Introducing the Forecasts API — Event-driven forecasts for precise demand planning. Fast, accurate, and easy to run.
Explore Now
LogoLogo
Visit websiteWebAppGet DemoTry for Free
  • Introduction
  • Swagger UI
  • Loop
  • System Status
  • Getting Started
    • API Quickstart
    • Data Science Notebooks
    • PredictHQ Data
      • Data Accuracy
      • Event Categories
        • Attendance-Based Events
        • Non-Attendance-Based Events
        • Unscheduled Events
        • Live TV Events
      • Labels
      • Entities
      • Ranks
        • PHQ Rank
        • Local Rank
        • Aviation Rank
      • Predicted Attendance
      • Predicted End Times
      • Predicted Event Spend
      • Predicted Events
      • Predicted Impact Patterns
    • Guides
      • Geolocation Guides
        • Overview
        • Searching by Location
          • Find Events by Latitude/Longitude and Radius
          • Find Events by Place ID
          • Find Events by IATA Code
          • Find Events by Country Code
          • Find Events by Placekey
          • Working with Location-Based Subscriptions
        • Understanding Place Hierarchies
        • Working with Polygons
        • Join Events using Placekey
      • Date and Time Guides
        • Working with Recurring Events
        • Working with Multi-day and Umbrella Events
        • Working with Dates, Times and Timezones
      • Events API Guides
        • Understanding Relevance Field in Event Results
        • Attendance-Based Events Notebooks
        • Non-Attendance-Based Events Notebooks
        • Severe Weather Events Notebooks
        • Academic Events Notebooks
        • Working with Venues Notebook
      • Features API Guides
        • Increase Accuracy with the Features API
        • Get ML Features
        • Demand Forecasting with Event Features
      • Forecasts API Guides
        • Getting Started with Forecasts API
        • Understanding Forecast Accuracy Metrics
        • Troubleshooting Guide for Forecasts API
      • Live TV Event Guides
        • Find Broadcasts by County Place ID
        • Find Broadcasts by Latitude and Longitude
        • Find all Broadcasts for an Event
        • Find Broadcasts for Specific Sport Types
        • Aggregating Live TV Events
        • Live TV Events Notebooks
      • Beam Guides
        • ML Features by Location
        • ML Features by Group
      • Demand Surge API Guides
        • Demand Surge Notebook
      • Guide to Protecting PredictHQ Data
      • Streamlit Demo Apps
      • Guide to Bulk Export Data via the WebApp
      • Industry-Specific Event Filters
      • Tutorials
        • Filtering and Finding Relevant Events
        • Improving Demand Forecasting Models with Event Features
        • Using Event Data in Power BI
        • Using Event Data in Tableau
        • Connecting to PredictHQ APIs with Microsoft Excel
        • Loading Event Data into a Data Warehouse
        • Displaying Events in a Heatmap Calendar
        • Displaying Events on a Map
    • Tutorials by Use Case
      • Demand Forecasting with ML Models
      • Dynamic Pricing
      • Inventory Management
      • Workforce Optimization
      • Visualization and Insights
  • Integrations
    • Integration Guides
      • Keep Data Updated via API
      • Integrate with Beam
      • Integrate with Loop Links
    • Third-Party Integrations
      • Receive Data via Snowflake
        • Example SQL Queries for Snowflake
        • Snowflake Data Science Guide
          • Snowpark Method Guide
          • SQL Method Guide
      • Receive Data via AWS Data Exchange
        • CSV/Parquet Data Structure for ADX
        • NDJSON Data Structure for ADX
      • Integrate with Databricks
      • Integrate with Tableau
      • Integrate with a Demand Forecast in PowerBI
      • Google Cloud BigQuery
    • PredictHQ SDKs
      • Python SDK
      • Javascript SDK
  • API Reference
    • API Overview
      • Authenticating
      • API Specs
      • Rate Limits
      • Pagination
      • API Changes
      • Attribution
      • Troubleshooting
    • Events
      • Search Events
      • Get Event Counts
    • Broadcasts
      • Search Broadcasts
      • Get Broadcasts Count
    • Features
      • Get ML Features
    • Forecasts
      • Models
        • Create Model
        • Update Model
        • Replace Model
        • Delete Model
        • Search Models
        • Get Model
        • Train Model
      • Demand Data
        • Upload Demand Data
        • Get Demand Data
      • Forecasts
        • Get Forecast
      • Algorithms
        • Get Algorithms
    • Beam
      • Create an Analysis
      • Upload Demand Data
      • Search Analyses
