In a previous post, we talked about how difficult it can be to build a standard Machine Learning (ML) system for Betfair from scratch. It requires a lot of work, including handling data, coding live pipelines, and managing databases. But now, a new method is changing everything: AI Agents using Large Language Models (LLMs) combined with betting software like BfExplorer.
Let's explore how the "Old Way" (Traditional ML) compares to the "New Way" (AI Agents).
The Traditional Machine Learning Approach
Traditional ML systems work a lot like software engineering. Here's an overview of the process:
Data Collection: You gather years of data stored in files, often in CSV (spreadsheet) format.
Feature Engineering: You create mathematical formulas to explain patterns, like calculating an average speed.
Training: You feed that data into a machine learning model to teach it how to predict results by minimizing errors.
Deployment: Finally, you build a complex app that connects to Betfair, processes data, applies the model, and places bets.
The Problem: This is very technical. You need to be both a programmer and a data scientist to make it work.
The AI Agent Approach (e.g., BfExplorer)
The "AI Agent" uses a Large Language Model (like GPT-4) instead of relying on complicated numerical models. It also uses apps like BfExplorer to handle the technical tasks.
Here, instead of writing Python code, you write simple instructions in plain language (called a Prompt). The app takes care of connecting to Betfair and running the instructions for you.
"Semantic" vs. "Numeric" Thinking
This is where AI Agents really shine. Traditional ML focuses on numbers, like speed or weights. It struggles with text, like "the horse looked tired." AI Agents, on the other hand, love working with text. They can read race reviews and actually understand subtle details.
Example: EV Analysis Strategy
Let’s imagine an example. Suppose you want to create a betting strategy using an AI Agent. Instead of coding, you would give this prompt:
Sample Prompt:
1. Find the active market.
2. Look up data about all the horses from a source like "Racing Post".
3. Read the descriptions of the last race for each horse and analyze the text. Notice positive phrases like "ran on well" or negative ones like "bad mistake".
4. Use this analysis to calculate a “True Probability.”
5. Compare that to the current Betfair odds to calculate Expected Value (EV).
6. If a horse has over 10% EV, place a bet of 10 Euros.
Why This is a Big Deal
No Advanced Coding Needed: You don't have to write complicated code to connect to Betfair or manage complex data files. The AI handles that by using the app’s tools.
Understanding Text: AI Agents can read and make sense of comments from race reviews, like "the jockey said the horse gave a strong finish." In comparison, a traditional ML model usually ignores text unless you convert it into numbers, which takes lots of extra work.
Quick Execution: Once the AI understands the instructions, it can act immediately. It connects to the app to place bets for you. You don’t code the logic—it’s built into the app.
Comparison: Traditional ML vs. AI Agent
Logic Setup: Traditional ML requires coding and math, while AI Agents use simple prompts in natural language.
Data Focus: Traditional systems work with numbers, while AI Agents process text, summaries, and rich descriptions.
Development Time: Building a traditional ML setup might take months. With AI Agents, it’s a matter of hours or days.
Maintenance: Traditional ML models can break if APIs change. AI Agents don’t worry about this because their app handles API updates.
Key Skill: Traditional ML demands programming and mathematics expertise. AI Agents only need basic domain knowledge and the ability to write clear prompts.
Conclusion
Traditional Machine Learning is still great for fast-paced and statistical strategies, like high-frequency trading. However, if your goal is "smart betting"—copying the decision-making process of a real expert reading race reports—then AI Agents are the way to go. This new approach transforms your role from a technical coder into a creative strategy manager.