Quantifying Cakewalks, or The Time Rafa Finally Got Lucky

During this year’s US Open, much has been made of some rather patchy sections of the draw. Many great players are sitting out the tournament with injury, and plenty of others crashed out early. Pablo Carreno Busta reached the quarterfinals by defeating four straight qualifiers, and Rafael Nadal could conceivably win the title without beating a single top-20 player.

None of this is a reflection on the players themselves: They can play only the draw they’re dealt, and we’ll never know how they would’ve handled a more challenging array of opponents. The weakness of the draw, however, could affect how we remember this tournament.  If we are going to let the quality of the field color our memories, we should at least try to put this year’s players in context to see how they compare with majors in the past.

How to measure draw paths

There are lots of ways to quantify draw quality. (There’s an entire category on this blog devoted to it.) Since we’re interested in the specific sets of opponents faced by our remaining contenders, we need a metric that focuses on those. It doesn’t really matter that, say, Nick Kyrgios was in the draw, since none of the semifinalists had to play him.

Instead of draw difficulty, what we’re after is what I’ll call path ease. It’s a straightforward enough concept: How hard is it to beat the specific set of guys that Rafa (for instance) had to play?

To get a number, we’ll need a few things: The surface-weighted Elo ratings of each one of a player’s opponents, along with a sort of “reference Elo” for an average major semifinalist. (Or finalist, or title winner.) To determine the ease of Nadal’s path so far, we don’t want to use Nadal’s Elo. If we did that, the exact same path would look easier or harder depending on the quality of the player who faced it.

(The exact value of the “reference Elo” isn’t that important, but for those of you interested in the numbers: I found the average Elo rating of every slam semifinalist, finalist, and winner back to 1988 on each of the three major surfaces. On hard courts, those numbers are 2145, 2198, and 2233, respectively. When measuring the difficulty of a path to the semifinal round, I used the first of those numbers; for the difficulty of a path to the title, I used the last.)

To measure path ease, then, we answer the question: What are the odds that an average slam semifinalist (for instance) would beat this particular set of players? In Rafa’s case, he has yet to face a player with a weighted-hard-court Elo rating above 1900, and the typical 2145-rated semifinalist would beat those five players 71.5% of the time. That’s a bit easier than Kevin Anderson‘s path the semis, but a bit harder than Carreno Busta’s. Juan Martin del Potro, on the other hand, is in a different world altogether. Here are the path ease numbers for all four semifinalists, showing the likelihood that average contenders in each round would advance, giving the difficulty of the draws each player has faced:

Semifinalist   Semi Path  Final Path  Title Path  
Nadal              71.5%       49.7%       51.4%  
del Potro           9.1%        7.5%       10.0%  
Anderson           69.1%       68.9%       47.1%  
Carreno Busta      74.3%       71.2%       48.4%

(We don’t yet know each player’s path to the title, so I averaged the Elos of possible opponents. Anderson and Carreno Busta are very close, so for Rafa and Delpo, their potential final opponent doesn’t make much difference.)

There’s one quirk with this metric that you might have noticed: For Nadal and del Potro, their difficulty of reaching the final is greater than that of winning the title altogether! Obviously that doesn’t make logical sense–the numbers work out that way because of the “reference Elos” I’m using. The average slam winner is better than the average slam finalist, so the table is really saying that it’s easier for the average slam winner to beat Rafa’s seven opponents than it would be for the average slam finalist to get past his first six opponents. This metric works best when comparing title paths to title paths, or semifinal paths to semifinal paths, which is what we’ll do for the rest of this post.

Caveats and quirks aside, it’s striking just how easy three of the semifinal paths have been compared to del Potro’s much more arduous route. Even if we discount the difficulty of beating Roger Federer–Elo thinks he’s the best active player on hard courts but doesn’t know about his health issues–Delpo’s path is wildly different from those of his semifinal and possible final opponents.

Cakewalks in context

Semifinalist path eases of 69% or higher–that is, easier–are extremely rare. In fact, the paths of Anderson, Carreno Busta, and Nadal are all among the ten easiest in the last thirty years! Here are the previous top ten:

Year  Slam             Semifinalist               Path Ease  
1989  Australian Open  Thomas Muster                  84.1%  
1989  Australian Open  Miloslav Mecir                 74.2%  
1990  Australian Open  Ivan Lendl                     73.8%  
2006  Roland Garros    Ivan Ljubicic                  73.7%  
1988  Australian Open  Ivan Lendl                     72.2%  
1988  Australian Open  Pat Cash                       70.1%  
2004  Australian Open  Juan Carlos Ferrero            69.2%  
1996  US Open          Michael Chang                  68.8%  
1990  Roland Garros    Andres Gomez                   68.4%  
1996  Australian Open  Michael Chang                  66.2%

In the last decade, the easiest path to the semifinal was Stan Wawrinka‘s route to the 2016 French Open final four, which rated 59.8%. As we’ll see further on, Wawrinka’s draw got a lot more difficult after that.

