HOCKEY ANALYTICS: PREDICTIVE MODELING OF TEAM AND PLAYER PERFORMANCE

Open Access
- Author:
- Bollendorf, Steven Matthew
- Area of Honors:
- Industrial Engineering
- Degree:
- Bachelor of Science
- Document Type:
- Thesis
- Thesis Supervisors:
- Dr. Guodong Pang, Thesis Supervisor
Dr. Catherine Mary Harmonosky, Thesis Honors Advisor - Keywords:
- hockey
sports analytics
NHL
regression analysis
optimization
analytics
predictive modeling
player evaluation - Abstract:
- Analytics in hockey is growing in popularity. Deciding which game strategies to implement and which players make a team more competitive is extremely valuable information for coaches and general managers (GMs) of National Hockey League (NHL) teams. The goal of this applied research is to look at two of aspects of the sport to find outcomes that can help with in-game strategies and help find the right players for reasonable salaries on NHL teams. This research looks at The Pennsylvania State University 2016-2017 hockey team and the 2015-2016 Pittsburgh Penguins to discover any scoring rate patterns that winning hockey teams possess. Likewise, a linear regression model based on a team’s Goals For (GF), or goals scored by a team, and Goals Against (GA), or goals scored against a team, predicts that GF contribute less to a team’s success than GA. In addition, the data from four NHL seasons on every NHL player is used to cluster players into specific player types in order to predict their value to team success. The key clustering metric used is the Corsi For Percentage (CF%), which measures a player’s puck possession skill. According to this research, elite forwards, second line forwards, and defensive defenseman provide the most value to a team. Lastly, specific teams during the 2016-2017 season are analyzed to determine if they have underperformed or overperformed relative to the model’s predicted team point total.