Five Upsets Did Not Make the World Cup Ad Forecasts False

Based on the reported snapshot, favored sides won 12 of 17 Kalshi contracts concerning which brands would advertise during the 2026 World Cup final. The five losing favorites were DoorDash, Airbnb, Walmart, New Balance, and OpenAI/ChatGPT, according to Yahoo Sports’ 17-contract summary.

Those outcomes are genuine forecast misses or upsets. They do not prove that Kalshi’s probabilities were mathematically false, systematically miscalibrated, or displayed incorrectly in an advertisement.

The verdict: surprising outcomes, not proof of wrong odds

DoorDash was the clearest upset: Yes was reportedly priced at 69%, but the contract resolved No. Airbnb and Walmart appeared despite reported Yes prices below 50%. New Balance and OpenAI/ChatGPT did not appear even though their reported No prices were below 50%, making Yes the favored side at those snapshots.

A probabilistic forecast can favor one outcome while assigning a substantial chance to the alternative. When the alternative occurs, the favorite has lost, but the quoted probability has not been logically disproved.

The contracts addressed which outside brands would advertise during the World Cup final. They were not an audit of Kalshi’s own World Cup marketing. Separate reporting examined Kalshi’s campaign strategy, sportsbook-like presentation, partnerships, and regulatory disputes, but did not establish that an advertisement displayed incorrect values for these 17 advertiser markets.

All prices and results discussed here come from secondary publications. They have not been independently matched to timestamped Kalshi records, so they should be treated as reported snapshots rather than verified closing prices.

All 17 reported contracts: prices, favorites, and results

Yahoo Sports supplied the complete list. Covers corroborated only a subset, including Airbnb and Walmart, rather than all 17 contracts.

Brand	Reported quote	Opposite probability; favorite	Resolution; favorite result
Adidas	Yes 94%	No 6%; Yes	Yes; won
Polymarket	No 75%	Yes 25%; No	No; won
Anthropic/Claude	No 73%	Yes 27%; No	No; won
Nike	Yes 72%	No 28%; Yes	Yes; won
Home Depot	Yes 72%	No 28%; Yes	Yes; won
DoorDash	Yes 69%	No 31%; Yes	No; lost
Casamigos	No 66%	Yes 34%; No	No; won
McDonald’s	No 60%	Yes 40%; No	No; won
Gemini	Yes 60%	No 40%; Yes	Yes; won
Modelo	Yes 59%	No 41%; Yes	Yes; won
UberEats	No 59%	Yes 41%; No	No; won
Pepsi	No 57%	Yes 43%; No	No; won
Jeep	Yes 51%	No 49%; Yes	Yes; won
Airbnb	Yes 45%	No 55%; No	Yes; lost
Walmart	Yes 39%	No 61%; No	Yes; lost
New Balance	No 38%	Yes 62%; Yes	No; lost
OpenAI/ChatGPT	No 29%	Yes 71%; Yes	No; lost

Source note: The complete figures are Yahoo Sports’ reported pre-game probabilities and resolutions. They have not been checked against Kalshi tickers or historical records. The reports do not specify the exact snapshot times or whether each figure was a bid, ask, last trade, midpoint, or another price representation. The figures therefore should not be labeled closing odds.

New Balance and OpenAI/ChatGPT can look counterintuitive because the report quoted their No prices. A 38% No price for New Balance implies approximately 62% for Yes. A 29% No price for OpenAI/ChatGPT implies approximately 71% for Yes. Both resolved No, so both count among the five favored-side losses.

Why DoorDash losing at 69% does not make 69% wrong

A binary Yes share pays $1 if the specified event occurs and $0 if it does not. A price of 69 cents is therefore commonly interpreted as roughly a 69% implied probability, subject to market frictions. It is a tradable forecast, not a guarantee.

For DoorDash, 69% Yes leaves approximately 31% for No:

• Yes: approximately 69%
• No: approximately 31%
• Resolution: No

A 31% outcome is less likely than its alternative, but it is not remotely impossible. Across a large set of genuinely well-calibrated 69% forecasts, the alternative would be expected to occur about 31 times in every 100 cases. One No resolution cannot determine whether this particular estimate was accurate or mispriced.

Airbnb and Walmart illustrate the same point from the other direction. Airbnb’s reported 45% Yes price represented a substantial minority chance. Walmart’s 39% Yes price was closer to four chances in ten than to a remote long shot. Their Yes resolutions were upsets relative to the favorite, not contradictions of probability.

Three propositions must remain separate:

1. The favored side lost. Established for five reported contracts.
2. The probability was miscalibrated or the contract was mispriced. Not established by one realized outcome.
3. An advertisement displayed an objectively incorrect value. Not established without the advertisement and matching market records.

