Heavy Topspin – Page 84 – The Tennis Abstract blog

The Tennis Abstract Podcast

Today, Carl Bialik and I are excited to announce our new podcast, called–you guessed it–the Tennis Abstract Podcast.

In the inaugural episode, recorded Monday, we discuss the results from Indian Wells and Miami, with a special focus on the renewed relevance of Roger Federer and Rafael Nadal. We also take a wider look at the upcoming clay season for both the ATP and WTA tours.

We’re still working out all the kinks, so please forgive us for the bare bones presentation and the occasional audio glitch. If all goes well, we’ll have a new episode most weeks, maybe even with fewer glitches to apologize for.

Anyway, we both really enjoyed recording this first episode, and we hope you like it as well!

Click here to listen to Episode 1.

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We’ll soon be listed on iTunes and elsewhere. In the meantime, our xml feed — http://www.tennisabstract.com/podcast/feed.xml — may come in handy for those of you who would like to subscribe.

Cool Down Tennis

This is a guest post by Carl Bialik.

Imagine you’re named boss of tennis. Right after being sworn in by Rod Laver and Martina Navratilova, you’re handed an empty wall calendar. You make the schedule for 2018. What’s your first move?

Mine would be to move Indian Wells and Miami earlier in the calendar, and the Australian Open later, after the two U.S. Masters tournaments.

I never wanted this more than while sweating my way around the Indian Wells grounds in search of shade last month. I wasn’t alone. The only full sections of the main stadium during day sessions were the ones protected from the sun. Around the fan-friendly venue, there are plenty of seats in the shade — under tents, or in Adirondack chairs that shade-seeking people push ever closer to the screen as the sun shifts. The players can only wait for shade to slowly descend on the court. Jack Sock needed a towel holding 50 ice cubes to cool down.

Sure, it was unusually hot at this year’s Indian Wells tournament. But the climatological averages are clear: It’s hot in the California desert and in the Florida sunshine in March, and in the antipodean summer in January. It’d be cooler in Indian Wells, Miami and Melbourne if the two Masters events moved two months earlier and led up to the year’s first Grand Slam in March. Each of the two-week events would be, on average, 4 to 10 degrees Fahrenheit cooler each year. (The precipitation would be about the same, so Miami men’s finalist Rafael Nadal might continue to bemoan humidity, request sawdust and show more than he’d planned beneath his shorts; while women’s champ Johanna Konta might keep having to change clothes midmatch because they’ve accumulated approximately five kilograms of sweat.)

I’m using the averages because I don’t want to make too much of an unseasonably hot Indian Wells, or too little of an unusually cold March in Miami. But the averages might understate the problem because it’s precisely the outliers we’re worried about. A nudge downward of a few degrees, on average, could translate into a big drop in the probability of an unbearably hot fortnight — say, from 25 percent to 5 percent.

Changing the tennis calendar would also mean less daylight. That wouldn’t be so good for the nickname Sunshine Double, but it’d be good for tennis. Until more tennis stadiums adopt overhanging partial roofs — but for sun, not for rain — shorter days means less sun for fans to contend with and more reason to fill the seats. Plus, night tennis is exciting. The venues already have plenty of lights and evening sessions.

Scrambling the schedule would do more than cool down tennis. The three midyear majors’ proximity to each other helps the sport carry some momentum and mainstream buzz from one to the next. The Australian Open squanders all that in the four-month gap between its end and the start of the French Open. There’s even a month between the Aussie Open and the next big event.

The other three majors also get opening acts, to help players build up familiarity with the surface and for fans to build anticipation. The Australian Open gets two weeks at the start of the season — without so much as a 500 event on the men’s side.

The lack of buffer between the offseason and Melbourne also means it loses some players still recovering from the end of the previous season. That was the case this year with Juan Martin del Potro, who skipped this year’s first major after winning the Davis Cup with Argentina in November.

Imagine instead starting the season with Indian Wells and Miami — or Miami, then Indian Wells, while we’re scrambling things, for the convenience of travel from the sport’s power center of Europe — using the same courts and balls as Melbourne. Follow that month — or less, if one or both of the U.S. early-year Masters succumbs to the reality that they could be just a week — by Doha and Dubai, then Brisbane, Sydney and the like, before the main event in Melbourne at the start of March. We’d start the season with a real hard-court swing, ending with the first major.

From Australia, the tour could stay in the southern hemisphere. The swing through South America has a long history and a terrible spot on the current calendar. It was traditionally played on clay but some of its biggest events are moving to hard courts — first (North American) Acapulco, now, maybe, Rio, in search of Masters status — to the chagrin of Nadal and others. Too many players simply don’t think it’s worth it to compete on clay for a few weeks if that’s followed by a month of hard-court events. But move Indian Wells and Miami, and South American clay could move a month later in the calendar — while slightly tempering what Nadal bemoans as “too extreme” weather conditions by an average of 1 degree. The swing would give way seamlessly to Houston, Charleston and the European clay spell — which, by the way, would absorb Bucharest, Hamburg, Umag, Bastad and Gstaad from their awkward post-Wimbledon calendar slots. And no one would suggest Miami move to green clay.

We’d be left with a coherent calendar with five seasons of roughly equal length and importance, four with a major and one with the year-end finals: (1) Outdoor hard courts in the U.S., the Middle East and Oceania, followed by (2) clay in the Americas and Europe, (3) English and German grass (with Newport for those who want to visit the sport’s hall of fame), (4) North American and Asian outdoor hard courts, and (5) European indoor hard courts (absorbing the current winter events such as St. Petersburg and Rotterdam) culminating in wherever the tours’ multiplying year-end finals are calling home that year. And let’s play Davis Cup and Fed Cup at the same time — the tours acting in sync; what a concept! — on weekends at the edge of the five new seasons, giving hosts a wider range of sensible surfaces to choose from, and creating the option for combined venues if men and women from the same country are hosting the same round. (Prague in 2012 would’ve been tennis nirvana.) Or, hell, consider merging the events.

Could all this happen? Sure — if tennis power were centralized in a person or people who prioritize the overall good of the global game. Without a radical transformation of tennis, though, it’ll be slow going: It took years for the idea of lengthening the grass-court season by a week to become reality.

