Roger FedererTennis AnalysisTennis Statistics

New Player DNA Rates Players By Technical, Tactical, Physical and Mental Attributes

This new data highlights the strength of a player’s skills in each area relative to their competitors.

Is there a way to score a tennis player out of 100 based on data taken from tournament play? The Game Insight Group certainly thinks so and in partnership with Tennis Australia they've come up with a new Player DNA dataset that takes a look at what they believe are the 4 pillars of the perfect player.

I first noticed the data last week after @rfswissmaestro tweeted me when Roger replied to the Aus Open on Instagram after he scored poorly on footspeed 😀 . We'll be seeing more of this new data on our screens during the Australian Open so I thought I'd take a closer look. It's all very interesting but is it quantifiable and does it really tell you anything? Or is it just another case of lies, damned lies, and statistics?

fed-playergig

What is GIG Player DNA?

GIG’s Player DNA is a combination of four key areas: Technical, Tactical, Physical, and Mental. The Game Insight Group was formed by Tennis Australia and they break down the major findings within each metric to illustrate the strength of a player’s attributes relative to their competitors. Ratings are given up to a maximum score of 100.

What Data Are The Numbers Calculated From?

GIG has pulled together multiple data sources to try and get a reliable view of player performance. The data includes point-level data describing the outcomes of every point in Grand Slam matches and tracking data describing ball and player movement throughout points in matches at the Australian Open.

For all datasets, they focus on the period from 2016 to 2018. They don't actually give any specifics of where the data is sourced from which is a shame but you have to assume Hawkeye is involved here somewhere.

How Is Each Area Scored?

Each DNA score rates how much better or worse a player performs in that specific area or skill relative to an average Grand Slam player.

I rarely like these statistic based approaches as tennis is not played in a vacuum (one of the reasons ELO is completely flawed for tennis rankings).

However GIG has accounted for this realising the situation and strength of the opponent, they adjust for both situational factors and the difficulty of their opponent. In other words, using statistical models to compare players on a like for like basis.

Obviously it's not great for players who lost early and only played 1 or 2 matches in Australia as opposed to Federer who played 14, so to account for differences in sample size, each player effect was measured with ‘shrinkage’: shrinking values towards an effect size of zero in proportion to GIG's uncertainty about a player’s performance.

How Does GIG Overcome Biases?

GIG states they faced three major challenges when rating player skill
in each area of the Player DNA:

  • Sample size
  • Playing style
  • Opponent effect

For this they say:

Our main strategy for dealing with these issues was to setup a statistical model for each measure that would help us to estimate a player’s how much better or worse a player’s expected performance is comparable with an average player controlling for contextual factors and opponent strength.

To give a concrete example of the modeling approach, consider the Court Control measure of the Tactical DNA.

To assess each player’s Court Control, we took all instances in our AO tracking data when the impact player had a spatial advantage, which we defined as playing a rally shot from a central location while their opponent was out wide.

The outcome of interest was a player’s ability to win the point within two shots from this situation controlling for each of the following factors: exact player positions, incoming shot characteristics, opponent’s ranking group, and opponent’s general rally ability. Using random effects for the player, the model yields a shrinkage estimate of how much better or worse a player’s Court Control is than the average player, accounting for differences in sample size.

What Does Each Pillar Look At?

Technical DNA

fed-forehand
  • Serve (First and Second)
  • Return
  • Forehand
  • Backhand

Each stroke is broken down into subcomponents:

  • Speed
  • Potency
  • Accuracy, Placement and/or Reliability.

1. Serve

First and Second Serve

  • Speed: rates a player’s average serve speed.
  • Placement: rates how close to the lines a player hits their serve.
  • Reliability: rates how often a player gets their serve in-play.
  • Potency: rates how often a player is able to use their serve to win quick points.

2. Return

  • Speed: rates a player’s average return speed.
  • Reliability: rates how often a player gets their return in-play given the quality of the incoming serve.
  • Potency: rates how often a player is able to use their return to win points.

3. Forehand

  • Speed: rates a player’s top forehand speed.
  • Potency: rates how often a player is able to win points with their forehand.
  • Accuracy: rates how often a player uses placement rather than speed to win points with their forehand.

