Are forfeitures of Olympic medals predictable? : A test of the efficiency of the international anti-doping system

As the country that currently provides the highest cash pay-out for an Olympic gold medal (Soh, 2013), it is clear that Singapore's government is committed to pushing for greater excellence in sports. However, in a small island state where participation rather than excellence has tended to be the function of sport, this objective appears problematic. A controversial scheme used to overcome this is the Foreign Sports Talent Scheme (FSTS), a sports labour migration innovation. This article discusses the scheme and contextualizes it in light of Singaporean government sports policy over the past two decades. It is argued that despite some public dissatisfaction, the Singapore government's objective to augment its Olympic medal capacity will lead to continuance, if not augmentation, of the programme. It is thus clear that tension between the PAP and Singaporean citizens will continue to grow unless a more efficient system for identifying and developing the local sporting talent pool is developed.

The Olympic Gold Quest (OGQ) was founded as a Non-profit to support Indian athletes in their quest to win Olympic Gold medals by bridging the gap between the best athletes in India and in the world. The support from OGQ has been instrumental to India in winning its highest number of medals at any summer Olympics. Buoyed by this success, OGQ has set up a target of achieving eight Olympic medals at the 2016 Rio Olympic Games. With OGQ relying on donations to support the athletes, the challenge is to market the Olympic cause by creating, communicating, and delivering the right offering for its donors.

Background Predicting the number and distribution of Olympic medals in the future has become a hot topic, but predicting the number of Olympic medals is not easy and requires comprehensive consideration of multiple factors such as historical data, athlete performance, and host country effects. Method This article uses the GA-BP algorithm model, combined with genetic algorithm (GA) and backpropagation neural network (BPNN), to optimize the weights and bias parameters of the BP neural network using the global search capability of genetic algorithm, thereby improving training efficiency and prediction performance. By estimating the number of Olympic gold medals and total medals, verifying the accuracy of the model, and predicting the medal table for the 2028 Los Angeles Olympics. Meanwhile, based on the synthetic control model, Estonia and China were selected as research subjects to construct a virtual control group and two experimental groups for analysis. Result The experimental results showed that Estonia and China won more medals with a head coach than without one. In 1992, Estonia won 1 gold medal and 2 bronze medals under the guidance of excellent coaches, indicating the significant role of head coaches in improving athletes’ performance. Conclusion This study provides valuable insights for the decision-making of the Olympic Committee, revealing key factors in medal distribution, optimizing the allocation of national strategic resources, and predicting the performance of countries at future Olympic Games.

Predicting the number and distribution of Olympic medals in the future has become a hot topic, but predicting the number of Olympic medals is not easy and requires comprehensive consideration of multiple factors such as historical data, athlete performance, and host country effects. This article uses the GA-BP algorithm model, combined with genetic algorithm (GA) and backpropagation neural network (BPNN), to optimize the weights and bias parameters of the BP neural network using the global search capability of genetic algorithm, thereby improving training efficiency and prediction performance. By estimating the number of Olympic gold medals and total medals, verifying the accuracy of the model, and predicting the medal table for the 2028 Los Angeles Olympics. Meanwhile, based on the synthetic control model, Estonia and China were selected as research subjects to construct a virtual control group and two experimental groups for analysis. The experimental results showed that Estonia and China won more medals with a head coach than without one. In 1992, Estonia won 1 gold medal and 2 bronze medals under the guidance of excellent coaches, indicating the significant role of head coaches in improving athletes' performance. This study provides valuable insights for the decision-making of the Olympic Committee, revealing key factors in medal distribution, optimizing the allocation of national strategic resources, and predicting the performance of countries at future Olympic Games.

BackgroundPredicting the number and distribution of Olympic medals in the future has become a hot topic, but predicting the number of Olympic medals is not easy and requires comprehensive consideration of multiple factors such as historical data, athlete performance, and host country effects.MethodThis article uses the GA-BP algorithm model, combined with genetic algorithm (GA) and backpropagation neural network (BPNN), to optimize the weights and bias parameters of the BP neural network using the global search capability of genetic algorithm, thereby improving training efficiency and prediction performance. By estimating the number of Olympic gold medals and total medals, verifying the accuracy of the model, and predicting the medal table for the 2028 Los Angeles Olympics. Meanwhile, based on the synthetic control model, Estonia and China were selected as research subjects to construct a virtual control group and two experimental groups for analysis.ResultThe experimental results showed that Estonia and China won more medals with a head coach than without one. In 1992, Estonia won 1 gold medal and 2 bronze medals under the guidance of excellent coaches, indicating the significant role of head coaches in improving athletes’ performance.ConclusionThis study provides valuable insights for the decision-making of the Olympic Committee, revealing key factors in medal distribution, optimizing the allocation of national strategic resources, and predicting the performance of countries at future Olympic Games.