Carl Bialik has written about tennis for fivethirtyeight.com and The Wall Street Journal. He lives and plays tennis in New York City and has a Tennis Abstract page.

The Five Big Questions in Tennis Analytics

Italian translation at settesei.it

The fledgling field of tennis analytics can seem rather chaotic, with scores of mini-studies that don’t fit together in any obvious way. Some seem important but unfinished while others are entertaining but trivial.

Let me try to impose some structure on this project by classifying research topics into what I’ll call the Five Big Questions, each of which is really just an umbrella for hundreds more (like these). As we’ll see, there are really six categories, not five, which just goes to show: analytics is about more than just counting.

1. What’s the long-term forecast?

Beyond the realm of the next few tournaments, what does the evidence tell us about the future? This question encompasses everything from seasons to entire careers. What are the odds that Roger Federer reclaims the No. 1 ranking? How many Grand Slams will Nick Kyrgios win? How soon will Catherine Bellis crack the top ten?

The most important questions in this category are the hardest ones to answer: Given the limited data we have on junior players, what can we predict–and with what level of confidence–about their future? These are questions that national federations would love to answer, but they are far from the only stakeholders. Everyone from sponsors to tournaments to the players’ families themselves have an interest in picking future stars. Further, the better we can answer these questions, the more prepared we can be for the natural follow-ups. What can we (as families, coaches, federations, etc.) do to improve the odds that a player succeeds?

2. Who will win the next match?

The second question is also concerned with forecasting, and it is the subject that has received–by far–the most analytical attention. Not only is it fun and engaging to try to pick winners, there’s an enormous global industry with billions of dollars at stake trying to make more accurate forecasts.

As an analyst, I’m not terribly interested in picking winners for the sake of picking winners. More valuable is the quest to identify all of the factors that influence match outcomes, like the role of fatigue, or a player’s preference for certain conditions, or the specifics of a given matchup. Player rating systems fall into this category, and it’s important to remember they are only a tool for forecasting, not an end to themselves.

As a meta-question in this category, one might ask how accurate a set of forecasts could possibly become. Or, posed differently, how big of a role does chance play in match outcomes?

3. When and why does the i.i.d. model break down?

A lot of sports analysis depends on the assumption that events are “identically and independently distributed”–i.e. factors like streakiness, momentum, and clutch are either nonexistent or impossible to measure. In tennis terms, the i.i.d. model might assume that a player converts break points at the same rate that she wins all ad-court points, or that a player hold serve while serving for the set just as often as he holds serve in general.

The conventional wisdom strongly disagrees, but it is rarely consistent. (“It’s hard to serve for the set” but “this player is particularly good when leading.”) This boils down to yet another set of forecasting questions. We might know that a player wins 65% of service points, but what are her chances of winning this point, given the context?

I suspect that thorough analysis will reveal plenty of small discrepancies between reality and the i.i.d. model, especially at the level of individual players. More than with the first two topics, the limited sample sizes for many specific contexts mean we must always be careful to distinguish actual effects from noise and look for long-term trends.

4. How good is that shot?

As more tennis data becomes available in a variety of formats, the focus of tennis analytics will become more granular. The Match Charting Project offers more than 3,000 matches worth of shot-by-shot logs. Even without the details of each shot–like court position, speed, and spin–we can start measuring the effectiveness of specific players’ shots, such as Federer’s backhand.

With more granular data on every shot, analysts will be able to be even more precise. Eventually we may know the effect of adding five miles per hour to your average forehand speed, or the value of hitting a shot from just inside the baseline instead of just behind. Some academics–notably Stephanie Kovalchik–have begun digging into this sort of data, and the future of this subfield will depend a great deal on whether these datasets ever become available to the public.

5. How effective is that tactic?

Analyzing a single shot has its limits. Aside from the serve, every shot in tennis has a context–and even serves usually form part of the backdrop for other shots. Many of the most basic tactical questions have yet to be quantified, such as the success rate of approaching to the backhand instead of the forehand.

As with the previous topic, the questions about tactics get a lot more interesting–and immensely more complicated–as soon as Hawkeye-type data is available. With enough location, speed, and spin data, we’ll be able to measure the positions from which approach shots are most successful, and the type (and direction) that is most effective from each position. We could quantify the costs and benefits of running around a forehand: How good does the forehand have to be to counteract the weaker court position that results?

We can scrape the surface of this subject with the Match Charting Project, but ultimately, this territory belongs to those with camera tracking data.

6. What is the ideal structure of the sport?

Like I said, there are really just five questions. Forecasting careers, matches, and points, and quantifying shots and tactics encompass, for me, the entire range of “tennis analytics.”

However, there are plenty of tennis-related questions that we might assign to the larger field of “business of sports.” How should prize money be distributed? What is the best way to structure the tour to balance the interests of veterans and newcomers? Are there too many top-level tournaments, or too few? What the hell should we do with Davis Cup, anyway?

Many of these issues are–for now–philosophical questions that boil down to preferences and gut instincts. Controlled experiments will always be difficult if only because of the time frames involved: If we change the Davis Cup format and it loses popularity, is it causation or just correlation? We can’t replicate the experiment. But despite the challenges, these are major questions, and analysts may be able to offer valuable insights.

Now … let’s get to work.

3,000 Matches!

Italian translation at settesei.it

Last week, the Match Charting Project hit an exciting milestone: 3,000 matches!

The MCP has been logging shot-by-shot records of professional matches for about two and a half years now, and in doing so, we’ve built an open dataset unlike anything else in the tennis world. We have detailed records of at least one match from almost every player in the ATP and WTA top 200s, and extensive data on the top players of each tour. Altogether, we’ve tracked 450,000 points and over 1.7 million shots.

The research that could be conducted using this data is almost inexhaustible, and we’ve barely scraped the surface. My work on Federer’s new-and-improved backhand was just one example of what the Match Charting Project has made possible.

One of the most valuable aspects of the project last year was the addition–spearheaded by Edo–of nearly all men’s and women’s Grand Slam finals back to 1980. (We’re still missing a handful of them–if you can help us find video, we’d be very grateful!) This year, we’ve taken on another challenge: All of the head-to-heads of the ATP Big Four. Already, we’ve covered the 37 meetings of Federer and Nadal (through yesterday’s Miami final), and we’re near the 75% mark for the 216 total matches contested among these four all-time-greats.