4. Backhand

  • Speed: rates a player’s top backhand speed.
  • Potency: rates how often a player is able to win points with their backhand.
  • Accuracy: rates how often a player uses placement rather than speed to win points with their backhand.

Tactical DNA

Murray Slice

GIG takes into account five components to rate how well each player is tactically: Rallying Craft, Attacking Balance, Spatial Control, Time Control and End Range Defence.

1. Rallying Craft

This measures how successful a player is at rally exchanges of 4 or more shots.

2. Attacking Balance

This measures how well a player balances risk and reward when looking to attack. A good balance would result in more winners than unforced errors.

3. Court Control

This measures how successful a player is when they have the space advantage. A player has the space advantage when they can play their shot from a central location and their opponent is out wide.

4. Time Control

This measures how successful a player is when they have the time advantage. A player has the time advantage when they have more time to play their shot than their opponent just had. This is more time for decision-making, positioning and shot execution.

5. Wide Defence

This measures how good a player is at defending from a wide position (end range’)when their opponent is central. The best players are able to overturn their opponents space advantage and win the point.

Physical DNA

Federer Sprint

GIG look at five stats to rate a players Physical DNA: Foot Speed, Power, Repeat SprintsAgility and Match Endurance.

1. Foot Speed

This stat looks at players who are able to hit the highest speeds in a point and still have a successful outcome.

2. Acceleration

This stat looks at players explosive acceleration power when in a winning position.

3. Repeat Sprints

The Repeat Sprints stat measures how well a player can perform multiple running actions and still have the advantage in the point.

4. Agility

This measure assesses how well a player is able to quickly change direction during points and still be successful. A quick change’ is a highintensity change of direction.

5. Match Endurance

A player’s Match Endurance is measured by their win rate in Grand Slam matches 3 hours in length or more for men, and 2 hours in length or more for women.

Mental DNA

mental-focus

Winning the mental game is all about handling pressure. We break down a players ability to handle pressure into four componentsKiller InstinctGrit, Clutch and Winning Edge.

1. Killer Instinct

This measure gets at a players ability to be clinical when they are in control of the match. The specific stat looks at how well a player is able to close out matches with minimal pressure faced during Grand Slams.

2. Grit

The Grit measure of mental performance focuses on a players mental doggedness. To evaluate player Grit we look at Grand Slam matches when a players back was to the wall and see how well they were able to raise the pressure of the match, keeping the match close even if it was ultimately a loss.

3. Clutch

A player who can raise their level in key moments is considered ‘Clutch’: they bring their best game when it matters most. To evaluate clutch we look at player’s pressure win rate (PWR) on serve and return and compare these rates to their overall win rate on serve and return. The higher the differential on serve and return, the more ‘Clutch’ a player is.

4. Winning Edge

Most matches are won by the player who wins more of the key points than their opponent. Being able to maintain a high edge in big moments over opponents takes more than talent, it takes mental strength. The Winning Edge gets at this ability by looking at a players PWR on serve relative to the opponents they have faced at Grand Slams.

We think the rating method we have used to create Player DNA scores has a lot going for it. It looks at a number of dimensions of performance we rarely see analysed in tennis. And for each measure, we have attempted to make a statistical comparison that is robust and doesn’t cherry pick to favour popular players just because they are popular. Still, our approach isn’t without limitations. We hope that by sharing our method with readers, we can get more of the tennis community thinking about how we can improve the number and usefulness of advanced stats
in our sport.

The Latest Player DNA Data

The Player DNA scores for 56 male players. These scores are based on point-level data from Grand Slam matches and tracking data from the Australian Open matches played from 2016 to 2018.