Del Potro’s draw so far isn’t quite as extreme, but it is quite difficult in the historical context. Of the nearly 500 major semifinalists since 1988, all but 15 are easier than his 9.1% path difficulty. Here are the top ten, all of whom faced draws that would have given the average slam semifinalist less than an 8% chance of getting that far:

Year  Slam             Semifinalist              Path Ease  
2009  Roland Garros    Robin Soderling                1.6%  
1988  Roland Garros    Jonas Svensson                 1.9%  
2017  Wimbledon        Tomas Berdych                  3.7%  
1996  Wimbledon        Richard Krajicek               6.4%  
2011  Wimbledon        Jo Wilfried Tsonga             6.6%  
2012  US Open          Tomas Berdych                  6.8%  
2017  Roland Garros    Dominic Thiem                  6.9%  
2014  Australian Open  Stan Wawrinka                  7.0%  
1989  Roland Garros    Michael Chang                  7.1%  
2017  Wimbledon        Sam Querrey                    7.5%

Previewing the history books

In the long term, we’ll care a lot more about how the 2017 US Open champion won the title than how he made it through the first five rounds. As we saw above, three of the four semifinalists have a path ease of around 50% to win the title–again, meaning that a typical slam winner would have a roughly 50/50 chance of getting past this particular set of seven opponents.

No major winner in recent memory has had it so easy. Nadal’s path would rate first in the last thirty years, while Carreno Busta’s or Anderson’s would rate in the top five. (If it comes to that, their exact numbers will depend on who they face in the final.) Here is the list that those three men have the chance to disrupt:

Year  Slam             Winner                  Path Ease  
2002  Australian Open  Thomas Johansson            48.1%  
2001  Australian Open  Andre Agassi                47.6%  
1999  Roland Garros    Andre Agassi                45.6%  
2000  Wimbledon        Pete Sampras                45.3%  
2006  Australian Open  Roger Federer               44.5%  
1997  Australian Open  Pete Sampras                44.4%  
2003  Australian Open  Andre Agassi                43.9%  
1999  US Open          Andre Agassi                41.5%  
2002  Wimbledon        Lleyton Hewitt              39.9%  
1998  Wimbledon        Pete Sampras                39.1%

At the 2006 Australian Open, Federer lucked into a path that was nearly as easy as Rafa’s this year. His 2003 Wimbledon title just missed the top ten as well. By comparison, Novak Djokovic has never won a major with a path ease greater than 18.7%–harder than that faced by more than half of major winners.

Nadal has hardly had it easy as he has racked up his 15 grand slams, either. Here are the top ten most difficult title paths:

Year  Slam             Winner                Path Ease  
2014  Australian Open  Stan Wawrinka              2.2%  
2015  Roland Garros    Stan Wawrinka              3.1%  
2016  Us Open          Stan Wawrinka              3.2%  
2013  Roland Garros    Rafael Nadal               4.4%  
2014  Roland Garros    Rafael Nadal               4.7%  
1989  Roland Garros    Michael Chang              5.0%  
2012  Roland Garros    Rafael Nadal               5.2%  
2016  Australian Open  Novak Djokovic             5.4%  
2009  US Open          J.M. Del Potro             5.9%  
1990  Wimbledon        Stefan Edberg              6.2%

As I hinted in the title of this post, while Nadal got lucky in New York this year, it hasn’t always been that way. He appears three times on this list, facing greater challenges than any major winner other than Wawrinka the giant-killer.

On average, Rafa’s grand slam title paths haven’t been quite as harrowing as Djokovic’s, but compared to most other greats of the last few decades, he has worked hard for his titles. Here are the average path eases of players with at least three majors since 1988:

Player           Majors        Avg Path Ease  
Stan Wawrinka         3                 2.8%  
Novak Djokovic       12                11.3%  
Rafael Nadal         15                13.6%  
Stefan Edberg         4                14.6%  
Andy Murray           3                18.8%  
Boris Becker          4                18.8%  
Mats Wilander         3                19.8%  
Gustavo Kuerten       3                22.0%  
Roger Federer        19                23.5%  
Jim Courier           4                26.4%  
Pete Sampras         14                28.9%  
Andre Agassi          8                32.3%

If Rafa adds to his grand slam haul this weekend, his average path ease will take a bit of a hit. Still, he’ll only move one place down the list, behind Stefan Edberg. After more than a decade of battling all-time greats in the late rounds of majors, it’s fair to say that Nadal deserved this cakewalk.

Update: This post reads a bit differently than when I first wrote it: I’ve changed the references to “path difficulty” to “path ease” to make it clearer what the metric is showing.

Nadal and Anderson advanced to the final, so we can now determine the exact path ease number for whichever one of them wins the title. Rafa’s exact number remains 51.4%, and should he win, his career average across 16 slams will increase to about 15%. Anderson’s path ease to the title is “only” 41.3%, which would be good for ninth on the list shown above, and just barely second easiest of the last 30 US Opens.

Putting the Antalya Draw Into Perspective

This is a guest post by Peter Wetz.

When the pre-Wimbledon grass court tournament in Antalya was announced by the ATP in May 2016, some people were scratching their heads: Which top players will be willing to play in Antalya, Turkey one week ahead of Wimbledon? Even more so, because one week earlier two events are played in London and Halle, the latter being considerably closer to London. If a player wanted to participate in Antalya, he would have to fly from Halle (or London) to Antalya and then back to London for Wimbledon, not an ideal itinerary.

