The Pervasive Role of Luck in Tennis

Italian translation at settesei.it

No matter what the scale, from a single point to a season-long ranking–even to a career–luck plays a huge role in tennis. Sometimes good luck and bad luck cancel each other out, as is the case when two players benefit from net cord winners in the same match. But sometimes luck spawns more of the same, giving fortunate players opportunities that, in turn, make them more fortunate still.

Usually, we refer to luck only in passing, as one possible explanation for an isolated phenomenon. It’s important that we examine them in conjunction with each other to get a better sense of just how much of a factor luck can be.

Single points

Usually, we’re comfortable saying that the results of individual points are based on skill. Occasionally, though, something happens to give the point to an undeserving player. The most obvious examples are points heavily influenced by a net cord or a bad bounce off an uneven surface, but there are others.

Officiating gets in the way, too. A bad call that the chair umpire doesn’t overturn can hand a point to the wrong player. Even if the chair umpire (or Hawkeye) does overrule a bad call, it can result in the point being replayed–even if one player was completely in control of the point.

We can go a bit further into the territory of “lucky shots,” including successful mishits, or even highlight-reel tweeners that a player could never replicate. While the line between truly lucky shots and successful low-percentage shots is an ambiguous one, we should remember that in the most extreme cases, skill isn’t the only thing determining the outcome of the point.

Lucky matches

More than 5% of matches on the ATP tour this year have been won by a player who failed to win more than half of points played. Another 25% were won by a player who failed to win more than 53% of points–a range that doesn’t guarantee victory.

Depending on what you think about clutch and momentum in tennis, you might not view some–or even any–of those outcomes as lucky. If a player converts all five of his break point opportunities and wins a match despite only winning 49% of total points, perhaps he deserved it more. The same goes for strong performance in a tiebreaks, another cluster of high-leverage points that can swing a match away from the player who won more points.

But when the margins are so small that executing at just one or two key moments can flip the result–especially when we know that points are themselves influenced by luck–we have to view at least some of these tight matches as having lucky outcomes. We don’t have to decide which is which, we simply need to acknowledge that some matches aren’t won by the better player, even if we use the very loose definition of “better player that day.”

Longer-term luck

Perhaps the most obvious manifestation of luck in tennis is in the draw each week. An unseeded player might start his tournament with an unwinnable match against a top seed or with a cakewalk against a low-ranked wild card. Even seeded players can be affected by fortune, depending on which unseeded players they draw, along with which fellow seeds they will face at which points in the match.

Another form of long-term luck–which is itself affected by draw luck–is what we might call “clustering.” A player who goes 20-20 on a season by winning all of his first-round matches and losing all of his second-round matches will not fare nearly as well in terms of rankings or prize money as someone who goes 20-20 by winning only 10 first-round matches, but reaching the third round every time he does.

Again, this may not be entirely luck–this sort of player would quickly be labeled “streaky,” but combined with draw luck, he might simply be facing players he can beat in clusters, instead of getting easy first-rounders and difficult second-rounders.

The Matthew effect

All of these forms of tennis-playing fortune are in some way related. The sociologist Robert Merton coined the term “Matthew effect“–alternatively known as the principle of cumulative advantage–to refer to situations where one entity with a very small advantage will, by the very nature of a system, end up with a much larger advantage.

The Matthew effect applies to a wide range of phenomena, and I think it’s instructive here. Consider the case of two players separated by only a few points in the rankings–a margin that could have come about by pure luck: for instance, when one player won a match by walkover. One of these players gets the 32nd seed at the Australian Open and the other is unseeded.

These two players–who are virtually indistinguishable, remember–face very different challenges. One is guaranteed two matches against unseeded opponents, while the other will almost definitely face a seed before the third round, perhaps even a high seed in the first. The unseeded player might get lucky, either in his draw or in his matches, cancelling out the effect of the seeding, but it’s more likely that the seeded player will walk away from the tournament with more points, solidifying the higher ranking–that he didn’t earn in the first place.

Making and breaking careers

The Matthew effect can have an impact on an even broader scale. Today’s tennis pros have been training and competing from a young age, and most of them have gotten quite a bit of help along the way, whether it’s the right coach, support from a national federation, or well-timed wild cards.

