Author: Frank, PANews

The World Cup is never short of predictions. Professional institutions, betting companies, fan communities, and data models all offer their answers before the tournament begins. But in prediction markets, a judgment is not just an opinion—it is a choice that must be backed with real money.

When the price for a strong team to win a match is continuously bought up, when draw shares are suddenly swept up just before kickoff, or when a single wallet splits its bets into dozens of orders for the same match, the prediction market no longer merely presents the question of "who will win." It becomes a real-time experiment in capital, information, and bias.

PA Beacon analyzed the trading data of completed World Cup contracts on Polymarket. As of June 17, 2026, the data covers 20 group-stage matches with finalized results, including all single transactions exceeding $5,000. The total pre-match buy-in amount was $89.5457 million, with $43.4504 million placed on the correct outcome, resulting in a dollar-weighted accuracy rate of 48.5%.

This result does not align with many people's intuitive image of "smart money." At least in this World Cup sample, large capital did not act like a crystal ball, revealing all the answers in advance. More interestingly, if we estimate based on holding all aggregated pre-match buy-in positions until settlement, the total cost for 1,278 combined trading positions was $89.5457 million, with total returns of $87.7863 million. This represents an overall loss of approximately $1.7594 million, with an ROI of -2.0%.

In other words, the true value of prediction markets may not lie in telling us "who will definitely win," but in revealing something more complex: when capital bets on its own judgment, which consensuses are validated, which biases are punished, and which so-called smart money also falters in the face of the pitch's uncertainty.

Draws Remain the Biggest Risk, But the Favorite Script Begins to Recover

Among the 20 completed matches, 12 ended with a winner and loser, and 8 were draws; 10 matches had total goals over 2.5, and 14 matches saw both teams score.

On June 17, the latest four matches finally produced no upsets. France beat Senegal 3-1, Norway beat Iraq 4-1, Argentina beat Algeria 3-0, and Austria beat Jordan 3-1. Favorites and strong sides delivered in these games, pushing the dollar-weighted accuracy rate of pre-match buy-ins from 45.8% to 48.5%.

However, overall, draws remain the most significant risk factor in this round of the prediction market. Eight out of 20 matches were draws, accounting for 40.0%. For large capital betting on strong teams to win, the most dangerous outcome is often not a weak team pulling off an upset victory, but the favorite failing to convert its advantage into a win, with profits ultimately swallowed by a draw.

Belgium vs. Egypt is the most typical case. This match attracted the highest pre-match buy-in amount in the sample, reaching $12.3855 million, with 145 pre-match buy-in groups involving 53 wallets. But the match ended in a 1-1 draw, and the dollar-weighted accuracy rate for pre-match buy-in funds was only 5.4%. Judging by the trading results, a large amount of capital clearly treated a Belgium victory as the main script, but the football pitch delivered a draw. However, the unusually high buy-in for a match that drew little attention is itself suspicious. Overseas analyst @ORamosBets suggested this match may have involved $8.6 million in "money laundering" transactions.

Netherlands vs. Japan showed a similar structure. The pre-match buy-in amount for this match was $6.0814 million, the final score was 2-2, and the dollar-weighted accuracy rate was only 18.9%. Spain vs. Cape Verde was even more extreme: 210 pre-match buy-in groups and $4.3117 million in capital entered the market, but the match ended 0-0, resulting in a dollar-weighted accuracy rate of 23.0%. These three matches absorbed a combined $22.7715 million in pre-match buy-ins, yet all saw mainstream capital direction significantly skewed by the draw results.

But the market was not entirely ineffective. Germany vs. Curaçao is a sample of "correct consensus," where Germany ultimately won 7-1. The pre-match buy-in amount for this match was $2.8883 million, with a dollar-weighted accuracy rate reaching 98.9%. In Iraq vs. Norway, Norway won 4-1, with a pre-match buy-in amount of $1.4464 million and an accuracy rate of 91.6%. The accuracy rate for France vs. Senegal also reached 76.7%. These cases show that when the strength gap is sufficiently clear and the match outcome path is relatively straightforward, large capital can still reflect high information efficiency in advance.

What is truly worth pondering is when the market is more efficient and when it is more easily led astray by sentiment. The larger the strength gap, the more likely prices become containers of information; when the gap is not large enough to cover the risk of a draw, prices can become amplifiers of popular narratives.

"Buy No" Continues to Outperform, But the Advantage is Narrowing

From the perspective of outcome shares, in the latest 20-match sample, "Buy No" still significantly outperformed "Buy Yes."

Among 2,645 pre-match buy-in groups, the amount for buying "Yes" shares was $49.9188 million, with a correct amount of $18.7170 million, yielding a dollar-weighted accuracy rate of 37.5%; the amount for buying "No" shares was $39.6270 million, with a correct amount of $24.7334 million, yielding a dollar-weighted accuracy rate of 62.4%.

This gap remains very significant. It does not mean "always buy No" is a stable strategy, but rather indicates that in this sample, the market's pricing of favorite outcomes may still be overly full. Once a match ends in a draw, a favorite fails to win, or the market overprices the probability of one team winning, buying "No" shares provides greater margin for error.

Iran vs. New Zealand is one of the best examples. The match ended in a 2-2 draw, with a pre-match buy-in amount of $9.9282 million and a dollar-weighted accuracy rate of 74.5%. Among them, the wallet "mintblade" concentrated on buying "Iran Not to Win," with an aggregated cost of $6.4705 million at an average price of about 0.49. If held to settlement, this position would have returned approximately $13.2443 million, yielding a profit of about $6.7738 million and an ROI of 104.7%.

