Continuing with our point-by-point player profiles, let’s look at Andy Murray. The Scot finished strong and performed up to expectations at the grand slams despite a dreadful stretch following the Australian Open.
Using all of his grand slam matches from 2011, we can begin to analyze his tendencies on serve and return.
The first table shows the frequency of different outcomes in the deuce court, in the ad court, and on break point, relative to Murray’s average. For instance, the 1.014 in the upper left corner means that Murray wins 1.7% more points than average in the deuce court.
OUTCOME Deuce Ad Break
Point% 1.017 0.981 1.020
Aces 1.034 0.963 1.048
Svc Wnr 1.036 0.960 1.043
Dbl Faults 1.104 0.886 0.872
1st Sv In 1.007 0.993 0.957
Server Wnr 1.009 0.990 0.860
Server UE 0.968 1.035 1.013
Return Wnr 0.775 1.246 0.558
Returner Wnr 1.019 0.979 1.012
Returner UE 0.988 1.013 1.003
Rally Len 1.015 0.984 1.037
Like most righties, Murray is a little better in the deuce court. The substantial difference in return winners hints at a larger issue: When he serves cautiously, he serves very cautiously, leading to horrible second-serve results. That’s a topic for another day.
What’s remarkable about the above table, though, is Andy’s results serving against break point. Sure, 2% better than average doesn’t sound like much, but keep in mind that when fighting off breakers, he’s generally playing his best opponents. As we’ve seen, both Nadal and Federer perform serve more than 10% worse than average on break point for this reason; Murray bucks that trend, all the more remarkable because most break points are in the ad court.
Next, this is how he performs on a point-by-point basis. Win% shows what percentage of points he wins at that score; Exp is how many he would be expected to win (given how he performs in each match), and Rate is the difference between the two. A rate above 1 means he plays better on those points; below 1 is worse.
SCORE Pts Win% Exp Rate
g0-0 398 66.6% 65.5% 1.02
g0-15 131 58.8% 64.5% 0.91
g0-30 54 61.1% 63.3% 0.97
g0-40 21 66.7% 61.0% 1.09
g15-0 262 62.2% 66.0% 0.94
g15-15 176 68.2% 65.1% 1.05
g15-30 89 65.2% 63.5% 1.03
g15-40 45 66.7% 61.5% 1.08
g30-0 163 69.9% 66.7% 1.05
g30-15 169 60.4% 65.5% 0.92
g30-30 125 64.0% 64.7% 0.99
g30-40 75 65.3% 63.0% 1.04
g40-0 114 64.9% 68.0% 0.96
g40-15 142 66.2% 66.5% 1.00
g40-30 128 72.7% 65.0% 1.12
g40-40 148 60.8% 62.0% 0.98
g40-AD 58 58.6% 59.6% 0.98
gAD-40 90 66.7% 63.5% 1.05
None of the numbers in this table are that extreme, but the overall picture they paint is of a player with better clutch serving abilities than Murray gets credit for. He serves better than expected at both 15-40 and 30-40, and he is barely below average at 30-30, 40-40, or 40-AD. According to these numbers, his game doesn’t change much according to the score–at least at the slams this year.
Serving Against Murray
We can go through the same exercises for Murray’s return points. The next two tables are trickier to read. Look at them as Serving against Murray. Thus, the number in the upper-left corner means that when serving against him, players win 1.5% more points than average in the deuce court; he is a better returner in the ad court. That’s mostly attributable to the fact that righties serve better in the deuce court, regardless of who is returning.
(I’ve excluded return points against lefty servers. Since lefties and righties have such different serving tendencies, limiting the sample to righty servers gives us clearer results, even as the sample shrinks a bit.)
OUTCOME Deuce Ad Break
Point% 1.015 0.984 0.977
Aces 1.018 0.980 0.741
Svc Wnr 0.993 1.008 0.979
Dbl Faults 0.956 1.049 1.811
1st Sv In 0.998 1.003 0.974
Server Wnr 1.066 0.927 0.974
Server UE 1.016 0.982 1.148
Return Wnr 0.704 1.324 1.287
Returner Wnr 0.885 1.126 0.883
Returner UE 0.917 1.091 1.170
Rally Len 0.999 1.001 0.920
These numbers continue to challenge the conventional wisdom on Murray. What sticks out is the rally length on break points: 8% shorter than usual. I would have expected that Murray plays extremely cautiously in converting break points, but instead, he hits more return winners, makes more unforced errors, and keeps points shorter.
Here’s more on Murray’s return game, again with numbers from the perspective of players serving against him.
