{"id":95,"date":"2011-03-11T00:23:56","date_gmt":"2011-03-11T00:23:56","guid":{"rendered":"http:\/\/heavytopspin.com\/?p=95"},"modified":"2011-03-11T00:23:56","modified_gmt":"2011-03-11T00:23:56","slug":"indian-wells-projections","status":"publish","type":"post","link":"https:\/\/www.tennisabstract.com\/blog\/2011\/03\/11\/indian-wells-projections\/","title":{"rendered":"Indian Wells Projections"},"content":{"rendered":"<p><em>If you&#8217;ve found your way here from the Wall Street Journal, welcome!  If you don&#8217;t know what I&#8217;m talking about, go read <a href=\"http:\/\/online.wsj.com\/article\/SB10001424052748704823004576192782049730532.html\">what Carl Bialik has to say<\/a> in today&#8217;s paper, and <a href=\"http:\/\/blogs.wsj.com\/dailyfix\/2011\/03\/10\/tennis-rankings-get-a-tweak\/\">in an online follow-up<\/a>.<\/em><\/p>\n<p>I&#8217;ve developed a fairly sophisticated algorithm to predict the outcome of tennis matches. &nbsp;It seeks to remedy some of the flaws in the present ranking system and do a better job of forecasting which players will perform better at certain times, on certain surfaces, against certain opponents.<\/p>\n<p>In the past, I&#8217;ve written about the <a href=\"http:\/\/summerofjeff.wordpress.com\/2009\/08\/28\/predicting-the-outcome-of-mens-tennis-matches\/\">predictiveness<\/a> of ATP ranking points&#8211;which are pretty darn good, for all their flaws. &nbsp;By just about any standard, however, my system is better. &nbsp;It&#8217;s not perfect&#8211;it&#8217;s far, far from it&#8211;but it does give you a valid second opinion on a player&#8217;s abilities at any given time.<\/p>\n<p><strong>The components<\/strong><\/p>\n<p>My algorithm does several things that traditional ranking points do not. &nbsp;Here are a few of the components:<\/p>\n<ul>\n<li>Points are awarded based on the <strong>quality of opponents<\/strong>, not on the round or tournament. &nbsp;Thus, beating Mikhail Youhzny in the quarterfinals in Moscow is worth the same as the semifinals of Indian Wells. &nbsp;Losing to a low-ranked player counts against you more than losing against Roger Federer.<br \/>&nbsp;<\/li>\n<li>These points, and everything else, are <strong>adjusted for surface<\/strong>. &nbsp;Beating Federer counts for more on hard courts than on clay; beating Juan Carlos Ferrero is the opposite.<br \/>&nbsp;<\/li>\n<li>The algorithm generates a set of overall rankings, and it also generates <strong>two sets of surface-specific rankings<\/strong>, one for clay courts, one for everything else. &nbsp;(There isn&#8217;t enough data on indoor hard courts or grass courts to treat them separately from any other type of fast court.) &nbsp;So for Indian Wells, I&#8217;m using the hard-court rankings. &nbsp;Of course, this drastically impacts the chances of many players.<br \/>&nbsp;<\/li>\n<li>The points awarded for any tournament are also based on <strong>how recent the event was<\/strong>. &nbsp;Beating Andy Murray last week is more relevant than beating him last year. &nbsp;Thus, Milos Raonic does better in my rankings (24th overall) than in the ATP rankings (37th). &nbsp;Sure, it would help if Raonic had played more ATP-level events last year, but my algorithm recognizes that February results count for more than wins from last June.<br \/>&nbsp;<\/li>\n<li>My system considers <strong>matches from the last two years<\/strong>, not just one year, as the ATP rankings do. &nbsp;This and the &#8216;recency&#8217; adjustment remedy what I consider to be the most ridiculous part of the ATP ranking system. &nbsp;A player can fall dozens of spots in the rankings simply because a tournament result &#8220;falls off.