We’ve all seen it before.
“Since 2005, underdogs of more than six points have gone 162-138 (54%) against the spread in the first round of conference tournaments.” – some crappy “analyst”.
I get it. Trends are easy to comprehend and fit a narrative to. Media companies and sportsbooks (which are becoming increasingly intertwined) love them because they can be queried very quickly, and they give the illusion that you have an edge.
But that’s exactly what trends are. An illusion. If you look hard enough anywhere, you can manipulate any data set to craft a narrative about a trend that doesn’t actually exist.
Most trends are a result of variance. And as a result, you can find a trend for just about anything. Thankfully, we have the tools to assess variance which can help explain why trends can be 1) so prevalent yet 2) so meaningless at the same time.
A binomial distribution is a discrete probability distribution of the number of successes in n-numbered trials. Binomial distributions help us answer questions such as “what’s the probability of getting 7 or more ‘heads’ if I flip a coin 10 times?” (17.2% for those wondering). Using binomial distributions, we could answer the question: “If a monkey picked the ATS winner in 300 games, what’s the probability of Mr. Monkey wins at least 162 games (54%)?”
Back to Trends
So how do we apply this to trends? Can you say – if pull a random 300-game sample, there is a 9.2% probability of finding a trend with a 54% win probability? Not quite.
You see 9.2% represents a single tail of the distribution of outcomes as displayed below.