Understanding the complex logic behind online sport manager games to inform our design strategy

First and foremost, a main challenge of building Playmaker was the mixed audience: doing so in a way that targeted users who were interested in NFTs but not well versed in basketball—and vice versa. Therefore, our goal was to give our varied players control over their teams and decisions, making their actions impactful and meaningful. Users needed to be able to adjust team parameters and see the game simulated automatically based on selected strategy parameters.

And so, we began by studying the mechanics of Lympo’s already existing game, Basketpulse, to understand its key functionalities and identify areas for improvement. We found that Playmaker and Basketpulse needed to be distinct because of their different target audiences. Basketpulse used sports terminology and game mechanics, while Playmaker aimed to be user-friendly for those new to basketball or NFTs while keeping experts engaged.

Along with the audit of Basketpulse, we also conducted a comprehensive competitive analysis. We analyzed six competitors’ platforms, looking for ways to simplify basketball team management through gamification and personalization, and presented our findings to the Lympo team in SWOT tables.

Moreover, we researched ways to incorporate level progression and leaderboards without creating a “pay-to-win” experience. So, we looked at how mobile and video games used loot boxes, daily rewards, and achievements to encourage and incentivize repeat visits from users.

Developing understanding and empathy for our users to create an intuitive feature set and incredible UX

We identified four user groups for the Lympo project: basketball fans, NFT collectors, crypto fans, and physical collectors. To understand each group’s unique characteristics, we created user personas with biographies, goals, needs, and frustrations. These insights helped us tailor our features to meet their exclusive needs.

Given the wide scope of the project, we developed a feature set and prioritized it into two buckets: minimum viable product and full application features. The features were ranked based on their potential value to users.

• Onboarding that’s interactive and intuitive for attracting and retaining new users
• Tactics management as users set up their gameplay
• An internal wallet for a streamlined and convenient workflow

We also created a list of high-level guidelines for the project, including:

• Prioritizing desktop experience over mobile to encourage a more attentive experience
• Offering a free version to establish a lower barrier-to-entry in a field that’s typically heavily paywalled or hard to casually enter
• Cultivating an active community with whom users could interact and trade players and NFTs within the platform. 

We were ready for the low-fidelity phase of development after researching and strategizing. With a good understanding of our users, we were confident in creating an engaging experience.

Designing complex flows in low-fidelity

In the wireframing phase, we worked on creating simple and intuitive flows while offering a high level of customization to our users without overwhelming them.

To tackle this challenge, we first identified and prioritized the importance of each feature within the flow to ensure a smooth learning curve for our users. We added a progress bar component on the dashboard to help users stay informed and engaged in the process.

Then, we created an interactive onboarding experience with a helper figure to guide users through the game mechanics and setup. We used strategies like progressive disclosure to introduce features gradually, allowing users to learn at a natural pace without realizing they were in a tutorial.

Developing the tactics setup flow for lo-fis was challenging as it needed to manage a multitude of complex game tactics. To make it intuitive and streamlined, we introduced a gameplay setup hub screen that allowed users to “bulk-manage” their tactics while seeing the big picture on their basketball court.

After extensive revisions and discussions with the Lympo team, we successfully devised optimal flows for every feature. To test these flows, we decided to conduct a series of Moderated Usability Tests with our a low-fidelity prototype.