Who competes
Participant Profile drawn from the authorised event record: demographics, origin and race category.
- Age
- Gender
- Origin
- Category
Race Intelligence Platform
Understand who competes, what they use, how they perform
Eyerace turns what happens in competition into market intelligence, connecting observed race-day behaviour with authorised event data.
Explore Eyerace as
Understand which products actually make it to competition
Eyerace measures which brands and models participants use, who uses them and how adoption changes by athlete profile and performance.
Explore Brand Intelligence
Adidas · major European city race, 2026
Race-day Share among participants with identified kit
23.1%
Race-day Share
6,670
Observed runners
35
Median age
1h51
Median finish
Observed in competition · Event anonymised · 2026
Real anonymised event, major European city race, 2026: over 28,000 participants analysed, 93% detection coverage, more than 90 nationalities, 40% women, 40% international participation.
What Eyerace measures
Three layers of data that once required three separate sources
Surveys for the profile, manual counts for the kit, results for the performance. Eyerace brings them together in a single record per participant, using the same methodology at every race.
What they use
Brand and model of the kit detected in competition, with its confidence level and product class.
- Brand
- Model
- Product class
- Confidence
How they perform
Actual performance for every participant, so kit usage can be segmented by competitive level.
- Official time
- Pace
- Position
- Level
One connected record per participant
Every observation is matched to a bib number and to the authorised event record before it enters the aggregated analysis. The result is a dataset that is comparable across events, editions and regions.
The output
From single frame to a report that holds up in the room
Detection is the means. What we deliver is a report covering usage share in competition, penetration by segment, observed sponsor presence and change between editions. The usage share below uses real data from a marathon analysed by Eyerace in 2026.
01 · Observation
Bib and kit detected at control points.
02 · Data
Matched to the authorised record, then quality-checked.
03 · Insight
Aggregated metrics ready for decisions and negotiations.
Illustrative view · demo dataset · N=4,812
Share by performance level
Your brand · % within each band
Sub 3 h31.2%
3 – 4 h26.4%
4 – 5 h21.8%
5 h +17.1%
Share by age band
Your brand
18 – 2922.6%
30 – 3928.7%
40 – 4924.9%
50 +18.4%
Gender split
Participants using your brand
Men 58.3% Women 41.7%
Penetration by origin
Index 100 = event average
| Origin | Participants | Index |
|---|---|---|
| Metropolitan area | 2,104 | 112 |
| Rest of country | 1,876 | 97 |
| International | 832 | 84 |
Interpretation
Segmentation shows where a brand is strong and where it is not, without relying on self-reported samples.
Illustrative view · demo dataset · N=4,812
What is measured: the % of runners competing in the official race shirt carrying the sponsor logo, detected control point by control point.
Official shirt with sponsor logo in use
% of participants by control point
Interpretation
The organiser can document how many runners wore the official shirt carrying the sponsor logo, point by point, instead of estimating it.
Sponsor summary
One edition
38.4%
Runners in the official sponsor shirt
1,848
Identified wearers
30 – 44
Dominant age band
+2.1 pp
Change on the previous edition
Eyerace measures observed presence and profile. It does not estimate sponsorship value or media return.
Illustrative view · demo dataset
Your brand's share across events in one region
Illustrative example · same season
Interpretation
Comparing the same brand across different events in one region shows whether its strength is consistent in the local market or tied to a single type of race.
Detail by event
Same region · same season
| Event | Analysed | Your share |
|---|---|---|
| Metropolitan marathon | 2,989 | 24.8% |
| Coastal half marathon | 1,640 | 21.3% |
| City 10K | 980 | 19.6% |
| Mountain trail | 540 | 14.2% |
Edition-on-edition change for the same event becomes available as we build up years of history. Comparing your brand across different events in one region within a single season is possible today.
Usage share: real data from an event analysed by Eyerace
Segmentation, sponsorship and trend: illustrative view of the report structure
Brand Intelligence · for sports brands
See who really uses your products in competition
Point of sale explains what is bought. Competition explains what is used, who uses it and at what level of performance. Eyerace measures that second layer systematically.
Competition Share
The real presence of each brand and model among the participants analysed, race by race.
Participant Profile
Who chooses your product to compete in, described from event registration data rather than self-reported samples.
Competitive intelligence
A like-for-like comparison of the brands identified at the same event under the same methodology.
Performance segmentation
Product usage by competitive level, so you can see where each range works.
Affinity Index
Your brand's representation within a segment against its benchmark representation at the event: where you gain and where you lose.
Trends and cross-event comparison
Your brand's share across different events in one region today; edition-on-edition change for the same race as we build up years of history.
