When executed correctly and deployed at the right moment, the down-the-line backhand is one of the most devastating shots in tennis. How valuable is it, and which players use it the most effectively? These are surprisingly complicated questions, and I don’t yet have solid answers. But the preliminary work, of determining the frequency with which players use the down-the-line backhand, as well as their success rate when they do, is illuminating in itself.<\/p>\n\n\n\n
The Match Charting Project<\/a> offers a lot of data on tactics like this. MCP charts record the type and direction of every shot. In a rally between right-handers, a down-the-line (DTL) backhand is simple to identify: a backhand from<\/em> the backhand corner to<\/em> the opponent’s forehand corner. (Or in MCP parlance, 3b1<\/em>.) From the 2010s alone, the MCP has logged close to 100,000 DTL backhands, roughly evenly split by gender. <\/p>\n\n\n\n MCP charts also give us an idea of the bigger picture. We can identify opportunities<\/em> to hit DTL backhands, in which a player might choose instead to hit a backhand in a different direction, or even to use a different shot entirely. A player who hits a lot of slices, or runs around the backhand to hit forehands, might hit a very high percentage of their backhands down the line, but those DTL backhands wouldn’t make up a high proportion of their total chances in that corner.<\/p>\n\n\n\n DTL opportunities<\/strong><\/p>\n\n\n\n Let’s start with a look at those opportunities. The following table shows three rates for each tour, covering charted matches, 2010-present. The first rate is the percentage of backhand-corner opportunities that resulted in backhands. (For today’s purposes, I’m excluding slice backhands. DTL slices can be devilish, but they are an entirely different weapon. I’ve also excluded service returns, which present their own complications.) The second is the percentage of opportunities that results in down-the-line<\/em> backhands, and the third is a combination of those two, the percentage of backhands from the backhand corner that were hit down the line.<\/p>\n\n\n\n Women are much more likely than men to hit a non-slice backhand from their backhand corner. There are two reasons for that. First: men, on average, hit more slices, largely because a small number of men hit a lot of slices. Second, men are somewhat more likely to run around the backhand and hit a forehand from that corner. Because men hit fewer backhands in total, men also hit fewer DTL backhands as a percentage of all shots from that corner.<\/p>\n\n\n\n However, once they choose to hit a backhand<\/em>, men and women go down the line at exactly the same rate, 17.4%, or roughly once per six backhands.<\/p>\n\n\n\n DTL results<\/strong><\/p>\n\n\n\n Let’s look at the results of those DTL backhands. Here’s another table with aggregate ATP and WTA numbers, showing the percentage of DTL backhands that go for winners (including shots that induce forced errors), the percentage that are unforced errors, and the percentage that lead to the most important thing–ultimately winning the point:<\/p>\n\n\n\n Both men and women have a “positive” ratio of winners to unforced errors. But women hit a lot more of both. (As we’ll see, some<\/em> women have eye-poppingly aggressive numbers.) And both genders, on average, end points more frequently with DTL backhands than they do with other shots, whether we look at all shots, or all backhands. The average non-slice backhand–counting those from every position, hit in every direction–goes for approximately 10% winners and 10% unforced errors.<\/p>\n\n\n\n The percentage of points won doesn’t look very dramatic, at 51.6% and 52.4% for men and women, respectively. Yet both numbers reverse the usual expectation for backhands. Backhands occur more often in defensive positions, so backhands are slightly more likely to occur in points lost<\/em> than in points won, so the corresponding numbers for all<\/em> backhands are below 50%. This is a good example of what makes shot analysis so difficult: Do DTL backhands result in more points won because they are a better tactical decision, or because players hit them more often in response to weak balls? It’s probably a combination of both, but more a reflection of the latter. <\/p>\n\n\n\n A lefty digression<\/strong><\/p>\n\n\n\n I will get to some player-by-player numbers shortly, but first, let’s look at an interesting comparison between lefties and righties. I’ve long speculated that lefties–because they mostly face right-handed opponents–must learn to play “backwards.” While righties can whack crosscourt forehands at each other, a lefty rarely has the chance to do that. As a result, left-handers spend more time practicing unusual shots, like inside-out groundstrokes and the DTL backhand. That’s my theory, anyway.