sports-betting 0.4.0

Python sports betting toolbox.

sports-betting

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Introduction

Python sports betting toolbox.

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.

Sports betting in practice

You can think of any sports betting event as a random experiment with unknown probabilities for the various outcomes. Even for the most unlikely outcome, for example scoring more than 10 goals in a soccer match, a small probability is still assigned. The bookmaker estimates this probability P and offers the corresponding odds O. In theory, if the bookmaker offers the so-called fair odds O = 1 / P in the long run, neither the bettor nor the bookmaker would make any money.

The bookmaker's strategy is to adjust the odds in their favor using the over-round of probabilities. In practice, it offers odds less than the estimated fair odds. The important point here is that the bookmaker still has to estimate the probabilities of outcomes and provide odds that guarantee them long-term profit.

On the other hand, the bettor can also estimate the probabilities and compare them to the odds the bookmaker offers. If the estimated probability of an outcome is higher than the implied probability from the provided odds, then the bet is called a value bet.

The only long-term betting strategy that makes sense is to select value bets. However, you have to remember that neither the bettor nor the bookmaker can access the actual probabilities of outcomes. Therefore, identifying a value bet from the side of the bettor is still an estimation. The bettor or the bookmaker might be wrong, or both of them.

Another essential point is that bookmakers can access resources that the typical bettor is rare to access. For instance, they have more data, computational power, and teams of experts working on predictive models. You may assume that trying to beat them is pointless, but this is not necessarily correct. The bookmakers have multiple factors to consider when they offer their adjusted odds. This is the reason there is a considerable variation among the offered odds. The bettor should aim to systematically estimate the value bets, backtest their performance, and not create arbitrarily accurate predictive models. This is a realistic goal, and sports-betting can help by providing appropriate tools.

Using sports-betting CLI, the betting process includes the following steps:

• Create a dataloader's configuration file that selects the training and fixtures data.

• Create a bettor's configuration file that selects and configures the betting strategy.

You can find examples of configuration files in sports-betting/configs.

Extract the training data:

sportsbet dataloader training -d dataloader_config.py

Extract the fixtures data:

sportsbet dataloader fixtures -d dataloader_config.py

Apply backtesting to estimate the performance of the model on future data:

sportsbet bettor backtest -b bettor_config.py -d dataloader_config.py

Get the value bets:

sportsbet bettor bettor -b bettor_config.py -d dataloader_config.py

Installation

For user installation, sports-betting is currently available on the PyPi's repository, and you can install it via pip:

pip install sports-betting

Development installation requires to clone the repository and then use PDM to install the project as well as the main and development dependencies:

git clone https://github.com/georgedouzas/sports-betting.git
cd sports-betting
pdm install

Usage

API

The sports-betting package makes it easy to download 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()

X_train are the historical/training data and X_fix are the test/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 use the trained bettor model to predict the value bets using the fixtures data:

bettor.bet(X_fix, O_fix)

CLI

The command sportsbet provides various sub-commands to download data and predict the value bets. For any sub-command you may add the --help flag to get more information about its usage.

Dataloader

The sub-command sportsbet dataloader requires a dataloader configuration file. You can find examples of such configuration files in sports-betting/configs/dataloaders. The following conventions apply:

• The configuration file has a Python extension.

• It should include a dictionary called MAIN and two key-value pairs. A 'dataloader' key with a dataloader class as value and a 'path' key with a relative to the configuration file path as value. It will be the path of the pickled dataloader.

• It may include a dictionary called OPTIONAL and up to three key-value pairs. The three keys are 'param_grid', 'drop_na_thres' and 'drop_na'. You can refer to the API for more details about their values.

Using a dataloader configuration file and the following commands you can extract training and fixtures data.

Show available parameters for dataloaders:

sportsbet dataloader params -d dataloader_config.py

Show available odds types:

sportsbet dataloader odds-types -d dataloader_config.py

Extract training data:

sportsbet dataloader training -d dataloader_config.py

Extract fixtures data:

sportsbet dataloader fixtures -d dataloader_config.py

Bettor

The sub-command sportsbet bettor requires both bettor and dataloader configuration files. The dataloader configuration files are explained above. For the bettor configuration files, you can find examples of such configuration files in sports-betting/configs/bettors, while the following conventions apply:

• The configuration file has a Python extension.

• It should include a dictionary called MAIN and two key-value pairs. A 'bettor' key with a bettor class as value and a 'path' key with a relative to the configuration file path as value. It will be the path of the pickled bettor.

• It may include a dictionary called OPTIONAL and multiple key-value pairs. Two of the optional keys are 'tscv' and 'init_cash' The rest of the keys are the initialization parameters of the selected bettor. You can refer to the API for more details about their values.

Using a bettor configuration file and the following commands you can backtest the bettor and estimate the value bets.

Backtest the bettor:

sportsbet bettor backtest -b bettor_config.py -d dataloader_config.py

Get the value bets:

sportsbet bettor bettor -b bettor_config.py -d dataloader_config.py