Meanwhile, we’re continuing to add a broad range of matches almost as soon as they happen, including over 20 each from Indian Wells and Miami, along with the occasional ITF and Challenger contest. While the data is skewed toward a handful of popular players, we’ve been careful to amass several matches for nearly every player of consequence on both tours.

If you’re interested in tennis analytics, I hope you’ll consider contributing to the project by charting matches. This data doesn’t magically collect itself, and like most volunteer-driven endeavors, a small number of contributors are responsible for a substantial percentage of the work. Even a single match is a useful addition, and the biggest risk you face is that you’ll get hooked.

Click here to find out how to get started.

Here’s to the next 3,000 matches!

Del Potro’s Draws and the Possible Persistence of Bad Luck

Italian translation at settesei.it

Tennis’s draw gods have not been kind to Juan Martin del Potro this year.

In Acapulco and Indian Wells, he drew Novak Djokovic as his second-match opponent. In Miami, Delpo got a third-rounder with Roger Federer. In each of the March Masters events, with 1,000 ranking points at stake, del Potro was handed the most difficult opponents for his first round against a fellow seed. Thanks in part to the resulting early exits, one of the most dangerous players on tour is still languishing outside of the top 30 in the ATP rankings.

When I wrote about the Indian Wells quarter of death–the section of the draw containing del Potro, Djokovic, Federer, Rafael Nadal, and Nick Kyrgios–I attempted to quantify the effect of the draw on each player’s expected ranking points. Before each player’s name was placed in the bracket, my model predicted that Delpo would earn about 150 ranking points–the weighted average of his likelihood of reaching the third round, the fourth round, and so on–and after the draw was conducted, his higher probability of a clash with Djokovic knocked that number down to just over 100. That negative effect was one of the worst of any player in the tournament.

The story in Miami is similar, if less extreme. Pre-draw, Delpo’s expected points were 183. Post draw: 155. In the four tournaments he has entered this year, he has been uniformly unlucky:

Tournament    Pre-Draw  Post-Draw  Effect  
Delray Beach      89.3       74.0  -17.1%  
Acapulco         121.5       97.1  -20.1%  
Indian Wells     154.6      102.5  -33.7%  
Miami            182.9      155.4  -15.0%  
TOTAL            548.2      429.0  -21.7%

*The numbers above for Indian Wells are slightly different than what I published in the Indian Wells article, since the simulations I ran for this post consider the entire 96-player field, not just the 64-player second round.

The good news, as we’ll see, is that it’s virtually impossible for this degree of misfortune to continue. The bad news is that those 119 points are gone forever, and at Delpo’s current position in the ranking table, that disadvantage will affect his tournament seeds, which in turn will result in worse draws (earlier meetings with higher-ranked players, independent of luck) for at least another few weeks.

Before we go any further, let me review the methodology I’m using here. (If you’re not interested, skip this paragraph.) For “post-draw” expected points, I’m taking jrank-based forecasts–like the ones on the front page of Tennis Abstract–and using each player’s probability of each round to calculate a weighted average of expected points. “Pre-draw” forecasts are much more computationally demanding. In Miami, for instance, Delpo could’ve faced any of the 64 unseeded players in the second round and been slated to meet any of the top eight seeds in the third round. For each tournament, I ran a Monte Carlo simulation with the tournament seeds, generating a new draw and simulating the tournament–100,000 times, then summing all those outcomes. So in the pre-draw forecast, Delpo had a one-eighth chance of getting Fed in the third round, a one-eighth chance of getting Kei Nishikori there, and so on.

It seems clear that a 22%, 119-point rankings hit over the course of four tournaments is some seriously bad luck. Last year, there were about 750 instances of a player being seeded at an ATP tournament, and in fewer than 60 of those, the draw resulted in an effect of -22% or worse on the player’s expected ranking points. And that’s just one tournament! The odds that Delpo would get such a rough deal in all four of his 2017 tournaments are 1 in more than 20,000.

Over the course of a full season, draw luck mostly evens out. It’s rare to see an effect of more than 10% in either direction. Last year, Thiemo de Bakker saw a painful difference of 18% between his pre-draw and post-draw expected points in 12 ATP events, but everyone else with at least that many tournaments fell between -11% and +11%, with three-quarters of players between -5% and +5%. Even when draw luck doesn’t balance itself out, the effect isn’t as bad as what Delpo has seen in 2017.

Del Potro’s own experience in 2016 is a case in point. His most memorable event of the season was the Olympics, where he drew Djokovic in the first round, so it’s easy to recall his year as being equally riddled with bad luck. But in his 12 other ATP events, the draw aided him in six–including a +34% boost at the US Open–and hurt him at the other six. Altogether, his 2016 ATP draws gave him a 5.9% advantage over his “pre-draw” expected points–a bonus of 17 ranking points. (I didn’t include the Olympics, since no ranking points were awarded there.)

Taken together, Delpo’s 2016-17 draws have deprived him of about 100 ranking points, which would move him three spots up the ranking table. So even with a short stretch of extreme misfortune, draw luck hasn’t affected him that much. Last year’s most extreme case among elite players, Richard Gasquet, suffered a similar effect: His draws knocked down his expected take by 9%, or 237 points, a difference that would bump him up from #22 to #19 in this week’s ranking list.

There are many reasons to believe that del Potro is a much better player than his current ranking suggests, such as his Elo rating, which stands at No. 7. But his ATP ranking reflects his limited schedule and modest start last year much more than it does the vagaries of each week’s brackets. The chances are near zero that he will continue to draw the toughest player in each tournament’s field in the earliest possible round, so we’ll soon have a better idea of what exactly he is capable of, and where exactly he should stand in the rankings.

Playing Even Better Than Number One

Italian translation at settesei.it

Last night in Miami, Venus Williams beat newly re-minted WTA No. 1 Angelique Kerber. Venus, of course, has plenty of experience clashing with the very best in women’s tennis, with 15 Grand Slam finals and three spells at the No. 1 ranking herself.

