Everything You Need to Know for Understanding the Upsets in NCAA Basketball Betting Predictions

Before you start your office betting pool, you need to understand a few things about upsets. Mostly, upsets are the best part of March madness. Very few experiences even come close to the joy and excitement of the underdog being victorious.

But predicting upsets is easier said than done. Here are a few things you should know about understanding these upsets in NCAA.

What Are Upsets?

Every year 64 teams are divided into four groups according to 16 regions. A ‘seed’ is assigned to every team that denotes the teams from best to worst from 1 to 16.  This set up means that one team in each game is the favorite. But it’s not a surprise when the 6th seed is beaten by the 7th seed or even beaten by the 11th seed. This is an ‘upset.’

The matchups are simply a tossup and based on chance.  But to be able to predict truly shocking and game-changing upsets is what we’re talking about here.

A good rule of thumb to predict an upset is to pick a winning team that’s seeded at least 4 slots lower—for example, the 6 seed beating the 1 seed, and so on.  By following the seed system, you can follow the matchups to quickly and easily identify any potential upsets by studying them.

Pro Tip: A good range to pick upsets is to keep the margin between both teams between 4 to 8.

Data

You can obtain data on the performance of a team or individual player during the regular season. This data may include what’s known as ‘upset potential,’ which means that you can get a rough idea of the teams that can beat their opponents that seeded at least 4 slots higher in that season.

This data can also be obtained for tournaments and games played by every eligible team to play throughout that season.

Spotting Underdogs Using Team Efficiency Margin

The data obtained can also reflect their ‘adjusted efficiency margin’ over another team with a single number.  This number denotes the overall strength of play and the expected margin of victory by a team over an average team.

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