Kalshi API for Super Bowl Prop Bet Exploration
If you're interested in building trading bots for prediction markets, one of the first things you need to understand is how the exchange organizes its data. Today we're going to look at Kalshi, a regulated prediction market where you can trade on everything from inflation numbers to NFL touchdowns.
We'll start with the conceptual stuff, including how Kalshi structures its markets Then we'll drill down into a specific example: finding Super Bowl player props using Python.
By the end of this video, you'll understand the Kalshi data model well enough to do your own data exploration. In the follow up, we'll actually authenticate and programmatically place orders using our actual account.
How Kalshi Markets are Organized
Kalshi organizes everything into three levels: Series, Events, and Markets.
Series
A series is a template for a type of prediction that happens repeatedly.
Think about inflation. The Bureau of Labor Statistics releases CPI data every single month. Kalshi doesn't want to manually set up new markets from scratch each time. So they create a series, let's call it KXINFL. That defines the structure: "Will monthly inflation exceed X percent?"
That series becomes a reusable template. Every month, they spin up new markets based on it. Same idea for quarterly GDP, weekly jobless claims, daily weather forecasts.
Since sports is the most popular category on Kalshi and the Super Bowl is tomorrow, we'll focus on the NFL.
NFL player props follow the same pattern. "Will this player score 2 or more touchdowns?" That's a series. It gets reused every single game, every single week, for every player.
Events
An event is a specific instance within a series. It is a particular time period or occurrence.
For inflation, the event might be "January 2026 CPI release." For NFL, the event is a specific game. So the Super Bowl event is the one on Sunday between Seattle and New England.
Markets
A market is the actual contract you can trade. It has a YES and NO side, order books, and eventually settles to 0 or 100.
For inflation: "Will January 2026 CPI exceed 3.0%?" is a market.
For the Super Bowl: "Will Kenneth Walker score 2+ touchdowns?" is a market.
Why This Structure?
This hierarchy exists because predictions often recur on a schedule. The series captures what stays the same: the rules, the settlement criteria, the structure. Events capture when it happens. Markets capture the specific tradeable question.
When you're building bots, understanding this structure is important because it's exactly how the API is organized.
Finding Your Edge
Now, before we dive into code, let's talk about strategy.
Different traders on Kalshi tend to specialize. They focus on categories where they feel they have an informational or analytical edge.
Some people focus on economic data. Maybe they have models for predicting inflation, or they closely follow Fed communications and can anticipate how markets will move.
Others specialize in weather. If you understand meteorological models better than the average trader, hurricane and temperature markets might be your thing.
Political and geopolitical markets attract people who follow elections, policy, and international relations closely.
And then there's sports. If you've built models for NFL player performance, or you understand matchups and game scripts better than the market, that's where your edge might be.
The point is: you don't need to trade everything. Find the series where you have insight, and go deep.
For this tutorial, we're going to focus on NFL player props. Specifically Super Bowl props. But everything we cover applies to any series on Kalshi.
Kalshi API
Alright, let's talk about the API. Kalshi has a REST API that lets you access market data, place orders, and manage positions programmatically.