Technical DNA

PLAYER SERVE RETURN FOREHAND BACKHAND TECHNICAL DNA
Roger Federer 93.1 88.5 94 92.1 95.7
Novak Djokovic 85.7 95.4 84.3 82 94.4
Gael Monfils 82.4 73.5 86.7 91.4 93.2
Kyle Edmund 78.4 84.3 87.3 74.4 92.2
Rafael Nadal 39.1 95.1 94.5 93.9 92
Dominic Thiem 88.2 53.1 87.3 93.3 91.9
Andy Murray 74.1 88.7 75 80 91.4
Tomas Berdych 82.5 94.8 84.3 55.6 91.3
Alexander Zverev 56.4 78.1 93.3 87.8 91.1
Kei Nishikori 52.5 81.9 86.3 88.5 90.2
Diego Sebastian Schwartzman 53.9 94.3 68.8 88.5 89.6
Marin Cilic 90.5 84.2 94 34.4 89.2
Stan Wawrinka 90.3 79.1 71.4 56.2 88.1
Hyeon Chung 18.1 94 90.6 92.5 87.8
Fernando Verdasco 63.7 81.9 60.9 84.5 86.9
David Goffin 50.2 93.6 70 72.1 85.8
Richard Gasquet 72.6 70.4 40.9 94.9 84.1
Grigor Dimitrov 49.7 40.7 94.3 93.8 84.1
Andreas Seppi 73.9 50.9 69.5 78.1 82.4
Ryan Harrison 61.1 85.8 75.4 46.2 81.3
Albert Ramos-Vinolas 34.2 65.4 81.5 85.8 80.8
Andrey Kuznetsov 41.7 72.6 72.3 78.7 80.3
Fabio Fognini 8.5 80.5 88 85.5 79.4
Jo-Wilfried Tsonga 84.3 52 85.9 40.1 79.3
Roberto Bautista Agut 61.8 61.2 74.7 62.4 78.6
Jack Sock 55.9 66.1 91.2 44.7 77.9
Nick Kyrgios 92.9 33.6 39.1 84.1 74.8
Mischa Zverev 64.8 39.1 57.6 86 73.9
Juan Martin Del Potro 84.4 53.2 86.7 22.9 73.9
Milos Raonic 92 87.6 50.1 15.9 73.2
Gilles Simon 12.1 63.1 80.3 82.7 70.1
Philipp Kohlschreiber 45.1 47.7 86.8 50.1 66.1
Guido Pella 36.7 53.4 81.5 55.3 64.7
Julien Benneteau 49.5 85.4 22.2 66.1 62.9
Sam Querrey 89.9 17.2 24.5 84 58.9
Denis Istomin 63 84.2 8.9 58.1 58.2
David Ferrer 11 38.3 76.3 70 47.9
Jordan Thompson 15.2 23 88.1 68.6 47.6
Marcos Baghdatis 49.5 49.2 70.1 24.3 46.6
Damir Dzumhur 10.7 45 55.2 82 46.5
Andrey Rublev 9.3 84.8 59.9 35.3 44.5
John Isner 93.5 43.7 37.5 10.5 42.3
Pablo Carreno-Busta 49.6 42 55.3 38.1 42.1
Benoit Paire 54 89.4 7.8 31.9 41.1
Paolo Lorenzi 12.6 15.3 69.4 84.9 40.7
Guillermo Garcia Lopez 10.7 48 89.7 27.7 37.5
Pablo Cuevas 67.7 43.7 21.9 31.8 32.1
Bernard Tomic 85.1 33 11.6 13.2 22.9
Viktor Troicki 27 52.4 17.3 43.4 21.9
John Millman 20.1 14.9 40.3 57.6 19.6
Daniel Evans 14.3 36.1 28.1 49.7 18.2
Alex De Minaur 9 34.1 6.9 75.3 17.3
Gilles Muller 88.3 15.7 9.2 8.7 16.4
Ivo Karlovic 92.2 3.9 11.4 4.7 14.1
Joao Sousa 22.4 18.8 41.1 7.2 9.8
Yoshihito Nishioka 8.8 26.8 18.2 30.3 9.1

