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What detrended fluctuation analysis can tell us about NBA results

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  • Ferreira, Paulo

Abstract

Basketball is one of the favourite sports in the United States and NBA the most popular basketball league in the world, followed not only by Americans but by fans everywhere. At present, people are increasingly interested in betting on sports in general and on basketball in particular. So analysis of trends in the results could be important for gamblers. In this paper, detrended fluctuation analysis is applied to 28 NBA teams, in order to understand if their results have (or not) any kind of memory. Our results show that all the teams are persistent in their results. Nevertheless, some teams have higher persistence than others, which could be important for gamblers in deciding how to bet.

Suggested Citation

  • Ferreira, Paulo, 2018. "What detrended fluctuation analysis can tell us about NBA results," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 92-96.
    • Handle: RePEc:eee:phsmap:v:500:y:2018:i:c:p:92-96
    DOI: 10.1016/j.physa.2018.02.050
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    Cited by:

    1. Song, Kai & Gao, Yiran & Shi, Jian, 2020. "Making real-time predictions for NBA basketball games by combining the historical data and bookmaker’s betting line," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    2. Kononovicius, A., 2019. "Illusion of persistence in NBA 1995–2018 regular season data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 250-256.
    3. Zhou, Hanchu & Chang, Fangrong, 2022. "The long-memory temporal dependence of traffic crash fatality for different types of road users," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    4. Ferreira, Paulo & Quintino, Derick & Wundervald, Bruna & Dionísio, Andreia & Aslam, Faheem & Cantarinha, Ana, 2021. "Is Brazilian music getting more predictable? A statistical physics approach for different music genres," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).

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