Research on Olympic medal prediction based on GA-BP and logistic regression model

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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. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-245/v1", "name": "Research on Olympic medal prediction based on GA-BP and logistic regression..." } } ] } Home Browse Research on Olympic medal prediction based on GA-BP and logistic regression... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Zhao S, Cao J and Steve J. Research on Olympic medal prediction based on GA-BP and logistic regression model [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :245 ( https://doi.org/10.12688/f1000research.161865.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Research on Olympic medal prediction based on GA-BP and logistic regression model [version 1; peer review: 1 approved with reservations] Sanglin Zhao https://orcid.org/0009-0004-4616-7129 1 , Jikang Cao 1 , Jackon Steve https://orcid.org/0009-0003-0046-768X 2 Sanglin Zhao https://orcid.org/0009-0004-4616-7129 1 , Jikang Cao 1 , Jackon Steve https://orcid.org/0009-0003-0046-768X 2 PUBLISHED 28 Feb 2025 Author details Author details 1 School of Engineering Management, Hunan University of Finance and Economics, Changsha, Hunan, China 2 School of Management, University of Khartoum, Khartoum, Khartoum, Sudan Sanglin Zhao Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Writing – Original Draft Preparation, Writing – Review & Editing Jikang Cao Roles: Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Jackon Steve Roles: Conceptualization, Data Curation, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract 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. READ ALL READ LESS Keywords Genetic algorithm, Logistic regression, Virtual control group, Olympic medals, Coach effect Corresponding Author(s) Sanglin Zhao ( [email protected] ) Close Corresponding author: Sanglin Zhao Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Zhao S et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Zhao S, Cao J and Steve J. Research on Olympic medal prediction based on GA-BP and logistic regression model [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :245 ( https://doi.org/10.12688/f1000research.161865.1 ) First published: 28 Feb 2025, 14 :245 ( https://doi.org/10.12688/f1000research.161865.1 ) Latest published: 16 Jul 2025, 14 :245 ( https://doi.org/10.12688/f1000research.161865.3 )  There is a newer version of this article available. Suppress this message for one day. 1. Introduction At the recent 2024 Summer Olympic Games in Paris, athletes from all over the world contributed countless wonderful moments to the audience. However, in addition to paying attention to the fierce competition of individual events, the audience also showed strong interest in the ranking of countries in the medal list. In this Olympic Games, the United States ranked first in the overall medal list with 126 medals, while China and the United States tied for the first place in the gold medal list, each winning 40 gold medals. France, the host country, ranked fifth in the gold medal list and fourth in the total medal list by virtue of its home court advantage. Although Britain ranked seventh in the gold medal list with only 14 gold medals, it rose to third place in the overall medal list with 22 silver medals and 29 bronze medals. In addition, small countries such as Albania, Cape Verde, Dominica and Saint Lucia won Olympic medals for the first time, among which Dominica and Saint Lucia won a gold medal respectively, and these achievements also triggered extensive discussions among the public and the media ( Olympics.com , 2024). 1 Medal list is not only a competition of sports strength between countries, but also reflects the resource input and strategic planning of countries in sports development. Continued attention to the medal list has aroused people’s interest in medal trend prediction, especially when the next Olympic Games is coming, and when the participation plans and athlete lists of various countries are gradually clear, predicting the medal distribution in the future Olympic Games has become a compelling topic. It is not easy to predict the number of Olympic medals, and many factors such as historical data, athletes’ performance and host country effect need to be considered comprehensively from multiple dimensions. Research shows that the host country can often significantly improve the medal performance, and this “home advantage effect” has been verified in the past summer and winter Olympic Games (Bernard & Busse, 2004). 2 In addition, the economic level and population size of a country are also significantly related to medal performance, for example, Johnson and Ali (2004). 3 Our research shows that there is a positive correlation between GDP and the number of medals. Analyzing the data of previous Olympic Games can not only help us better understand the distribution law of medals, but also provide theoretical support for future medal prediction. This prediction model is not only of great significance for the media and the public to better understand the results of the Olympic Games, but also provides a reference for the strategic planning of Olympic committees in various countries, helping them to optimize the allocation of resources and concentrate on developing advantageous projects in future events. In addition, with the continuous increase and adjustment of Olympic events, as well as the change of resources input in sports events in various countries, the medal distribution trend has become more complex and dynamic. Studies have shown that different events have different effects on the distribution of national medals. For example, swimming and track and field have made great contributions to the United States, while gymnastics and weightlifting have made significant contributions to China’s medals (Bian, 2015). 4 We need to explore the driving factors behind the medal performance of various countries, including the long-term investment of the country in sports development, the individual performance of athletes, the ability of the coaching team, and the influence of emerging projects on the medal list. This can not only help to predict the medal distribution, but also reveal the competitive advantages of different countries in specific events, and provide more comprehensive guidance for future Olympic preparations. Through these data-driven analysis, medal prediction is no longer just a simple estimation of the results, but an important tool to provide insight and decision-making basis for the national sports strategy. 