With the foundation of the regression introduced and explained, we are now able to use the above mentioned to find our empirical results. The estimated model provides a deep exploration into our hypothesis, with variables such as Position played, and Games Played used in this experiment that would have a direct effect on the hypothesis of whether skin tone affects the referee’s decisions when it comes to disciplinary sanctions. Time dummies are included in the framework (relevant seasons) in order to account for any potential altercations in refereeing policy over time in the Premier League. For example, the rule that players will receive bookings for simulation/diving was only implemented in 2017 which would affect our dataset and the potential outcome of the results in comparison to seasons prior. The main catalyst for disciplinary sanctions is expected to be the number of fouls committed due to obvious reasons, and this variable will also feature in the empirical specification, with the linear and poisson model exhibiting 1605 and 1142 fixed effects respectively, specific to each observation found across all 3 seasons. Further analysis of the empirical results calculated using the regression found in Table 4 will be discussed in the next section. Relevant notes for the table are as follows: ***, **, * represent statistical significance at the 1%, 5% and 10% level respectively and – represents the base group of estimation and these variables have been omitted in the regression. The number of club controls within the database is set at 21, with one club omitted as the base club.