Will Ronaldo Cry​? World Cup Fans Bet Billions Through Prediction Markets

Football fans placed bets totaling over $14 billion on the FIFA World Cup through prediction markets Polymarket and Kalshi, according to an analysis by Bellingcat. These platforms allowed users to wager on a wide range of outcomes, including the identity of the Golden Boot sponsor and whether Cristiano Ronaldo would cry during a Portugal match. The World Cup, held in the United States, Canada, and Mexico from June to July, was anticipated to become the largest betting event in history, with forecasts suggesting up to $50 billion in wagers. Prediction markets function similarly to stock exchanges, enabling users to trade contracts based on their belief in the likelihood of specific real-world events occurring. Unlike conventional sports betting sites, these platforms operate through an order book system, where prices shift depending on market sentiment. Both Polymarket and Kalshi impose transaction fees on each trade, generating revenue from the activity. The World Cup featured 48 teams competing in 104 matches, marking it as the most extensive sporting event ever. According to Bellingcat's findings, users engaged in nearly 60,000 different trades across both platforms throughout the tournament. On Polymarket alone, traders bet $10 billion, $5.7 billion on individual matches and $4.3 billion on predicting the eventual champion. The highest-stakes match on Polymarket was the Spain versus Argentina final, drawing $212 million in bets, followed by the France versus Spain semi-final at $165 million and the England versus Argentina semi-final at $142 million. Kalshi saw slightly less action, with users placing over $4.3 billion in bets, primarily focused on the outcome of individual games and the overall tournament winner. While detailed win-loss statistics could not be fully analyzed for Kalshi due to limited access to user account data, Bellingcat noted that on Polymarket, just 1% of trading accounts captured 86% of all winnings, while the lowest half of participants accounted for only 0.1% of profits. The average winning trader earned $21, whereas the average losing trader lost $32, figures derived from the median to minimize the impact of extreme gains or losses. More than 12% of traders who participated in two or more games lost all their bets. One Polymarket account achieved a profit exceeding $13 million, while another suffered a loss of $11.6 million. The most frequently traded teams across both platforms were Argentina, Spain, and France, with respective volumes of $1.068 billion, $876 million, and $836 million. Individual player-related trades included $40 million on Lionel Messi, $36 million on Kylian Mbappé, and $16 million on Erling Haaland. The prediction market sector has drawn scrutiny regarding its susceptibility to insider trading and market manipulation, alongside concerns about promoting unregulated gambling. Reports from the Wall Street Journal highlighted how a select group of traders employing algorithmic strategies secured disproportionate portions of the winnings. Although Bellingcat's study indicated similar patterns, the depth of data obtained prevented confirmation of whether these high-earning accounts used such automated techniques. Data transparency varies between the two platforms. Polymarket offers direct visibility into the actual traded volume, showing the total U.S. dollar amount of shares bought and sold since the market began. In contrast, Kalshi presents notional volume, which measures every contract at its maximum payout value of $1, regardless of the price paid for the contract. This discrepancy affects how the volume is interpreted and compared between the two services. As the World Cup continues to draw global attention, the role of prediction markets in shaping public engagement with major sporting events remains under observation. With ongoing discussions around regulation and ethical considerations, the future trajectory of these platforms will likely depend on addressing current challenges and ensuring fair practices for all participants.