Taking a glance at the entry list, the doubts are verified: After Dominic Thiem, the only top 10 player entered in the event, there were just three other men (Paolo Lorenzi, Viktor Troicki and Fernando Verdasco) ranked within the top 40. Only three (Thiem, Verdasco, and Lorenzi) of the 28 players who were directly accepted to the main draw of the event, will be seeded at Wimbledon.

But how weak is the field really compared to others? Of course there are countless ways to measure the strength of a draw, but for a quick and dirty approach we will simply look at two measures, that is, the last direct acceptance (LDA) and the mean rank of quarterfinalists.

The LDA is the rank of the last player who gained direct entrance into a tournament’s main draw excluding lucky losers, qualifiers and special exempts. Comparing the last direct acceptance of the Antalya draw (86, Radu Albot) to all other ATP Tour level events with a draw size of 32 or 28 players, it turns out that Antalya is at the 39th percentile. This means that 39% of the other tournaments have a better/lower (or equal) LDA and that 61% have a worse/higher LDA, respectively. The following image shows a percentile plot of LDAs of tournaments since 2012, highlighting this week’s event in Antalya:

The fact that the LDA compares well against the other tournaments tells us that despite the lack of top ranked seeds, the field seems to be more dense at the bottom. Not that bad after all?

Let us take a look at the mean rank of the eight players who made it into the quarterfinals. Choosing quarterfinalists limits the calculation to the players who were able to perform well at the event, winning at least one, and usually two, matches. This should reduce some of the noise in the data that would be otherwise included due to lucky first round wins.

The mean rank of the quarterfinalists at the Antalya Open 2017 is 109. Out of the 726 tournaments since 2000 with 32 or 28 player draws which were considered in this analysis, only 35 tournaments had a higher mean rank of players at the quarterfinal stage. With nine out of those 35 tournaments, the Hall of Fame Tennis Championships at Newport–which takes place each year after Wimbledon–stands out from the pack. As the following plot shows, the Antalya Open is at the 95th percentile in this category. This seems to be more aligned with what we would have expected.

To provide some context, the following table lists the top 10 tournaments with links to the draws having the worst mean rank of quarterfinalists.

#  Tournament           Mean QF Rank
1  Newport '10          240
2  Newport '01          197
3  Delray Beach '16     191
4  Moscow '13           166
5  Newport '11          166
6  Newport '07          165
7  s-Hertogenbosch '09  164
8  Newport '08          163
9  Gstaad '14           156
10 Amsterdam '01        152
36 Antalya '17          109

The seeds are to blame for this: Of the eight seeds, only Verdasco managed to win a match. The other seven went winless. We have to go back as far as 1983’s Tel Aviv tournament to find a draw where only one seed won a match. In Tel Aviv, however, the third seed Colin Dowdeswell won three matches all in all, whereas Fernando Verdasco crashed out in the second round. By the way, Tel Aviv 1983 marks the first title of the then 16 years and 2 months old Aaron Krickstein, still the youngest player to win a singles title on the ATP Tour. That only two out of eight seeds win their first match happens about once per year. The last time this happened at the 2016 Brasil Open, where only Pablo Cuevas and Federico Delbonis won matches as seeds.

Despite the presence of only one top 30 player in this year’s Antalya draw, the middle and bottom of the field looked surprisingly solid, as we saw when considering the last direct acceptance. However, if we take into account the development of the tournament and calculate the mean rank of quarterfinalists, it becomes clear that the field got progressively weaker. Still, there have been worse draws in the past and there will doubtless be worse draws in future. Maybe even in the not too distant future, if we take a glance at this year’s Newport entry list.

Peter Wetz is a computer scientist interested in racket sports and data analytics based in Vienna, Austria.

Diego Schwartzman’s Return Game Is Even Better Than I Thought

Diego Schwartzman is one of the most unusual players on the ATP tour. Even shorter than David Ferrer, his serve will never be a weapon, so the only way he can compete is by neutralizing everyone else’s offerings and winning baseline battles. Up to No. 34 in this week’s official rankings and No. 35 on the Elo list, he’s proven he can do that against some very good players.

Using the ATP stats leaderboard at Tennis Abstract, we can get a quick sense of how his return game compares with the elites. At tour level in the last 52 weeks (through Monte Carlo), he ranks third with 42.3% return points won, behind only Andy Murray and Novak Djokovic. He is particularly effective against second serves, winning 56.6% of those, better than anyone else on tour. He has broken in 31.8% of his return games, another third-place showing, this time behind Murray and Rafael Nadal.

Yet the leaderboard warns us to tread carefully. In the last year, Murray’s opponents have been far superior to Schwartzman’s, with a median rank of 24 and a mean rank of 41.5. The Argentine’s opponents have rated at 45.5 and 54.8, respectively. Murray, Djokovic, and Nadal are far better all-around players than Schwartzman, so they regularly reach later rounds, where the quality of competition goes way up.

Competition quality is one of the knottiest aspects of tennis analytics, and it is far from being solved. If we want to compare Murray to Djokovic, competition quality isn’t such a big factor. One or the other might get lucky over a span of months, but in the long run, the two best players on tour will face roughly equivalent levels of competition. But when we expand our view to players like Schwartzman–or even a top-tenner such as Dominic Thiem–we can no longer assume that opponent quality will even out. To use a term from other sports, the ATP has a very unbalanced schedule, and the schedule is always more challenging for the best players.

