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.

T his paper presents an application of optimization modeling to the winning of a popular cycling game. The application includes real-life data of contempory cyclists. It also has the potential to motivate students with a competitive but fun "race" for a solution. Because the developed optimization model contains features of knapsack problems, multiperiod inventory problems, and logical constraint modeling, it is perfectly suitable for a concluding case study in an undergraduate operations research/management science course. The application was originally developed for an MBA operations research course focusing on spreadsheet modeling skills, but it can also be used in courses that focus on algebraic modeling of optimization problems.

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This article explores Russian TV viewership for football games at seven international football tournaments from 2006 to 2018. The research goal is 2-fold. First, we identify the determinants of Russian viewership for football mega-events. We focus on patriotism effects, and we check for any hosting impact of the 2018 FIFA World Cup in Russia. Second, we analyze how these determinants differ in explaining two distinct TV metrics: Audience Size and Reach. Results indicate that the metrics are partially driven by different determinants which can be linked to two types of viewers: seasoned football fans and occasional watchers of football games.

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