Tactical DNA

PLAYER RALLY CRAFT ATTACKING BALANCE COURT CONTROL TIME CONTOL WIDE DEFENCE TACTICAL DNA
Rafael Nadal 95.4 97.3 97.3 94.2 94 96.3
Roger Federer 95.4 97.8 91.4 92 93.5 96
Novak Djokovic 97.6 94.1 85.2 93 95.1 95.8
Kei Nishikori 91 84.7 80.1 93.8 89 94.4
Andy Murray 86.4 94.9 83.8 80.5 88.5 94.2
Tomas Berdych 90.9 89.5 78.7 84.5 67 92.4
David Ferrer 94.8 51.1 64.4 97.1 94 91.5
Mischa Zverev 82.2 89 79.6 59.7 79.8 90.4
Dominic Thiem 86.7 87.9 59.7 55.8 97 90
Richard Gasquet 90 94.9 79.3 91.1 20 88.4
Roberto Bautista Agut 85.1 84.3 63.9 52.1 89.2 88.3
Kyle Edmund 48 75.9 91.8 65.5 92.8 88.3
Grigor Dimitrov 92.2 62.8 63.8 97.1 57.9 88.2
Jo-Wilfried Tsonga 80.1 76.9 94.7 35 84.5 87.9
Fabio Fognini 72.3 21 91 86.7 97.2 87.4
Philipp Kohlschreiber 58.5 77 50.6 87.8 89.2 86.6
Gilles Simon 86 35.8 59.3 90.2 91.1 86.4
Gael Monfils 75.3 74.9 88 29.8 91.4 85.9
Marin Cilic 93.5 31.6 76.4 88.1 65 85.1
Albert Ramos-Vinolas 87.4 82.7 56.7 79.6 35.1 82.4
Hyeon Chung 72.4 68.1 98.1 59.9 32.8 80.1
Fernando Verdasco 71.4 46 91.2 59.5 47.4 75.8
David Goffin 71.9 67 85 34.6 53.1 74.6
Pablo Carreno-Busta 73.9 96.1 29.5 90.8 16.1 73
Stan Wawrinka 54 61.2 83.4 40.7 62.7 71.6
Bernard Tomic 67.1 45.1 60.2 61.1 59.4 68.3
Juan Martin Del Potro 44.1 68.3 75.8 74.1 29.1 67.8
Damir Dzumhur 76.9 22.4 72.3 23.9 91.3 66.1
John Millman 69.2 18.8 77.1 39.5 81.8 65.9
Guillermo Garcia Lopez 72.9 40.3 75.2 77.9 19.2 65.6
Guido Pella 48.2 87 61.7 37.3 47.5 64.2
Nick Kyrgios 74.6 58.7 35.2 24.6 87.5 63.7
Paolo Lorenzi 52.5 61 49.3 50.2 65 62.7
Andreas Seppi 71.3 34.9 64.3 44.6 58.2 60.8
Daniel Evans 71.5 22.9 91.6 48.2 38.7 60.7
Jack Sock 51.2 70.8 70 65.1 4.8 56.1
Andrey Kuznetsov 81 80.2 21 53.4 25.6 55.8
Andrey Rublev 61.1 37 58.2 77 11.9 48.9
Milos Raonic 72.7 76.2 24.9 44.9 24.8 48.2
Denis Istomin 24.9 82.2 25.5 19.3 87.9 46.6
Alex De Minaur 23.6 37.7 39.4 40.6 93.6 44.5
Julien Benneteau 57 42.5 36.7 51.1 41.4 41.9
Sam Querrey 71 53.6 5.3 66.6 31.9 41.8
Yoshihito Nishioka 40.1 19.2 61.8 41.6 56.1 37.8
Marcos Baghdatis 25.4 45 77.1 49.8 17.3 36.2
Alexander Zverev 58.6 15.1 19.6 72.4 47.2 35.6
Diego Sebastian Schwartzman 67.3 16.1 68.3 31.4 13.9 29.7
Gilles Muller 21 82.2 8.1 47.1 23.7 24.9
Jordan Thompson 29.4 11.5 45.9 53.9 28.6 21.3
Viktor Troicki 48 38.5 14.1 5.4 49.5 17.9
Ivo Karlovic 20.8 80.7 27.7 12.8 6.7 16.5
Pablo Cuevas 44.5 28.9 20.3 19.3 17.4 13.1
John Isner 24.6 31.2 25.7 4.5 42.6 12.8
Joao Sousa 16.2 55.7 15.2 21 6.6 10.8
Ryan Harrison 15.2 14.9 29.2 3.5 29.4 8.3
Benoit Paire 19 45.8 6 1.9 12.6 7.6