Correcting for competition quality is also key to understanding how any particular player evolves over time. If a player’s results improve, he’ll usually start facing more challenging competition, as Schwartzman is doing this spring in his first shot at the full slate of clay-court Masters events. If his return numbers decline, is he actually playing worse, or is he simply competing at his past level against tougher opponents?

Adjusting for competition

To properly compare players, we need to identify similarities in their schedules. Any pair of tour regulars have played many of the same opponents, even if they’ve never played each other. For instance, since the beginning of last season, Murray and Djokovic have faced 18 of the same players–some more than once. Further down the ranking list, players tend to have fewer opponents in common, but as we’ll see, that’s an obstacle we can overcome.

Here’s how the adjustment works: For a pair of players, find all the opponents both men have faced on the same surface. For example, both Murray and Djokovic have played David Goffin on clay in the last 16 months. Murray won 53.7% of clay return points against the Belgian, while Djokovic won only 42.1%, meaning that Djokovic returned about 22% worse than Murray did. We repeat the process for every surface-player combination, weight the results so that longer matches (or larger numbers of matches) count more heavily, and find the average.

When we do that for the top two men, we find that Djokovic has returned 2.3% better. (That’s a percentage, not percentage points. A great returner wins about 40% of return points, and a 2.3% improvement on that is roughly 41%.) Our finding suggests that Murray has faced somewhat weaker-serving competition: Since the beginning of 2016, he has won 42.9% of return points, compared to Djokovic’s 43.3%–a smaller gap than the competition-adjusted one.

It takes more work to reliably compare someone like Schwartzman to the elites, since their schedules overlap so much less. So before adjusting Diego’s return numbers, we’ll take several intermediate steps. Let’s start with the world No. 3 Stanislas Wawrinka. We follow the above process twice: Once for Wawrinka and Murray, then again for Stan and Novak. Run the numbers, and we find that Wawrinka’s return game is 22.5% weaker than Murray’s and 24.3% weaker than Djokovic’s. Wawrinka’s rates relative to the other two players correspond very well with what we already found, suggesting that Djokovic is a little better than his rival. Weighting the two numbers by sample size–which, in this case, is almost identical–we slightly adjust those two comparisons and conclude that Wawrinka’s return game is 22.4% worse than Murray’s.

Generating competition-adjusted numbers for each subsequent player follows the same pattern. For No. 4 Federer, we run the algorithm three times, one for each of the players ranked above him, then we aggregate the results. For No. 34 Schwartzman, we go through the process 33 times. Thanks to the magic of computers, it takes only a few seconds to adjust 16 months worth of return stats for the ATP top 50.

Below are the results for 2016-17. Players are ranked by “relative return points won” (REL RPW), where a rating of 1.0 is arbitrarily given to Murray, and a rating of 0.98 means that a player wins 2% fewer return points than Murray against equivalent opposition. The “EX RPW” column puts those numbers in a more familiar context: The top-ranked player’s rating is set equal to 43.0%–approximately the best RPW of any player in the last few seasons–and everyone else’s is adjusted accordingly.  The last two columns show each player’s actual rate of return points won and their rank among the ATP top 50:

1     Diego Schwartzman         1.04   43.0%   42.4%     4  
2     Novak Djokovic            1.02   42.1%   43.3%     1  
3     Andy Murray               1.00   41.2%   42.9%     2  
4     Rafael Nadal              0.98   40.3%   42.6%     3  
5     David Goffin              0.97   40.1%   41.3%     5  
6     Gilles Simon              0.96   39.6%   40.1%     9  
7     Kei Nishikori             0.95   39.3%   40.1%    10  
8     David Ferrer              0.95   39.1%   40.6%     7  
9     Roger Federer             0.94   38.7%   38.7%    15  
10    Gael Monfils              0.93   38.5%   39.8%    11  

11    Roberto Bautista Agut     0.93   38.3%   40.3%     8  
12    Ryan Harrison             0.92   37.9%   36.7%    33  
13    Richard Gasquet           0.92   37.9%   40.8%     6  
14    Daniel Evans              0.91   37.6%   36.9%    27  
15    Juan Martin Del Potro     0.91   37.5%   36.8%    32  
16    Benoit Paire              0.90   37.0%   38.1%    19  
17    Mischa Zverev             0.90   36.9%   36.9%    28  
18    Grigor Dimitrov           0.89   36.4%   38.2%    18  
19    Fabio Fognini             0.88   36.4%   39.7%    12  
20    Fernando Verdasco         0.88   36.4%   38.3%    16  

21    Joao Sousa                0.88   36.2%   38.3%    17  
22    Dominic Thiem             0.88   36.2%   38.1%    20  
23    Stani Wawrinka            0.88   36.1%   37.5%    22  
24    Alexander Zverev          0.88   36.0%   37.5%    23  
25    Albert Ramos              0.87   35.9%   38.9%    14  
26    Kyle Edmund               0.86   35.5%   36.1%    37  
27    Jack Sock                 0.86   35.5%   36.6%    34  
28    Viktor Troicki            0.86   35.4%   37.1%    26  
29    Marin Cilic               0.86   35.4%   37.3%    25  
30    Pablo Carreno Busta       0.86   35.3%   39.4%    13  

