Hawkeye Data Damien Demaj looks at the restricted use of Hawkeye Data in professional tennis
The Big Interview: We Talk to Darren Roberts, High Performance Manager at Red Bull
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A couple of years ago I was listening to a fantastic presentation from Marco Cardinale, the head of analytics at the British Olympic Association. During this presentation I realized that I hadn’t really heard about this level of analytics being used elsewhere in sport.
Editors Letter
I did some more research and found that it was being used but had not reached the mainstream. Sports analytics were spreading. Fast forward to September 2012 and I sat in a room of around 200 senior sports analysts and I realized that this was something that had really expanded. It needed somewhere to learn about new techniques and hear what peers in the same industry were doing. So this magazine was born, with the sole purpose of the spreading new sports analytics, technology and training ideas.
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I wanted to create a publication that is more approachable than traditional academic papers, but which also gives people the same kind
of insight and cutting edge information that you want to hear to push the industry forward. I want to create a publication where peers are sharing new ideas in an approachable way. If you have any feedback or you are interested in writing an article, then please let me know.
George Hill Editor
Managing Editor: George Hill Art Director: Gavin Bailey Advertising:
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Contributors: David Barton George Hill Damien Demaj Becci Barrie Freddie Faull General Enquiries:
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Contents Damien Demaj looks at
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spatial analytics in the Andy Murray vs Roger Federer Olympic Gold Medal match in summer 2012
David Barton looks at the issue surrounding intern usage within sports analytics and the current media interest in it
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We look at the reasons behind hawkeye data confidentiality and how it may be used in the future
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We speak to Darren Roberts High Performance manager at Red Bull about his use of Analytics in extreme sports
We review the Nike+ Fuelband, the device leading the way in advanced consumer performance analytics
Becci Barrie looks at the increasing relevance of business analytics in on field performance
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email us: info@performasports.com like us: facebook.com/performasports follow us: @performasports call us: 0843 5328 982
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Interview: Darren Roberts, High Performance Manager Red Bull
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6 When people think of sports analytics it is normally the treadmill, mask, several tubes or stopwatches and cameras. However, in modern sports this is not always possible. So what do you do when you are not working in a sport where you have the luxury of predictable environments, limited space and clearly trackable attributes? I caught up with Darren Roberts, the High Performance Manager at Red Bull, at the Sports Analytics Innovation Summit in March to discuss these challenges, that he faces every day.
player or sprinter. Darren and I took some time to discuss the various aspects of his role in measurements.
Adaptation One of the interesting aspects of many of the sports that Red Bull cover is that they are not traditional. Working with guys who not only perform in their sports but have to perform within their performance often means that analytics gathering has it's own challenges. Darren makes it clear that Traditional technologies simply would not work in many of
Red Bull has been known for it's drinks and big stunts (think skydiving from space) and many people will have seen the brand advertising throughout shows and festivals. In addition to this Red Bull has it's own teams of extreme athletes from parkour runners to BMX riders and snow boarders. Taking an active role in their performance, development and results. This means that Darren takes a leading role in the training and development of these athletes to help them achieve everything they can within their respective sports. The difficulty with many of these is that analytics is often a difficult thing to get from somebody who doesn't necessarily train in the same way as a footballer, basketball
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the environments in which his athletes operate. They will seldomly be found in a static gym or a lab or even a stadium. The majority of the time these guys will be out on the side of a mountain, in a desert or just in a field. The issue here is that these places do not necessarily offer the kinds of measurement opportunities from static devices. The way that this issue is overcome, is through the use of wearable technologies. Many of these technologies are not designed for the purpose that many of these athletes need them to be used for. Darren describes the adaptation of these technologies as 'it’s
'It’s about adapting the technology and equipment to work in their performance playground' about adapting the technology and equipment to work in their performance playground'. This technology needs to be adapted to the individual. Given the freestyle nature of much of what is attempted by his athletes,
the technology needs to adapt to the highly dynamic nature of extreme sports.
Peformance within a Performance One of the unique aspects of the Red Bull athletes is that within their sport they are required to not only compete, but that they are required to have almost a dramatic performance within that. I asked Darren how or if this is measured to help the success of his athletes. Darren recognises that this is a difficult aspect to actually measure, instead it is a case of both allowing them to stay in peak physical condition in order to help their performance, but it is also encouragement through the data to allow them to adapt this unique part of their sports. In the words of Darren this is 'a performance within a performance'.
