How we rate shoes
We use modern data science to turn real reviews from over 15,000 experienced runners into shoe quality ratings.
The top-rated shoes we track, and their ratings.
TLDR
Each shoe's rating starts with ratings from prior shoes in the same product line, the brand's other shoes, and external retailers. We then use ratings and head-to-head shoe comparisons from our reviews to score the shoe. Evidence from our own shoe reviews has more influence as it accumulates. We publish a confidence score alongside each rating to give readers an idea of how sure we are about each shoe rating. Often-reviewed shoes from product lines with long review histories will have higher confidence ratings.
Introduction
Running shoe buyers today have access to endless ratings and reviews: retailer star ratings by the thousand, in-depth review sites, influencers on YouTube. Experienced runners see all of this and still wonder whom to trust. How different is a 4.6 from a 4.7 when there are ten ratings? Is the charismatic reviewer sincere, and do their feet differ from yours? And above all: will this shoe work for me?
Star ratings from retail sites are a useful signal, and they are one of the inputs to our own model. Interpreting them well, however, requires context. How many ratings are there? Who left them? How are similar shoes rated? Making sense of this context is difficult for ordinary buyers. Our model, however, combines information from retailers, ratings from prior versions of a shoe, the star ratings of reviewers, and head-to-head comparisons of specific shoes. This information is then used to generate a quality rating. Modern statistical methods make this possible.
Every major sports team now uses similar methods to figure out who deserves large salaries. Those teams who adopted these methods first have won championships and changed how all major sports are coached and played. Why not apply the same techniques to shoe buying? Our model lets you do just that. We think it's a much better way to pick a shoe than relying on the judgments of only a few reviewers, however sincere and knowledgeable those reviewers are.
We are able to provide you this information because we have been collecting detailed shoe reviews for over ten years from some of the most knowledgeable and experienced runners in the world — over 15,000 so far, from LetsRun.com readers and visitors to this site. Different from retailer ratings, these reviewers rate sub-dimensions of shoes like comfort and durability, directly compare each shoe to other pairs they've run in, and tell us about themselves: whether they overpronate, how much mileage they run and how fast, whether they're injury prone. Alongside all of this, we have painstakingly collected hundreds of thousands of outside ratings from retailers.
This has left us with one of the most in-depth and rich data sets on shoe quality in the world, allowing for higher quality shoe ratings than any other magazine, review site, or individual shoe reviewer could ever provide.
How a Rating Score is built
We realize we're making a big claim, so let us explain a little bit about why we think you should believe us.
Our model reasons the way an experienced runner would, starting from what the product line has done before and revising as reviews arrive. An intuitive way to see this at work is to follow one shoe through it, one source of evidence at a time. The HOKA Clifton 9 is one of our highest-rated shoes, at 98 out of 100 from 130 reviews. Its newest sibling, the Clifton 11, just landed with 4 reviews and shows 88. Here is where those numbers come from.
1. Building a baseline estimate from real data
Before any of our runners review a shoe, we have the brand (HOKA), the product line it belongs to (Clifton), and the aggregate ratings of the shoe from shoe retailers. We use this information to form an initial estimate. For the HOKA Clifton 9, that initial estimate was already high, because earlier Cliftons were rated well. A brand-new shoe sits at this same first step: the Clifton 11, which at the time of writing had only 4 reviews, was rated an 88, because we are basing our best guess for the shoe's quality on the product line as a whole. This score is thus a forecast based upon the product line's history, but is not definitive. That is why our model gives it a confidence score of 82, below the Clifton 9's 100. (We explain confidence scores below.)
The model does not naively use the last version of a score alone. It also considers the brand's reputation, although this does not contribute much to our knowledge, as our analysis of what brands predict sets out. More importantly, not all shoes have rich product line data. When no earlier version of a shoe has been rated, its baseline comes from the brand alone, and the shoe page labels it "Brand rating". When little is known about the brand either, the model pulls the baseline closer to the average score of all shoes. Put plainly, our model rewards shoes with a long history of quality and is skeptical of one-hit wonders.
