RatingsLib is a Python library dedicated to rating/ranking systems implementation with applications in sports and other fields.
RatingsLib requires Python 3.8 or newer. More details about requirements can be found in requirements.txt
.
You can install ratingslib
directly ::
pip install git+https://github.com/ktalattinis/ratingslib
or by cloning the repository ::
git clone https://github.com/ktalattinis/ratingslib
cd ratingslib
pip install .
Rating/Ranking systems:
- WinLoss
- Colley
- Massey
- Keener
- Elo
- Offense - Defense
- GeM
- AccuRATE
Ranking Aggregation methods:
- Borda Count
- Average Rank
Rating Aggregation methods:
- Markov
- Perron
- Offense-Defense
Comparison metrics:
- Kendall's Tau
Applications & Examples:
-
Sports (the main application of the library):
- Soccer Teams rating
- Soccer Teams ranking lists comparison
- Hindsight and foresight prediction of the final outcome of soccer matches
- Combining rating systems and machine learning methods to predict soccer matches outcome
- Ranking NFL teams
-
Other Applications & Examples:
- Finance:
- Examples from investment selection and portfolios rating and ranking.
- Domain Market:
- An illustrative example is provided and shows the ranking of domain names.
- Movies:
- Application on real-world dataset from MovieLens
- Finance:
The documentation is available at: https://ktalattinis.github.io/ratingslib/