Category: Deep thoughts

Italian translation at settesei.it

In sports analytics, the topic of streakiness–the “hot hand“–is a popular one. Nearly everyone believes it exists, that players (or even teams) can go on a hot or cold streak, during which they temporarily play above or below their true level.

To a certain extent, streakiness is inevitable–if you flip a coin 100 times, you’ll see segments of 5 or 10 flips in which most of the flips are heads. That’s not because the coin suddenly got “better,” it’s a natural occurence over a long enough time span. So if you watch an entire tennis match, there are bound to be games where one player seems to be performing better than usual, perhaps stringing together several aces or exceptional winners.

The question, then, is whether a player is more streaky than would occur purely at random. To take just one example, let’s say a player hits aces on 10% of service points. If he did occasionally serve better than usual, we would observe that after he hits one ace, he is more likely (say, 15% or 20%) to hit another ace. A missed first or second serve might make it more likely than he misses his next try.

My last couple of topics–differences in the deuce/ad court, and the “reverse hot hand” at 30-40–have hinted that tennis may be structured in a way that prevents players from getting hot.

One of the most popular subjects for hot hand research is basketball free throw shooting. Researchers like it because it’s as close as basketball players get to a laboratory: every shot is from the same distance, there’s no defensive quality to consider, and even better, players usually get two tries, one right after the other. There’s nothing like it in tennis.

The one thing that seems a bit akin to free throw shooting is serving, especially for more dominant players. John Isner, Roger Federer, and Milos Raonic seem to go on serving streaks; certainly they can play game after game and control play with unreturnable serving. But when we look closer, their experience is much more nuanced. As we’ve seen, players generally are better in the deuce or ad court. It would be as if basketball player shot one free throw, then took two steps to the left and one step forward before attempting his next shot.

And, of course, there’s another player on the court. If Federer uses a relatively slow serve out wide in the deuce court for a service winner at 15-15, he is much less likely to use the same tactic at 30-30 or 40-15. Even if he was capable of hitting 50 perfect serves of that nature, he would never do so in a match. If it has any relevance for professional tennis, the hot hand must refer to something broader than a single skill.

On a more general level, the rules of tennis involve alternation more than more sports. Sure, most sports give the ball to the other team after a goal, but the length of possession–or in baseball, the length of an inning–can vary widely. In tennis, you can only add one game to your tally before handing the ball to your opponent. And even within that game, you are constantly moving from your stronger court to your weaker court; your opponent might be doing the same.

My question to you is this: If there is a hot hand in tennis, where would you expect to find it? Consecutive aces? Aces specifically in the deuce court? Service winners? Short service points? Points won? Return points won? Games won? First serves in? Point-ending winners? Avoidance of unforced errors? It’s possible that any or all of these things could occur in bunches, but which of them would indicate what we think of as a tennis player on a hot streak?

Italian translation at settesei.it

In any sport, there are a handful of stats that are frequently cited, but are ultimately of limited use.  Often, these statistics tell you something, but are misunderstood to imply something more.  Simple examples are many “counting” stats — points scored in basketball, touchdowns thrown in football, RBI in baseball.  In all of those cases, they indicate something good, but don’t give you context — lots of field goal attempts, a great offensive line, or good hitters on base in front of you, to take those three cases.

The stat in tennis that aggravates me most is the unforced error.  Not only does it ignore some important context (as in the other-sport stats I just mentioned), but it relies on the judgment of a scorer.

Misjudgment

The second problem is the more problematic one.  How much does a number mean if two people watching the same match wouldn’t come up with the same result?  This was a hot-button issue during Wimbledon, when the scorers were assigning an unusually small number of UEs, especially on serve returns.

If you’re watching the match, you might not notice.  If the end-of-set stats show that Nadal had 8 UEs and Federer had 17, that does tell you something … Federer was making more obvious mistakes.  But if you want to compare that to a Nadal/Federer match three weeks ago, or last year, those numbers are all but useless.

I suspect that, at events like Wimbledon, someone from the ITF, or maybe IBM, is giving standardized instructions to scorers with general rules for categorizing errors.  That would be a good start, especially if it were implemented across all tournaments at all professional levels.

…but it doesn’t matter

I suspect that no matter how consistent scorers are, the distinction between “unforced” and “forced” errors will always be arbitrary.  Consider the case of service returns.  There are occasional points, especially on second serve returns, where the returning player misses an easy shot.  But more frequently, the returning player is immediately on defense.  When is an error “unforced” on the return of a 130 mile-per-hour shot?

Ultimately, we will probably have computerized systems that classify errors for us.  If you have all the necessary data and crunch the numbers, a 125-mph serve down the T in the ad court might be returned 60% of the time, meaning there is a 40% chance of an error or non-return.  With those numbers on every serve (and every other shot, eventually), we could set the line for an “unforced” error on a shot that the average top-100 player would make, say, 75% of the time.  Or we could have different classifications: “unforced errors,” “disastrous errors,” “mildly forced errors,” and so on, indicating different percentage ranges.

The problem we have now is that professionals are so good (and their equipment is so advanced), that almost every shot can be offensive, meaning that players are almost always–to some extent–on defense.  If you’re rallying with Nadal, you might hit some winners, but you’re always fighting the spin.  If you’re rallying with Federer, the spin isn’t so bad, but you’re always trying to keep the ball away from his forehand.  (If you’re rallying with Djokovic, you’re wishing you had hit a better serve.)  That perpetual semi-defensive posture means that nearly every error is, to some extent, forced.  And because players are so good, we expect them to return every reachable ball, suggesting that nearly every error is, to some extent, unforced.

Yikes!

The wisdom of baseball analysts

A very similar problem arises in baseball.  If a fielder makes a misplay (according to the official scorer), he is charged with an “error.”  Paradoxically, some of the best fielders end up with the highest error totals.  If, say, a shortstop has great range, he’ll reach a lot of groundballs, and have more chances to make bad throws, thus racking up the errors.

For decades, fans considered errors to be the standard measure of defensive prowess–a stat called “fielding percentage” measures the ratio of plays-successfully-made to chances.   (In other words, 1 minus “error rate.”)  But because of the paradox mentioned above, the highest fielding percentages do not necessarily belong to the best fielders.

The solution: Ignore errors, look only at plays made.  (This is an oversimplification, but not by much.)  If Shortstop A makes more plays than Shortstop B, it doesn’t matter whether A makes more errors.  The guy you want on your team is the one who makes more plays.

Essentially, baseball errors correspond to tennis unforced errors, and baseball plays-not-made (shortstop dives for the ball and can’t reach it) correspond to tennis forced errors.  The stat that ends up mattering to baseball analysts–“plays made”–corresponds to “shots successfully returned.”  The analogy is imperfect, but it illustrates the problem with separating one type of non-play from another.

Solutions

If we don’t distinguish between different types of errors, we’re left with “shots made” and “shots not made,” or–even less satisfactorily–“points won” and “points lost.”  Not exactly a step in the right direction, since we’re already counting points!

Still, I suspect it’s better to have no stat than to have a misleading stat.  Rally counts are a positive step, since we can look at outcomes for different types of points.  If you win a lot of 10-or-more-stroke rallies, that identifies you as a certain type of player (or playing a certain kind of match).  It doesn’t matter whether you lose that sort of point on an unforced error or your opponent’s winner–both outcomes might stem from the same tactical mistake three or four strokes sooner.

Either that, or we can wait until we can calculate real-time win probability and start categorizing errors with extreme precision.  “Unforced errors” aren’t going away any time soon, but as fans, we can be smarter about how much attention we grant to individual numbers.