Original research · 2026-07 edition

AI SEO Statistics: Sports Supplies (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-06

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The question bank

The questions we tested — sampled from real buyer journeys in sports supplies.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

I'm starting a home gym in a small apartment, what's the most versatile equipment I can get for under $500?
Is it worth buying a high-end mountain bike online or should I go to a local shop for the assembly and fit?
What are the red flags I should look for when buying professional grade soccer cleats from a website I've never used before?
How can I tell if a pair of boxing gloves is actually genuine leather or just cheap synthetic material before I hit buy?
I need a heavy-duty basketball hoop for my driveway that won't rust in a coastal climate, what specs should I check?
What's the difference in durability between basic foam rollers and the expensive vibrating ones for muscle recovery?
I have a marathon in two weeks and my shoes just ripped, who offers the most reliable overnight shipping for running gear?
Is there a significant performance difference between a $50 pickleball paddle and a $200 one for a beginner player?
Show all 15 questions
How do I choose the right tension for a badminton racket if I'm transitioning from casual to competitive play?
What's the best way to compare weights and dimensions for kayak paddles online to make sure it's not too heavy for long trips?
Are those online mystery boxes for golf supplies actually a good deal or just a way for stores to dump old stock?
What kind of warranty should I expect when buying a motorized treadmill from an e-commerce retailer versus a big box store?
I need to buy 15 sets of uniforms for a youth volleyball team, how do I ensure the colors match across different sizes and styles?
Which online sports stores allow you to filter equipment specifically by skill level or player height rather than just price?
How do I know if a weight bench is sturdy enough for 300 plus pounds without being able to test it in person?

Model by model

26-point average divergence: which AI you ask changes the answer.

The divergence index is the average gap between the most and least likely model per behavior. Higher = the models disagree more about sports supplies buyers.

Behavior rates across 15 sports supplies buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional27%20%13%73%
Suggests DIY first0%0%0%100%
Names specific providers20%67%47%13%
Gives price or cost info13%40%27%67%
Tells to check reviews40%33%0%47%
Tells to verify credentials20%0%0%80%
Mentions case studies / portfolio7%0%0%93%
Mentions local proximity33%20%0%60%
Gives selection criteria73%80%47%20%
Warns about red flags27%27%13%67%
Asks a clarifying question53%73%0%7%
Recommends multiple quotes0%0%0%100%

By model

How each assistant handled Sports Supplies questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same sports supplies questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 26.7% (ChatGPT) down to 13.3% (Gemini), a 13-point gap on an identical question set.

Across the 15 sports supplies answers it produced, ChatGPT recommended hiring a professional in 26.7% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 20% of answers (about 0.9 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 53.3% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 20%, averaging 545 words per answer. On the remaining cues it told the buyer to check reviews in 40%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 33.3%; a selection-criteria checklist appeared in 73.3% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 sports supplies answers it produced, Claude recommended hiring a professional in 20% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 66.7% of answers (about 3.4 distinct providers per answer) and included price or cost information 40% of the time. Claude asked a clarifying question before answering in 73.3% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 0%, averaging 297 words per answer. On the remaining cues it told the buyer to check reviews in 33.3%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 80% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 sports supplies answers it produced, Gemini recommended hiring a professional in 13.3% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 46.7% of answers (about 1.1 distinct providers per answer) and included price or cost information 26.7% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 0%, averaging 214 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a sports supplies buyer to a professional (26.7%) and Gemini the least (13.3%). ChatGPT produced the longest answers, at 545 words on average. Specific providers were named most often by Claude (66.7%) — even there, roughly one answer in 1 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 26.3 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a sports supplies buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 73.3% (Claude) — a 73-point spread.
  • Names a specific provider: from 20% (ChatGPT) to 66.7% (Claude) — a 47-point spread.
  • Tells the buyer to check reviews: from 0% (Gemini) to 40% (ChatGPT) — a 40-point spread.
  • Mentions local proximity: from 0% (Gemini) to 33.3% (ChatGPT) — a 33-point spread.
  • Gives selection criteria: from 46.7% (Gemini) to 80% (Claude) — a 33-point spread.

