Discovering the crucial factors that contribute to goal success in sports analytics, this thesis aimsto utilize Random Forest classification to predict the outcome of shots and pre-shot events in powerplay situations. Through three experiments, the study evaluated the use of shots, shots with pre-shotevents, and shots with pre-shot events over sections. The first experiment used only shots, while thesecond experiment focused on shots with pre-shot events, where both compared it with shots over anexpected goal value of 0.08 or higher. The third experiment examined shots with pre-shot events acrossdifferent sections. Our findings demonstrated that the models in our experiments achieved accuracyscores ranging from 78% to 96% and F1 scores between 0% and 24%. Notably, the models in experiment3 demonstrated lower recall scores. The feature importance analysis revealed that pre-shotevents played a significant role in the predictive models of the second and third experiments, indicatingtheir substantial impact on the outcomes. A noteworthy conclusion arising from the discussion isthe recommendation for future research to conduct a more comprehensive exploration into the impactof pre-shot events, given their demonstrated significance in predicting goals. Such an investigation isdeemed necessary and justified.