Predicting Break Point Performance

Break points are some of the most important points in a tennis match. Whether for the server or the returner, winning those points is often the difference between winning and losing the match. This raises the question, are there players that perform better than others on these big points?

The most commonly quoted statistics in this area are % BP Saved and % BP Won. Earlier in the year, the ATP wrote about how Ivo Karlovic had the highest % BP Saved for the year, ahead of Tomas Berdych and Jo-Wilfried Tsonga. Now that we have virtually reached the end of the season, looking at the list, we see John Isner, Ivo Karlovic and Feliciano Lopez at the top of the pile. However, the truth is that the big servers are always going to top this list. They could be mentally weak and under-perform on the break points, but because of their big serves, they will save plenty of break points.

The same is true on the % BP Won. The top returners will always top this list, while the 'servebots' will always find themselves at the bottom. Looking at the list, we find John Isner and Ivo Karlovic at the bottom with Feliciano Lopez fourth from bottom.

However, let us look more closely at Ivo Karlovic and John Isner. On a standard return point in 2014, Ivo Karlovic has won 28.0%, but when he creates a break point opportunity, he has won 31.6%. In other words, he is more likely to win on break point than on a standard return point. John Isner wins 30.1% of return points in 2014, but on break point, he is winning just 24.4% of points. In other words, he is less likely to win on break point than on a standard return point.

They both show up at the bottom of the list in terms of the actual number of break points converted, but we might suggest that Karlovic is better on converting break points than John Isner is. We can divide the % of BP Won by the % of Return Points Won to create a new measure - BP Conversion Rate. We can do the same with % of BP Saved divided by the % of Service Points Won to create the BP Save Rate.

These new measures give us an idea of how players perform on the break points and whether they over-perform or under-perform compared to their own average level. To give an idea, here are the BP Conversion and BP Save Rates for the top 10 in the WTA rankings:

Player	2014 BP Conversion Rate	2014 BP Save Rate
Serena Williams	101.7	97.4
Maria Sharapova	104.8	95.3
Simona Halep	105.3	93.0
Petra Kvitova	94.8	97.6
Li Na	109.1	102.4
Agnieszka Radwanska	104.3	95.5
Eugenie Bouchard	103.0	98.3
Ana Ivanovic	101.5	96.1
Caroline Wozniacki	106.2	99.8
Angelique Kerber	97.9	103.5

As we can see, the BP Conversion rate tends to be above 100, while the BP Save rate is usually below 100. This makes sense given that the server is usually under more pressure on break point and that the returner is likely to go for it more. Overall, the average BP Conversion rate is 104.5 and the average BP Save rate is 96.4.

Based on those averages, we might suggest that Li Na, Caroline Wozniacki and Simona Halep are particularly good at converting break points, while Petra Kvitova, Angelique Kerber and Ana Ivanovic are relatively poor. Similarly, Angelique Kerber, Li Na and Caroline Wozniacki are all strong at saving break points, while Simona Halep, Maria Sharapova and Agnieszka Radwanska are all below average at saving break points this year.

How does this compare with last year? The table below shows the conversion and save rates for the same players in 2013:

Player	2013 BP Conversion Rate	2013 BP Save Rate
Serena Williams	105.1	96.8
Maria Sharapova	103.5	98.4
Simona Halep	111.2	91.6
Petra Kvitova	107.6	105.5
Li Na	109.5	101.4
Agnieszka Radwanska	103.6	99.3
Eugenie Bouchard	101.3	93.6
Ana Ivanovic	102.1	97.3
Caroline Wozniacki	106.8	100.9
Angelique Kerber	103.7	101.9

Simona Halep, Li Na and Petra Kvitova all show up well in conversion rates in 2013, while Eugenie Bouchard, Ana Ivanovic and Maria Sharapova all show up poorly. In terms of save rate, Petra Kvitova, Angelique Kerber and Li Na show up well, while Simona Halep, Eugenie Bouchard and Serena Williams show up poorly.

