Overview Leaderboard Methodology
Industry Report

Tennis Shoes

Court-specific shoes engineered for lateral stability, durability, and grip on various tennis surfaces, offered by major athletic and tennis-focused footwear brands.

Brands tracked: 30
Brands analyzed: 30
Last updated: 2026-04-23
Model: OpenAI GPT-5
Prompts: 245
Total responses: 81
Top Brand Overall?
ASICS
33/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
ASICS
0

Highest score on the LBA metric in this industry.

Authority Leader?
ASICS
100

Highest score on the Authority metric in this industry.

TOM Leader?
ASICS
100

Highest score on the TOM metric in this industry.

Tennis Shoes is an emerging or highly fragmented category

No brand in Tennis Shoes scored above 40 on overall AI visibility. That means AI assistants do not currently recommend specific brands for this category with any consistency. Either the category is too new or too regional for the models to have converged on a default set of answers, or the queries users ask don't lend themselves to brand-specific responses.

Brand Leaderboard All 30 Tennis Shoes 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
ASICS
asics.com
 0
100
100
33
2
adidas
adidas.com
 0
95
92
31
3
New Balance
newbalance.com
 0
56
84
25
4
Nike
nike.com
 0
62
77
25
5
Babolat
babolat.com
 0
39
38
16
6
K-Swiss
kswiss.com
 0
25
33
13
7
Wilson
wilson.com
 0
0
41
5
8
Mizuno
mizuno.com
 0
0
15
3
9
HEAD
head.com
 0
0
2
1
10
Yonex
yonex.com
 0
0
3
1
11
Anta
anta.com
 0
0
0
0
12
BIDI BADU
bidibadu.com
 0
0
0
0
13
Diadora
diadora.com
 0
0
0
0
14
Dunlop
dunlopsports.com
 0
0
0
0
15
Fila
fila.com
 0
0
0
0
16
Joma
joma-sport.com
 0
0
0
0
17
Lacoste
lacoste.com
 0
0
0
0
18
Le Coq Sportif
lecoqsportif.com
 0
0
0
0
19
Li-Ning
lining.com
 0
0
0
0
20
Lotto
lotto.it
 0
0
0
0
21
Munich
munichsports.com
 0
0
0
0
22
N
Nivia
nivia.in
 0
0
0
0
23
On
on.com
 0
0
1
0
24
Prince
princetennis.com
 0
0
0
0
25
Sergio Tacchini
sergiotacchini.com
 0
0
0
0
26
Skechers
skechers.com
 0
0
0
0
27
Slazenger
slazenger.com
 0
0
0
0
28
Tecnifibre
tecnifibre.com
 0
0
0
0
29
Tretorn
tretorn.com
 0
0
0
0
30
Under Armour
underarmour.com
 0
0
0
0

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 245 shared Tennis Shoes 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 →