Functional ratings in sports
-
Brad Lowery
,
Abigail Slater
Abstract
In this paper, we present a new model for ranking sports teams. Our model uses all scoring data from all games to produce a functional rating by the method of least squares. The functional rating can be interpreted as a team average point differential adjusted for strength of schedule. Using two team’s functional ratings we can predict the expected point differential at any time in the game. We looked at three variations of our model accounting for home-court advantage in different ways. We use the 2018–2019 NCAA Division 1 men’s college basketball season to test the models and determined that home-court advantage is statistically important but does not differ between teams.
Funding source: University of Sioux Falls Natural Science Research Fellowship Grant
Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: This research was funded by the University of Sioux Falls Natural Science Research Fellowship Grant.
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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Articles in the same Issue
- Frontmatter
- Research Articles
- Functional ratings in sports
- Profiting from overreaction in soccer betting odds
- Understanding draws in Elo rating algorithm
- Modeling time loss from sports-related injuries using random effects models: an illustration using soccer-related injury observations
- The relative roles of skill and luck within 11 different golfer populations
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Articles in the same Issue
- Frontmatter
- Research Articles
- Functional ratings in sports
- Profiting from overreaction in soccer betting odds
- Understanding draws in Elo rating algorithm
- Modeling time loss from sports-related injuries using random effects models: an illustration using soccer-related injury observations
- The relative roles of skill and luck within 11 different golfer populations
- A parametric family of Massey-type methods: inference, prediction, and sensitivity
