Cognitive biases in betting: seven traps and how to counter them

1. The gambler's fallacy: "it's due"

The belief that after a run of one result, the other result becomes more likely. Tversky and Kahneman described it in 1971 as part of the "belief in the law of small numbers": people expect short sequences to look like the long-run average.

Betting example: "Team B hasn't drawn in eight matches, so a draw is due." Football matches are not a coin that remembers its past. If the draw probability in the next match is 27%, it is 27% whether the last eight matches were draws or not; the odds already reflect what is known about both teams.

Counter-habit: before a bet, write down your reason in one sentence. If the sentence contains "due", "has to" or "overdue", skip the bet.

2. The hot hand: "he is on fire"

The mirror image: believing a streak will continue. Gilovich, Vallone and Tversky (1985) found that basketball fans saw "hot hands" in random-looking shooting data. Later work by Miller and Sanjurjo (2018) showed a small hot-hand effect may exist in shooting after all, but in betting results, streaks are mostly noise.

Betting example: a tipster has won 7 of their last 10 bets at odds of about 2.00. That feels like skill. But someone with no skill at all, picking coin-flip bets, wins 7 or more of 10 with a probability of (120 + 45 + 10 + 1) ÷ 1,024 = 17%. Among a hundred tipsters, about seventeen will show that "form" by chance.

Counter-habit: judge yourself and others on hundreds of bets, not on the last ten. Our variance guide shows how wide normal swings are.

3. Confirmation bias: seeing only what fits

Once we lean towards an answer, we look for information that supports it and skim past what contradicts it. Nickerson (1998) called it one of the most widespread problems in human reasoning.

Betting example: you fancy the home side, so you read the article about their unbeaten home run and miss the line about their first-choice centre-back being injured.

Counter-habit: write down the strongest argument against your bet before you place it. If you cannot find one, you have not looked.

4. Recency bias: the last match counts too much

Recent, vivid events feel more important than older, duller ones.

Betting example: a team wins 5–0 on Sunday, and on Monday you want to back them again. The market saw the 5–0 too, and usually moved its prices straight after the match. Often the opponent was down to ten men, or the scoreline flattered the performance.

Counter-habit: look at a whole season of data, or at least the last 10 to 15 matches, before you let one result change your view.

5. The near-miss: "I almost won"

A loss that looks close to a win feels like progress. A brain-imaging study by Clark and colleagues (2009) found that near-misses activate reward-related brain areas and increase the urge to keep playing, although they pay nothing.

Betting example: a five-leg accumulator, each leg at odds of 1.80, loses because of one late goal. Suppose each leg really has a 52% chance of winning. Then:

• all five win: 0.52⁵ = 3.8%;
• exactly four win, one fails ("so close"): 5 × 0.52⁴ × 0.48 = 17.5%.

"One leg short" happens more than four times as often as a win. And the bet loses money on average: the payout is 1.80⁵ = 18.90, so EV = 0.038 × 18.90 − 1 = about −28% of the stake.

Counter-habit: record an accumulator that lost by one leg simply as a loss, with no "almost" column.

6. Sunk cost: "I have put too much in to stop"

Money already spent should not affect the next decision, but it does. Arkes and Blumer (1985) showed that people keep investing in losing projects because of what they have already put in.

Betting example: "I'm €300 down this month. If I stop now, the €300 is gone." It is gone either way. The only question is whether the next bet is a good one on its own. This trap is the engine behind chasing losses.

Counter-habit: ask: "If I were starting fresh today with the same balance, would I place this bet?" If the answer is no, don't.

7. The illusion of control

Langer (1975) found that people behave as if they can influence chance outcomes when a situation feels skilful: choosing, practising, being involved. In betting, that feeling is strongest in live betting, where you watch every minute and act fast.

Betting example: you watch a match, "feel" a goal coming and bet in play. Watching gives you a sense of insight, but the bookmaker's live price is driven by faster data feeds than your screen, which usually runs a few seconds behind.

Counter-habit: for every bet, write the probability you believe in. After 200 bets, compare those numbers with what actually happened. A betting log makes this easy.

How a rule-based method helps, and where it does not

Most of these traps live in one question: "who will win?" Sure bets skip that question. When two bookmakers disagree enough on a price, you back every outcome, and the stakes come from a formula, not a feeling. If all legs are accepted at the shown odds and stand, the payout is about the same whatever the result: a small margin, not a guarantee. There is no "due" draw and no hot team.

That reduces bias. It does not remove it. The same traps come back in different clothes:

• Chasing after a failed leg: the second price drops before you place it, and you are tempted to raise the next stakes to "make it back".
• Hot hand after a good week: twenty sure bets went smoothly, so you stake more than your bankroll plan allows.
• Sunk cost with a limited account: you keep money at a bookmaker that now accepts only tiny stakes, because of the effort you put in.
• Illusion of control: skipping the check of a bookmaker's rules because "it worked last time".

The fix is the same as above: fixed rules for stakes and bankroll, decided in advance, and a log you actually read. If betting stops feeling good, our responsible gambling page lists free, confidential help.

Frequently asked questions

What is the gambler's fallacy in sports betting?

It is the belief that a result becomes more likely because it has not happened for a while, for example "a draw is due". Each match has its own probability, and the odds already reflect what is known about both teams.

Is the hot hand real?

In some sports skills a small hot-hand effect may exist, as later research suggests. In betting results, short streaks are mostly chance: someone with no edge wins 7 or more of 10 coin-flip bets about 17% of the time.

Why do near-misses make me want to bet again?

Research shows near-misses activate reward-related brain areas although they pay nothing. In an accumulator, losing by one leg is far more common than winning, so the feeling of "almost" is misleading.

Does arbitrage betting remove cognitive biases?

It reduces them, because you do not predict the result and stakes come from a formula. Biases still appear around it, for example chasing after a failed leg or raising stakes after a good week.

Sources

• Tversky, A., Kahneman, D. (1971): Belief in the law of small numbers. Psychological Bulletin 76(2), 105–110
• Gilovich, T., Vallone, R., Tversky, A. (1985): The hot hand in basketball. Cognitive Psychology 17(3), 295–314
• Miller, J. B., Sanjurjo, A. (2018): Surprised by the hot hand fallacy? Econometrica 86(6), 2019–2047
• Nickerson, R. S. (1998): Confirmation bias: a ubiquitous phenomenon in many guises. Review of General Psychology 2(2), 175–220
• Clark, L. et al. (2009): Gambling near-misses enhance motivation to gamble and recruit win-related brain circuitry. Neuron 61(3), 481–490
• Arkes, H. R., Blumer, C. (1985): The psychology of sunk cost. Organizational Behavior and Human Decision Processes 35(1), 124–140
• Langer, E. J. (1975): The illusion of control. Journal of Personality and Social Psychology 32(2), 311–328

As of October 2026. Calculations by ArbVantage; the examples are illustrative.

Betting involves risk, sure bets included: odds change, bets get voided and accounts get limited. Only bet money you can afford to lose. Our responsible gambling page lists free help.

Not betting, legal or tax advice. 18+. As of October 2026. See our disclaimer.