Overview Leaderboard Methodology
Industry Report

Running & Cycling Apps

Apps focused on outdoor activities like running, cycling, and triathlon training that offer GPS tracking, route planning, and performance analytics.

Brands tracked: 30
Brands analyzed: 30
Last updated: 2026-04-23
Model: OpenAI GPT-5
Prompts: 245
Total responses: 1,225
Top Brand Overall?
Nike
89/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
Nike
93

Highest score on the LBA metric in this industry.

Authority Leader?
Strava
90

Highest score on the Authority metric in this industry.

TOM Leader?
Strava
100

Highest score on the TOM metric in this industry.

Nike is the default answer in AI responses for Running & Cycling Apps

Nike and Strava together dominate AI responses for Running & Cycling Apps. Both brands consistently surface unprompted, with the model treating them as the default answers for most category queries. Brands outside the top two face a structural disadvantage: users are usually given these two before the model even considers alternatives.

Brand Leaderboard All 30 Running & Cycling Apps brands ranked

Ranked by overall AI Visibility Score (smoothed geometric mean of LBA, Authority, and TOM with an LBA-based floor on Authority and TOM, see methodology). Click any brand for the full report.

# Brand LBA Authority TOM Overall
1
Nike
nike.com
 93
85
90
89
2
Strava
strava.com
 77
90
100
89
3
Garmin
garmin.com
 79
45
65
62
4
Runkeeper
runkeeper.com
 63
35
67
53
5
adidas
adidas.com
 77
10
63
38
6
MapMyRun
mapmyrun.com
 70
7
65
35
7
Runna
runna.com
 65
36
17
34
8
Under Armour
underarmour.com
 68
7
45
30
9
TrainingPeaks
trainingpeaks.com
 88
24
9
28
10
COROS
coros.com
 74
7
17
23
11
Komoot
komoot.com
 69
7
14
21
12
Ride with GPS
ridewithgps.com
 78
8
8
19
13
Stryd
stryd.com
 80
8
8
19
14
Polar
polar.com
 72
7
8
18
15
Zwift
zwift.com
 73
7
7
18
16
Suunto
suunto.com
 68
7
7
17
17
Cyclemeter
abvio.com
 59
6
6
15
18
Final Surge
finalsurge.com
 58
6
6
14
19
Smashrun
smashrun.com
 55
6
6
14
20
Wahoo
wahoofitness.com
 56
6
6
14
21
MapMyRide
mapmyride.com
 51
5
5
13
22
Outdooractive
outdooractive.com
 50
5
5
13
23
Sports Tracker
sports-tracker.com
 51
5
5
13
24
Trailforks
trailforks.com
 47
5
5
12
25
Bikemap
bikemap.net
 43
4
4
11
26
OpenRunner
openrunner.com
 32
3
3
9
27
iSmoothRun
ismoothrun.com
 27
3
3
7
28
MyWhoosh
mywhoosh.com
 26
3
3
7
29
Relive
relive.com
 27
3
3
7
30
OnTheGoMap
onthegomap.com
 8
1
1
3

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 245 shared Running & Cycling Apps prompts. The same prompts, same model, same iterations. So differences in scores reflect actual differences in AI visibility, not differences in measurement.

Overall AI Visibility Score
Smoothed geometric mean of LBA, Authority and TOM. Authority and TOM are floored at LBA × 0.1 before the geometric mean (the same floor used in the per-metric cards above, so brand cards and the composite tell the same story). Formula: composite = ((LBA + 5)(Authority + 5)(TOM + 5))^(1/3) - 5. The floor keeps brands the model clearly recognises but doesn't yet recommend from collapsing to zero, while a single genuinely weak metric still pulls the composite down. Full methodology.
Shared industry prompts
For Authority and TOM, all brands in the industry are scored against the same 245 category prompts (e.g. "best SEO tools for agencies"). This makes brand-to-brand comparisons valid - everyone faces identical inputs. LBA prompts are per-brand because they ask brand-specific questions.
Latent Brand Association (LBA)
5 brand probes + 1 control prompt, each run 5 times in recall mode (no web search). LBA = quality × meta × stability × share × recognition × 100. Read the full LBA methodology →
LLM Authority
50 organic category prompts (discovery, comparison, problem and transactional intents), each run once in recall mode and once in retrieval mode. Score = frequency × log-decayed prominence × intent weight, then 50/50 averaged across the two modes. Read the full Authority methodology →
Top of Mind (TOM)
15 high-volume discovery prompts (sourced from Keywords Everywhere search-volume data), each run 5 times in pure recall mode (no web). Score = frequency × (0.5 + 0.5 × log-prominence), volume-weighted. Read the full TOM methodology →