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Breaking: Bettors wagered millions on this weekend expected snowfall across the U . S . (Cbsnews)

created: Jan 26, 2026, 09:23 PM
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Bettors wagered millions on prediction markets like Polymarket and Kalshi regarding snowfall totals across the U.S. during a recent winter storm. Over $6 million was bet on New York City's snowfall totals alone, with the implied consensus being 11 inches.

Millions of dollars were wagered on prediction markets concerning the amount of snowfall expected across the U.S. during a recent weekend. Prediction markets such as Polymarket and Kalshi saw significant betting activity, with over $6 million wagered on New York City's snowfall totals alone. On Polymarket, more than $1.2 million was bet on the question, "How many inches of snow in NYC this weekend? (Jan 24-26)".

The implied consensus for New York City's total snowfall, based on bets placed, was 11 inches. The official outcome will be determined by data from the National Oceanic and Atmospheric Administration (NOAA). The most popular wager for New York City was in the 10- to 12-inch range.

Bettors also placed wagers on snowfall in other cities, including Washington, D.C., Atlanta, and Seattle. On Kalshi, over $5 million was wagered on a similar question regarding snow in New York City, with the market presuming a 99% chance of more than 10 inches of accumulation.

Prediction markets allow individuals to buy and sell shares in the outcomes of future events, providing insights into consumer and investor sentiment. Recently, an anonymous trader earned over $463,000 by correctly predicting that Venezuelan leader Nicolás Maduro would be captured. Key takeaways:

  • Millions were wagered on snowfall totals using prediction markets like Polymarket and Kalshi.
  • Over $6 million was bet on New York City snowfall totals alone.
  • The implied consensus for New York City snowfall was 11 inches.
  • Bettors also wagered on snowfall in Washington, D.C., Atlanta, and Seattle.
  • Prediction markets track wagers in real-time and provide insights into consumer sentiment.