Locations Another unique aspect of many of the Red Bull athletes is that they are undertaking their sports in unconventional locations which
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'When we are moving forward it will be about not being bogged down in the numbers to make your decisions but to use the numbers to justify decisions that have already been made'
Taking measurements from the side of a mountain or whilst upside down after jumping from a ramp is not something that normal sports measurement equipment can effectively measure. Therefore Darren's team have taken innovative steps to make sure that they are measured to the best of their ability.
The athletes tend to wear the technology rather than measuring their movement against a static piece of equipment. This creates additional challenges as in traditional sports the kinds of movements that need to be measured are not available in traditional technologies. To combat this limitation Darren has used the multiple industries in which Red Bull works to access a wider range of technologies. Rather than looking purely at what is currently available within the sports analytics and measurement space, he has looked to the
gaming and film industry’s use of movement sensors to plot the complex movements made by many of his athletes. This means that there are more diverse measurement opportunities using technology that was not designed for this purpose, but can be adapted to his needs better than traditional sports measurement technology.
Growing Industry As somebody who has been working in athlete development for a few
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years and has the success of analytics in many athletic performances, I wanted to get Darren's perspective on where he thought the industry was heading. He thinks that the technology will not necessarily change significantly in terms of what it will measure, but will be far more inclusive. This could be a single wearable garment that measures what a dozen devices will measure today. However, Darren thinks that the biggest change will not be in the measurements that take place but in the analysis of the data. The limit to the technology will not be in the technology itself, but will instead be in the questions that are being asked. Being able to drill down into the data to answer more specialised and individual questions will be where the innovations will truly take place. Darren puts it best 'When we are moving forward it will be about not being bogged down in the numbers to make your decisions but to use the numbers to justify decisions
that have made'.
already
been
This kind of analytics, with athletes than aren't necessarily given the same spotlight as those within more mainstream sports is where we are going to be finding the biggest innovations. Being able to adapt technologies to specific needs is going to be increasingly important to push the industry forward and with open source ideas in existing technologies being explored we are likely to see an increase in the velocity of change.
George Hill Editor
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Internships: The Current Hot Topic in Sports Analytics Being an intern is unfortunately part and package of getting a job today. I myself worked for a year as an unpaid intern, working full time over weekends in order to make sure that I could work towards my long-term career goals during the week. Ask anybody working within finance, law and especially journalism whether or not they had to intern before they had their first fully paid job and the vast majority will have done some sort of unpaid work to make sure that they get the experience on their CV. Football analysts have rocked this past month by the media's interpretation of sports analysts requiring interns. There have been claims that this is exploitative and unfair to the interns, however I wanted to investigate whether or not this was the case. The issue that the press has with this is that football clubs have a perceived wealth as they can afford to pay their players extraordinary wages well beyond what many would consider to be justified. However, the wages paid to players are within a
been
separate bubble that has been created not through justification, but through competition. Added to this is the fact that many clubs currently operate at a loss or close to the break even point. Clearly saving money where possible is important. At the Sports Analytics Innovation Summit, London, in March, I heard from several sports analysts who are working in similar positions across youth teams for clubs in similar situations as those currently being targeted by the press. One of the main aspects that they discussed is that analytics is not just about creating players for the first team, but maximising the value of players that could be sold. This means that unlike what many would perceive many football teams to be i.e. the squad and those who will be in the squad in
12 x amount of years, it is almost a fully functional separate business. This means that there is clearly an ROI for analytics departments throughout the football world, lower division clubs especially benefit hugely when one of their youth players, such as Wilfried Zaha from Crystal Palace, is brought for large fees. The fact that they have this ROI does not necessarily justify an unpaid internship as it is, but justifies a push for the best that they can possibly get to have maximum rewards. A recent graduate with no experience is going to be a risk. Having no prior experience in any industry is dangerous when appointing somebody new and as such, if people are unwilling to take that kind of risk we would find ourselves in a situation where the
only people who are getting the jobs are the ones already in the industry, creating a vicious skills circle. In order to create a system where the industry can become inclusive and available to all, there needs to be a system whereby young people have the opportunity to learn and gain experience whilst companies do not have the risk associated with financial investment. We can look at individuals during their internship and realize that this is a hardship, that earning money is necessary to create an easier life and without adequate financial backing (through either additional work or benefactors) that for many this would be impossible. However, I would argue that we should be looking at the overall picture and how this kind of work placement benefits the industry as a whole and will allow those currently at the bottom of the pile to rise to the top in the future.