31    Milos Raonic              0.86   35.2%   36.1%    38  
32    Pablo Cuevas              0.85   35.1%   36.9%    29  
33    Tomas Berdych             0.85   35.1%   36.9%    30  
34    Borna Coric               0.85   34.9%   36.1%    39  
35    Nick Kyrgios              0.85   34.9%   35.7%    41  
36    Philipp Kohlschreiber     0.84   34.7%   37.9%    21  
37    Jo Wilfried Tsonga        0.84   34.6%   36.2%    36  
38    Sam Querrey               0.83   34.3%   34.6%    44  
39    Lucas Pouille             0.82   33.9%   36.9%    31  
40    Feliciano Lopez           0.81   33.2%   35.2%    43  

41    Robin Haase               0.80   33.0%   36.1%    40  
42    Paolo Lorenzi             0.80   32.9%   37.5%    24  
43    Donald Young              0.78   32.2%   36.3%    35  
44    Bernard Tomic             0.78   32.1%   34.1%    45  
45    Nicolas Mahut             0.76   31.4%   35.4%    42  
46    Steve Johnson             0.75   31.0%   33.8%    46  
47    Florian Mayer             0.74   30.3%   33.5%    47  
48    John Isner                0.73   30.0%   29.8%    49  
49    Gilles Muller             0.72   29.8%   32.4%    48  
50    Ivo Karlovic              0.63   25.9%   26.4%    50

The big surprise: Schwartzman is number one! While the average ranking of his opponents was considerably lower than that of the elites, it appears that he has faced bigger-serving opponents than have Murray or Djokovic. The top five on this list–Schwartzman, Murray, Djokovic, Nadal, and Goffin–do not force any major re-evaluation of who we consider to be the game’s best returners, but the competition-adjusted metric does offer more evidence that Schwartzman really belongs there.

There is a similar predictability at the bottom of the list. The five players rated the worst by the competition-adjusted metric–Steve Johnson, Florian Mayer, John Isner, Gilles Muller, and Ivo Karlovic–are the same five who sit at the bottom of the actual RPW ranking, with only Isner and Muller swapping places. This degree of consistency at the top and bottom of the list is reassuring: The metric is correcting for something important, but it isn’t spitting out any truly crazy results.

There are, however, some surprises. Three players do very well when their return games are adjusted for competition: Ryan Harrison, Daniel Evans, and Juan Martin del Potro, all of whom jump from the bottom half to the top 15. In a sense, this is a surface adjustment for Harrison and Evans, both of whom have played almost exclusively on hard courts. Players win fewer return points on faster surfaces (and faster surfaces attract bigger-serving competitors, magnifying the effect), so when adjusted for competition, someone who plays only on hard courts will see his numbers improve. Del Potro, on the other hand, has been absolutely hammered by tough competition, so in his case the correction is giving him credit for the difficult opponents he has had to face.

Several clay court specialists find their return stats adjusted in the wrong direction. Last week’s finalist, Albert Ramos, falls from 14th to 25th, Pablo Carreno Busta drops from 13th to 30th, and Roberto Bautista Agut and Paolo Lorenzi see their numbers take a hit as well. This is the reverse of the effect that pushed Harrison and Evans up the list: Clay-court specialists spend more time on the dirt and they play against weaker-serving opponents, so their season averages make them look like better returners than they really are. It appears that these players are all particularly bad on hard courts: When I ran the algorithm with only clay-court results, Bautista Agut, Ramos, and Carreno Busta all appeared among the top 12 in competition-adjusted return points won. It’s their abysmal hard-court performances that pull down their longer-term numbers.

Beyond RPW

This algorithm–or something like it–has a great deal of potential beyond simply correcting return points won for tour-level competition quality. It could be used for any stat, and if competition-adjusted return rates were combined with corrected rates of service points won, it would generate a plausible overall player rating system.

Such a rating system would be more valuable if the algorithm were extended to players beyond the top 50, as well. Just as Schwartzman doesn’t yet have that many common opponents with the elites, Challenger-level stalwarts don’t have share many opponents with tour regulars. But there is enough overlap that, when combining the shared opponents of dozens of players, we might be able to get a better grip on how Challenger-level competition compares to that of the highest levels. Essentially, we can compare adjacent levels–the elites to the middle of the pack (say, ATP ranks 21 to 50), the middle of the pack to the next 50, and so on–to get a more comprehensive idea of how much players must improve to achieve certain goals.

Finally, adjusting serve and return stats so that we have a set of competition-neutral numbers for every player, for each season of his career, we will gain a clearer picture of which players are improving and by how much. Official rankings and Elo ratings tell us a lot, but they are sometimes fooled by lucky breaks, close wins, or inconsistent opposition. And they cannot isolate individual stats, which may be particularly useful for developmental purposes.

Adjusting for opposition quality is standard practice for analysts of many other sports, and it will help tennis analytics move forward as well. If nothing else, it has shown us that one extreme performance–Schwartzman’s return game–is much more than a fluke, and that service return greatness isn’t limited to the big four.