This raises the question of whether certain players are consistently good at saving or converting break points, while other players are poor on the big points. This would fit in well with the idea that mental strength on the big points is hugely important in becoming a top level tennis player. Let us look at a plot of the 2014 BP Conversion Rate against the 2013 BP Conversion Rate to see whether a value in 2013 can allow us to predict what is likely to happen in 2014:

The chart shows all the players in the top 100 of the WTA rankings who have played at least 10 matches in both 2013 and 2014. At first glances, everything seems to look pretty random. In total, there were 14 players with BP Conversion Rates of over 110 in 2013. In 2014, just three of these players had values of over 110 - Yanina Wickmayer, Shuai Zhang and Francesca Schiavone. A further three of these players had values between the WTA average of 104.5 and 110, while three more players showed values of below 100.

If we look at the reverse, there were 19 players that had BP Conversion Rates of below 100 in 2013. None of these players flipped to a value of over 110 in 2014, but eight of them improved to values of above the WTA average in 2014. Seven of them remained below 100 in 2014.

The R squared value for this is 0.0032, which suggests that as a sample, there is virtually no correlation between the value in 2013 and the value in 2014.

Now, let us look at the same chart, but for the BP Save Rate:

There are 15 players with a BP Save Rate in 2013 that was above 102.0. Moving forward to 2014, there are four of these players that still have a BP Save Rate of above 100, while seven have dropped to below the WTA average of 96.4. Those four players that have remained above 100 in the second year are Barbora Zahlavova Strycova, Mirjana Lucic-Baroni, Elena Vesnina and Roberta Vinci.

As with the conversion rate, we find an R squared value of 0.0021, so again there is no overall correlation at all.

For those four players that we found with successive high values on the BP Save rate, let us look further back. The table below shows the values:

Player	2014	2013	2012	2011	2010
Barbora Zahlavova Strycova	102.1	108.2	103.8	99.4	102.7
Mirjana Lucic-Baroni	105.5	105.2	90.8	96.5	94.1
Elena Vesnina	100.9	104.1	92.7	94.1	93.2
Roberta Vinci	101.1	103.0	97.6	97.9	102.5

Barbora Zahlavova Strycova's figures are massively impressive. Four out of five years, she has finished with a value above 100.0, while the other year, she is still well above the WTA average. Mirjana Lucic-Baroni has scored above 100.0 for the past two years, but the three years before that, she was below average. Elena Vesina is pretty much the same as Lucic-Baroni in terms of three below average years before her recent improvement, while Roberta Vinci has three values of over 100.0 with two further above average values.

We cannot completely rule out certain players being particularly good at break points or certain players being particularly bad without further investigation, but on the whole, we cannot use the previous year's performance to determine how a player is likely to perform in the coming year on break points.

Serena Williams: The Decline?

Serena Williams has been, without a doubt, the best women’s tennis player in the past few years. In 2012 and 2013 combined, she racked up a win-loss record of an astonishing 136-8. She won no fewer than 18 titles in the 28 tournaments that she entered during that period. It is one of the most dominant periods for any woman in the history of tennis.

However, there have been indications in 2014 that she is beginning to slow down. It is no surprise – keeping the incredibly high level that she had been playing at was never going to be easy and she is not getting any younger. At 33-years old, she is the third oldest woman in the WTA top 100 and is the oldest world number one in history. She is the second oldest woman to win a Grand Slam singles title, only behind Martina Navratilova. She is performing at a higher level than virtually any woman of her age has ever done before.

While she is still the world number one, her advantage over the rest of the field has fallen slightly in 2014. At the end of 2013, she had 13,260 ranking points, giving her an enormous 5,214 lead over Victoria Azarenka. At the current moment in time, she currently has 7,146 ranking points and a lead of just 466 points, which could even be smaller come the end of the week. She has a record of 48-7 in 2014 thus far, which the rest of the field would die for, but which is relatively poor by her standards. Her six titles in 15 tournaments is also a drop for her.

