Meeting Expectations at the 2016 Rio Olympic Games: Country Potential and Competitiveness

Abstract

Objective Develop a framework to measure the extent to which countries meet their performance expectations at major sporting events using economists, experts, and fan knowledge. Method Long‐term expectations based on socioeconomic potential are calculated using sport‐agnostic econometric modeling. Short‐term expectations based on performance and competitiveness are calculated using betting odds, which incorporate both expert knowledge and “wisdom of the crowd.” Robust statistics based on the chi‐squared distribution are used to measure the extent to which countries’ expectations are met. Our method is tested using real data from the Rio 2016 Summer Olympic Games. Results Twelve countries over‐ or underperformed in terms of meeting either long‐ or short‐term expectations. Conclusions Uzbekistan was the only country that exceeded both long‐ and short‐term expectations.

Legend of leagues: heterogeneity in the revenue structure of European national leagues

“…Country (macro) level and club (micro) level are the most common examples, but the number of research is relatively few at the league (meso) level. At a macro level, we can highlight the Olympic/professional elite sport (Nessel and Ko sci ołek, 2022;Otamendi et al, 2020) and football (Gasparetto and Barajas, 2020) related articles that mainly work with sport data and macro-level economic data. In the current paper, we emphasise that UEFA coefficient is key to understanding if different revenue structures correlate with higher or lower sporting performance.…”

Connection between State Funding and International Sporting Success: The Case of Croatia

“…One of the main reasons for this might be the fact that "it is unattainable to develop indicators for each and every participating country based on publicly available data." (Otamendi, et al, 2020, p. 671) According to De Bosscher et al (2006, majority of research dealt with similarities and differences between nations regarding their sport systems and analysed organizational and management context of elite sport in former communist countries (Houlihan, 1997;Kruger, 1984;Riordan, 1991;Semotiuk, 1990). Some of the research conducted after the year 2000 showed that national elite sport systems are becoming the same, homogenous in every country (Green & Oakley, 2001;Houlihan & Green, 2008), but there is still room to differ among them (Green & Oakley, 2001).…”

“…Some of the research conducted after the year 2000 showed that national elite sport systems are becoming the same, homogenous in every country (Green & Oakley, 2001;Houlihan & Green, 2008), but there is still room to differ among them (Green & Oakley, 2001). Macro-level research also emphasize this conclusion and discuss sport policy when accounting for differences in results (Otamendi, et al, 2020), explain some of the used variables in the research (Forrest et al, 2017) or refer to needed future actions (Otamendi & Doncel, 2014).…”

“…(Bernard & Busse, 2004, p. 413) The starting premises are that larger countries have larger talent pools to choose from, and richer countries can invest more in sport and provide better infrastructure needed to practise sport (Bernard & Busse, 2000;Bernard & Busse, 2004;Grimes et al, 1974;Kiviaho & Mäkelä, 1978;Levine, 1974;Lui & Sen 2008;Morton, 2002). This baseline model was expanded to include variables such as hosting an event, climate conditions, political system, number of female athletes, even corruption or schooling indicators (Andrade Rosas & Flegl, 2019;Andreff, M., Andreff, W. & Poupaux, 2008;Bernard & Busse, 2000;Bernard & Busse, 2004;Forrest et al, 2017;Grimes et al, 1974;Kiviaho & Mäkelä, 1978;Levine, 1974;Lui & Sen 2008;Morton, 2002;Otamendi & Doncel, 2014;Otamendi et al, 2020;Scelles et al, 2020;Soos et al, 2020;Trivedi & Zimmer, 2014;Vagenas & Palaiothodorou, 2019). Due to already mentioned problems with data gathering, variables concerned with finances, such as the level of public expenditure on recreational, cultural, and religious affairs (Blais-Morisset, Boucher & Fortin, 2017;Forrest, Sanz & Tena, 2010) or on health (Vagenas & Vlachokyriakou, 2012) are seldomly used.…”

A method in bayesian network knowledge engineering for state-space reduction

“…Earlier practices on this matter attempted to predict the investment return an asset is expected to pay out in stock markets [52] or the risk of an earthquake at a given location in order to help planning prevention strategies [42]. More recent works applied prediction to estimate the probability of a song to become popular based on popularity metrics assessed from chart data [61]; quantify the relevance of urban metrics and socioeconomic indicators in predicting homicides [6]; anticipate engagement issues with students in an online course and adapt its methodology to improve the learning experience [69]; determine how likely it is a patient to be readmitted to a hospital soon after they had been discharged [8]; develop suicide prediction tools for preventive interventions on target patients [53]; forecast wind speed in wind farms with more accuracy to enhance system reliability and efficiency [102]; and measure how well national teams meet performance and medal expectations in major sporting events such as the Olympic Games [74], to name a few.…”