Rafael Nadal’s Wide-Open Monte Carlo Draw

This afternoon, Rafael Nadal will take on Albert Ramos for a chance at his tenth Monte Carlo Masters title. Since 2005, Nadal has faced the best clay-court players in the sport and, with very few exceptions, beaten them all.

Yet this year, Nadal’s path to the trophy has been remarkably easy. The three top seeds–Andy Murray, Novak Djokovic, and Stan Wawrinka–all lost early, leaving Nadal to face David Goffin in the semifinals and Ramos (who ousted Murray) in the final. Goffin, at No. 13, was Rafa’s highest ranked opponent, followed by Alexander Zverev, at No. 20, who Nadal crushed in the third round.

When we run the numbers, we’ll see that this competition isn’t just weak: It’s the weakest faced by any Masters titlist in recent history. I’ll get into the mechanics and show you some numbers in a minute.

First, a disclaimer. By saying a draw is weak, I’m not arguing that the title “means less” or is somehow less deserved. It’s not in any way a reflection on the player. For all we know, Rafa would’ve cruised through the draw had he faced the toughest possible opponent in every round. The only thing a weak draw tells us about the champion is how to forecast his future. Had Nadal beaten multiple top-ten players this week, we might be more confident predicting future success for him than we are now, after he has beaten up on a bunch of players we already suspected he’d have no problem with.

Back to the numbers. To measure the difficulty of a player’s draw, I used jrank–my own surface-adjusted rating system, roughly similar to Elo–at the time of each Masters event back to 2002. For each tournament, I found the jrank of each player the titlist defeated, and calculated the likelihood that a typical Masters winner would beat that group of players.

That’s a mouthful, so let’s walk through an example. In the last 15 years, the median Masters winner was ranked No. 3, with a jrank (for the surface of the tournament) of about 4700, good for fourth at the moment. A 4700-rated player would have an 85.7% chance of beating Ramos, a 75.7% chance of defeating Goffin, and 87.3%, 68.4%, and 88.7% chances of knocking out Diego Schwartzman, Zverev, and Kyle Edmund, respectively. Multiply those together, and our average Masters winner would have a 34.3% chance of claiming the trophy, given that competition.

I’m using a hypothetical average Masters winner so that we measure the level of competition against a constant level. It doesn’t matter whether 2017 Nadal, peak Nadal, or someone else entirely played that series of opponents. If Djokovic had faced the same five players, we’d want the numbers to come out the same.

Here are the ten easiest paths to a Masters title since 2002, measured by this algorithm:

Year  Event                Winner          Path Ease  
2017  Monte Carlo Masters  Rafael Nadal*       34.3%  
2016  Shanghai Masters     Andy Murray         33.0%  
2011  Shanghai Masters     Andy Murray         30.8%  
2013  Madrid Masters       Rafael Nadal        30.8%  
2012  Paris Masters        David Ferrer        30.4%  
2010  Monte Carlo Masters  Rafael Nadal        27.3%  
2012  Canada Masters       Novak Djokovic      25.8%  
2014  Madrid Masters       Rafael Nadal        25.3%  
2016  Paris Masters        Andy Murray         24.7%  
2010  Rome Masters         Rafael Nadal        24.6%

* pending; extremely likely

The average ‘Path Ease’ is 15.6%, and as we’ll see in a moment, some players have had it much, much harder. In Shanghai last year, Murray certainly did not: His draw turned out much like Rafa’s this week, complete with Goffin along the way and a three-named Spaniard in the final–in his case, Roberto Bautista Agut.

Here are the ten most difficult paths:

Year  Event                 Winner              Path Ease  
2007  Madrid Masters        David Nalbandian         4.1%  
2007  Paris Masters         David Nalbandian         6.2%  
2014  Canada Masters        Jo Wilfried Tsonga       6.6%  
2011  Rome Masters          Novak Djokovic           6.6%  
2009  Madrid Masters        Roger Federer            7.0%  
2010  Canada Masters        Andy Murray              7.7%  
2004  Cincinnati Masters    Andre Agassi             7.9%  
2007  Canada Masters        Novak Djokovic           8.0%  
2009  Indian Wells Masters  Rafael Nadal             8.0%  
2002  Canada Masters        Guillermo Canas          8.4%

Those of us who remember the end of David Nalbandian‘s 2007 season won’t be surprised to see him atop this list. In Madrid, he beat Nadal, Djokovic, and Roger Federer in the final three rounds, and in Paris, he knocked out Federer and Nadal again, along with three other top-16 players. Making his paths even more difficult, he didn’t earn a first-round bye in either event.

Given that Monte Carlo is the one non-mandatory Masters event, I expected that, over the years, it would prove to have the weakest competition. That was wrong. Entering this week, Monte Carlo is only fourth-easiest of the nine current 1000-series events. Indian Wells–which requires at least six victories for a title, unlike most of the others, which require only five–has been the toughest, while Miami, which also requires six wins, is closer to the middle of the pack:

Event         Avg Path Ease  
Indian Wells          12.8%  
Canada                14.3%  
Rome                  14.6%  
Miami                 15.3%  
Cincinnati            15.7%  
Monte Carlo*          16.5%  
Madrid**              16.7%  
Paris                 16.8%  
Shanghai              21.5%

* through 2016; ** hard- and clay-court eras included

Finally, seeing the presence of Nadal, Djokovic, and Murray on the list of easiest title paths raises another question. How have the big four’s levels of competition differed at the Masters events?