SCORE Pts Win% Exp Rate
g0-0 388 59.8% 57.0% 1.05
g0-15 152 52.0% 55.9% 0.93
g0-30 73 49.3% 55.6% 0.89
g0-40 37 51.4% 54.8% 0.94
g15-0 231 60.6% 57.7% 1.05
g15-15 170 53.5% 56.6% 0.95
g15-30 115 54.8% 55.2% 0.99
g15-40 71 53.5% 54.5% 0.98
g30-0 140 57.9% 58.2% 0.99
g30-15 150 60.0% 58.0% 1.04
g30-30 123 56.1% 55.8% 1.01
g30-40 92 56.5% 54.1% 1.04
g40-0 81 56.8% 58.8% 0.97
g40-15 125 59.2% 58.6% 1.01
g40-30 120 50.0% 57.3% 0.87
g40-40 209 56.5% 55.9% 1.01
g40-AD 91 53.8% 54.8% 0.98
gAD-40 118 59.3% 56.8% 1.05
Murray’s results when returning at 40-30 are the only ones that really stick out. He returns much better than expected, winning exactly half of those points. He also appears to string together more streaks than expected at 0-15 and 0-30. Beyond that, he is fairly steady, much like Djokovic in the return game.
Jeff – I’m trying to think whether the four men and their coaches, or other opponents could benefit from from your analysis. You picked what may be the best year ever to do this, as collectively they probably won more Major matches this year than any 4 men in a previous year (90 of a possible 92 that 4 men could win in the same tournaments; if not for Rafa’s loss to Ferrer in Oz and Roger’s loss to Tsonga At Wimbledon, they’d have been the semi-finalists in all 4 events):
26 Joker
23 Rafa
21 Murray
20 Roger
One can’t know too much about an opponent unless you misuse the knowledge, and if I were in a position to play these great champions in majors next year, I’d look for any possible insight which could be a small edge. In addition to your summaries of their most notable serve and return characteristics, I don’t come up with a lot of other take-aways after reviewing and comparing their data a bit, but here’s one observation from the data you present:
– Both when serving and returning, Joker and Murray had much less variation between their extremes of performance than both Rafa and Roger. At the extremes in each man, the younger men had little more than half the variation:
.4 vs. .72 variation from their average on service points
.35 vs. .59 variation from their average on return points
Less of this kind of variation does not – if it means anything to look at just the extremes you identified – make one or the other characteristic easier or more difficult to play against – unless it surprises you AND bothers you. On the other hand, the take-away from knowing their tendencies is only valuable if it helps you NOT to overreact to it when it hits you in a match.
An example of when a certain knowledge hurts players: Rafa’s feared reputation as a warrior when he’s down makes him harder for most players when they get ahead of him. Perhaps quantifying that reputation can help frame it, and keep it in perspective to tame your fear.
How do you think other players and coaches could benefit from what you’ve done, if you do?
And, in what ways (if any) do you think the 4 players themselves (and their coaches) could benefit.
Rick Devereix
That’s a really good question, Rick, and one that I wish I had a better answer for. Certainly, if Paul Annacone came calling, I would want a better sales pitch than I could come up with right now!
My cop-out answer is that I think the real, applicable-to-players-and-coaches breakthrough will come when we have shot-by-shot data, so you can predict how opponents will react after, say, an ad-court serve wide, or a down-the-line backhand return, and so on. That data will be a huge step toward quantifying tennis points in the way we can currently talk about patterns of play in chess.
If I were a coach, I’d want all the data I could get my hands on, about my player and about possible opponents. But I’m not sure what I’d do with it. Your point, about simply quantifying bothersome tendencies to better manage them, is a good start. From the coach’s perspective, this sort of data might provide some direction for practice–perhaps it would come as a surprise that the player doesn’t hit many return winners from the deuce court, and that’s something that could improve.
For in-match strategy, a good starting point would be having better numbers on serving tendencies — direction and speed at various points throughout a game (or, if it changes, throughout a match). But even 26 matches isn’t enough to know, for instance, which way Novak is likely to go at 15-40. And since players have a split second to read the serve (which they’ve spent their lives training to do), maybe knowing that there’s a 70% chance he’ll go down the T would be too much information. Maybe some players (Janko?) would benefit from it while others would be distracted.
Thanks, Jeff – intelligent reply, as always. The data you mention needing – shot selection by situation in a quantity that allows valid analysis – is stuff that Dave Fish has gathered by video of his Harvard players. I believe his players learned and improved as a result of his intelligence. I know that one of the things they looked at was how a player returned serve in both deuce and ad courts, depending on the serve (wide, body, middle). Patterns of errors and lost points emerged from their tapes.
Rick