Jan 16, 2020
Jan 16, 2020

Do the ATP top ten have 'clutch' ability?

Is 'clutch' ability real or a myth?

Do players over and underperform on break points?

ATP top ten players' stats analysed

Tennis trends and insights

Do the ATP top ten have 'clutch' ability?

With this year’s tennis season getting underway, now is a good time to take an alternative look at how success and failure can emerge on the court. In the first article of a four-part series, Dan Weston will study the luck, variance and fine margins which frequently decide outcomes in professional tennis.

When was the last time you looked at a tennis player and thought something along the lines of “They are just so good at key points, almost every time they need to win a point they somehow manage it” or “They are so clutch when it matters”?

It is anticipated that many readers will have thought this about certain players, particularly those at the top of the rankings. This article will question such views and specifically whether ‘clutch’ play, or the ability to perform best when it really matters, actually exists in tennis.

Do players overperform on break points?

To begin this study, a look at both the short-term and long-term key point data for the top ten players on the ATP Tour is extremely useful. The short-term data taken from the 2019 season is detailed below:

ATP Tour Top Ten 2019 Player Break Point Over and Underperformance Data

Player

2019 service points won (%)

2019 break points saved (%)

2019 return points won (%)

2019 break points saved (%)

Break point over/underperformance (%)

Rafael Nadal

70.1%

68.0%

42.5%

45.1%

0.5%

Novak Djovokic

69.1%

63.8%

41.3%

48.9%

2.3%

Roger Federer

71.3%

71.1%

38.9%

40.9%

1.8%

Dominic Thiem

67.2%

62.3%

37.0%

42.1%

0.2%

Daniil Medvedev

67.0%

61.7%

40.3%

43.7%

-1.9%

Stefanos Tsitsipas

67.9%

65.0%

35.7%

36.0%

-2.6%

Alexander Zverev

64.5%

59.6%

38.9%

38.7%

-5.1%

Matteo Berrettini

68.6%

69.7%

35.0%

37.5%

3.6%

Roberto Bautista-Agut

66.7%

59.9%

38.7%

39.8%

-5.7%

Gael Monfils

64.2%

63.8%

39.1%

41.1%

1.6%

There are a number of interesting observations that can be drawn from the table above. Firstly, Roger Federer leads the way for break point save figures (71.1%), meaning those who feel that the Swiss legend is formidable at saving break points on his serve may be inclined to give themselves a pat on the back.

Matteo Berrettini’s data suggested he enjoyed a 3.6% overperformance, the largest margin amongst the group.

However, it is important to view these numbers in complete context, and the reason why Federer is so good at saving break points on serve is actually rather simple. He wins more service points than the rest of the top ten, recording 71.3% across the last year.

Effectively, his ability to save break points is a mere natural consequence of being an elite-level server to begin with, as opposed to somehow being able to summon up considerable mental strength at vital moments to stave off the threat of his opposition.

We can also see that several top ten players underperformed on break points based on their expectations from their service and return points won, most notably Alexander Zverev and Roberto Bautista-Agut.

Zverev dropped to seventh in the world rankings after a seemingly disappointing 2019. However, he should be heartened by the fact that he underperformed by 5.1% on break points based on expectations throughout the season, and this key point deficit is unlikely to continue.

Matteo Berrettini’s data suggested he enjoyed a 3.6% overperformance, the largest margin amongst the group. The Italian will do well to maintain such a high win percentage (66%) with just 103.6% combined service and return points won in 2019. Ascertaining win percentage expectations based on service and return points won will be covered more in the next article of this series.

Links between break and service points

Looking at numbers across the ATP Tour in 2019, 63.9% of service points were won and 61.2% of break point chances were saved by the server. Therefore, 36.1% of return points were won and 38.8% of break point chances were converted by the returner.

These figures are in line with longer term data as well, which indicates that the average server saves around 2.7-2.8% fewer break points than they win service points, and conversely, the average returner converts more break point chances than they win return points by around the same margin.

These numbers are both stable and have a logical explanation. If a player reaches break point on return, it is often because they are either playing particularly well or their opponent is playing badly or even injured.

This state of play will often persist throughout the match and in particular, the remainder of a specific game. Essentially, if a player reaches 0-40 on return then there is often a reason as to why that situation has occurred, which is likely to have an influence on subsequent break point chances.

Analysing service and return performance

Using the 2.7% differential from 2019, we can now look at the ATP top ten players’ data for last season from a different perspective: their over and underperformance split by service and return, assuming a linear relationship.

