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on Sports and Economics |
| By: | Tim Pawlowski (University of Tübingen); Dooruj Rambaccussing (University of Dundee); Philip Ramirez (Department of Economics, University of Reading); James (Department of Economics, University of Reading); Giambattista Rossi (Birkbeck University of London) |
| Abstract: | Previous research exploring the role of belief dynamics for consumers in the entertainment industry has largely ignored the fact that emotional reactions are a function of the content and a consumer’s disposition towards certain participants involved in an event. By analyzing 19m tweets in combination with in-play information for 380 football matches played in the English Premier League we contribute to the literature in three ways. First, we present a setting for testing how belief dynamics drive behavior which is characterized by several desirable features for empirical research. Second, we present an approach for detecting fans and haters of a club as well as neutrals via sentiment revealed in Tweets. Third, by looking at behavioral responses to the temporal resolution of uncertainty during a game, we offer a fine-grained empirical test for the popular uncertainty-of-outcome hypothesis in sports. |
| Keywords: | suspense, surprise, entertainment utility, football, tweets |
| JEL: | C10 D91 L83 |
| Date: | 2023–08–04 |
| URL: | http://d.repec.org/n?u=RePEc:rdg:emxxdp:em-dp2023-13&r=spo |
| By: | V\'elez Jim\'enez; Rom\'an Alberto; Lecuanda Ontiveros; Jos\'e Manuel; Edgar Possani |
| Abstract: | This paper presents a novel approach for optimizing betting strategies in sports gambling by integrating Von Neumann-Morgenstern Expected Utility Theory, deep learning techniques, and advanced formulations of the Kelly Criterion. By combining neural network models with portfolio optimization, our method achieved remarkable profits of 135.8% relative to the initial wealth during the latter half of the 20/21 season of the English Premier League. We explore complete and restricted strategies, evaluating their performance, risk management, and diversification. A deep neural network model is developed to forecast match outcomes, addressing challenges such as limited variables. Our research provides valuable insights and practical applications in the field of sports betting and predictive modeling. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.13807&r=spo |
| By: | Subhasish M. Chowdhury (Department of Economics, University of Sheffield, Sheffield S1 4DT, UK); Sarah Jewell (Department of Economics, University of Reading, Whiteknights Campus, RG6 6EL, UK); Carl Singleton (Department of Economics, University of Reading, Whiteknights Campus, RG6 6EL, UK) |
| Abstract: | Competition is often judged by official decision makers, such as judges, juries, and referees. Systematic bias in those judgements, frequently related to social identities, may have undesirable effects. We investigate whether raising awareness can correct or even reverse such bias. We use a natural experiment from international Test cricket to focus on the match umpires and their decisions. Previous research has found evidence of biased judgements favouring the home team when the umpires shared the same nationality. Policy makers solved this by employing neutral country umpires. From June 2020, home umpires temporarily returned, sometimes in empty stadiums, because of the COVID-19 pandemic. We argue that these umpires were then under substantial scrutiny, due to the previous bias being well-known and highlighted in the media, alongside a technology-driven decision review system. Through a behavioural model, we show that such circumstances may result in the in- group judgement bias being eliminated or reversed. We find no evidence of the historical bias in umpire judgements returning during the pandemic. Instead, we find over-compensating behaviour, with a pre-pandemic home team advantage of 26% in the frequency of subjective and difficult ‘leg before wicket’ decisions being eliminated by the return of home umpires. Tight decisions tended to go against the home team more frequently when home umpires were officiating. We conclude that awareness not only has a long-term effect on eliminating identity-driven judgement bias but also may reverse it against the in-group. |
| Keywords: | Natural Experiment; Identity; Judgement Bias; Social Pressure; Home Advantage |
| JEL: | D01 D91 L83 Z2 |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:shf:wpaper:2023017&r=spo |
| By: | Stefano Piasenti; Marica Valente; Roel van Veldhuizen; Gregor Pfeifer; Gregor-Gabriel Pfeifer |
| Abstract: | How do men and women differ in their persistence after experiencing failure in a competitive environment? We tackle this question by combining a large online experiment (N=2, 086) with machine learning. We find that when losing is unequivocally due to merit, both men and women exhibit a significant decrease in subsequent tournament entry. However, when the prior tournament is unfair, i.e., a loss is no longer necessarily based on merit, women are more discouraged than men. These results suggest that transparent meritocratic criteria may play a key role in preventing women from falling behind after experiencing a loss. |
| Keywords: | competitiveness, gender, fairness, machine learning, online experiment |
| JEL: | C90 D91 J16 C14 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10572&r=spo |
| By: | Rinne, Ulf (IZA); Sonnabend, Hendrik (Fern Universität Hagen); Wolters, Leonie (Fern Universität Hagen) |
| Abstract: | This paper examines the relationship between customer preferences and ethnic team composition in German professional soccer. Ethnic team composition is measured using facial recognition techniques, player names, and nationality. The study uses a difference-in-differences approach to show that after New Year’s Eve 2015-16, third-division teams focusing on local and regional fans increased the share of native players by 6.4 to 12.2 percent compared to first- and second-division teams. Additionally, we find that in strongholds of the right-wing populist party AfD, a one-standard-deviation increase in the regional voting share for this party is associated with an increase in the share of native players by 3.1 to 3.6 percentage points. When examining the impacts of these changes in ethnic team composition on team productivity and economic success, we find that a higher share of (native) German is neither associated with better performance outcomes nor higher attendance rates. |
| Keywords: | discrimination, labor market, soccer, ethnicity, facial recognition |
| JEL: | J15 J44 J71 Z22 |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16315&r=spo |
| By: | Hamed Amini; Maxim Bichuch; Zachary Feinstein |
| Abstract: | Prediction markets allow traders to bet on potential future outcomes. These markets exist for weather, political, sports, and economic forecasting. Within this work we consider a decentralized framework for prediction markets using automated market makers (AMMs). Specifically, we construct a liquidity-based AMM structure for prediction markets that, under reasonable axioms on the underlying utility function, satisfy meaningful financial properties on the cost of betting and the resulting pricing oracle. Importantly, we study how liquidity can be pooled or withdrawn from the AMM and the resulting implications to the market behavior. In considering this decentralized framework, we additionally propose financially meaningful fees that can be collected for trading to compensate the liquidity providers for their vital market function. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.08768&r=spo |