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 the 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 just another case of lies, damned lies and statistics?


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

  • 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


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

PlayerServeReturnForehandBackhandTechnical DNA
Roger Federer93.188.59492.195.7
Novak Djokovic85.795.484.38294.4
Gael Monfils82.473.586.791.493.2
Kyle Edmund78.484.387.374.492.2
Rafael Nadal39.195.194.593.992
Dominic Thiem88.253.187.393.391.9
Andy Murray74.188.7758091.4
Tomas Berdych82.594.884.355.691.3
Alexander Zverev56.478.193.387.891.1
Kei Nishikori52.581.986.388.590.2
Diego Sebastian Schwartzman53.994.368.888.589.6
Marin Cilic90.584.29434.489.2
Stan Wawrinka90.379.171.456.288.1
Hyeon Chung18.19490.692.587.8
Fernando Verdasco63.781.960.984.586.9
David Goffin50.293.67072.185.8
Richard Gasquet72.670.440.994.984.1
Grigor Dimitrov49.740.794.393.884.1
Andreas Seppi73.950.969.578.182.4
Ryan Harrison61.185.875.446.281.3
Albert Ramos-Vinolas34.265.481.585.880.8
Andrey Kuznetsov41.772.672.378.780.3
Fabio Fognini8.580.58885.579.4
Jo-Wilfried Tsonga84.35285.940.179.3
Roberto Bautista Agut61.861.274.762.478.6
Jack Sock55.966.191.244.777.9
Nick Kyrgios92.933.639.184.174.8
Mischa Zverev64.839.157.68673.9
Juan Martin Del Potro84.453.286.722.973.9
Milos Raonic9287.650.115.973.2
Gilles Simon12.163.180.382.770.1
Philipp Kohlschreiber45.147.786.850.166.1
Guido Pella36.753.481.555.364.7
Julien Benneteau49.585.422.266.162.9
Sam Querrey89.917.224.58458.9
Denis Istomin6384.28.958.158.2
David Ferrer1138.376.37047.9
Jordan Thompson15.22388.168.647.6
Marcos Baghdatis49.549.270.124.346.6
Damir Dzumhur10.74555.28246.5
Andrey Rublev9.384.859.935.344.5
John Isner93.543.737.510.542.3
Pablo Carreno-Busta49.64255.338.142.1
Benoit Paire5489.47.831.941.1
Paolo Lorenzi12.615.369.484.940.7
Guillermo Garcia Lopez10.74889.727.737.5
Pablo Cuevas67.743.721.931.832.1
Bernard Tomic85.13311.613.222.9
Viktor Troicki2752.417.343.421.9
John Millman20.114.940.357.619.6
Daniel Evans14.336.128.149.718.2
Alex De Minaur934.16.975.317.3
Gilles Muller88.315.79.28.716.4
Ivo Karlovic92.23.911.44.714.1
Joao Sousa22.418.841.17.29.8
Yoshihito Nishioka8.826.818.230.39.1

