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Forecasting Sports Popularity: Application of Time Series Analysis

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  • Ryan Miller
  • Harrison Schwarz
  • Ismael S. Talke

Abstract

Popularity trends of the NFL and NBA are fun and interesting for casual fans while also of critical importance for advertisers and businesses with an interest in the sports leagues. Sports leagues have clear and distinct seasons and these have a major impact on when each league is most popular. To measure the popularity of each league, we used search data from Google Trends that gives real-time and historical data on the relative popularity of search words. By using search volume to measure popularity, the times of year, a sport is popular relative to its season can be explained. It is also possible to forecast how sport leagues are trending relative to each other. We compared and discussed three different univariate models both theoretically and empirically: the trend plus seasonality regression, Holt-Winters Multiplicative (HWMM), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models to determine the popularity trends. For each league, the six forecasting performance measures used in this study indicated HWMM gave the most accurate predictions.

Suggested Citation

  • Ryan Miller & Harrison Schwarz & Ismael S. Talke, 2017. "Forecasting Sports Popularity: Application of Time Series Analysis," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 6, July.
    • Handle: RePEc:bjz:ajisjr:1665
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    References listed on IDEAS

    as
    1. Martin Spann & Bernd Skiera, 2009. "Sports forecasting: a comparison of the forecast accuracy of prediction markets, betting odds and tipsters," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(1), pages 55-72.
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