2. Data selection and analysis 2.1 Data preprocessing The data of multiple data sets (data set references) are merged into an excel form, and descriptive statistical analysis is made. Taking the 2024 Paris Olympic Games as an example, as shown in Figure 1 , and the missing values are replaced and filled. The descriptive statistical results are shown in Tables 1 - 2 and Table 3 model accuracy evaluation in the Table 4 . Figure 1. Medals for the 2024 Paris Olympic Games. Table 1. Symbol description table. Symbol Meaning Unit N Number of countries / M i Total number of medals in country I trunk/horsewhip/used in connection with coins G i Total number of gold medals in country I trunk/horsewhip/used in connection with coins S i Total number of silver medals in country I trunk/horsewhip/used in connection with coins B i Total number of bronze medals in country I trunk/horsewhip/used in connection with coins GOLD i Gold medal changes in country I trunk/horsewhip/used in connection with coins HOST Is it the host country? {0,1} Total events Total projects individual NOC Country name N/A X sample N/A Year age N/A Gold_Increment Gold medal increment trunk/horsewhip/used in connection with coins Table 2. Descriptive statistical results. Descriptive statistics N minimum value maximum total average value standard deviation variance skewness kurtosis Year 6300 1896 2024 12364800 1962.67 38.762 1502.461 -.151 .031 -1.201 .062 Gold 6300 0 83 5809 .92 4.348 18.909 8.887 .031 102.564 .062 Silver 6300 0 seventy-eight 5777 .92 3.786 14.330 8.117 .031 93.295 .062 host 6300 0 one 24 .00 .062 .004 16.113 .031 257.709 .062 Total sports 6300 11 33 132510 21.03 5.339 28.503 .375 .031 -.463 .062 Bronze 6300 0 77 6292 1.00 3.739 13.978 6.849 .031 67.209 .062 Total disciplines 6300 10 50 181860 28.87 9.437 89.063 .535 .031 -.391 .062 Total events 6300 43 339 1210860 192.20 82.039 6730.428 .293 .031 -1.141 .062 Total 6300 0 231 17878 2.84 11.548 133.349 7.925 .031 85.435 .062 Gold_Change 6300 -80 83 0 .00 3.815 14.554 -.348 .031 147.811 .062 Gold_Increment 6300 -80 83 0 .00 3.815 14.554 -.348 .031 147.811 .062 Table 3. Classification and summary of the average medals in Olympic events. Sport Medals Total Archery 17.7 81.75 Art Competitions 6.556 159.944 Artistic Gymnastics 5.053 78.158 Athletics 134.35 1040.15 Basketball 43.278 158.056 Beach Volleyball 4.706 28.824 Boxing 25.1 122.65 Canoe Slalom 1.471 8.647 Canoe Sprint 5.25 23 Canoeing 34.65 180.3 Cycling 51.4 315.05 Diving 20.35 100.2 Equestrian 4.35 29.3 Equestrianism 38.5 224.65 Fencing 73.35 375.95 Figure Skating 2.857 six Football 61.85 204.35 Golf 2.947 17.158 … … … … … … Table 4. Model evaluation index parameters. Variable Definition Computing formula MSE Mean-squared error ∑ i = 1 n ( y i − y i ¯ ) 2 n R2 Goodness of fit 1 − ∑ i ( y ̂ i − y i ) 2 ∑ i ( y i − y ¯ ) 2 Evar Explanatory variance value 1 − ∑ i = 1 n ( ( y i − y ̂ ) − E ( y → − y ̂ ) ) 2 ∑ i = 1 n ( y i − y ¯ ) 2 MAE Average absolute error 1 m ∑ i = 1 m | h ( x i ) − y i | 2.2 Correlation analysis The variables were analyzed for correlation, and Pearson correlation test was used because the data passed the normality test. The result is shown in the following Figure 2 : Figure 2. Correlation coefficient thermogram. As shown in Figure 3 , the axis of the icon lists the correlation coefficients of gold, silver, bronze and total medals. These correlation coefficients reflect the degree of correlation between the number of medals and other columns in the table. Among them, it is shown that there is a very close relationship between them. The correlation between the HOST variable and the total number of medals is 0.3, which shows that the athletes in the host country have the advantage of gold medal, but it is not significant. Through further analysis, we can find that gold medals occupy an important position in the total number of medals. In the Olympic Games, the gold medal is the symbol of the highest honor, and every participating country or region will strive for more gold medals. At the same time, although the number of silver medals and bronze medals is also included in the total number of medals, their value is relatively low. Therefore, the change of the number of gold medals will often affect the total number of medals. It can also be seen from the figure that the correlation between gold medals and silver medals and bronze medals is also high, which is 0.933 and 0.898 respectively, but it is still not as large as the correlation coefficient between gold medals and total medals, which further proves the important position of gold medals in the total number of medals. This also shows that in the Olympic Games, when countries compete for medals, they often pay attention to the number of gold medals, silver medals and bronze medals at the same time, but the gold medal is always the most important goal. Figure 3. Proportion of awards in Olympic events. 2.3 Analysis of the number of medals won in sports events In the provided grouping summary table, the data show Medals and Total of different Sport. Through the analysis of these data, we can draw the following rigorous, refined, accurate, complete, coherent and logical interpretations: from traditional Athletics and swimming to Cricket and Croquet. Medals, as a key index to measure the performance of each event, reveals the competitive situation and strength distribution among events. Total may reflect the participation, influence or overall scale of the events. In order to deeply analyze the competitive level and popularity of each event, swimming (83) and track and field (52, on average) are the two events with the largest number of medals, indicating that these two events have a very high level of competition and extensive participation. At the same time, basketball (11.5), boxing (18.5), gymnastics (if rhythmic gymnastics and competitive gymnastics medals are considered together, the total number is 50.5, but the analysis here is based on separate data) and other projects have also won more medals, revealing the popularity and competitiveness of these projects in the global scope. 2.4 Evaluation of indicators 3. Establishment and solution of medal prediction model 3.1 Establishment of medal prediction model 3.1.1 BP neural network model BP network is a nonlinear system, and its most remarkable feature is its high adaptability (Sanglin Zhao, 2024). 5 The algorithm takes 100 samples of the research object as the input nodes of the neural network, and the expected results as the corresponding output nodes, calculates the weights and thresholds, and calculates the error between the actual results and the predicted results, as shown in Formula (1) . Fitness function is the standard to measure whether the error value meets the requirements. For the calculation results that do not meet the requirements, the network will carry out error back propagation. Equation (2) is the correction of the hidden layer and the output layer. Through repeated iterations, if the error meets the expectations, the model is successfully established. nX = ( x 1 , x 2 , ⋯ , x j , ⋯ , x n ) Y = ( y 1 , y 2 , ⋯ , y j , ⋯ , y n ) (1) E ( ω ) = 1 2 ∑ k , j [ y i ( n ) − y i ′ ( n ) ] (2) Δ W i y ‾ = − η ∂ E ∂ W kj Where: the expected result is the prediction result, the learning rate is the hidden layer, the output value is the output layer, and the output value of this node is the correction. y i ( n ) ; y i ′ ( n ) ; η ; E ; Δ W kj ; Δ W kj . 