<\/p>\n\n\n\n Sure enough, left-handed men hit quite a few more DTL backhands than their right-handed peers. Righties go down the line on 16.9% of their backhands from the backhand corner, while lefties do so 21.4% of the time. Rafael Nadal<\/a> plays a sizable part in this, as he represents a lot of the charted matches of left-handers, and he goes down the line 24.4% of the time, more than almost any other man. (Another lefty, Martin Klizan<\/a>, is one of the few to be more extreme than Rafa, at 25.2%.) Still, a gap of several percentage points remains even if we exclude Nadal.<\/p>\n\n\n\n But this is hardly a physical law. Women show the opposite trend, in the aggregate. Right-handed women go down the line 17.6% of the time, while lefties do so 15.8% of the time. A few female lefties fit the mold of Nadal and Klizan, including Lucie Safarova<\/a> (26.3%) and Ekaterina Makarova<\/a> (26.1%). But in general, it is the most aggressive women–regardless of their dominant hand–who use the DTL backhand the most often. Jelena Ostapenko<\/a> tops 27%, and Dayana Yastremska<\/a> forces us to rescale the y-axis with a rate of 33%.<\/p>\n\n\n\n The DTL trade-off<\/strong><\/p>\n\n\n\n I mentioned above a prime difficulty in evaluating shot selection. The most important measurement of any tactic is whether it results in more points won. If hitting more DTL backhands didn’t improve a player’s rate of points won, why would she do it? But if hitting more DTL backhands does<\/em> improve her rate of points won, she should look to hit more of them, which means finding opportunities from slightly more challenging positions … which means winning points at a slightly lower rate. Push that logic to its extreme, and a superior tactic will no longer result in many more points won than the inferior tactic it replaces.<\/p>\n\n\n\n This problem, combined with the obvious fact that players have different skills and preferences, means that there’s not a strong relationship between a player’s rate of DTL backhands and their success–measured in points won–when they hit them. There is a very slight negative correlation (for both and women) between the frequency with which a player hits DTL backhands and the number of DTL backhand winners he or she hits, suggesting that there are limited opportunities to swing away and hit a clean winner. For women, there is no relationship, however, between the rate of DTL backhands and the rate of points won.<\/p>\n\n\n\n There is one minor exception to the barrage of non-relationships. For men, there is a weak negative correlation (r^2 = 0.13) between the rate at which the player hits DTL backhands and the rate of points won. That result tracks with the intuition described above, that as a player opts for the tactic more often, his results will decline–not because he plays worse, but because he is opting for the tactic in riskier situations. A player who goes down the line on 10% of his backhands is just picking the low-hanging fruit, while a player who does so 25% of the time is sometimes hitting an awfully low-percentage shot.<\/p>\n\n\n\n DTL by player: ATP<\/strong><\/p>\n\n\n\n Thus, we might–very cautiously!–conclude a player who is winning a high percentage of points when he hits a DTL backhand should do so even more often. Here are 25 of the most prominent ATPers, sorted by the frequency with which they hit the DTL backhand:<\/p>\n\n\n\n If nothing else, these numbers show us that there are a lot of different ways to win tennis matches. Nadal hits a lot of backhands down the line, but he rarely ends the point that way. Only a bit further down the list, we find players who end the point with DTL backhands more than twice as often. The bottom of the table is filled with players who don’t win many points going down the line, but they are mixed with Diego Schwartzman<\/a> and Borna Coric<\/a>, two men who are very effective on the rare occasions they hit the more difficult shot.