According to nearly every tennis commentator I’ve ever heard, 15-30 is a crucial point, especially in men’s tennis, where breaks of serve are particularly rare. One reasonable explanation I’ve heard is that, from 15-30, if the server loses either of the next two points, he’ll face break point.<\/p>\n
Another way of looking at it is with a theoretical model<\/a>. A player who wins 65% of service points (roughly average on the ATP tour) has a 62% chance of winning the game from 15-30. If he wins the next point, the probability rises to 78% at 30-all, but if he loses the next point, he will only have a 33% chance of saving the game from 15-40.<\/p>\n Either way, 15-30 points have a lot riding on them. In line with my analysis of the first point of each game<\/a> earlier this week, let’s take a closer look at 15-30 points–the odds of getting there, the outcome of the next point, and the chances of digging out a hold, along with\u00a0a look at which\u00a0players are particularly good or bad in these situations.<\/p>\n Reaching 15-30<\/b><\/p>\n In general, 15-30 points come up about once every four games, and no more or less often than we’d expect. In other words, games\u00a0aren’t particularly likely or unlikely to reach\u00a0that score.<\/p>\n On the other hand, some particular\u00a0players are quite a bit more or less likely. \u00a0Oddly enough, big servers show up at both extremes. John Isner<\/a> is the player who–relative to expectations–ends up serving at 15-30 the most often: 13% more than he should. Given the very high rate at which he wins service points, he should get to 15-30 in only 17% of service games, but he actually reaches 15-30 in 19% of service games.<\/p>\n The list of players who serve at 15-30 more often than they should is a very mixed crew. I’ve extended this list to the top 13 in order to include another player in Isner’s category:<\/p>\n (In all of these tables, “Games” is the number of service games for that player in the dataset, minimum 1,000 service games. “ExpW” is the expected number of occurences as predicted by the model, “ActW” is the actual number of times it happened, and “Ratio” is the ratio of actual occurences to expected occurences.)<\/p>\n While getting to 15-30 this often is a bit of a disadvantage, it’s one that many of these players are able to erase. Isner, for example, not only remains the favorite at 15-30–his average rate of service points won, 72%, implies that he’ll win 75% of games from 15-30–but from this score, he wins 11% more often\u00a0than he should.<\/p>\n To varying extents, that’s true of every player on the list. Joao Sousa<\/a> doesn’t entirely make up for the frequency with which he ends up at 15-30, but he does win 4% more often from 15-30 than he should. Rafael Nadal<\/a>, Tomas Berdych<\/a>, and Gael Monfils<\/a> all win between 6% and 8% more often from 15-30 than the theoretical model suggests that they would. In Nadal’s case, it’s almost certainly related to his skill in the ad court, particularly in saving break points.<\/p>\n At the other extreme, we have players we might term “strong starters” who avoid 15-30 more often than we’d expect. Again, it’s a bit of a mixed bag:<\/p>\n With some exceptions, many of the players\u00a0on this list are thought to be weak in the clutch. (The Dutch pair of Robin Haase<\/a> and Igor Sijsling<\/a> are 12th and 13th.) This makes sense, as the pressure is typically lowest early in games. A player who wins points more often at, say, 15-0 than at 40-30 isn’t going to get much of a reputation for coming through when it counts.<\/p>\n The same analysis for returners isn’t as interesting. Juan Martin del Potro<\/a> comes up again as one of the players least likely to get to 15-30, and Isner–to my surprise–is one of the most likely. There’s not much of a pattern among the best returners: Novak Djokovic<\/a> gets to 15-30 2% less often than expected; Nadal 1% less often, Andy Murray<\/a> exactly as often as expected, and David Ferrer<\/a> 3% more often.