Last night’s quarterfinal was Venus’s 37th match against a WTA No. 1 and her 15th win. Kerber became the sixth different top-ranked player to lose at the hands of the elder Williams sister.

All of these numbers are very impressive, especially when you consider that, taken as a whole, WTA No. 1s have won just over 88% of their nearly 2,300 matches since the modern ranking system was instituted. However, Venus doesn’t hold the record in any of these categories.

Records against No. 1s are a somewhat odd classification, since the best players tend to reach the top spot themselves. For example, Martina Hingis played only 11 matches against top-ranked opponents, barely one-fifth as many as the leader in that category. On the other hand, injuries and other layoffs have meant that many all-time greats have found themselves lower in the rankings for long stretches. That is particularly true of Venus and Serena Williams.

With her 37 matches played against No. 1s, Venus is approaching the top of the list, but it will take a superhuman effort to catch Arantxa Sanchez Vicario, at 51:

Rank  Player                   Matches vs No. 1
1     Arantxa Sanchez Vicario                51
2     Gabriela Sabatini                      38
3     Venus Williams                         37
4     Lindsay Davenport                      34
5     Conchita Martinez                      33
6     Helena Sukova                          31
7     Serena Williams                        28
8     Svetlana Kuznetsova                    27
-     Jana Novotna                           27
10    Amelie Mauresmo                        25
11    Maria Sharapova                        23

Wins against No. 1s is a more achievable goal. Martina Navratilova holds the current record at 18*, followed by Serena at 16, and then Lindsay Davenport and Venus at 15:

Rank  Player               Wins  Losses
1     Martina Navratilova    18*      
2     Serena Williams        16      12
3     Lindsay Davenport      15      19
-     Venus Williams         15      22
5     Steffi Graf            11       8
6     Gabriela Sabatini      10      28
7     Amelie Mauresmo         8      17
8     Svetlana Kuznetsova     7      20
-     Maria Sharapova         7      16
-     Mary Pierce             7      15
-     Justine Henin           7       9

*My database does not have rankings throughout Navratilova’s entire career, but other sources credit her with 18 wins.

Win percentage against top-ranked opponents is a bit trickier, as it depends where you set the minimum number of matches. I’ve drawn the line at five. That’s rather low, but I wanted to include Alize Cornet and Elina Svitolina, active players who have each won three of their six matches against No. 1s. By this standard, Venus ranks eighth, though equally reasonable thresholds of 8 or 10 matches would move her up two or three places:

Rank  Player             Wins  Losses   Win%
1     Steffi Graf          11       8  57.9%
2     Serena Williams      16      12  57.1%
3     Petra Kvitova         5       4  55.6%
4     Elina Svitolina       3       3  50.0%
-     Alize Cornet          3       3  50.0%
6     Lindsay Davenport    15      19  44.1%
7     Justine Henin         7       9  43.8%
8     Venus Williams       15      22  40.5%
9     Vera Zvonareva        4       7  36.4%
-     Dinara Safina         4       7  36.4%

Remember that the average player wins fewer than 12% of matches against No. 1s!

Finally, Venus’s defeat of Kerber gave her a win against her sixth different No. 1, moving her into second place in that department. As is so often the case, she trails only her sister, who has beaten seven. Oddly enough, there is very little overlap between Serena’s and Venus’s lists: Their only common victims are Hingis and Davenport. The full list:

Rank  Player               No. 1s defeated
1     Serena Williams                    7
2     Venus Williams                     6
3     Steffi Graf                        5
-     Kim Clijsters                      5
-     Amelie Mauresmo                    5
-     Maria Sharapova                    5
7     Petra Kvitova                      4
-     Lindsay Davenport                  4
-     Justine Henin                      4
-     Svetlana Kuznetsova                4

If Karolina Pliskova–who now stands within 1500 points of No. 1 and could further close the gap in Miami–reaches the top spot, Venus may get a chance to beat a 7th top player. Of course, Serena could get that chance, as well.

Regulations for Returning Rule-Breakers

Italian translation at settesei.it

Next month, Maria Sharapova will complete her 15-month doping ban and return to the WTA tour in Stuttgart, where she has been granted a wild card. It’s no surprise that tournaments are eager to invite an extremely marketable former No. 1, and Sharapova has already lined up wild cards for the Premier-level events in Madrid and Rome.

This has generated no small amount of controversy. Many people see wild cards as a sort of reward or gift that is inappropriate for a player caught breaking such a serious rule. Many fans and fellow players think that, even after she has undergone a severe penalty, Sharapova doesn’t deserve this type of advantage.

Crucially, neither the ITF–which handles drug testing and issued the suspension–nor the WTA–which sets the guidelines for tournament entry–has anything to say about the situation. Each event must make its own decision. The French Open may refuse to invite Sharapova this year (and Wimbledon could follow suit) but any other tournament organizer who cares about selling tickets and sponsorships would want her in the draw.

In other words, with the possible exception of Paris and London, Sharapova will be able to pick up where she left off, entering whichever tournaments she wishes. The only disadvantage is that she won’t be seeded, meaning that we could see some draws that will make the Indian Wells quarter of death look like a friendly club tournament. If she plays well and stays healthy, she’ll probably earn her way to some seeds before the end of the season.

I’m not interested the argument about whether Sharapova “deserves” these wild cards. I’m not a fan of tournaments handing prize money and ranking points opportunities to favorites in any case, but on the other hand, Maria’s penalty was already severe. It doesn’t seem right that she would spend months scrambling for points in lower-level ITFs. When Viktor Troicki was suspended for one year in 2013, he was granted only two tour-level wild cards, so he needed six months to regain his former ranking.

My concern is for the Troickis of the tennis world. Both Sharapova’s and Troicki’s comebacks will ultimately be shaped by the decisions of individual tournaments, so Sharapova–an immensely marketable multiple-Slam winner–will get in almost everywhere she wants, while Troicki was forced to start almost from zero. Put another way: Sharapova’s 15-month ban will last 15 months (exactly 15 months, since she’ll play her first-round match in Stuttgart on the first possible day) while Troicki’s 12-month suspension knocked him out of contention for almost 18 months.

