My project will concern goal scoring in the NHL and insights I can offer to anyone who is interested in what factors in shots contribute the most to goals. The graphic above displays goal scoring heat maps of two teams: the Boston Bruins who missed the playoffs on the left, and the Florida Panthers who won hockey’s ultimate prize, the Stanley Cup just a few weeks ago. Using data from moneypuck.com, I have a tremendous amount of data available to explore goal scoring. For the above figure, X variable would be team and the Y variable would be the prevalance of goal scoring mapped on the ice sheet. Immediately, your eyes should be drawn to the Panthers’ ability to score goals in the areas to the diagonal right and left of the crease. I will also utilize a specific player to further explore the most important factors when it comes to goal scoring. Using logistic regression, I modeled factors including shot distance, shot type, and shot angle to examine which would be the most significant predictor of a goal. Overall, shot distance and powerplay opportunities were the two most significant factors when predicting goals. Furthermore, For the player Sam Reinhart, each additional foot away from the net he attempted a shot, the log-odds of scoring a goal decreased by 0.033, with a 95% confidence interval from -0.045 to -0.022.