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Forecasting Tour de France TV audiences: A multi-country analysis

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  • Van Reeth, Daam

Abstract

The Tour de France is the world’s biggest cycling event. The race attracts up to 25 million TV viewers per stage worldwide. In this article, we forecast TV audiences for individual stages of the Tour de France for five European countries where cycling is popular: Belgium, Denmark, France, The Netherlands and Spain. The predictions follow from on a multivariate ordinary least squares regression model that explains historical viewing habits for the Tour de France as a function of attributes of the individual stages, and contextual information such as TV channel and day. Although the accuracy of the forecasts changes from year to year and can be very different between TV markets, in most cases our predictions clearly outperform forecasts based on naive models. Our findings illustrate that a large part of the variation in TV viewership is determined by how the race route is designed by the race organizer, independent of actual race developments.

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  • Van Reeth, Daam, 2019. "Forecasting Tour de France TV audiences: A multi-country analysis," International Journal of Forecasting, Elsevier, vol. 35(2), pages 810-821.
    • Handle: RePEc:eee:intfor:v:35:y:2019:i:2:p:810-821
    DOI: 10.1016/j.ijforecast.2018.06.003
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    Cited by:

    1. Alexander Genoe & Ronald Rousseau & Sandra Rousseau, 2021. "Applying Google Trends’ Search Popularity Indicator to Professional Cycling," Journal of Sports Economics, , vol. 22(4), pages 459-485, May.
    2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    3. Buraimo, Babatunde & Forrest, David & McHale, Ian G. & Tena, J.D., 2022. "Armchair fans: Modelling audience size for televised football matches," European Journal of Operational Research, Elsevier, vol. 298(2), pages 644-655.
    4. Uribe, Rodrigo & Buzeta, Cristian & Manzur, Enrique & Alvarez, Isabel, 2021. "Determinants of football TV audience: The straight and ancillary effects of the presence of the local team on the FIFA world cup," Journal of Business Research, Elsevier, vol. 127(C), pages 454-463.
    5. Bram Janssens & Matthias Bogaert & Mathijs Maton, 2023. "Predicting the next Pogačar: a data analytical approach to detect young professional cycling talents," Annals of Operations Research, Springer, vol. 325(1), pages 557-588, June.
    6. Giwoong Bae & Hye-jin Kim, 2022. "The impact of online video highlights on TV audience ratings," Electronic Commerce Research, Springer, vol. 22(2), pages 405-425, June.

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