3.1.2 Genetic algorithm The basic idea of genetic algorithm is a population that needs to be optimized. On the basis of the survival of the fittest, it evolves from generation to generation, leaving the evolved population with good fitness and inheriting it to the next generation. In this process, it needs to be crossed and mutated to produce a new population with better fitness. The optimization process is shown in Figures 4 and 5 . Figure 4. Schematic diagram of genetic algorithm. Figure 5. GA-BP flow chart. A. Chromosome coding Floating-point coding is selected as the coding rule, which can obtain higher precision network weights and thresholds on the one hand, and a wider search range than before on the other hand, with the length s as shown in Formula (3) . Where is the number of neurons in input layer, hidden layer and output layer. (3) s = n 1 × n 2 + n 2 × n 3 + n 2 + n 3 B. Fitness function During the training of a BP neural network, the network’s outputs and desired outputs are selected as training samples. After training, the resulting weights and thresholds are chosen as the optimal parameters. Genetic algorithm metrics are employed as indicators for the optimization process. Subsequently, the sum of squared errors between the predicted values and actual values is computed., as shown in the formula. y ¯ ( k ) y ( k ) (4) J m = 1 2 ∑ k = 1 N [ y ¯ ( k ) − y ( k ) ] 2 In this context: k represents the logarithmic transformation of both input and output sampling data. n denotes the total count of network output nodes. The kth node’s anticipated output is compared against its predicted output value. In order to avoid the phenomenon of dividing by zero, a value close to zero is introduced here, and the fitness function is Formula (5) . y ¯ ( k ) y ( k ) δ (5) fit = ( J m + δ ) − 1 C. Choose Let the population size be n and fi be the fitness, and this fitness is for the individual I in the population, then the probability of I being selected is shown in Formula (6) . (6) P si = f i / ∑ i = 1 n f i D. Crossing Arithmetic crossover method is widely used in individuals with floating-point crossover. Assuming that arithmetic crossover is performed between individuals, the new individuals generated after the operation are shown in the Formula (7) . x A t and x B t (7) { x A t + l + α x B t + ( l − α ) x A t x B t + l + α x A t + ( l − α ) x B t Where: α is a random number evenly distributed in the interval [0,1]. E. Variation In order to prevent premature phenomenon, based on a small range of random numbers, genetic variation occurs in a small range of probability, and the local search ability of the algorithm will also be strengthened in this step. At this time, it is assumed that the maximum and minimum values of the initial individuals of the population are respectively summed, and then the mutated gene is cut as shown in Formula (8) . x min and x max then, the mutated gene can be seen in Formula (8) (8) x k = x min + β ( x min + x max ) Where: β is a random number evenly distributed in the interval [0,1]. Figure 4 is a schematic diagram of genetic algorithm, and Table 5 is a flow chart of combinatorial optimization model. Table 5. Comparison chart of model accuracy. Comparison of model accuracy GA-BP BP R2 0.9881 0.9001 MSE 0.629 0.992 Evar 0.9975 0.9905 MAE 0.1338 0.3326 3.2 The solution of medal prediction model Figure 6 shows the fitting effect of the prediction model: Figure 6. Comparison between predicted value and true value of GA-BP model. Figure 6 , Figure 7 and Table 5 show the training process of GA-BP algorithm model. According to the information analysis in the figure, the R value of BP algorithm model on the target data set is 0.9001, while the R value of GA-BP algorithm model on the target data set is 0.9881, which is closer to 1 than BP algorithm model, which indicates that the fitting degree of GA-BP model is higher than that of BP model. In addition, MSE can also be observed in Figure 7 . The models of the two algorithms have different MSE values after different rounds of training. After three rounds of training, the MSE value of GA-BP algorithm reaches the minimum value of 0.629, which greatly reduces the mean square error compared with the BP algorithm model, which shows that the model is simpler and the prediction accuracy is higher. This paper shows the performance comparison results of GA-BP algorithm and BP algorithm on different data sets through several performance indicators. As can be seen from the figure, GA-BP algorithm is superior to BP algorithm in training error, R value, MSE value, Evar value and MAE value. Figure 7. Model training process diagram. The prediction results are shown in Table 6 : Table 6. Forecast results of gold medals in various countries. NOC Gold_Predicted United States forty-two China 37 South Korea 16 Australia 15 Japan 13 Germany 11 Netherlands 11 Great Britain 10 Italy 9 ROC 9 France 8 Brazil 7 New Zealand 7 Uzbekistan 7 Canada 6 Hungary 5 Kenya 5 Iran 4 Spain 4 Croatia 3 Georgia 3 … … … … Serbia 2 As shown in Figure 8 , data collection and analysis based on GA-BP algorithm show that United States is expected to win 42 gold medals in the 2028 Los Angeles Olympic Games, ranking first in the gold medal list, followed by China with 37 gold medals and South Korea with 16 gold medals. The number of gold medals predicted by other countries is shown in the figure. As a global sports power, the United States has always performed well in international sports events such as the Olympic Games. The United States has strong competitiveness in various sports, especially track and field, swimming, gymnastics and other events, which are often the major events that produce gold medals. The number of gold medals predicted this time is as high as 42, which reflects the leading position of the United States in global sports competition. As a new sports power, China has performed better and better in international sports events such as the Olympic Games in recent years. The number of gold medals predicted this time is 37, although it is slightly less than United States, but it is still a very impressive number, which is far from the third place. China has traditional advantages in table tennis, badminton, diving, weightlifting, etc. At the same time, it is also striving to enhance the competitiveness of other new sports and strive to achieve the all-round development of national sports. Other countries such as Great Britain and Italy have relatively few gold medals. This does not mean that the sports strength of these countries is weak, but that the number of gold medals predicted may be low due to fierce competition or other factors. In addition, there are some countries behind, although the number of gold medals predicted is not much, but they may also make breakthroughs in some advantageous events. Figure 8. American gold medal forecast chart. 