# Achievement Prediction and Performance Assessment System for Nations in the Asian Games
## 2 Materials and Methods
### Back-Propagation DEA Algorithm
Their key observations were the better performance of Northeast Asian nations, the superior performance of female athletes over males in Asia, the significant impact of state support (including the influence of communism and the use of performance-enhancing drugs on competition), and the influence of economic power (noting that comparatively poorer Asian nations tend to lag in athletic performance). [PERSON] and [PERSON] [33] studied and forecasted the performance of 75 nations that won medals in the 2004 Athens Olympic Games. They revealed that winning Olympic medals depends on the combined potential of population, wealth, growth rate, unemployment, former hosting experience, and social-sport expenditures. The size of the team was also identified as a crucial influencing factor. More populated and wealthier nations tend to produce larger Olympic teams, which leads to winning more medals. [PERSON] et al. [51] utilized an ANN to predict the success of nations in the Asian Games by incorporating macropolitical, economic, social, and cultural variables. The study, spanning the years 1970 to 2006, constructed a model using variables such as urban population, education expenditures, age structure, GDP real growth rate, GDP per capita, unemployment rate, population, inflation average, current account balance, life expectancy at birth, and merchandise trade. Their model was then tested with 2010 data to predict the number of gold medals nations would win. The correlation coefficient between the predicted and actual rankings was 86%. Success in Asian sports has become increasingly crucial for numerous nations. Despite the growing financial investments in sports by many nations to compete internationally, no clear evidence indicates how sports variables influence international sports success. Artificial neural network applications in the sports industry and sports management have garnered significant attention. Neural network technology has been widely used in industrial and commercial applications, broadly categorized into pattern classification, prediction and financial analysis, control, and optimization [52]. Additionally, AI technology is extensively employed in the sports industry, focusing on three major industry segments: (1) Application in the North American regional sports industry, including computer vision, emerging gaming equipment, and connected devices. (2) Utilizing AI to realize benefits in improving sports planning, enhancing sports information, meeting fan/player demands, and increasing sports accuracy. (3) Effectively enhancing goals through AI, encompassing improved sports regulations, periodic AI data reviews, detection and mitigation of fraud cases, increased transparency and accountability, event-specific training, enhanced athlete performance, AI-assisted coaching, automatic video highlights, optimized coaching guidance, computer-assisted referee vision, and intelligent ticketing [53]. Furthermore, the sports industry demonstrates significant potential in improving national economies, guiding social consumption patterns, and reshaping industry structures. Using neural network-based predictive models in the sports industry has been validated for predicting growth with demonstrated efficiency and accuracy [30]. Asian nations must first select a performance assessment system before investing in mega-sports events to integrate PAS with environmental, social, and governance (ESG). Thus, it is essential to identify issues to evaluate a nation's sports promotion comprehensively and follow this with strategic planning, goal setting, progress monitoring, and proactive improvement. The recommended steps are: (1) Form an understanding of ESG: Nations need a clear understanding of the definition, values, and importance of the ESG Sustainable Development Goals. They should confirm the overall planning and development of ESG for the nation.