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A Comprehensive Data Pipeline for Comparing the Effects of Momentum on Sports Leagues

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  • Jordan Truman Paul Noel

    (Department of Computer Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada)

  • Vinicius Prado da Fonseca

    (Department of Computer Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada)

  • Amilcar Soares

    (Department of Computer Science and Media Technology, Linnaeus University, 352 52 Växjö, Sweden)

Abstract

Momentum has been a consistently studied aspect of sports science for decades. Among the established literature, there has, at times, been a discrepancy between conclusions. However, if momentum is indeed an actual phenomenon, it would affect all aspects of sports, from player evaluation to pre-game prediction and betting. Therefore, using momentum-based features that quantify a team’s linear trend of play, we develop a data pipeline that uses a small sample of recent games to assess teams’ quality of play and measure the predictive power of momentum-based features versus the predictive power of more traditional frequency-based features across several leagues using several machine learning techniques. More precisely, we use our pipeline to determine the differences in the predictive power of momentum-based features and standard statistical features for the National Hockey League (NHL), National Basketball Association (NBA), and five major first-division European football leagues. Our findings show little evidence that momentum has superior predictive power in the NBA. Still, we found some instances of the effects of momentum on the NHL that produced better pre-game predictors, whereas we view a similar trend in European football/soccer. Our results indicate that momentum-based features combined with frequency-based features could improve pre-game prediction models and that, in the future, momentum should be studied more from a feature/performance indicator point-of-view and less from the view of the dependence of sequential outcomes, thus attempting to distance momentum from the binary view of winning and losing.

Suggested Citation

  • Jordan Truman Paul Noel & Vinicius Prado da Fonseca & Amilcar Soares, 2024. "A Comprehensive Data Pipeline for Comparing the Effects of Momentum on Sports Leagues," Data, MDPI, vol. 9(2), pages 1-17, February.
  • Handle: RePEc:gam:jdataj:v:9:y:2024:i:2:p:29-:d:1331323
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    References listed on IDEAS

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    1. Brett Green & Jeffrey Zwiebel, 2018. "The Hot-Hand Fallacy: Cognitive Mistakes or Equilibrium Adjustments? Evidence from Major League Baseball," Management Science, INFORMS, vol. 64(11), pages 5315-5348, November.
    2. Miller, Joshua Benjamin & Sanjurjo, Adam, 2018. "Surprised by the Hot Hand Fallacy? A Truth in the Law of Small Numbers," OSF Preprints sv9x2, Center for Open Science.
    3. Joshua B. Miller & Adam Sanjurjo, 2018. "Surprised by the Hot Hand Fallacy? A Truth in the Law of Small Numbers," Econometrica, Econometric Society, vol. 86(6), pages 2019-2047, November.
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