3.3 Logistic Regression Prediction Model and Solution of First Gold Medal Countries The sports events and winning trends are used as data training sets for logical probability regression, and the logical variables of winning gold medals or not are used as dependent variables for analysis. (9) P ( Medal i = 1 ) = 1 1 + e − ( α + β 1 ⋅ Athletes i + β 2 ⋅ Trend i ) The binary logistic regression results in Tables 7 , 8 and 9 show that: Table 7. Likelihood chi-square ratio results. Likelihood ratio chi-square value P AIC BIC 115.633 0.000*** 121.633 129.448 Table 8. Model parameter settings. Experimental group = 1.0 coefficient of regression standard error Wald P OR 95% confidence interval of OR value upper limit lower limit constant -0.561 0.485 1.342 0.247 0.57 0.221 1.475 Total 0.011 0.003 11.334 0.001*** 1.011 1.005 1.017 Sport -0.005 0.014 0.131 0.718 0.995 0.969 1.022 Table 9. Evaluation table of logistic regression model. Accuracy rate Recall rate Accuracy rate F1 AUC 0.72 0.72 0.75 0.715 0.802 The significance p value of Total is 0.001***, which is significant horizontally, rejecting the original hypothesis. Therefore, Total will have a significant impact on Y (whether it wins the prize or not), which means that the probability of Y (whether it wins the prize or not) is 1.099% higher than that of 0.0 for every additional unit of Total. Sport has no significant influence on Y (whether winning or not) (p=0.718). ROC curve As can be seen from Table 10 and Figures 9 - 11 , the true positive rate (TPR) increases with the increase of false positive rate (FPR). When the FPR is at a low level of 0-0.2, the TPR increases slowly, ranging from 0-0.4; When FPR starts to rise from 0.2, the rising speed of TPR is accelerated, and when FPR is about 0.4, TPR reaches about 0.8; After that, FPR continued to increase, and the rising trend of TPR slowed down, finally approaching 1.0. On the whole, the chart shows typical ROC curve characteristics, which reflects the classification performance of the model under different thresholds. With the increase of the allowable false positive rate, the true positive rate also increases, but the increase amplitude decreases when the FPR is high, indicating the performance of the model in balance misjudgment and correct recognition. Table 10. Countries likely to win gold for the first time. NOC Year Gold Silver Bronze Total Albania 2028 0 0 2 2 Algeria* 2028 2 1 1 4 Argentina* 2028 2 1 1 4 Australasia 2028 0 0 0 0 Figure 9. ROC curve. Figure 10. Predicting the probability of winning gold medals in American events (part). Figure 11. Probability map of winning gold by countries (some top countries). Probability map of winning gold by countries (some top countries): Based on the logistic regression prediction model, data collection and analysis show that United States will be the first in the gold medal list of the 2028 Los Angeles Olympic Games, and all its events have considerable winning probability, with the winning probability of Athletics and Badminton even as high as 1.0. The track and field event United States has always been in a dominant position in the Olympic history, and badminton is an Olympic event that the United States has paid more attention to in recent years. Although the winning probabilities of baseball, baseball/softball, basketball, beach volleyball and boxing are lower than 0.8, they are all higher than 0.6. The winning probability of the remaining Olympic events, such as Breaking, Canoe Slalom, Canoe Sprint, Canoeing and Cycling, is between 0.6 and 0.4, and the winning probability of Dividing, Equestrianism and Fencing is between 0.2 and 0.4. It can be seen that the advantages and disadvantages of the United States coexist in Olympic events. Its advantage lies in that track and field and badminton are traditional strengths, and there are many gold medals in these two events. With excellent physical fitness and advanced training system, the United States has strong competitiveness in many of them. The United States also has outstanding strength in collective big ball games, such as basketball, volleyball and rugby, with a mature professional league system and many high-level athletes. In addition, baseball/softball and other new events in the 2028 Los Angeles Olympic Games have a broad mass base and profound cultural heritage in the United States, and American players occupy the advantage of home court. However, there are also some inferior events in the United States. In table tennis, diving and other technical events, it is difficult for the United States to compete with traditional strong teams such as China, and the results are often poor. Although the United States may win gold medals in fencing, equestrian and other events, the overall probability is small, and it does not have obvious advantages compared with some countries that focus on these events. In the statistics of predicted medals in some countries in 2028, Algeria (Algeria) and Argentina (Argentina) are outstanding, both of which are regarded as the countries most likely to win the championship for the first time. Driven by policies and GDP, it is predicted that Algeria and Argentina will win two gold medals, one silver medal and one bronze medal in 2028, totaling four medals. In recent years, Algeria has invested a lot of policy support in sports development. The country has made great efforts to build sports infrastructure and built a number of modern stadiums nationwide, providing athletes with a high-quality training and competition environment. At the same time, the training plan for sports talents has been implemented, and potential young talents have been selected from the youth groups for systematic professional training, and excellent athletes have been provided with opportunities for overseas exchanges and competitions. On the economic level, global macro models and analysts of Trading Economics predict that Algeria’s GDP will reach 249.02 billion US dollars by the end of 2024; In the long run, it is estimated that it will be about $256.49 billion in 2025 and $262.90 billion in 2026, which provides a strong financial guarantee for the government in sports. Argentina also has a positive policy orientation in sports development. The government attaches great importance to the inheritance and development of traditional advantageous sports such as football, and at the same time, it is constantly tapping the potential of other sports and encouraging more young people to participate in various sports. In the field of football, Argentina has a perfect youth training system, which continuously transports talents for the national team. In addition, Argentina’s GDP ranks high in South America. Although the GDP has declined in recent years, Argentina’s economy is recovering strongly, and its developed agriculture, animal husbandry and industry provide economic support for sports development. 