## 2 Literature Review  
Football is a popular sport in Europe, and it attracts special attention. In this section, we briefly present some research papers dealing with the possibilities of football team evaluation. These works mainly concentrate on predicting the results of matches. In [36], the English Premier League was investigated by a refined score-driven method, and this method was also extended to the best national leagues (English, German, Spanish, French, Italian, and Dutch) in [37]. The authors elaborated a bivariate Poisson model for predicting the number of goals scored by the teams. The success of the method was shown by the fact that the predictions concerning the differences in the number of goals coincided well with the odds provided by some bet offices. In [38], the results of the UEFA Champions League were forecast using a cumulative probit model. The teams' strengths were supposed to be Gauss distributed, and the forecasting was expressed by linear regressions for the UEFA club rankings. In [7], seven evaluation methods applying a gradient descent algorithm were analyzed from a predictive point of view. In this paper, the authors also considered the time effects. The starting point was the Elo method [3], which was modified according to the authors' aims. The predictive abilities of the algorithms were presented for the results of the \"Big Six\" in the Premier League (Arsenal, Chelsea, Liverpool, Manchester City, Manchester United, and Tottenham). The authors of [39] measured the predictive abilities of different methods. In this paper, the authors compared ten scoring methods for predicting teams' rank probabilities. Similarly to [39], in [40], the comparison of different methods was again the focus of the research. The authors investigated the Thurstone model, the Bradley-Terry model, the Bradley-Terry-Davidson model, and Poisson models. The article concluded that the Poisson models outperformed the Thurstone and Bradley-Terry models from a predictive point of view. However, we have to note that the Poisson models use more information than the Thurstone and Bradley-Terry models, as they also consider the number of goals scored by teams in addition to the relations (better, equal, worse) between them. The predictive power of fuzzy logic was analyzed in [41] through a football case study. Nowadays, machine learning provides new possibilities for predicting sports results. Although neural-network-based methods are popular, their disadvantage is that they require lots of data to provide reliable results. In [42], the authors built a neural network for predicting football match results, and they demonstrated its effectiveness using data from the Chinese Football Super League. In [43], Premier League match results were also analyzed by machine learning methods. In [44], a similarity learning approach was proposed to predict football match rankings in the English Premier League. The study utilized transfer learning, and the performances of standard neural networks, Siamese networks, the RankNet method, the traditional sports tally ranking method, and graph-based PageRank methods were evaluated. Besides using the English Premier League seasonal team statistics, the data were also augmented with financial and transfer data. The authors concluded that no model consistently performed the best from all perspectives. However, machine learning methods have been applied to not only evaluate the performances of football teams but also determine the values of football players [45]. This information can also be used to estimate the results of matches [46]. Hybrid methods for evaluating football results have also been developed. For example, in [47], the random forest machine learning method was combined with the Poisson ranking method and the bookmaker consensus. In this article, the FIFA Women's World Cup 2011 and 2015 were used to train the proposed method, and then the FIFA Women's World Cup 2019 was simulated 100,000 times to determine the winning probabilities of the 24 participating teams.