# Analysis and Parameterization of Sports Performance: A Case Study of Soccer
## 2 Related Work  
Once defined the research work aim and scope it is necessary to explore other similar works in order to see the degree of innovation of our approach and also to compare the results obtained. There are several initiatives exploring similar issues, such as those listed below:  
* Predictive systems about issues of soccer matches, such as a simple framework based on scoring models [6], capable of obtaining accurate forecasts for binary outcomes in soccer matches, are proposed. In this sense and in order to analyze the usefulness of the proposed model, experiments were conducted with the English Premier League and the Italian Serie A, where certain events such as red cards in the match, the score under/over, and the goal/no goal outcome are predicted. Other work related to this kind of prediction is described in [7]. It mainly highlights the effectiveness of the black-box modeling capability of neural networks in the case of soccer match outcome prediction. As the authors indicate, input parameter selection is a serious problem in soccer match prediction systems based on neural networks or statistical methods. In order to do so, they tested two different input vector parameters by learning vector quantization networks in order to emphasize the importance of input parameter selection. * The use of mathematical methods, including betting odds, for performance analysis, such as the work proposed by [8], defines an approach that uses the information included in betting odds in a soccer forecasting model based on the well-known ELO (proposed by [PERSON], [PERSON]) ranking system. In this way, the authors show how the proposed model can help to obtain valuable information about the quality of soccer teams and their evolution over time, which is of practical benefit in performance analysis. * The application of statistical modeling of soccer match results [9] is quite popular among researchers in both academia and industry, not only because of the potential for financial returns but also because of the challenges presented by such modeling. However, the modeling of scores and results, as well as other match outcomes, both simple (e.g., the first player to score) and unusual (e.g., number of player cautions), motivated by the search for inefficiencies in the betting market, has been an important motivating factor in the development of state of the art. The authors of [9] describe the state of the art in the statistical modeling of match outcomes and scoreboards in particular. Use of a fuzzy knowledge base relying on the outcome of previous matches [10]. The conclusions presented in this research indicate that it is possible to predict the match outcome based on previous results. Other research related to this topic is presented in [11], which describes an improved system that uses fuzzy logic and ANN (Artificial Neural Networks) to assist managers in the team selection operation. The research results show that the proposed decision support system improves the accuracy in determining the player selection decision. * Expert support in the formation of a soccer team. The authors of [12] proposed a model to select the best formation of a soccer team through two phases. In the first phase, the players are chosen through fuzzy ranking, selecting the best players, while in the second phase, the best formation is chosen by evaluating the different combinations of the players. Although the judgments of the coaches are essential in evaluating players, this model can help coaches make decisions. On the other hand, sports performance optimization software tools are engineered to assist athletes, coaches, and sports teams in enhancing their performance and attaining their objectives across various sporting disciplines [13; 14]. These applications bear certain common characteristics [15]:  
* They incorporate data collection tools suitable for different sporting activities, either individual or group-based.