Does binary thinking impact success in sports betting?
What is binary bias in the first place?
And what is a good bet?
Read on to find out.
What Is Binary Thinking?
Binary thinking sorts information into mutually exclusive options.
It’s 1s and 0s.
They are the only options.
There is no grey area.
It’s thought humans instinctively use binary thinking to sort information – and during our primitive years, this made perfect sense.
Survival depended on these sort of decisions, especially when a quick decision was required.
Back then, deciding if the rustle in the bush behind you was a friend or foe was the difference between life and death.
Debating what the sound could’ve been wasn’t worth the risk of being killed.
Simplifying it down to a friend or foe situation made far more sense in terms of risk vs reward.
Richard Dawkins claims the need for straight yes/no answers is “the tyranny of the discontinuous mind“.
He suggests people seek assurances because it’s easier for the brain to think in this binary way, rather than remaining in the grey area.
Binary thinking is perfect when a snap decision is required, but the lines blur in our now nuanced world.
And there’s no other industry that reflects this sentiment more than sports betting.
So, how does binary thinking impact how we process information?
Fisher and Keil set out to find out with a series of studies on “binary bias”.
Participants in the study were presented with evidence on a subject, before being asked to summarize it and rate their overall impression of the argument’s strength.
If people are evaluating data from different studies investigating the relationship between caffeine and health, for example, they would quickly categorize data as either showing an effect or not, regardless of the relative strength of the evidence.
Fisher said of the results, “The binary bias influenced how people interpret sequences of information and a wide variety of graphical displays.”
The participants ignored relative strength and favored categorizing the evidence and looking at the sum of all evidence within each category.
All continuous data was removed.
As a result, a single conclusion with a 25% likelihood in a single direction was collected with all conclusions that also leaned that way, regardless of how strong the evidence was.
This made it easier for the participants to process the data.
However, it also lessened the value of the data.
YouTube discovered this for themselves when they revamped their video rating system.
The original five-star system just didn’t work as most people would just vote one or five stars.
The culprit: binary thinking.
When the user liked the video, they gave it a five-star rating.
If they didn’t, they gave it a one-star rating.
The meaning of all the ratings in-between was lost.
YouTube switched to the thumbs up/thumbs down system that we rate videos with now.
We highlighted above that as humans, we like sorting information into two distinct categories.
The same applies in sports betting.
Inexperienced bettors think that winning bets are good bets and losing bets are bad.
Their binary way of thinking takes over to make the nuances of sports betting easy to understand.
But this is not how it works.
Terrible bets can win and the best bet of all time can lose.
Categorizing bets into “winning means good” and “losing means bad” removes all the vital information out of the equation.
This was particularly highlighted after the Baltimore Ravens failed with a two-point conversion in a match against the Kansas City Chiefs in the 2019 NFL season.
This was the correct decision, mathematically speaking.
However, because they didn’t convert the play, pundits categorized this as a “bad decision”.
The analytics behind this play was lost on these pundits due to a combination of binary bias (needing to place the play into the “good” or “bad” decision bucket) and outcome bias (they failed so it must have been a bad decision).
Had the Ravens scored, chances are the pundits would have thought differently.
Thinking Like A Sports Bettor
The grey area between a winning bet and a losing bet is what determines if it was a good bet or a bad bet.
Sports bettors operate in percentages.
Sports bettors that have more accurate percentages than the sportsbooks will profit over the long-term.
But how do you tell if a percentage is accurate?
It’s impossible to answer this question without a large enough sample size.
There’s one famous stat that serves as an excellent example; FiveThirtyEight had Donald Trump at just 30% to become the President of the United States.
Trump won, of course, but that didn’t stop some quarters labelling the prediction “wrong”.
Given how people like to take a binary approach, you can see why this happened.
As shown by Fisher and Keil, people disregarded the strength of this prediction (30% chance of winning rather 0%) and categorized it into something they were more comfortable with – that the prediction must be wrong.
This is total nonsense, of course.
A 30% chance of winning implies that Trump should win three times out of ten.
The fact that Trump did go on to win doesn’t show anything new about the prediction’s accuracy.
The sample size must be increased substantially by running the election over and over again.
This isn’t possible, of course, but it’s the only way we’d be able to know how close the prediction was to the real figure.
The final sentence above is obviously a bit disconcerting.
We don’t like not knowing if a prediction was ever a good one.
We may never know.
As sports bettors, we operate within the grey area between that 1 and 0.
We must avoid the easy route and embrace the percentages for what they are:
Pure attempts at creating a “good” bet knowing we may never be able to tell if it really was.