Player          Titles  Avg Path Ease  
Roger Federer       26          14.6%  
Novak Djokovic      30          16.1%  
Rafael Nadal        28          16.7%  
Andy Murray         14          18.1%

not including 2017 Monte Carlo

Federer has had the most difficult paths, followed by Djokovic, Nadal, and then Murray. Assuming Rafa wins today, his number will tick up to 17.3%.

To reach ten titles at a single event, as Nadal is on the brink of doing in Monte Carlo, requires one to thrive regardless of draw luck. Rafa’s path to the trophy last year was tougher than any of his previous Monte Carlo campaigns, rating a Path Ease of 9.1%, almost difficult enough to show up on the top ten list displayed above. His 2008 title was no cakewalk either–a typical Masters winner would have only a 10.0% chance of coming through that draw successfully.

This year, Rafa’s luck has decidedly changed. To no one’s surprise, the best clay court player in history is taking full advantage.

Is Grand Slam Qualifying Worth Tanking For?

Earlier today in Hobart, Naomi Osaka lost her second-round match to Mona Barthel. Coming into the match, she was in a tricky position: If she won, she wouldn’t be able to play Australian Open qualifying. For a young player outside the top 100, a tour-level quarterfinal would be nice, but presumably Melbourne was intended to be the centerpiece of her trip to Australia.

Since she lost the match, she’ll be able to play qualifying. But what if she hadn’t? Is this a situation in which a player would benefit from losing a match?

Put another way: In a position like Osaka’s, what are the incentives? If she could choose between the International-level quarterfinal and the Slam qualifying berth, which should she pick? Or, put more crassly, should a player in this position tank?

Let’s review the scenarios. In scenario A, Osaka wins the Hobart second-rounder, reaches the quarterfinal, and has a chance to go even further. She can’t play the Australian Open in any form. In scenario B, she loses the second-rounder, enters Melbourne qualifying and has a chance to reach the main draw.

Before we go through the numbers, take a guess: Which scenario is likely to give Osaka more ranking points? What about prize money?

Scenario A is more straightforward. By reaching the quarterfinals, she earns 30 additional ranking points and US$2,590 beyond what a second-round loser makes. Beyond that, we need to calculate “expected” points and prize money, using the amounts on offer for each round and combining them with her odds of getting there.

Let’s estimate that Osaka would have about a 25% chance of winning her quarterfinal match and earning an additional 50 points and $5400. In expected terms, that’s 12.5 points and $1,350. If she progresses, we’ll give her a 25% chance of reaching the final, then in the final, a 15% chance of winning the title.

Adding up these various possibilities, from her guaranteed QF points to her 0.94% chance (25%*25%*15*) of winning the Hobart title, we see that her expected rewards in scenario A are roughly 48 ranking points and just under $4,800.

Scenario B starts in a very different place. Thanks to the recent increases in Grand Slam prize money, every player in the qualifying takes home at least US$3,150. That’s already close to Osaka’s expected financial reward from advancing in Hobart. The points are a different story, though: First-round qualifying losers only get 2 WTA ranking points.

I’ll spare you all the calculations for scenario B, but I’ve assumed that Osaka would have a 70% chance of winning qualifying round 1, a 60% chance of winning QR2, and a 50% chance of winning QR3 and qualifying. Those might be a little bit high, but if they are, consider it compensation for the possibility that she’ll reach the main draw as a lucky loser. (Also, if we knock her chances all the way down to 50%, 45%, and 40%, the conclusions are the same, even if the points and prize money in scenario B are quite a bit lower.)

Those estimated probabilities translate into an expectation of about 23 ranking points and US$11,100. Osaka isn’t guaranteed any money beyond the initial $3,150, but the rewards for qualifying are enormous, especially compared to the prize money in Hobart. A first-round main draw loser in Melbourne takes home more money than the losing finalist does in Hobart.

And, of course, if she does qualify, there’s a chance she’ll go further. Since 2000, female Slam qualifiers have reached the second round 41% of the time, the third round 9% of the time, the fourth round 1.8% of the time, and the quarterfinals 0.3% of the time. Those odds, combined with her 21% chance of reaching the main draw in the first place,  translate into an additional 7 expected ranking points and $2,600 in prize money.

All told, scenario B gives us 30 expected ranking points and US$13,600 in expected prize money.

The Slam option results in far more cash, while the International route is worth more ranking points. In the long term, those ranking points would have some financial value, possible earning Osaka entry into a few higher-level events than she would otherwise qualify for. But that value probably doesn’t overcome the nearly $9,000 gap in immediate prize money.

I hope that no player ever tanks a match at a tour-level event so they can make it in time for Slam qualifying. But if one does, we’ll at least understand the logic behind it.

Will the US Open First-Round Bloodbath Benefit Serena Williams?

After only two days of play, the US Open women’s draw is a shell of its former self.

Ten seeds have been eliminated, only the fifth time in the 32-seed era that the number of first-round upsets has reached double digits. Four of the top ten seeds were among the victims, marking the first time since 1994 that so many top-tenners failed to reach the second round of a Grand Slam.

