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Academy / Model Library / Evaluation, testing and behaviour / Behavioural Biases
Model Library · Evaluation, testing and behaviour

Behavioural Biases

The predictable mental shortcuts that distort betting prices and bettors' own decisions, and how to test whether any of them are still exploitable.

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In one sentence

Behavioural biases are systematic errors in human judgement, such as overreacting to recent results or backing popular teams, that can push odds away from fair value and push bettors into bad decisions.

How it works

Markets are made of people, and people are not perfect probability machines. Some biases show up in prices: the favourite-longshot bias, where longshots are overbet; popularity bias, where big clubs attract money; and recency bias, where a thumping win makes a team look better than it is.

Other biases show up in your own behaviour. Loss chasing after a bad run, locking in profits too early, sticking with a losing position to avoid admitting a mistake, and remembering winners more vividly than losers all damage results even when the model is sound.

The first group is a possible source of edge; the second is a certain source of leakage. Both are worth measuring, but only the second is fully under your control.

The maths

There is no single formula; biases are patterns to be tested. The usual test is whether the market's implied probability differs from the true rate for a group of selections:

bias=qˉ−Wn,EVlay=(1−p) S (1−c)−p S (O−1)\text{bias} = \bar{q} - \frac{W}{n}, \qquad \text{EV}_{\text{lay}} = (1 - p)\,S\,(1 - c) - p\,S\,(O - 1)
  • q with a bar: the average implied probability, 1 ÷ odds, for a group such as teams after a big win.
  • W and n: winners and total selections in that group.
  • p: your estimate of the true win probability.
  • S: the lay stake; O: the lay odds; c: commission.

In plain English: if the market says 55% and similar teams only win 50% of the time, the market is overrating them, and laying may have value.

Worked betting example

A team wins 5-0 at the weekend. Next match, the market makes them 1.80 to win, implying 1 ÷ 1.80 ≈ 55.6%. Your model, which weights performance over many matches rather than the last one, says 50%.

Laying £10 at 1.80:

  1. Liability if they win: £10 × 0.80 = £8.
  2. If they do not win (50%): you win £10, or £9.80 after 2% commission.
  3. EV = 0.5 × £9.80 − 0.5 × £8 = +£0.90 per £10 laid.

That is only worth doing if the overreaction is real. You would test it by collecting every match after a big win, comparing the average implied probability with the actual win rate, and checking the gap is larger than luck and commission can explain.

A personal-bias example: after losing five bets in a row, doubling stakes to "get it back" does not change the edge on the next bet at all. It only increases the damage if the losing run continues.

Where it's good

  • Generating ideas for market inefficiencies to test, such as overreaction to recent results, popular teams, or big-name managers.
  • Understanding the favourite-longshot bias, one of the best-documented patterns in racing markets.
  • Auditing your own betting log for loss chasing, early cash-outs and stake creep after wins.
  • Trading around predictable crowd behaviour, such as money arriving for popular teams close to kick-off.

Limitations and pitfalls

  • Well-known biases get traded away. Many have shrunk on Betfair as sharper money and bots have arrived; do not assume a pattern from older research still pays.
  • A bias story is easy to believe and hard to prove. Test it like any other system, with enough data and a correction for the number of ideas you tried.
  • The size of most price biases is small compared with commission, so a real bias may still not be profitable.
  • Biases often vary by market and time: stronger in smaller leagues, weaker or absent in the biggest ones.
  • You are subject to the same biases when judging your own research, especially confirmation bias.
  • Laying into perceived overreaction carries large liabilities at short prices; stake with care.

How to build it

  • Python: pandas for grouping bets by situation; statsmodels logistic regression with implied probability plus a "situation" flag to test whether the flag adds anything.
  • Data: historical Betfair prices and results, and your own full betting log including stakes and timestamps.
  • Tip: keep a pre-bet note of your reason for each bet; reviewing them monthly shows your own biases far more clearly than memory does.
Learn it step by step
18+ only. Educational content, not financial or betting advice. Past results do not guarantee future returns. If gambling stops being fun, get free, confidential help at BeGambleAware.org.
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