Achievement Prediction and Performance Assessment System for Nations in the Asian Games
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Envelopment Analysis—Artificial Neural Networks
2.2. Back-Propagation DEA Algorithm
Algorithm 1: Back-propagation DEA algorithm |
2.3. Confusion Matrix
Actual Positive | Actual Negative | |
Predicted Positive | TP | FP |
Predicted Negative | FN | TN |
2.4. Data Collection
- (1)
- Data collection, data processing, and coding were conducted using Eviews 7.0 software, and the predictive analysis was carried out for a total of 14 nations or regions participating from the 11th Asian Games in Beijing in 1990 to the 19th Asian Games in Hangzhou in 2023, excluding nations that did not win any medals (Table 2).
- (2)
- The BCC output-oriented model was utilized to explore efficiency analysis (using DEA-Solver Pro 7.0 software) for historical Asian Games from the 11th Asian Games in Beijing in 1990 to the 18th Asian Games in Jakarta in 2018 for participating Asian nations.
- (3)
- A neural network simulation software (NeuralWorks Professional II/PLUS), was used to predict the efficiency of participating nations and their athletic performance at the 19th Asian Games in Hangzhou in 2023.
No. | Nation | Participating Years |
---|---|---|
1 | Afghanistan | 51, 54, 58, 62, 66, 74, 82, 90, 94, 02, 06, 10, 14, 18, 23 |
2 | Bahrain | 74, 78, 82, 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
3 | Bangladesh | 78, 82, 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
4 | Bhutan | 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
5 | Brunei | 90, 94, 98, 02, 06, 10, 14, 18, 23 |
6 | Cambodia | 54, 58, 62, 70, 74, 94, 98, 02, 06, 10, 14, 18, 23 |
7 | China | 74, 78, 82, 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
8 | Chinese Taipei | 54, 58, 66, 70, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
9 | Hong Kong | 54, 58, 62, 66, 70, 74, 78, 82, 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
10 | India | 51, 54, 58, 62, 66, 70, 74, 78, 82, 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
11 | Indonesia | 51, 54, 58, 62, 66, 70, 74, 78, 82, 86, 90, 94, 98, 02, 06, 10, 14, 18 |
12 | Iran | 51,58,66, 70, 74, 82, 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
13 | Iraq | 74, 78, 82, 86, 06, 10, 14, 18, 23 |
14 | Israel | 54, 58, 66, 70, 74 |
15 | Japan | 51, 54, 58, 62, 66, 70, 74, 78, 82, 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
16 | Jordan | 86, 94, 98, 02, 06, 10, 14, 18, 23 |
17 | Kazakhstan | 94, 98, 02, 06, 10, 14, 18, 23 |
18 | Korea | 18, 23 |
19 | Kuwait | 74, 78, 82, 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
20 | Kyrgyzstan | 94, 98, 02, 06, 10, 14, 18, 23 |
21 | Laos | 74, 82, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
22 | Lebanon | 78, 82, 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
23 | Macau | 90, 94, 98, 02, 06, 10, 14, 18, 23 |
24 | Malaysia | 54, 58, 62, 66, 70, 74, 78, 82, 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
25 | Maldives | 82, 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
26 | Mongolia | 74, 78, 8290, 94, 98, 02, 06, 10, 14, 18, 23 |
27 | Myanmar | 51, 54, 58, 62, 66, 70, 74, 78, 8290, 94, 98, 02, 06, 10, 14, 18, 23 |
28 | Nepal | 51,58,66, 70, 74, 78, 82, 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
29 | North Borneo | 54, 58, 62 |
30 | North Korea | 74, 78, 82,90,98, 02, 06, 10, 14, 18, 23 |
31 | North Yemen | 82, 86 |
32 | Oman | 82, 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
33 | Pakistan | 54, 58, 62, 66, 70, 74, 78, 82, 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
34 | Palestine | 90, 94, 98, 02, 06, 10, 14, 18, 23 |
35 | Philippines | 51, 54, 58, 62, 66, 70, 74, 78, 82, 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
36 | Qatar | 78, 82, 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
37 | Sarawak | 62 |
38 | Saudi Arabia | 78, 82, 86, 90, 942, 06, 10, 14, 18, 23 |
39 | Singapore | 51, 54, 58, 62, 66, 70, 74, 78, 82, 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
40 | South Korea | 54, 58, 62, 66, 70, 74, 78, 82, 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
41 | South Yemen | 82 |
42 | Sri Lanka | 51, 54, 58, 62, 66, 70, 74, 78, 82, 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
43 | Syria | 78, 82, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
44 | Tajikistan | 94, 98, 02, 06, 10, 14, 18, 23 |
45 | Thailand | 51, 54, 58, 62, 66, 70, 74, 78, 82, 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
46 | Timor-Leste | 02, 06, 10, 14, 18, 23 |
47 | Turkmenistan | 94, 98, 02, 06, 10, 14, 18, 23 |
48 | United Arab Emirates | 78, 82, 86, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
49 | Uzbekistan | 94, 98, 02, 06, 10, 14, 18, 23 |
50 | Vietnam | 54, 58, 62, 66, 70, 74, 82, 90, 94, 98, 02, 06, 10, 14, 18, 23 |
51 | Yemen | 90, 94, 98, 02, 06, 10, 14, 18, 23 |
2.5. Operational Definitions
- (1)
- Population: The number of people in a nation or region, indicating demographic strength. The larger the population is, the higher the proportion of talented individuals in various sports, leading to victory. Conversely, when two nations achieve the same number of medals, the one with a smaller population is likely more efficient [2].