Background Predicting the number and distribution of Olympic medals in the future has become a hot topic, but predicting the number of Olympic medals is not easy and requires comprehensive consideration of multiple factors such as historical data, athlete performance, and host country effects. Method This article uses the GA-BP algorithm model, combined with genetic algorithm (GA) and backpropagation neural network (BPNN), to optimize the weights and bias parameters of the BP neural network using the global search capability of genetic algorithm, thereby improving training efficiency and prediction performance. By estimating the number of Olympic gold medals and total medals, verifying the accuracy of the model, and predicting the medal table for the 2028 Los Angeles Olympics. Meanwhile, based on the synthetic control model, Estonia and China were selected as research subjects to construct a virtual control group and two experimental groups for analysis. Result The experimental results showed that Estonia and China won more medals with a head coach than without one. In 1992, Estonia won 1 gold medal and 2 bronze medals under the guidance of excellent coaches, indicating the significant role of head coaches in improving athletes’ performance. Conclusion This study provides valuable insights for the decision-making of the Olympic Committee, revealing key factors in medal distribution, optimizing the allocation of national strategic resources, and predicting the performance of countries at future Olympic Games.

During the 2024 Paris Olympics, the medal table attracted intense attention, and there was a surge of enthusiasm for predicting the medals of the 2028 Olympics. This study focuses on constructing a model to indicate the number of Olympic medals, aiming to explore the factors influencing a country's performance in winning Olympic medals and make accurate predictions. First, we conducted rigorous data cleaning operations on the collected data to ensure that the data input into the model met the requirements. Then we adopted a two-stage model construction approach to predict the number of medals these countries could obtain. The virtual medal prediction leaderboard shows that Bangladesh and Benin might win their first Olympic medals in 2028. Next, we used the Kmeans++ clustering model to group similar countries together. We also analyzed the relationship between sports events and medal counts. We identified their advantageous sports events and visualized the clustering results through data visualization. Overall, this model provides a practical tool for reasonably predicting the number of medals. By considering multiple factors, it offers insights for countries on how to win more medals.

THE PHENOMENON OF THE FOURTH PLACE IN SPORT THROUGH THE SOCIAL AND MEDIA PRISM: THE OLYMPIC AND PARALYMPIC GAMES

This paper analyzes several influencing factors by constructing a predictive model for the Olympic medal table, providing valuable insights to assist national Olympic committees in optimizing their Olympic strategies. In the preprocessing stage, the study analyzed each country’s performance over the past four Olympic Games regarding gold and total medals. Countries were grouped into two categories—developed and developing sports nations — and visualized through Principal Component Analysis (PCA). This classification is a foundational element for the predictive model in the first question. The study converted Olympic medals from past Games into corresponding scores in the first question. Calculating Pearson correlation coefficients showed that the various Olympic sports can broadly be categorized into ball and non-ball. A linear regression model was then constructed to predict medals in ball and non-ball sports using the performance data from the past three Olympic Games as independent variables. In the second question, we analyzed the relationships between coaching assignments and the performance of the women’s volleyball and gymnastics teams for three pairs of countries: China-USA and Romania-USA. Through analysis, we concluded that the “coach effect” exists, with national team scores strongly correlated with outstanding coaches. Additionally, the contribution of exceptional coaches to a nation’s total medal count exceeded 0.5. In the third question, we pointed out that the prediction of Olympic medals requires careful consideration of the leader effect, host effect, candidate effect, excellent coach effect, etc., providing a reference for the Olympic Committee to designate competition rules.

Introduction: Elite performance in sports is known to be influenced by heritable components, but the magnitude of such an influence has never been quantified.Hypothesis/Objectives: We hypothesized that having a former world-class champion in the family increases the chances of an athlete to repeat the achievement of her or his kinship. We aimed to measure the heritability of a medal in the Olympic Games (OG) among Olympians and to estimate the percentage of the genetic contribution to such a heritance.Study Design: Twin-family study of a retrospective cohort.Methods: All the 125,051 worldwide athletes that have participated in the OG between 1896 and 2012 were included. The expected probability to win a medal in the OG was defined as the frequency of medallists among Olympians without any blood kinship in the OG. This expected probability was compared with the probability to win a medal for Olympians having a kinship (grandparent, aunt/uncle, parent, or siblings) with a former Olympian that was a (1) non-medallist or (2) medallist. The heritability of the genetically determined phenotype (h2) was assessed by probandwise concordance rates among dizygotic (DZ) and monozygotic (MZ) twins (n = 90).Results: The expected probability to win a medal in the OG was 20.4%. No significant difference of medal probability was found in the subgroups of Olympians with a Non-medallist kinship, except among siblings for whom this probability was lower: 13.3% (95% CI 11.2–14.8). The medal probability was significantly greater among Olympians having a kinship with a former Olympic Medallist: 44.4% for niece/nephew (33.7–54.2); 43.4% for offspring (37.4–48.6); 64.8% for siblings (61.2–68.8); 75.5% for DZ twins (63.3–86.6); and 85.7% for MZ twins (63.6–96.9); with significantly greater concordance between MZ than DZ (p = 0.01) and h2 estimated at 20.5%.Conclusion: Having a kinship with a former Olympic medallist is associated with a greater probability for an Olympian to also become a medallist, the closer an athlete is genetically to such kinship the greater this probability. Once in the OG, the genetic contribution to win a medal is estimated to be 20.5%.

