We love hearing about clients doing new things with Watershed. It’s such a flexible platform, that we’re often surprised and inspired by what they create and the insights they uncover along the way. So, we were absolutely thrilled to hear how Bryan Rose, a technical architect at Visa, has been using Watershed for something very different indeed: tracking his activity and performance in Rocket League.
Copyright © 2017 Psyonix, Inc.
Rocket League is an action-packed game that combines driving and soccer. Colorful rocket-powered cars drive about the pitch, knocking a giant metal ball into soccer goals. It’s massively popular—it has more 300,000 "Very Positive" reviews on Steam and is one of the top-viewed games on Twitch.tv. It looks insanely fun and is definitely going on my Christmas list.
And know what’s even cooler? Rocket League also has a statistics API, which means that an xAPI aficionado, like Bryan, can pull the data out with a connector and push it into Watershed as xAPI statements.
And that's exactly what Bryan is doing. He's tracking high-level statistics of wins and losses, plus granular in-game interactions—such as goals, assists, saves, and shots. Bryan has set up more than 15 reports to visualize that data, which he can easily share with his team.
Why track non-L&D data in Watershed?
So, why is Bryan using Watershed to track performance that's unrelated to professional learning and development? He wants to use this data to demo Watershed when he's unable to share real data from Visa. Here are four examples of the visualizations he's created:
1) A group of cards looking at trends over time.
Currently, he only has a few days’ worth of data; but, as it accumulates over weeks and months, he’ll start to see trends of how he’s changing and improving his play.
2) Wins and losses by day of the week and hour of the day.
This shows when he’s most active and when he plays his best game.
3) Individual stats leaderboard.
This spans across 10 metrics to compare his performance to the performance of his friend Alex.
4) Overall metrics.
This includes a variety of visualizations that include bar, pie, and spider charts.
Keep it real with xAPI.
Bryan's gameplay is an example of a real data set with a variety of metrics, but it's not one that’s commercially sensitive or where he needs to worry about data privacy. Setting up the tracking and dashboards was also a useful xAPI learning experience for Bryan, giving him the opportunity to design statements that are very different to those used by Visa’s data sources—which also meant he had to think out of the box with new verbs, activity types, and extensions.
What have you built with Watershed?
What have you built in Watershed, or what would you like to build? Send us a note and tell us!
NOTE: Rocket League is used here only to illustrate the examples in this blog post. Watershed is not associated with, sponsored by, or affiliated with Psyonix, Inc.
About the author
As one of the authors of xAPI, Andrew Downes has years of expertise in data-driven learning design. With a background in instructional design and development, he’s well versed in creating learning experiences and platforms in corporate and academic environments.
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