- (2)
- Gross domestic product (GDP): All economic activities within a nation, regardless of who owns the production assets. For instance, if a foreign company establishes a subsidiary in the United States and even repatriates profits to its parent company in another nation, those profits are still part of the United States GDP [3]. A higher GDP signifies more substantial national economic power, indicating that wealthier nations can provide better public sports facilities, and sports professionals are more likely to receive social support [2,47].
- (3)
- Athletes: Athletes representing various Asian nations participate in the Asian Games.
- (4)
- Zero-sum game (ZSG): Lins et al. [2] proposed that the traditional ranking based on gold, silver, and bronze medals constitutes a lexicographic multicriteria approach, leading to the overvaluation of gold medals. This results in nations that have not won gold but have silver and bronze being significantly ranked lower, creating a situation in which the overall sporting prowess of a nation is not accurately represented. The original data envelopment analysis model allows decision units to choose the weights of various output categories freely [49]. In this study, assigning higher weights to silver or bronze medals than gold could distort the findings. To avoid this scenario, Lins et al. [2] integrated output to create a single output to reduce bias and achieve a more accurate assessment, serving as the calculation model for the relative efficiency of the integrated output. Through mathematical derivation, the calculation formula is as follows: single output = 0.5814 × Gold+ 0.2437 × Silver+ 0.1749 × Bronze.
- (5)
- Efficiency: This study considers the input and output items of Asian nations related to the Asian Games, measuring the efficiency values obtained by each nation.
3. Results
- (1)
- Accuracy: The prediction accuracy for gold, silver, bronze, and total medals won ranges between 0.825 and 1.000, with the lowest accuracy for bronze medals at 0.825. Higher accuracy is generally considered ideal, depending on the specific application context.
- (2)
- Precision: Precision indicates the accuracy of optimistic predictions. Prediction precision for gold, silver, bronze, and overall medals ranges between 0.824 and 1.000, varying depending on the type of medal. Higher precision is crucial to ensure prediction accuracy, while low precision might lead to misjudgments.
- (3)
- Recall: Recall represents the proportion of correct predictions among positives. Prediction recall for gold, silver, bronze, and overall medals ranges from 0.966 to 1.000. Higher recall is essential to capture all genuine medals, while low recall might result in missing some actual medals.
- (4)
- F1 score: The F1 score is the harmonic mean of precision and recall, offering a combined assessment of these two metrics. The F1 score ranges between 0.889 and 1.000, contingent on the specific types of medals. Higher F1 scores are generally considered ideal because they consider precision and recall.
- (1)
- Preprocess the Asian Games dataset using DEA: set inputs and outputs and evaluate Asian nations’ efficiency from the 1990 to the 2022 Asian Games.
- (2)
- Determine the DMUs of Asian nations: fifty-one nations participated from the 1951 to 2023 Asian Games, excluding those that were absent from any of the 1990 to 2023 Asian Games or did not win any medals, and a total of fourteen Asian nations were involved.
- (3)
- Set the DEA-ANN model’s input and output layers: five input layers (GDP, population, athletes, ZSG, efficiency) and one output layer as medals won were set in this model.
- (4)
- Conduct the initial training of ANN: showcase the predictions of the medals earned for gold, silver, bronze, and total medals across 14 nations in the 2023 Asian Games.
