Edited by Wolfgang Maennig and Andrew Zimbalist
Ruud H. Koning and Ian G. McHale 1 INTRODUCTION Without any doubt, the Soccer World Cup is the most important single sports tournament that is organized on a regular basis. Whole nations come to a standstill during games of the tournament (for example, more than eight million Dutch people watched the final of the 2010 World Cup of Netherlands against Spain, 56 per cent of the total population of the Netherlands). In the run-up to each Continental and World Cup tournament many people take an active interest in discussing the most likely winner. For fans, pundits and the media this question is more of incidental interest while for bookmakers and bettors answering the question has financial implications. As such, in recent years statisticians and econometricians have been in high demand in the gambling industry as odds setters and punters attempt to gain an edge over the competition. Forecasting in sport has attracted growing attention in academia too, with an increasing number of papers appearing in the statistics and economics literature. Of course, academics use their forecasting models not for making money (the ones they publish anyway!), but rather to address other questions such as: is the betting market efficient? (see, for example, Goddard and Asimakopoulos, 2004), the effect of a red card in football (Ridder et al., 1994), or what effect does the tournament structure have on the probability of winning (see, for example, Scarf and Yusof, 2010). Forecasting winners in academia is not just confined to football. Tennis (McHale...
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