How to bend it like Federer

Roger Federer claimed his 23rd ATP World Tour 1000 Masters title on the weekend by beating Gilles Simon 7-6(6), 7-6(2) in Shanghai. Whilst this wasn’t Federer’s most memorable match of his career, he was able to get the job done when it mattered most. However, the match that everyone is still talking about is his semi-final win over Novak Djokovic. For it was Federer that turned back the clock by putting on a masterclass of serve and volleying.

Last night I pulled down some Hawk-Eye data from a match Federer played against Paul-Henri Mathieu back in 2012 at the Swiss Indoors. I ran a quick visualisation of a serve and volley point played by Federer to illustrate how Federer sets up his serve and volley points using a beautifully executed slice serve.

Federer Hawk-Eye Serve

Figure 1: Federer v Paul Henri Mathieu, Swiss Indoors, 2012. Federer serving. Red lines are Federer. Blue line is Mathieu’s return of serve. Click to enlarge.

In this example Federer slices his serve out wide to Mathieu’s forehand, drawing Mathieu off court. Mathieu picks up the Federer serve (very well actually) and returns it right at Federer’s feet. Unfortunately for Mathieu, Federer makes a rather tricky half court volley look seemingly easily as he punches the Mathieu return into the open court to finish off the point.

Federer Hawk-Eye visualisationFigure 2: Mathieu’s look off of the Federer racket. The red lines are Federer. Blue line is Mathieu serve return. Click to enlarge.

Let’s take a look at how Federer is using sidespin to shape the ball off his racket. Figure 2 gives you a first hand look at what Mathieu sees coming off of the Federer racket. The moment the ball leaves Federer’s racket the ball begins swinging away from Mathieu’s forehand pulling him off court and creating a negative court position for him. The shadow of the serve trajectory illustrates just how much curvature Mathieu has to deal with. Let’s take a look at this from Federer’s end of the court (see Figure 3).

Federer serve trajectory Hawk-EyeFigure 3: The green line is Federer’s serve trajectory off his racket. This particular serve was recorded at 172 km/h. Click to enlarge.

From Federer’s end the sidespin is even more evident. Take a look at the right to left movement of the ball as seen on the green line above. You will also notice how little margin of error there is as the ball crosses the net, this is a typical property of a sidespin serve. The lack of top spin means the serve doesn’t rip up-and-over the net, instead it’s slicing down towards the court more quickly which results in tighter clearance over the net. In order to generate this amount of sidespin players pull back their serve speed in order to get the racket head around the serve on impact. This first serve of Federer’s was hit at only 172 km/h and landed in an OK position in the service box. Had the location of the serve been closer to the sideline, the serve may have well been an ace, as the ball would have been too far off court by the time it got to Mathieu for him to get a racket on the ball.

Federer has never had the biggest serve on the tour, but his precision and work on the ball has caused many of his opponents a headache or two in their day. The sharp curve and heavy sidespin gives Federer an instant advantage in the point, and puts his opponents in an immediately poor court position. What this does is force the returner to come up with a great return (which in this case Mathieu did, but Federer was too classy in this exchange) otherwise the point is quickly over with a volley, or one-two play. Djokovic experienced the Federer serve in full flight on Saturday, with the Swiss maestro bending it around like Beckham (as they say!). To top it off Federer brought his soft hands to the court and played a number of exquisite volleys, giving Novak no chance of getting into the long grinding baseline rallies that he thrives on. Let’s hope we see more of this attacking serve volley game from Federer as the 2014 ATP season draws to a close!

Visualisations created using 3D ArcGIS.

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An interview with Courtney Walsh at the US Open

Recently I met with Courtney Walsh at the US Open. Courtney is a sports journalist at The Australian newspaper, specializing in tennis. He contacted me a while back about the work I was doing with Hawk-Eye data and suggested we meet up at the US Open. We talked about all things tennis, in particular the potential of Hawk-Eye and how it might influence player’s tactical preparation, training and post-game analysis. Courtney’s article titled “Hawkeye data could serve up maps, sports data and analysis” can be read here. Enjoy!

Hawkeye data could serve up maps, sports data and analysis

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Mining Hawk-Eye data. It’s time to show the tennis world what they’re missing out on.

It’s been a little quiet around here recently, so I wanted to update you all on what’s been happening at Gamesetmap. The good news is there has been plenty of great work going on.

As many of you know I have been chasing down access to Hawk-Eye data for over 18 months now. One of my earliest posts on Gamesetmap outlined the challenges of getting access to the data. You can check out my post titled “Unlocking Hawk-Eye data: What it means for tennis, the ATP, WTA and ITF”.

Today, I’m please to announce that I now have access to a select number of matches, with the opportunity to purchase further matches in future. This is an enormous breakthrough for tennis, and for Gamesetmap!

