Email: Tennistrades@gmail.com Skype: @TennisRatings SUBSCRIBE TO THE TENNISRATINGS YOUTUBE CHANNEL FOR THE LATEST TENNIS TRADING & TENNIS BETTING VIDEOS! I decided to find out! What I found, and you can see full findings on the table below, was that actually between 1st January, 2011 and 18th September, 2013 (today), backing every top seed in all of their matches in 250 events would have pretty much broken even. The ROI based on Pinnacle closing starting prices across the 338 matches was -1.22%, which is effectively break even when you consider their profit margins. However, if we look at each year individually, we can see that in 2011 this approach would have made a profit of £579 (based on level £100 stakes), in 2012 it would have lost £140 and this year would have lost an incredible £850! So is a new trend developing or is it just variance? Only time will tell but if this continues in 2014 there may be the basis for a concrete strategy. I also split the results up into some other categories, by surface and also I had a look at player genres. Here were the surface stats:- (based on level £100 stakes) Indoor Hard +£447 Hard -£182 Grass -£253 Clay -£423 Whilst sample size is less strong having split the 338 matches across 4 surfaces, it's interesting to see Indoor Hard is notably the best surface for the top seeds, showing £870 more profit than clay. I have no theory as to why clay was so bad for the top seeds, although the likes of Nadal and Ferrer in low calibre events are often priced sub 1.10 and often even sub 1.05 so a couple of shock defeats may have skewed this data. Grass is more explainable as it's often used as a warm-up event for Wimbledon and top players may 'tank' after they get several matches under their belt. I took a look at player genres, with the elite top 4 (Djokovic, Nadal, Murray & Federer) showing a £209 loss if you had backed them in the 250 events they were top seeds in across the 3 years. Considering there were only a total of 13 tournaments where one of these 4 was top seeds in the sample, that's a pretty bad loss. When I added 4 other players to that sample who were staple top 10 players over the sample (Ferrer, Berdych, Tsonga & Del Potro), the loss was slightly reduced to £73. The table with the data can be seen below. Any comments/feedback gratefully received as usual!
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