S.L. Benfica—Portugal’s top football team and one of the best teams in the world—makes as much money from carefully nurturing, training, and selling players as actually playing football.

Football teams have always sold and traded players, of course, but Sport Lisboa e Benfica has turned it into an art form: buying young talent; using advanced technology, data science, and training to improve their health and performance; and then selling them for tens of millions of pounds—sometimes as much as 10 or 20 times the original fee.

Let me give you a few examples. Benfica signed 17-year-old Jan Oblak in 2010 for €1.7 million; in 2014, as he blossomed into one of the best goalies in the world, Atlético Madrid picked him up for a cool €16 million. In 2007 David Luiz joined Benfica for €1.5 million; just four years later, Luiz was traded to Chelsea for €25 million and player Nemanja Matic. Then, three years after that, Matic returned to Chelsea for another €25 million. All told, S.L. Benfica raised more than £270 million (€320m) from player transfers over the last six years.

At Benfica’s Caixa Futebol Campus there are seven grass pitches, two artificial fields, an indoor test lab, and accommodation for 65 youth team members. With three top-level football teams (SL Benfica, SL Benfica B, and SL Benfica Juniors) and other youth levels below that, there are over 100 players actively training at the campus—and almost every aspect of their lives is tracked, analyzed, and improved by technology. How much they eat and sleep, how fast they run, tire, and recover, their mental health—everything is ingested into a giant data lake.

With machine learning and predictive analytics running on Microsoft Azure, combined with Benfica’s expert data scientists and the learned experience of the trainers, each player receives a personalized training regime where weaknesses are ironed out, strengths enhanced, and the chance of injury significantly reduced.

Sensors, lots of sensors

Before any kind of analysis can occur, Benfica has to gather lots and lots of data—mostly from sensors, but some data points (psychology, diet) have to be surveyed manually. Because small, low-power sensors are a relatively new area with lots of competition, there’s very little standardization to speak of: every sensor (or sensor system) uses its own wireless protocol or file format. “Hundreds of thousands” of data points are collected from a single match or training session.

Processing all of that data wouldn’t be so bad if there were just three or four different sensors, but we counted almost a dozen disparate systems—Datatrax for match day tracking, Prozone, Philips Actiware biosensors, StatSports GPS tracking, OptoGait gait analysis, Biodex physiotherapy machines, the list goes on—and each one outputs data in a different format, or has to be connected to its own proprietary base station.

Benfica uses a custom middleware layer that sanitises the output from each sensor into a single format (yes, XKCD 927 is in full force here). The sanitised data is then ingested into a giant SQL data lake hosted on the team’s own data centre. There might even be

a few Excel spreadsheets along the way, Benfica’s chief information officer Joao Copeto tells Ars—”they exist in every club,” he says with a laugh—but they are in the process of moving everything to the cloud with Dynamics 365 and Microsoft Azure.

Once everything is floating around in the data lake, maintaining the security and privacy of that data is very important. “Access to the data is segregated, to protect confidentiality,” says Copeto. “Detailed information is only available to a very restricted group of professionals.” Benfica’s data scientists, which are mostly interested in patterns in the data, only have access to anonymised player data—they can see the player’s position, but not much else.

Players have full access to their own data, which they can compare to team or position averages, to see how they’re doing in the grand scheme of things. Benfica is very careful to comply with existing EU data protection laws and is ready to embrace the even-more-stringent General Data Protection Regulation (GPDR) when it comes into force in 2018.

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At VRLA this past month, I had the opportunity to see first-hand how the technology gap is closing in terms of photorealistic rendering in virtual reality. Using the ODG R-9 Smartglasses, Otoy was showing a CG scene rendered using Octane Renderer that was so realistic I couldn’t tell whether or not it was real. The ORBX VR media file that results when you build a scene using Octane can be played back at 18K on the GearVR. Unity and Otoy are actively working to integrate their rendering pipeline in the Unity2017 release of the engine. And in short, with a light-field render option, you can move your head around if the device’s positional tracking allows for it.

Octane is an unbiased renderer. In computer graphics, unbiased rendering is a method of rendering that does not introduce systematic errors or distortions in the estimation of illumination. Octane became a pipeline mainly used for visualization work, everything from a tree to a building for architects, in the early 2010s. About 13 years ago, Sony Pictures Imageworks enabled VFX knowledge that is coming to VR content from Magnopus. How VR, AR, & MR Are Driving True Pipeline Convergence, Presented by Foundry

change of pace with the voxel, so named as a shortened form of “volume element”, is kind of like an atom. It represents a value on a regular grid in three-dimensional space.

This is analogous to a texel, which represents 2D image data in a bitmap (which is sometimes referred to as a pixmap). As with pixels in a bitmap, voxels themselves do not typically have their position (their coordinates) explicitly encoded along with their values. Instead, the position of a voxel is inferred based upon its position relative to other voxels (i.e., its position in the data structure that makes up a single volumetric image). In contrast to pixels and voxels, points and polygons are often explicitly represented by the coordinates of their vertices. A direct consequence of this difference is that polygons are able to efficiently represent simple 3D structures with lots of empty or homogeneously filled space, while voxels are good at representing regularly sampled spaces that are non-homogeneously filled. [1]

Within the last year, I’ve seen Google’s Tango launch in the Lenovo Phab 2 Pro (and soon the Asus ZenFone AR), Improbable’s SpatialOS Demo Live, Otoy’s ODG R-9’s at Otoy’s booth at VRLA of an incredibly realistic scene completely rendered using some form of point cloud data.

“The ability to more accurately model reality in this manner should come as no surprise, given that reality is also voxel based. The difference being that our voxels are exceedingly small, and we call them subatomic particles.”

[1] Wikipedia : Voxel
from Pocket

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