# Achievement Prediction and Performance Assessment System for Nations in the Asian Games
## 2 Materials and Methods
### Back-Propagation DEA Algorithm
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Nations** & **Gold** & **Gold Pre-** & **Accuracy** & **Silver** & **Silver** & **Accuracy** & **Bronze** & **Bronze** & **Accuracy** & **Total** & **Medals** & **Prediction** & **Accuracy** \\  & **Medals** & **diction** & **Action** & **Medals** & **Prediction** & **Action** & **Medals** & **Prediction** & **Action** & **Medals** & **Prediction** & **Accuracy** \\ \hline 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 & 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 \\ \hline average & & & 0.900 & & 0.880 & & & 0.987 & & & 0.938 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Medals won, medal prediction, and accuracy of 14 nations in the 2023 Asian Games. 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. Based on the provided data, the prediction results for gold, silver, bronze, and overall medals exhibit varying degrees of ideality. As [PERSON] et al. [43] mentioned, a performance assessment system provides a comprehensive range of Asian game performance management to effectively achieve the intended goals of Asian nations in this study (Figure 3), excluding the steps of starting PAS and stopping PAS. There are seven steps, and the details are described as follows:

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. Figure 3: The Asian Games performance assessment system. 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.