Erasmus university rotterdam
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- ATP matches (Men)
- Number of games Mean return category Standard deviation
- Mean return category Standard deviation T-test
- Total 24.152
Results genderTable 8: Results of all ATP and WTA tournaments separately in the last 5 years (2009-2013)
Graph 2: Development of the mean return of ATP and WTA tournaments separately in the last 5 years (2009-2013) Graph 2 shows the line of the mean return for ATP tournaments (men) and WTA tournaments (women) separately. The higher the probability category, the closer the line gets to the x-as, so it is clear that there is a favorite longshot bias in both genders. The line of the WTA (women) gets faster to the x-as than the ATP (men). For the WTA tournaments from category 4 the mean return is higher than -0,2 and fluctuate between -0,2 and 0,00 in categories 4 to 20. The line of the mean return in ATP tournaments shows a more stable upward curve. The higher the category, the higher the return. Only at category 12 the mean return bounces a bit back. After all, looking at category 1 (longshot), we see that the WTA tournaments have a lower return than the ATP tournaments. Looking at category 20 (favorites) we see that in WTA tournaments you have no profit or loss (return is €0) and in ATP tournament you have a loss of €0,018. Looking at the last column we see a lot of t-values below -2 of above 2 which means a lot of significant results. Looking at the ATP tennis, in categories 1-7 there are 6 significant t-values and in category 15-20 there are 2 significant t-values. In WTA tennis, in categories 1-7 there are 2 significant t-values and in category 15-20 there are 3 significant t-values. The significant results in the underdogs categories are an indication for the existence of a favorite longshot bias. In WTA tennis there are only 2 significant t-values in underdog categories. Based on the lack of significant results, we can doubt about the existence of a favorite longshot bias. In the underdog categories of ATP tournaments there are 6 out of 7 significant t-values, but also in the favorite categories there are a lot of significant t-values. Based on this you cannot really speak of the existence of a bias.
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