{"id":10825,"date":"2015-04-30T14:07:58","date_gmt":"2015-04-30T13:07:58","guid":{"rendered":"http:\/\/www.betangel.com\/blog_wp\/?p=10825"},"modified":"2015-05-07T12:45:01","modified_gmt":"2015-05-07T11:45:01","slug":"best-worst-players-winning-positions-atp","status":"publish","type":"post","link":"https:\/\/www.betfairtradingblog.com\/blog_wp\/2015\/04\/30\/best-worst-players-winning-positions-atp\/","title":{"rendered":"The best and worst players from winning positions in the ATP"},"content":{"rendered":"

Further to last week\u2019s article which looked at the application of a blanket strategy<\/span><\/a>, and the usage of WTA stats to illustrate why this is a bad idea, I thought I\u2019d move onto some more detailed applications of statistics in the ATP, which is something I\u2019ve been looking at in great detail lately.<\/span><\/p>\n

\u00a0An objective way to categorise front runners has been my major focus, with a particular plan of trying to work out which players are lucrative to lay at low prices<\/strong>.\u00a0\u00a0In the vast majority of cases, a player will be strong odds-on, and in some cases very low priced indeed, when they are either a set and break up in a match, or a break up in the final set.\u00a0 This allows us to lay the player with a low risk if we lose the trade, but a high upside if we win the trade, or indeed the player we have laid ends up losing the set or the match.<\/span><\/p>\n

\u00a0I settled on three metrics to use to assess front-runners.<\/span><\/p>\n

    \n
  1. Taking the\u00a0first break when player wins the 1st set.<\/span><\/li>\n
  2. Retention of the first set and\u00a0break lead in the 2nd set.<\/span><\/li>\n
  3. Retention of the first\u00a0break lead in\u00a0the 3rd set.<\/span><\/li>\n<\/ol>\n

    \u00a0Researching players’ statistics in these areas allowed me to create a ‘front-runner’ score for the ATP top 100, and a few other notable players –\u00a0effectively a combined percentage that these players succeeded in retaining all three leading situations. \u00a0Undertaking this research also allowed me to see individual player dynamics that exist – for example some players perform much better in gaining the first break of the 2nd set, but much worse at retaining a set and break lead, or\u00a0vice-versa.<\/span><\/p>\n

    \u00a0I ploughed through the point by point history of results since July 2014 (quite some job!) and found a number of amazing statistics (Min 15 leading situations – data correct 1st July 2014 to 24th April 2015):-<\/span><\/p>\n

    \u00a0Most leading situations:-<\/strong><\/span><\/p>\n