{"id":10802,"date":"2015-04-23T16:26:46","date_gmt":"2015-04-23T15:26:46","guid":{"rendered":"http:\/\/www.betangel.com\/blog_wp\/?p=10802"},"modified":"2015-05-07T12:45:11","modified_gmt":"2015-05-07T11:45:11","slug":"tennis-trading-blanket-strategy-usually-unsuccessful","status":"publish","type":"post","link":"https:\/\/www.betfairtradingblog.com\/blog_wp\/2015\/04\/23\/tennis-trading-blanket-strategy-usually-unsuccessful\/","title":{"rendered":"Tennis Trading – Why a blanket strategy is usually unsuccessful"},"content":{"rendered":"

Frequently I get asked whether a given blanket Tennis trading strategy is a good idea, and almost always my answer is \u2018it depends\u2019.\u00a0 This probably frustrates the person posing the question, but it\u2019s a genuine answer – it really does depend on the given situation, and the two players participating in the match.<\/span><\/p>\n

The simple (and obvious) reason why this is the case is because not all players are created equally.\u00a0 Some players are good at serving, others\u2019 strengths lie on the return.\u00a0 Some players are top level, and others are struggling to stay in the top 100.\u00a0 Some players are good as front-runners, others buckle under pressure when leading.\u00a0 I could go on, but I think you\u2019ve probably taken my point!<\/span><\/p>\n

I thought I\u2019d run through a few WTA stats for this article to illustrate the difference between the best and worst players for a few metrics, and hopefully it will give you an idea of some trading angles which you can move forward with in the future.\u00a0 I update my statistics in these areas on a monthly basis, and these stats were correct for the 12 months preceding 1st April, 2015 (minimum 15 leads obtained in this time period):-<\/span><\/p>\n

Gaining a break lead when the set is on serve:<\/span><\/p>\n

Best:-<\/span><\/p>\n

    \n
  1. Simona Halep, 71.55%<\/span><\/li>\n
  2. serena Williams, 70.99%<\/span><\/li>\n
  3. Vania King, 66.10%<\/span><\/li>\n<\/ol>\n

    No surprise at the first two names on the list, but Vania King will shock a few people!\u00a0 The US player hasn\u2019t played this season due to injury, and her matches frequently feature leading by a break and then losing it.<\/span><\/p>\n

    Worst:-<\/span><\/p>\n

      \n
    1. Julia Glushko, 31.51%<\/span><\/li>\n
    2. Maria Kirilenko, 32.20%<\/span><\/li>\n
    3. Anna-Lena Friedsam, 34.62%<\/span><\/li>\n<\/ol>\n

      Immediately you can see the difference between the best and worst players for gaining a break lead.\u00a0 It\u2019s a mark of how much injury affects players to see ex-top 10 player Maria Kirilenko in this list, with journeywoman Glushko and prospect Friedsam.<\/span><\/p>\n

      Some players are strong when leading, rarely allowing opponents back into the set when they are a break up – below are the best and worst players for the percentage they lost a break lead to go back on serve:-<\/span><\/p>\n

      Best:-<\/span><\/p>\n

        \n
      1. Serena Williams, 14.78%<\/span><\/li>\n
      2. Ana Ivanovic, 21.26%<\/span><\/li>\n
      3. Yanina Wickmayer, 22.22%<\/span><\/li>\n<\/ol>\n

        Again, the best two players probably won\u2019t surprise anyone – Williams is not only the best player on Tour, but she possesses the best serve by some distance, and Ivanovic is noted for being a great front-runner by many.\u00a0 Yanina Wickmayer is in the Vania King envelope – a real shock inclusion.<\/span><\/p>\n

        Worst:-<\/span><\/p>\n

          \n
        1. Katarzyna Piter, 68.42%<\/span><\/li>\n
        2. Alla Kudryavtseva, 62.96%<\/span><\/li>\n
        3. Klara Koukalova, 61.33%<\/span><\/li>\n<\/ol>\n

          Here we have mediocre players, and in the case of Koukalova, a veteran in decline with a very weak serve and negative mentality.\u00a0 What is also worth noting is the difference of over 50% between the best (Williams) and the worst (Piter).<\/span><\/p>\n

          Finally, a list of players who are good and bad a break up – ranked by the percentage they get a break deficit back on serve:-<\/span><\/p>\n

          Best:-<\/span><\/p>\n

            \n
          1. Maria Sharapova, 60.00%<\/span><\/li>\n
          2. Venus Williams, 57.69%<\/span><\/li>\n
          3. Andrea Petkovic, 57.04%<\/span><\/li>\n<\/ol>\n

            All three players are strong players currently, and should be renowned for fighting in matches.\u00a0 Certainly Sharapova is noted for her fightback ability, although this is frequently down to game state – something to discuss another day perhaps\u2026<\/span><\/p>\n

            Worst:-<\/span><\/p>\n

              \n
            1. Vania King, 10.00%<\/span><\/li>\n
            2. Alexandra Dulgheru, 15.29%<\/span><\/li>\n
            3. Polona Hercog, 24.49%<\/span><\/li>\n<\/ol>\n

              Three very mediocre players here and certainly a trio you wouldn\u2019t be keen on backing when a break down in a set!\u00a0 King again features, whilst Dulgheru\u2019s recent improvement might see her figure rise in future months.\u00a0 Hercog is a rare breed (a serve-orientated WTA player), so it\u2019s not a shock to see her being poor for recovering deficits as her return game is pretty weak.<\/span><\/p>\n

              Hopefully this gives you some insight into why blanket strategies are almost always unsuccessful – if it was that easy, everyone would be doing it!<\/span><\/p>\n

              Many Thanks to\u00a0Dan Weston<\/a>\u00a0who was the guest poster for this article, visit Dan’s excellent\u00a0Tennis ratings site for lots of useful Tennis stats<\/a>.<\/em><\/strong><\/p>\n

              \"\"

              Even Ms King was surprised by Dan’s statistics<\/p><\/div>\n

               <\/p>\n

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              Frequently I get asked whether a given blanket Tennis trading strategy is a good idea, and almost always my answer is \u2018it depends\u2019.\u00a0 This probably frustrates the person posing the question, but it\u2019s a genuine answer – it really does depend on the given situation, and the two players participating in the match. The simple […]<\/p>\n

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