<\/p>\n\n\n\n DTL by player: WTA<\/strong><\/p>\n\n\n\n There is no similar tour-wide correlation for women, but that doesn’t mean that each player’s shot selection is optimal. Here are the same stats for 25 prominent WTAers:<\/p>\n\n\n\n A dramatic example is that of Belinda Bencic<\/a>, who hits more DTL backhands than almost anyone else on this list and <\/em>is one of the most successful, in terms of points won, when she does so. It’s tough to avoid the hypothesis that she is squandering some opportunities to deploy this weapon. At the opposite extreme, Ostapenko and Madison Keys<\/a> are extremely aggressive, hitting almost as many errors as winners, and in the case of Keys, winning considerably fewer than half of those points.<\/p>\n\n\n\n As it says on the tin, this is just a first look at the DTL backhand. Evaluating shot selection is hard, and quantifying the effects of shot-level tactics is even harder. But we can’t do it unless we’ve pinned down some of the basics, picking out some useful metrics and doing a first pass for any correlations that might (or probably don’t) exist. While it’s a long process, we’re one baby step closer to some answers.<\/p>\n","protected":false},"excerpt":{"rendered":" When executed correctly and deployed at the right moment, the down-the-line backhand is one of the most devastating shots in tennis. How valuable is it, and which players use it the most effectively? These are surprisingly complicated questions, and I don’t yet have solid answers. But the preliminary work, of determining the frequency with which … Continue reading A First Look at the Down-the-Line Backhand<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","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":[10,65,110],"tags":[],"class_list":["post-3805","post","type-post","status-publish","format-standard","hentry","category-backhands","category-match-charting","category-tactics"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/posts\/3805","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=3805"}],"version-history":[{"count":0,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/posts\/3805\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/media?parent=3805"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/categories?post=3805"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/tags?post=3805"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Tour BH\/Opps DTL BH\/Opps DTL\/All BH \nATP 63.7% 11.1% 17.4% \nWTA 73.6% 12.8% 17.4% <\/pre>\n\n\n\n
Tour Winner% UFE% Points Won% \nATP 22.1% 18.1% 51.6% \nWTA 26.0% 22.2% 52.4% <\/pre>\n\n\n\n
Player DTL\/BH Wnr% UFE% Pts Won% \nRafael Nadal 24.5% 12.1% 11.1% 54.7% \nJohn Isner 22.0% 23.2% 27.3% 38.2% \nNovak Djokovic 21.2% 16.7% 16.1% 54.2% \nJo Wilfried Tsonga 21.0% 20.6% 27.7% 45.8% \nDenis Shapovalov 20.5% 20.1% 23.5% 49.1% \nStan Wawrinka 19.1% 28.8% 26.8% 51.4% \nKei Nishikori 18.8% 27.7% 19.1% 56.7% \nDominic Thiem 18.4% 28.5% 28.2% 51.6% \nFabio Fognini 18.3% 20.4% 23.8% 49.3% \nDavid Goffin 18.2% 23.5% 23.8% 49.5% \nRoger Federer 18.2% 25.5% 21.0% 53.2% \nGrigor Dimitrov 17.7% 27.4% 23.6% 50.5% \nNick Kyrgios 17.7% 19.5% 23.5% 44.4% \nAndy Murray 16.8% 21.7% 16.5% 54.2% \nRichard Gasquet 16.6% 33.5% 23.1% 55.2% \nJuan Martin del Potro 15.5% 24.6% 15.7% 52.2% \nAlexander Zverev 15.3% 32.5% 19.0% 56.1% \nGael Monfils 14.3% 25.9% 17.6% 54.7% \nDaniil Medvedev 14.3% 17.0% 16.9% 49.6% \nDavid Ferrer 14.2% 16.9% 18.1% 48.0% \nStefanos Tsitsipas 14.1% 24.3% 22.9% 49.3% \nBorna Coric 13.6% 29.3% 24.1% 55.4% \nKevin Anderson 13.3% 25.3% 24.9% 45.9% \nRoberto Bautista Agut 10.4% 17.3% 20.2% 46.3% \nDiego Schwartzman 10.3% 32.5% 22.3% 55.7%<\/pre>\n\n\n\n
Player DTL\/BH Wnr% UFE% Pts Won% \nDayana Yastremska 33.7% 27.4% 24.7% 54.8% \nJelena Ostapenko 27.1% 35.0% 33.6% 51.0% \nSerena Williams 25.2% 28.3% 19.6% 57.4% \nBelinda Bencic 21.6% 28.1% 14.6% 59.1% \nAryna Sabalenka 21.2% 38.7% 25.5% 57.1% \nMadison Keys 20.4% 27.7% 39.9% 46.7% \nSimona Halep 20.1% 25.3% 21.7% 55.8% \nVenus Williams 19.2% 26.1% 19.7% 49.7% \nBianca Andreescu 19.2% 22.6% 17.9% 59.7% \nVictoria Azarenka 19.1% 25.9% 16.2% 57.3% \nKarolina Pliskova 18.9% 26.6% 23.1% 51.6% \nGarbine Muguruza 18.1% 28.2% 18.9% 57.5% \nMaria Sharapova 18.0% 27.1% 21.4% 53.2% \nNaomi Osaka 17.9% 28.2% 27.7% 48.6% \nJohanna Konta 16.1% 33.4% 29.9% 53.6% \nPetra Kvitova 15.8% 30.9% 24.0% 54.0% \nCaroline Wozniacki 15.6% 25.5% 15.9% 56.8% \nSloane Stephens 15.1% 25.9% 26.4% 53.2% \nKiki Bertens 14.7% 21.6% 21.7% 49.0% \nMonica Niculescu 13.2% 29.7% 14.7% 62.9% \nAngelique Kerber 13.2% 26.7% 18.5% 56.2% \nAshleigh Barty 13.1% 26.9% 29.0% 50.6% \nMarketa Vondrousova 11.5% 29.8% 18.5% 52.3% \nCarla Suarez Navarro 10.9% 33.1% 25.8% 55.9% \nElina Svitolina 10.2% 27.6% 20.5% 53.9%<\/pre>\n\n\n\n