4. Analysis of Great Coach Effect It is difficult for athletes to change their nationality, but their coaches can change teams for guidance, in order to explore the role of coaches in Olympic games. Using the Synthetic Control Method model, we construct a virtual control group and two experimental groups, one is the change of the number of medals in different periods, and the other is the change of the number of medals in the same period with or without coaches, and then analyze them separately to compare the difference between the actual medal growth and the synthetic control group (the team without coaching effect): (10) Δ Y it = Y it treated − ∑ j ∈ control w j Y jt Among them, the weight is determined by minimizing the difference of pretreatment period: W j (11) min w ∥ Y i , pre − ∑ j w j Y j , pre ∥ 2 s . t . w j ≥ 0 , ∑ w j = 1 Two countries, Estonia and China, are selected to analyze the head coach effect. Based on Figures 12 and 13 and Table 11 , it can be found that: Figure 12. Analysis of Estonia coaching effect. Figure 13. Analysis of China's coaching effect. Table 11. Coach list. Name Team2 Year_2 Medal Erika Salume Estonia 1992 Gold Qin Yiyuan China 2000 Bronze Tnu Tniste Estonia 1992 Bronze Toomas Tniste Estonia 1992 Bronze Firstly, from 1920 to 2020, the number of gold, silver and bronze medals won by Estonia in the Olympic Games fluctuated to some extent. In 1992, Estonia won a gold medal and two bronze medals. From 2000 to 2020, Estonia’s Olympic performance was not as good as that in 1992. This shows that with a coach, Estonia has more medals overall and more balanced medal types. The coach’s guidance has a positive effect on athletes’ good performance in the competition. Without a coach, the number of medals is small, and it is mainly concentrated in some years and medal types. This further emphasizes the importance of coaches in athlete training, competition preparation and strategy formulation. Secondly, From 1985 to 2024, the number of gold medals, silver medals and bronze medals in China showed a significant upward trend, which showed that China paid more and more attention to the international Olympic Games and invested more and more resources in its sports development. Among them, the number of gold medals and bronze medals reached the highest point in 2000, but there is still some fluctuation. To sum up, based on the synthetic control method, combined with the virtual control group, it is found that the number of medals won by Estonia and China with head coaches is higher than that without head coaches. It shows that the great head coach effect has a significant positive effect on the performance of athletes. 5. Analysis of advantages and disadvantages of the model Based on the problem background of the 2028 Los Angeles Olympic Games, this paper adopts GA-BP algorithm model and logistic regression model to analyze and predict the problems respectively. The advantages and disadvantages of the two models are as follows: Firstly, the GA-BP algorithm model comprehensively optimizes the weights and thresholds of the BP neural network by introducing genetic algorithm, thus cleverly avoiding the inherent defect of BP neural network being prone to getting stuck in local optimal solutions. During the training phase of the model, the GA-BP algorithm model demonstrated a significantly better fit than the BP model, demonstrating significant performance advantages. Compared with the BP algorithm model, the GA-BP algorithm model achieves a significant reduction in mean square error, thereby significantly improving the prediction accuracy of the model. Secondly, the model has strong robustness. Its genetic algorithm does not depend on gradient information, and it can provide better performance and strong stability in complex or nonlinear training data when predicting the number of medals in Olympic Games. Finally, the model has strong generalization ability. The trained GA-BP algorithm model can adapt to the new data well, and its MSE reaches the minimum in the third round of training, so it has strong prediction ability for unknown data. However, this model also has some limitations. GA-BP algorithm model combines genetic algorithm and BP neural network, which leads to high computational complexity of the model. In the process of predicting Olympic medals, the calculation process of genetic algorithm is usually time-consuming, especially when dealing with large-scale data sets, which requires long training time and high computing resources. The performance of GA-BP algorithm model largely depends on the parameter settings in genetic algorithm, such as population size, crossover probability and mutation probability. The selection of these parameters has a great influence on the prediction results of the model, and improper parameter setting will affect its optimization effect, so it needs careful adjustment. Although the GA-BP algorithm model has a strong learning ability, if the training data is insufficient or there is noise, the model may be over-fitted, resulting in a decline in the ability to predict new data. For logistic regression model, this model is a classification algorithm in machine learning, and it is widely used in practical applications. First of all, the model has strong explanatory power, and the influence degree of independent variables on dependent variables can be understood through regression coefficients. Based on predicting whether countries win gold medals for the first time in the Olympic Games and their probability, we can understand the influence of different sports on the probability of winning by regression coefficient. Secondly, the logistic regression model has high computational efficiency and is suitable for processing large-scale data. It can not only predict the data category, but also get approximate probability prediction. The prediction results can be given quickly in the probability prediction of winning gold medals in Olympic Games countries, which makes up for the shortcomings of GA-BP algorithm model. Thirdly, the logistic regression model does not require strict data distribution, and it is robust to some extent. This makes the model still maintain good prediction performance when dealing with non-normal distribution data. However, the model also has some shortcomings, which are mainly reflected in: the logistic regression model assumes that there is a linear relationship between independent variables and dependent variables, which limits the application scope of the model. In practical application, if there is a nonlinear relationship between independent variables and dependent variables, the prediction performance of logistic regression model may decline, and GA-BP algorithm model skillfully makes up for this shortcoming. At the same time, the model is sensitive to multicollinearity, and if there is a high correlation between independent variables, it may lead to instability and inaccuracy of the model. Logistic regression model is mainly used for binary classification problems, but its prediction ability for multi-classification problems or regression problems is limited. Although the model is used to predict whether countries win gold medals for the first time, it may not be accurate enough in predicting the specific number of medals. To sum up, the combination of GA-BP algorithm model and logistic regression model adopted in this paper makes up for the shortcomings in gold medal prediction in various countries and broadens the application scope of this combination model. 