Formula 1 venues use sensors and analytics to track fan movement and behavior. Over 50 million data points are collected from sensors at gates, grandstands, food areas and more across 9 races in 2018. The data is processed to remove identifying information before automated queries analyze dwell time, arrival patterns, and engagement with activations. Insights found variation between circuits that can help improve fan experience, such as bringing concessions to grandstands for fans who don't leave their seats. The system will be expanded to more races in 2019 to further optimize the fan experience.

1. Formula 1
Engineered Insanity?
Making sense of footfall patterns at Formula 1 venues

2. The 2017 takeover signalled a new direction for F1
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Our mission is “to unleash the greatest racing spectacle on the planet”

3. IT ALL STARTS WITH
THE FANS
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4. So what did we already know about F1 spectators…?
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5. So we launched two research projects to help us fill that gap…
Post-Race Spectator Surveys
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Spectator Footfall Analysis

6. How exactly do you measure people movement at live events ?
• Little white boxes . . . .
• Analytics sensors enabled with software, HD cards and 4G connectivity listen to
the mobile ‘pings’ given off by devices
• Lightweight, battery or mains powered, water resistant and ideal for temporary
environments.
• Discrete and easily deployable.
• The clever bit is on the inside.
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7. And that starts at the Edge . . .
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Edge computing is a way to streamline the flow of traffic from
IoT devices and provide real-time local data analysis
It’s a network of micro data centers that process or store
critical data locally and push all received data to a central data
center or cloud storage repository.
In this case the first point of process is the sensor / dongle, the
2nd is the collection api, then a “dequeuer” ie there are 3 point
of process on the edge of the big bucket of information.
The primary result of this is that no personally identifiable
information is being passed from the sensor to the cloud.
GDPR regulation.

8. It starts at the Edge . . .
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Oh and its fast
Very, very fast . .
Which is important because an average Grand Prix
weekend sees us wrestling with over 50 million data
points.

9. From volume to value
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50+ MILLION DATA POINTS
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4
3
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Ping Pong
We take out any “Fake” Id’s, background noise, staff
etc
Sample of 1
Automated Query Mapping
KPI output250K
DATA POINTS

10. Step 1
SENSORS PERFORM 3 PRIMARY FUNCTIONS
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PROCESS
KEY
INFORMATION1
CREATE A SECURE
CONNECTION
TO THE CLOUD2 PASS BACK
NON PII INFORMATION3

11. Step 1
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its our need for speed again !
An active sensors sees “pings” and collates sensor source ie the location name we have given it.
This is then routed into a distributed queuing system based on Redis.

12. Steps 2 & 3
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All our data is now in the cloud, however it is still “dirty”. Its at this stage we run a series of process over the top to
remove, randomized MAC iDs, fixtures and fittings, staff etc
This is a combination of black list of the certain identifiers eg printers, security cameras, digital signage etc
Behaviours, if a ping remains the same ie same frequency, distance, timing etc it isn’t moving and therefore unlikely to be
associated with a person. Or in the case of staff if someone is in situ for 12 hours they are probably not an end
consumer.
At this point we also remove a sample of 1 . . .
There are many reasons why we may only see an ID once however it isn’t valuable to us and skews the data so we
remove it automatically from further analysis.

13. Steps 4 & 5
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This is where the magic happens :-0
Next process is to attribute more values to the data. We append tags that enable us to query on a deeper, more
meaningful level.
This is a library of standard naming conventions e.g. each ping belongs to an individual sensor, that sensor is likely to be
part of a cluster or behavioural group and will also contribute to the total event.
An F1 example is Sensor 33 is Silverstone Gate 2, it is part of the Entrance and Exit cluster and simultaneously part of
the British GP event.

