Visualizing Player Movement in Sport using 3D Web GIS

In this blog I present a Diorama of Player Movement using ArcScene and Esri’s CityEngine Web Viewer. This unique 3D GIS visualization helps better understand the interaction of the spatial and temporal components of optical player tracking data and offers an unrivalled viewing experience of player movement data.

Building the Diorama

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.

The scene was generated in ArcScene then exported to the CityEngine Web Viewer using the Export to 3D Web Scene geoprocessing tool found in the 3D Analyst/CityEngine toolbox (Figure 1). The export process is really simple, and is a great way to share your 3D scenes with your colleagues and peers. For tips on using the Export to 3D Web Scene geoprocessing tool check out this white paper.

Figure 1. Using the Export To 3D Web Scene geoprocessing tool to create the 3D Web Scene.

For the purpose of this example, I created a player velocity map using a static 2D representation. In order to create the player velocity map I classified the data into four categories (Figure 2). For more information about drawing layers using categories check out this handy reference on the ArcGIS Resources Center.

Figure 2. The player velocity classification used in Figure 2 – walking, jogging, running and sprinting.

Figure 3 (below) is a simple way of presenting relative speed using a green to red color scheme for each point in the dataset. However the representation makes it difficult to see how the path of the player and their velocity is changing over time. We also only see a portion of the data at any one time. In this case the most recent player movement ‘paths’ are drawn on top of the earlier ‘paths’, making it difficult to identify the distribution and frequency of player velocity.

 Figure 3. Creating a static 2D map of player velocity using ArcGIS Desktop 10.2. White = walking, green = jogging, orange = running, and red = sprinting.

In order to improve the representation we might consider animating the data (using the animation tool in ArcScene), or, create a series of small static maps which each present a time period from the match. We may also consider introducing an interactive element to the map like a time slider. Each of these methods has the potential to enable us to see how the data is changing over time, and therefore eliminate the issue of overlapping data. Whatever approach is taken the fundamental issue of viewing the data in a two-dimensional plane remains.  Animation, small multiples or time sliders all allow us ways to slice through the data and see different moments but none give us clarity when trying to look at the match as a whole.

Introducing a Diorama of Player Movement using 3D Web GIS

Perhaps a more suitable, but rarely seen method for visualizing spatio-temporal data in sport is to use a Space Time Cube. The Space Time Cube is a 3D visualization method introduced by Swedish geographer Torsten Hägerstrand in the 1970’s. Last year my colleague Ken Field built a Space Time Cube visualizing Napoleon’s March on Moscow. Be sure to check out Ken’s awesome 3D web scene here.

By building a Space Time Cube I was able to disaggregate the overlapping player movement lines by using the z axis of the cube to represent time. The min z value represents the start of the match and the max z value the end of the match. Along the base of the cube represents the x, y movement of the players – the planar court (Figure 4).

Figure 4. The Diorama of Player Movement. A Space Time Cube visualization.

In order to map the player movement points to the z axis of the cube I used a simple expression in ArcScene based on the time field in the data (Figure 5). This allowed me to spread the data vertically up the z-axis.

Figure 5. Setting the base height of the player movement points using an expression in ArcScene.

Some things to consider when working with Space Time Cubes

A drawback of the Space-Time cube has been the presentation of a single view as a static image (as in Figure 4). The inherent problems of trying to understand the data on a perspective view mean that in some respects it creates a mass of data points that are difficult to visually disentangle, much like a 2D static map. Therefore orientation, navigation and human interaction of the cube are central to its appeal and usability. The rapid advancement of web technology, in particular WebGL means these complex visualizations can be rendered in the browser using the CityEngine Web Viewer to create an interactive version (the app is rendering over 25,000 points in the browser). This gives analysts and sport scientists an opportunity to explore the scene by panning, zooming and titling from any viewpoint and overcomes the drawbacks of a static view. Using Web GIS as a platform means the visualizations can then be shared amongst players, and other stakeholders (Figure 6).

Figure 6 The interactive Diorama of Player Movement application is hosted on ArcGIS.com in an organisational account. 

Observations

The 360° view of the scene means we can also quickly compare patterns between both players at any angle. For example, from behind each player we can visualize over time the extent of their lateral movement throughout the match (Figure 7).

Figure 7. A side-by-side comparison of each player’s lateral movement. The 1m distance markers can be used as a reference for court position. The line surrounding the cube roughly half way up represents the end of set 1, start of set 2.

We are also able to analyze who is attacking and playing up on the baseline. In order to better represent the baseline in 3D we extruded the baseline in ArcScene which gave us a plane in space to compare from. The extruded baseline allows us to very quickly see the frequency of forward and backward movement over time by each player, whether in attack or defense (Figure 8).

Figure 8. The baseline walls allow us to visually explore the players position change over time in relation to the baseline. (Federer – right, Mathieu – left).

Click here to view the interactive application and explore these observations in more detail.

***The application is best viewed in Google Chrome, on a computer or laptop with high-speed internet.***

Conclusion – 3D Web GIS Rocks Sports Analytics!

The Diorama of Player Movement presents a unique way of visualizing player movement in a three-dimensional space using 3D Web GIS. The single, comprehensive view offered by a Space Time Cube enables us to see the spread and frequency of player movement more clearly.  Using ArcScene, we were able to make use of the third-dimension of the cube to disaggregate the data meaning we can be more confident about making judgments about movement patterns because of the full view of the dataset.

3D Web GIS is providing teams, coaches, and GIS analyst with a powerful platform to view, share and collaborate their projects. Browsers are fast becoming very capable of rendering large quantities of big data, meaning that representations of data being collected from Optical sensors and GPS, like player tracking can be viewed and interacted with on mass.

For more information about CityEngine visit their resource page where you’ll find links to videos, help files and a great gallery of 3D scenes to inspire you’re next 3D map making adventure! And be sure to check out other cool CityEngine web scenes here.

There is also a great blog post about the CityEngine Viewer here.

For a deeper analysis on the player tracking data featured above please check out the latest issue the Sports Performance and Tech magazine where I cover the analysis of the data in more detail.

Damien Saunder (formerly Demaj) is a Geospatial Designer at Esri where he designs and builds online interactive maps and applications. @damiensaunder

References

[1]       Per Ola Kristensson et el, “An Evaluation of Space Time Cube Representation of Spatio Temporal Patterns,” IEEE Trans. Visualization and Computer Graphics, vol. 15, no. 4, pp. 696-702, July/Aug. 2009.

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One Comment

  1. timw1984 says:

    Great Article!!! Where did you get this data, is this data openly available? I would love to do something like that with NBA data.