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bl-predictor-1.2
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مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
A simple application for predicting game results for the German Bundesliga
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | bl-predictor-1.2 |
| نام | bl-predictor |
| نسخه کتابخانه | 1.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Lukas Gehring, Anabel Stammer, Alex Brylka and Fabricio Aguilera-Galviz |
| ایمیل نویسنده | l.gehring@student.uni-tuebingen.de |
| آدرس صفحه اصلی | https://github.com/lgehring/bl-predictor |
| آدرس اینترنتی | https://pypi.org/project/bl-predictor/ |
| مجوز | - |
# bl-predictor <img src="https://www.python.org/static/community_logos/python-powered-w-70x28.png" alt="Python powered" align="right">
<img src="https://raw.githubusercontent.com/lgehring/bl-predictor/master/bl-predictor_logo.svg" width="150" align="right">
[](https://frontend.code-inspector.com/public/project/17966/bl-predictor/dashboard)
[](https://coveralls.io/github/lgehring/bl-predictor)
[](https://pypi.org/project/bl-predictor/)
[](LICENSE.txt)
[](https://github.com/lgehring/bl-predictor/tree/main/tests)
[](https://www.python.org/dev/peps/pep-0008/)
[](https://codeql.github.com/docs/codeql-overview/)
<br />
<span style="font-family:Helvetica; font-size:1em; font-weight:bold">
Bl-predictor is a simple application for predicting game results for the German 1. Bundesliga.
It features a clean graphical user interface (including DarkMode to spare your eyes), automatic data crawling, a variety of
prediction-models to choose from, and a few built-in model evaluation tools.
</span>
## Usage
#### Install:
```bash
pip install bl-predictor
```
#### and start the GUI:
```bash
bl-predictor-gui
```
The left column shows you the next upcoming matches. These are automatically crawled from [OpenligaDB](https://www.openligadb.de) when the application starts.
The center column gives you the option to tweak your prediction preferences:
- choose the seasons used for training the model via the slider
- select a [model](#prediction-models) to train
- choose a home and guest team
Your result and additional information about the model used will appear in the righthand column.
To make another prediction just use one of the reset-options on the bottom-left.
<center> <img src="https://media.giphy.com/media/nD4GGlxODQoGXUw5lJ/giphy.gif" alt="demo"/></center>
You can switch to dark mode or exit the application under "Options" in the top-left corner.
<center> <img src="https://media.giphy.com/media/dQ8b4Lf5XasFzFpUEQ/giphy.gif" alt="dark mode"/></center>
## Prediction models
#### PoissonModel
A model that predicts the winning team out of two given teams, based on a poisson regression model.
**Caution:** The model is sensitive to the order of given teams, because the home_team scores better on average!
This model is heavily based on a [guideline from David Sheehan](https://dashee87.github.io/football/python/predicting-football-results-with-statistical-modelling/).
#### BettingPoissonModel
A adaptation of the PoissonModel improved for betting.
If no relevant (>10%) difference in the teams winning probabilities is present, "Draw" is returned.
#### FrequencyModel
A model that uses all results of the last seasons to predict a winner based on the relative frequency of wins.
## Model Evaluation
The model evaluation features no graphical user interface.
To access it you will need to go into the package source files to [prediction_evaluation.py](bl_predictor/prediction_evaluation.py)
and call the functions given at the bottom of the file. You can:
- generate a plot about the accuracy / F1-score of all models with different trainset sizes
- evaluate a single models performance
- trainset information
- performance measures
- ((Betting-)PoissonModel also returns a team-ranking based on the models coefficients)
- compare two models
- get general statistics about a trainset
The results will either be given as printout in the console or as plots.png and will look something like this:
<a href="https://ibb.co/2d6dhHW"><img src="https://i.ibb.co/zZ5ZxDQ/Model-Evaluation.jpg" alt="Model-Evaluation" border="0"></a>
<center>
<a href="https://ibb.co/2YyFdws"><img src="https://i.ibb.co/Wp6HfQP/Poisson-Conf-Mat.png" alt="Poisson-Conf-Mat" border="0"></a>
</center>
<a href="https://imgbb.com/"><img src="https://i.ibb.co/X51FwxH/Model-Compare.jpg" alt="Model-Compare" border="0"></a>
<center>
<a href="https://ibb.co/CMH88xf"><img src="https://i.ibb.co/wwzggDf/Accuracy-over-time.png" alt="Accuracy-over-time" border="0"></a>
</center>
## License
bl-predictor is made available under the [MIT-License](LICENSE.txt)
نیازمندی
| مقدار | نام |
|---|---|
| ~=1.1.3 | pandas |
| ~=50.3.0 | setuptools |
| ~=0.12.1 | statsmodels |
| ~=1.19.3 | numpy |
| ~=1.5.2 | scipy |
| ~=3.3.4 | matplotlib |
| ~=6.1.2 | pytest |
| ~=2.24.0 | requests |
| ~=8.1.0 | pillow |
| ~=4.6.1 | lxml |
| ~=0.8.7 | tabulate |
| ~=0.24.1 | scikit-learn |
| ~=3.2.2 | ttkthemes |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8 | Python |
نحوه نصب
نصب پکیج whl bl-predictor-1.2:
pip install bl-predictor-1.2.whl
نصب پکیج tar.gz bl-predictor-1.2:
pip install bl-predictor-1.2.tar.gz