English Premier League (EPL) is the top-tier competition in English football and become the most-watched league internationally. This study aims to examine the correlation between an explanatory variable and goal scoring in EPL; to analyse the consistency metrics between goal scoring and the explanatory variable in EPL by using Poisson and Probit Regression Models and to propose an appropriate Poisson and Probit Regression Models that fit to the dataset. The methods that considered in this study are Poisson and Probit Regression Model. The dataset is based on the match final scores for the 2021/2022 season with 380 matches. Therefore, the dataset consists of 380 observations, each representing a match and 22 attributes. The results revealed that the Probit Regression Model is a better fit for the dataset with Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) values of 407.38 and 420.8 compared to the Poisson Regression Model with the values of 1169.2 and 1139.7. In conclusion, Manchester City and Liverpool had the highest average goal count among clubs. Both clubs are identified as having high probabilities of securing the top ranks in the season. This is substantiated by the fact that both teams indeed finished at the top in the end as first and second place. Furthermore, both models demonstrate significance in fulfilling the Chi-Square test and overdispersion for the Poisson Regression Model, when it comes to model selection, the Probit Regression Model outperforms the Poisson Regression Model in terms of AIC and BIC values.