<\/p>\n Before moving on, one final note about reaching 15-30. Returners are much less likely to apply enough pressure to reach 15-30 when they are already in a strong position\u00a0to win the set. At scores such as 0-4, 0-5, and 1-5, the score reaches 15-30 10% less often than usual. At the other extreme, two of the games in which a 15-30 score is most common are 5-6 and 6-5, when the score reaches 15-30 about 8% more often than usual.<\/p>\n The high-leverage next point<\/strong><\/p>\n As we’ve seen, there’s a huge difference between winning and losing a 15-30 point. In the 290,000 matches I analyzed for this post, neither the server or returner has an advantage at 15-30. However, some players do perform better than others.<\/p>\n Measured by their success rate serving at 15-30 relative to their typical rate of service points won, here is the top 11, a list unsurprisingly dotted with lefties:<\/p>\n There’s Isner again, making up for reaching 15-30 more often than he should.<\/p>\n And here are the players who win 15-30 points less often than other service points:<\/p>\n When we turn to return<\/em> performance at 15-30, the extremes are less interesting. However, returning at this crucial score\u00a0is something that is at least weakly correlated with overall success: Eight of the current top ten (all but Roger Federer<\/a> and Milos Raonic<\/a>) win more 15-30 points than expected. Djokovic wins 4% more than expected, while Nadal and Tomas Berdych win 3% more.<\/p>\n Again, breaking down 15-30 performance by situation is instructive. When the server has a substantial advantage in the set–at scores such as 5-1, 4-0, 3-2, and 3-0–he is less likely to win the 15-30 point. But when the server is trailing by a large margin–0-3, 1-4, 0-4, etc.–he is more likely to win the 15-30 point. This is a bit of evidence, though peripheral, of the difficulty of closing out a set–a subject for another day.<\/p>\n Winning the game from 15-30<\/strong><\/p>\n For the server, getting to 15-30 isn’t a good idea. But compared to our theoretical model, it isn’t quite as bad as it seems. From 15-30, the server wins 2% more often than the model predicts. While it’s not a large effect, it is a persistent one.<\/p>\n Here are the players who play better than usual from 15-30, winning games much more often than the model predicts they would:<\/p>\n Naturally, this list has much in common with that of the players who excel on the 15-30 point itself, including many lefties. The big surprise is Nikolay Davydenko<\/a>, a player who many regarded as weak in the clutch, and who showed up on one of the first lists among players with questionable reputations in pressure situations. Yet Davydenko–at least at the end of his career–was very effective at times like these.<\/p>\n Another note on Nadal: He is the only player on this list who is also near the top among men who\u00a0overperform from 15-30 on return. Rafa exceeds expectations in that category by 7%, as well, better than any other player in the last few years.<\/p>\n And finally, here are the players who underperform from 15-30 on serve:<\/p>\n Tentative conclusions<\/strong><\/p>\n This is one subject on which the conventional wisdom and statistical analysis agree, at least to a certain extent. 15-30 is a very important point, though in context, it’s no more important than some of the points that follow.<\/p>\n These numbers show that some players are better than others at certain stages within each game. In some cases, the strengths balance out with other weaknesses; in others, the stats may expose pressure situations where a\u00a0player falters.<\/p>\n While many of the extremes I’ve listed here are significant, it’s important to keep them in context. For the average player, games reach 15-30 about one-quarter of the time, so performing 10% better or worse in these situations affects only one in forty<\/em>\u00a0games.