sports-betting 0.2.1

Python sports betting toolbox.

sports-betting

Introduction

The sports-betting package is a collection of tools that makes it easy to create machine learning models for sports betting and evaluate their performance. It is compatible with scikit-learn.

Usage

The sports-betting package makes it easy to download training and fixtures sports betting data:

>>> from sportsbet.datasets import SoccerDataLoader
>>> dataloader = SoccerDataLoader(param_grid={'league': ['Italy'], 'year': [2020]})
>>> X_train, Y_train, O_train = dataloader.extract_train_data(odds_type='market_maximum', drop_na_thres=1.0)
>>> X_fix, Y_fix, O_fix = dataloader.extract_fixtures_data()

The historical data can be used to backtest the performance of a bettor model:

>>> from sportsbet.evaluation import ClassifierBettor
>>> from sklearn.dummy import DummyClassifier
>>> bettor = ClassifierBettor(DummyClassifier())
>>> bettor.backtest(X_train, Y_train, O_train)

We can get the value bets using fixtures data:

>>> bettor.bet(X_fix, O_fix)

Installation

sports-betting is currently available on the PyPi’s repositories and you can install it via pip:

pip install -U sports-betting

The package is released also in Anaconda Cloud platform:

conda install -c gdouzas sports-betting

Documentation

Installation documentation, API documentation, and examples can be found in the documentation.