# Achievement Prediction and Performance Assessment System for Nations in the Asian Games
## 2 Materials and Methods
### Back-Propagation DEA Algorithm
(2) Assess the current situation: Nations should evaluate the current state of the national sports environment, considering environmental, social, and governance aspects, which involves understanding strengths, weaknesses, and challenges. (3) Set goals and indicators: Nations should establish clear, measurable ESG goals, considering recommendations from impartial and objective entities (such as third-party organizations). They should also develop concrete plans and strategies, measurable indicators, and mechanisms for tracking and evaluation. (4) Implement the plan: Nations need to actively implement the plan by carrying out specific measures and actions in environmental, social, and governance aspects that align with core national policies. (5) Monitor and report: Nations should regularly monitor and evaluate their ESG performance. Additionally, they should periodically report their progress and implementation outcomes to impartial and objective organizations. ## 5 Conclusions

The significant contribution of this study is to establish a PAS system predicting the efficiency of Asian nations participating in mega-sports events. Analyzing the results aligns with the practical issues of national sports policies and the operation and development of the sports industry. This approach provides insights and development possibilities for national sports policy formulation as a point of reference. Based on the results of the Asian nations' PAS with the ESG concept of mega-sports events in the study, to establish more appropriate practical applications for national sports policy, experts in sports management should identify the meso-level factors of elite sports policies leading to success in international competitions, such as \"financial support\", \"governance\", \"participation\", \"talent identification\", \"athletic career support\", \"training facilities\", \"coaching development\", and \"scientific research\" [54]. Based on national differences and the eight factors above, Asian nations formulate distinct sports policies to enhance efficiency in mega-sports events. Particularly noteworthy is the remarkable success of their female athletes, considering Asia's initial failure to include women athletes and the relatively low status of women in many Asian countries [19; 50]. Additionally, the positive influence of host nations [4; 12; 17; 19] and the distinctive development of featured and dominant sports [19] are equally crucial factors that Asian nations cannot overlook. The literature suggests that integrating the performance and advancement of female athletes, the influence of host nations, and the development of featured sports can contribute to formulating specialized and professional sports policies, ultimately fostering the expansion of national sports development and effectiveness. From the practical applications, Asian nations formulate different sports policies based on their performance in mega-sports events. Taking Chinese Taipei as an example, based on the prediction results of the performance assessment system (PAS) and considering the upgrade performance and upward trends of Chinese Taipei in this study, the Chinese Taipei Sports Administration has initiated the \"Golden Plan\" for the 2028 Olympic Games to assistant elite athletes and teams to pursue better performance and, in addition, advance entire performance in Asian Games. The ANN-based methods have been applied successfully in many fields and are usually effective for large amounts of data. The DEA-ANN back-propagation algorithm has been used to measure the efficiency of a set of DMUs, and the result indicates that the DEA-ANN prediction for efficiency score appears to be a reasonable estimate for most DMUs [26]. An analysis of error shows that the larger the dataset, the smaller the error. Several computational intelligence techniques have been proposed to overcome learning limitations from small- and medium-sized datasets [55; 56].