ATP World Tour Interactive Map – 2014

With the 2014 ATP World Tour underway in Brisbane, Doha and Chennai this week, I thought I would put together a quick interactive map that locates this year’s 61 tournaments around the world. The ATP World Tour map allows fans to follow the world’s greatest players around the world as they battle it out in 2014.

ATP World Tour Map 2014

The ATP World Tour Map – 2014. Click here to access the map.

The map shows the location of the four Grand Slam tournaments, the nine Masters 1000′s, the eleven 500 and the forty 250 series tournaments, as well as the season ending World Tour Finals played in London.

Use the map to check out the site of the newest tournament on the tour this year, The Rio 500 in Brazil. Remind yourself of where the clay tournament in Umag (Croatia) is played. A tournament that returns to the circuit this year after a one year absence. Check out the latest stop in China in 2014 at the Shenzhen Open, a city that is already familiar with the women on the WTA tour.

Explore each tournament in more detail by clicking on each of the icons to reveal further information about the tournament like the prize money on offer, who is the defending champion, what the tournament surface is and other important information.

ATP World Tour Map WimbledonInteract with each tournament to reveal important statistics about each event.

The map features some of the highest resolution available satellite imagery of the globe, meaning you can see the tournaments up close like never before. Click on the “Zoom To” link in the pop-up to quickly navigate to each tournament. You will find yourself being blown away by some of the global landscapes that sit at the doorstep of some of the lesser known tournaments, like the Credit Agricole Suisse Open.

Gstaad TournamentThe 250 series Credit Agricole Suisse Open, set against the backdrop of the beautiful Swiss Alps.

Valencia Open

Discover the Valencia Open 500 (Spain) played at the stunning Ciutat de les Arts i les Ciències where Mikhail Youzhny triumphed in 2013.

In each of the pop-up’s there is a link to the official tournament website where you can see who’s down to play in 2014, and where you can get tickets. I hope you have fun exploring the map throughout the year, and I hope it inspires you to plot your next tennis adventure!

Read the ATP press release about the 2014 tour here.

To view the full 2014 ATP World Tour calendar in PDF format, click here.

The Comeback. An interactive Game Tree of Nadal’s extraordinary 2013 season.

On February 5, 2013, following a 222 day break from the game, Rafael Nadal returned to tennis ranked #8 in the world. Just 9 months later he completed an almost flawless comeback ending the year ranked the #1 player in the world for the third time.

To celebrate Nadal’s historic season we present his 2013 interactive Game Tree. Nadal’s Game Tree allows you to explore how his 600+ service games played out in the Grand Slams, Masters 1000 and World Tour Finals.

NadalGameTree_new

Click here to access Nadal’s interactive Game Tree application. 

This rare point-by-point summary shows where Nadal’s history breaking season was won and rarely lost.

The Game Tree presents an alternate way of visualizing game momentum. The challenge was to come up with a visualization that better reflects game momentum, and therefore shows how easily, or not a player wins their service game.

Each point in Nadal’s Game Tree is colour coded to reflect the momentum in each game. Blue representing positive momentum, and red negative momentum. The spine of the game tree is coloured white indicating neutral territory for Nadal.

About Nadal Game Tree

Mapping momentum through the Game Tree. Each point in the Game Tree diagram is tagged with a colour that matches its momentum classification.

When Nadal dominates a match you will see him flow through the outside ‘positive’ points of the Game Tree. When he struggles to hold serve, or looses his serve his flow will tend to move the through the ‘neutral’, or outside ‘negative’ points. The Game Tree clearly shows how Nadal dominated his opponents on serve this season by the frequency of points through the positive side of the Game Tree.

Game Tree’s are perfect for coaches and players to see where in the game a player is making it, or breaking it. Once they identify the breaking points, they can go to the tape and see what’s happening on court.

And the great news is we have coded the app so we can plug in any ATP, WTA or Challenger match/es into their own Game Tree!

We believe this is the first interactive point-by-point Game Tree that maps an entire season of service games for one player. The Game Tree interactive is an experiment to see what we can do with traditional forms of tennis data. Please let us know what you think; we’d love to hear from you.

A massive thanks to David Webb for coding this up, and Ella Ling for allowing me to use her fantastic Nadal pic on the opening page. Next week we’ll go into a little more detail about the concept, design and development of the app.

Enjoy!

Pinpointing the serve. Who missed, and by how much.