So, things would suggest that Serena has not enjoyed such a good year, but can we pinpoint where exactly the decline in her results and performances might have come. To achieve this, we shall look at her statistical performance and compare it with 2013. The table below shows some basic statistics. For an more effective comparison, we shall focus on her primary surface – hard courts.

Statistic	2014	2013
% Won on 1st Serve	75.4%	75.1%
% Won on 2nd Serve	51.4%	49.6%
% Won on Return	46.7%	49.7%
BP Created/Game	0.88	0.90
BP Conversion Rate	102.7	112.6
DF/Game	0.38	0.28
Aces/Game	0.84	0.64
BP Faced/Game	0.46	0.46
BP Save Rate	92.5	100.7

What can we gather from this? First thing we notice is that Serena is actually winning more points on serve than she was last year - on both first and second serve. She is serving significantly more aces this year – an extra 0.2 per game – but also more double faults. Combine these facts together and we can probably conclude that she is going all out on the serve. She is looking for big aces and unreturnable serves with the first serve and taking more risks on the second serve, resulting in winning more points quickly, but also in more double faults. It is interesting that, despite the increase in points won on her serve, she is still facing the same number of break points per game as she was in 2013. It would suggest that she is maybe enjoying more quick and comfortable service games, but this is being counteracted by having more service games where her opponent is getting chances. In other words, her service games are verging toward the two extremes.

However, it is the return game that would appear to have declined this year. The points won on return has dropped by 3.0%, which is a significant margin, although again, it has only resulted in a small drop in the break points created per game, which has fallen by just 0.02. Again, this may suggest that she is still getting break point opportunities, but that she is also giving her opponent more easy holds during the match.

Her two break point rates* have also dropped significantly in 2014. The conversion rate of 112.6 in 2013 was an exceedingly high value – previously in her career, this value has tended to be between 100 and 103, where it has returned in 2014. The save rate of 92.5 in 2014 is slightly low, but in the past, she has managed to overcome a low save rate by the rest of her game being at such a high level.

So, how do those figures convert to actual games won? The table below gives an indication:

Statistic	2014	2013
% of Service Games Won	81.8%	84.3%
% of Return Games Won	42.0%	50.3%

Despite winning more points behind both her first and second serve, Serena has actually held serve less often in 2014 than she did in 2013. The bigger change, though, is in the return game. Last year, she was winning over half of her return games. However, this year, this figure has dropped by 8.3%, which is a far bigger change than the earlier stats might have suggested.

From what we have learned so far, we might draw the conclusion that Serena’s actual serve itself is working as well as it has ever done, but that her ground game is beginning to decline. Whether this is because her speed around the court is slowing slightly, meaning that she is unable to get into position, or whether it is simply age slowing her reactions, we cannot tell, but this would seem to be indicated by the stats.

We can dig further into this to try and find further evidence to support our theory. Here are some further statistics to look at:

Statistic	2014	2013
% Non-Ace 1st Serve Points Won	67.7%	70.2%
% Non-DF 2nd Serve Points Won	60.2%	56.1%

I have used the first of those statistics – the % Non-Ace 1^st Serve Points Won – as a proxy for looking at how players perform in rallies. We can see that on points where Serena gets her first serve into play, but does not serve an ace, she is winning 2.5% fewer points than she was in 2013. Interestingly though, she is actually winning more of the points on her second serve when she does not serve a double fault. This ties in with our idea earlier that she might be going for it more on the second serve to try to shorten points and avoid getting into rallies.

I now want to look in more detail at those break point numbers from earlier. If you recall, Serena’s break points created dropped from 0.90 to 0.88 per game, while the break points faced on her own serve remained constant at 0.46. This seems slightly out with the game win percentages that we saw, so could do with some further investigation.

Before looking at the statistics, my theory was that she was creating almost the same number of break points, but that many of these were coming in the same games, hence the lower conversion rate. It may show in the basic statistics as the same number of break points created, but this is a less desirable way of achieving that.

