World Cup Betting Bonanza: $14B Wagered on Prediction Markets, Analysis Reveals

Football fans wagered more than $14 billion on the FIFA World Cup through prediction markets Polymarket and Kalshi, according to an analysis by Bellingcat, an independent journalism group.

Prediction markets operate like stock exchanges, allowing users to trade shares based on the likelihood of real-world events occurring. Participants traded on nearly 60,000 outcomes during the tournament, ranging from the sponsor of the Golden Boot winner to whether Cristiano Ronaldo would cry during a match. The World Cup, hosted in the U.S., Canada, and Mexico from June to July, was expected to generate $50 billion in bets, making it the largest betting event in history.

Polymarket, a crypto-based platform, reported $10 billion in trades, with $5.7 billion wagered on individual games and $4.3 billion on the tournament winner. Kalshi, a U.S.-based platform, saw over $4.3 billion in trades, with $4.1 billion on games and $200 million on the winner. The largest single game on Polymarket was Spain vs. Argentina, with $212 million in trades, followed by France vs. Spain ($165 million) and England vs. Argentina ($142 million).

Bellingcat’s analysis found that on Polymarket, 1% of trading accounts collected 86% of all winnings, while the bottom 50% of winners shared just 0.1% of profits. The median winning account earned $21, while the median losing account lost $32. More than 12% of traders who bet on two or more games lost every bet. The top-winning Polymarket account made over $13 million, while the biggest loser lost $11.6 million. Kalshi does not disclose individual trading accounts, so a similar analysis could not be conducted for that platform.

The most-traded teams across both sites were Argentina ($1.068 billion), Spain ($876 million), and France ($836 million). The most-traded players were Argentina’s Lionel Messi ($40 million), France’s Kylian Mbappé ($36 million), and Norway’s Erling Haaland ($16 million).

Prediction markets have faced criticism for their susceptibility to insider trading, market manipulation, and concerns about unregulated gambling. A May report by *The Wall Street Journal* highlighted that a small number of individuals using algorithmic trading models were profiting disproportionately, a trend that appears consistent with Bellingcat’s findings.

To ensure a fair comparison between Polymarket and Kalshi, Bellingcat adjusted Kalshi’s trading volume data, which displays notional volume rather than actual dollar amounts. The analysis relied on data scraping supported by Oxylabs’ Project 4β.

Bellingcat is a non-profit organization that relies on public donations for its investigations. The group encourages readers to support its work through donations or by following its platforms on social media.