<\/p>\n Over the course of a career, it adds up, but we’re rarely going to be able to spot these trends during a single match, or even within a tournament. While outperforming expectations on 15-30 points (or any other small subset) is helpful, it’s rarely something the best players rely on. If you play as well as Djokovic does, you don’t need to play even better in clutch situations. Simply meeting expectations is enough.<\/p>\n","protected":false},"excerpt":{"rendered":" According to nearly every tennis commentator I’ve ever heard, 15-30 is a crucial point, especially in men’s tennis, where breaks of serve are particularly rare. One reasonable explanation I’ve heard is that, from 15-30, if the server loses either of the next two points, he’ll face break point. Another way of looking at it is … Continue reading The Pivotal Point of 15-30<\/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":[18,96,123],"tags":[],"class_list":["post-1844","post","type-post","status-publish","format-standard","hentry","category-clutch","category-research","category-win-probability"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/posts\/1844","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=1844"}],"version-history":[{"count":0,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/posts\/1844\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/media?parent=1844"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/categories?post=1844"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/tags?post=1844"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Player Games ExpW ActW Ratio \nJohn Isner 3166 537 608 1.13 \nJoao Sousa 1390 384 432 1.12 \nJanko Tipsarevic 1984 444 486 1.09 \nTommy Haas 1645 368 401 1.09 \nLleyton Hewitt 1442 391 425 1.09 \nTomas Berdych 3947 824 894 1.08 \nVasek Pospisil 1541 361 390 1.08 \nRafael Nadal 3209 661 713 1.08 \nPablo Andujar 1922 563 605 1.08 \nPhilipp Kohlschreiber 2948 652 698 1.07 \nGael Monfils 2319 547 585 1.07 \nLukasz Kubot 1360 381 405 1.06 \nIvo Karlovic 1941 299 318 1.06<\/pre>\n
Player Games ExpW ActW Ratio \nDustin Brown 1013 249 216 0.87 \nVictor Hanescu 1181 308 274 0.89 \nMilos Raonic 3050 514 462 0.90 \nDudi Sela 1066 297 270 0.91 \nRichard Gasquet 2897 641 593 0.93 \nJuan Martin del Potro 2259 469 438 0.93 \nErnests Gulbis 2308 534 500 0.94 \nKevin Anderson 2946 610 571 0.94 \nNikolay Davydenko 1488 412 388 0.94 \nNicolas Mahut 1344 314 297 0.94<\/pre>\n
Player Games ExpW ActW Ratio \nDonald Young 1298 204 229 1.12 \nRobin Haase 2134 322 347 1.08 \nSteve Johnson 1194 181 195 1.08 \nBenoit Paire 1848 313 336 1.08 \nFernando Verdasco 2571 395 423 1.07 \nThomaz Bellucci 1906 300 321 1.07 \nJohn Isner 3166 421 449 1.07 \nXavier Malisse 1125 175 186 1.06 \nVasek Pospisil 1541 243 258 1.06 \nRafael Nadal 3209 470 497 1.06 \nBernard Tomic 2124 328 347 1.06<\/pre>\n
Player Games ExpW ActW Ratio \nCarlos Berlocq 1867 303 273 0.90 \nAlbert Montanes 1183 191 173 0.91 \nKevin Anderson 2946 377 342 0.91 \nGuillermo Garcia-Lopez 2356 397 370 0.93 \nRoberto Bautista-Agut 1716 264 247 0.93 \nJuan Monaco 2326 360 338 0.94 \nMatthew Ebden 1088 186 176 0.94 \nGrigor Dimitrov 2647 360 341 0.95 \nRichard Gasquet 2897 380 360 0.95 \nAndy Murray 3416 473 449 0.95<\/pre>\n
Player Games ExpW ActW Ratio \nNikolay Davydenko 1488 194 228 1.17 \nSteve Johnson 1194 166 190 1.14 \nDonald Young 1298 163 185 1.13 \nJohn Isner 3166 423 470 1.11 \nNicolas Mahut 1344 172 188 1.09 \nBenoit Paire 1848 266 288 1.08 \nLukas Lacko 1162 164 177 1.08 \nRafael Nadal 3209 450 484 1.08 \nMartin Klizan 1534 201 216 1.08 \nFeliciano Lopez 2598 341 367 1.07 \nTomas Berdych 3947 556 597 1.07<\/pre>\n
Player Games ExpW ActW Ratio \nDustin Brown 1013 122 111 0.91 \nTommy Robredo 2140 289 270 0.93 \nAlexandr Dolgopolov 2379 306 288 0.94 \nFederico Delbonis 1110 157 148 0.94 \nJuan Monaco 2326 304 289 0.95 \nSimone Bolelli 1015 132 126 0.96 \nPaul-Henri Mathieu 1083 155 148 0.96 \nGilles Muller 1332 179 172 0.96 \nCarlos Berlocq 1867 256 246 0.96 \nGrigor Dimitrov 2647 333 320 0.96 \nRichard Gasquet 2897 352 339 0.96<\/pre>\n