Tactical DNA

PlayerRally CraftAttacking BalanceCourt ControlTime ContolWide DefenceTactical DNA
Rafael Nadal95.497.397.394.29496.3
Roger Federer95.497.891.49293.596
Novak Djokovic97.694.185.29395.195.8
Kei Nishikori9184.780.193.88994.4
Andy Murray86.494.983.880.588.594.2
Tomas Berdych90.989.578.784.56792.4
David Ferrer94.851.164.497.19491.5
Mischa Zverev82.28979.659.779.890.4
Dominic Thiem86.787.959.755.89790
Richard Gasquet9094.979.391.12088.4
Roberto Bautista Agut85.184.363.952.189.288.3
Kyle Edmund4875.991.865.592.888.3
Grigor Dimitrov92.262.863.897.157.988.2
Jo-Wilfried Tsonga80.176.994.73584.587.9
Fabio Fognini72.3219186.797.287.4
Philipp Kohlschreiber58.57750.687.889.286.6
Gilles Simon8635.859.390.291.186.4
Gael Monfils75.374.98829.891.485.9
Marin Cilic93.531.676.488.16585.1
Albert Ramos-Vinolas87.482.756.779.635.182.4
Hyeon Chung72.468.198.159.932.880.1
Fernando Verdasco71.44691.259.547.475.8
David Goffin71.9678534.653.174.6
Pablo Carreno-Busta73.996.129.590.816.173
Stan Wawrinka5461.283.440.762.771.6
Bernard Tomic67.
Juan Martin Del Potro44.168.375.874.129.167.8
Damir Dzumhur76.922.472.323.991.366.1
John Millman69.218.877.139.581.865.9
Guillermo Garcia Lopez72.940.375.277.919.265.6
Guido Pella48.28761.737.347.564.2
Nick Kyrgios74.658.735.224.687.563.7
Paolo Lorenzi52.56149.350.26562.7
Andreas Seppi71.334.964.344.658.260.8
Daniel Evans71.522.991.648.238.760.7
Jack Sock51.270.87065.14.856.1
Andrey Kuznetsov8180.22153.425.655.8
Andrey Rublev61.13758.27711.948.9
Milos Raonic72.776.224.944.924.848.2
Denis Istomin24.982.225.519.387.946.6
Alex De Minaur23.637.739.440.693.644.5
Julien Benneteau5742.536.751.141.441.9
Sam Querrey7153.65.366.631.941.8
Yoshihito Nishioka40.119.261.841.656.137.8
Marcos Baghdatis25.44577.149.817.336.2
Alexander Zverev58.615.119.672.447.235.6
Diego Sebastian Schwartzman67.316.168.331.413.929.7
Gilles Muller2182.
Jordan Thompson29.411.545.953.928.621.3
Viktor Troicki4838.514.15.449.517.9
Ivo Karlovic20.880.727.712.86.716.5
Pablo Cuevas44.528.920.319.317.413.1
John Isner24.631.225.74.542.612.8
Joao Sousa16.255.715.2216.610.8
Ryan Harrison15.214.929.23.529.48.3
Benoit Paire1945.861.912.67.6

Physical DNA

PlayerFoot SpeedAccelerationSprints RepeatAgilityEndurance MatchPhysical DNA      
Rafael Nadal86.994.193.788.194.595.1
Novak Djokovic648394.292.797.793.7
Dominic Thiem92.282.561.291.581.291.9
Marin Cilic56.987.692.386.382.791.7
Jo-Wilfried Tsonga71.789.477.379.98191.1
Hyeon Chung36.185.786.892.189.990.2
Viktor Troicki78.586.668.26493.190.1
Damir Dzumhur78.675.482.383.967.689.8
Andy Murray8159.681.966.398.589.8
Tomas Berdych79.35787.185.676.189.5
Richard Gasquet8378.784.375.262.789.4
Grigor Dimitrov28.976.882.889.793.487.8
Andreas Seppi92.23680.271.689.987.6
Mischa Zverev48.736.889.793.788.985.7
Kyle Edmund87.184.87979.625.285.4
Albert Ramos Vinolas53.853.379.573.49384.9
Fabio Fognini49.881.893.794.722.783
Roger Federer21.234.892.798.592.582.4
Philipp Kohlschreiber66.245.255.376.188.780.7
Diego Sebastian Schwartzman91.191.545.233.169.580.5
Juan Martin Del Potro77.975.952.630.192.680.2
Kei Nishikori42.157.890.282.454.279.6
Gael Monfils98.293.15550.918.476.9
Yoshihito Nishioka75.775.148.381.733.976.7
David Ferrer53.454.696.197.612.576.5
Stan Wawrinka82.135.86534.696.376.4
Daniel Evans35.549.687.568.569.175.4
David Goffin7570.17225.96775.3
Guido Pella52.857.588.873.832.274
Gilles Simon64.249.592.964.721.270.1
Alex De Minaur89.374.830.817.379.369.8
John Millman89.729.6614653.465.8
Paolo Lorenzi46.958.267.920.372.160.7
Andrey Kuznetsov45.146.580.355.932.458.7
Jack Sock85.846.956.166.53.458.1
Pablo Carreno Busta52.343.764.618.373.155.5
Roberto Bautista Agut4930.483.941.139.252.2
Benoit Paire7827.622.623.190.951.6
Guillermo Garcia-Lopez21.362.270.641.644.350.7
Alexander Zverev79.38541.65.823.448.8
Andrey Rublev35.663.863.919.245.546
Julien Benneteau18.583.727.961.434.345.1
Ryan Harrison77.647.52111.467.244.7
Pablo Cuevas41.142.73233.47444.1
Denis Istomin22.145.333.342.274.842
Fernando Verdasco15.827.474.629.869.441.7
Bernard Tomic9.
Milos Raonic25.421.371.276.717.940
Nick Kyrgios14.943.173.648.41935
Sam Querrey14.514.365.257.647.535
Gilles Muller9.55442.226.759.732.6
Joao Sousa2143224653.130.3
Jordan Thompson76.435.453.97.17.828.9
Marcos Baghdatis72.480.916.23.74.527.9
Ivo Karlovic22.66.58.951.533.815.1
John Isner16.621.715.12220.111