The WTA needs a set of rules that determine exactly what a player can expect upon return from a suspension. Fortunately, they already have something in place that can be adapted to serve the purpose: the “special ranking” for those with long-term injuries. (The ATP’s “protected ranking” rule is similar.) If a player is out of action for more than six months, she can use the ranking she held when she last competed to enter up to eight events, including up to two Premier Mandatories and two Grand Slams. Whether the player is iconic or anonymous, she has a fair chance to rebuild her ranking after recovering from injury.

This is my proposal: When a player returns from suspension, treat her like a player returning from injury, with one difference: For the first year back, no wild cards. Sharapova would get into eight events–she might choose Stuttgart, Rome, Madrid, Roland Garros, Birmingham, Wimbledon, Toronto, and Cincinnati. If she played well in her first two months back, she would probably have a high enough ranking to get into the US Open without help, and the whole issue would cease to matter.

The details don’t need to be exactly the same as post-injury comebacks. I can imagine including two to four additional special ranking entries into ITFs or qualifying, in case a player wants to work her way back to tour level, as a sort of rehab assignment. The important thing here is that the rules would be the same for everyone. As harsh as Sharapova’s penalty is, it pales in comparison to the effect a 15-month ban could have on a less popular tour regular, as Troicki’s example demonstrates.

Like it or not, there will be more doping bans, and unless the tours institute this sort of standardized treatment, there will be more controversies about whether this player or that player deserves wild cards after they return to the tour. The ultimate severity of a penalty will always depend on many factors, but a player’s popularity should never be one of them.

The Indian Wells Quarter of Death

Italian translation at settesei.it

The Indian Wells men’s draw looks a bit lopsided this year. The bottom quarter, anchored by No. 2 seed Novak Djokovic, also features Roger Federer, Rafael Nadal, Juan Martin del Potro, and Nick Kyrgios. It doesn’t take much analysis to see that the bracket makes life more difficult for Djokovic, and by extension, it cleared the way for Andy Murray. Alas, Murray lost his opening match against Vasek Pospisil on Saturday, making No. 3 seed Stan Wawrinka the luckiest man in the desert.

The draw sets up some very noteworthy potential matches: Federer and Nadal haven’t played before the quarterfinal since their first encounter back in 2004, and Fed hasn’t played Djokovic before the semis in more than 40 meetings, since 2007. Kyrgios, who has now beaten all three of the elites in his quarter, is likely to get another chance to prove his mettle against the best.

I haven’t done a piece on draw luck for awhile, and this seemed like a great time to revisit the subject. The principle is straightforward: By taking the tournament field and generating random draws, we can do a sort of “retro-forecast” of what each player’s chances looked like before the draw was conducted–back when Djokovic’s road wouldn’t necessarily be so rocky. By comparing the retro-forecast to a projection based on the actual draw, we can see how much the luck of the draw impacted each player’s odds of piling up ranking points or winning the title.

Here are the eight players most heavily favored by the pre-draw forecast, along with the their chances of winning the title, both before and after the draw was conducted:

Player                 Pre-Draw  Post-Draw  
Novak Djokovic           26.08%     19.05%  
Andy Murray              19.30%     26.03%  
Roger Federer            10.24%      8.71%  
Rafael Nadal              5.46%      4.80%  
Stan Wawrinka             5.08%      7.14%  
Kei Nishikori             5.01%      5.67%  
Nick Kyrgios              4.05%      2.62%  
Juan Martin del Potro     4.00%      2.34%

These odds are based on my jrank rating system, which correlates closely with Elo. I use jrank here instead of Elo because it’s surface-specific. I’m also ignoring the first round of the main draw, which–since all 32 seeds get a first-round bye–is just a glorified qualifying round and has very little effect on the title chances of seeded players.

As you can see, the bottom quarter–the “group of death”–is in fact where title hopes go to die. Djokovic, who is still considered to be the best player in the game by both jrank and Elo, had a 26% pre-draw chance of defending his title, but it dropped to 19% once the names were placed in the bracket. Not coincidentally, Murray’s odds went in the opposite direction. Federer’s and Nadal’s title chances weren’t hit quite as hard, largely because they weren’t expected to get past Djokovic, no matter when they faced him.

The issue here isn’t just luck, it’s the limitation of the ATP ranking system. No one really thinks that del Potro entered the tournament as the 31st favorite, or that Kyrgios came in as the 15th. No set of rankings is perfect, but at the moment, the official rankings do a particularly poor job of reflecting the players with the best chances of winning hard court matches. The less reliable the rankings, the better chance of a lopsided draw like the one in Indian Wells.

For a more in-depth look at the effect of the draw on players with lesser chances of winning the title, we need to look at “expected ranking points.” Using the odds that a player reaches each round, we can calculate his expected points for the entire event. For someone like Kyle Edmund, who would have almost no chance of winning the title regardless of the draw, expected points tells a more detailed story of the power of draw luck. Here are the ten players who were punished most severely by the bracket:

Player                 Pre-Draw Pts Post-Draw Pts  Effect  
Kyle Edmund                    28.8          14.3  -50.2%  
Steve Johnson                  65.7          36.5  -44.3%  
Vasek Pospisil                 29.1          19.4  -33.2%  
Juan Martin del Potro         154.0         104.2  -32.3%  
Stephane Robert                20.3          14.2  -30.1%  
Federico Delbonis              20.0          14.5  -27.9%  
Novak Djokovic                429.3         325.4  -24.2%  
Nick Kyrgios                  163.5         124.6  -23.8%  
Horacio Zeballos               17.6          14.1  -20.0%  
Alexander Zverev              113.6          91.5  -19.4%

At most tournaments, this list is dominated by players like Edmund and Pospisil: unseeded men with the misfortune of drawing an elite opponent in the first round. Much less common is to see so many seeds–particularly a top-two player–rating as the most unlucky. While Federer and Nadal don’t quite make the cut here, the numbers bear out our intuition: Fed’s draw knocked his expected points from 257 down to 227, and Nadal’s reduced his projected tally from 195 to 178.