As an example, let us imagine that two players both have break point figures of 2/12 in a match. The first player created six break points in one return game and six in a second return game. The second player created one break point opportunity in twelve different service games. From this, you would probably suggest that the second player had demonstrated the better return game as he had created opportunity in virtually every service game. The first player had created plenty of chances, but all in one or two service games.

So, does this pan out for Serena Williams in 2014? The table below shows the figures:

Statistics	2014	2013
% of Service Games with BP Faced	29.5%	28.8%
% of Return Games with BP Created	52.9%	56.9%

We can see here that Serena is having to defend break points in 0.7% more of her service games and that she is creating break points in 4.0% fewer of her return games. So, combining several of these figures together, Serena is creating break points in fewer return games and is converting those break points less regularly. These are combining to give us the 8.3% fall in return games that she is winning in 2014.

It also helps to shows us where the fall in service holds is coming from. Despite winning more points, she is facing break points in more service games than she was before and is saving those break points less regularly. She may win plenty of cheap service games, but she is struggling to win those games where her opponent is forcing her into rallies.

These struggles on return also seem to emphasis themselves on those occasions when Serena falls behind in sets. The excellent Dan Weston of TennisRatings has looked at this recently – in 2013, Serena was able to recover a break deficit and get back on serve a massive 75.6% of the time. However, in 2014, this has dropped remarkably to just 57.5% - still top 5 in the WTA, but a decline of 18.1% nonetheless.

In conclusion, there is plenty of evidence to suggest a reasonable decline in Serena William’s performance in the past twelve months. To be clear, she is still comfortably the best player in women’s tennis – her advantage over the rest meant that she could decline yet remain the best. However, compared to her previous level, this year has been a worry. She is being forced to go for quick finishes on her serve to compensate for a declining ground game, which is working to an extent, but means that when her serve is not working at 100%, she becomes beatable. Her overall ground game is causing a decline in her ability to create break points and to recover deficits.

As a final overall statistic to show that Serena’s dominance is decline, in 2013, she won 67.6% of all the games in the 47 hard court matches that she played. In 2014, this had fallen to 61.9% in her 42 matches. A fall of 5.7% in the total games that she has won suggests that her matches are becoming closer and she is becoming more beatable.


* The BP Conversion Rate is calculated by dividing the % of break points won by the player's % won on return to determine whether he performs better or worse compared to an average point when he creates a break point on his opponent's serve. A value of 100 corresponds to performing exactly the same, whether it is break point or not, a value greater than 100 corresponds to performing better on break point than an average point and a value lower than 100 corresponds to under-performing on break point. In the same way, the BP Save Rate divides the % of break points saved on a player's serve by the % of points won on serve to determine whether he performs better when facing break point.

Understanding the Tennis Radars

Recently, you may have noticed some of the player radars that I have been posting on Twitter. They do seem to have garnered some interest, but there have been a number of questions about them, so I have put together a brief guide as to how they work, what we can learn from them and, just as importantly, their limitations.

The first thing to mention is that they do not really convey any new information. Rather, they are simply a way to try and generate an easy visualisation of statistics. Many people find looking at or reading numbers quite dull or find it difficult to read the significance into certain statistics. The radars are simply an attempt to make it easier to get an immediate impression of a player’s statistics in a more accessible manner. There is nothing new outside of the standard information that is relatively easily accessible on the internet. The idea for them came from the excellent @mixedknuts on Twitter, who has used them successfully in visualising football statistics.

Statistics and data in tennis are really quite poor compared to the majority of major sports. Compared to a similar sized sport, such as cricket, there is significantly less useful data out there. The availability and complexity of available cricket data is like a separate continent from tennis. Compared to football, which has undergone a revolution over the past few years in terms of availability and quantity of data, it is in a whole different world. Compared to the American sports, such as baseball, NFL and hockey, it is simply incomparable. The data available to analyse those sports is quite simply outstanding. It is equivalent to a separate galaxy.