David Barton Contributor
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Damien Demaj on Andy Murray and Roger Federer at the 2012 Olympic Tennis Final
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Using Spatial Analytics To Study Spatio-Temporal Patterns In Sport Late last year I introduced ArcGIS users to sports analytics, an emerging and exciting field within the GIS industry. Using ArcGIS for sports analytics can be read here. Recently I expanded the work by using a number of spatial analysis tools in ArcGIS to study the spatial variation of serve patterns from the London Olympics Gold Medal match played between Roger Federer and Andy Murray. In this blog I present results that suggest there is potential to better understand players’ serve tendencies using spatio-temporal analysis. The Most Important Shot in Tennis? The serve is arguably the most important shot in tennis. The location and predictability of a player’s serve has a big influence on their overall winning serve percentage. A player is who is unpredictable with their serve and can consistently place their serve wide into the service box, at the body or down the T is more likely to either win a point outright, or at least weaken their opponent’s return [1]. The results of tennis matches are often determined by a small number of important points during the game. It is common to see a player win a match who has won the same number of points as his opponent. The scoring system in
Figure 1: Igniting further exploration using visual analytics. Created in ArcScene, this 3D visualization depicts the effectiveness of Murray’s return in each rally and what effect it had on Federer’s second shot after his serve.
tennis also makes it possible for a player to win fewer points than his opponent yet win the match [2]. Winning these big points is critical to a player’s success. For the player serving their aim is to produce an ace or, force their opponent into an outright error, as this could make the difference between winning and losing. It is of particular interest to coaches and players to know the success of the player’s serve at these big points. Geospatial Analysis In order to demonstrate the effectiveness of geo-visualizing spatio-temporal data using GIS we conducted a case study to determine the following: Which player served with more spatiotemporal variation at important points during the match? To find out where each player served during the match we plotted the x,y coordinate of the serve bounce. A
Figure 2. The K Means algorithm in the Grouping Analysis tool in ArcGIS groups features based on attributes and optional spatial temporal constraints.
16 total of 86 points were mapped for Murray, and 78 for Federer. Only serves that landed in were included in the analysis. Visually we could see clusters formed by wide serves, serves into the body and serves hit down the T. The K Means algorithm [3] in the Grouping Analysis tool in ArcGIS (Figure 2) enabled us to statically replicate the characteristics
Figure 5. Mapping the spatial serve clusters using the K Means Algorithm. Serves are grouped according to the direction they were hit. The direction of each serve is indicated by the thin green trajectory lines. The direction of serve was used to statistically group similar serve locations.
of the visual clusters. It enabled us to tag each point as either a wide serve, serve into the body or serve down the T. The organization of the serves into each group was based on the direction of serve. Using the serve direction allowed us to know which service box the points belong to. Direction gave us an advantage over proximity as this would have grouped points in neighbouring service boxes. To determine who changed the location of their serve the most we arranged the serve bounces into a temporal sequence by ranking the data according to the side of the net (left or right), by
Figure 3. Calculating the Euclidean distance (shortest path) between two sequential serve locations to identify spatial variation within a player’s serve pattern.
court location (deuce or ad court), game number and point number. The sequence of bounces then allowed us to create Euclidean lines (Figure 3) between p1 (x1,y1) and p2 (x2,y2), p2 (x2,y2) and p3 (x3,y3), p3 (x3,y3) and p4 (x4,y4) etc in each court location. It is possible to determine, with greater spatial variation, who was the more predictable server using the mean Euclidean distance between each serve location. For example, a player who served to the same part of the court each time would exhibit a smaller mean Euclidean distance than a player who frequently changed the position of their serve. The mean Euclidean distance was calculated by adding all of the distances linking the sequence of serves in each service box divided by the total number of distances. To identify where a player served at key points in the match we assigned an importance value to each point based on the work by Morris [4]. The table in Figure 4 shows the importance of points to winning
Figure 4. The importance of points in a tennis match as defined by Morris. The data for the match was classified into 3 categories as indicated by the sequential color scheme in the table (dark red, medium red and light red).