ATP Tour Top Ten 2019 Player Service, Break and Return Points Over and Underperformance Data

Player

2019 service points won (%)

2019 break points saved (%)

2019 break points saved expectation (%)

2019 return points won (%)

2019 break points saved (%)

2019 return points won expectation (%)

Serve break point over/underperformance (%)

Return break point over/underperformance (%)

Rafael Nadal

70.1%

68.0%

67.4%

42.5%

45.1%

45.2%

0.6%

-0.1%

Novak Djovokic

69.1%

63.8%

66.4%

41.3%

48.9%

44.0%

-2.6%

4.9%

Roger Federer

71.3%

71.1%

68.6%

38.9%

40.9%

41.6%

2.5%

-0.7%

Dominic Thiem

67.2%

62.3%

64.5%

37.0%

42.1%

39.7%

-2.2%

2.4%

Daniil Medvedev

67.0%

61.7%

64.3%

40.3%

43.7%

43.0%

-2.6%

0.7%

Stefanos Tsitsipas

67.9%

65.0%

65.2%

35.7%

36.0%

38.4%

-0.2%

-2.4%

Alexander Zverev

64.5%

59.6%

61.8%

38.9%

38.7%

41.6%

-2.2%

-2.9%

Matteo Berrettini

68.6%

69.7%

65.9%

35.0%

37.5%

37.7%

3.8%

-0.2%

Roberto Bautista-Agut

66.7%

59.9%

64.0%

38.7%

39.8%

41.4%

-4.1%

-1.6%

Gael Monfils

64.2%

63.8%

61.5%

39.1%

41.1%

41.8%

2.3%

0.7%

This table highlights that it was extremely difficult for players to maintain an over or underperformance for break points on both their serve and return throughout the season.

Only three players, Zverev, Bautista-Agut and Stefanos Tsitsipas, underperformed on break points on both serve and return based on their service and return points won expectations. Not a single player managed to both save and convert more break points than their expected figures.

This immediately brings into question the ability of individual players to be ‘clutch’ and somehow win important points when it really matters. If they were able to possess such talents, it is likely that some of the ATP top ten would have been able to outperform their expected figures on break points, but they have evidently been unable to do so.

Longer-term trends

A look at longer-term data also indicates that this is the case. The table below shows the three-year data for the same current ATP top ten players:

ATP Tour Top Ten 2017-2019 Player Service, Break and Return Points Over and Underperformance Data

Player

3-year service points won (%)

3-year break points saved (%)

3-year break points saved expectation (%)

3-year return points won (%)

3-year break points saved (%)

3-year return points won expectation (%)

Serve break point over/underperformance (%)

Return break point over/underperformance (%)

Overall break point over/underperformance (%)

Rafael Nadal

69.3%

69.7%

66.6%

42.8%

43.7%

45.5%

3.1%

-1.8%

1.3%

Novak Djovokic

68.0%

63.9%

65.3%

41.6%

44.1%

44.3%

-1.4%

-0.2%

-1.6%

Roger Federer

71.8%

69.0%

69.1%

39.0%

40.9%

41.7%

-0.1%

-0.8%

-0.9%

Dominic Thiem

66.4%

64.9%

63.7%

38.1%

40.4%

40.8%

1.2%

-0.4%

0.8%

Daniil Medvedev

64.9%

62.6%

62.2%

38.1%

41.2%

40.8%

0.4%

0.4%

0.8%

Stefanos Tsitsipas

66.9%

63.6%

64.2%

35.2%

37.8%

37.9%

-0.6%

-0.1%

-0.7%

Alexander Zverev

66.0%

60.7%

63.3%

38.6%

41.8%

41.3%

-2.6%

0.5%

-2.1%

Matteo Berrettini

67.6%

65.8%

64.9%

34.6%

35.2%

37.3%

0.9%

-2.1%

-1.2%

Roberto Bautista-Agut

65.3%

61.1%

62.6%

39.8%

41.6%

42.5%

-1.5%

-0.9%

-2.4%

Gael Monfils

64.3%

61.6%

61.6%

38.3%

42.7%

41.0%

0.0%

1.7%

1.7%

As mentioned, there were some fairly wild fluctuations between break expectation and reality in the data looking at over and underperformance in 2019, with three players in the top ten having greater than 3.5% swings from their expectations last season.

The longer-term data calms these swings markedly. Using the three-year data, only two players – Zverev and Bautista-Agut – had deviations of greater than 2% away from their expectations, illustrating that players fall more into line on this front as the sample size gets larger.

Interestingly, the three-year sample also highlights that only Daniil Medvedev was able to both save and convert more break points than expected, and the Russian talent only did so by 0.4% across each metric. In other words, his overperformance was basically negligible.

Overall, not a single player in the current ATP top ten can boast notably better break point saving and conversions than is expected of them, based on their ability to simply win service or return points. This assertion lays to rest any viable theory that these players are as ‘clutch’ as many people would likely think.

The next article in this series will look at the influence of service and return points on a player’s win percentage, and will help establish which players won more or less matches last season compared to their expectation. This enables us to identify who is perhaps more likely than others to be over or under-rated by the betting markets as the season gets underway.

You can read the second article in this series analysing the links between points won and win percentages in tennis here, the third looking at the influence of tiebreak points in tennis here and the last on how to identify players undervalued and overvalued by the betting market here.

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