Hawk-Eye tennis dataLet the Hawk-Eye data mining journey begin. A screen dump of the raw ball trajectory data from Hawk-Eye.

Generating the ball trajectory from the raw Hawk-Eye data was made possible with the help of Darren O’Shaughnessy. Darren runs a small consulting company in sports analytics and informatics called Ranking Software. He was able to extract the information I needed from the raw Hawk-Eye files in order to get started analysing the data. The image above shows the ball trajectory from the matches I have thus far.

Late last year I created a 3D Diorama using Hawk-Eye player tracking data from the same matches (see below):

A Diorama of Player Movement in Sport

The two datasets (player tracking and ball trajectory) provide an insight into tennis matches that has rarely be seen before.

Stay tuned over the next few months as I dig through these fabulous datasets to uncover the spatial patterns that exist in tennis.

Visualisations created using 3D ArcGIS.

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Roland Garros Men’s Final 2014 – Game Tree

Rafael Nadal continued his dominance on court Philippe Chatrier with an emphatic 3-6, 7-5, 6-2, 6-4 win over Novak Djokovic to take out the French Open for an incredible 9th time. Below are Novak and Rafa’s interactive Game Trees. You can explore the interactive game tree here.

Roland Garros Game Tree

Roland Garros Men’s Final 2014 Game Trees  (click to enlarge).

How the Game Tree Works

Each point is color-coded to reflect the momentum in each game. A match that is dominated by the server is highlighted with a thicker, outside flow through the ‘positive’ blue points of the Game Tree. More tightly contested service games result in thicker lines through the ‘neutral’ (white) and ‘negative’ (red) points of the Game Tree. You can click on each line to reveal how many times the score passed through that point in the match.

A Summary of the Roland Garros Game Tree

  • Nadal won 16 service games. He was broken 3 times.
  • Djokovic won 14 service games. He was broken 6 times.
  • Nadal won 6 service games from 40-30 (38%), and 5 from 40-0 (31%).
  • Djokovic won 9 service games from 40-15 (64%), and 2 from 40-30 (14%).
  • Djokovic lost his serve 3 times (50%) from 40-Ad. He lost his serve twice at 30-40 (33%), and once from 15-40 (16%).
  • Nadal lost his serve 2 times from 30-40 (67%). The one other time he lost serve was at 40-Ad.
  • Nadal won 74% of first points while serving.
  • Djokovic won 70% of first points while serving.
  • Neither player served out a shocker of a service game (loosing from 0-40). However both players found themselves at 0-30 once, but each time they were able to push the score back to 15-30.

Most Frequently Played Points

Game Tree Roland Garros

Roland Garros Men’s Final Most Frequently played points (click to enlarge).

Nadal played most of his points down the outside positive section of the game tree (as indicated by the rich yellow markers on the above diagram). Other commonly played points by Nadal were played at 15-15, 30-30 and 40-30 (as indicated by the lighter yellow markers).

Djokovic played most of his points down the positive section of the game tree, but with a little less safety than Nadal. Most of Djokovic’s points were played within one point of the neutral section of the game tree, whereas Nadal’s were played within two points of neutral section of the game tree.

The Big Difference: 30-30

The big difference between the two player’s game trees is at 30-30.

Nadal played 30-30 seven times, and was able to convert 100% of those into a very positive position, 40-30 (see below). Djokovic also played 30-30 seven times, but was only able to get to 40-30, 3 times (43%). He was forced into a very dangerous position (30-40) 4 times (57%).

Roland Garros_30-30

Nadal                                          Djokovic

Conclusion

The Game Tree was originally developed to give us a better understanding of the final score, and how close a match was. It provides one of many ways to analyse the final result of a tennis match. In this match the game trees for each player were remarkably similar hinting that the match was tightly contested. No player likes to see too many (if any) thick lines extending from the bottom four red circles of the game tree like. In this match Djokovic saw his service broken 6 times (once at 15-40, twice at 30-40, and three times at 40-Ad). (Note: lines extending from any of the four bottom red circles means a player lost his serve). Djokovic played 15-15 point better than Nadal, but clearly Nadal played 30-30 far better than Djokovic. Nether player played through deuce a lot (in comparison to other matches). However, Nadal was able to convert more winning games from deuce than Djokovic. Nadal converted 75% of games played through Deuce, while Djokovic converted only 50% of games played through deuce. At 40-30, Nadal also had a better conversion rate than Djokovic (66% to 40%).

Like we see in so many of these matches, games are won and lost at a few very important points. The game tree suggest there wasn’t much in this match between these two players. It does however suggest that Nadal was perhaps the better ‘big point’ player, and as we know in tennis, big points win matches.

Make your own conclusion about the final using the interactive Game Tree here.