The widest single gap — asks a clarifying question, 73 points — means a sports supplies buyer can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the sports supplies market.

Where they agree

The points of near-consensus in Sports Supplies.

On other behaviors the three models move almost in lockstep — the points of near-consensus for sports supplies, where all three landed within a few points of each other:

  • Suggests a DIY approach first: 0% across all three models.
  • Recommends multiple quotes: 0% across all three models.
  • Mentions case studies or portfolio: 0%–6.7% across all three (a 7-point spread).
  • Recommends hiring a professional: 13.3%–26.7% across all three (a 13-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "suggests a DIY approach first" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (6.7%).

Every behavior, measured

All twelve coded behaviors for Sports Supplies, averaged across the three models.

The behaviors AI models reproduce most often for sports supplies are gives selection criteria (66.7% on average), names a specific provider (44.5%) and asks a clarifying question (42.2%); the rarest are recommends multiple quotes (0%), suggests a DIY approach first (0%) and mentions case studies or portfolio (2.2%). Each figure below is the share of a model's 15 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:

  • Gives selection criteria: 66.7% on average (ChatGPT 73.3%, Claude 80%, Gemini 46.7%) — a 33-point spread.
  • Names a specific provider: 44.5% on average (ChatGPT 20%, Claude 66.7%, Gemini 46.7%) — a 47-point spread.
  • Asks a clarifying question: 42.2% on average (ChatGPT 53.3%, Claude 73.3%, Gemini 0%) — a 73-point spread.
  • Gives price or cost information: 26.7% on average (ChatGPT 13.3%, Claude 40%, Gemini 26.7%) — a 27-point spread.
  • Tells the buyer to check reviews: 24.4% on average (ChatGPT 40%, Claude 33.3%, Gemini 0%) — a 40-point spread.
  • Warns about red flags or scams: 22.2% on average (ChatGPT 26.7%, Claude 26.7%, Gemini 13.3%) — a 13-point spread.
  • Recommends hiring a professional: 20% on average (ChatGPT 26.7%, Claude 20%, Gemini 13.3%) — a 13-point spread.
  • Mentions local proximity: 17.8% on average (ChatGPT 33.3%, Claude 20%, Gemini 0%) — a 33-point spread.
  • Tells the buyer to verify credentials: 6.7% on average (ChatGPT 20%, Claude 0%, Gemini 0%) — a 20-point spread.
  • Mentions case studies or portfolio: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.
  • Suggests a DIY approach first: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
  • Recommends multiple quotes: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the sports supplies buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the sports supplies buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 24.4% of answers on average. Verifying credentials or certifications appeared in 6.7%. Warning about red flags or scams appeared in 22.2%.

On structuring the decision, a selection-criteria checklist showed up in 66.7% of answers on average and a recommendation to gather multiple quotes in 0%. The single least-reproduced protective signal for sports supplies is "recommends multiple quotes" at 0% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

Referral behavior

Do AI models name Sports Supplies providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 sports supplies answers, a specific provider was named in 44.5% of responses on average — roughly 1.8 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for sports supplies: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Sports Supplies questions cover.

The 15 questions behind every percentage on this page were drawn from real sports supplies (ecommerce / online retail; buyer hiring decisions for this specific service) buyer journeys. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact sports supplies question set — not a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.

How to read this

A note on the numbers.

A percentage here is the share of a model's 15 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific sports supplies question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.

Methodology

A controlled snapshot, documented end to end.

15 standardized buyer questions per industry, one response per model per question (ChatGPT (gpt-5-mini), Claude (claude-sonnet-5), Gemini (gemini-3-flash-preview)), collected 2026-07-06, coded against a fixed 12-behavior rubric with human QA. AI outputs vary with model version, location and time — figures describe this sample and window, and are refreshed each edition. Read the full methodology →