Event Intelligence · for race organisers
Turn what happens on race day into commercial intelligence
Understand your participants, measure sponsor-asset adoption and identify commercial opportunities from observed race-day behaviour.
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The real profile of your race
Demographics, origin, performance level and kit used, with the same methodology every year.
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Stronger sponsorship proposals
Specific data on who the event reaches, instead of generic audience descriptions.
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Documented Sponsor Asset Adoption
Observed presence of sponsor kit among participants, control point by control point.
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An objective post-event report
The same report structure every edition, comparable and auditable by both parties.
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Evidence for renewals
Built for edition-on-edition comparison. Once a race has several analysed editions, Eyerace keeps the same metric definitions to build a comparable series.
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Organic Brand Demand
See which brands already have a meaningful following inside your event before you open a commercial conversation.
The kind of claim you can back with data
Participant Profile
“40% of our participants are women, with more than 90 nationalities represented: a clear profile to steer the commercial conversation.”
Sponsor Asset Adoption
“38% of finishers competed in your brand's official asset; among those wearing it, 60% are aged 25 to 44.”
Organic Brand Demand
“We identify brands with real presence at your race before any sponsorship agreement exists.”
Commercial opportunity
“Brand and model detail lets you take the commercial conversation to specific categories, not just to the total entry list.”
These figures come from real anonymised data from events analysed by Eyerace in 2026. Eyerace does not estimate the financial return on sponsorship: it supplies the evidence to negotiate it.
How it works
Four steps, one repeatable methodology
Eyerace works with the event organiser. The deployment is light and does not disrupt the running of the race.
01
Capture
Cameras at strategically selected points on the course. Light installation, no permanent infrastructure.
02
Detect
Computer vision identifies the bib number and the brand and model of the kit.
03
Connect
Every detection is matched to the authorised event record and to the official results.
04
Analyse
Eyerace produces aggregated market intelligence and the event report.
6 participants · 6 models classified
Low-confidence detections are reviewed before entering the aggregate
Why Eyerace
A different market source, not an automated version of the manual count
Surveys give you stated behaviour. Manual counts give direct observation, usually on narrower samples. Eyerace adds observation at scale, connected to registration and performance.
| Dimension | Surveys | Manual counts | Eyerace |
|---|---|---|---|
| Behaviour | Stated | Observed | Observed in competition |
| Brand and model identification | From recall | Brand; model rarely | Brand and model |
| Demographic information | Self-reported | Not available | From the event record |
| Performance data | Not available | Not available | Time, pace and position |
| Scalability | Limited by response rate | Small samples | Thousands of participants per race |
| Competitive comparison | Partial | Visible brands only | Brands identified in the sample |
| Longitudinal analysis | Different samples | Rarely practical | Yes, where history or a comparable multi-event study exists |
A methodological comparison of the information sources commonly used in the sector. All three can be complementary, depending on the objective of the study.
Applied Race Intelligence
From observed behaviour to insight
Real race analysis designed to understand product adoption, athlete behaviour and the commercial dynamics of an event.
What competition reveals about product adoption
European half marathon · 2026 · Event anonymised
23.1%
Competition Share, leading brand
62%
Concentration in two models
1.43x
Affinity Index, women
Headline share hides real differences between consumer segments.
Explore the analysisFrom race-day presence to commercial opportunity
European half marathon · 2026 · Event anonymised
28,875
Participants analysed
39.2%
Top 10 product concentration
38%
Sponsor Asset Adoption
Which brands already have a community inside the event, before any agreement.
Explore the analysisBrands shown in the analyses are part of the data observed in competition. The event name is published only when the organiser authorises it in writing. See the full analyses.
Disciplines
Built for endurance sport
We start where equipment is visible, the participant is identified by a bib number and an official race record exists.
Running
Marathon, half marathon and 10K. The highest participant volume per event.
Trail running
Mountain races and ultras, where technical kit varies sharply by brand and model.
Cycling
Sportives and gran fondos, with high-value, highly visible equipment.
Triathlon
Three disciplines in one race, and therefore three equipment categories.
The model applies to any sport with a participant register and identifiable equipment. We prioritise endurance sport to keep data quality high.
About
Built from inside the sport
Eyerace comes from working inside endurance events, and from a simple observation: every race generates a large amount of market information that nobody measures. Thousands of participants choose, buy and use equipment in real competition conditions, and that evidence disappears the moment they cross the line.
We built Eyerace to turn that signal into a rigorous, reusable data source — useful both to the people who organise the race and to the people who make the equipment.
Would you rather speak to the team directly? Write to us at hello@eyerace.io.
Contact
Turn competition into intelligence
Tell us about your event or your product category and we will set out exactly what analysis is possible and what you would receive.