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Researchers are interested in defining decision support systems that can act in contexts characterized by uncertainty and info-incompleteness. The present study proposes a learning model for assessing the relevance of probability, plausibility, credibility, and possibility opinions in the conditions above. The solution consists of an Artificial Neural Network acquiring input features related to the considered set of opinions and other relevant attributes. The model provides the weights for minimizing the error between the expected outcome and the ground truth concerning a given phenomenon of interest. A custom loss function was defined to minimize the Mean Best Price Error (MBPE), while the evaluation of football players’ was chosen as a case study for testing the model. A custom dataset was constructed by scraping the Transfermarkt, Football Manager, and FIFA21 information sources and by computing a sentiment score through BERT, obtaining a total of 398 occurrences, of which 85% were employed for training the proposed model. The results show that the probability opinion represents the best choice in conditions of info-completeness, predicting the best price with 0.86 MBPE (0.61% of normalized error), while an arbitrary set composed of plausibility, credibility, and possibility opinions was considered for deciding successfully in info-incompleteness, achieving a confidence score of 2.47±0.188 MBPE (1.89±0.15% of normalized error). The proposed solution provided high performance in predicting the transfer cost of a football player in conditions of both info-completeness and info-incompleteness, revealing the significance of extending the feature space to opinions concerning the quantity to predict. Furthermore, the assumptions of the theoretical background were confirmed, as well as the observations found in the state of the art regarding football player evaluation.

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