The opposite list–those who enjoyed the best draw luck–features a lot of names from the top half, including both Murray and Wawrinka. Murray squandered his good fortune, putting Wawrinka in an even better position to take advantage of his own:

Player              Pre-Draw Pts  Post-Draw Pts  Effect  
Malek Jaziri                21.9           31.6   44.4%  
Damir Dzumhur               29.1           39.0   33.9%  
Martin Klizan               27.6           36.4   32.1%  
Joao Sousa                  24.7           31.1   25.9%  
Peter Gojowczyk             20.4           25.5   24.9%  
Tomas Berdych               93.6          116.6   24.6%  
Mischa Zverev               58.5           72.5   23.8%  
Yoshihito Nishioka          26.9           32.6   21.1%  
John Isner                  80.2           97.0   21.0%  
Andy Murray                369.1          444.2   20.3%  
Stan Wawrinka              197.8          237.7   20.1%

Over the course of the season, quirks like these tend to even out. Djokovic, on the other hand, must be wondering how he angered the draw gods: Just to earn a quarter-final place against Roger or Rafa, he’ll need to face Kyrgios and Delpo for the second consecutive tournament.

If Federer, Kyrgios, and del Potro can bring their ATP rankings closer in line with their true talent, they are less likely to find themselves in such dangerous draw sections. For Djokovic, that would be excellent news.

Are Taller Players the Future of Tennis?

This is a guest post by Wiley Schubert Reed.

This week, the Memphis Open features the three tallest players ever to play professional tennis: 6-foot-10″ John Isner, 6-foot-11″ Ivo Karlovic, and 6-foot-11″ Reilly Opelka. And while these three certainly stand out among all players in the sport, they are by no means the only giants in the game. Also in the Memphis draw: 6-foot-5″ Dustin Brown, 6-foot-6″ Sam Querrey, and 6-foot-8″ Kevin Anderson. (Brown withdrew due to injury, and with Opelka’s second-round loss yesterday, Isner and Karlovic are the only giants remaining in the field.)

https://www.instagram.com/p/BQjI1gJBKgE/

There is no denying that the players on the ATP and WTA tours are taller than the ones who were competing 25 years ago. The takeover by the tall has been obvious for some time in the men’s game, and it’s extended to near the very top of the women’s game as well. But despite alarms raised about the unbeatable giants among men, the merely tall men have held on to control of the game.

The main reason: The elegant symmetry at the game’s heart. The tallest players have an edge on serve, but that’s just half of tennis. And on the return, extreme height–at least for the men–turns out to be a big disadvantage. But a rising crop of tall men have shown promise beyond their service games. If one of the tallest young stars is going to challenge the likes of Novak Djokovic and Andy Murray, he’ll have to do it by trying to return serve like them, too.

Sorting out exactly how much height helps a player is a complicated thing. Just looking at the top 100 pros, for instance, makes the state of things look like a blowout win in favor of the tall. The median top-100 man is nearly an inch taller today than in 1990, and the average top-100 woman is 1.5 inches taller [1]. The number of extremely tall players in the top 100 has gone up, too:

                                    1990  Aug 2016  
Top 100 Men      Median Height  6-ft-0.0  6-ft-0.8  
               At least 6-ft-5        3%       16%  
Top 100 Women    Median Height  5-ft-6.9  5-ft-8.5  
                 At least 6-ft        8%        9%

Height is clearly a competitive advantage, as taller young players rise faster through the rankings than their shorter peers. Among the top 100 juniors each year from 2000 to 2009 [2], the tallest players (6-foot-5 and over for men and 6-foot and over for women) [3] typically sit in the middle of the rankings. But they do better as pros: They were ranked on average approximately 127 spots higher than shorter players their age after four years for men and approximately 113 spots higher after four years for women.

Thus, juniors who are very tall have the best chance to build a solid pro career. But does that advantage hold within the top 100 of the pro rankings? Are the tallest pros the highest ranked?

For the women, they clearly are. From 1985 to 2016, the median top 10 woman was 1.2 inches taller than the median player ranked between No. 11 and No. 100, and the tallest women are winning an outsize portion of titles, with women 6-foot and taller winning 15.0 percent of Grand Slams, while making up only 6.6 percent of the top 100 over the same period. Most of these wins were by Lindsay Davenport, Venus Williams and Maria Sharapova. Garbiñe Muguruza became the latest 6-foot women’s champ at the French Open last year [4].

It’s a different story for the men, however. From 1985 to 2016, the median height of both the top 10 men and men ranked No. 11 to No. 100 was the same: 6-foot-0.8. And in those same 32 years, only three Grand Slam titles (2.4 percent) were won by players 6-foot-5 or taller (one each by Richard Krajicek, Juan Martin del Potro and Marin Cilic), while over the same period, players 6-foot-5 and above made up 7.7 percent of the top 100. In short, the tallest women are overperforming, while the tallest men are underperforming.

Why have all the big men accomplished so little collectively? One big reason is that whatever edge the tallest men gain in serving is cancelled out by their disadvantage when returning serve. I compared total points played by top-100 pros since 2011, and found that while players 6-foot-5 and over have a clear service advantage and return disadvantage, their height doesn’t seem to have a major impact on overall points won:

Height            % Svc Pts Won  % Ret Pts Won  % Tot Pts Won  
6-ft-5 and above          66.8%          35.7%          51.2%  
6-ft-1 to 6-ft-4          64.5%          37.8%          51.1%  
6-ft-0 and below          62.3%          39.1%          51.1%

Taller players serve better for two reasons. First, their height lets them serve at a sharper angle by changing the geometry of the court. With a sharper angle available to them, they have a greater margin for error to clear the top of the net while still getting the ball to bounce on or inside the service line. And a sharper angle also makes the ball bounce higher, up and out of returners’ strike zone [5].

Disregarding spin, for a 6-foot player to serve the ball at 120 miles per hour at the same angle as a 6-foot-5 player, he would need to stand more than 3 feet inside the baseline.

Second, a taller player’s longer serving arm allows him to whip the ball faster. For you physics fans, the torque (in this case magnitude of force imparted on the ball) is directly proportional to the radius of the lever arm (in this case the server’s extended arm and racket). As radius (arm length) increases, so does torque. There is no way for shorter players to make up this advantage. Six-foot-8 Kevin Anderson, current No. 74 in the world and one of the tallest players ever to make the top 10, told me, “I always say it’ll be easier for me to move like Djokovic than it will be for Djokovic to serve like me.”

