sports are tennis and volleyball. Papers dealing with this approach usually choose tennis for modeling [9,10]. In this paper, the focus will be put on volleyball since it has been relatively scarcely presented in scientific papers to date. Despite this fact, shown principles are generalizable and can be applied to other discrete sports which use a similar, tightly defined scoring scheme. Most published approaches focus on building models (pre-match and in-play) which base their estimates of match outcomes on historical data and the known average service percentages of both teams. These values are precomputed and subsequently fed to mathematical equations based on a Markov chain to produce the probability of a given team winning the match or to estimate the duration of the match in terms of the total number of points that will be played in the match. These models are created with the common assumption that points in discrete sports are _identically and independently distributed_ (commonly called the _iid distribution_). This means that the average service percentages of both teams, once calculated from historical data, never change throughout the match whose outcome is to be predicted. The notion of independence stems from the presumption that the probability of winning a point on serve is not influenced in any way by the outcome of previous points. The assumption that each point is equal, regardless of whether it is a point of little importance at the beginning of the match or a crucial point in the final set, is the identical part of the iid annotation. [PERSON] [11] was the first to describe a tennis match through a Markov chain with constant probabilities of transition between states. [PERSON] [12] presented an analytical approach that was used to calculate game, set, and match win probabilities. He also obtained solutions for the mean and variance of the number of points played in a game, set, and match, respectively. After that, many more papers were published that also proposed solutions to predict the outcome of tennis matches, either before the match started, pre-match models [9,13,14], or during the match whose outcome is to be predicted, or in-match models [15,16,17,18]. Although tennis has been the main focus in Markovian sports modeling, there are also papers that focus on volleyball analysis. The milestone of volleyball analysis was published in 2004 [19], where the authors derived expressions for set winning probability in terms of the Legendre polynomials. They also showed the importance of information about who is serving first in a deciding set in volleyball. [PERSON] et al. [20] had a first attempt to estimate the set winning probabilities and the duration of a volleyball set (in terms of the number of points played in a set) using the Markovian chain model. They offered recurrence formulas to estimate those values. [PERSON] and [PERSON] [21] refactored the previous paper from the combinatorial point of view, while making a distinction between the two scoring systems. A common assumption which all these papers are based on is the identical and independent distribution of points. The assumption of the independent and identical distribution of points greatly facilitates the modeling of volleyball and tennis matches, but conflicts with a phenomenon very popular in psychology--psychological momentum [22]. Since the end of the twentieth century, the existence of psychological momentum has been a topic of research in the sports domain, where the synonymous term \"hot-hand\" is used more often. Briefly, \"hot-hand\" or psychological momentum is a belief that a player has a higher chance of scoring a point after a few successful points. The research has been conducted on a variety of sports, mainly in basketball [23,24,25,26], baseball, [27] and tennis [28,29,30,31,32,33,34,35]. Quite extensive research on the existence of psychological momentum has also been conducted in volleyball [36,37,38].