6. Conclusion and enlightenment to that Olympic committee Based on the in-depth analysis of the distribution and influencing factors of Olympic medals, combined with various statistical and machine learning models, this paper provides valuable insights and strategic suggestions for Olympic committees in the future. The study not only reviews the historical medal data, but also discusses the key factors that affect the medal distribution, such as the host country effect, the characteristics of sports events and the ability of the coaching team. Firstly, this paper makes a correlation analysis, which reveals that there is a very close relationship between the number of gold medals and the total number of medals. Through the analysis of historical data, we find that the medal list not only reflects the competition of sports strength of various countries, but also reveals the resource input and strategic planning of various countries in sports development. Based on GA-BP algorithm model, this paper predicts the number of medals that countries may win in the 2028 Los Angeles Olympic Games. The results show that the United States and China, as sports powers, are expected to occupy a leading position in the gold medal list, which reflects their global influence in sports competition. Secondly, this paper also analyzes the possibility and influencing factors of countries winning gold medals for the first time in the Olympic Games through the logistic regression model. The results of the model show that there are significant differences in the winning probability of different sports, which provides an important reference for the National Olympic Committee to formulate preparation strategies. For projects with traditional advantages, the National Olympic Committee can increase investment to maintain and enhance competitiveness; For emerging or potential projects, athletes and coaches can be encouraged to actively participate in the Olympic Games through policy support and resource allocation. In addition, based on the control grouping model, this paper deeply analyzes the important influence of “great coaching effect” on athletes’ performance. Through comparative analysis, we find that countries with high-level coaches can often get better medal results in the Olympic Games. This provides an important inspiration for the National Olympic Committee in selecting and training coaches, that is, we should pay attention to the professionalism and coaching ability of the coaching team and provide more scientific and systematic training guidance for athletes. Ethics and consent Ethical approval and consent were not required. Author contributions Sanglin Zhao: Conceptualization, Data curation, formal analysis, Software, Writing–original draft, Visualization, Writing–review and editing;Jikang Cao: Conceptualization, Data curation, formal analysis, Software, Writing–original draft, Visualization, Writing–review and editing Jackon Steve: Validation, Writing review and editing. All authors have read and approved the final version of the manuscript. Data availability statement Underlying data Figshare: Prediction of Olympic medals based on GA-BP and logistic regression model https://doi.org/10.6084/m9.figshare.28307321.v2 . 6 The project contains the following underlying data: • results_of_problem_1.xlsx • summerOly_medal_counts.csv • Processed data.xlsx • data_dictionary.csv • summerOly_programs.csv • summerOly_athletes.csv Extended data Fishare: Research on Olympic medal prediction based on GA-BP and logistic regression model Extended data. https://doi.org/10.6084/m9.figshare.28382315.v1 . 7 This project contains the following extended data: • Sheet 1 • Sheet 2 Reporting guidelines Figshare: STROBE checklist ‘[Research on Olympic medal prediction based on GA-BP and logistic regression model checklist’. https://doi.org/10.6084/m9.figshare.28374260.v4 . 8 • STROBE-checklist-v4-combined (1) Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication). Software availability description The software used in this study is all free and publicly available genuine software. WPS Excel (or office Excel can also replace it) is used for data processing, and WPS Excel is used for image drawing. References 1. Olympics.com: Medal count and statistics.2024. Reference Source 2. Bernard AB, Busse MR: Who wins the Olympic Games: Economic resources and medal totals. Rev. Econ. Stat. 2004; 86 (1): 413–417. Publisher Full Text 3. Johnson DKN, Ali A: A tale of two seasons: Participation and medal counts at the Summer and Winter Olympic Games. Soc. Sci. Q. 2004; 85 (4): 974–993. Publisher Full Text 4. Bian X: National strategies and Olympic success: Comparative analysis of swimming and gymnastics. Int. J. Sport Policy Politics. 2015; 7 (2): 150–170. 5. Zhao S, et al. : Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm. Front. Environ. Sci. 2024; 12 : 1497941–1497941. Publisher Full Text 6. Zhao S, Cao J, Steve J: Prediction of Olympic medals based on GA-BP and logistic regression model. figshare. [Dataset]. 2025. Publisher Full Text 7. Zhao S: Research on Olympic medal prediction based on GA-BP and logistic regression model Extended data. figshare. [Dataset]. 2025. Publisher Full Text 8. Zhao S, Cao J, Steve J: Research on Olympic medal prediction based on GA-BP and logistic regression model checklist. figshare. Online resource. 2025. Publisher Full Text Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 28 Feb 2025 ADD YOUR COMMENT Comment Author details Author details 1 School of Engineering Management, Hunan University of Finance and Economics, Changsha, Hunan, China 2 School of Management, University of Khartoum, Khartoum, Khartoum, Sudan Sanglin Zhao Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Writing – Original Draft Preparation, Writing – Review & Editing Jikang Cao Roles: Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Jackon Steve Roles: Conceptualization, Data Curation, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (3) version 3 Revised Published: 16 Jul 2025, 14:245 https://doi.org/10.12688/f1000research.161865.3 version 2 Revised Published: 28 Mar 2025, 14:245 https://doi.org/10.12688/f1000research.161865.2 version 1 Published: 28 Feb 2025, 14:245 https://doi.org/10.12688/f1000research.161865.1 Copyright © 2025 Zhao S et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Zhao S, Cao J and Steve J. Research on Olympic medal prediction based on GA-BP and logistic regression model [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :245 ( https://doi.org/10.12688/f1000research.161865.