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.
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+ |
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.
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.
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
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
- Population density
- Ticket sales
- And more
The ML features used by the performing arts model to predict how many people will attend a concert event are listed below:
- Type of event
- Venue capacity
- Ticket sales
- Duration
- Start time
- Number of performers
- Population density
- And more
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)
Last modified 2mo ago