Extracting collective probabilistic forecasts from Web games

David M. Pennock, Steve Lawrence, Finn Årup Nielsen, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

42 Scopus citations

Abstract

Game sites on the World Wide Web draw people from around the world with specialized interests, skills, and knowledge. Data from the games often reflects the players' expertise and will to win. We extract probabilistic forecasts from data obtained from three online games: the Hollywood Stock Exchange (HSX), the Foresight Exchange (FX), and the Formula One Pick Six (F1P6) competition. We find that alt three yield accurate forecasts of uncertain future events. In particular, prices of so-called "movie stocks" on HSX are good indicators of actual box office returns. Prices of HSX securities in Oscar, Emmy, and Grammy awards correlate well with observed frequencies of winning. FX prices are reliable indicators of future developments in science and technology. Collective predictions from players in the F1 competition serve as good forecasts of true race outcomes. In some cases, forecasts induced from game data are more reliable than expert opinions. We argue that web games naturally attract well-informed and well-motivated players, and thus offer a valuable and oft-overlooked source of high-quality data with significant predictive value.

Original languageEnglish (US)
Title of host publicationProceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
EditorsF. Provost, R. Srikant, M. Schkolnick, D. Lee
Pages174-183
Number of pages10
StatePublished - Dec 1 2001
EventProceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001) - San Francisco, CA, United States
Duration: Aug 26 2001Aug 29 2001

Publication series

NameProceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

OtherProceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001)
CountryUnited States
CitySan Francisco, CA
Period8/26/018/29/01

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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    Pennock, D. M., Lawrence, S., Nielsen, F. Å., & Giles, C. L. (2001). Extracting collective probabilistic forecasts from Web games. In F. Provost, R. Srikant, M. Schkolnick, & D. Lee (Eds.), Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 174-183). (Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).