Mental DNA

PlayerKiller InstinctGritClutchWinning EdgeMental DNA
Rafael Nadal97.596.297.197.595.7
Roger Federer98.892.498.598.295.7
Novak Djokovic97.691.498.898.295.6
Andy Murray94.894.697.696.495.5
Juan Martin Del Potro88.684.69695.794.4
Jo Wilfried Tsonga8887.190.992.294
Milos Raonic91.574.395.792.393.7
Marin Cilic91.171.592.190.493
Stan Wawrinka51.39496.694.592.2
John Isner66.798.375.28490.9
Dominic Thiem63.18785.385.390.5
Gael Monfils64.280.984.288.590.1
Roberto Bautista Agut77.272.783.58189.6
Grigor Dimitrov85.567.677.18489.6
Richard Gasquet9433.186.987.287.7
Nick Kyrgios86.155.67682.687.6
David Ferrer63.365.77889.386.9
David Goffin82.447.981.283.986.7
Kei Nishikori59.549.394.491.286.6
Sam Querrey6961.979.476.285.1
Tomas Berdych8117.4939184.2
Gilles Simon44.961.575.382.680
Alexander Zverev60.991.342.968.479.7
Andreas Seppi39.57766.977.979.1
Fabio Fognini62.184.640.265.476.5
Jack Sock5475.94570.374.2
Pablo Carreno Busta60.972.647.95270.1
Philipp Kohlschreiber86.137.346.758.268.2
Fernando Verdasco4.591.364.167.867.9
Bernard Tomic34.988.841.958.366.5
Marcos Baghdatis77.64741.456.966.1
Albert Ramos Vinolas55.214.8727965.3
John Millman3257.562.160.161.3
Andrey Kuznetsov41.758.252.258.761
Julien Benneteau47.282.340.735.858.8
Kyle Edmund71.736.341.15558
Ryan Harrison6730.155.347.656.2
Mischa Zverev67.19.167.853.755.2
Guido Pella48.865.553.528.954.7
Paolo Lorenzi38.67238.345.353.5
Pablo Cuevas70.946.938.835.252.4
Alex De Minaur42.421.759.757.447.6
Damir Dzumhur60.355.624.236.245.3
Diego Schwartzman17.152.850.355.244.9
Andrey Rublev8.372.643.448.143.6
Gilles Muller4.957.65647.840.8
Daniel Evans19.742.150.45039
Guillermo Garcia Lopez51.526.744.236.737.7
Benoit Paire7.949.845.953.837
Viktor Troicki30.822.160.343.836.8
Hyeon Chung39.325.530.923.323
Ivo Karlovic9.818.650.64023
Jordan Thompson39.839.621.616.322.5
Denis Istomin25.513.844.428.120.9
Joao Sousa37.513.223.429.518.8
Yoshihito Nishioka42.

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.


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.

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  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).

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