Product Thinking
User Research
Exploratory
We've seen a promising improvement rate since testing out our MVP with a selection of professional and university-level teams. The product most notably improved player performance by 10%.
Tested the product
Player performance
Net Promoter Score
Former professional soccer player turned skills trainer for the Vancouver White Caps FC, Leigh, discovered that the difference between the elite and average athletes lies in their cognitive ability to understand and shape the environment around them to make the right play. In his book “Preception,” Leigh details the different facets of developing these skills, leading to the founding idea for Preception.
Leigh (Middle)
The team had conducted preliminary research before I joined. Therefore, I focused my efforts on synthesizing relevant insights.
Because the product has a big hardware dependency, we encountered setbacks that would later influence feasibility.
Access to our target users was challenging to coordinate. Therefore I used guerrilla methods to carry out usability testing.
Kevin Harmse,
Former MLS player, current SFU coach
First, I interviewed Leigh to understand his vision for the solution; I then proceeded to sort through relevant data from user research to get as much perspective as possible. Lastly, I performed secondary research to get more context of the market and business drivers.
Stakeholder interview notes
Data is a crucial leverage factor during contract negotiations, and this type of data was unquantifiable to help re-enforce a player's value.
In sports, visualization is key to understanding actions. Previous tools they used relied too heavily on text-heavy data or complex graphs.
Players have dedicated time to review game film after training, but they lack further context without data to support what they're seeing.
Given our users' familiarity with video analysis, we felt it would be important to continue that familiarity and use data as a supporting actor.
The hardware used will be tracking the players' movement and record key moments in time. This will then give a micro-level view of the player's awareness.
Breaking down the data into primary KPIs based on the OODA loop will empower the player to understand what areas need improvement.
Leigh (Right), testing out our hardware
The Preception dashboard needed to handle seemingly complex data and present it familiarly. Leveraging my research insights, I began by examining comparative experiences in the market.
Competitive analysis and visual references
I began ideating concepts based on each solution proposition and competitive analysis. Our main objective was to simplify and contextualize data down to their primary key performance indicators.
Early wireframe concepts
Giving early-stage prototypes in my team’s hands reveals a few significant technical considerations as well as overall information architecture.
Leigh (Left) and lead engineer Taric (Right) during early prototype testing
Get alignment on feasibility
Evaluate information architecture
Can the user navigate to view a logged moment?
Does the timeline view provide enough affordance?
After conducting two rounds of moderated usability testing
Improvement in task completion rate
Improvement in task flow
Having the most up-to-date breakdown of your data allows the user to gather context around their performance.
Paired with video review, key moments provides the player tangible information that can be used for future training.
Having film sessions easily accessible is an integral part of maintaining engagement and allows discoverability to relevant content.
We've seen a promising improvement rate since testing out our MVP with a selection of professional and university-level teams. The product most notably improved player performance by 10%.
Tested the product
Player performance
Net Promoter Score
