Create Your Own Positive Expected Value Odds Scraper using Python — Part 11 — Scraping Data and Getting Player Names

Continuing with our Scraping Journey!

Introduction

Today we will go through how we will scrape selections and odds and then query our databases to build a dictionary of odds.

Yesterdays article can be found here-

Create Your Own Positive Expected Value Odds Scraper using Python — Part 10— Posting to DB Tables

Code Walkthrough

First run all of the scrapers ideally, in Jupyter Notebooks. Post the data to your DB as shown in the last tutorial.

Now create a dictionary called players. We will now query the table playerNames in order to get the corresponding names for all of our betting sites. I will use the Bet365 player names list as the base list.

players = {}
for i in playerNamesFinal:
    data = supabase.table('playerNames').select('id,playerName,Bet365Name,PaddyPowerName,WillHillName,UnibetName,Team').eq('Bet365Name',i).execute()
    if(len(data.data) > 0):
        if(data.data[0]['playerName'] not in players):
            players[data.data[0]['playerName']] = {
                "id" : data.data[0]['id'],
                "Bet365Name" : data.data[0]['Bet365Name'],
                "PaddyPowerName" …