Estimating offensive and defensive contributions of NHL hockey players since 2015, using hierarchical latent variable modeling.
The application can be viewed here.
Note: I have disabled the webserver for now and so the application must be run locally.
Often, we hear countless debates regarding whether X player contributes more than Y. Crosby vs. Ovechkin, Heiskanen vs. Makar, Hughes vs Kakko - the list goes on. In general, the majority of these debates come down to either the so called "eye-test" or excessive reliance on a single particular statistic to support what already wants to be seen. I wanted to provide a more statistically sound way to compare players that looked at all of these stats at once.
This model is not perfect - there are still aspects of players that aren't being captured (zone entries for example). In addition, the "eye-test" is still useful - it is difficult to capture all contexts with just numbers.
Other individuals have attempted similar ideas to what this application and model does - however, a common problem with some other models is that they are typically biased towards those who generate above average offence (specifically high Corsi/points per game players) while ignoring defensive contributions. Thus, players like Valeri Nichushkin or Zach Aston-Reese are often assigned poor or lack luster ratings even though both players are third liners who exhibit strong defensive play in their own end.
- To be completed. Essentially, the scores are all estimated using hierarchical structural equation models.
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Python 3.7 or higher:
- numpy==1.18.1
- pandas==1.0.1
- selenium==3.141.0
- BeautifulSoup4==4.8.2
- docopt==0.6.2
- chromedriver
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R 4.00 or higher:
- tidyverse==1.3.0
- recipes==0.1.13
- fuzzyjoin==0.1.6
- docopt==0.7.1
- lavaan==0.6-6
In addition, you must be a Patreon subscriber to Evolving Hockey.
To run the app locally, install Docker and then run the following command:
docker run -p 3838:3838 btang101/nhl_lv
Visit 0.0.0.0:3838 in a web browser to view the application.