&#8221; &nbsp;<br \/>&nbsp;<br \/>&nbsp;So, a match from 51 weeks ago tells us a lot about a player&#8217;s current skill level, but a match from 53 weeks ago does not? &nbsp;In my system, both are counted; a match from 51 weeks ago counts for about 55-60% of the value of a match from last week, while a match from a few weeks earlier counts for a little less.<br \/>&nbsp;<\/li>\n<li><strong>Grand slams count for a bit more<\/strong>, but not a lot more. &nbsp;The main reason for this is that the winner of a five-setter is more likely to the more skilled player than the winner of a three-setter. &nbsp;A couple of bad bounces in a tiebreak can turn a three-setter against you, but it&#8217;s awfully hard to win a five-setter with luck.<br \/>&nbsp;<\/li>\n<li>There is a bit of <strong>home court advantage<\/strong> in tennis, though with the increasing use of the challenge system (which limits officiating bias), it seems to be decreasing. &nbsp;It still exists, and it&#8217;s considered.<br \/>&nbsp;<\/li>\n<li>For whatever reason, it appears that <strong>qualifiers and wild cards do worse<\/strong> in ATP main draw matches than my system would otherwise expect. &nbsp;So they are penalized a small amount.<br \/>&nbsp;<\/li>\n<li>Finally, there is a <strong>head-to-head component<\/strong>. &nbsp;It turns out that the head-to-head component can&#8217;t improve that much on the rankings-based algorithm, but it does have some value. &nbsp;So I do consider the history of each matchup, giving a slight edge to the player who has won more matches in the past. &nbsp;(Depending, of course, on how long ago it was, what surface the matches were on, and so on.)<\/li>\n<\/ul>\n<p>Whew!<\/p>\n<p>Thanks for reading this far.<\/p>\n<p>As I post this, a few matches have already been played. &nbsp;But these numbers were generated this morning, after the full draw was released. &nbsp;It shows the probability that each player reaches each round of the tournament. &nbsp;I&#8217;ll have a little more to say at the bottom.<\/p>\n<p><pre>Player            R64   R32   R16    QF    SF     F     W \n(1)Nadal         100% 94.6% 78.3% 56.3% 40.1% 24.1% 13.0% \n(q)De Voest       54%  3.1%  0.8%  0.1%  0.0%  0.0%  0.0% \nRiba              46%  2.3%  0.5%  0.1%  0.0%  0.0%  0.0% \n(q)Sweeting       42%  8.4%  0.8%  0.1%  0.0%  0.0%  0.0% \nGranollers        58% 17.2%  2.0%  0.5%  0.1%  0.0%  0.0% \n(27)Monaco       100% 74.4% 17.7%  7.5%  2.9%  0.8%  0.2% \n(19)Baghdatis    100% 86.1% 52.9% 21.3% 11.3%  4.7%  1.6% \n(q)Devvarman      43%  5.0%  1.0%  0.1%  0.0%  0.0%  0.0% \nMannarino         57%  8.9%  2.2%  0.2%  0.0%  0.0%  0.0% \n(q)Cipolla        28%  4.0%  0.7%  0.1%  0.0%  0.0%  0.0% \nMalisse           72% 22.1%  6.6%  1.5%  0.4%  0.1%  0.0% \n(15)Tsonga       100% 73.9% 36.7% 12.2%  5.9%  2.0%  0.6% \n\n(11)Almagro      100% 81.5% 51.0% 22.4%  7.8%  2.7%  0.8% \n(q)Russell        45%  8.1%  2.0%  0.3%  0.0%  0.0%  0.0% \nAnderson          55% 10.4%  3.1%  0.6%  0.1%  0.0%  0.0% \nIstomin           41% 13.1%  4.6%  1.0%  0.2%  0.0%  0.0% \nNieminen          59% 24.4%  9.3%  2.8%  0.6%  0.1%  0.0% \n(23)Montanes     100% 62.5% 30.2% 10.8%  3.1%  0.8%  0.2% \n(28)Simon        100% 73.1% 27.2% 14.5%  4.6%  1.4%  0.4% \nSchuettler        40%  8.3%  1.2%  0.3%  0.0%  0.0%  0.0% \nHaase             60% 18.7%  4.0%  1.3%  0.2%  0.0%  0.0% \n(q)Matosevic      29%  2.7%  0.6%  0.1%  0.0%  0.0%  0.0% \nKarlovic          71% 12.7%  5.0%  1.8%  0.4%  0.1%  0.0% \n(6)Ferrer        100% 84.6% 61.9% 44.1% 22.2% 10.8%  4.4% \n\n(4)Soderling     100% 89.0% 71.0% 46.8% 27.3% 15.8%  7.6% \nPhau              37%  3.0%  0.9%  0.2%  0.0%  0.0%  0.0% \nBerrer            63%  8.0%  3.4%  0.9%  0.2%  0.0%  0.0% \n(q)Smyczek        48% 10.5%  1.1%  0.2%  0.0%  0.0%  0.0% \nMarchenko         52% 13.4%  1.5%  0.3%  0.0%  0.0%  0.0% \n(32)Kohlsch.     