Model Comparison for Professional Basketball’s Player Efficiency Rating
2021
- 70Usage
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage70
- Abstract Views70
Artifact Description
The Player Efficiency Rating (PER) is a statistic commonly used by Sports Networks to compare and contrast Professional Basketball players by attempting to quantify an individual player’s value while on the court. Though it is widely used, there are still several flaws that need to be addressed which Hollinger himself has openly admitted. For example, it is evident in the formula that offense is overvalued whereas defense is undervalued since there are more intangible and unrecognized factors on defense that contribute to a player’s success. This research will seek to develop a comparable model that will be used to compare against existing models. Using the existing models as the standard, I will compare each developed model using linear regression, residual sum of squares, and finding the coefficients of determination — which will attempt to prove that there is a better model that addresses these flaws accordingly and more accurately quantifies a player’s contributions.
Bibliographic Details
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know