This is not betting on an underdog win or loss, but betting on "the favorite failing to deliver." In prediction markets, such trades are more intriguing than directly buying a draw. It does not require the trader to accurately judge that the match will definitely be a draw, only to judge that a certain favorite outcome is overvalued. In a low-scoring, high-randomness tournament environment like the World Cup, this approach is often closer to the risk itself than betting on a single win/loss outcome.

However, the matches on June 17 also showed that the "Buy No" advantage is not immune to correction. After favorites like France, Norway, and Argentina successfully delivered, the accuracy rate for buying "Yes" has risen from 28.8% in the previous sample to 37.5%. This shows that prediction markets do not always punish favorites, but punish them when their prices are overly full.

Some Made $6.77 Million Overnight, Others Lost $8 Million on a Single Match

If we shift the sample from the match level to the position level, the high-volatility nature of prediction markets becomes even more apparent.

In this analysis, there were 1,278 aggregated pre-match buy-in positions, of which 694 were correct and 584 were incorrect. The number of correct positions already exceeds the incorrect ones, but due to the vast differences in position sizes, the final outcome still hinges on the success or failure of a few large positions.

The largest correct case came from mintblade. This wallet bought "Iran Not to Win" in the Iran vs. New Zealand match, with a cost of about $6.4705 million and an estimated profit of $6.7738 million, as mentioned earlier.

The second-largest correct case came from LEEEROYJENKINS, who bought "Turkey Not to Win" in the Australia vs. Turkey match, with a cost of about $3.7511 million at an average price of about 0.44. Australia ultimately won 2-0. If held to settlement, this position is estimated to have profited $4.7976 million, with an ROI of 127.9%. However, LEEEROYJENKINS also bought "Belgium to Win" in the Belgium vs. Egypt match, with a cost of about $8.3943 million at an average price of about 0.66. This position ultimately went to zero, resulting in an estimated loss of $8.3943 million. This turned the account's profit from $5 million directly to -$2.57 million, effectively wiping out gains overnight.

The 0-0 draw between Spain and Cape Verde also created a low-cost, high-return case. The wallet "fishalive" bought "Spain Not to Win," with a cost of about $306,500 at an average price of just 0.09. Because the match ended in a draw, this position is estimated to have profited about $3.1572 million, with an ROI exceeding 1000%. The appeal of such trades is clear: when the market strongly believes a favorite will win, the price of the opposing share is low enough that if the result deviates from the mainstream script, the return elasticity can be enormous.

Latina bought "Argentina to Win" in the Argentina vs. Algeria match, with a cost of about $888,300. Argentina ultimately won 3-0, yielding an estimated profit of about $499,300 and an ROI of 56.2%.

FlickRaw bought "Netherlands to Win" in the Netherlands vs. Japan match, with a cost of $3.29 million. The match ended 2-2, and the position also went to zero. In the new sample, weatherman12 and wr0ngw4yb3tt0r both bought "Argentina Not to Win" in the Argentina vs. Algeria match, but Argentina ultimately won 3-0, resulting in estimated losses of $1.1759 million and $471,600 for their respective positions.

These cases collectively point to one fact: large capital in prediction markets behaves more like high-volatility information trading than low-volatility arbitrage. When correct, low-priced shares can bring returns close to doubling or even several times over; when wrong, the binary settlement mechanism can wipe out the principal entirely.

Often, we see a wallet "making millions of dollars by betting correctly on one match," but we don't see that, under the same market structure, other equally large sums of capital also went to zero in another match.

Consistent Wallets Are More Worth Tracking Than Single-Match Whales

From a wallet perspective, those covering multiple matches with consistent accuracy are often more worth tracking in the long term.

Sorted by pre-match buy-in amount, mintblade represents another extreme. This wallet had a buy-in amount of $7.2889 million, covering 2 matches, with a dollar-weighted accuracy rate of 100.0%. However, with only 2 matches covered, the sample size remains small.

In contrast, swisstony has more sustained observational value. This wallet covered 16 matches, correctly predicted 11 at the match level, with a pre-match buy-in amount of $1.9284 million and a dollar-weighted accuracy rate of 73.3%. NiNo999 covered 9 matches with a dollar-weighted accuracy rate of 76.2%; Cannae covered 12 matches with a match-level accuracy rate of 66.7%. The individual trade amounts for these wallets may not be the most astonishing, but because they cover more matches, their behavior more closely resembles an observable trading pattern.

The latest sample also revealed some accounts with small amounts but high consistency. For example, zhqzhq, anon.1980.123, and NiFengFanPan all covered 5 matches and correctly predicted all at the match level, but their buy-in amounts were around $290,000, $110,000, and $80,000 respectively. Whether such accounts have lasting value requires verification over more matches.

The charm of the World Cup lies precisely in its unpredictability. In this capital experiment involving tens of millions of dollars, Polymarket did not become a crystal ball for foreseeing the future, but rather acted more like a mirror, clearly reflecting the crowd's fervor, biases, and blind obedience to popular narratives.

The heavy losses and huge gains of large capital once again validate a simple truth: in the face of absolute uncertainty, no one can forever transcend rules and probability. The true intelligence of so-called "smart money" lies not in possessing a supernatural ability to see through the future, but in knowing how to find pricing discrepancies within uncertainty and always maintaining reverence for risk.

PA Beacon has launched a World Cup Fund Watch, updated daily based on the latest large-scale fund buying and selling activities. Interested readers can click to read the original article for more details. Once again, please note that the above content is compiled based on Polymarket trading data, and the amounts, hit rates, and profit/loss figures are all analytical estimates and do not constitute betting or investment advice.