sports-betting 0.8.1

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.

Daily bet tips

This section will contain daily updated value bets of a betting strategy based on a machine learning model. You can read the quick start guide below to understand the details or reproduce the results. Alternatively, you can visit regularly this page to use the predictions for your own betting.

Value bets

Date	League	Home Team	Away Team	Home Win	Draw	Away Win	Over 2.5	Under 2.5
2023-10-14	England	Grimsby	Accrington	2.36	4.08	3.52	1.86	2.16
2023-10-14	England	Forest Green	Colchester	2.62	4.08	2.78	1.86	2.17
2023-10-14	England	Lincoln	Burton	2.29	4.08	3.55	1.91	2.09
2023-10-14	England	Notts County	Mansfield	2.41	3.71	3.5	1.84	2.19
2023-10-14	England	AFC Wimbledon	Bradford	2.78	4.01	3.53	1.87	2.15
2023-10-14	England	Crewe	Tranmere	2.04	4.08	3.55	1.87	2.16
2023-10-14	England	Doncaster	Sutton	2.29	4.08	3.55	1.86	2.17
2023-10-14	England	Milton Keynes Dons	Barrow	2.36	4.08	3.33	1.99	2.01
2023-10-14	England	Harrogate	Stockport	2.66	4.61	2.45	1.9	2.11
2023-10-14	England	Blackpool	Stevenage	2.38	4.12	3.52	1.88	2.13
2023-10-14	England	Swindon	Newport County	2.1	4.14	3.94	1.83	2.21
2023-10-14	England	Walsall	Gillingham	2.6	4.1	3.37	1.92	2.09
2023-10-14	England	Wrexham	Salford	1.91	2.38	4.17	1.88	2.14
2023-10-14	England	Cambridge	Shrewsbury	2.36	4.08	3.53	1.9	2.11
2023-10-14	England	Carlisle	Leyton Orient	2.62	4.22	3.52	1.88	2.13
2023-10-14	Spain	Mirandes	Tenerife	2.63	4.03	3.48	1.93	2.07
2023-10-14	Spain	Espanol	Valladolid	2.24	4.08	3.59	1.85	2.18
2023-10-14	Spain	Sp Gijon	Zaragoza	2.37	4	3.65	1.97	2.03
2023-10-14	Spain	Cartagena	Santander	2.38	4.55	3.64	1.76	2.32
2023-10-15	Spain	Eibar	Huesca	1.64	4.18	5	2	2
2023-10-15	Spain	Eldense	Elche	2.6	4.12	3.42	1.92	2.09
2023-10-15	Spain	Leganes	Amorebieta	2.11	4.09	4.11	1.97	2.03
2023-10-15	Spain	Burgos	Villarreal B	2.35	3.94	3.79	1.93	2.07
2023-10-15	Spain	Andorra	Alcorcon	2.36	4.38	3.67	1.96	2.05
2023-10-16	Spain	Levante	Ferrol	2.1	4.08	4.22	1.86	2.16

Backtesting results

Training Start	Training End	Training Period	Testing Start	Testing End	Testing Period	Start Value	End Value	Total Return [%]	Total Bets	Win Rate [%]	Best Bet [%]	Worst Bet [%]	Avg Winning Bet [%]	Avg Losing Bet [%]	Profit Factor	Sharpe Ratio	Avg Bet Yield [%]	Std Bet Yield [%]
2016-01-08 00:00:00	2017-09-25 00:00:00	626 days 00:00:00	2017-09-25 00:00:00	2018-11-25 00:00:00	427 days 00:00:00	1000	1000	0	0	nan	nan	nan	nan	nan	nan	inf	nan	nan
2016-01-08 00:00:00	2018-11-25 00:00:00	1052 days 00:00:00	2018-11-25 00:00:00	2020-01-24 00:00:00	426 days 00:00:00	1000	1204.4	20.44	680	48.3824	674	-175	80.0253	-64.0311	1.1945	1.52356	6.13755	97.9237
2016-01-08 00:00:00	2020-01-24 00:00:00	1477 days 00:00:00	2020-01-24 00:00:00	2021-04-24 00:00:00	457 days 00:00:00	1000	1626.66	62.666	1641	48.9336	857.143	-181.818	83.8059	-68.3494	1.2479	2.92606	6.14726	102.423
2016-01-08 00:00:00	2021-04-24 00:00:00	1933 days 00:00:00	2021-04-24 00:00:00	2022-08-16 00:00:00	480 days 00:00:00	1000	1551.96	55.196	1490	49.7987	536.667	-185.714	80.3641	-65.3676	1.23447	2.56372	7.29261	97.4973
2016-01-08 00:00:00	2022-08-16 00:00:00	2412 days 00:00:00	2022-08-16 00:00:00	2023-10-09 00:00:00	420 days 00:00:00	1000	1350.43	35.043	1444	48.0609	522	-186.667	75.4121	-68.3366	1.15157	1.70726	0.845068	93.7018