(Part 3 of 3)

In the final part of this three part series, I determine who picks up the most free drinks as a result of hitting the centre of the USTA target zone, and by how much. I also extend the analysis to see how much each player missed the ‘optimum’ serve locations.

Who picks up the most free drinks?

For a bit of fun let’s see who would have picked up the most free drinks by hitting the ‘imaginary’ cone in the center of each target zone. We know coaches run this drill with their players, so let’s see how well each player fared in a match environment. Let’s assume the cone is 20 cm in diameter.

Federer Murray Serve Map Spider DiagramFigure 1. Federer v Murray. Mapping spatial serve patterns from the centre of each target zone. (click to enlarge)

The results show us that Federer picked up 4 free drinks, while Murray picked up only 3.   I don’t feel too bad since each player hit 100 or so serves each. That’s a pretty poor strike rate given these guys are best players in the world!

Each player missed the target by almost the same amount. Federer was on average     0.76 m from the centre of the each target  zone, while Murray was out by an average of 0.82 m.

Let’s take a look at the School Boys…

NCAA Tennis Serve Spider DiagramFigure 2. School Boy A v School Boy B. Mapping spatial serve patterns from the centre of each target zone. (click to enlarge)

The results show us that School Boy A picked up only 1 free drink, while School Boy B went thirsty not hitting the center of any of the targets! Ok, so now I’m feeling really good.

School Boy A on average missed the centre of the target zone by 0.94 m, while School Boy B was only out by an average of 0.80 m.

As discussed in part 2 of the blog, it’s reasonable to assume that perhaps the players weren’t targeting the centre of each zone. What if they were aiming for a ‘optimum’ but higher risk serve position? In part 2 of the blog we argued that the corners and lines were the ‘optimum’ positions to land your serve. So let’s see how far each player was from these ‘optimum’ serve positions.

Federer Murray Serve Map Spider Diagram 2Figure 3. Federer v Murray. Mapping spatial serve patterns from the ‘optimum’ serve locations. (click to enlarge)

Figure 3 shows us that Federer missed the ‘optimum’ serve locations on average by     0.88 m, while Murray missed on average by 1.04 m.

NCAA Serve map Spider DiagramFigure 4. School Boy A v School Boy B. Mapping spatial serve patterns from the ‘optimum’ serve locations. (click to enlarge)

Figure 4 shows us that School Boy A missed the ‘optimum’ serve locations on average by 1.15 m, while School Boy B missed on average by 1.22 m.

What can we learn from this?

Well we know that Federer takes home as many free drinks as the other three put together! We also know that Federer was on average serving closer to the ‘optimum’ locations than Murray which supports our analysis in part 2 of the blog, where we found Federer to target the high risk zones more than any other player.

We all expected the spread of the School Boy serves around the ‘optimum’ zones to be greater than the Big Boys due the results in part 2, where the Big Boys landed more balls in these ‘optimum’ areas. When we changed the target position back to the centre of each zone the School Boys and Big Boys numbers pretty much evened up, again supporting the results in part 2.

Spider Diagrams: The spider diagrams allowed us to visually link the serves to their target points and see the spread (length and direction) around each point. The spider lines for each zone allow us to very quickly see any bias in direction and distance towards the spread of serve around the points.  Without the lines it would be difficult to identify the serve clusters, and which central point they belong to.

Outliers: There were a couple of serve outliers for the Big Boys but these didn’t affect their averages enough to remove them from the calculations. The School Boys certainly had some big misses, but because there were multiple instances of these so they were left in the calculations.

More Data: With a larger dataset across different players we would be able to determine what is the expected norm, and whether these results are above or below that. Unfortunately, large serve datasets that are easily accessible to the players, coaches or analyst do not exist in tennis (hint hint ATP and WTA).

0.75 m: Let’s think back to part 2 of the blog for a minute. The size of the USTA target zones are 0.75 m square. Perhaps this tells us something. On average the four players missed by 0.83 m. Maybe the USTA set their targets knowing these missed averages and that is the reason for the particular size of the boxes?

To Summarize…

Over the course of the three blogs I have presented an alternative way of assessing a player’s serve accuracy using the USTA defined serve zones, and an additional two ‘higher risk’ zones. When comparing serve accuracy around the USTA zones there was very little difference between the four players. However once we started to analyze the serve towards the higher risk zones (the ‘optimum’ serve areas) the results started to lean in favor of the Big Boys, Federer and Murray.