Physical DNA

PLAYER FOOT SPEED ACCELERATION SPRINTS REPEAT AGILITY ENDURANCE MATCH PHYSICAL DNA
Rafael Nadal 86.9 94.1 93.7 88.1 94.5 95.1
Novak Djokovic 64 83 94.2 92.7 97.7 93.7
Dominic Thiem 92.2 82.5 61.2 91.5 81.2 91.9
Marin Cilic 56.9 87.6 92.3 86.3 82.7 91.7
Jo-Wilfried Tsonga 71.7 89.4 77.3 79.9 81 91.1
Hyeon Chung 36.1 85.7 86.8 92.1 89.9 90.2
Viktor Troicki 78.5 86.6 68.2 64 93.1 90.1
Damir Dzumhur 78.6 75.4 82.3 83.9 67.6 89.8
Andy Murray 81 59.6 81.9 66.3 98.5 89.8
Tomas Berdych 79.3 57 87.1 85.6 76.1 89.5
Richard Gasquet 83 78.7 84.3 75.2 62.7 89.4
Grigor Dimitrov 28.9 76.8 82.8 89.7 93.4 87.8
Andreas Seppi 92.2 36 80.2 71.6 89.9 87.6
Mischa Zverev 48.7 36.8 89.7 93.7 88.9 85.7
Kyle Edmund 87.1 84.8 79 79.6 25.2 85.4
Albert Ramos Vinolas 53.8 53.3 79.5 73.4 93 84.9
Fabio Fognini 49.8 81.8 93.7 94.7 22.7 83
Roger Federer 21.2 34.8 92.7 98.5 92.5 82.4
Philipp Kohlschreiber 66.2 45.2 55.3 76.1 88.7 80.7
Diego Sebastian Schwartzman 91.1 91.5 45.2 33.1 69.5 80.5
Juan Martin Del Potro 77.9 75.9 52.6 30.1 92.6 80.2
Kei Nishikori 42.1 57.8 90.2 82.4 54.2 79.6
Gael Monfils 98.2 93.1 55 50.9 18.4 76.9
Yoshihito Nishioka 75.7 75.1 48.3 81.7 33.9 76.7
David Ferrer 53.4 54.6 96.1 97.6 12.5 76.5
Stan Wawrinka 82.1 35.8 65 34.6 96.3 76.4
Daniel Evans 35.5 49.6 87.5 68.5 69.1 75.4
David Goffin 75 70.1 72 25.9 67 75.3
Guido Pella 52.8 57.5 88.8 73.8 32.2 74
Gilles Simon 64.2 49.5 92.9 64.7 21.2 70.1
Alex De Minaur 89.3 74.8 30.8 17.3 79.3 69.8
John Millman 89.7 29.6 61 46 53.4 65.8
Paolo Lorenzi 46.9 58.2 67.9 20.3 72.1 60.7
Andrey Kuznetsov 45.1 46.5 80.3 55.9 32.4 58.7
Jack Sock 85.8 46.9 56.1 66.5 3.4 58.1
Pablo Carreno Busta 52.3 43.7 64.6 18.3 73.1 55.5
Roberto Bautista Agut 49 30.4 83.9 41.1 39.2 52.2
Benoit Paire 78 27.6 22.6 23.1 90.9 51.6
Guillermo Garcia-Lopez 21.3 62.2 70.6 41.6 44.3 50.7
Alexander Zverev 79.3 85 41.6 5.8 23.4 48.8
Andrey Rublev 35.6 63.8 63.9 19.2 45.5 46
Julien Benneteau 18.5 83.7 27.9 61.4 34.3 45.1
Ryan Harrison 77.6 47.5 21 11.4 67.2 44.7
Pablo Cuevas 41.1 42.7 32 33.4 74 44.1
Denis Istomin 22.1 45.3 33.3 42.2 74.8 42
Fernando Verdasco 15.8 27.4 74.6 29.8 69.4 41.7
Bernard Tomic 9.2 30.1 64.1 39.2 73.7 41.4
Milos Raonic 25.4 21.3 71.2 76.7 17.9 40
Nick Kyrgios 14.9 43.1 73.6 48.4 19 35
Sam Querrey 14.5 14.3 65.2 57.6 47.5 35
Gilles Muller 9.5 54 42.2 26.7 59.7 32.6
Joao Sousa 21 43 22 46 53.1 30.3
Jordan Thompson 76.4 35.4 53.9 7.1 7.8 28.9
Marcos Baghdatis 72.4 80.9 16.2 3.7 4.5 27.9
Ivo Karlovic 22.6 6.5 8.9 51.5 33.8 15.1
John Isner 16.6 21.7 15.1 22 20.1 11