17 a game, when a server has 0.62 probability of winning a point on serve. This shows the two most important points in tennis are 30-40 and 40Ad, highlighted in dark red. To simplify the rankings we grouped the data into three classes, as shown in Figure 4. In order see a relationship between outright success on a serve at the important points we mapped the distribution of successful serves and overlaid the results onto a layer containing the important points. If the player returning the serve made an error directly on their return, then this was deemed to be an outright success for the player. An ace was also deemed to be an outright success for the server. Results Federer’s spatial serve cluster in the ad court on the left side of the net was the most spread of all his clusters. However, he served out wide with great accuracy into the deuce court on the left side of the net by hugging the line 9 times out 10 (Figure 5). Murray’s clusters appeared to be grouped overall more tightly in each of the service boxes. He showed a clear bias by serving down the T in the deuce court on the right side of the net. Visually there appeared to be no other significant differences between each player’s patterns of serve. By mapping the location of the players serve bounces and grouping them into spatial serve clusters we were able to quickly identify where in the service box each player was hitting their serves. The spatial serve clusters, wide, body or T were symbolized using a unique color, making it easier for the user to identify each group on the
map. To give the location of each serve some context we added the trajectory (direction) lines for each serve. These lines helped link where the serve was hit from to where the serve landed. They help enhance the visual structure of each cluster and improve the visual summary of the serve patterns. The Euclidean distance calculations showed Federer’s mean distance between sequential serve bounces was 1.72 m (5.64 ft), whereas Murray’s mean Euclidean distance was 1.45 m (4.76 ft). These results suggest that Federer’s serve had greater spatial variation than Murray’s. Visually, we could detect that the network of Federer’s Euclidean lines showed a greater spread than Murray’s in each service box. Murray served with more variation than Federer in only one service box, the ad service box on the right side of the net. The directional arrows in Figure 6 allow us to visually follow the temporal sequence of serves from each player in any given service box. We have maintained the colors for each spatial serve cluster (wide, body, T) so you can see when a player served from one group into another. At the most important points in each game (30-40 and 40-Ad), Murray served out wide targeting Federer’s backhand 7 times out of 8 (88%). He had success doing this 38% of the time, drawing 3 outright errors from Federer. Federer mixed up the location of his 4 serves at the big points across all of the spatial serve clusters, 2 wide, 1 body and 1 T. He had success 25% of the time drawing 1 outright error from Murray. At other less important points Murray tended to favour going down
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the T, while Federer continued his trend spreading his serve evenly across all spatial serve clusters (Figure 7). The proportional symbols in Figure 7 indicate a level of importance for each serve. The larger circles represent the most important points in each game – the smallest circles the least important. The ticks represent the success of each serve. By overlaying the ticks on-top of the graduated circles we can clearly see a relationship between the success at big points on serve. The map also indicates where each player served. The results suggest that Murray served with more spatial variation across the two most important point categories, recording a mean Euclidean distance of 1.73 m (5.68 ft) to Federer’s 1.64 m (5.38 ft). Conclusion Successfully identifying patterns of behavior in sport in an ongoing area of work [5] (see figure 8), be that in tennis, football or basketball. The examples in this blog show that GIS can provide an effective means to geovisualize spatiotemporal sports data, in order to reveal potential new patterns within a tennis match. By incorporating
Figure 7. A proportional symbol map showing the relationship of where each player served at big points during the match and their outright success at those points.
space-time into our analysis we were able to focus on relationships between events in the match, not the individual events themselves. The results of our analysis were presented using maps. These visualizations function as a convenient and comprehensive way to display the results, as well as acting as an inventory for the spatiotemporal component of the match [6]. Expanding the scope of geospatial research in tennis, and other sports relies on open access to reliable spatial data. At present, such data is not publically available from the governing bodies of tennis. An integrated approach with these organizations, players, coaches, and sports scientists would allow for further validation and
Figure 6. A comparison of spatial serve variation between each player. Federer’s mean Euclidean distance was 1.72m (5.64 ft) - Murrray’s was 1.45m (4.76 ft). The results suggest that Federer’s serve had greater spatial variation than Murray’s. The lines of connectivity represent the Euclidean distance (shortest path) between each sequential service bounce in each service box.
19 development of geospatial analytics for tennis. The aim of this research is to evoke a new wave of geospatial analytics in the game of tennis and across other sports. Furthermore, to encourage statistics published on tennis to become more time and space aware to better improve the understanding of the game, for everyone.
Damien Demaj Contributor
Figure 8. The heatmap above shows Federer’s frequency of shots passing through a given point on the court. The map displays stroke paths from both ends of the court, including serves. The heat map can be used to study potential anomalies in the data that may result in further analysis.
Neale Cousland / Shutterstock.
References [1] United States Tennis Association, “Tennis tactics, winning patterns of play”, Human Kinetics, 1st Edition, 1996. [2] G. E. Parker, “Percentage Play in Tennis”, In Mathematics and Sports Theme Articles, http://www.mathaware.org/mam/2010/ essays/ [3] J. A. Hartigan and M. A. Wong, “Algorithm AS 136: A K-Means Clustering Algorithm”, Journal of the Royal Statistical Society. Series C (Applied Statistics), vol. 28, No. 1, pp. 100-108, 1979.