I also set out to determine whether serve location really matters in tennis. The results suggest that it depends on what level of tennis is being played. The Big Boys clearly had more outright success on serves that landed in the USTA zones, and the higher risk zones than if they missed these zones. It was a different story for the School Boys however, as it didn’t appear to make any difference to their outright success rate whether they served in or outside the zones.

There is much work to be done in expanding the analysis of serve accuracy, serve success, and general serve patterns. Let’s hope we start to see more meaningful statistics from broadcasters and commentators about the serve in order to better understand who really are the best servers in the game!

Pinpointing the serve. Who’s better? The Big Boys or the School Boys?

(Part 2 of 3)

In part 1 of this 3 part series, I set out to find which player out of Federer, Murray and two NCAA Division 1 players were able to land the highest proportion of their serves in the USTA target zones.

Surprisingly the School Boys outranked the big boys in this simple comparison. However once we moved the target to include zones closer to the lines, Federer’s serving clearly stood out as being the most accurate. See part 1 for the complete results of the analysis. In order to gain some real value out of this analysis, I set out to determine if there was a positive relationship between serve position and outright serve success.

To explore this relationship I classified each serve into an ‘outright success’ category. Throughout the blog I will refer to an outright success point as a free point (to keep things simple).

Free Point definition: An error made by the player returning serve OR an ace made by the server. The remaining serves were either classified as being “returned in play” or “out” (fault).

For each player I generated a Serve Map (see Figures 4 A-D) showing the position of their serves in relation to the three target zones and their free point success.

Click to enlarge each map.

Federer ServeFigure A. Federer’s Serve Map

Murray ServeFigure B. Murray’s Serve Map

NCAA Tennis PlayerAFigure C. School Boy A Serve Map

NCAA Tennis PlayerBFigure D. School Boy B Serve Map.

Mapping the relationship between serve location and the effectiveness of serve. The Serve Maps also show where each player served when it mattered most.

School Boy A was able to collect 3 (50%) free points from his serves inside the zones, compared to 5 (42%) for School Boy B.

Federer picked up 13 (76%) free points from his serves inside the zones, compared to 18 (82%) for Murray.

Summary: The Big Boys picked up 31 (79%) free points from serves that landed in the target zones, compared to 8 (44%) for the School Boys.

Across all four players, 39 (68%) serves out of 57 that landed in the target zones earned the players a free point.

Serves that missed the zones: To test the importance of serve position I calculated how many free points each player picked up off of their serve that landed outside the target zones, but still within the service box.

Federer picked up only 4 (24%) free points on serves outside the zones compared to 13 (76%) inside the zones. While Murray picked up 4 (18%) outside the zones, compared to 18 (82%) inside the zones.

School Boy A picked up 3 (50%) free points when serving outside the target zones, which equalled his inside count 3 (50%), while School Boy B picked up 7 (58%) free points outside, which was more than his inside count of 5 (42%).

Summary:  The Big Boys picked up only 8 (21%) free points from serves that landed outside the target zones, compared to a surprisingly high 10 (56%) for the School Boys.

Across all four players, 18 (31%) serves out of 57 that landed outside the target zones earned the players a free point.

Take-aways:

Based on the data in this analysis the Big Boys clearly had more success on their serve when they landed their serve into the target zones (79% to 21%). This is a significant difference. At this level the Big Boys almost quadruple their chances of getting a free point off of their serve if they land it in the target zones!

Interestingly, the same trend didn’t occur for the School Boys. Player B recorded more success outside the zones than inside (58% to 42%), while School Boy A had the same level of success inside to out. So does it mean at the lower levels of the game that serve position is not all that important? Well it is quite possible. However we need to be a little careful about the above statement given the small-ish sample size and the fact that the study only included two players. It would be interesting to see what the numbers would do over a larger sample size, and with more players. Likewise for the Big Boys, would the high level of success remain with a larger sample spread over different players?

Overall across the four players free points were easier to get inside the target zones than out.

The USTA suggest that improving and practicing your serve location will help strengthen your game, and with some luck you might just pick up some free points along the way! Well that may well be the case, but it also might depend on which level of the game you’re playing!

In part 3…

In the final part of this three-part blog we are going to have some fun and address the most important question of all. Which player picked up the most free drinks by landing their ball in the center of the target zones? I present another series of maps showing spider diagrams to visualize how far each player was from the centre of each zone!