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 28 Feb 2025 Views 0 Cite How to cite this report: Wang Y and Huang TY. Reviewer Report For: Research on Olympic medal prediction based on GA-BP and logistic regression model [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :245 ( https://doi.org/10.5256/f1000research.177956.r369223 ) The direct URL for this report is: https://f1000research.com/articles/14-245/v1#referee-response-369223 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 07 Mar 2025 Yiquan Wang , Xinjiang University, Xinjiang, China; Shenzhen X-Institute, Shenzhen, Guangdong, China Tin-Yeh Huang , Department of Industrial and System Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.177956.r369223 The manuscript presents an interesting study that combines a genetic algorithm–optimized backpropagation neural network (GA-BP) and logistic regression to predict Olympic medal outcomes and evaluate the impact of coaching. While the multi-model approach is innovative, several revisions are recommended for ... Continue reading READ ALL The manuscript presents an interesting study that combines a genetic algorithm–optimized backpropagation neural network (GA-BP) and logistic regression to predict Olympic medal outcomes and evaluate the impact of coaching. While the multi-model approach is innovative, several revisions are recommended for clarity, reproducibility, and balanced interpretation. Focus and Literature: The introduction should clearly state the research objectives and hypotheses. It would be beneficial to explain how GA-BP improves upon traditional methods and why logistic regression is used alongside the neural network. Additionally, updating the literature review to include recent advances in sports analytics and machine learning would better contextualize the study. Methodological Detail: Key aspects such as data preprocessing steps, neural network architecture (e.g., layers, neurons, activation functions, learning rate), and genetic algorithm settings (e.g., population size, mutation rates) need more thorough explanation. Providing detailed model validation processes (like cross-validation and sensitivity analyses) would also help ensure reproducibility. Data and Code Transparency: The manuscript should include a clear description of available datasets (e.g., file structures and variables) and ensure that analysis scripts and code are accessible via a public repository. This transparency is essential for replicability and further research. Interpretation and Conclusions: While the results are promising, the causal claims regarding the “great coach effect” should be presented more cautiously. Separating correlation from causation and tempering policy recommendations based on the current methodological limitations would strengthen the discussion. Overall, addressing these points would enhance the manuscript's clarity, methodological rigor, and scientific contribution. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: artificial intelligence，machine Learning, deep Learning We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Wang Y and Huang TY. Reviewer Report For: Research on Olympic medal prediction based on GA-BP and logistic regression model [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :245 ( https://doi.org/10.5256/f1000research.177956.r369223 ) The direct URL for this report is: https://f1000research.com/articles/14-245/v1#referee-response-369223 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 28 Feb 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 Version 3 (revision) 16 Jul 25 read read Version 2 (revision) 28 Mar 25 read Version 1 28 Feb 25 read Yiquan Wang , Xinjiang University, Xinjiang, China; Shenzhen X-Institute, Shenzhen, China Tin-Yeh Huang , The Hong Kong Polytechnic University, Hong Kong SAR, China C.V. Suresh Babu , Hindustan Institute of Technology and Science, Chennai, India Garrett Bullock , Wake Forest School of Medicine, Winston-Salem, USA Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Bullock G. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 24 Sep 2025 | for Version 3 Garrett Bullock , Wake Forest School of Medicine, Winston-Salem, North Carolina, USA 0 Views copyright © 2025 Bullock G. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Thank you for allowing my to review this paper. This study is developing prediction models on medal counts for future Olympic games. This is an interesting question. There are some questions that are required concerning the methods and construction of the paper. 1. The flow of the paper is not in a a traditional scientific sense. IMRD would be a much more streamlined way of putting this paper together. Right now, it is a few successive analyses with interpretation within the same sections. 2. There is a mixxing of prediction, association, and causal inference. You interchangeably use the words effect (e.g., coach effect, country effect) and also correlation (e.g., correlation between gold and silver and bronze medals) and prediction (e.g., the US is preicted to have the highest medal count in the next Olympics). The overall aims of this paprr is to develop and predict medal counts for future Olympics. It is advised to stick to the aims: develop the GA-BP, and forecast medal counts. Some of the data development and quality checks that are in the earlier methods are good to keep, but the focus should be on development and forecasting of medal counts. 3. Deep learning requires massive amounts of data. The data itself is not the largest. This can be OK, particularly in a proof of concept perspective. However, within the prediction metrics, the variance of these predictions needs to be included, not just the point estimates. This will allow for improved interpretation. 4. This study should be informed of reporting by TRIPOD AI+. