  Technology has had an unquestionable impact on the way people watch sports.
Along with this technological evolution has come a higher standard to ensure a
good viewing experience for the casual sports fan. It can be argued that the
pervasion of statistical analysis in sports serves to satiate the fan's desire
for detailed sports statistics. The goal of statistical analysis in sports is a
simple one: to eliminate subjective analysis. In this paper, we review previous
work that attempts to analyze various aspects in sports by using ideas from
Markov Chains, Bayesian Inference and Markov Chain Monte Carlo (MCMC) methods.
The unifying goal of these works is to achieve an accurate representation of
the player's ability, the sport, or the environmental effects on the player's
performance. With the prevalence of cheap computation, it is possible that
using techniques in Artificial Intelligence could improve the result of
statistical analysis in sport. This is best illustrated when evaluating
football using Neuro Dynamic Programming, a Control Theory paradigm heavily
based on theory in Stochastic processes. The results from this method suggest
that statistical analysis in sports may benefit from using ideas from the area
of Control Theory or Machine Learning