One would think that height could be an advantage on return as well, with increased wingspan offering greater reach. 18-year-old, 6-foot-11 Reilly Opelka, who is already as tall as the tour’s reigning giant Ivo Karlovic and who ESPN commentator Brad Gilbert said will be “for sure the biggest ever,” told me his height gives him longer leverage. “My reach is a lot longer than a normal tennis player, so I’m able to cover a couple extra inches, which is pretty huge in tennis.”

But Gilbert and Tennis Channel commentator Justin Gimelstob said they believe tall players struggle on return because their higher center of gravity hurts their movement. If a very tall man can learn to move like the merely tall players that have long dominated the sport––Djokovic, Murray (6-foot-3), Roger Federer (6-foot-1) and Rafael Nadal (6-foot-1)–– Gilbert thinks he could be hard to stop. “If you’re 6-foot-6 and are able to move like that, I can easily see that size dominating,” he said.

Interestingly, Gilbert pointed out that some of the best returners in the women’s game––such as Victoria Azarenka (6-foot-0) and Maria Sharapova (6-foot-2)––are among its tallest players [6]. Carl Bialik asked three American women — 5-foot-11 Julia Boserup, 5-foot-10 Jennifer Brady and 5-foot-4 Sachia Vickery — why they think taller women aren’t at a disadvantage on return. They cited two main reasons: 1) Women are returning women’s serves, which are slower and have less spin, on average, than men’s serves, so they have more time to make up for any difficulty in movement; and 2) Women play on the same size court that men do, but a height that’s relatively tall for a woman is about average for men, and it’s a height that works well for returning, no matter your gender.

“On the women’s side, we don’t really have anyone who’s almost 6-foot-11 or 7-foot tall,” Brady said. While she’s above average height on the women’s tour, “I’m not as tall as Reilly Opelka,” she said.

Another reason players as tall as Opelka tend to struggle on return could be that they focus more in practice on improving their service game, which exacerbates the serve-oriented skew of their games. “Being tall helps with the serve and you maybe tend to focus on your serve games even more,” Karlovic, the tallest top 100 player at 6-foot-11 [7], said in an interview conducted on my behalf by members of the ATP World Tour PR & Marketing staff at the Bucharest tournament in April. “Shorter players aren’t as strong at serve so they work their return more.”

Charting the careers of all active male players 6-foot-5 and above who at some point ranked year-end top 100 bears this out. Their percentage of service points won increased by about 6 percentage points over their first eight years on tour [8], while percentage of return points won only increased by about 1.5 percentage points. In contrast, Novak Djokovic has steadily improved his return points won from 36.7 percent in 2005 to 43.9 percent in 2016.

When very tall men break through, it’s usually because of strong performance on return: del Potro and Cilic, who are both 6-foot-6, boosted their return performances to win the US Open in 2009 and 2014, respectively. At the 2009 US Open, del Potro won 44 percent of return points, up from his 40 percent rate on the whole year, including the Open. At the 2014 US Open, Cilic won 41 percent of return points, up from 38 percent that year. And they didn’t improve their return games by facing easy slates of opponents: Each man improved on his return-point winning rates against those same opponents over his career by about the same amount as he elevated his return game compared to the season as a whole.

“It’s a different type of pressure when you’re playing a big server who is putting pressure on you on both the serve and the return,” Gimelstob said. “That’s what Cilic was doing when he won the US Open. That’s the challenge of playing del Potro because he hits the ball so well, but obviously serves so well, also.” To put things into perspective, if del Potro and Cilic had returned at these levels across 2016, each would have ranked among the top seven returners in the game, joining Djokovic, Nadal, Murray, 5-foot-11 David Goffin, and 5-foot-9 David Ferrer. Neither man, though, has been able to return to a Slam final; del Potro has struggled with injury and Cilic with inconsistency.

For the tallest players, return performance is the difference between making the top 50 and the top 10. On average, active players 6-foot-5 and above who finished a year ranked in the top 10 won 67.7 percent of service points that year, while those who finished a year ranked 11 through 50 won 68.1 percent of service points, on average. That’s a difference of only 0.4 percentage points. The difference in return performance between merely making the top 50 and reaching the top 10, however, is far more striking: Tall players who made the top 10 win return points at a rate nearly 4 percentage points higher than do players ranked 11 through 50.

A solid-serving player 6-foot-5 or taller who can consistently win more than 38 percent of points on return has an excellent chance of making the top 10. Tomas Berdych and del Potro have done it, and Milos Raonic is approaching that mark, one reason he reached his first major final this year at Wimbledon. Today there are several tall young men who look like they could eventually win 38 percent of return points or better. Alexander Zverev (ranked 18) and Karen Khachanov (ranked 48) are both 6-foot-6, each won about 38 percent of return points in 2016, and neither is older than 20. Khachanov has impressed Gilbert and Karlovic. “That guy moves tremendous for 6-foot-6,” Gilbert said.

Other giants have impressed recently. Jiri Vesely, who is 23 and 6-foot-6, beat Novak Djokovic last year in Monte Carlo and won nearly 36 percent of return points in 2016. Opelka reached his first tour-level semifinal, in Atlanta. Most of the top 10 seeds at Wimbledon lost to players 6-foot-5 or taller. Del Potro won Olympic silver, beating Djokovic and Nadal along the way.

But moving from the top 10 to the top 1 or 2 is another question. Can a taller tennis player develop the skills to move as well as the top shorter players, and win multiple major titles? Well, it’s happened in basketball. “We haven’t had a big guy play tennis that’s like 6-foot-6, 6-foot-7, 6-foot-8, that’s moved like an NBA guy,” Gilbert said. “When you get that, that’s when you get a multiple Slam winner.” Anderson agrees that height is not the obstacle to movement people play it up to be: “You know, LeBron is 6-foot-8. If he can move as well as somebody who’s 5-foot-10, his size now is a huge advantage; there’s not a negative to it.”

