Crafting a Gambling Predictive Model: Strategies, Techniques, and Insights

Let's dive into the wild world of the 2020s, where every other bob officious seemingly thinks they can shake up the sports betting scene just like that. Sure, the Internet's made it easier than ever to spout off about predicting sports outcomes, but let's face it - not all of us are ready for the limelight.

That said, I've had my fair share of conversations with aspiring betting model folks, so I figured I'd write up some common topics, and drop a of knowledge on those folks who are eager to improve their betting prowess.

I've got 0 clue where to start, doc:

You're not alone, friend. My advice? Focus on one sport in the beginning, and let yourself take it slow. Building a model ain't a walk in the park, and you don't want to overwhelm yourself. Plus, a learning curve comes with automation - it's not something most people've got down pat yet, so be prepared for a bit of a journey.

Once you've got your sport, start looking at stats and try to figure out which ones would be most helpful. As a general rule, keep it simple - you don't want to go overboard, trying to cram in every advanced metric you can get your hands on. Remember, simplicity brings clarity, and clarity can make all the difference.

What now? I've got my stats, but I've no idea how to make projections:

This is a common stumbling block, but it doesn't have to be a deal-breaker. Think: how do those stats you've chosen connect with the points (or goals, or runs, or whatever scoring metric you've got going)? Take football, for example - if you're obsessed with QBR (Quality-Based Rating), it might be great for determining who the best quarterback is, but it's not so hot for projecting the game outcome. You might consider a metric like points per play or points per drive, but maybe even points per game can work if you're able to adjust for opponent strength.

My projections ain't nowhere near the market line:

This question comes up so often, and more often than not, the problem is that raw stats are being used to make projections. But any good model isn't just going to spit out raw data - it's about making opponent adjustments. That's the real key to stand out from the crowd.

Some people might base their projections on raw stats and then try to adjust for the market number, or copy the market number altogether. But why bother making a model if that's what you're gonna do? Don't get me wrong, it ain't a crime to have a market-aligned projected outcome, but if you aim to make it your own, a bit of opponent-adjusting'll do the trick.

Okay, how do I make that opponent adjustment stuff happen?

Alright, but let me start by saying that there ain't one right way to go about this - it's all about how you roll, and what makes sense for you. Generally, you've got two options:

Do an opponent adjustment for every game: This method's a bit nuanced, and requires a fair bit of time and organizational know-how. It starts with establishing a baseline rating for how good each team is using raw performance metrics.
Adjust for the overall schedule: This method's a bit simpler than option 1 - take a team's raw average stats, and adjust it to account for the overall schedule to date. If you ain't got access to your own rating system, you can use a famously reliable SOS (Strength-of-Schedule) metric like Sagarin or KenPom.

Important to note: Regardless of which method you choose, you're gonna need some serious organizational skills and patience. The road ain't easy, but the results could be worth every drop of sweat you put in.

Data automation's my worst nightmare:

Fear not, because I've been there, and I got out alive, friend. Remember, I ain't no rocket scientist, and I built the TSI (I don't know what it stands for, either) using nothing but Google Sheets (oh, and a little help from my robot pal called ChatGPT). It isn't fancy, but it keeps me super organized, and I can integrate all the tools I need to run my model.

That being said, there might come a time when manual data input ain't gonna cut it anymore - I mean, you're gonna accumulate a whole lot of data over time, and your spreadsheet ain't gonna be able to keep up. That's when you're gonna have to graduate to more advanced tools like Python or R. Again, I ain't no expert on the subject, but if you wanna start dabbling in these platforms, there's plenty of YouTube tutorials and helpful materials online to get you started.

So, there you have it - some key insights for those looking to get into sports betting modeling. Remember, there ain't a right or wrong way to build a model, and you'll learn a whole lot along the way.

As you set sail on this new adventure, keep in mind that there ain't no easy button for creating and maintaining a sports betting model. But hey, the reward is worth the struggle - just ask all the other fellas out there who've done it and made 'em some dough.

Good luck!

FUN FACT:

Sports-related discussion has been a popular topic on social media platforms like Twitter, Facebook, and (believe it or not) even email for decades. In the 2000s and 2010s, it seemed like every high school kid with a mixtape wanted to talk about music, but in the 2020s, it's all about sports predictions.

SOURCES:

[1] "Guide to Building a Sports Betting Model" by Improbable Wins, 2021[2] "Building and Managing a Betting Model" by Sports Analytics Source, 2020[3] "Units of Stake and Risk Management" by Profit Confidential, 2018[4] "Top Web Scraping Tools and APIs for sports data" by DataWeave, 2021[5] "A Beginner's Guide to Live Betting" by Covers.com, 2019

If you're aiming to shake up the sports-betting scene, focus on analyzing a single sport first, such as college football, to build a strong foundation for your modeling skills.
Live betting can offer exciting opportunities in sports betting, but remember to be prepared for the unpredictability that comes with real-time sports events.
Automation tools like Python or R can greatly speed up data handling and analysis for sports-betting modeling, but it may require a learning curve for those new to programming.
The importhtml function in Google Sheets can be used to import data from various sports websites, simplifying the initial data collection process for sports-betting modeling.
Some notable sports-betting models can be found in publications such as "Guide to Building a Sports Betting Model" by Improbable Wins and "Building and Managing a Betting Model" by Sports Analytics Source.