March Madness Mistakes to Avoid When Trading on Kalshi: Fade the Favorites in These Six Games

Every sports trader on Kalshi should be aware of the mispriced markets for these six games in Round 1 of the Men's NCAA Tournament.

Mar 16, 2026; Dayton, OH, USA; Detailed viewed of March Madness logo during a practice session ahead of the first four of the men's 2026 NCAA Tournament at University of Dayton Arena. / Rick Osentoski-Imagn Images

It’s called March MADNESS for a reason—upsets come early and often throughout the tournament.

The first round of the Men’s NCAA Tournament is a perfect storm of market inefficiencies. Typically, it features strong public bias toward blue-blood programs, limited information on mid-majors, and emotional reasoning rather than math-based logic. Smart traders take advantage of it, while uninformed casual traders are left pulling their hair out asking how a 14 seed just took out a 3 seed.

But every year, the MADNESS happens—and every year, the general public is just as surprised.

With prediction markets like Kalshi or Polymarket, the key to trading games in the Men’s Tournament (or any sporting event) isn’t about “who wins.” It’s about “where is the price wrong?”

That’s where sharp traders find their edge.

For example, the public says a 5 seed should beat a 12 seed, and the market implies a 70% win probability—but a sharp trader may see it closer to 55%. That gap is where opportunity lives.

The 90% Trap: Where the Market Breaks

The biggest inefficiency in Round 1 lives in the 90% range.

There are 32 games in the first round, and nearly half (14, to be exact) have favorites priced at 90% or higher to win. Below, we identify where the 90% trap actually exists, the true probability based on historical data, and how traders can take advantage.

First Round Reality: Identifying the 90% Trap

Matchup	Upsets	Total Games	Favorite Win %
1 vs. 16	2	160	98.8 %
2 vs. 15	11	160	93.1 %
3 vs. 14	23	160	85.6 %
4 vs. 13	33	156	79 %
5 vs. 12	55	156	65 %
6 vs. 11	75	156	52 %
7 vs. 10	60	156	51 %

If your eyes aren’t popping after looking at the data, go back and look again.

A 2 seed has lost 11 times in Round 1

A 3 seed has lost 23 times

A 4 seed has lost 33 times

At some point, these aren’t “upsets”—they’re patterns the market continues to misprice. That’s the illusion: the public wants certainty, but history consistently delivers volatility.

This data is based on more than 150 games per matchup since the tournament expanded to 64 teams in 1985. The results show the same thing every year:Outside of 1 vs. 16, there are no truly “safe” favorites in March Madness.

In fact, a No. 1 seed has only been upset twice in the first round:

UMBC over Virginia (2018)

Fairleigh Dickinson over Purdue (2023)

That’s a 98.8% win rate for No. 1 seeds. But from there, the drop-off is steep:

No. 2 vs. No. 15 → 93.1%

No. 3 vs. No. 14 → 85.6%

No. 4 vs. No. 13 → ~79%

No. 5 vs. No. 12 → ~65%

No. 6 vs. No. 11 → ~52%

A 14 seed wins roughly 1 out of every 6 games.
A 13 seed wins 1 out of every 5.
A 12 seed has won at least one game in 34 of the last 40 tournaments.

The public isn’t underestimating underdogs, we all expect upsets. The mistake is overestimating favorites, especially when nearly half of Round 1 games are priced above 90%.

Mar 18, 2026; Greenville, SC, USA; Duke Blue Devils forward Cameron Boozer (12) reacts with forward Maliq Brown (6) during a practice session ahead of the first round of the men's 2026 NCAA Tournament at Bon Secours Wellness Arena. / Bob Donnan-Imagn Images

March Madness Games Priced Correctly on Kalshi

Let’s start by removing the obvious:

All No. 1 vs. No. 16 matchups are priced around 99%:

Duke vs. Siena, Arizona vs. LIU, Michigan vs. Howard, Houston vs. Idaho

These are efficient markets. There’s no edge here.

Next, the No. 2 vs. No. 15 matchups:

Illinois vs. Penn, Purdue vs. Queens, Iowa State vs. Tennessee State, UConn vs. Furman

These are priced between 95–97%, while the historical baseline is 93.1%. There’s a slight edge, but not enough to prioritize. That removes 8 of the 14 games. Now we get to where the real opportunity lives.

Mar 18, 2026; Oklahoma City, OK, USA; Nebraska Cornhuskers forward Rienk Mast (51) leads the team onto the floor for a practice session ahead of the first round of the men's 2026 NCAA Tournament at Paycom Center. / William Purnell-Imagn Images

The Matchups to Exploit on Kalshi

These are the six matchups where the market does not reflect the historical win probability.

No. 3 Gonzaga vs. No. 14 Kennesaw State

No. 3 Virginia vs. No. 14 Wright State

No. 3 Michigan State vs. No. 14 North Dakota State

No. 4 Nebraska vs. No. 13 Troy

No. 4 Arkansas vs. No. 13 Hawai’i

No. 4 Kansas vs. No. 13 Cal Baptist

Three of these immediately stand out:

Nebraska (90%)

Kansas (91%)

Arkansas (91%)

Historically, No. 4 seeds win this matchup about 79% of the time. The market is pricing these games 10–12% higher than reality.

That’s not a small gap, that’s a massive inefficiency. The market says these are safe but history says the favorite loses 1 out of every 5 times

Nebraska vs. Troy is the most mispriced game on the board.

March 10, 2026; Las Vegas, NV, USA; Gonzaga Bulldogs guard Mario Saint-Supery (17) and guard Davis Fogle (4) celebrate against the Santa Clara Broncos during the second half at Orleans Arena. / Kyle Terada-Imagn Images

Don’t Ignore the 3 vs. 14 Games

The No. 3 vs. No. 14 matchups are also overpriced:

Gonzaga → 96%

Virginia → 95%

Michigan State → 93%

Historically, favorites win 85.6% of the time. That means Gonzaga, for example, is being priced roughly 10% too high. Again, real edge.

Remember the MADNESS in March Madness

We just threw a lot of math, percentages, and numbers your way.

Here’s the simple version: The market is extremely efficient at 99%. Still solid at 95%. But starts to break down badly in the 90–93% range.

That’s the trap.

That’s where confidence is highest, and pricing is most wrong. And that’s where sharp traders win.

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EDITOR'S NOTE: Markets are constantly evolving, changing on Kalshi. The above market prices reflect the the percentages at the time of the published article.

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