Unlocking Hawk-Eye data: What it means for tennis, the ATP, WTA and ITF.

Since 2005 the governing bodies of tennis (ATP, WTA and ITF) have been collecting data using Hawk-Eye for many top-level tournaments and the Grand Slams. So what have the governing bodies been doing with this data? Where is it stored? Who owns it? Who has access to it?

Hawk-Eye WimbledonHawk-Eye was introduced to tennis in 2005. Since then, the governing bodies of tennis have been collecting valuable data about match play. Image: Hawk-Eye Innovations.

Some background

Early in 2012 I set out to start mapping tennis matches. As a Cartographer, and tennis player this kind of made sense and excited me! Tennis is a spatial game, meaning that the location of the ball and the players are linked spatially to the court. So at any time during a match we can plot where and when a stroke, or player is. The concept of mapping sports matches is not new. It has been around for some time now and is commonly referred to as Sports Analytics or Spatial Analytics. Many sports like Football (Soccer), Basketball and Baseball have been using analytics for years to explore potential unknown patterns about the game, their players and their opponent’s tactics. We have all seen Moneyball right?

To kick off my research into maps about tennis I manually plotted the ball location and player movement from the London Olympics Men’s tennis final using video footage and a 3D visualization application. The results of the research can be read here. This method of data capture was perfect at the time because it allowed me to captured the tags I needed to run my analysis on. As a result of the research I have had tennis players, coaches and other tech companies contact me wanting help analyzing their players patterns, strengths and weaknesses using similar methods as outlined in my research. Sure, I replied with over-the-top enthusiasm. But, we have to manually capture the data first, and that tends to be time-consuming and a tad laborious. So the client says, “Can’t we use Hawk-Eye?” That’s a great question I tell them, but it’s not that easy…

The search begins for Hawk-Eye data

So how would one go about getting access to this infamous Hawk-Eye data that everyone apparently everyone knows about (like its their brother), has seen on TV, but no one knows where it is or who to contact to get access to it? Go direct to Hawk-Eye?

To cut a long story short: Hawk-Eye state that they don’t own the data they capture. The tournaments do. Or do they? After spending the last 6 month trying to track down the right people in the right place at the right time I receive this response recently from Tennis Properties, the management group who runs the ATP. “Tennis Properties own all of the Hawk-Eye data from the Masters 1000 tournaments. We don’t license this data to 3rd parties”. Well at least that clears up who owns the data. But of course that wasn’t the response I had hoped for!

I then turned to Tennis Australia. I figured they might care to share some Hawk-Eye data with another Aussie. This was their response “The Hawk-Eye data is owned by our commercial/IT teams…. but it is not for use for commercial or external endeavors”. So they own their Hawk-Eye data, not Tennis Properties. Confused yet?

So my search started targeting the ATP 500 series tournaments. Tennis Properties had told me that each of these 500 series tournaments has their own agreements in place with Hawk-Eye and that the ATP does not control the data captured at these tournaments. Sounds promising right? Well it was. The team running the Swiss Indoors tournament in Basel granted me permission to all of their match data for their 2012 tournament. I was ecstatic. Finally I would be able to grow my research, and potentially help some of the pending requests from other interested parties. However, they didn’t have the Hawk-Eye data in-house (sigh). I was then directed to Hawk-Eye themselves to retrieve the data….

Swiss Indoors BaselThe Swiss Indoors at Basel granted me access to their Hawk-Eye data from their 2012 tournament.  Image: Swiss Indoors.

A further six long months has passed and I am yet to see any sight of the data from Hawk-Eye. Apparently they are too busy to attend to the request of the Swiss Indoors to release the data (grrrggh!).

Why is Hawk-Eye data so protected?

The answer is simple. The data that Hawk-Eye collects is very powerful. It collects the location of the ball and player, the spin of the ball, speed and flight of the ball (just to name a few). If the data lands in the hands of someone who can pull it apart and reveal patterns about players and opponents (that may not have been seen before) then it becomes a potential sticking point for the ATP, WTA or ITF. Or does it? Let’s take a look at this from another point of view.