@damiensaunder

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Journal of Medicine and Science in Tennis publish Spatial Serve Variation article

I’m proud to announce that the Journal of Medicine and Science in Tennis (JMST) recently published my research on spatio-temporal serve variation in their October 2013 issue.

stms-logo

The Journal of Medicine and Science (JMST) in Tennis is an international, peer-reviewed journal produced by the Society for Tennis Medicine (STMS) in co-operation with the ITF, the ATP, and the WTA Tour. It is published three times a year (February, June, and October).

The goal of the STMS is to be a forum and a disseminator of tennis specific and tennis related information, and as a catalyst for advancement in tennis sports medicine and science.

Journal of Medicine and Science in Tennis

The cover of the October 2013 issue of the JMST.

JMST Article

It is an honor to have my work published in such a prestigious and well respected journal. It’s great to see this type of analytical tennis work getting some serious traction in well respected areas of the tennis profession. Stay tuned throughout 2014 for more geo-tennis work from GameSetMap!

The article was prepared alongside Dr Mark Kovacs.

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The Federer Backhand – Using Analytics from a Coaching Perspective

Earlier this month Dr Mark Kovacs and I published an article in the May/June edition of Tennis Pro Magazine titled “The Federer Backhand – Using Analytics from a Coaching Perspective“.

TennisPro is the award winning magazine of PTR (Professional Tennis Registry), published bi-monthly and distributed to PTR members in more than 117 countries.

Tennis Pro Magazine

TennisPro. The official magazine from the PTR.

The article analyzed what specific areas of the Federer backhand produce errors, and where these errors occur. The purpose of the article was to help coaches better understand how to use analytics to help player’s improve, but also how to potentially scout opponents to help find areas of weakness in future opponents.

Federer BackhandPage 1 from the article.

Federer Backhand 2Page 2 and 3 from the article.

The article evaluates the 2012 Olympic Final between Roger Federer and Andy Murray. Andy Murray won the match 6-2, 6-1, 6-4.

You can read the article in full at the PTR website.

I hope you enjoy this example of how statistics and analytics are becoming more crucial in modern tennis at every level of the game.

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Animating Player Movement using Hawk-Eye Optical Tracking Data

Over the last few nights I have been experimenting with ways to animate player movement in tennis, using optical tracking data from Hawk-Eye. Displaying how movement is changing over time (or any temporal data for that matter) has, and still provides a great challenge to data visualization experts, animators and cartographers. This very quick animation highlight some of the advantages and disadvantages of 2D animations.

Over the next month or so as the project progresses I’ll start sharing some of my experiences of what I’ve learnt.

Until then…

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IBM “Data Wall” @ 2013 US Open

Late last year I received a call from the very talented crew at Hush in New York to help on a project they were building for the 2013 US Open and IBM. You can check out a video they put together to promote the work below.

The IBM Data Wall had three layers of interaction, Playground View, Simple View and Detail View. Each digital “tennis ball” represented a match and the wealth of data that every match produces, and sometimes even included predictions of the outcome.

My primary role on the project was to:

  • contribute ideas and design sketches
  • be a source of tennis knowledge
  • contribute as source of interpretation of data

Sean Klassen in a recent article on the Communication Arts website had this to say about the project…

“Sports are chock-full of data enthusiasts, so it’s great to see IBM and the U.S. Open taking advantage of that fact with some of the most beautiful infographics I’ve ever seen.”

The full article by Communication Arts which reviews the application can be read here.

Some of my design sketches and ideas were transformed by the guys at Hush into these fabulous interactive infographics which were used in the final application (see below).

IBM Data Wall Important Point

TennisServe Speed Infographic

Source: http://heyhush.com/work/ibm/

The main interface to exploring the Detail View ended up like this:

US Open 2013 IBM Data Wall

The guys at Hush had some fun with concept of an exploding tennis ball for the Playground View – they literally built this overnight!

Exploding Yellow Tennis Ball

A more comprehensive collection of screen shots from the application can be seen at Hush’s official website.

Enjoy!

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Presenting a Diorama of Player Movement in Sport

Earlier today Sports Performance and Tech magazine published my article on visualizing Hawk-Eye player tracking data. The article explores the value of 3D and Space Time Cubes when displaying spatio-temporal data.

To create the Diorama I used 3D optical tracking data from an official Hawk-Eye tennis match played between Roger Federer and Paul-Henri Mathieu at the Swiss Indoors in Basel, 2012.

Sports Performance and Tech Magazine

A Diorama of Player Movement in Sport

A Diorama of Player Movement in Sport

The full article can be read here.

A Diorama of Player Movement in Sport

To explore the 3D Diorama in more detail please visit the app here.

*** The app is best viewed on a computer or laptop using Google Chrome ***

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