'"Human Swarms” of novice sports fans beat professional handicappers when forecasting NFL football games

Abstract: The biological phenomenon of Swarm Intelligence (SI) enables social species to converge on group decisions by interacting in real-time systems. Studied in schools of fish, bee swarms, and bird flocks, biologists have shown for decades that SI can greatly amplify group intelligence in natural systems. Artificial Swarm Intelligence (ASI) is a computer-mediated technique developed in 2015 to enable networked human groups to form real-time systems that can deliberate and converge on decisions, predictions, estimations, and prioritizations. A unique combination of real-time HCI methods and AI algorithms, ASI technology (also called “Human Swarming” or “Swarm AI”) has been shown in many studies to amplify group intelligence in forecasting tasks, often enabling small groups of non-professionals to exceed expert level performance. In the current study, small groups of approximately 24 amateur sports fans used an online platform called Swarm to collaboratively make weekly predictions (against the spread) of every football game in four consecutive NFL seasons (2019 - 2022) for a total of 1027 forecasted games. Approximately 5 games per week (as forecast by the human swarm) were identified as “predictable” using statistical heuristics. Performance was compared against the Vegas betting markets and measured against accepted performance benchmarks for professional handicappers. It is well known that professional bettors rarely achieve more than 55% accuracy against the Vegas spread and that top experts in the world rarely exceed 58% accuracy. In this study the amateur sports fans achieved 62.5% accuracy against the spread when connected as real-time “swarms.” A statistical analysis of this result (across 4 NFL seasons) found that swarms outperformed the 55% accuracy benchmark for human experts with significance (p=0.002). These results confirmed for the first time that groups of amateurs, when connected in real-time using ASI, can consistently generate forecasts that exceeded expert level performance with a high degree of statistical certainty.Keywords: Swarm Intelligence, Artificial Swarm Intelligence, Collective Intelligence, Wisdom of Crowds, Hyperswarms,

Keywords: Human Computer Interaction, Collective Intelligence, Social Computing, Artificial Intelligence, Swarm Intelligence, ASI, Human Swarming, AI

DOI: 10.54941/ahfe1003287

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