NBA_Celtics_Report

Summary

Obtaining data from a sports API to generate a pdf report with NBA team Boston Celtic's advanced stats as charts and tables for Brad Stevens (Boston Celtic´s GM). Additionally, predicting the result for the following Celtic's match based on betting house odds using webscraping

Previous requirements

Once you have cloned the git repository in your computer, for the program to work correctly, some non-default libraries need to be installed before executing the main .py program ETL_Celtics.py. All these indispensable libraries are included in a requirements.txt file, specifying the versions.

Before downloading these libraries you need to check the version of Google Chrome on your computer (open Google Chrome > Menu > Help > About Google Chrome). Then you need to go to this web https://chromedriver.chromium.org/downloads and download a zip file containing the Chrome Driver according to your version of Google Chrome and your operating system. Once you have decompressed the zip file, you´ll need to remember the path of the file chromedriver.exe in your computer.

Now you can download all the libraries by running the following command in your terminal.

pip install -r requirements.txt

Moreover, you also need to edit the other txt file, config.txt, including your API key and the path of the executable file you have just downloaded (chromedriver.exe). To obtain the API key you need to create an account in the web https://sportsdata.io/ and subscribe to the NBA-API. The API key will be sent to you by email or you´ll be able to find it in here https://sportsdata.io/developers/api-documentation/nba The config.txt file must be edited and left with this format:

{
    'api-key': '<your_api_key>',
    'path': '<the_full_path_of_the_chromedriver.exe_file_on_your_computer>'
}

You are now ready to proceed

Files

A brief explanation of the three .py files in this git repo will be done in the following paragraphs.

• ETL_Celtics.py

This is the only file that must be runned for the report and the prediction to be created. The two other .py files only contain functions and are used as libraries (if you try to run them nothing will happen), which are imported in the beginning of ETL_Celtics.py. This file uses an ETL (Extract Transform Load) to extract the data from the API with three get requests using your API key from the config.txt file, to transform and filter the data using pandas dataframes, and to load the resulting information in 4 csv files which are saved in the directory archivos_generados, and in 16 images including charts and tables which are saved in the directory imagenes.

Now the program calls the function crear_reporte_GM() from the library Crear_pdf.py to generate the pdf report and save it in the directory archivos generados, and the function pronostica() from the library Pronosticador.py to predict the result of the following match for the Boston Celtics.

• Crear_pdf.py

This file uses fpdf library to generate a pdf executive report for Brad Stevens (Boston Celtic´s GM) including charts and tables with all te stats obtained from the API that have been saved as images in the directory imagenes. The crear_report_GM() is the only callable function in this library, which is called in ETL_Celtics.py after generating the images.

• Pronosticador.py

This file uses selenium library to do webscraping of this web https://www.sportytrader.es/ to obtain the betting odds for the next NBA games. The function pronostica() creates a webdriver and searches for the odds in the web for the next Boston Celtics match. It also gives you a chance to predict the result of the next match for any other team that you wish. This function is called in ETL_Celtics.py after the ETL process and the generating of the pdf, and with that, our program comes to an end.