venv/bin/jupyter lab Navigating inside JupyterLab If you need to restart JupyterLab (eg after rebooting your computer) you can do so by running the following commands in your terminal: cd Downloads/bitodds-data-analysis-master You will then be able to go to in your browser to access JupyterLab.
venv/bin/pip install -r requirements.txt Installing the dependencies and launching JupyterLabĪfter downloading and extracting the zip, open a terminal and then run the following commands to create a Python virtual environment, install the requirements and start JupyterLab: cd Downloads/bitodds-data-analysis-master If you are on macOS we recommend using Homebrew. If you don’t have Python 3 installed, you can install it with your system package manager. You can find out by running python3 in the terminal.
If you are on macOS or Linux you may already have Python 3 installed. If you are on Windows you can download the Python installer. Running the code on your computer Installing Python If you want to be able to save your changes you should follow the instructions in the next section to install and run the project on your own computer. This is great for trying out the notebook, however, any changes you make will be lost when you close your browser. This will allow you to run the notebook in your browser without installing anything on your computer. The easiest way to run the notebook is to launch it in Binder with the following link (note it may take a minute to launch): To make it easy to execute the code in this article, we have created a git repository containing the CSV files and a Jupyter notebook that you can use to run the analysis and explore the data for yourself. The date ranges for the exports are:Īfter downloading the CSV files for this two date ranges you will have two files in your Downloads folder: The archive only allows exporting 1,000 events at a time so we need to perform two exports and then combine the data in Pandas. Since the 2020-21 NBA season ran from December 2020 to July 2021, we need to make sure the date range covers this period. Select NBA in the competition dropdown.
Exporting the data from BitOddsįor this example, we will export NBA data for the 2020-21 season. In this post, we will demonstrate how to load and analyze a CSV export using the Python programming language and the Pandas data analysis tool, and how to apply machine learning to this data to construct a model to predict the winners of NBA games. For this reason, we have now added the ability to export the archive data in two easy-to-use formats: CSV and Google Sheets. With odds and results for events going right back to 2018, we wanted to make it easy for anyone to utilize this data to take their betting to the next level. The BitOdds archive provides a treasure trove of data for sports bettors looking to use data-driven betting strategies.
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