Catapult Vision: Importing third-party data

One of the main benefits of the Catapult Vision platform is the ability to import data from many sources, align it to video and wearable data, then deliver flexibility in analysing those data streams together.

In this article, we look at the power of importing Opta data into Vision.

Game Event Data

As the sports industry has developed, the volume of performance data available to teams has grown exponentially. Nowhere has this been more evident than in match event data, with companies offering thousands of game events tagged live for all major football and rugby competitions. This has altered how teams collect their own data. Perhaps most significantly, it has freed analysts up to focus on tagging their own tailored KPIs or subjective clips specific to their tactics or players, safe in the knowledge that standard, objective game events have already been tagged for them.

This game event data has a huge range of uses in reviewing performance or scouting opponents. It offers a play-by-play account of every action by every player, which–when aligned to video–provides powerful search options to highlight the key stories from each game. The challenge for analysts has always been how to best manage the volume of this data and use it in a way that answers specific performance questions.

Vision’s Custom Import

Vision’s approach to importing data is to first allow users to structure the data in a manner that best sets them up to analyse it. This could be redefining what fields data is grouped by or associating fields that overlap in time, through to just allowing users to import specific categories and defining the lead or lag times that they prefer for each field. This means that, from the onset, the data is tailored to be relevant to the questions you want to answer.

Analyze

We often see users exporting data to Excel in order to restructure in ways their applications don’t support. In Vision, once data is imported, users can leverage the power of a pivot table within the application. Pivot tables are commonly used when working with large data sets as a means of reorganising, filtering and summarising data into the desired report.

In Vision this can all be done within the application and aligned to video. Not only does this provide a report, but it also drives your video search and playlist creation.

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Filters

In this example, we’re looking at Opta rugby data. The Analyze menu allows you to select from any of the fields within your data with the flexibility to sort, filter, or create new custom parameters from it. This specific example shows how we can leverage the power from Opta’s tagged events with a wide range of descriptive labels. This feature allows us to build a query with many layers, giving us the key events and outcomes we’re looking for.

For example, if we want to see the tackles made in this game we can see there are 408. However, we specifically want to see which were missed (68), by one specific team (38), and of those which resulted in a try being scored (9), of which four were committed by two specific players.

By filtering in this way, we can quickly go from an import of 1,958 events to the four events that matter most to us. We can also build customised values around this search and save the filter for future reference, so it’s possible to import this same data set and immediately jump to the same insight.

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Multi-Game

Vision’s multi-game capabilities mean we can import three further games and start to ask broader questions regarding the frequency and occurrence of certain events to breakdown any possible underlying trends. At this stage, managing now over 6,000 events, visualising the data in chart form can be more impactful than the tabular view, so Vision comes fully equipped with charting capabilities to display your data sets in a variety of ways that help you build up your report.

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Review

From here, the search across the dataset provides us with a curated playlist of clips relevant to our initial question. These can then be saved as a playlist, edited using Vision’s built-in telestration tools, and published directly to the player app. Ultimately this can all help to provide more informed feedback to prepare athletes for the next game through this highly efficient workflow.

In addition, Vision can export this data in formats compatible with many other solutions and also import them into Catapult’s OpenField software for deeper contextual analysis alongside wearable datasets.

Want to discover how Catapult Vision can take your team’s video analysis to the next level? Click here to find out more.