Italian translation at settesei.it
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.