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Most people think bookmakers try to estimate the true probability of a match and then add a margin.

In practice, their real problem is predicting how people will bet.

A simple coin-flip example shows why:

Even with a 10% overround, if 80% of money lands on one side, the bookmaker becomes exposed to extreme short-term variance. Three consecutive popular outcomes can wipe out the theoretical edge.

The article breaks down:

– why money distribution matters more than probability – how emotional teams distort football markets – why positive expected value does not prevent bankruptcy

Curious how people here see the analogy with market makers in financial markets.

Full breakdown here: https://www.playaiodds.com/en/blog/money-made-on-public-bets

Great article — the idea of “injectable micro-apps” or widgets inside a chatbot feels like a natural next step toward more fluid interfaces

This article explores why bookmaker odds often diverge from the actual statistical probabilities of sports outcomes.

It touches on topics like overrounds, market efficiency, and how bettor psychology and liquidity shape the implied probabilities.

I found it interesting because it connects data modeling, behavioral economics, and prediction theory — fields that are usually discussed separately.

Curious what the HN community thinks: – Are betting markets efficient in the long run? – How would you model the “bias” in odds vs true probability?

Happy to answer any questions about how I used Poisson and Glicko models to calculate match probabilities. I’m especially looking for feedback on how to visualize the predictions better.