Improving a Hockey Team
(3 pages of text)
In November 2017, a fan of hockey watched his favourite team lose 6–4 against a contending team. After a great start to the season with a record of 6 wins and 1 loss, his favourite team had lost 6 of the last 8 games, dropping their record to 8 wins and 7 losses. The fan knew that the time was approaching for the inevitable discussions with his friends regarding the team's success. Which players should they have signed? Which player should the team trade for? What line combinations would serve the team best? Who was overvalued, and who was undervalued? Knowing that conversations among his friends typically took on a random and unstructured format, the fan planned to provide a more analytical approach to the discussion this year. His research would raise the level of the conversation and help back up his suggestions.
This case is suitable for data analytics courses focusing on optimization at the masters or undergraduate level. After completion of this case, students should be able to:
- understand correlation and multicollinearity;
- assess continuous and categorical variables;
- assign criteria for using linear regression;
- calculate statistical significance and p-values;
- use and interpret the linear regression model;
- understand the limitations of a model and identify potential improvements to address those limitations; and
- evaluate discontinuous optimization problems.
Arts, Entertainment, Sports and Recreation
Canada, Small, 2017
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