LEARNMODELS

Why a Good Model Still Loses Bets

Positive expected value loses often. Variance doesn’t care about your edge.

2 MIN READ · BETTORWISE RESEARCH · EXAMPLES MARKED AS ILLUSTRATIVE

Suppose a model is genuinely 55% on a bet priced at even money. Solid edge. It still loses 45% of the time — and loses five in a row about twice in every hundred sequences. That’s not the model failing; that’s the shape of randomness.

Judge the process

Over a small window, results are dominated by variance. Over a large one, they reflect the quality of decisions. CLV and calibration give you the large-one answer before the results do.

Surviving the middle

The practical discipline: size stakes so streaks are survivable, grade decisions on process, and let the record accumulate instead of narrating it.

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01What Makes a Sports Model Well Calibrated?

A calibrated model says 60% and it happens about 60% of the time. Calibration is checkable — which is exactly why it matters.

MODELS · 2 MIN02What Is Closing Line Value?

CLV compares the price you took with the price the market closed at. It grades your timing and your process, not your luck.

EXPECTED VALUE & MARKETS · 2 MIN03How to Track Your Betting Results Properly

Dollars distort. Units, prices and CLV show what your process is actually doing.

TRACKING · 2 MIN

Compare the market yourself.

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