Skill vs Chance: What sports fans get wrong about probability

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Numbers and sports go hand in hand. Watch any live broadcast and you will see and hear commentators and pundits talking about possessions, chances created, and the new expected goals (xG) statistic. Numbers are an extension of the game. Yet, despite this, many modern sports fans still struggle when interpreting probability and setting it against results.
When a team dominates possession, has triple the number of shots, and is bulldozing the opposition in every part of the pitch, it’s natural that people expect goals. When Donald Bradman took the crease for his final Test innings, everybody expected him to score a century. While the probability of that team scoring goals is high, as too was the likelihood of Sir Donald scoring runs, that does not equal a guarantee.
Seeing a probability of 80% and not associating that with success is a hard concept to accept. But for sports fans, it is a vital adjustment to make, especially when dealing with situations where chance and skill intertwine.
The false comfort of certainty in uncertain outcomes
Expectation statistics are a new addition to most sports broadcasts. Football pundits talk about expected goals. Cricket shows expected run rates. However, the problem arises in the way our brains interpret the numbers.
If Manchester City has a 70% win probability, fans take that as a certainty. Whenever a figure is above 50% we convince ourselves that it will be the outcome. There is a comfort in certainty, and when dealing with variables, we look for a way to create certainties from them. The truth is that even at 70%, the team still loses three matches out of ten. Individual performance follows the same pattern.
When we see an xG of above one, we assume the team has already scored. If we see that a striker has an xG of 0.8, it is assumed that he should have scored. Yet the truth is xG tells us that he misses entirely in 20% of situations.
The emotional lens that distorts the numbers
Another problem with sports fans and probability is the emotional factor. Fandom restricts our probability assessment. Being objective about the team you support, the team you invest your heart and passion in, is not easy, especially in the build-up to a derby game or a big cup game. We want our team to do well. The illusion of control allows us to convince ourselves that our passion is enough to influence performance, regardless of what the statistics say.
Recency bias is another consideration. In our minds, one standout performance last week is a better indicator than two months of form.
As fans, we don’t see things down the middle. Asymmetry gives us the freedom to adjust our views based on circumstance. A win is down to the team playing well. They were more skilful. They were clinical and dominant. But a loss? That’s down to bad refereeing, bad luck, or downright dirty play. This protects the team in our eyes.
Optimism bias is a powerful reasoning tool, more so than realism. That is why people consistently overrate their preferred team’s chances by up to 20%.
Pattern recognition: A survival tool gone wrong
Our brains are designed to seek out patterns. This tendency can be traced back to understanding predatory behaviour in ancestral times. While it worked as a survival mechanism, it doesn’t function effectively when dealing with sports probabilities.
Sports fans love patterns in randomness. In football, fans will cling to the belief that a team always scores in the last ten minutes. Cricket fans convince themselves that India will always collapse when they are chasing more than 250 runs. There are times when these do reflect a minor trend, but we amplify it and begin to use it as a basis for expectations.
Often, sports fans fall foul of the gambler’s fallacy, where they convince themselves that a striker is due a good game on the back of a series of missed chances. Probability has no memory. Every game, every moment starts with a blank slate.
Game analysis with heat maps and streak analysis also fuels the hunt for patterns. A team wins two or three in a row, and suddenly, they are on a hot streak.
In reality, that is just probability in action.
The fine line between ability and randomness
The divide between skill and chance is tiny. In theory, better players produce better results. However, from match to match, there is the capacity for massive variance, which has nothing to do with skill.
The most famous example of skill versus chance comes from cricket. Sir Donald Bradman, going back to our previous example, was on course to retire with an average of nearly 100 per Test innings. But what happened? He was bowled on the first ball for zero. His swing was driven by skill. The rest was up to chance. A good ball, the right bounce in the right weather conditions. None of these are excuses, but variables that impact the chance of something happening while negating skill.
Across the course of his career, Bradman hit that ball more often than not. Skill commonly prevails over time, but when viewed as an isolated incident, it is a different story.
What video games teach us about real-world probability
RNG is used in video games and digital entertainment as a way to capture the unpredictable nature of probability. FIFA uses RNG to catch these moments to make the game more realistic, much to the ire of fans. When a 90-rated striker misses an open goal, they blame the game, when really, the game is accurate. Even the best players miss their lines on occasion.
Fans dislike this level of realism because in a real game they can lay the blame elsewhere, but in the video game, there are no such excuses. Player fallacies are laid bare, and players are made to realize that nobody is perfect.
Exposure to RNG in video games has helped improve people’s probability literacy, making them view more situations from the perspective of chance rather than certainty.
The psychology of managing uncertain results

Sports fans don’t like randomness. They don’t like the unknown, and so they seek out frameworks that they can then apply to different situations. They study form tables, evaluate xG trends, and look for streaks or any form of consistency that can be attributed to recent results or upcoming games.
Finding sense in randomness is a coping mechanism that sports fans have perfected. It extends beyond football and into any domain where chance and expectation meet. The way football fans pore over match stats looking for answers is the same as fans who read slot reviews from CasinoHawks to find games that will “guarantee” a win sooner or later. They are trying to find something that doesn’t just explain payout rates but gives them a way to categorize their own results against anything other than chance.
Variance levels and hit frequencies discussed in slot reviews are the same as xG totals and possession statistics in football. They both provide a framework that goes some way to explaining the randomness of results.
Why understanding probability improves your experience
Understanding probability is important for sports fans as it allows them to better understand the games they love. Sport is now a data-driven pastime. Analysts, coaches, pundits, and betting markets all work with a probabilistic framework. Fans are now realizing they need to understand what the numbers are saying, because the numbers are what is driving the game.
Sometimes the better team wins, and when fans understand the general concept of probability, a loss is disappointing but no longer as frustrating. Probability literacy helps guide fans’ support into the right space. Wins and losses are still important, but fans’ expectations are better aligned.
Without randomness, sport would be boring. The best teams would win every time. The starting players would excel, and the others would languish behind. There would be no excitement. No atmosphere at the games. The simple fact that anybody can beat anybody on their day keeps all sports exciting and gives people a reason to watch.
Whether you are a betting fan or not, understanding what people are talking about changes the way you view the game and makes it more interesting.