Doubles has long been a sort of final frontier in tennis analytics. Double is interesting, at least in part, for the same reason that all team sports are compelling–contributions can come from either player, or a combination of the two. From an analytics perspective, that poses a challenge: Can we isolate what each player brings to the court? I’ve tried to do so with my doubles Elo ratings, but that method relies on players changing partners. It’s not possible to identify how much each half contributed simply by looking at match results.
The problem, as usual, is limited data availability. To know how much value to assign to each player, we need to know what he or she did, even at the basic level of aces, double faults, winners, and errors. The tours report matchstats for many doubles contests, but do not separate the players. Knowing that the Bryan brothers hit 12 aces doesn’t tell us anything about Bob or Mike. The grand slam websites have been better, often providing sequential point-by-point data for some matches, but the same problem persists: They don’t differentiate between players.
That is, until now! The Australian Open website specified the server for each point of every doubles match. (It doesn’t identify the returner on each point, but … baby steps.) That opens up whole new vistas for analytics to separate the contributions of each player.
There’s no I in mixed
A natural place to start is mixed doubles, an event that, due to lack of data, has been almost entirely ignored by analysts. Yet mixed doubles is one of things that everyone seems to have at least a moderate interest in, either because it’s a popular amateur pastime, or because gender differences in sport are inherently fascinating. Due to the variety of skillsets on court at all times, mixed doubles presents tactical puzzles that are different from those posed by same-gender matches.
Let’s start with the basics. There are only 32 teams in a grand slam mixed doubles event, so it’s possible to extend the dataset even further by manually recording which players returned from which sides. (Thanks to Jeff M for a big assist with this.) Thus, for over 3,000 points, we have the gender of the server and the returner. The following table shows several aggregates: Overall mixed doubles averages, typical performance for male and female servers, and rates for male and female returners, including serve points won, first-serve-in rates, and average first serve speed:
Subset Hold% SPW First In Avg 1st Average 76.0% 63.3% 66.2% 103.1 Men serving 78.6% 65.1% 65.0% 110.2 Women serving 72.4% 61.3% 67.6% 94.9 Men returning - 60.4% 64.6% 103.5 Women returning - 65.9% 67.6% 102.8
I was a bit surprised by how narrow the gap is between men and women serving. In men’s doubles at the Australian Open, servers won 67.8% of points, and in women’s doubles, servers won 58.5%. The pool of players is very similar, but in the mixed event, men won fewer serve points and women won more.
Perhaps there is more insight to be gained by looking at more specific matchups:
Server Returner SPW First In Avg 1st Male Male 61.7% 63.5% 111.0 Male Female 68.1% 66.3% 109.5 Female Male 58.9% 66.0% 94.6 Female Female 63.3% 69.0% 95.1
Tactics appear to change a bit depending on the gender of the returner. Both men and women land more first serves when facing a female returner. However, first serve speed doesn’t vary much. This suggests that David Marrero–who got himself in hot water by possibly fixing a 2016 Australian Open mixed match and then making some questionable comments about inter-gender competition afterward–is unusual in his reluctance to hit hard against female opponents.
Interestingly, the averages from same-gender doubles matches pop up in this table. When men serve to women in mixed doubles, they win 68.1% of points, almost exactly the same rate of serve points won in men’s doubles. When women serve to men, they take 58.9% of points, just a bit higher than the usual rate in women’s doubles. This suggests that while the server-returner matchup is important, the gender of the net player is a key factor as well.
Beware of Melichar
Individual player results against each gender will tell us more, but a single tournament worth of no-ad, third-set super-tiebreak matches doesn’t give us a lot of data on many players. Many members of first-round losing teams served only 20-25 points each. Of the finalists, John Patrick Smith had the biggest gender gap, winning 54.9% of service points against men and 74.4% against women, and his opponent Barbora Krejcikova was similar, winning 59.6% against men and 73.0% against women. Their partners, Astra Sharma and Rajeev Ram, both had narrower gaps of just a few percentage points.
Over the course of the entire event, Sharma was the best server of the four, winning 69.7% of total service points compared to Ram’s 69.0%. But neither came close to semi-finalist Nicole Melichar, who won a whopping 78.4%, narrowly besting her partner, Bruno Soares, who won 77.7%. The Melichar/Soares duo appears to be particularly effective as a unit: Melichar won only 72.6% of service points in her three women’s doubles matches, and Soares won only 70.2% in his men’s doubles quarter-final run alongside Jamie Murray.
The first step toward analyzing any sporting event is simply understanding what’s going on. In the case of mixed doubles, a big part of that is getting a sense of the gender gap on both serve and return. There’s still a painful dearth of data–we now have a mere 31 matches with servers and returners identified for each point–but the next time you watch a mixed doubles match, you’ll be that much smarter about what to expect and what sorts of performances are worthy of further study.