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https://github.com/georgedouzas/sports-betting

Collection of sports betting AI tools.
https://github.com/georgedouzas/sports-betting

machine-learning python scikit-learn sports-analytics sports-betting

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Collection of sports betting AI tools.

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# 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].



The main components of `sports-betting` are dataloaders and bettors objects.



- Dataloaders download and prepare data suitable for predictive modelling.

- Bettors provide an easy way to backtest betting strategies and predict the value bets of future events.



## Quick start



`sports-betting` supports all common sports betting needs i.e. fetching historical and fixtures data as well as backtesting of

betting strategies and prediction of value bets. Assume we would like to backtest the following scenario and use the bettor object

to predict value bets:



- Selection of data

  - First and second division of German, Italian and French leagues for the years 2021-2024

  - Maximum odds of the market in order to backtest our betting strategy

- Configuration of betting strategy

  - 5-fold time ordered cross-validation

  - Initial cash of 10000 euros

  - Stake of 50 euros for each bet

  - Use match odds (home win, away win and draw) as betting markets

  - Logistic regression classifier to predict probabilities and value bets



```python

# Selection of data

from sportsbet.datasets import SoccerDataLoader



leagues = ['Germany', 'Italy', 'France']

divisions = [1, 2]

years = [2021, 2022, 2023, 2024]

odds_type = 'market_maximum'

dataloader = SoccerDataLoader({'league': leagues, 'year': years, 'division': divisions})

X_train, Y_train, O_train = dataloader.extract_train_data(odds_type=odds_type)

X_fix, _, O_fix = dataloader.extract_fixtures_data()



# Configuration of betting strategy

from sklearn.model_selection import TimeSeriesSplit

from sklearn.compose import make_column_transformer

from sklearn.linear_model import LogisticRegression

from sklearn.impute import SimpleImputer

from sklearn.pipeline import make_pipeline

from sklearn.preprocessing import OneHotEncoder

from sklearn.multioutput import MultiOutputClassifier

from sportsbet.evaluation import ClassifierBettor, backtest



tscv = TimeSeriesSplit(5)

init_cash = 10000.0

stake = 50.0

betting_markets = ['home_win__full_time_goals', 'draw__full_time_goals', 'away_win__full_time_goals']

classifier = make_pipeline(

  make_column_transformer(

    (OneHotEncoder(handle_unknown='ignore'), ['league', 'home_team', 'away_team']), remainder='passthrough'

  ),

  SimpleImputer(),

  MultiOutputClassifier(LogisticRegression(solver='liblinear', random_state=7, class_weight='balanced', C=50)),

)

bettor = ClassifierBettor(classifier, betting_markets=betting_markets, stake=stake, init_cash=init_cash)



# Apply backtesting and get results

backtesting_results = backtest(bettor, X_train, Y_train, O_train, cv=tscv)



# Get value bets for upcoming betting events

bettor.fit(X_train, Y_train)

bettor.bet(X_fix, O_fix)

```



## 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.



## Installation



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



```bash

pip install sports-betting

```



Development installation requires to clone the repository and then use [PDM](https://github.com/pdm-project/pdm) to install the

project as well as the main and development dependencies:



```bash

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

cd sports-betting

pdm install

```



## Usage



You can use the Python API or the CLI to access the full functionality of `sports-betting`. Nevertheless, it is recommended to be

familiar with the Python API since it is still needed to write configuration files for the CLI.



### API



The `sports-betting` package makes it easy to download sports betting data:



```python

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')

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:



```python

from sportsbet.evaluation import ClassifierBettor, backtest

from sklearn.dummy import DummyClassifier

bettor = ClassifierBettor(DummyClassifier())

backtest(bettor, X_train, Y_train, O_train)

```



We can use the trained bettor model to predict the value bets using the fixtures data:



```python

bettor.fit(X_train, Y_train)

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.



#### Configuration



In order to use the commands, a configuration file is required. You can find examples of such configuration files in

`sports-betting/configs/`. The configuration file should have a Python file extension and contain a few variables. The variables 

`DATALOADER_CLASS` and `PARAM_GRID` are mandatory while the rest are optional.



The following variables configure the data extraction:



- `DATALOADER_CLASS`: The dataloader class to use.



- `PARAM_GRID`: The parameters grid to select the type of information that the data includes.



- `DROP_NA_THRES`: The parameter `drop_na_thres` of the dataloader's `extract_train_data`.



- `ODDS_TYPE`: The parameter `odds_type` of the dataloader's `extract_train_data`.



The following variables configure the betting process:



- `BETTOR`: A bettor object.



- `CV`: The parameter `cv` of the function `backtest`.



- `N_JOBS`: The parameter `n_jobs` of the function `backtest`.



- `VERBOSE`: The parameter `verbose` of the function `backtest`.



#### Commands



Once these variables are provided, we can select the appropriate commands to select any of the `sports-betting`'s functionalities.



##### Dataloader



Show available parameters for dataloaders:



```bash

sportsbet dataloader params -c config.py

```



Show available odds types:



```bash

sportsbet dataloader odds-types -c config.py

```



Extract training data and save them as CSV files:



```bash

sportsbet dataloader training -c config.py -d /path/to/directory

```



Extract fixtures data and save them as CSV files:



```bash

sportsbet dataloader fixtures -c config.py -d /path/to/directory

```



##### Bettor



Backtest the bettor and save the results as CSV file:



```bash

sportsbet bettor backtest -c config.py -d /path/to/directory

```



Get the value bets and save them as CSV file:



```bash

sportsbet bettor bet -c config.py -d /path/to/directory

```