So, how do the radars work? The statistics available on the radar may change over time as more data or new measures become available, but the overall theory behind them will remain the same. Including the inner circle and the outer edge, there are ten rings that make up the radar. The mid-point of each axis represents the ATP or WTA mean value for that particular statistic. So, the perfectly average ATP player will have a radar that joins up the midpoint of each axis.

The inner circle represents a value that is two standard deviations below the mean. Similarly, the outer edge of the radar represents a value that is two standard deviations above the mean. Getting slightly more complex, if we roughly assume that all the values in the sample follow a standard normal distribution, then 95.45% of all players will lie within two standard deviations above or below the mean on each statistic. Thus, if a player reaches the outer edge on a statistic, we can roughly suggest that he is within the top 2.2% in the ATP/WTA in that attribute. Similarly, if a player is within the inner circle, we can suggest that they are within the bottom 2.2% in that attribute.

So, that is roughly how the radar itself works. Now, we will look at each of the current attributes. Most of them are fairly self-explanatory, but one or two of them are slightly more unusual.

‘% Won on 1^st Serve’ and ‘% Won on 2^nd Serve’ are fairly straightforward and simply represent the percentage of points won on each of the player’s two serves. Similarly, the ‘% Won on Return’ represents the percentage of points won on the opponent’s serve. Related to the serve, ‘Aces/Game’ and ‘DF/Game’ are both as they suggest – it is the expected number of aces and double faults per service game. Dividing through by the number of service games helps to negate the effect of having played lots of long matches, where one might expect a player to serve more aces than in a series of shorter matches. Similarly, BP Faced/Game and BP Created/Game are based around the same principle for break points faced on the player’s own serve and created on his opponent’s serve.

The two slightly more unusual statistics are the Break Point Save Rate and Break Point Conversion Rate. The BP Conversion Rate is calculated by dividing the % of break points won by the player's % won on return to determine whether he performs better or worse compared to an average point when he creates a break point on his opponent's serve. A value of 100 corresponds to performing exactly the same, whether it is break point or not, a value greater than 100 corresponds to performing better on break point than an average point and a value lower than 100 corresponds to under-performing on break point. In the same way, the BP Save Rate divides the % of break points saved on a player's serve by the % of points won on serve to determine whether he performs better when facing break point.

Now, let us look at a couple of standard shapes of radars. The first is the typical 'servebot', who has a huge serve, bangs down plenty of aces, but has very little on return. The example of this is Ivo Karlovic on grass - pretty much the definition of a typical 'servebot'. We can see plenty of area filled at the top and on the left-hand side where the serve statistics dominate, but very little on the right-hand side in the return and break point creation areas of the radar.

The second example is a player with a very weak serve, but whose return game is crucial to remaining competitive in matches. Here, we have a young Argentinean player - Diego Sebastian Schwartzman - on all surfaces. We can see the right-hand side of the radar is now dominant with high outcomes in the return and break point creation areas, while the top and left-hand side is virtually unfilled, illustrating the lack of ability of serve.

In reality, the vast majority of players will lie somewhere between the two extremes. It is also important to remember that different abilities can go into generating high statistics in certain areas - the top players are likely to generate high serving statistics, even if their serve is not that great, simply due to their superior ability in rallies.

So, the radars can give an idea of the style of game that certain players adopt and it can give us an idea of the overall quality of a player. Certain of the statistics are likely to be highly repeatable - a big server is likely to have high values for the first serve statistic, for aces and break points faced in each separate year, while top returners are likely to have high values in the return and break point creation. However, without further work, one can only speculate as to whether break point conversion and save rates are repeatable across years.

Andy Murray: What Has Gone Wrong in 2014?

Before his back surgery after the US Open last year, Andy Murray had become an undisputed member of the ‘Big 4’ in men’s tennis. Long grouped with Roger Federer, Rafael Nadal and Novak Djokovic, Andy Murray had finally started to justify his inclusion in that group. When he lifted the Wimbledon trophy and realised a lifelong ambition, it seemed to mark the start of a rise to potential superstardom.

