# Achievement Prediction and Performance Assessment System for Nations in the Asian Games
###### Abstract
While obtaining a summary of the evolution of efficiency, productivity, and analysis of medals in major sporting events, it was discovered that the characteristics of fixed-sum outputs (FSO) using an input-oriented approach [9] were considered. Another method, introduced implicitly considering FSO, was proposed by [PERSON] et al. [2] through an integrated output model known as zero-sum gains (ZSG). ZSG considers integration as a single output (weighted sum of gold, silver, and bronze medals) and predicts the performance of each decision-making unit (DMU) by assuming that other DMUs adopt specific input reduction strategies, which was the efficiency analysis model used in their study. Subsequently, [PERSON] et al. [20] expanded the ZSG DEA model to include multiple outputs. [PERSON] and [PERSON] [21] argued that the ZSG efficiency scores by [PERSON] et al. [2] could not be used to genuinely compare DMUs. Hence, they proposed an alternative performance indicator consistent with FSO constraints, emphasizing output interdependence, which required a centralized DEA or standard DEA model to explicitly impose FSO constraints, demanding fixed total numbers of different medals. [PERSON] et al. [22] employed the equilibrium efficient frontier (EEF) explicitly considering FSO constraints, but they needed to consider common sets of weights (CSW) while computing the EEF, resulting in efficiency evaluations without a common platform. Subsequently, [PERSON] et al. [23] simultaneously utilized FSO constraints and CSW using the generalized equilibrium efficient frontier (GEEF) to address this issue. Additionally, [PERSON] et al. [16] extended GEEF to a two-stage network DEA configuration similar to [PERSON] et al. [24]. [PERSON] and [PERSON] [18] pointed out that the ZSG and GEEF methods did not consider the completeness of output indicators for the Olympic Games or calculate output targets. Instead, these approaches solely calculated efficiency scores and rankings. Thus, they proposed a multi-objective centralized DEA approach capable of fairly computing DMUs compliant with FSO constraints. [PERSON] et al. [25] investigated the efficiency and quality of banking services, giving more attention to performance analysis. They utilized data envelopment analysis and artificial neural networks (ANNs) to measure the relative efficiency of branches of Canadian banks. The measurement results were compared with conventional DEA outcomes. They found that ANNs could be applied for short-term performance prediction, and both sets of results could be mutually compared to provide suggestions for bank branches to enhance their performance. [PERSON] and [PERSON] [26] regarded data envelopment analysis as one of the most widely used methods for measuring the efficiency and productivity of decision-making units (DMUs). However, they highlighted the substantial computational resources required for DEA, particularly in datasets with numerous inputs/outputs. The study proposed neural network back-propagation data envelopment analysis to address the challenges posed by large-scale datasets. Additionally, the study argued that the computational demands of neural networks for memory and central processing unit (CPU) time are significantly lower than those of traditional DEA methods, making it a valuable tool for measuring the performance of large datasets. The study further applied back-propagation neural network data envelopment analysis to five large datasets and compared the results with traditional DEA outcomes. [PERSON] [6] introduced a hybrid model using data envelopment analysis, decision trees (DT), and ANNs to explore procurement management and assess supplier performance. Their model consisted of two modules. Module one applied DEA to classify suppliers into effective and ineffective clusters based on efficiency scores. Module two utilized enterprise performance-related data to train the DT and ANN models, applying the well-trained decision models to new suppliers.