Favourites are justifiably shorter in ATP events<\/strong> with the best of five set format lending itself to the \u2018better\u2019 players, both in a fitness and a variance sense \u2013 however in the WTA, the best of three format still applies and realistically there should be little difference between Grand Slams and general WTA tournaments.<\/p>\n Whilst financial and ranking point incentives are highest in Grand Slams, it\u2019s taken as a given that players will give maximum effort, and whilst this is reasonable to assume, it\u2019s also going to be the case for both higher and lower ranked players.<\/p>\n Our conversation was further exacerbated by Sloane Stephens\u2019 demise from 1.01 against Johanna Larsson.\u00a0 At the point she was a set and break up (2-0) in the second set, she was 1.01 to back and 1.02 to lay, and I highlighted this as a price that was too short with over half a potential match left, given the Americans\u2019 propensity to go walkabout mentally during periods of matches.<\/p>\n Laying at low prices is obviously an attractive proposition, with there being little risk but a potentially huge upside if the trade turns in your favour<\/strong>.\u00a0 Therefore, with this week\u2019s events naturally piquing my interest further, I decided to analyse some WTA historical data to see if there were any trends worth discovering with a view to profiting in the markets.<\/p>\n \u00a0The following table illustrates the records of WTA players priced under 1.25 in Grand Slams from 2013-2014, in both the first set, and the second set when they won the first set.<\/p>\n As we can see from the stats, the percentage that WTA players lose the first set in Grand Slams is significantly higher at this year\u2019s US Open than any other Grand Slam<\/strong> in the sample, so our observation that heavy favourites seem to be struggling more currently was correct.<\/p>\n However, with the preceding Grand Slam \u2013 Wimbledon \u2013 having the lowest percentage, it\u2019s difficult to start making any judgements as to whether this is a trend, although the 2014 statistics for WTA players winning the first set and losing the second set show a significant 2.69% increase from 2013.<\/p>\n Overall, from the 302 matches sampled, 84 were not won in straight sets by the heavy favourite (27.8%), and very interestingly, there was a very small difference between the percentage that heavy favourites lost the first set, and the percentage that they won the first, but lost the second.\u00a0 From that, we can infer that there is very little momentum taken from winning the first set<\/strong>, and that in itself is very useful information.<\/p>\n Given that the tick risk is even lower for laying WTA players when a set up \u2013 typically they will be around 1.05 or so to lay by this point \u2013 being able to know that there is little momentum taken from the first set to the second when the heavy favourite wins the set, gives us an attractive entry point to lay the favourite.<\/p>\n Further filtering of players with a primary focus on those without high projected service hold percentages<\/strong> \u2013 enabling us to have an even better chance of upward price movement \u2013 should allow us excellent chances of profiting in the long run with this approach.<\/p>\n\n\n
\n Lost 1st Set<\/td>\n Won 1st, Lost 2nd Set<\/td>\n<\/tr>\n \n Yes<\/td>\n No<\/td>\n %<\/td>\n Yes<\/td>\n No<\/td>\n %<\/td>\n<\/tr>\n \n 2013 Aus Open<\/td>\n 6<\/td>\n 40<\/td>\n 13.04<\/td>\n 5<\/td>\n 35<\/td>\n 12.50<\/td>\n<\/tr>\n \n 2013 French Open<\/td>\n 5<\/td>\n 33<\/td>\n 13.16<\/td>\n 4<\/td>\n 29<\/td>\n 12.12<\/td>\n<\/tr>\n \n 2013 Wimbledon<\/td>\n 6<\/td>\n 27<\/td>\n 18.18<\/td>\n 5<\/td>\n 22<\/td>\n 18.52<\/td>\n<\/tr>\n \n 2013 US Open<\/td>\n 7<\/td>\n 29<\/td>\n 19.44<\/td>\n 3<\/td>\n 26<\/td>\n 10.34<\/td>\n<\/tr>\n \n 2014 Aus Open<\/td>\n 5<\/td>\n 34<\/td>\n 12.82<\/td>\n 6<\/td>\n 28<\/td>\n 17.65<\/td>\n<\/tr>\n \n 2014 French Open<\/td>\n 6<\/td>\n 35<\/td>\n 14.63<\/td>\n 5<\/td>\n 30<\/td>\n 14.29<\/td>\n<\/tr>\n \n 2014 Wimbledon<\/td>\n 3<\/td>\n 33<\/td>\n 8.33<\/td>\n 5<\/td>\n 28<\/td>\n 15.15<\/td>\n<\/tr>\n \n 2014 US Open<\/td>\n 9<\/td>\n 24<\/td>\n 27.27<\/td>\n 4<\/td>\n 20<\/td>\n 16.67<\/td>\n<\/tr>\n \n 2013<\/td>\n 24<\/td>\n 129<\/td>\n 15.69<\/td>\n 17<\/td>\n 112<\/td>\n 13.18<\/td>\n<\/tr>\n \n 2014<\/td>\n 23<\/td>\n 126<\/td>\n 15.44<\/td>\n 20<\/td>\n 106<\/td>\n 15.87<\/td>\n<\/tr>\n \n Overall<\/td>\n 47<\/td>\n 255<\/td>\n 15.56<\/td>\n 37<\/td>\n 218<\/td>\n 14.51<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n ![\"\"](\"https:\/\/en.africatopsports.com\/wp-content\/uploads\/2014\/05\/Sloane-Stephens-1.jpg\")
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