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Suspicious Blood and Performance in Professional Cycling by Tom

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Suspicious Blood and Performance in Professional Cycling by Tom Suspicious Blood and Performance in Professional Cycling By Tom Coupe* and Olivier Gergaud** * Kyiv Economics Institute, Kyiv School of Economics ** Bordeaux Management School and University of Reims We thank Wladimir Andreff, Nicolas Eber, Victor Ginsburgh, Lionel Page and a referee for helpful comments. Tom Coupe: Kyiv School of Economics. Yakira 13, 04119 Kyiv, Ukraine Phone: 38 044 492 8012, Fax: 38 044 492 8011 Email: [email protected] Olivier Gergaud: Bordeaux Management School and University of Reims, 680 Cours de la Libération, 33405 Talence Cedex, France. Phone: 33 032 608 2235 Email: [email protected] 1 Abstract In this note, we analyze whether the International Cycling Union’s ‘index of suspicion’, which reflects the extent to which a rider is suspected of using doping, correlates with performance during the 2010 Tour de France and the one year period before and after the 2010 Tour de France. Though our point estimates suggest a medium sized performance improving effect of being suspected of doping, the index of suspicion can only explain a very small part of the variation in performance. This could be due to the fact that doping has little effect on the outcome of the cycling races. Keywords : Doping, Cycling, Tour de France JEL codes : L83 (Industry Studies - Sports) 2 Introduction The use of past blood tests, the so-called biological passports, to detect suspicious changes in blood values is becoming a more and more widespread weapon in the fight against doping in sports. For example, the 2012 London Olympics will be the first Olympics where such tests will be used for certain competitions (McGrath 2011 1). Despite this increasing popularity, the usefulness of such tests to detect doping is still controversial, from an ethical and legal point of view but also from the point of view of its capacity to detect actual doping. One of the reasons that it is hard to check the usefulness of these tests is that it is very hard to get the biological passport data for a large sample of athletes. In this paper, we use, what is as far as we know, the first such large dataset with publicly available information on the biological passports of almost 200 top level cyclists. On May 13, 2011, the French Sport Newspaper L’Equipe published an ‘Index of Suspicion’, a list containing the names of about 200 professional cyclists and the degree to which their blood values were ‘suspicious’, that is, showed signs of the possible use of doping, at the eve of the 2010 Tour de France, arguably the most important international cycling race. This list, made by a doping (detection) specialist, on demand of the International Cycling Union, is based on an evaluation of blood tests of the cyclists, tests submitted between 2008 and the eve of the Tour 2010. On the basis of this evaluation, the riders were categorized into eleven categories, ranging from 0 (not suspect) to 10 (very suspect). In this note, we first analyze whether this ‘index of suspicion’ can help to predict the performance during the 2010 Tour de France and the year before and after the start of the 2010 Tour de France. If suspicious blood values are a good indicator of the use of performance enhancing drugs, and if, in addition, performance enhancing drugs actually do enhance performance significantly, there should be a positive correlation between the ‘index of suspicion’ and performance. The absence of such correlation would suggest either that doping is not effective or alternatively that the ‘index of suspicion’ is not a good indicator of doping. We then try to distinguish between these two explanations by running an instrumental variables regression, where we instrument the ‘index of suspicion’ by other doping-related variables such 1 http://www.bbc.co.uk/news/science-environment-14307262 3 as belonging to a team where managers have been suspected of doping, or such as having been suspected of doping based on other indicators than the biological passport. The reason to use instruments is the idea that if the ‘index of suspicion’ is an imperfect indicator of the use of effective performance enhancing drugs, then the coefficient of the ‘index of suspicion’ in a performance regression will be biased towards zero due to the so-called ‘errors in variables” problem. To get an unbiased coefficient of the effectiveness of doping, we can instrument one (imperfect) indicator of doping by other (equally imperfect) indicators of doping (see Wooldridge, pp. 526-527). This paper is not the first paper that investigates doping in sports (see for example Berentsen (2001), Haugen (2004) or Eber (2008) for a theoretical analysis of doping behavior or Dilger et al. (2007) for a selective survey of doping cases in cycling and other sports) or the determinants of success in cycling (for example, Torgler, 2007). This is the first paper however that studies, using a large sample of athletes, the effectiveness of biological passports as a weapon in the fight against doping 2. Analysis a. Does the ‘index of suspicion’ predict performance? 