## 1 Introduction  
Sports competitions promise hard effort for competitors and exciting entertainment for supporters. However, sports and sporting competitions are not just about entertainment; they are also important from an economic point of view. Organizing and running them usually costs a lot of money; furthermore, sports betting, advertising, and player transfer fees also involve vast amounts. Perhaps this is why the importance of sports can be observed not only in the economic and entertainment sectors, but also in scientific research, which deals with predicting the results of matches and competitions. Evaluating matches' results plays a crucial role in setting out the rankings and predicting the results of subsequent matches. The most frequently used method for ranking competitors is the point-based method. When two competitors face each other, the winner receives one, two, or three points (in chess, handball, and soccer, respectively, as examples) according to the scoring system used in the sport. At the same time, the loser does not receive any points. In the case of a tie, both competitors receive one or half a point. This method is called the row sum method [1]. If the number of matches played differs between competitors, the number of points collected does not reflect the strength of the competitors. The row sum method does not take into consideration the strength of the opponent: a win guarantees the same number of points whoever the opponent is, either weak or strong. This drawback can be partly eliminated by applying the generalized row sum method elaborated by [PERSON] [2]. However, the generalized row sum method requires a parameter that can be changed arbitrarily, and its value significantly impacts the evaluation result. Another branch of evaluation methodologies is the set of Elo evaluation methods originally elaborated by [PERSON] [3]. An Elo method is applied, for example, in chess, where players are ranked with their Elo points, but it can also be applied in evaluating horse sports [4]. The method changes the strengths of competitors in pairs. If two players play a match, then their Elo points change as a function of the result of the match played and the difference in the players' strengths. As this can be achieved in various ways, we can speak about different Elo methods. The Elo methods can be considered local methods, as only the strengths of the competitors participating in the current competition are changed. It is worth mentioning that, besides chess players' rankings, an Elo-type method has been used for the FIFA ranking of women's national football teams and the FIFA ranking of men's national teams since 2018 [5]. For further details about the Elo methods, see [6]; additionally, their predicting properties are detailed in [7]. The most popular method of evaluating through comparisons in pairs is the analytic hierarchy process (AHP) method elaborated by [PERSON] [8; 9]. This method starts with a reciprocal symmetric matrix, with the elements expressing the priorities of the objects relative to each other. There are several possible ways to evaluate the matrix; the most frequently used is the eigenvector method. The result is a priority vector, which expresses the weights of the objects to be evaluated. The method has thousands of applications in every field of life, for example, in education [10], psychology [11], security [12], technology [13], business [14], and decision-making [15]. Its application for evaluating sports results is presented, for example, in [16; 17]. The latter paper used the pairwise comparison method for ranking the best tennis players in the world, but \"comparison in pairs\" methods can also be used for racing sports [18], handball [19], chess [20], e-sports [21], and football [22]. An alternative means of comparison in pairs is the stochastic Thurstone-type methods. In this case, the performances of the competitors are considered random variables. This idea is not far from reality. Think of the surprising changes in the performances of teams.