Bob Kramer, the former tournament director of the Farmer’s Classic* in Los Angeles, said the technology ran at his tournament cost about $60,000-$70,000 for one court, with much of that cost going to installing the infrastructure. Now if I was a tournament director and I was spending that kind of money on new technology then I would be keen to explore ways I can recoup some of those costs. One of those ways may be selling/licensing the Hawk-Data back to its players, the media and fans. Oh but wait, the tournaments can’t do this because the ATP, WTA and ITF control the data. Or do they?

So who really owns Hawk-Eye data?

The tournaments seem to be funding the implementation of the technology (the richer tournaments like Indian Wells have more Hawk-Eye courts than say Miami) so is it their data to share and/or commercialize? Or is the data in fact the player’s data? They are the ones putting on the show; the data is about them, not the tournament. What if Roger Federer or Serena Williams wanted access to the Hawk-Eye data? How quickly would the ATP, the tournaments and Hawk-Eye react to their request? Are they permitted to even access the data?

Tennis unlike Basketball, Baseball and Football (Soccer) is an individual sport, played mostly on neutral territory (with the exception of Davis Cup). In team sports, it is the teams who are collecting the data at their home games, not the governing bodies of each sport. So where does this leave the players? Does Novak Djokovic have to bring his own data capture equipment on court to trace him movements and map his shots? Let’s hope not!

Novak DjokovicWorld number 1, Novak Djokovic may have to bring his own data capture equipment to matches to record his shot patterns and movements! Image: Reuters

What’s in it for the ATP, WTA and ITF to unlock (open) Hawk-Eye data?

Open data initiatives have been actively gaining momentum (outside of sport) as governments and private industry see the benefit of making their data freely available. Late last year however, the Manchester City Football Club (MCFC) opened up some of its match data so it could crowd source new ways of visualizing the data and encourage innovative ways of making use of it (read the Forbes article about the MCFC program here). They were essentially tapping into the crowd’s knowledge and passion for the game to better understand their players and opposing teams. If the governing bodies of tennis were to do this it would open up a unique opportunity to engage with the fans and media like never before. Tim Davies whom is an open data advocate calls this making use of “social infrastructure” that surrounds sports.  Opening up the vast of amounts of tennis match data available at a relatively low cost (or for free), would lead to third party innovation, where the next generation of tennis fans could design innovative products, which may result in a new wave of interest in tennis analytics and spawn many new products in tennis. Imagine what IBM could do with data, or anyone else that has an interest in commenting and reporting on the game? Imagine the maps and graphics that the tournaments could supply to the pressroom at the end of the day to help report on the days play!

Opening data can be scary (but it’s time to be brave!)

Opening up your data to the whole world can seem scary at first. There is no doubt the ATP, WTA and ITF will have reservations about doing so. But think of the increased two-way interaction, between the innovators and the data suppliers. Perhaps Hawk-Eye data can be extended way beyond what it is currently being used for? Perhaps there is a revenue stream back to the tournaments that may offset their cost of installing the technology. The data may even be turned into physical products, like artwork for Nike’s next Rafael Nadal t-shirt! Who knows? History has shown that opening up data is not in fact scary, it is incredibly exciting and the possibilities appear endless.

Andy Murray Tennis ArtAndy Murray poses in front of ‘tennis art’ at the O2 Arena in London last year. Andy created the unique portrait of himself that was auctioned off for charity late last year.

Natural Evolution for Tennis

Unlocking Hawk-Eye data is a natural evolution for tennis. As pressure builds on the ATP, WTA and ITF to-be-seen-to-be-keeping up with other sports, perhaps the locks will come off the data. At present, only the TV broadcasters and national tennis associations appear to have a key to the data. Sadly, there is a very valuable stockpile of data gathering dust on some internal server at Hawk-Eye with no use for it all! Of course you might get lucky and be granted access to a portion of that data but fail to ever see it! It will only take one of the ‘next gen’ of players, like a Sloan Stevens or Milos Raonic who understand what modern analytics can do for their game, or one commentator (hint hint, Justin Gimelstob) to lean hard on the governing bodies to move this issue in the right direction. Imagine how powerful the ATP FedEx Reliability Stats could be if they integrated space into their stats by using Hawk-Eye data! Let’s hope that happens quickly. Then we can sit back and watch it open up a whole new world of tennis analytics, third party products and applications that will benefit the players, tournaments, the fans, the media and most of all the great game of tennis itself!

 * The Farmers Classic will not be returning to the ATP circuit in 2013. After 86 years, and being the longest running annual professional sporting event in Los Angeles, it ran its last event in 2012.