He had reached at least the semi-final of nine of the preceding ten Grand Slams, had appeared in five finals and had won two Grand Slam titles. He had won the Olympic gold medal at Wimbledon the previous summer. At that moment, the idea that it would be 14 long months until Andy Murray would reach his next final at any level would have been laughable.

Can Andy Murray rediscover the form that saw him win Wimbledon last year?

His comeback has been slow – much slower than he might have hoped. His relationship with Ivan Lendl came to an end, with the Czech potentially anticipating that the path back to the top would be long and, potentially even, impossible. However, looking at his statistics, it is difficult to pinpoint what is different about Andy Murray since his comeback.

As a starting point, let us look at Andy Murray on hard courts – a surface that he has thrived on in the past. The table below shows various statistics comparing his performance in 2013, where he won a Masters series title in Miami, reached the final of the Australian Open and won the title in Brisbane, with his performance in 2014, where he has won just the one title in Shenzhen last week and failed to pass the quarter-finals in any Grand Slam or Masters event.

Statistic	2013	2014
1st Serve %	61.2%	59.7%
% Points Won on 1st Serve	74.1%	73.5%
% Non-Ace Points Won on 1st Serve	70.3%	69.7%
% Points Won on 2nd Serve	51.1%	52.8%
% Points Won on Return	47.1%	43.1%
Break Points Created/Game	0.71	0.71
Break Point Conversion Rate	112.5	111.3
Aces/Game	0.52	0.47
Double Faults/Game	0.21	0.21
Break Points Faced/Game	0.45	0.49
Break Point Save Rate	97.5	97.1

So, what can we see from these figures? His first serve percentage is slightly down this year, although he would appear not to have lost any speed on his serve – the average serve of his first and second serves this year are virtually identical to both 2012 and 2013. When he gets the first serve into play, he is winning slightly less, but it is only down by 0.6% - not a huge amount. His percentage of non-ace points won on first serve, which acts as a proxy of the quality of his ground game is down very slightly, but again not by a huge amount. In terms of the rest of the ATP, his 74.1% in 2013 put him at 28^th, while he has dropped just two places in 2014 to 30^th.

His second serve has always been an issue, but he is actually winning a greater percentage of points behind his second serve this year than he did last year. It is still slightly down from the 53.5% that he won in 2012, but this improvement is encouraging. His 52.8% puts him at number 18 in the ATP this year, up eight places from last year.

His return game is slightly more concerning, but also slightly puzzling. He is winning a huge 4% fewer points on return this year compared to last year, but interestingly, he is still creating the same number of break points per game. This is intriguing and something that we shall come back to later.

Both his break point conversion rate and break point save rates are very fractionally down, but not by any significant margin, suggesting that we cannot necessarily point to his performance on the important points as the difference. He is serving 0.05 fewer aces per game this year, but this is again pretty negligible, while his double faults per game is identical to 2013. He is facing slightly more break points per game, but again a small change.

So, how do these very small changes affect his performance when we scale them up to game level, rather than point level?

In 2013, Andy Murray held serve in 83.6% of his service games on hard courts and was able to break his opponent in 34.4% of return games. In comparison, this year, he has held serve in 82.2% of service games and has broken in 33.3% of return games.

Again, these appear to be very minimal changes. He holds serve in 1.4% fewer service games and breaks serve in 1.1% fewer return games. Can these seemingly small changes really cause such a big difference in his results?