Quick start

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

Parameters

Assume that we would like to fetch historical data of various leagues for specific years, including the maximum odds of the market and dropping columns that contain more than 20% of missing values:

leagues = ['England', 'Scotland', 'Germany', 'Italy', 'Spain', 'France', 'Netherlands', 'Belgium', 'Portugal', 'Turkey', 'Greece']
years = [2017, 2018, 2019, 2020, 2021, 2022, 2023]
odds_type = 'market_maximum'
drop_na_thres = 0.8

We would like also to use a GradientBoostingClassifier to support our betting strategy:

from sklearn.compose import make_column_transformer
from sklearn.ensemble import GradientBoostingClassifier
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

classifier = make_pipeline(
  make_column_transformer(
    (OneHotEncoder(handle_unknown='ignore'), ['league', 'home_team', 'away_team']), remainder='passthrough'
  ),
  SimpleImputer(),
  MultiOutputClassifier(GradientBoostingClassifier(random_state=5)),
)

Finally, our backtesting parameters would include a 5-fold time ordered cross-validation and initial portfolio value of 1000:

from sklearn.model_selection import TimeSeriesSplit
tscv = TimeSeriesSplit(5)
init_cash = 1000

Process

Using the above selections, the betting process is the following:

• Create a dataloader that is used to fetch the training and fixtures data.

from sportsbet.datasets import SoccerDataLoader
dataloader = SoccerDataLoader({'league': leagues, 'year': years})
X_train, Y_train, O_train = dataloader.extract_train_data(drop_na_thres=drop_na_thres, odds_type=odds_type)
X_fix, _, O_fix = dataloader.extract_fixtures_data()

• Create a bettor that selects and configures the betting strategy.

from sportsbet.evaluation import ClassifierBettor
bettor = ClassifierBettor(classifier)

• Backtest the bettor on the training data to evaluate the betting strategy:

bettor.backtest(X_train, Y_train, O_train, tscv=tscv, init_cash=init_cash)
bettor.backtest_results_[['Sharpe Ratio', 'Total Return [%]', 'Testing Period']].mean()

• Predict the value bets:

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:

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

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:

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.

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 dictionary CONFIG:

CONFIG = {
  'data': {
    'dataloader': ...,
    'param_grid': {

    },
    'drop_na_thres': ...,
    'odds_type': ...
  },
  'betting': {
    ...: ...,
    'bettor': ...,
    'tscv': ...,
    'init_cash': ...
  }
}

The dictionary CONFIG has the following structure:

• Two mandatory keys 'data' and 'betting' that configure the data extraction and betting strategy, respectively and contain other nested dictionaries as values.
• The 'data' key has a nested dictionary as a value with a mandatory key 'dataloader' and the optional keys 'param_grid', 'drop_na_thres' and 'drop_na'. You can refer to the API for more details about their values.
• The 'betting' key has a nested dictionary as a value with a mandatory key 'bettor' and the optional keys 'tscv', and 'init_cash'. You can refer to the API for more details about their values.

Dataloader

Show available parameters for dataloaders:

sportsbet dataloader params -c config.py

Show available odds types:

sportsbet dataloader odds-types -c config.py

Extract training data and save them as CSV files:

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

Extract fixtures data and save them as CSV files:

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

Bettor

Backtest the bettor and save the results as CSV file:

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

Get the value bets and save them as CSV file:

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