BetMGM
Integrating stat based research to the betting process & enhancing user decision making.
Role
UX/UI designer
Test moderator
Job duties
UX research
User interviews
Prototyping
Usability testing
Tools
Figma
Figjam
Case Summary
This capstone challenged students to add a feature to an existing product while preserving the brand’s identity. I chose BetMGM, a major online sports betting (OSB) platform, and focused on a critical user need that surfaced repeatedly through research: the lack of accessible, integrated sports statistics during the betting experience.
My goal was to explore whether providing team & player statistics could increase user engagement and confidence when placing wagers.
Problem Solving
Even though among the leading OSB platforms, BetMGM only offer limited stat info on game logs, matchup histories, or player stats within the betting flow.
As a result, bettors frequently leave the app to consult external stat sites, breaking their decision-making rhythm and diminishing BetMGM’s value as an all-in-one betting destination.
Along with enhancing my abilities as a UX designer, this capstone taught me how to introduce a new feature into an existing brand ecosystem without disrupting the established user experience.
UX Research & Competitive Analysis
User Interviews
Usability Test Moderating
Prototyping
The visual hierarchy of player points data needed alignment relocation that allowed for faster recognition.
Interactive icons with bigger touch points linking to deeper stat categories helped testers feel more in control of their research process.
Old
New
Nearly all tested bettors emphasized the importance of player injury reports, prompting me to integrate an injury status table into the research flow.
Users responded positively to the expanded dataset and noted that these additions aligned well with how they naturally research bets.
High-fidelity testing with both experienced and casual bettors revealed refinement opportunities to the UI's information architecture:
Final Iterations to Bring it All Together
Access to stat research within alerts for last second changes to game odds allowing users to reaffirm their final betting decisions.
Added stat table content for player points research similar to stat info provided for Money Line wagers.
Color-coded win/loss records for easier quick view eye scanning
Game records of previous 3 head-to-head matchup histories.
| Feature | BetMGM | DraftKings | FanDuel | Dabble |
| ------------------------- | ------ | ---------- | ------- | ------ |
| Sportsbook | ✅ | ✅ | ✅ | ⚠️ |
| Casino Games | ✅ | ✅ | ✅ | ❌ |
| Daily Fantasy | ❌ | ✅ | ✅ | ✅ |
| Social Features | ❌ | ❌ | ❌ | ✅ |
| Copy Betting | ❌ | ❌ | ❌ | ✅ |
| Player/Team Stat Research | ⚠️ | ✅ | ✅ | ⚠️ |
The final prototype provided bettors with an intuitive, research-supported betting experience. Testers described the feature as:
“Something I wish BetMGM already had”
and noted that having integrated statistics increased their overall confidence in placing wagers.
This project allowed me to further develop my skills with:
These refinements culminated in a cohesive, brand-aligned prototype that delivered statistics exactly where users needed them.
Initial tests validated that users understood the placement of the information but wanted a broader range of stats.
With this feedback I was able to expand the scope of content needed for iterations into high-fidelity wireframes to include:
Task flows based the user personas lead to mid-fidelity wireframes that explore how basic statistics could support Money Line (Win/Loss) wagers that included quick-scan stat information for team and player performance:
Terry
Age: 38
Location: Las Vegas
Occupation: Chef
The Expert Better
I’ll pretty much bet on anything. I’m a gambler, so the more information I have the better!
This guided the creation of two primary personas whose needs shaped the feature requirements and user flows.
After uncovering the patterns and expectations through affinity mapping several insights quickly emerged:
Katherine
Age: 31
Location: New Jersey
Occupation: Cocktail Server
The Social Better
Betting on the games while watching with my friends & family makes it that much more fun to watch!
Bettors rely heavily on third-party sites for statistics because OSB apps do not present robust data during the betting process.
BetMGM offers significantly fewer research tools compared to the market share leaders.
Both casual and experienced bettors want stats that are quick to scan and easy to interpret.
Users desired an integrated research-and-betting flow rather than switching between multiple apps.
Insights from user interviews described that having to rely on outside sources for player or team research as a consistent pain point, “It’s so annoying having to constantly go back and forth between the app and looking up stats”, and expressed a desire for a more streamlined process that eliminated the need to reference third-party resources.
Through secondary research I discovered BetMGM ranked 3rd in the OSB market share, which shows there is room to improve their betting process. Competitive analysis revealed the top OSB platforms, ahead of BetMGM, did offer some form of stat information for users within their operating system.
Process
Closing Insights
Research & Discover
Insights Into Initial Designs
Adding Stat Content for User Research
The Last 6 games Win/Loss results for Money Line wagers.
Player points averages for over/under bets