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Prediction, Epidemiology, AI, Sport, Sport Medicine I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Bullock G. Peer Review Report For: Research on Olympic medal prediction based on GA-BP and logistic regression model [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :245 ( https://doi.org/10.5256/f1000research.184275.r414238) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-245/v3#referee-response-414238 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Babu C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 25 Jul 2025 | for Version 3 C.V. Suresh Babu , Hindustan Institute of Technology and Science, Chennai, Tamil Nadu, India 0 Views copyright © 2025 Babu C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions All suggestions have been satisfactorily incorporated and justified in the revised manuscript. The authors not only addressed the feedback but provided extended and contextual elaborations, particularly on the synthetic control method and the rationale for authorship changes. Competing Interests No competing interests were disclosed. Reviewer Expertise AI, ML I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Babu CVS. Peer Review Report For: Research on Olympic medal prediction based on GA-BP and logistic regression model [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :245 ( https://doi.org/10.5256/f1000research.184275.r398763) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-245/v3#referee-response-398763 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Babu C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 25 Jun 2025 | for Version 2 C.V. Suresh Babu , Hindustan Institute of Technology and Science, Chennai, Tamil Nadu, India 0 Views copyright © 2025 Babu C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Responses to Review Questions Is the work clearly and accurately presented and does it cite the current literature? Partly The study is presented with reasonable clarity, but several sections, particularly the introduction and methodology, would benefit from better articulation of objectives and more detailed citations of recent work. Some key claims (e.g., the coach effect) lack robust sourcing. Is the study design appropriate and is the work technically sound? Yes The dual-model approach combining GA-BP and logistic regression is appropriate for the multifaceted nature of Olympic medal prediction. The use of the synthetic control model adds depth to the coach effect analysis. Are sufficient details of methods and analysis provided to allow replication by others? Partly While the core methodology is outlined, important implementation details are omitted: Network architecture specifics (e.g., number of layers/neurons, activation functions) Genetic algorithm parameters (e.g., population size, mutation rate) Training-validation split, cross-validation methods These gaps hinder full reproducibility. If applicable, is the statistical analysis and its interpretation appropriate? Yes The use of R², MSE, MAE, and Evar for model evaluation is standard and appropriate. Logistic regression outputs are well interpreted, and ROC/AUC analysis is correctly applied. Are all the source data underlying the results available to ensure full reproducibility? Partly The manuscript claims to provide datasets on Figshare, but in-text descriptions of these datasets (structure, variables) and analysis code are missing. Full transparency would require direct links and documentation on how each dataset was used. Are the conclusions drawn adequately supported by the results? Partly The conclusions align with the presented results. However, the causal claims regarding the coach effect should be toned down. The synthetic control method assumes group similarity, but the paper does not verify covariate balance or control for potential confounders. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise AI, ML I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 10 Sep 2025 Jackon Steve, School of Management, University of Khartoum, Khartoum, Sudan We appreciate your feedback on the revisions. Overall, the newly submitted manuscript focuses on strengthening the literature review and discussion of synthetic control methods and head coach effects in the introduction section. We have added code to the data involved in the article. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Babu CVS. Peer Review Report For: Research on Olympic medal prediction based on GA-BP and logistic regression model [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :245 ( https://doi.org/10.5256/f1000research.179311.r385994) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-245/v2#referee-response-385994 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Wang Y et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 07 Mar 2025 | for Version 1 Yiquan Wang , Xinjiang University, Xinjiang, China; Shenzhen X-Institute, Shenzhen, Guangdong, China Tin-Yeh Huang , Department of Industrial and System Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China 0 Views copyright © 2025 Wang Y et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The manuscript presents an interesting study that combines a genetic algorithm–optimized backpropagation neural network (GA-BP) and logistic regression to predict Olympic medal outcomes and evaluate the impact of coaching. While the multi-model approach is innovative, several revisions are recommended for clarity, reproducibility, and balanced interpretation. Focus and Literature: The introduction should clearly state the research objectives and hypotheses. It would be beneficial to explain how GA-BP improves upon traditional methods and why logistic regression is used alongside the neural network. Additionally, updating the literature review to include recent advances in sports analytics and machine learning would better contextualize the study. Methodological Detail: Key aspects such as data preprocessing steps, neural network architecture (e.g., layers, neurons, activation functions, learning rate), and genetic algorithm settings (e.g., population size, mutation rates) need more thorough explanation. Providing detailed model validation processes (like cross-validation and sensitivity analyses) would also help ensure reproducibility. Data and Code Transparency: The manuscript should include a clear description of available datasets (e.g., file structures and variables) and ensure that analysis scripts and code are accessible via a public repository. This transparency is essential for replicability and further research. Interpretation and Conclusions: While the results are promising, the causal claims regarding the “great coach effect” should be presented more cautiously. Separating correlation from causation and tempering policy recommendations based on the current methodological limitations would strengthen the discussion. Overall, addressing these points would enhance the manuscript's clarity, methodological rigor, and scientific contribution. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise artificial intelligence，machine Learning, deep Learning We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. reply Respond to this report Responses (0) Wang Y and Huang TY. Peer Review Report For: Research on Olympic medal prediction based on GA-BP and logistic regression model [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :245 ( https://doi.org/10.5256/f1000research.177956.r369223) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-245/v1#referee-response-369223 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. 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