It’s tough to quantify things like the effect of a good or bad coach at age 15, but wild cards are a more easily understood example of the phenomenon. The unlucky unseeded player I discussed above at least got to enter the tournament. But when a Grand Slam-hosting federation decides which promising prospect gets a wild card, it’s all or nothing: One player gets a huge opportunity (cash and ranking points, even if they lose in the first round!) while the other one gets nothing.

This, in a nutshell, is why people like me spend so much time on our hobby horses ranting about wild cards. It isn’t the single tournament entry that’s the problem, it’s the cascading opportunities it can generate. Sure, sometimes it turns into nothing–Ryan Harrison’s career is starting to look that way–but even in those cases, we never hear about the players who didn’t get the wild cards, the ones who never had the chance to gain from the cumulative advantage of a small leg up.

Why all this luck matters

If you’re an avid tennis fan, most of this isn’t news to you. Sure, players face good and bad breaks, they get good and bad draws, and they’ve faced uneven challenges along the way.

By discussing all of these types of fortune in one place, I hope to emphasize just how much luck plays a part in our estimate of each player at any given time. It’s no accident that mid-range players bounce around the rankings so much. Some of them are truly streaky, and injuries play a part, but much of the variance can be explained by these varying forms of luck. The #30 player in the rankings is probably better than the #50 player, but it’s no guarantee. It doesn’t take much misfortune–especially when bad luck starts to breed more opportunities for bad luck–to tumble down the list.

Even if many of the forms of luck I’ve discussed are truly skill-based and, say, break point conversions are a matter of someone playing better that day, the evidence generally shows that major rises and falls in things like tiebreak winning percentage and break point conversion rates are temporary–they don’t persist from year to year. That may not be properly classed as luck, but if we’re projecting the rankings a year from now, it might as well be.

While match results, tournament outcomes, and the weekly rankings are written in stone, the way that players get there is not nearly so clear. We’d do well to accept that uncertainty.

4 thoughts on “The Pervasive Role of Luck in Tennis”

  1. Another example of luck that’s worth considering:

    Imagine two equally matched players who all things being equal win 65% of their points on serve when they play each other.

    On this particular day one wins 59%, the other 71% and the second guy wins easily in two sets.

    So what happened? The first player play badly and the second play great? Well, maybe not. Two quick sets could be as few as 50 or 60 points on serve each so both players are only 1 standard deviation (sqrt[0.65*0.35/60]) away from what you would expect. I.e. it’s completely consistent with the two players playing just as well as each other.

    You’d never hear that from fans or commentators though.

  2. In consider the Matthew effect as being completely embedded, ‘deep-seated’ so to speak, in the construction of the ATP rankingsystem itself. The more “seats” you create in a tennis draw and the more rules you create, the less randomness you leave in the composition of a draw.
    I presume that one of the reasons that Federer, Nadal and Djokovic have been able to collect so many slam titles, is the creation of the 32-seats system for slams in 2001, in combination with the specific rules that were put in place. To a much lesser extent also the change of the points-system in 2008-2009, which makes it imo more difficult for lower ranked players to climb in the rankings. The top players benefit from this system, and get richer and richer… (Matthew by the way didn’t intend this to be interpreted in its material sense).
    I agree with you that luck can play a big role in a players tennis career, and have a cumulative impact. I don’t have to look any further than the case of my compatriot David Goffin. Entering as “lucky lose” in the 2012 Roland Got and playing a couple good matches propelled him temporarily in the top 50, without having the level to stay there. This season he had his share of luck again with Andy Murray’s withdraw in the R16 of the Rome Master, which allowed him his first QF in a Master event, and – some time later – to be 16th seed in the Wimbledon draw, which “allowed” him in turn to advance for the first time to a Wimbledon R16, and stay in the top 20.

    1. I think you’re being a bit harsh on Goffin there. Sure he had a good run in 2012, and then didn’t quite back it up, but he’s been improving and is now playing better than he ever has. My model (which is based on who players beat and lose to) has him as #14 in the world at the moment.

  3. Jeff,

    Really good discussion on luck in its short term and long term forms.

    One data point was a set of “picks games” I ran in 2007 for Pete Bodo’s TennisWorld, where participants were invited to pick the outcomes of GS R32 and later matches. I think I ran this over 3 or 4 tournaments: I found that, on average, the more popular pick won about 80% of the matches.

    This seemed to be close to a Goldilocks point: matches were not random, but neither were they so predictable that the contest wasn’t worth watching.

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