Mental DNA

[table id=4 /]

Final Thoughts

So all very interesting and it's obviously very clever how it's all been calculated to produce a final number. Looking through the chart some of the scores are what I'd have expected them to be, other's not.

For example, we all know Gasquet's forehand is an absolute joke compared to many of the top guys and he scores accordingly. But why is Wawrinka's backhand so low? Is it because he lost to Sandgren last year early?

Like I said above tennis is not played in a vacuum so it's very hard to just throw numbers like this around. How do you weight each particular area? From my understanding, all four pillars are weighted equally, but this will always vary from match to match. Some days are mental battles, other physical.

Another thought I had was the numbers show Roger's foot speed is below average. But can you measure his anticipation and understanding of the court geometry? He often has a very good idea of where the ball is going before it even leaves the strings. Without eye-tracking data, probably not. So lacking in one area can be made up for in another that essentially helps produce the same end result – getting to the ball in time.

And does something like this take into account what one player knows about the other from past matches and practice so they will vary their game style accordingly?

Overall I find it interesting, but not that useful.  I feel like you can determine a lot of this stuff just by watching without the need to quantify it. I know De Minaur is fast and I know Nadal saves a lot of break points with his mindset. Does a score out of a 100 help? I'm not sure. 

I just like the numbers that can be measured in a straight line and are very easy to quantify. Distance run/covered in the tournament, break points saved, break points converted etc.

Perhaps something like this works better on a tournament by tournament basis, so you can see who is performing well in certain areas during that week. Then combined at the year-end to see who comes out on top over 12 months and see how that relates to their results and titles. Rather than this data which is from just one tournament over 3 years.

What do you guys think of the Player DNA? I know some of you guys are very stats-minded, so is this any good? Or is it flawed? Let me know your thoughts in the comments.

Jonathan

Huge fan of Roger Federer. I watch all his matches from Grand Slam level right down to ATP 250. When I'm not watching or writing about tennis I play regularly myself and have a keen interest in tactics, equipment and technicalties of the sport.

Related Articles

37 Comments

  1. @Jonathan : I agree with you, all these data aren’t very useful and are too robotic in my opinion. It removes the human part of tennis. I don’t think we need all this to know how to qualify a player. I hope it won’t be a real metric in the future.
    Thanks for sharing.

  2. I quite like it. As you say, Jon, the ultimate reality is that this is a crap model which doesn’t really tell you much beyond what you can see and DOES get a few things wrong, like for example Djokovic foot speed. I’m in favour of it though. You won’t improve it if you don’t keep trying to, so when this stuff comes out it makes me happy because it confirms someone out there is working on it.

    1. Looking at foot speed and how they determine it, I don’t think Djoker’s is necessarily wrong? I wouldn’t have expected him to be ultra fast with his build. I’d be backing Nadal over 60m for example.

      It’s just what speed the player gets to, I know from other data I was shown that Wawrinka hit’s some of the highest speeds on tour and he’s right up there in the chart. But we all think Stan is an average mover for tennis, which is true as he’s not as agile as Djok or Fed.

      Del Potro too, can move quickly from a to b but he’s also unagile compared to those guys.

