Markov Chain Algorithm in Sports

As a sports enthusiast, you have always been wandering your thoughts to select a better player in fact which team will be going to win. For example, if you can predict what type of ball will be bowled by a bowler or shot played by a batsman or how a player will tackle the football in the ground if football passed to him. It will add up better insights for a player, a manager or a coach in context with tactical and technical aspects. For the scope of this discussion, I will be covering two sports that are cricket and football. The objective of using the Markov chain model is to provide tactile or technical advice in cricket and football.

A Markov Reward Process (MRP) is a Markov model where transitions may have an associated reward or cost. Specifically, an MRP is a tuple (S, P, R, γ) where S is the set of states, P: S × S → [0, 1] is the transition function, R: S × S → R is a reward function associated with every transition and γ ∈ [0, 1] is a discount factor.

Why we are discussing Markov Chain Model in context with sports?

In sports most of the time, we have to decide the changes in tactics of sports depending upon the present situation of sports rather than the historical data. As we know that in Markov Chain we have to predict the probabilities through present events and not with the past events.

Elements of Markov models: Cricket

Suppose a bowler is bowling an over if in current over he bowled 3 Swing bowl, 2 bouncer and 1 yorker then present…