14. Steps 4 & 5
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In the case of F1 these have been built up over 12 months of working together, we know that understanding the
Paddock Club is part of the brief so data from Paddock Club sensors is identified.
This data is then run through a series of pre established, productised queries. E.g. a repeat visitor is someone not seen
for a minimum of 90 minutes. Dwell time is calculated based on first seen, last seen by sensor and by event.
These queries are SQL and Python based and the push the “answers” to a front end API which feeds a real time
dashboard or is an CSV or excel output.
Clean raw data is also manually interrogated for new hypothesis testing before we map and automate the query and add
it to the library.

15. OUR KPIS
WE START WITH A HYPOSTHESIS, ITS NOT WALLPAPER
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FOOTFALL ENGAGEMENT DWELL TIME FREQUENCY TIME & DAY FLOW SEGMENTATION

16. The F1 Implementation in action
Meshh have deployed c55 Analytics sensors across 9 races in 2018.
Entrances & exits
Grandstands
Food & beverage
Merchandise stands
Fan zone including activations
The Paddock Club
Circuit specific points of interest
Sponsor / Partner Activity
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17. The results are used for, among other things –
Identifying when and where visitors arrive on the circuit, helps to understand gate opening hours and staffing.
Journey patterns throughout the event, where are the blockages and opportunities ?
Engagement with the fan zone for both F1 and Partner / Sponsor assets, Johnny Walker use Meshh data as part of their experiential
measurement KPIs
Engagement with entertainment assets, Driver signings, concerts etc for example do they drive incremental visitors ?
Dwell time at different grandstands ( proxy for different ticket type holder ), how does general admission behaviour differ from premium
ticket holders ?
Wayfinding hotspots and much more . . . .
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18. All of which needs to be shown to stakeholders in a clear and concise way…
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Arrival Times
USA – October 2018
Grandstand & Fan Zone overlap
Italy – September 2018
Activation conversion in Fan Zone
Belgium – August 2018
Merchandise store visits
Britain – July 2018

19. Every circuit is different, which presents challenges…
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Ongoing challenges
• Difficult to create standardised reporting when every circuit is
different and has different priorities
• Different clusters require different analysis (eg. you can engage
with a stage from a distance, but closer to food & drink or a
merchandise stand)
• Weather can cause delays in deployment (eg. heavy rain at the
Italian GP)
• Spectator pinch points occasionally cause issues in reaching
broken sensors (eg. French GP)
• Important to have a team on the ground to provide visual context
to the numbers
• And we need the circuits to buy into it!

20. Dwell time varies hugely across different circuits…
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Azerbaijan BelgiumBahrain
This illustrates the importance of building a distinct offering for each circuit
Average dwell time…
2 hrs 2 mins
Average dwell time…
2 hrs 24 mins
Average dwell time…
4 hrs 14 mins

21. Footfall to merchandise outlets and the fan zone also varies hugely…
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Azerbaijan Belgium
• Dwell time is a factor…
• …but do we have the right locations?
• …do fans actually know about the merchandise
and fan zone offering?
• Other research shows that fan zone visitors are
more likely to buy tickets again.
• Only 9% visited the fan zone
• Only 13% visited the merchandise
megastore
• Only 38% visited any merchandise store
• 46% visited the fan zone
• 21% visited the merchandise megastore
• 80% visited any merchandise store
Why?

22. A surprising number of fans just spend the whole time in their seat…
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France Britain
• Some spectators might not want to miss a
second on the track (including support races)…
• …but maybe that means we should bring food /
drink / merchandise to them?
• …are they just too far away from the fan zone to
want to walk there?
• …or is lack of information the problem again?
% of fans spending 3 or more hours in their
seat…
• 46% in Sainte Baume
• 43% in Chicane
• 41% in Beausette
% of fans spending 3 or more hours in their
seat…
• 35% in Luffield
• 34% in Village
• 33% in Club Corner
So what?

23. The future…
• Project has also already been extended to Fan Festivals during 2018 season
• Plans already in place to cover 8 races in 2019
• A mixture of repeat visits and circuits that we haven’t yet covered
• Some promoters interested in contributing to costs to make sure their race is covered
• But, planning meeting required pre-2019 to work on some of the obstacles discussed today!
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24. THANK YOU
ANY QUESTIONS?
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