1. Regressing Performance on the ‘Index of Suspicion’ Our main variable of interest is the categorical variable which reflects the score on the ‘Index of Suspicion’. Categorical variables are typically included as explanatory variables in regression analysis by creating a dummy variable for each specific category. However, given that there are a lot of categories (11 categories, ranging is from 0 to 10) and some of these categories have only few observations in them, one might want to consider this index as almost continuous and hence, include the index as just one variable. Below we also present a specification where, instead of the index, we included 2 dummies each grouping a number of categories – an omitted first dummy for category 0 and 1 reflecting little or no suspicion of doping, a second dummy for categories 2, 3, and 4 reflecting a medium level of suspicion, the third dummy being for categories 5 and up, reflecting high levels of suspicion. In this way, we allow for a non-linear effect of the doping 2 Zorzoli (2011) presents a graph showing that after the introduction of the passports the percentage of suspicious blood samples has decreased substantially. 4 categories on performance. The first two dummies represent each about 40 % of the riders in the sample, while the last category represents about 20 percent. A third specification compares the performance of those with little or no suspicion of doping (categories 0 and 1) to the other cyclists. Table 1 gives the results of regressions where we run a specific dimension of performance on a constant and our doping variable(s). We use two performance measures based on the performance during the Tour de France: the logarithm of the total time needed to finish the Tour de France and the logarithm of the final ranking in the yellow jersey competition 3. As additional measures of performance, we use the log of the number of points a rider collected in the widely used Cycling Quotient (CQ) ranking. The CQ ranking measures the riders’ performance throughout the year 4 and we use both the points collected in the year leading up to the 2010 Tour de France (CQ2010) and year following the start of the 2010 Tour de France (CQ2011). These different performance measures are used to capture different time periods: the results of the Tour de France have the advantage that they capture the performance immediately after the index was composed but have the disadvantage that they measures the performance in only one specific race. The CQ2011 gives the performance over a longer stretch of time and is based on the riders’ performance in many different races but has the disadvantage that, if athletes’ doping status changes over time, the index of suspicion measured at the eve of the 2010 Tour might become less and less relevant as the year progresses. Finally, CQ2010 has the advantage it measures performance during the period the blood measurements were taken but has the disadvantage that this is before the actual index was composed. Hence, one cannot exclude that the (interpretation of the) blood samples were (was) affected by the riders’ performance. [Insert table 1 here] We find a significant and sizeable effect on the time needed to finish the race: depending on the specific specification, compared to an unsuspected rider (category 0), a rider in the highest category of doping (category 10) needs about 0.4 to 1 percent less time to finish the 2010 Tour 3 Using the time and ranking rather than the logarithm of time and ranking does not affect our conclusions. 4 The CQ ranking gives points to riders based on their performance in a large number of cycling competitions. The amount of points a rider gets depends both on the difficulty of the specific cycling competition and the place a rider obtained in that competition. We use the ranking of June 27, 2010 and June 26, 2011 which captures the performance of the riders in the 12 month period up to these respective dates. More information about the CQ ranking can be found at http://www.cqranking.com/men/asp/info/whats.asp 5 de France. Given it took the riders somewhere between 5500 and 6000 minutes to finish de Tour de France, this means an estimated time gain of roughly between half an hour and one hour. Similarly, in terms of ranking a highly suspected rider would be ranked 30 to 50 percentage points better. That is, if competing with unsuspected riders, he would rank first rather than second or fifth to seventh rather than tenth.
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