Artificial Intelligence and Machine Learning in Sport Research: An Introduction for Non-data Scientists

Chmait

Westerbeek

2021

Front. Sports Act. Living

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In the last two decades, artificial intelligence (AI) has transformed the way in which we consume and analyse sports. The role of AI in improving decision-making and forecasting in sports, amongst many other advantages, is rapidly expanding and gaining more attention in both the academic sector and the industry. Nonetheless, for many sports audiences, professionals and policy makers, who are not particularly au courant or experts in AI, the connexion between artificial intelligence and sports remains fuzzy. Likewise, for many, the motivations for adopting a machine learning (ML) paradigm in sports analytics are still either faint or unclear. In this perspective paper, we present a high-level, non-technical, overview of the machine learning paradigm that motivates its potential for enhancing sports (performance and business) analytics. We provide a summary of some relevant research literature on the areas in which artificial intelligence and machine learning have been applied to the sports industry and in sport research. Finally, we present some hypothetical scenarios of how AI and ML could shape the future of sports.

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Separately measuring home‐field advantage for offenses and defenses: A panel‐data study of constituent channels within collegiate American football

McMahon,

Quintanar

2024

Southern Economic Journal

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We improve constituent‐channel estimates of home‐field and neutral‐site advantage for collegiate American football's top division by utilizing a richer, 12‐season data set and by exploiting the COVID‐19 pandemic as a random shock. Novel to the literature, we separately examine points scored by each team, allowing us to identify impacts on each team's offense and defense individually. The information set provided by our model is a strict superset of that provided by the previous standard in the literature, making ours a strictly dominant modeling choice. We demonstrate this improvement theoretically and empirically. Physiologically, away‐team travel distance does not impact their own score, but it increases home‐team scores, consistent with the notion that defenses tire faster than offenses. There is also similar but limited evidence of this effect for neutral‐site teams. Time zones may play a minor role, too. Psychologically, crowd size and density hurt away‐team scores but do not impact home or neutral‐site teams. The away‐team effect disappears in 2020, however, indicating that the pre‐2020 effect is caused by the crowd's noise, not their mere presence. We also find that increasing stadium capacity while holding crowd size constant hurts home‐team scores, highlighting the importance of considering ticket demand when considering stadium expansion. Tactically, stadium familiarity helps offenses, not defenses, while team‐opponent familiarity has the opposite effect. Weather also plays a role. At median values for key variables, we find an overall home‐field advantage of 4.1 points.

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Tennis superstars: The relationship between star status and demand for tickets

Cited by 14 publications

References 50 publications

Stadium Attendance Demand Research: A Scoping Review

Stadium Attendance Demand Research: A Scoping Review

Artificial Intelligence and Machine Learning in Sport Research: An Introduction for Non-data Scientists

Separately measuring home‐field advantage for offenses and defenses: A panel‐data study of constituent channels within collegiate American football

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