- (5)
- Identify the measurement accuracy: determine that the confusion matrix reveals and fits the accuracy, precision, recall, and F1 score.
- (6)
- Assess whether the accuracy is satisfactory: if predictions fit, verify whether actual medals surpass predictions and PAS stops; if predictions do not fit, go to dataset configuration.
- (7)
- Perform configuration of the dataset being analyzed: start the previous six steps over again.
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Authors | Inputs | Outputs | Methods | Events and Targets |
---|---|---|---|---|
Lins et al. [2] | Gross domestic product (GDP), population | single output = 0.5814 × gold + 0.2437 × silver + 0.1749 × bronze | VRS (variable returns to scale) output-oriented DEA | nations participating in the 2000 Sydney Olympics |
Churilov et al. [3] | GDP per capita, disability-adjusted life expectancy, the index of equality of child survival | gold, silver, and bronze medals | self-organizing maps in a two-stage DEA methodology | nations participating in the 2000 Sydney Olympics |
Lin [4] | GDP, population | gold, silver, and bronze medals | data envelopment analysis | 1990 Beijing Asian Games to 2006 Doha Asian Games |
Li et al. [5] | GDP per capita (in USD), population | gold, silver, and bronze medals | assurance region output-oriented DEA | 1984 to 2004 Olympic Games |
Wu et al. [6] | GDP per capita, population | gold, silver, and bronze medals | cross-efficiency DEA and Spearman test | 1984 to 2004 Olympic Games |
Zhang et al. [7] | GDP, population | gold, silver, and bronze medals | VRS output-oriented DEA model with lexicographic preferences | 2004 Athens Olympic Games |
Wu et al. [8] | GDP, population | gold, silver, and bronze medals | integer-valued DEA model | 2008 Beijing Olympic Games |
Mello et al. [9] | GDP, population | gold, silver, and bronze medals | nonradial DEA model | 2008 Beijing Olympic Games |
Azizi and Wang [10] | GDP, population | gold, silver, and bronze medals | improved bounded DEA models | 2004 Athens Olympic Games |
Lei et al. [11] | GDP, population | gold, silver, and bronze medals | a parallel DEA approach | 2012 London Summer Olympics, 2010 Vancouver Winter Olympic Games |
Lin [12] | GDP, population | gold, silver, and bronze medals | DEA, metafrontier, technology gap ratio | 1992 Barcelona to 2012 London Olympic Games |
Calzada-Infante and Lozano [13] | GDP, population | gold, silver, and bronze medals | dominance network DEA | 2008 Beijing Olympic Games |
Jablonsky [14] | GDP, population | gold, silver, and bronze medals | a parallel DEA approach | 2016 Rio Olympic Games |
Flegl and Andrade [15] | GDP, economic active population, corruption perception index | gold, silver, and bronze medals | data envelopment analysis | 2016 Rio Olympic Games |
Li et al. [16] | population, GDP per capita, athletes | gold, silver, and bronze medals | two-stage multiplier DEA | 2018 PyeongChang Winter Olympic Games |
Sekitani and Zhao [17] | GDP, population, medals won in the last Olympic Games | gold, silver, and bronze medals | a complex restricted multiplier DEA | 2016 Rio Olympic Games |
Lozano and Villa [18] | GDP, population | gold, silver, and bronze medals | weighted Tchebycheff solution method | 2020 Tokyo Olympic Games |
Lin et al. [19] | GDP, population | gold, silver, and bronze medals | DEA, metafrontier | 1992 to 2020 Olympic and Paralympic Games |