[4] C. Morris, “The most important points in tennis”, In Optimal Strategies in Sports, vol 5 in Studies and Management Science and Systems, , North-Holland Publishing, Amsterdam, pp. 131-140, 1977. [5] M. Lames, “Modeling the interaction in games sports – relative phase and moving correlations”, Journal of Sports Science and Medicine, vol 5, pp. 556-560, 2006. [6] J. Bertin, “Semiology of Graphics: Diagrams, Networks, Maps”, Esri Press, 2nd Edition, 2010.
20 and the Underarmour 39.
Review: The Nike+ Fuelband
The Nike fuel band is realistically the first iteration of consumer wearable fitness measurement. It has been around for a couple of years and undoubtedly there are more complex wearable technologies to track aspects of personal fitness, but Nike seem to have created a truly versatile and durable wearable technology. Some have claimed that the fuel band is a glorified pedometer, but in reality it has paved the way to additional wearable technologies such as the Amiigo
The current iteration with it’s ability to track important aspects in fitness regimes such as calories burnt and steps taken whilst creating a genuine aspirational element in it’s target setting and lighting system is a step up from the previous consumer tracking techniques. Another aspect of the Fuelband that has created a considerable improvement on older technologies is that it is genuinely wearable. Whilst previous wearable technologies have been either bulky or lacking aesthetics, the Fuelband looks good, simple and non-imposing. I have been using the Fuelband for just over a week and in this time, seen a genuine improvement in both my fitness and motivation to exercise. One of the aspects of the device that I think has a real resonance is that the measurement display is not a constant. In order to see your progress you press a subtly placed button. This means that rather than having a device where you are being constantly reminded of how much you have achieved
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and how far you still need to go, you choose when you want to find out. However, the really exciting aspect of the Fuelband is not what it is, but what it may become. Nike are currently running the Nike+ Accelorator, an initiative to push forward the technology and leverage the capabilities of the Fuelband in order to develop new ideas and apps to revolutionize the use of the band. This element of self creation, which you can find on Android and IOS devices in the form of apps is yet to find it’s way to wearable technologies. This kind of element is what those working within the sports industry have been calling out for. The interview with Darren Rogers elsewhere in this magazine shows the need for wearable technologies when tracking various aspects of athlete movement. Having to currently adapt technologies from other industries, despite having many benefits is both an additional expense and due to the original use of the technology, time consuming. Having a durable wearable technology like the Nike+
Fuelband will allow companies to develop software for the device to create truly innovative sports performance usage. Overall a review of the Fuelband is a difficult thing to assimilate due to the current usage compared to the potential future iterations. As it is at the moment the Fuelband is an incredibly useful fitness tracking device which is genuinely wearable 24/7. What it will be in future is limitless.
Freddie Faull Contributor
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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?
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? Some background
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. 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
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 Hawk-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.
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The Swiss Indoors at Basel granted me access to their Hawk-Eye data from their 2012 tournament. Image: Swiss Indoors.
over-the-top enthusiasm. But, we have to manually capture the data first, and that tends to be timeconsuming 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 HawkEye data So how would one go about getting access to this infamous Hawk-Eye data that apparently everyone knows about (like it’s 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 received 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 HawkEye 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.
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Confused yet?
to release the data.
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 HawkEye data in-house (sigh). I was then directed to HawkEye themselves to retrieve the data…
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
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).
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
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
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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?
request? Are they permitted to even access the data? Tennis, unlike Basketball, Baseball and Football (Soccer) is an individual sport, played mostly on
So who really owns Hawk-Eye data? The tournaments seem to be funding the implementation of the technology (the richer tournaments like Indian Wells World number 1, Novak Djokovic may have to bring his own data capture have more Hawkequipment to matches to record his shot patterns and movements! Image: Eye courts than say Reuters Miami) so is it their data to share and/ neutral territory (with the or commercialize? Or is the exception of Davis Cup). In data in fact the player’s team sports, it is the teams data? They are the ones who are collecting the data putting on the show; the at their home games, not data is about them, not the the governing bodies of tournament. What if Roger each sport. So where does Federer or Serena Williams this leave the players? Does wanted access to the HawkNovak Djokovic have to Eye data? How quickly would bring his own data capture the ATP, the tournaments equipment on court to trace and Hawk-Eye react to their
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him movements and map his shots? Let’s hope not! 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 industries 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 who 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 day’s play!
