sports-betting 0.2.0

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

You can download sports betting data:

from sportsbet.datasets import FTESoccerDataLoader
dataloader = FTESoccerDataLoader()
X_train, Y_train, O_train = dataloader.extract_train_data()

Use the historical data to backtest the performance of models:

from sportsbet.evaluation import ClassifierBettor
num_features = [
  col
  for col in X_train.columns
  if X_train[col].dtype in (np.dtype(int), np.dtype(float))
]
X_train = X_train[num_features]
bettor = ClassifierBettor(KNeighborsClassifier())
bettor.backtest(X_train, Y_train, O_train)

Get the value bets using fixtures data:

X_fix, Y_fix, O_fix = dataloader.extract_fixtures_data()
value_bets = bettor.bet(X_fix[num_features], 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.