“Consistency” is one of the many terms that commentators frequently use but rarely define. \u00a0It’s often misused, too: we say we want a player to be more consistent, when we really just want him to stop playing badly.<\/p>\n
To me, consistency for a tennis player is similar to the notion of “playing up to his ranking.” \u00a0In other words, if a player is consistent, he usually beats players ranked lower, and he usually loses to players ranked higher. \u00a0No player is perfect in this regard, but clearly, some are much more reliable than others.<\/p>\n
A recent poster boy for inconsistency<\/em>\u00a0is Ernests Gulbis. \u00a0At Roland Garros, he lost to Blaz Kavcic, ranked 82nd in the world. \u00a0That was on clay, a surface on which Gulbis had posted some excellent results the previous year. \u00a0Two months later, in Los Angeles, he beat<\/em>\u00a0Juan Martin del Potro, someone he shouldn’t have even challenged on a hard court.<\/p>\n Quantifying consistency<\/strong><\/p>\n With my player rankings and match prediction system, I’m able to assign a win probability to each player for every match. \u00a0For instance, when Ivan Dodig beat Nadal last week, I had given him a 14.4% chance of doing so. \u00a0As you might imagine, that’s a major upset–as I wrote the next day<\/a>, it was the 10th-biggest upset of the season.<\/p>\n In these terms, an ideally consistent player will never be on either end of an upset. \u00a0If he is the favorite, he wins; if he is the underdog, he loses. \u00a0In practice, no tour-level player accomplishes this, though over the last two years, Florent Serra and Eduardo Schwank have come very close.<\/p>\n I’ve come up with a metric to measure consistency. \u00a0This is how it works:<\/p>\n Whew! \u00a0In something more like layman’s terms, we’re taking all the upsets a player was involved in, coming up with a number to represent how big<\/em>\u00a0(or surprising) the upset was, then averaging the results.<\/p>\n Using this method, we give big upsets considerably more weight than mini-upsets. \u00a0If a player had a 45% chance of winning a match and ends up winning, it barely counts as an upset–and this system treats it accordingly. \u00a0By dividing by the total number of matches, we give consistency credit to players who win the matches they’re “supposed to” win, and lose those they are supposed to lose.<\/p>\n Most importantly, the numbers this algorithm spits out are completely believable, matching up well with the conventional wisdom of which players are consistent and inconsistent.<\/p>\n The consistency of the top ten<\/strong><\/p>\n The most consistent player on the tour, since the beginning of 2010, has been … Florent Serra<\/strong>. \u00a0Amazingly, Igor Kunitsyn comes in second. \u00a0But I doubt many of you care much about the consistency of guys like that.<\/p>\n Let’s start with the current top 10, ranked from most to least consistent:<\/p>\n The relevant column is the rightmost, “UpsetScore,” which is the result of the algorithm described above. \u00a0Ferrer has been part of more upsets than any of the top three (“Up%”), but his upsets are more minor. \u00a0Except for losses to Ivo Karlovic and Jarkko Nieminen early in the year on hard courts, Ferrer has not lost a match he had a 60% or better chance of winning.<\/p>\n The two ends of this list certainly line up with what I would have expected: Ferrer and Nadal are rock-solid (last week’s loss to Dodig notwithstanding), while Soderling and Murray both can be picked off by anybody, and<\/em>\u00a0frequently threaten higher-ranked players.<\/p>\n Right now, you may be tempted to put Djokovic higher on the list–after all, he’s ranked #1 and he’s beating everybody. \u00a0However, in the slightly longer term of 20 months, his movement around the top three has included some unexpected results, like losing to Ljubicic at Indian Wells last year, and victories over Federer and Nadal before his ranking suggested he would do so.<\/p>\n Tour wild cards<\/strong><\/p>\n Outside of the top 10, there are a handful of players who are almost impossible to predict. \u00a0Some names that come to mind are Marcos Baghdatis, Ernests Gulbis, and Nikolay Davydenko, men who can take out one of the top three on a good day (well, maybe not Gulbis), but can lose to a qualifier on the next.<\/p>\n The “upset score” number tells the story for Davydenko. \u00a0The man who beat Nadal at the beginning of the year and threatened Djokovic last week recently suffered defeat at the hands of Cedrik-Marcel Stebe (twice!) and Antonio Veic.<\/p>\n While no one is in Davydenko’s league, names like Cilic, Baghdatis, Murray, and Verdasco seem appropriate. \u00a0Verdasco, along with Melzer and Milos Raonic suggest a flaw in this approach: the algorithm reads very fast improvement or decline as inconsistency, which isn’t quite right. \u00a0Yes, Raonic has shocked the tennis world repeatedly this season, but he hasn’t mixed in too many disastrous losses alongside the surprise upsets. \u00a0I tinkered with ways to include that in the model, but nothing worked very well.