Hypeshot - "Dashboard" and "Your Bets" Screens, with your monetary returns and bets


Graphic Design, Research, UI/UX, Web Dev

Figma, React.js, HTML, CSS

Design Lead

Ravi Bakhai, Nar Bharadwaj, Anand Kannappan

Virtual, with team members across the U.S.

Fall 2019 - Spring 2020



Sports bettors are paying high fees to handicappers, who provide bet recommendations that give bettors little edge. The process is also inconvenient, and makes it hard to track bets on an individual level.


Hypeshot is an algorithmic sports betting service and streamlined dashboard on the web, that is cheaper, clearer, and more effective than traditional handicapping services.


Handicapper: a paid consultant who gives sports bettors recommendations on bets to make. They do not place the bets on behalf of their clients—the client has to place the bets manually through a registered sports book (an authorized platform that takes bets). The entire set of bets would have to be placed as is to potentially work as intended.

How it began

Hypeshot is a personal side project I worked on with a few others.

Sports and sports betting are industries that some of the members of our team had prior experience with, and we all had an interest in. We wanted to develop a product that addressed the needs of current and prospective sports bettors.


Market Insights

First, we did some research online to understand the terminology, problems, and trends of sports betting. Concretely, here are some statistics around what we learned:

Hypeshot - market insights graphic, showing handicapper use/cost and market growth

1. Use of handicappers is widespread
40% of bettors have paid for handicappers. In the U.S. legal market, that's 3M bettors.

2. Hiring a handicapper is expensive
On average, handicappers charge $600 an NBA season and $1000 an NFL season.

3. The space is growing in value
$10B been invested in the legal market since 2019, and the market is growing at a 40% compound annual growth rate.

Target User

It was important for us to have a clear sense of who we were targeting with our initial product early in our process. We agreed that our target user would be an amateur sports bettor who uses a handicapper and is between the ages 20 and 26.

This individual is already seeking out a paid solution to some problems through handicapping services. Therefore, they can be our early adopter, and we can expand to other types of sports bettors in the future.

Bettor Insights

Having a better understanding of the market and who we wanted to target, we conducted 25 interviews. We learned about the different issues these amateur bettors are facing, and realized the large scale of their problems.

Hypeshot - how one interviewee keeps track of bets—in a pile of paper bet slips
Hypeshot - an interviewee's online bet slip. A bit more organized, but still hard to decipher
How one interviewee keeps track of bets—in a pile of paper slips
An interviewee's online bets. More organized, still hard to decipher
Hypeshot - top three user interview insights

There are a few key ideas we kept hearing from our users:

1. Handicappers are ineffective
Users want to outperform the stock market, but they often miss the mark despite paying for handicappers. 

2. The experience is subpar
Bettors have to place the bets manually, but they could amend, miss, or choose not to place recommended bets. This flow is outdated and inconvenient.

3. There is a lack of transparency
Recommendations do not always make sense to the bettor, and bets are hard to keep track of.

problem statement

Using these three insights, I crafted a guiding question for the rest of our work. We want to help bettors make the highest returns, through a seamless experience that's at a more accessible price point.

"How Might We... design a more seamless and accessible experience for sports betting to make bettors the highest returns?"


creating a feature set

To come up with an optimal set of features for our first version of Hypeshot, I created a flow chart that started from problems and lead to solutions.

The result was a feature set that includes: a dashboard UI, accessible help, and an optimized algorithm to make bets.

Hypeshot - flow chart showing how problem connects to solution set


I drew ideas out by hand, creating quick sketches that showed the flow and features at a high-level.

Hypeshot - storyboard sketch
Hypeshot - potential components for home screen

Final betting platform

Afterwards, I translated the sketches into high-fidelity screens.

Hypeshot - splash screen
Hypeshot - sample risk question #1 ("How long do you want a deposit of $1k to last, guaranteed?")
Hypeshot - sample risk question #2 ("What type of game do you feel comfortable investing in?")
1. Answer risk questions
Set up your account and help us gauge your risk tolerance
Hypeshot - home screen showing your returns, bets, and cash
Hypeshot - Your Bets screen
2. Track bets and returns
Using ML to optimize bets we place on your behalf—view them all


Designing for Data Density

I learned how to design a system for an industry that has a lot of terminology and is information rich.

Takeaway: The Dashboard and Your Bets pages had to be thoughtfully organized due to all the bet details they display.

Building in Extensibility

Our design had to be extensible to beginners in the future.

Takeaway: I designed risk questions that were simple, with little industry jargon, and made help accessible throughout the setup process.

Next Steps

Personalize the product

An area of improvement is focusing more on personalization.

Takeaway: A lot of bettors participate in sports betting because they care about the teams, games, and actual bets—it's important to give them more control in the bets that are placed.