GDP | Population | Athletes | ZSG | Efficiency | Gold Medals | Silver Medals | Bronze Medals | |
---|---|---|---|---|---|---|---|---|
1990 | 121,400 | 20,546,664 | 288 | 6.11 | 0.22 | 0 | 10 | 21 |
1994 | 224,000 | 21,298,930 | 268 | 11.26 | 0.36 | 7 | 13 | 23 |
1998 | 308,000 | 21,908,135 | 384 | 22.36 | 0.73 | 19 | 17 | 41 |
2002 | 386,000 | 22,548,009 | 359 | 14.33 | 0.34 | 10 | 17 | 25 |
2006 | 386,450 | 23,036,087 | 391 | 12.39 | 0.37 | 9 | 10 | 27 |
2010 | 444,281 | 23,083,083 | 397 | 18.10 | 0.52 | 13 | 16 | 38 |
2014 | 535,332 | 23,422,513 | 416 | 14.22 | 0.38 | 10 | 18 | 23 |
2018 | 609,251 | 23,726,185 | 588 | 19.94 | 0.72 | 17 | 19 | 31 |
2022 | 761,400 | 23,923,276 | 524 | 17.18 | 0.59 | - | - | - |
Observations | Max | Min | Mean | SD | |
---|---|---|---|---|---|
GDP | 126 | 17,734,060 | 1700 | 1,186,524 | 2,594,255 |
population | 126 | 1,430,000,000 | 429,152 | 208,000,000 | 422,000,000 |
athletes | 126 | 1956 | 17 | 419.80 | 306.86 |
ZSG | 126 | 161.84 | 0.17 | 24.57 | 34.54 |
efficiency | 126 | 1.95 | 0.04 | 0.74 | 0.54 |
Nations | Gold Medals | Gold Prediction | Accuracy | Silver Medals | Silver Prediction | Accuracy | Bronze Medals | Bronze Prediction | Accuracy | Total Medals | Medals Prediction | Accuracy |
---|---|---|---|---|---|---|---|---|---|---|---|---|
China | 201 | 195 | 0.970 | 111 | 111 | 1.000 | 71 | 74 | 1.042 | 383 | 381 | 0.995 |
Japan | 52 | 73 | 1.404 | 67 | 58 | 0.866 | 69 | 74 | 1.072 | 188 | 205 | 1.090 |
South Korea | 42 | 54 | 1.286 | 59 | 56 | 0.949 | 89 | 66 | 0.742 | 190 | 176 | 0.926 |
India | 28 | 16 | 0.571 | 38 | 21 | 0.553 | 41 | 33 | 0.805 | 107 | 70 | 0.654 |
Chinese Taipei | 19 | 17 | 0.895 | 20 | 20 | 1.000 | 28 | 34 | 1.214 | 67 | 71 | 1.060 |
Iran | 13 | 20 | 1.538 | 21 | 20 | 0.952 | 20 | 22 | 1.100 | 54 | 62 | 1.148 |
Thailand | 12 | 12 | 1.000 | 14 | 16 | 1.143 | 32 | 45 | 1.406 | 58 | 73 | 1.259 |
Hong Kong | 8 | 8 | 1.000 | 16 | 18 | 1.125 | 29 | 15 | 0.517 | 53 | 41 | 0.774 |
Malaysia | 6 | 7 | 1.167 | 8 | 15 | 1.875 | 18 | 16 | 0.889 | 32 | 38 | 1.188 |
Qatar | 5 | 6 | 1.200 | 6 | 2 | 0.333 | 3 | 3 | 1.000 | 14 | 11 | 0.786 |
Philippines | 4 | 2 | 0.500 | 2 | 2 | 1.000 | 12 | 16 | 1.333 | 18 | 20 | 1.111 |
Singapore | 3 | 2 | 0.667 | 6 | 2 | 0.333 | 7 | 13 | 1.857 | 16 | 18 | 1.125 |
Mongolia | 3 | 3 | 1.000 | 5 | 10 | 2.000 | 13 | 10 | 0.769 | 21 | 23 | 1.095 |
Macau | 1 | 1 | 1.000 | 3 | 1 | 0.333 | 2 | 4 | 2.000 | 6 | 6 | 1.000 |
average | 0.900 | 0.880 | 0.987 | 0.938 |
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Yeh, C.-C.; Peng, H.-T.; Lin, W.-B. Achievement Prediction and Performance Assessment System for Nations in the Asian Games. Appl. Sci. 2024, 14, 789. https://doi.org/10.3390/app14020789
Yeh C-C, Peng H-T, Lin W-B. Achievement Prediction and Performance Assessment System for Nations in the Asian Games. Applied Sciences. 2024; 14(2):789. https://doi.org/10.3390/app14020789
Chicago/Turabian StyleYeh, Chin-Chang, Hsien-Te Peng, and Wen-Bin Lin. 2024. "Achievement Prediction and Performance Assessment System for Nations in the Asian Games" Applied Sciences 14, no. 2: 789. https://doi.org/10.3390/app14020789
APA StyleYeh, C.-C., Peng, H.-T., & Lin, W.-B. (2024). Achievement Prediction and Performance Assessment System for Nations in the Asian Games. Applied Sciences, 14(2), 789. https://doi.org/10.3390/app14020789