      • Get an Analysis
      • Update an Analysis
      • Partially Update an Analysis
      • Get Correlation Results
      • Get Feature Importance
      • Refresh an Analysis
      • Delete an Analysis
      • Analysis Groups
        • Create an Analysis Group
        • Get an Analysis Group
        • Search Analysis Groups
        • Update an Analysis Group
        • Partially Update an Analysis Group
        • Refresh an Analysis Group
        • Delete an Analysis Group
        • Get Feature Importance for an Analysis Group
    • Demand Surge
      • Get Demand Surges
    • Suggested Radius
      • Get Suggested Radius
    • Saved Locations
      • Create a Saved Location
      • Search Saved Locations
      • Get a Saved Location
      • Search Events for a Saved Location
      • Update a Saved Location
      • Delete a Saved Location
    • Loop
      • Loop Links
        • Create a Loop Link
        • Search Loop Links
        • Get a Loop Link
        • Update a Loop Link
        • Delete a Loop Link
      • Loop Settings
        • Get Loop Settings
        • Update Loop Settings
      • Loop Submissions
        • Search Submitted Events
      • Loop Feedback
        • Search Feedback
    • Places
      • Search Places
      • Get Place Hierarchies
  • WebApp Support
    • WebApp Overview
      • Using the WebApp
      • API Tools
      • Events Search
      • How to Create an API Token
    • Getting Started
      • Can I Give PredictHQ a Go on a Free Trial Basis?
      • How Do I Get in Touch if I Need Help?
      • Using AWS Data Exchange to Access PredictHQ Events Data
      • Using Snowflake to Access PredictHQ Events Data
      • What Happens at the End of My Free Trial?
      • Export Events Data from the WebApp
    • Account Management
      • Managing your Account Settings
      • How Do I Change My Name in My Account?
      • How Do I Change My Password?
      • How Do I Delete My Account?
      • How Do I Invite People Into My Organization?
      • How Do I Log In With My Google or LinkedIn Account?
      • How Do I Update My Email Address?
      • I Signed Up Using My Google/LinkedIn Account, but I Want To Log In With My Own Email
    • API Plans, Pricing & Billing
      • Do I Need To Provide Credit Card Details for the 14-Day Trial?
      • How Do I Cancel My API Subscription?
      • Learn About Our 14-Day Trial
      • What Are the Definitions for "Storing" and "Caching"?
      • What Attribution Do I Have To Give PredictHQ?
      • What Does "Commercial Use" Mean?
      • What Happens If I Go Over My API Plan's Rate Limit?
    • FAQ
      • How Does PredictHQ Support Placekey?
      • Using Power BI and Tableau With PredictHQ Data
      • Can I Download a CSV of Your Data?
      • Can I Suggest a New Event Category?
      • Does PredictHQ Have Historical Event Data?
      • Is There a PredictHQ Mobile App?
      • What Are Labels?
      • What Countries Do You Have School Holidays For?
      • What Do The Different Event Ranks Mean?
      • What Does Event Visibility Window Mean?
      • What Is the Difference Between an Observed Holiday and an Observance?
    • Tools
      • Is PHQ Attendance Available for All Categories?
      • See Event Trends in the WebApp
      • What is Event Trends?
      • Live TV Events
        • What is Live TV Events?
        • Can You Access Live TV Events via the WebApp?
        • How Do I Integrate Live TV Events into Forecasting Models?
      • Labels
        • What Does the Closed-Doors Label Mean?
    • Beam (Relevancy Engine)
      • An Overview of Beam - Relevancy Engine
      • Creating an Analysis in Beam
      • Uploading Your Demand Data to Beam
      • Viewing the List of Analysis in Beam
      • Viewing the Table of Results in Beam
      • Viewing the Category Importance Information in Beam
      • Feature Importance With Beam - Find the ML Features to Use in Your Forecasts
      • Beam Value Quantification
      • Exporting Correlation Data With Beam
      • Getting More Details on a Date on the Beam Graph
      • Grouping Analyses in Beam
      • Using the Beam Graph
      • Viewing the Time Series Impact Analysis in Beam
    • Location Insights
      • An Overview of Location Insights
      • How to Set a Default Location
      • How Do I Add a Location?
      • How Do I Edit a Location?
      • How Do I Share Location Insights With My Team?
      • How Do I View Details for One Location?
      • How Do I View My Saved Locations as a List?
      • Search and View Event Impact in Location Insights
      • What Do Each of the Columns Mean?
      • What Is the Difference Between Center Point & Radius and City, State, Country?
Powered by GitBook