Thanks to Dan Weston (@tennisratings), we can look in more detail at his performance during individual sets. The starred statistics are for all surfaces, rather than just hard court, but the overall trends should be similar. The table below shows some interesting statistics:

Statistic	2013	2014
*Early Games Hold %	84.8%	84.1%
*Late Games Hold %	84.5%	81.2%
*Early Games Break %	30.3%	36.9%
*Late Games Break %	34.7%	28.2%
*Lost Lead when Break Ahead	21.8%	23.7%
*Recovered Break Deficit	54.6%	35.4%
Set 1 Win %	77.4%	52.8%
Set 2 Win %	76.7%	80.6%
Set 3 Win %	76.5%	63.6%

There are a couple of things that immediately jump out from this table. The first and most obvious is the drop in recovered break deficit. This represents the percentage of times that a player breaks back and gets back on serve when down a break in a set. This is the first time that we have seen a huge change in a statistic – down 19.2%.

Despite the impression that Murray has thrown away matches from winning positions on far too many occasions in 2014, the percentage of times that he has been broken back when leading by a break is only fractionally higher than it was last year.

Another area that is interesting is how Murray’s game changes over the course of a set. The early games statistics refer to the opening two service games of the set for each player and we can see that while Murray’s serve has changed little here, he is actually breaking 6.6% more often at this early stage in sets.

Conversely, he appears to struggle slightly more at later moments in sets. He holds serve 3.3% less often this year compared with last year, but the big drop is in the number of games where he breaks late in sets, which is down by 6.5%.

How can we relate these statistics to what we might expect to see from Andy Murray? Well, it would seem to suggest that he actually comes out of the blocks more quickly in sets this year, but late in the sets, he is struggling to break serve. When we combine this with the fall in recovered break deficit, we can see that the big difference is that, where last year he would often either break to take the set or to get himself back into a set, this is not happening this year and players are serving out sets against him. Where he was able to battle to get back into sets last year and win tight sets, he is unable to do it this year.

We can also see that, despite starting well in sets, he is starting very slowly in matches as a whole. Where in 2013, he won 77.4% of opening sets, this has dropped to just 52.8% in 2014. Constantly, he is forcing himself to come back from losing the opening set and giving himself no room for error. His second set win percentage is slightly up, as we might expect given that he is dropping opening sets against weaker players and forcing himself to up his game, but his third set win percentage is down from 76.5% to 63.6% this year.

While the sample is relatively small, could we speculate that it is taking Murray time to get his back moving and, as a result, is struggling to get going in opening sets? Could we suggest that his fitness is not quite at the level that it was before, hence he is winning fewer matches in deciding sets?

We can combine some of these new statistics with what we found in some of the more basic ones. We noticed that, despite his points won on return being down, he is creating the same number of break points. However, these break points are not being created when he most needs them – when he is behind in a set.

To conclude, the major changes that we can find in Andy Murray’s statistics this year are his performance when he is behind in sets. He is serving just as well as last year, his return is very slightly down, but he is still creating break point opportunities and he is playing just as well on the break points. However, the big problem is that he is struggling to create and take these opportunities when he is behind in sets. He is creating and taking break point chances when the match is level and when he is ahead. However, if his opponent draws first blood, the ability to fight back into the set is not there this year.

This appears to be particularly problematic in the opening set. He is often coming out of the blocks relatively slowly in opening sets, going behind and then finds himself unable to get back into the set. It means that he is constantly having to go three sets to get back into matches, which against top 20 players is tough. Only once in his last nine matches against top 20 players has he dropped the opening set and come back to win. Fighting back from a set deficit against top players is incredibly difficult, so he simply cannot afford to keep starting so slowly.

Why he is finding it far tougher to come back from behind, we cannot tell from the statistics. Maybe it is mental – could it be that having achieved his dream of winning Wimbledon, the desire to fight for every point in every match has dwindled somewhat? Could it be that confidence is low after the injury layoff and his lack of titles and finals since his comeback? Could it simply be bad luck – when he has the opportunities to break back, his opponents are coming up with aces or huge winners which hit the line this year, whereas they might have just missed the line in the past?

Without having watched all of his matches this year, it is impossible to tell. There is almost certainly not one single reason. However, the encouraging news for Murray fans is that his game is not far off being back to where it was last year. He is still slightly off, but given a full and injury-free pre-season, it would be no surprise to see him back challenging again in Melbourne next January.