Predicted Attendance
Predicted Attendance represents the number of people predicted to attend an event.
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 events API response 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.
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)
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