Everything In Sports Betting Is A Guess

On the nature of information overload

Anyone that’s worked in statistics and data science can tell you that numbers can instil a false sense of confidence in ‘data driven’ decisions. Pick your tactic: 7 decimal points to convey the illusion of 7 decimal point accuracy; a long and multifaceted end-to-end modelling process to show sophisticated estimation techniques; P-value hunting from A/B tests. There’s no shortage of ways to launder fundamentally flawed analysis into a pretty end result loaded with pre-conceived bias.

It’s part of why I’ve always preferred to stay in sports betting - your bankroll doesn’t lie over the long term, and the betting markets reward being accurate and don’t care if you got there with a flashy approach.
What we’re increasingly seeing in tools and social area of the industry is nice packaging, bold claims and no results to back up anything. To be clear, there are excellent tools out there but they’re always making educated guesses, based on the biases of the creators, and need a certain amount of analysis of the true value of their output. The start of that is some insight into what’s going on under the hood of these engines.

One of the most common examples of tools out there are projections. You can find projections for just about anything in sports these days (team strengths, spreads / totals, prop values), and many places offer betting recommendations off of them. The projection-to-bet process is simple: produce your projections, see where they differ from the market, and bet accordingly. It’s easy to see why projections are as popular as they are: they have the feeling of elegance and simplicity all in one, giving confidence that someone is crunching numbers for you and all you have to do is find where they’re different. It’s even more intoxicating if you’re making your own projections and bet them: there’s a genuine rush you might get when your projections are different than market values, because you feel like you may have found and edge no one else has.
The UI for these projection-based tools gives it a sort of authority as well: they all have some explanation of their official-sounding methodology, and the numbers they display make it feel like there’s a robust maturity behind them. This, by the way, could apply to literally any sports betting tool that shows numbers and percentages: the way the numbers are displayed make them feel scientific, which ends up being a short circuit for questioning the accuracy or methodology of these numbers. 

In practice, most projections don’t actually beat the market, and if they show a different number than the market, it usually means the projection process is wrong, not the market. There are any number of common explanations why: their prediction process simply isn’t accurate enough, they can’t keep up with breaking news fast enough to produce actionable recommendations, or they have some flaw in converting projected outcomes to the distributions that betting requires.
Anyone who bets their own projections into markets has learned the hard way that profitable projections are hard; getting hit by negative returns remains one of the best ways to drive improvements in your projections process. All tools are powered by many different methodologies and assumptions, each of which has its mistakes that all get obfuscated by a seemingly official number at the end of the process.

What value, then, should be given to all this data? Should every ROI number just be thrown out entirely? Should projections be combined with home-grown opinions on the game?

Just knowing that the numbers you see on these tools can come from wildly different approaches is empowering alone. If you know what questions to ask about these tools, you can start to deduce for yourself which tools might be good and which ones might be bad. If you start to see certain patterns in the results they produce, you can start to question some of the assumptions these tools might be making. And even when you get good numbers from tools, you can pair those numbers with information beyond just their calculations to put together pieces of the puzzle for how to find profitable bets.

There are ways to intelligently incorporate this information. The arduous but reliable route is long-term ROI tracking of bet recommendations produced by betting tools. In the end, if something works, the money will show up. The burden will be on you to track tools results yourself, but there are no shortcuts here: this is a game of time and detail.