Fantasy sports have grown more and more sophisticated over the years, with data becoming more and more important. The result has been more advanced metrics, be it usage rates in the NBA or expected goals (xG) in the NHL, that are used to help players gain an edge. But outside of fantasy games, these very skills are being used in event trading and prediction markets.
What makes a successful daily fantasy sports (DFS) player is their aptitude at projecting outcomes more accurately than the consensus (the wisdom of the crowd). This can mean being better than others at synthesizing player news, coaching trends, historical data, and finding value.
Parallel to this is the growth of exchange-style wagering, and these DFS skills have become useful for identifying mispriced contracts. Sophisticated projections and tools used for fantasy leagues can be applied to the best prediction market apps, most notably Kalshi, to try to monetize some of these insights.
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Because these platforms are peer-to-peer exchanges rather than house-run sportsbooks, the odds fluctuate based on public sentiment. And, as we know, this can range from uninformed options and memes all the way to more serious wagers.
While major markets like the NFL Super Bowl winner or NBA Finals champion are normally highly efficient due to massive volume (as are fantasy league economics, because they’re so popular), it’s the niche prediction markets that suffer from a lack of price discovery.
Things like the NFL Offensive Rookie of the Year or NHL Vezina Trophy winners often receive little attention. In these lower-volume markets, even a small amount of fantasy-relevant information can really alter the probability of an outcome - and the more niche the market, the more time before the market pricing reacts. This lack of liquidity means that sharp information travels slower, and it’s often those using fantasy data who clean up and act as eventual price stabilizers.
The rise of prediction markets has actually captured a lot of attention from these experienced fantasy analysts, dividing their interests in two.
The edge in niche markets is usually just found in the details. So, in the MLB, a fantasy player tracking a pitcher’s increased spin rate or a change in pitch mix might predict a breakout season. While other fantasy players may have also picked up on this within their bubble, they may not be as active in prediction markets, where reactions are slower.
In a prediction market focused on season-long strikeouts or Cy Young voting, this information is of course gold. And by identifying these leading indicators in player performance before they manifest in the box score, fans may be able to buy low on event contracts.
The goal is the same in both fantasy leagues and prediction markets - it’s all about finding value and inefficiencies. The more participants, the more difficult this is, but it’s also about how informed those participants are - not all crowds are equally wise.