100% 76.1% 22.0%  7.7%  2.3%  0.6%  0.1% \n(20)Dolgopolov   100% 68.8% 24.4%  8.9%  2.8%  0.9%  0.3% \nHanescu           39% 10.5%  1.8%  0.3%  0.0%  0.0%  0.0% \nSeppi             61% 20.8%  4.9%  1.1%  0.2%  0.0%  0.0% \nStepanek          30% 12.1%  6.7%  2.3%  0.8%  0.2%  0.1% \n(PR)Del Potro     70% 46.4% 35.6% 20.8% 11.1%  6.1%  2.9% \n(14)Ljubicic     100% 41.6% 26.5% 10.6%  4.4%  1.7%  0.5% \n\n(9)Verdasco      100% 86.2% 60.7% 23.2% 10.1%  4.2%  1.3% \n(WC)Berankis      52%  7.4%  2.2%  0.3%  0.0%  0.0%  0.0% \n(q)Bogomolov      48%  6.3%  1.7%  0.2%  0.0%  0.0%  0.0% \nTipsarevic        71% 34.2% 12.2%  3.3%  0.9%  0.2%  0.0% \nKamke             29%  8.2%  1.7%  0.2%  0.0%  0.0%  0.0% \n(21)Querrey      100% 57.6% 21.5%  5.8%  1.5%  0.4%  0.1% \n(25)Robredo      100% 70.8% 16.9%  7.6%  2.2%  0.6%  0.1% \nZverev            62% 20.9%  2.9%  0.8%  0.1%  0.0%  0.0% \n(q)Ebden          38%  8.3%  0.8%  0.2%  0.0%  0.0%  0.0% \n(q)Young          37%  2.2%  0.6%  0.1%  0.0%  0.0%  0.0% \nStarace           63%  6.3%  2.6%  0.7%  0.1%  0.0%  0.0% \n(5)Murray        100% 91.4% 76.3% 57.7% 35.6% 21.5% 11.1% \n\n(8)Roddick       100% 84.9% 63.0% 43.4% 21.7%  8.7%  3.9% \n(WC)Blake         63% 11.3%  4.5%  1.4%  0.3%  0.0%  0.0% \n(q)Guccione       37%  3.8%  1.1%  0.2%  0.0%  0.0%  0.0% \nRam-Hidalgo       34%  5.1%  0.5%  0.1%  0.0%  0.0%  0.0% \nMello             66% 16.4%  2.7%  0.6%  0.1%  0.0%  0.0% \n(30)Isner        100% 78.4% 28.1% 12.6%  3.6%  0.8%  0.2% \n(18)Gasquet      100% 73.4% 34.8% 14.2%  4.6%  1.2%  0.3% \nCuevas            72% 22.8%  6.7%  1.7%  0.3%  0.0%  0.0% \nAndujar           28%  3.9%  0.5%  0.1%  0.0%  0.0%  0.0% \nBenneteau         46% 16.1%  7.1%  2.3%  0.6%  0.1%  0.0% \nLopez             54% 18.9%  9.0%  3.1%  0.8%  0.2%  0.0% \n(10)Melzer       100% 65.0% 41.9% 20.4%  8.2%  2.7%  0.9% \n\n(16)Troicki      100% 82.3% 40.1% 10.5%  4.3%  1.1%  0.3% \n(q)Bopanna        30%  3.1%  0.3%  0.0%  0.0%  0.0%  0.0% \n(WC)Tomic         70% 14.6%  3.1%  0.3%  0.1%  0.0%  0.0% \nGiraldo           55% 14.6%  6.0%  1.0%  0.3%  0.0%  0.0% \nGim-Traver        45% 10.9%  3.8%  0.6%  0.1%  0.0%  0.0% \n(24)Llodra       100% 74.5% 46.7% 15.8%  7.1%  2.2%  0.7% \n(31)Gulbis       100% 56.7% 12.5%  6.0%  2.3%  0.6%  0.1% \nHewitt            75% 37.3%  7.5%  3.7%  1.4%  0.4%  0.1% \nLu                25%  6.0%  0.6%  0.1%  0.0%  0.0%  0.0% \nMayer             66% 12.7%  7.2%  3.8%  1.6%  0.4%  0.1% \nGolubev           34%  3.7%  1.5%  0.5%  0.1%  0.0%  0.0% \n(3)Djokovic      100% 83.6% 70.8% 57.7% 42.5% 24.8% 15.4% \n\n(7)Berdych       100% 84.1% 64.8% 33.2% 12.6%  5.6%  2.3% \nKukushkin         48%  7.6%  2.8%  0.5%  0.1%  0.0%  0.0% \nKubot             52%  8.3%  3.1%  0.5%  0.1%  0.0%  0.0% \nDe Bakker         48% 20.6%  5.3%  1.3%  0.2%  0.0%  0.0% \nBecker            52% 21.9%  5.9%  1.5%  0.2%  0.1%  0.0% \n(26)Bellucci     100% 57.4% 18.1%  4.9%  0.9%  0.2%  0.0% \n(17)Cilic        100% 81.7% 37.2% 20.7%  6.6%  2.6%  1.0% \nGabashvili        49%  9.6%  1.5%  0.3%  0.0%  0.0%  0.0% \nSerra             51%  8.7%  1.2%  0.3%  0.0%  0.0%  0.0% \nDavydenko         84% 49.6% 32.8% 21.0%  8.7%  4.4%  2.1% \nFognini           16%  3.5%  1.1%  0.3%  0.1%  0.0%  0.0% \n(12)Wawrinka     100% 47.0% 26.2% 15.5%  