<\/p>\n A couple more interesting notes from the “most inconsistent” players are found in the upset percentage column. \u00a0Guys like Davydenko, Baghdatis, Mayer, Goloubev, and Chardy are involved in upsets nearly half the time<\/em>. \u00a0Chardy is highest in that category. \u00a0In fact, if I expanded the study to challenger events, he might rocket to the top of this list, as he plays quite a few, and often manages to lose against players outside the top 100.<\/p>\n The consistent ones<\/strong><\/p>\n The flip side is considerably less star-studded. \u00a0In the 20 most-consistent players of the last 19-20 months, Ferrer is the only top-10 guy present, though #11 Nicolas Almagro is there as well.<\/p>\n Here’s my seat-of-the-pants theory. \u00a0In this sense, “consistent” isn’t good. \u00a0Yes, “consistent” sounds good, especially when “inconsistent” means Davydenko losing to Antonio Veic or Mayer falling to Federico del Bonis. \u00a0But inconsistent means Davy beating Federer and Mayer beating Soderling. \u00a0So, the players who show up on as “most consistent” are in fact consistent, but they are also mediocre. \u00a0Their consistency (perhaps a mental advantage) has helped them move up from the top 200 to the top 50 or 100, but that’s all they can do.<\/p>\n Ferrer and Almagro are good examples of this, actually. \u00a0Neither has the weaponry that makes commentators say, “This guy could be number one!” \u00a0But they’ve earned their rankings by regularly reaching the quarters and semis of tournaments, not suffering the boneheaded losses that afflict the likes of Cilic and Baghdatis.<\/p>\n All that said, here’s the list:<\/p>\n These lists hardly represent the final word on who is or is not consistent–for one thing, I haven’t said anything about consistency within matches, which may be a completely separate issue. \u00a0But this approach does, I think, provide some insight into who is more likely to be part of an upset, and suggests that consistency might not be such a good thing after all.<\/p>\n","protected":false},"excerpt":{"rendered":" “Consistency” is one of the many terms that commentators frequently use but rarely define. \u00a0It’s often misused, too: we say we want a player to be more consistent, when we really just want him to stop playing badly. To me, consistency for a tennis player is similar to the notion of “playing up to his … Continue reading The Most (and Least) Consistent Players on the ATP Tour<\/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":[96],"tags":[],"class_list":["post-461","post","type-post","status-publish","format-standard","hentry","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\/461","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=461"}],"version-history":[{"count":0,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/posts\/461\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/media?parent=461"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/categories?post=461"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/tags?post=461"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
Player Upsets Matches Up% UpsetScore \nDavid Ferrer 25 119 21.0% 55 \nRafael Nadal 16 131 12.2% 68 \nNovak Djokovic 18 119 15.1% 69 \nRoger Federer 21 123 17.1% 69 \nJo-Wilfried Tsonga 20 80 25.0% 75 \nMardy Fish 19 77 24.7% 82 \nTomas Berdych 32 113 28.3% 106 \nGael Monfils 24 89 27.0% 107 \nRobin Soderling 23 115 20.0% 130 \nAndy Murray 24 97 24.7% 151<\/pre>\n
Player Upsets Matches Up% UpsetScore \nNikolay Davydenko 32 73 44% 273 \nMarin Cilic 27 91 30% 180 \nMarcos Baghdatis 38 89 43% 177 \nOlivier Rochus 20 52 38% 164 \nMilos Raonic 16 41 39% 164 \nJuan Martin del Potro 11 48 23% 154 \nAndy Murray 24 97 25% 151 \nJurgen Melzer 28 96 29% 150 \nFernando Verdasco 40 104 38% 150 \nIvan Ljubicic 27 69 39% 149 \nFlorian Mayer 30 72 42% 147 \nSamuel Querrey 26 66 39% 146 \nAndrei Goloubev 24 55 44% 143 \nErnests Gulbis 22 69 32% 140 \nJeremy Chardy 31 65 48% 133 \nJuan Monaco 26 73 36% 131 \nRobin Soderling 23 115 20% 130 \nMichael Llodra 26 67 39% 130 \nRainer Schuettler 15 42 36% 119 \nMikhail Youzhny 25 78 32% 116<\/pre>\n
Player Upsets Matches Up% UpsetScore \nFlorent Serra 11 56 20% 23 \nIgor Kunitsyn 14 40 35% 33 \nIlia Marchenko 14 46 30% 40 \nPotito Starace 28 81 35% 46 \nVictor Hanescu 26 77 34% 50 \nTobias Kamke 12 41 29% 52 \nAndreas Seppi 24 81 30% 53 \nJulien Benneteau 23 59 39% 53 \nViktor Troicki 25 101 25% 54 \nDavid Ferrer 25 119 21% 55 \nFabio Fognini 18 71 25% 55 \nPere Riba 13 41 32% 56 \nLukas Lacko 14 44 32% 57 \nIgor Andreev 17 62 27% 58 \nLukasz Kubot 26 63 41% 59 \nNicolas Almagro 22 112 20% 59 \nFrederico Gil 15 40 38% 60 \nDenis Istomin 25 76 33% 65 \nJarkko Nieminen 25 74 34% 66 \nJohn Isner 21 82 26% 67<\/pre>\n