Chapter 1: Introduction to Arbitrage in Sports Betting

Welcome back, everyone! In this segment, we will delve into the next phase of our project, which focuses on establishing two new database tables designed to hold information about various market types and their corresponding names.

To illustrate the concept, let's consider a common arbitrage scenario. Imagine we are backing both outcomes in the "Both Teams to Score" market across two different bookmakers. However, can we expect to encounter this kind of opportunity regularly? The short answer is no.

Typically, the primary markets for prominent football matches are very efficient. Popular betting markets such as match odds, over 2.5 goals, and both teams to score are heavily favored by bettors, meaning bookmakers are usually precise in their pricing. While occasional opportunities do arise, we cannot depend solely on these markets to yield arbitrage chances.

Consider a high-profile Premier League match, like one between Brentford and Chelsea. As the kickoff approaches, there are hundreds of markets with thousands of selections available. By exploring less popular auxiliary markets, we can uncover additional opportunities.

For instance, if we analyze "Chelsea Clean Sheet Yes" and "Brentford Over 0.5 Goals," while the displayed prices may not present an arbitrage situation, it highlights the potential benefit of betting across various markets, rather than limiting ourselves to only the most sought-after options.

To maximize our opportunities, we need to establish connections between markets that allow for backing both sides of an outcome. This requires the creation of two new database tables.

The first table, which we can name football_markets_common, will contain the following columns:

• id (int 8)
• market_name (text)
• market_common_id (int 8)
• market_display_name (text)
• arbitrage_markets (text array)

The key column to focus on here is the arbitrage_markets, which is of type array. When setting up this table in Supabase, click the settings icon next to the column and select "define as array."

The logic behind this structure is that as we scrape odds along with market names and types, we will compare those odds against an array of previously defined market common IDs. By ensuring that the current row's market and any markets in the array support both outcomes, we can identify more potential arbitrage opportunities.

The second table will be named football_markets_sites, which will store site-specific data since different sites often have unique naming conventions. This table will include:

• market_name_common (text)
• market_common_id (int 8)
• market_name_website (text)
• website (text)

In the coming days, I will focus on scraping market-specific names and creating the arbitrage_markets arrays for each. Please bear with me as this may take some time.

That's it for today! If you found this information helpful, please give a clap and follow along for more updates. Thank you!

Chapter 2: Practical Application of Market Analysis

To enhance your understanding, check out this insightful video that discusses retail arbitrage sourcing using Python.

This video explores straightforward methods to source retail arbitrage opportunities with Python, providing a practical perspective that complements our discussion.

As we move forward, it's essential to delve deeper into using Python and Selenium for scraping betting websites to uncover arbitrage opportunities during live matches.

This resource will help you grasp how to efficiently scrape data from betting platforms to identify potential arbitrage scenarios in real-time.