Footfall around stadiums on match days: how to see the spike and plan for it

Footfall around a stadium jumps on the day of a home match and falls back the next morning, and you can see it in footfall data if you look at the right window. The figure you get is an average over a run of days, so one match is diluted in a 30-day average. Measure a short window, a weekend-only window, the busiest day of the week and the busiest hour, and log those week by week: home weeks stand out against away weeks.

This is for the people who live off match days without running the stadium: bars and restaurants on the walk from the station, hotels, food delivery teams, shirt and souvenir shops, event security firms and brands that hand out samples. It explains what the data can and cannot say about a match, gives a made-up example, lists the mistakes we see most, and sets up an AI employee that keeps the log for you.

What footfall measures near a ground

Mobility Footfall adds up people-hours of presence around a place: a fan who spends three hours in a bar before kick-off counts three times. It is scaled from a panel of devices to the whole population and given as aggregates only, never a person or a trip. For each place you choose a distance (50 to 500 m), a window of days and whether to count all days, weekdays or weekends.

Three of its fields matter most on match days:

• footfall_peak_hour and footfall_peak: the busiest hour of the day in the place's own time, and the average footfall in that hour.
• busiest_day: the busiest day of the week. A ground that hosts on Saturdays tends to pull Saturday to the top for the streets around it.
• footfall_by_part: how the day splits between morning, midday, evening and late night. An evening kick-off moves weight into the evening.

Why one match hides in an average

Footfall per day (footfall_daily) is the average day across the window. A 30-day window around a ground that hosts two home matches in that month blends two big days with 28 ordinary ones. The spike is real but flattened. Two settings make it visible:

1. Set the window to 7 days and Day type to Weekends only (for a weekend fixture list). One home match is then one of two or three days in the figure, not one in thirty.
2. Run the same setting every week and keep each week's figure. A home week and an away week side by side show the difference.

Each day's data arrives about 10 days later, and the window always ends about 10 days ago. So the figure you read on a Monday covers a week that finished more than a week earlier. Keep a fixture list next to your log so you know which week was a home week. The data starts on 17 October 2026, so the first season you track builds up as it goes; it does not yet cover Australia.

A worked example in Manchester

A made-up case with invented numbers. A burger restaurant sits about 400 m from a football ground in Manchester, on the walk from the tram stop. The owner wants to know whether to open two hours earlier on match days and whether a second site on the other side of the ground would share the same rush.

She runs Mobility Footfall on both addresses every Monday, 200 m around each, a 7-day window, weekends only, with Footfall, Busy hours and Busiest days. After six weeks her sheet shows something like this (made up for illustration):

Invented figures for illustration: average weekend-day footfall in people-hours within 200 m, and the busiest hour. Not real data for Manchester.

Her street gains far more in home weeks than the other site, and the busiest hour moves to two hours before kick-off. The second site barely moves: fans do not walk that way. So she opens early on home weekends only, and does not count the match-day rush when she prices the second lease.

Mistakes to avoid

• A long window. Thirty or ninety days blur the matches into the ordinary days. Use 7 days and log each week.
• Treating people-hours as fans. A long pre-match drink counts more than a quick walk past. Compare like with like, week to week.
• Forgetting the delay. Last Saturday's match is not in this Monday's figure. Line your log up with the fixture dates.
• A circle that is too wide. At 500 m the circle may take in a quiet residential block and dilute the street that matters. Start at 200 m on the street itself.
• Ignoring the roads. Footfall says where people were. For closures and congestion on the day, use Traffic incidents and Traffic Flow, which are live.

Set it up as an AI employee

One AI employee keeps the weekly log; a second one helps on the day itself.

1. Schedule: every Monday at 09:00.
2. My data with your sites, or Nearby places to find the bars and restaurants within 500 m of the ground.
3. Mobility Footfall: 200 m, 7 days, Weekends only, with Footfall, Busy hours and Busiest days.
4. A Google Sheets step that adds one row per site with the week, footfall_daily, footfall_peak_hour and busiest_day.
5. Email the week's rows to you, with the dates the figures cover.

On match days, a second flow runs a few hours before kick-off: Traffic incidents around the ground, Traffic Flow on the roads next to your sites, and xMap LLM to write a two-line note for staff or riders. The templates road closures around the stadium on match days and match day near the stadium do this; road speeds around the stadiums runs every hour.

What lands in your inbox each Monday is a short table: each site, last measured week, its weekend footfall, its busiest hour and day, and the dates covered. After a few home and away weeks the pattern is in front of you without opening anything.