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

Jordan Truman Paul Noel (), Vinicius Prado da Fonseca and Amilcar Soares
Additional contact information
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

Data, 2024, vol. 9, issue 2, 1-17

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.

Keywords: feature engineering; machine learning; sports; momentum; NHL; NBA (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2306-5729/9/2/29/pdf (application/pdf)
https://www.mdpi.com/2306-5729/9/2/29/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:9:y:2024:i:2:p:29-:d:1331323

Access Statistics for this article

Data is currently edited by Ms. Cecilia Yang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().