That is why the type and time of the quoted price matter.

What five upsets among 17 can—and cannot—tell us

A 12-of-17 favorite hit rate is descriptive, not a calibration score. It answers how often the side above 50% won, but discards the magnitude of every forecast.

A miss at 51% and a miss at 94% count equally under favorite accuracy, even though they have very different forecasting implications. Likewise, correct 51% and 94% favorites each add one win.

Yahoo Sports and Covers both report $4.6 million in aggregate trading volume, but that figure does not show how activity was distributed across the contracts. It reveals neither contract-level liquidity nor bid-ask spreads, order-book depth, stale-price risk, or the quality of a particular snapshot, as reflected in the Covers World Cup ad-market recap.

Here, presenting either as authoritative would imply that all 17 probabilities were captured consistently at a meaningful common time. Without timestamps and price types, the apparent precision of such a score would exceed the precision of its inputs.

The DoorDash 212% return: payout math, not accuracy proof

Yahoo Sports reported that some DoorDash No backers bought at an earlier price of $0.32. If such a contract settled at $1, the gross-return calculation would be:

• Settlement value: $1.00
• Purchase price: $0.32
• Gross profit: $0.68
• Gross return: $0.68 ÷ $0.32 = 212.5%

That is approximately the reported 212% return before fees. It is not a verified net return.

The 32-cent entry was described as an earlier price and was not necessarily contemporaneous with the separate snapshot showing DoorDash Yes at 69%. Although 32-cent No and 69% Yes are arithmetically close, timing, spreads, and quote type could explain the difference.

A profitable trade also does not prove objective mispricing. A trader can earn a large return by buying an outcome that was reasonably considered less likely and then occurred. Establishing mispricing would require evidence that the contract’s true probability was materially higher than its market price given the information available at the time.

What would be needed to prove the odds or ad display were wrong

A proper audit would begin with the alleged display, not the final result. The necessary materials would include:

1. The original advertisement or an unedited screenshot
2. Its placement and exact timestamp
3. The relevant contract ticker
4. The displayed probability, price, payout, or odds
5. The display format, such as cents, percentages, potential payout, or American odds
6. The underlying price type, such as bid, ask, last trade, or midpoint

The timestamp would then need to be matched against primary records for bids, asks, last price, volume, liquidity, market status, rules, result, and settlement time. Kalshi’s historical-market API documentation lists fields that could support such an investigation, including bid and ask prices, last price, volume, liquidity, rules, results, and settlement timestamps. The documentation itself does not verify these 17 contracts or recreate what a consumer saw in an advertisement.

An audit would also need the exact resolution rules. “Brand appearance” could depend on whether a contract counted national commercials, regional inventory, sponsorship graphics, affiliated products, verbal mentions, or other integrations. Settlement evidence would have to be checked against the applicable wording rather than an informal understanding of what qualified as an advertisement.

Before alleging a false display, a reviewer should test ordinary explanations:

• Was the quote stale?
• Was the spread wide?
• Was trading thin?
• Was the number rounded?
• Was a contract price converted into American odds?
• Did the display use a bid, ask, or last trade without clearly labeling it?
• Did the market move between the data pull and the ad impression?

The available evidence establishes no specific advertisement containing mathematically false odds and no regulatory finding that such a display was deceptive. The narrower conclusion is supported: five favored outcomes lost, led by DoorDash, but probability never becomes a promise. Proving miscalibration or an incorrect display would require timestamped primary prices, price types, spreads, contract-level liquidity, rules, settlement evidence, and the alleged ad itself.

Frequently asked questions

Were the reported Kalshi probabilities closing odds?

Not on the available information. The publications describe pre-game probabilities but do not provide exact quote times or identify the figures as final trades immediately before market close. They also do not say whether the numbers were bids, asks, last trades, midpoints, or another representation.

The accurate label is reported snapshots, not verified closing odds.

Does $4.6 million in volume mean every advertiser contract was liquid?

No. The figure is aggregate volume across the advertiser markets. It does not reveal how much activity belonged to each contract or show contract-level spreads, order-book depth, active participation, or the timing of trades.

Volume and liquidity are related but different.

Is this the same controversy as Kalshi’s own World Cup advertising campaign?

No. These 17 contracts predicted which outside brands would appear during the final. Kalshi’s own World Cup campaign raised separate questions about how the platform marketed itself, presented sports contracts, and described its relationship to sportsbooks.

Reporting about that broader campaign does not establish that these advertiser-market probabilities were inaccurate. This analysis is informational, not trading advice.