Opelka, who qualified for his first grand slam main draw at the 2017 Australian Open where he pushed 11th-ranked David Goffin to five sets, says he is specifically focusing on the return part of his game in practice. “I’ve been spending a ton of time working on my return. When you look at the drills I’m doing in the gym, they work on explosive movement.” But he also points out that basketball players “move better than [tennis players] and are more explosive than [tennis players]” because of their incredible muscle mass, which won’t work for tennis. “I don’t know how they’d be able to keep up for four or five hours with that mass and muscle.” Put LeBron on Arthur Ashe Stadium at the U.S. Open in 100 degree heat for an afternoon, “it’s tough to say how they’ll compare.”

Zverev, who is 19 and 6-foot-6, agrees that tall tennis players face unique challenges: “Movement is much more difficult, and I think building your body is more difficult as well.” But the people I talked to believe that both Opelka and Zverev could be at the top of the game in a few years’ time. “Zverev––that guy could be No. 1 in the world,” Gilbert said. “He serves great, he returns great and he moves great.” And as for Opelka, Gilbert says: “Right now he’s got a monster serve. If he can develop movement, or a return game, who knows where he could go?”

Whether the tallest guys can develop the skills to consistently return at the level of a Djokovic or a Murray remains to be seen. But starting out with a huge serve is a major step toward eventually challenging them. As Opelka says, “every inch is important.”

Wiley Schubert Reed is a junior tennis player and fan who has written about tennis for fivethirtyeight.com. He is a senior at the United Nations International School in New York and will be entering Harvard University in the fall.

Continue reading Are Taller Players the Future of Tennis?

The Federer Backhand That Finally Beat Nadal

Italian translation at settesei.it

Roger Federer and Rafael Nadal first met on court in 2004, and they contested their first Grand Slam final two years later. The head-to-head has long skewed in Rafa’s favor: Entering yesterday’s match, Nadal led 23-11, including 9-2 in majors. Nadal’s defense has usually trumped Roger’s offense, but after a five-set battle in yesterday’s Australian Open final, it was Federer who came out on top. Rafa’s signature topspin was less explosive than usual, and Federer’s extremely aggressive tactics took advantage of the fast conditions to generate one opportunity after another in the deciding fifth set.

In the past, Nadal’s topspin has been particularly damaging to Federer’s one-handed backhand, one of the most beautiful shots in the sport–but not the most effective. The last time the two players met in Melbourne, in a 2014 semifinal the Spaniard won in straight sets, Nadal hit 89 crosscourt forehands, shots that challenges Federer’s backhand, nearly three-quarters of them (66) in points he won. Yesterday, he hit 122 crosscourt forehands, less than half of them in points he won. Rafa’s tactics were similar, but instead of advancing easily, he came out on the losing side.

Federer’s backhand was unusually effective yesterday, especially compared to his other matches against Nadal. It wasn’t the only thing he did well, but as we’ll see, it accounted for more than the difference between the two players.

A metric I’ve devised called Backhand Potency (BHP) illustrates just how much better Fed executed with his one-hander. BHP approximates the number of points whose outcomes were affected by the backhand: add one point for a winner or an opponent’s forced error, subtract one for an unforced error, add a half-point for a backhand that set up a winner or opponent’s error on the following shot, and subtract a half-point for a backhand that set up a winning shot from the opponent. Divide by the total number of backhands, multiply by 100*, and the result is net effect of each player’s backhand. Using shot-by-shot data from over 1,400 men’s matches logged by the Match Charting Project, we can calculate BHP for dozens of active players and many former stars.

* The average men’s match consists of approximately 125 backhands (excluding slices), while Federer and Nadal each hit over 200 in yesterday’s five-setter.

By the BHP metric, Federer’s backhand is neutral: +0.2 points per 100 backhands. Fed wins most points with his serve and his forehand; a neutral BHP indicates that while his backhand isn’t doing the damage, at least it isn’t working against him. Nadal’s BHP is +1.7 per 100 backhands, a few ticks below those of Murray and Djokovic, whose BHPs are +2.6 and +2.5, respectively. Among the game’s current elite, Kei Nishikori sports the best BHP, at +3.6, while Andre Agassi‘s was a whopping +5.0. At the other extreme, Marin Cilic‘s is -2.9, Milos Raonic‘s is -3.7, and Jack Sock‘s is -6.6. Fortunately, you don’t have to hit very many backhands to shine in doubles.

BHP tells us just how much Federer’s backhand excelled yesterday: It rose to +7.8 per 100 shots, a better mark than Fed has ever posted against his rival. Here are his BHPs for every Slam meeting:

Match       RF BHP  
2006 RG      -11.2  
2006 WIMB*    -3.4  
2007 RG       -0.7  
2007 WIMB*    -1.0  
2008 RG      -10.1  
2008 WIMB     -0.8  
2009 AO        0.0  
2011 RG       -3.7  
2012 AO       -0.2  
2014 AO       -9.9  
2017 AO*      +7.8 

* matches won by Federer

Yesterday’s rate of +7.8 per 100 shots equates to an advantage of +17 over the course of his 219 backhands. One unit of BHP is equivalent to about two-thirds of a point of match play, since BHP can award up to a combined 1.5 points for the two shots that set up and then finish a point. Thus, a +17 BHP accounts for about 11 points, exactly the difference between Federer and Nadal yesterday. Such a performance differs greatly from what Nadal has done to Fed’s backhand in the past: On average, Rafa has knocked his BHP down to -1.9, a bit more than Nadal’s effect on his typical opponent, which is a -1.7 point drop. In the 25 Federer-Nadal matches for which the Match Charting Project has data, Federer has only posted a positive BHP five times, and before yesterday’s match, none of those achievements came at a major.

The career-long trend suggests that, next time Federer and Nadal meet, the topspin-versus-backhand matchup will return to normal. The only previous time Federer recorded a +5 BHP or better against Nadal, at the 2007 Tour Finals, he followed it up by falling to -10.1 in their next match, at the 2008 French Open. He didn’t post another positive BHP until 2010, six matches later.

Outlier or not, Federer’s backhand performance yesterday changed history. Using the approximation provided by BHP, had Federer brought his neutral backhand, Nadal would have won 52% of the 289 points played—exactly his career average against the Swiss—instead of the 48% he actually won. The long-standing rivalry has required both players to improve their games for more than a decade, and at least for one day, Federer finally plugged the gap against the opponent who has frustrated him the most.