Building a Sports Betting Model Lesson 2: Starting to Code

In the second lesson of my series on building sports betting models, we dive deeper into the coding aspect of your model. If you missed the first lesson, make sure to check it out here. In this lesson, I aim to guide you through the initial steps of coding your betting model, emphasizing the importance of a structured approach. Let's get started!

The Overwhelming Journey of Model Building

Creating a sports betting model can feel like an overwhelming task. You might have a ton of ideas and strategies swirling in your head, but the key is to manage this chaos effectively. As someone who has built a model myself, I can tell you that the process is akin to turning over stones in a data project—each stone reveals complexities you hadn't anticipated.

When you embark on this journey, it's essential to recognize that you're not just playing a game; you're competing against multi-billion dollar sportsbooks and numerous other bettors. Therefore, being aware of the challenges ahead is crucial. Expect complications, setbacks, and a steep learning curve.

Fewer People, Faster Progress

Interestingly, having a smaller team can often lead to faster progress. Although it may seem counterintuitive, a larger team requires more coordination, resulting in inefficiencies. From my 25 years of experience in technology development, I've found that fewer people can streamline the decision-making process and reduce the time spent on meetings and documentation.

For instance, I'm building my model mostly alone, with minimal assistance from a business partner. This autonomy allows me to internalize various aspects of the model without the need for extensive documentation or meetings. So, if you're starting alone or with a small team, embrace that. You can move quickly!

Minimum Viable Product (MVP) Approach

Now, let's talk about the concept of the Minimum Viable Product (MVP). This principle can be applied to sports betting models as well. The idea is to create a "dirt path" before paving a multi-lane highway. When you're exploring, you want to test your hypothesis with minimal effort before investing significant resources.

Think of it this way: when camping in the forest, you wouldn't want to construct a complex road system if you're unsure about the terrain. Instead, create a simple path to see if it's worth pursuing further. This approach allows you to test whether your model has potential without overcommitting your time and resources.

Creating Your Model: Start Coding

Once you've established the groundwork, it's time to start coding. Whether you're writing code or building a spreadsheet, the focus should be on creating something functional, even if it's not perfect. You can iterate and improve your model later, but first, you need to get something out there.

Remember, a bad model can be refined into a good one. If you don't have any model at all, you have nothing to improve upon. The key is to complete the cycle—get something working so you can start learning from it.

Measuring Your Model's Effectiveness

As you develop your model, think critically about how you'll measure its effectiveness. You don't want to place bets blindly; instead, you should have a clear understanding of how your model performs. Consider options like paper trading, backtesting, and other metrics to evaluate your model's accuracy.

Knowing whether your model is effective before placing actual bets can save you money and frustration. You should be able to answer questions like: How does my model perform? What adjustments can I make? Understanding these aspects will help you refine your approach.

Learning from Mistakes

One of the most valuable lessons in building a sports betting model is to embrace the learning process. Acknowledge that mistakes will happen, and use them as opportunities for improvement. Measure your model's accuracy and identify areas that need enhancement. The iterative process of refining your model is crucial for long-term success.

Key Steps in Data Leadership

In my book, Data Leadership for Everyone, I outline a framework that can be applied to sports betting models as well. The key steps include:

1. Access: Gather the data you need.
2. Refine: Process the data to make it usable.
3. Use: Employ that data to form insights and make betting decisions.
4. Impact: Assess how your bets perform and where you can improve.
5. Govern: Create a system that allows for scaling and efficiency.

These steps provide a structured approach to building your model, ensuring that you maximize the value derived from your data.

Gathering Data and Making Projections

When building your model, start by gathering data. This could involve buying data, scraping it, or even creating it. Regardless of the method, ensure that you have reliable data to work with. Once you have your data, the next step is to make projections. What is your perspective of truth in the betting markets you've chosen to focus on?

Don't rush into making bets just yet. Instead, create projections that will help you understand market dynamics and identify potential edges. For instance, if you're focusing on baseball, you might want to create home run projections that serve as your model. This way, you have a clear focus, and you can refine your projections as you gather feedback.

Identifying Market Edges

Once you have your projections, it's time to shift your focus to identifying market edges. This involves analyzing where you can find discrepancies between your projections and the market odds. Understanding common betting behaviors, like the public's tendency to favor overs and favorites, can help you identify areas where your model might find an edge.

Think critically about your predictions and how they fit into the broader market landscape. This is where you can differentiate yourself from other bettors who may not be as diligent in their analysis.

Iterating and Improving Your Model

The iterative process is essential for refining your model. Once you've created a basic version, measure its performance and look for areas of improvement. Do you need more data? Is there a different way to process the data that could yield better results? These questions will guide you as you continue to develop your model.

Remember, building a sports betting model is a lot like running a business. You need to be methodical, data-driven, and willing to learn from your experiences. As you iterate on your model, you'll find that it becomes more effective over time, leading to better betting outcomes.

Conclusion

In this lesson, we've covered the initial steps of coding your sports betting model and the importance of a structured approach. Remember to start small, iterate, and measure your model's effectiveness. The journey of building a sports betting model is challenging but rewarding. As you embrace this process, you'll gain valuable skills that extend beyond sports betting.

For more resources and to connect with others on this journey, consider joining our free Discord community. We have a wealth of knowledge to share, and you'll find support from fellow bettors who are on the same path as you.

Lastly, don't forget to check out 8rain Station for the most powerful analytics software available to help you beat the books. Until next time, keep learning, iterating, and refining your model!

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