Neale Cousland / Shutterstock.com
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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. 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-seento-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!
History has shown that opening up data is not in fact scary, it is incredibly exciting and the possibilities appear endless. 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! Lets 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!
Damien Demaj Contributor
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Sports Analytics: More Than Sport
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30 Modern sport is a complex business machine. Performances on the field affect the amount of people sitting in your stadium, numbers of sponsors and then the amount of money you have to spend across the entire business. The amount of money that the franchise makes then directly contributes to the success of the team. To take a more holistic look at sport is incredibly important in the current economic climate. Just having players on the pitch performing well is not the only indicator of success. Therefore clubs today are using business analytics to extrapolate data from fans and their businesses in order to achieve success in their profits as well as in their sporting performance. Using algorithms to predict injuries and improve perfor-
mances are important, but not as important as making sure that club employees are paid. This is particularly important in sports like european footall where the financial fair play rules are coming into effect. This means that even if a team is winning competitions and performing well on the field, if they are making significant losses then they could have competition winnings docked or taken away, in addition to being banned from elite continental competitions. This kind of threat is especially daunting to clubs who may be bankrolled by rich owners or who are leveraging excessive credit. As such fan engagement and finding new revenue streams has become increasingly important to these teams to allow sustained success and important to other teams to help them max-
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31 imise their earning potential. So what kinds of analytics are teams and franchises leveraging to make the most of their fans and sponsorship income potential? In The Stadium How did fans get to the stadium? How did this affected their experience? Are people all entering through their allotted gates and if not why? Knowing this kind of thing allows franchises to not only streamline the entry and exit, but also strategically place food and merchandise to make the experience as pleasant as possible for their fans whilst maximising the potential to make money from them. Are different areas of the crowd expecting different experiences and are clubs acting accordingly? Season ticket holders for instance are likely to act differently to those who have bought tickets as a
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one off luxury. Many analytical models currently being used, especially across Europe, are based on the amount of season tickets being sold, changing this to a foundation of current data will help clubs to make more informed and profitable decisions. When people are exiting the game, what are they doing? Are they likely to linger in the stadium? It could be anything from the weather to the result of the team. For instance in the English FA Cup final the victorious team’s fans stayed in the stadium for a considerably longer time than those on the losing side. This means that these fans are more likely to contribute more to the club than if they are outside the stadium. This may be a one off given the importance of the match, but is there a way of attempting to replicate this during normal games? At Home
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If you are a major franchise, the majority of your fans won’t be in the stadium to watch a game. They will instead be watching at home on their TV, where additional analytics can be gathered. After all, one of the contentious issues that is currently affecting the English football league is the difference in TV rights between the Premier League and lower leagues in the country. Being able to use analytics to track what viewers are likely to be interested in can mean that advertisements during commercial breaks or even those being shown around the stadium can be targeted. With new technologies such as Tivo, viewers not only have the freedom to make the viewing experience their own through recording or skipping through
certain ad breaks or programs, but also gives companies additional insight into their viewing habits. Having statistics driven results for advertisers is a fantastic way to develop targeted campaigns,which is likely to see happier advertisers and an increase in advertising revenues. Purchasing Regardless of whether a fan is watching the match from home or a pitch/ court side seat, the likelihood is that you will be making a purchase based
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on your team support at some point. Analytics allow people to make quicker decisions and put the items that they are likely to buy in front of them when they want to buy. Through analysis of buying habits a team can see when somebody is likely to buy. People are more likely to buy after searching for an item online for instance. If you make the purchasing process easier based on this insight then sales are easier to come by. This form of analytics not only allows teams to benefit from their fans, but also means that it is easier for the fans to make a purchase. Although these kinds of analytics would not normally be something that coaches and performance managers would be looking at on a daily basis, in today’s world these are equally, if not more important, than the analytics used to measure on field performance. Taking these into consideration is increasingly important for the end results. long term team perform often comes down to making sure that the rest of the business is performing. The fall from grace that several teams have seen, especially in British football is testament to this. The concentration on team performance rather than overall company performance has seen teams like Portsmouth and Leeds United plummet from strong sporting positions due to weak business platforms. Creating models where you can measure
what your fans want and how you can give it to them is vital. Sports analytics goes further than simply asking how an athlete can run faster or make better decisions. It needs to have just as much focus on how the business can run smoother and make informed financial decisions.
Becci Barrie Contributor