Things are particularly dramatic in the top half of the draw, where Serena Williams can now reach the final without playing a single top-ten opponent. In a single day of play, my (conservative) forecast of her chances of winning the tournament rose from 42% to 47%, only a small fraction of which owed to her defeat of Vitalia Diatchenko.

However, plenty of obstacles remain. Serena could face Agnieszka Radwanska or Madison Keys in the fourth round, and then Belinda Bencic–the last player to beat her–in the quarters. A possible semifinal opponent is Elina Svitolina, a rising star who took a set from Serena at this year’s Australian Open.

The first-round carnage didn’t include most of the players who have demonstrated they can challenge the top seed. Five of the last six players to beat Serena–Bencic, Petra Kvitova, Simona Halep, Venus Williams, and Garbine Muguruza–are still alive. Only Alize Cornet, the 27th seed who holds an improbable .500 career record against Serena, is out of the picture.

What’s more, early-round bloodbaths haven’t, in the past, cleared the way for favorites. In the 59 majors since 2001, when the number of seeds increased to 32, the number of first-round upsets has had little to do with the likelihood that the top seed goes on to win the tournament.

In 18 of those 59 Slams, four or fewer seeds were upset in the first round. The top seed went on to win five times. In 22 of the 59, five or six seeds were upset in the first round, and the top seed won eight times.

In the remaining 19 Slams, in which seven or more seeds were upset in the first round, the top seed won only five times. Serena has “lost” four of those events, most recently last year’s Wimbledon, when nine seeds fell in their opening matches and Cornet defeated her in the third round.

This is necessarily a small sample, and even setting aside statistical qualms, it doesn’t tell the whole story. While Serena has failed to win four of these carnage-ridden majors, she has won three more of them when she wasn’t the top seed, including the 2012 US Open, when ten seeds lost in the first round and Williams went on to beat Victoria Azarenka in the final.

Taken together, the evidence is decidedly mixed. With the exception of Cornet, the ten defeated seeds aren’t the ones Serena would’ve chosen to remove from her path. While her odds have improved a bit on paper, the path through Keys, Bencic, Svitolina, and Halep or Kvitova in the final is as difficult as any she was likely to face.

Roger Federer’s Impressive but Not-Entirely-Relevant Dominance of the Istanbul Field

Roger Federer has faced 14 of the 27 other players in this week’s Istanbul field, and owns a career record of 59-1 against them. His one loss came to Jurgen Melzer, while more than half of his win total is thanks to his decade-long dominance of Mikhail Youzhny (16-0) and Jarkko Nieminen (14-0).

It’s rare that players of Federer’s stature contest such small events, so we don’t expect to see such lopsided head-to-heads very often. In fact, if we limit our view to events where a player faced at least 10 of the other entrants, it is only the 17th time since 1980 that someone has entered an event with a won-loss percentage of 95% or better against the field.

Federer himself represents two of the previous 16 times this has happened. The most notable of them is 2008 Estoril. He had previously faced 14 of the other players in the draw, and had never lost to any of them in 46 meetings. There are only four other instances of players undefeated against a field, all between 1980 and 1984 and in many fewer matches.

The most eye-grabbing of those early-80s accomplishments was Ivan Lendl‘s record entering the 1980 Taipei event. He had faced 15 of the men in the draw, posting a record of 24-0 up to that point. Lendl’s name is the most common on the list, having entered tournaments with a 95% won-loss record against the field on four different occasions, highlighted by a 79-4 mark against the other competitors at Stratton Mountain in 1988.

Federer won the 2008 title in Estoril and Lendl claimed the 1980 trophy in Taipei, but Lendl was ousted in the second round of the 1988 Stratton Mountain event. Federer has also demonstrated that a stratospheric record against the field is no guarantee of success.

After Estoril, Roger’s second-best record entering an event was in Gstaad in 2013. He held a 73-3 record against the field, with each of the three losses coming against different opponents. He lost his opening-round match in straight sets to Daniel Brands. His record against the field of the previous week’s Hamburg event was nearly perfect as well at 137-8, but Federico Delbonis stopped him in the semifinals there.

Rafael Nadal can tell a similar story. His best record against a field was in Santiago two years ago, coming back from injury. He had lost only 1 of 28 career matches against the other players in the draw. That week, Horacio Zeballos doubled Rafa’s loss count.

In fact, of the 16 times that a player went into an event with a 95% or better record against the field, the favorite won only six of them. Expanding the sample to records of 90% or better, the dominant player won 30 of 72 titles. Neither mark is as good as we’d expect if the historically great players continued to win matches at a 95% or 90% clip. In practice, head-to-head records just aren’t as predictive as they seem to be.

As is evident from some of the examples I’ve given, there are mitigating circumstances for many of these losses, and they aren’t entirely random. These days, when a player enters an event that seems below him, there’s a reason for it. Nadal rarely plays 250s; he was doing so to work his way back into match form. Federer rarely seeks out smaller events on clay; he was experimenting with a new racket.

This week, there’s no reason why Fed shouldn’t perform at his usual level–at least his usual level for clay–and win the four matches he needs to claim yet another title. But if he suffers his second loss against the players gathered in Istanbul this week, it won’t be quite as much of a shock as that 59-1 record implies.