  3. These stats may not take into consideration many factors like Roger’s anticipation of a return, his mental victory with the opponent even before start of the match in case of low ranked players.
    But ultimately proves why three players are so successful in this generation

  4. I do find it odd that Feds foot speed is so far below Delpossums considering he hardly ever seems to move at all?
    Still I am the most statistically challenged person in the world so what do I know.It does seem as Venkat says that it
    confirms what we already know,but it is interesting to have their various talents analysed and compared with others.
    At times though Fed seems to play by sheer instinct.I wonder how they quantify that?

    1. Del Potro is misleading, he moves slowly between points but during them he’s quick. His problem is that he’s not that agile, so a less efficient mover overall.

      1. Hmm,from side to side maybe,helped by his huge reach but when I saw him play in Basle
        slow coming forward.But only one match of course.

    1. I dunno if they are trying to simplify it. Just add a new flavour to how players are analysed?

      Thinking about it more, I think it’s use might be during commentary where someone can make an observation and see if the data backs that up. Say it looks like someone is serving well and they can then look at the raw ace count, % of pts won, then look at the DNA numbers as well to see if it ties up…

  5. I’m not at all stats-minded, so all the numbers just start to swim in front of my eyes after some time. Just looking at that graphic though, even if Roger’s foot speed is low (and even if he himself is laughing about it), his agility (ability to turn quickly between points) is the highest. That is pretty significant, especially is this data is from 2016-2018, with Federer being much older than many of the other players

    1. Yeah perhaps some metrics should be weighted higher than others. Foot speed will be more important for some players compared to others I think.

      It’s also going to depend on how far behind the baseline you play, as you have more space to reach higher speeds the further behind the baseline you play. I dunno if that’s accounted for.

  6. In a way, it helps explain the dominance of Fed (mental, technique and tactical genius) but also the challenges of not being a physical phenomenon in terms of foot speed and acceleration.

  7. Even for a non stats-minded, it’s fascinating post, Jonathan. Well, anything even remotely related to Fed, I like to read. I’m Fed-minded 😆
    98.8 killer instinct wow! Love that! (we often shout ‘Kill him!’ at him on the chat with frustration, though…)
    Okay, Fed had a great year in 2017 but the stats are from 2016-2018, right? It would be interesting to find the result from his prime years as well. Agreed with some, you can see how good Fed still is and the other top 3 guys.

    1. Yeah the numbers are purely from the Aus Open 16, 17 and 18 so it’s a pretty small sample size, unfortunately.

      If you had USO data for example on the same timeframe his killer instinct would be way down.

  8. Might be interesting as a (to be developed) base of analyzing matches. But I fear that it may be taken as objectively facts about comparing capabilities, machinelike. As some also have put it so nicely, there are still a lot of components in the obscure. For instance – Tactical – the instinct about where the opponent is shooting the ball, how to drive him around bewildered, teasing with fake prepared shots, surprise standing, running, shooting, finding his weak dimensions and so on.

    1. Ultimately I think there are too many variables for it to be completely accurate but it’s not a bad start. And it’s something that’s only going to improve as technology gets better.

      Maybe all players should wear a sensor like they do in most football teams.

  9. Hey all, keeping the tradition going: Roger to win AO 2019. BOOM. Called it. Nailed it. BOOM.
    But we do have to go through Monfils….

    I just have 2 wishes: Roger to win and Djoko NOT to win. Can you imagine if he won? He would be going for his second Grand Slam at RG. Nope. That cannot happen.
    I am watching Rafa vs Duckworth. Rafa already won the 1st set. I have the week off, so hopefully Roger can win many matches so that I can see them all 🙂

    Go Rog. Go Goat. No matter what… always Bel21ve 🙂

    Oh oh Muzz…. The tour won’t be the same without Andy… #heartbreaking

  10. Not a big fan of RBA, but he did good. Very respectful. Too bad Andy, but you did make it to 5 sets.
    Go Rogi, 6 more to win !!!
    Is it now time to worry about Gael??

  11. Interesting article. Nadal ranked 1st in 3 categories, that’s pretty amazing considering that the analysis is based only on the last 3 AO’s (he did not have his best tournaments).

Leave a Reply

Your email address will not be published. Required fields are marked *

Back to top button