The four sport data types you should be measuring right now

02.10.20

by Editorial Staff

We speak to sport data expert Holger Rahlfs to get the lowdown on the data that can and should be captured right now

02.10.20

by Editorial Staff

We speak to sport data expert Holger Rahlfs to get the lowdown on the data that can and should be captured right now

In a world where empty soccer stadiums are quickly becoming the norm, data has never been more important for telling a game’s unique story. But what can be measured today – and how? Plus, how will data collection evolve in the coming years? Read on...

“Put simply, the market is growing. The demand from clients is growing, and the interest from fans is growing,” says Holger, who works in a hybrid role in Sales Support for Deltatre as well as Head of Product for Sportec Solutions, a joint venture between DFL group and Deltatre. The company deploys the latest technologies to capture and share a wealth of data for the German Football League (DFL) and beyond.

He shares four important data types to capture now: 

1️⃣ Base data

Otherwise known as master data, base data is information that is often subject to very few changes and is ordinarily available at the start of a season.

For example:

  • The stadium that the game(s) will be played in
  • The clubs that will be involved
  • The players in each team, as well as fixed stats such as their birth date and height

How is base data captured?

Extensive research by Deltatre AG on behalf of Sportec Solutions. Much of the information, like birth dates for example, won’t change, and all the information can be stored in a database to enrich future games. However, it needs to be regularly fact-checked to ensure it’s up to date, for example, if players change.

2️⃣ Match information

This is all the information that makes the upcoming match unique.

For example:

  • The name of the referee
  • Which players within the teams
  • Which colors are being worn by the team
  • How many spectators there are
  • What the environment is like on the given day, i.e. the weather conditions

How is match information captured?

Via a mixture of research, DFL sources and observation of Deltatre operators either on-venue or at a remote production facility.

Next, is match data, which can be split into two further sections. 

3️⃣ Event data

This can include any information acquired as the game is playing out.

Event data could relate to player actions, such as:

  • The number of shots, passes, crosses, challenges, corners, forwards, offsides, or deflections, etc.

Or, team actions, such as:

  • The number of kick-offs, goal kicks, throw-ins, corner kicks, free kicks, or penalty kicks, etc.

Or, referee actions, such as:

  • The number of goals disallowed, dropped balls, substitutions, cautions or dismissals, etc.

How is event data captured?

Sportec Solutions’ partner, Deltatre AG, deploys one designated person, called the speaker, to record every action as it happens. Via a specialist speech code, developed by Deltatre AG, the speaker then verbally communicates every possible action as it happens. They’ll report the codes back to data gathering operators who will then input all the information into a live database, which can then be distributed to the media outlets.

“It’s very customizable,” says Holger, “you can have just this system of 1+1, but you can also have a high number of people to gather details. It’s designed to gather as many events as possible and is very fast and reliable – and to provide a full chronology of the game.”

4️⃣ Positional data

The locations of the players and the ball during the entirety of the match.

For example:

  • Data is stored as X-Y coordinates for every frame of the match – with this information, you can then calculate important information like distance, speed, and number of sprints

How is positional data captured?

Information is collected via an optimal tracking system, where images are automatically picked up by in-stadium cameras. “You can more or less create an animation like a radar view where you can see the location of players on the pitch and where they’re running,” he says.

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Combining data to predict outcomes

By capturing positional data and event data, it’s possible to create even deeper storytelling. “We have the algorithms to do this,” says Holger. “We can create new statistics out of this combination of event data and positional data – for example, the expected outcome of goal attempts.” Another level of information is unlocked, meaning the viewer can see a percentage likelihood of the player passing the ball or scoring a goal.

Data collection and Covid-19

Although the fan experience is still very much focused on remote viewing, Holger explains that little has changed in terms of the data that’s currently collected. However, the way it’s been collected has, of course, changed substantially. “How we gather data, i.e. how we protect people, what rules we have to consider such as operations and hygiene of course have been affected,” he says.

The future of sports data

Looking ahead, he sees no sign of the appetite for sports data slowing. Clients, i.e. clubs, are looking for data for performance reasons – they want to analyze the match to see if improvements can be made. Another vital customer, the media, is looking for new, innovative ways to tell stories around the game. “Especially for younger fans, statistics and analytics are getting more important,” says Holger.

There is already so much data available that future plans include how to get the most value out of existing data. “It’s about finding the treasures within the data,” he says. In the longer term, the development of event data gathering, he believes, will lie in automation. Watch this space.

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