PredictHQ

  • Terms of Service
  • Privacy Policy
  • GitHub

© 2025 PredictHQ Ltd

On this page
  • How Predicted Attendance is calculated
  • Examples of models used for Predicted Attendance
  • Expert systems and other types of models

Was this helpful?

  1. Getting Started
  2. PredictHQ Data

Predicted Attendance

Predicted Attendance represents the number of people predicted to attend an event.

PreviousAviation RankNextPredicted End Times

Last updated 10 months ago

Was this helpful?

Also known as PHQ Attendance. This value represents the number of people predicted to attend an event. The exact predicted attendance number is returned as the phq_attendance value in the for attendance events.

PHQ Rank also has a value between 0 and 100 that represents how many people will attend an event. For example, an event with a PHQ Rank 50 has around 1,000 attendance.

PHQ Rank, Local Rank, Aviation Rank, and PHQ Attendance use the following table to translate the numeric value to rank level and tier.

PHQ Rank Level
PHQ Rank Range
Expected Attendance

1 - Minor

0 - 10 11 - 20

~10 ~30

2 - Moderate

21 - 30 31 - 40

~100 ~300

3 - Important

41 - 50 51 - 60

~1,000 ~3,000

4 - Significant

61 - 70 71 - 80

~10,000 ~30,000

5 - Major

81 - 90 91 - 100

~100,000 ~300,000+

These mappings are sometimes referred to as ranking bands.

How Predicted Attendance is calculated

PredictHQ calculates Predicted Attendance via machine learning models (ML models) and expert systems in our pipeline. Our ML models are trained on historical data and predict the number of people that are predicted to attend a future event before they happen. Or models use a large number of inputs (called machine learning features) to make an accurate prediction.

PredictHQ monitors the accuracy of their models are periodically retrains them to ensure we retain high-accuracy predictions.

Examples of models used for Predicted Attendance

We have ML models to predict attendance for all our attended categories. Some types of events within some categories may use expert systems instead of machine learning models. For example, at the time of writing although most of our main sports within the sports category used ML models Formula 1 race events did not use a ML model.

ML models use machine learning features as inputs to predict attendance. These features are different pieces of data that allow the model to make an accurate prediction based on different factors. For example, the sports teams playing, the type of sport, and the venue a sports game is played all affect the predicted attendance. If two very popular sports teams play at a large stadium then they are more likely to have more people attending the game.

Below are examples of three ML models and what factors they used to predict attendance.

Sports model - features used

The ML features used by the sports model to predict how many people will attend a sporting event are listed below:

  • Teams

  • Venue

  • Sport

  • Duration

  • Weekday/Weekend

  • Gender

  • Recurring Event

  • International/Domestic

  • Tournament

  • And more

Concerts model - features used

The ML features used by the concerts model to predict how many people will attend a concert event are listed below:

  • Music genre

  • Record label

  • Population density

  • Venue capacity

  • Performer data

  • Ticket sales

  • And more

Performing arts model - features used

The ML features used by the performing arts model to predict how many people will attend a performing-arts event are listed below:

  • Type of event

  • Venue capacity

  • Ticket sales

  • Duration

  • Start time

  • Number of performers

  • Population density

  • And more

Conferences model - features used

The ML features used by the conferences model to predict how many people will attend a conference event are listed below:

  • Event density

  • Venue capacity

  • Location

  • Duration

  • Start time

  • Population density

  • And more

Expert systems and other types of models

We also have other specialist models for some types of events. For example, we have a specific model that predicts attendance for Youth Sports that uses features like age groups, student numbers, the number of teams in a tournament, and more to make specific attendance predictions for Youth Sports events.

Where we don’t have models we use the following data to predict attendance:

  • Venue capacity

  • Maximum Attendance

  • Recurring event group attendance

  • Provider future attendance

  • Provider-specific ranking methods

We also update sports events with actual attendance after they happen (mainly major US sports and major soccer leagues)

events API response