Analysis of basketball game states and transition probabilities using the Markov chains

Abstract

In this paper an abstract system of basketball games is set up. The parts of the game are marked by common characteristics, they are repeated, so they can be marked with the category: state of the game. The proposed model enables the identification and analysis of interactions between a system state. Discretization of the continuous flow of the game in a basketball game and the definition of the equivalence between the state of the game are a prerequisite for determining the transition probabilities between the state. Discrete stochastic processes, Markov Chains, were used to model events and the probability of transition between states. A matrix of transient probabilities between the individual states in the Markov chain is structured. The grounded model distinguishes the states of the game within the 4th phase of the game's game and allows the prediction of future conditions.