5.2%  2.2%  0.9% \n\n(13)Fish         100% 64.5% 41.9% 13.0%  6.4%  2.7%  1.1% \n(WC)Raonic        81% 33.0% 17.9%  4.3%  1.7%  0.6%  0.2% \nIlhan             19%  2.5%  0.6%  0.0%  0.0%  0.0%  0.0% \n(WC)Harrison      26%  5.7%  1.0%  0.1%  0.0%  0.0%  0.0% \nChardy            74% 32.1% 12.0%  2.4%  0.8%  0.2%  0.1% \n(22)Garcia-Lopez 100% 62.2% 26.6%  5.9%  2.3%  0.8%  0.2% \n(29)Chela        100% 59.2%  7.7%  2.6%  0.7%  0.2%  0.0% \nPetzschner        66% 30.5%  3.4%  1.1%  0.3%  0.0%  0.0% \nBrown             34% 10.3%  0.7%  0.1%  0.0%  0.0%  0.0% \nAndreev           41%  3.0%  1.4%  0.4%  0.1%  0.0%  0.0% \nNishikori         59%  6.4%  3.7%  1.4%  0.4%  0.1%  0.0% \n(2)Federer       100% 90.6% 83.1% 68.7% 52.4% 36.7% 24.5%<\/pre>\n<p>You&#8217;ll probably notice right off that Federer and Djokovic have the best chances of winning.  Indeed, they are the top two players on hard courts, according to my rankings.  Yes, Nadal has won the slams lately, but he has also lost to a few players he shouldn&#8217;t have (Baghdatis, Melzer, Garcia-Lopez) in the recent past.  I personally wouldn&#8217;t put money on Federer over Nadal in the final, but my algorithm disagrees.<\/p>\n<p>A few other players my system likes are Juan Martin Del Potro, Nikolay Davydenko, and Marcos Baghdatis.  It picks out some players for scoring wins over top-ranked players.  It likes Del Potro both because of his strong record in the last few weeks and because the algorithm still considers his torrid summer of 2009, leading up to his U.S. Open win.<\/p>\n<p>One more thing, and then I&#8217;ll shut up for now.  In the first-round matches, there are very few that stray beyond a 70\/30 split.  Even Tomic-Bopanna is 70\/30, and Bopanna barely plays singles.  The narrow divides are partly because no top players are involved in the first round, but it also shows you the depth of the men&#8217;s game &#8212; even someone ranked outside of the top 150, like Flavio Cipolla, has a decent chance of advancing.<\/p>\n<p>Of course, Flavio doesn&#8217;t have quite the same odds against Tsonga, and you can tell from Nadal&#8217;s second round odds that neither Pere Riba nor Rik de Voest stand much of a chance against him.<\/p>\n<p>Enjoy the tennis &#8230; and the numbers.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>If you&#8217;ve found your way here from the Wall Street Journal, welcome! If you don&#8217;t know what I&#8217;m talking about, go read what Carl Bialik has to say in today&#8217;s paper, and in an online follow-up. I&#8217;ve developed a fairly sophisticated algorithm to predict the outcome of tennis matches. &nbsp;It seeks to remedy some of &hellip; <a href=\"https:\/\/www.tennisabstract.com\/blog\/2011\/03\/11\/indian-wells-projections\/\" class=\"more-link\">Continue reading <span class=\"screen-reader-text\">Indian Wells Projections<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[40,56,91,96],"tags":[],"class_list":["post-95","post","type-post","status-publish","format-standard","hentry","category-forecasting","category-indian-wells","category-rankings","category-research"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/posts\/95","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/comments?post=95"}],"version-history":[{"count":0,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/posts\/95\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/media?parent=95"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/categories?post=95"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/tags?post=95"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}