BackgroundNational sporting achievement at the Olympic Games is important for national pride and prestige, and to promote participation in sport. Summer Olympic Games medal tallies have been associated with national wealth, and also social development and healthcare expenditure. It is uncertain however, how these socioeconomic factors translate into Olympic success. The objective of this study was therefore to examine the relationship between population muscle strength and Olympic medal tallies.Methods and ResultsThis study of handgrip strength represents a cross-sectional analysis of the Prospective Urban Rural Epidemiology (PURE) study, which is an ongoing population cohort study of individuals from high-, middle-, and low-income countries. Within participating countries, households from both urban and rural communities were invited to participate using a sampling strategy intended to yield a sample that was representative of the community. Households were eligible if at least one member was aged 35–70 years and if they intended living at the same address for a further four years. A total of 152,610 participants from these households, located in 21 countries, were included in this analysis. Handgrip strength was measured using a Jamar dynanometer. Olympic medal tallies were made over the five most recent Summer Games.There was a significant positive association between national population grip strength (GS) and medal tally that persisted after adjustment for sex, age, height, average daily caloric intake and GDP (total and per capita). For every 1kg increase in population GS, the medal tally increased by 36% (95% CI 13–65%, p = 0.001) after adjustment. Among countries that won at least one medal over the four most recent Summer Olympic Games, there was a close linear relationship between adjusted GS and the natural logarithm of the per capita medal tally (adjusted r = 0.74, p = 0.002).ConclusionsPopulation muscle strength may be an important determinant of Summer Olympic Games medal success. Further research is needed to understand whether population muscle strength is modifiable, and whether this can improve Olympic medal success. Extreme outcomes may reflect the average attributes of the population from which the individual experiencing the extreme outcome is drawn.

Olympic medals and demo-economic factors: Novel predictors, the ex-host effect, the exact role of team size, and the “population-GDP” model revisited

The story of the development of sporting talent in East Germany is a paradoxical one to say the least. First, the GDR system of talent-spotting and development is often held up as the most systematic attempt to date to produce elite sport performers from a very small population (around 17 million); second, and on close inspection, the system was wasteful, not as thorough as it is often portrayed, highly inflexible and beholden to ‘norms’ and ‘measurements’ and, towards the final period of the state’s existence, in danger of running out of the key ingredient with which to process champions through it: children. Finally, more recent research on talent identification and development portrays the system as not only archaic, but also misguided.2 Despite this latter assessment, East Germany produced more Olympic medals per capita than any other state and this was due, in no small part, to a cornerstone of its sports system: the systematic early development of children and youths into athletes through sports Training Centres — or Training Support Bases, as they were termed in rural areas3 — specialist Children’s and Youth Sports Schools and on, up the performance pyramid, to elite Sports Clubs.

The Olympic medal list is an important indicator to assess the competitive strength of countries, and the prediction and analysis of the distribution of the number of medals provide a scientific basis for countries to formulate sports development strategies. This paper takes the 2024 Paris Olympic Games and the previous Olympic Games as the basic data, combines the historical medal data, the distribution of each Olympic Games and the special characteristics of the host country, constructs a number of mathematical models, explores the law of medal distribution, and proposes a strategy to improve the number of medals.The model in this paper is comprehensive, flexible and practical, which provides a new way of thinking for the analysis of medal distribution in the Olympic Games, and also provides data support for the sports development strategy of each country.

The authors investigated the evaluative consequences of sequential performance judgments. Recent social comparison research has suggested that performance judgments may be influenced by judgments about a preceding performance. Specifically, performance judgments may be assimilated to judgments of the preceding performance if judges focus on similarities between the two. If judges focus on differences, however, contrast may ensue. The authors examined sequential performance judgments, using data gathered from the 2004 Olympic Games as well as data gathered in the laboratory with students or experienced gymnastics judges as participants. Sequential performance judgments were influenced by previously judged performances, and the direction of this influence depended on the degree of perceived similarity between the successive performances.