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Discipline

The Psychology of Losing Streaks

The Yoseri Desk·June 2026·5 min

Losing streaks are normal, not a sign you are broken

A strategy that wins 55% of the time still loses five, six, seven in a row on a regular basis — not because it stopped working, but because that is what randomness looks like over a long enough sample. The math guarantees these stretches. The mistake is reading a perfectly normal streak as proof that your edge has vanished, and then acting on that false signal.

Why a drawdown feels worse than it is

Two biases do most of the damage. Loss aversion makes a loss hurt about twice as much as an equivalent win feels good, so a flat stretch feels like a crisis. And recency bias makes the last few results loom far larger than the hundreds that came before. Together they push you toward exactly the wrong move: abandoning a sound plan at its low point.

Key idea: the danger of a losing streak is almost never the losses themselves — it is the unforced errors they tempt you into. Chasing, oversizing and plan-switching turn a survivable dip into real damage.

The behaviours that turn a dip into a disaster

Chasing — raising stakes to win it back fast — is the classic. So is tinkering, quietly rewriting your rules mid-drawdown to feel in control. And so is the opposite, freezing and skipping good opportunities out of fear. Each one trades a temporary, expected discomfort for a permanent, unnecessary loss of capital or edge.

Want to apply this?

Yoseri puts these tools to work on every pick.

Build the rules before you need them

The time to decide how you will behave in a drawdown is before you are in one. Pre-commit to your stake sizing, set a maximum drawdown that triggers a pause rather than a panic, and keep a record that lets you check whether your process is still sound. When the streak comes — and it will — you follow the plan you wrote on a calm day instead of the impulse you feel on a bad one.

YD
The Yoseri Desk

The analysts behind Yoseri's models — writing about value betting, bankroll math, and the discipline of a measured edge.

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