International Journal of Physical Sciences Research Vol.1, No.2, pp.1- 13, August 2017 ___Published by European Centre for Research Training and Development UK (www.eajournals.org) 1 STOCHASTIC MODELING/GAME THEORY ANALYSIS OF SCORELINE Etaga H.O, Umeokeke E.T, Nwosu C.R, Etaga N. C, Umeh E. U, Awopeju B, Eriobu N, Okoye V.C., Omoruyi F. A., Department of Statistics, Nnamdi Azikiwe University, Awka, Nigeria ABSTRACT: Prediction of a football match result arouses interest from different points of view; for different people, Hence the need for this work which aims at analysing the scores of the four top English clubs to enable prediction of future outcome of matches to be made in a more scientific manner. From the analysis of the scoreline of the top four EPL clubs; Manchester United (M.U), Chelsea (C), Arsenal (A), and Manchester City (M.C) from (2002- 2015) using Game theory and Stochastic modelling, Chelsea emerged the best team with a selection probability of 0.41 while Manchester United also emerged second best with a selection probability of 0.37. From the steady state transition probability matrix, for all the six possible pairs of the four clubs shows that the probability of M.U wining C at home is 0.44 while C wins M.U at home with probability 0.67 depicting C as the stronger club. Similarly M.U is stronger than A, with a 0.71 winning probability as against 0.25 winning probability for A, while M.U and M.C appears to be equally matched with 0.48 and 0.49 probability of winning. C against A reveals a probability of 0.58 and 0.25 for A vs C. while C vs M.C showed C to have an upper hand with a 0.71 probability of winning and 0.44 for M.C vs C. Finally A vs M.C gives the two teams 0.53 and 0.42 winning probabilities. Thus, the two most viable clubs out of the four clubs are Manchester United and Chelsea. Using the four step TPM we also predicted the 2015/2016 matches to obtain their various probabilities given the previous game. KEYWORDS: Game theory, Stochastic modelling, English Premier League, Football, Operations Research, Stochastic Modelling, Prediction. INTRODUCTION Football is the most popular game all over the world; in Europe and South America it is the dominant spectator sport. People find interest in soccer for various reasons and at different levels, with a clear dominance for the males, Reep and Benjamin (1968) came to the conclusion that “chance does dominate the game” while Hennessy (1969) is of the opinion that only chance was involved. Hill (1974) argued that anyone who had ever watched a football match could reach the conclusion that the game was either all skill or all chance. He justified his opinion by calculating the correlation between the expert opinions and the final league tables result, concluding that even though chance was involved, there was also a significant amount of talent affecting the final outcome of the match. However the first real model to predict football scores was put forward by Maher (1982).From his model, he obtained that the goals scored by two opposing teams in some particular match are drawn from independent Poisson distributions. Whilst introducing the home advantage factor, he assigned each team with a pair of fixed parameters ( α and β ) such that the model would simply consist in combining the respective attacking and defensive parameters of the opposing teams. Nelson Mandela (1992), avers that sports has the power to change the world. Lee (1997) relied on this