Lastly, while retailer ratings are informative, anyone who has scoured retailer sites' ratings will know they are often full of low quality reviews, often duplicate ratings (retailers will buy aggregated data from third parties), and clearly phony reviews. For this reason, our model gives them much less weight than our own reviewers' ratings, and it corrects for retailers whose ratings run consistently high or low. Nevertheless, when a shoe is released and we have not yet collected many of our own reviews, the product line and the ratings from external sources can still combine to give readers a very good idea of how a shoe will rate.
2. Incorporating head-to-head shoe comparisons
Our reviewers are asked to compare the shoe they are rating to their previous one. This links shoes into a large network that helps us correct for the fact that some people rate generously and others harshly.
Comparisons also provide crucial insight that star ratings cannot. The vast majority of star ratings, whether from external retailers or in our own data, are four or a five. This is common among reviewers of all products: people tend to buy positively-reviewed products they will like. However, this also means that qualitatively different shoes can end up with similar star averages. A direct comparison between two shoes cuts through this rating compression.
Nearly every review gives four or five stars
The distribution of overall star ratings.
3. Including our reviewers' ratings
Finally, we consider how reviewers rate shoes. While it is true that most shoes are rated well, the model is able to learn a lot about a shoe's quality by how frequently a shoe is not given top marks. The figure below shows how as reader ratings accumulate, the model converges upon a rating and its confidence increases.
Our reviews determine a shoe’s rating most when many exist
The HOKA Clifton 9’s rating at each stage of evidence. The gold line is the most likely quality estimate.
A reasonable concern is that a shoe from a reputable brand and highly-rated product line will always be rated highly even when changes to the shoe make it objectively worse. Our model detects the poor sentiment in the ratings and adjusts its rating accordingly, as shown in Figure 3 below, where our reviewers' ratings and comparisons lower the Nike Pegasus 41 while raising the HOKA Clifton 9.
Reviews can lower or raise a shoe’s rating from its starting point
The HOKA Clifton 9 and Nike Pegasus 41 at each stage of evidence. The gold line is the most likely quality estimate.
This comparison should reassure you that we don't just blindly rate shoes based upon the past version of a shoe, but intelligently use it to make our ratings not ignore useful signal, but not defer to it indiscriminately.
Uncertainty and the Confidence Score
Every rating we publish carries a confidence score on the same 0 to 100 scale. This score is designed to convey how confident or certain we are about the rating given how many reviews we have. For example, the HOKA Clifton 11 rates 88 today from 4 reviews, with a confidence of 82; the Clifton 9 rates 98 from 130 reviews, with a confidence of 100. A low confidence score means the rating is still based mostly on the product line and outside ratings and can move as reviews arrive; a high one means it is based on many reviews of the shoe itself. The score is a percentile: a confidence of 70 means the rating is more settled than 70% of the shoes we rate. Among shoes with five or more reader reviews the typical confidence is 87, and one in ten reaches 98 or higher. A low confidence score does not mean a shoe is bad. Instead, we provide this number to indicate to readers that we are less certain about a shoe's quality.
Ratings based on more reviews have higher confidence
Two shoes with nearly identical central estimates and different amounts of evidence. Each panel’s title shows what we publish.
Outside ratings can lift a new shoe's rating before our runners have reviewed it, but its confidence score stays modest until they do.
Reading the "Based On" Bars
Below each rating, three bars show what the rating is based on: our runners' ratings, the product line's track record, and ratings from outside retailers. When no earlier version of the shoe has been rated, the middle bar reads "Brand rating", since the brand is then the only history the model has. The bars show each source's share of the evidence behind the rating.
Readers are sometimes surprised by how much influence the product line's track record has, even for a shoe our runners have rated many times. This is by design. A product line's track record is the accumulated judgment of many runners across earlier versions, and new versions usually land close to their predecessors. As more reviews accumulate, the balance shifts toward our runners, and for the most-reviewed shoes their ratings carry most of the weight.
External ratings add the views of a wider group of buyers. Because we cannot verify them, we give them less weight in the confidence score.
Reading the External Retailer Ratings Box
Beside the bars, a second box shows where the shoe ranks with outside reviewers. We split retailers into two groups, specialty running shops and mass-market retailers, since they serve different buyers. Each row places the shoe among the shoes rated at that type of retailer, after correcting for each retailer's tendency to rate high or low. A group appears once the shoe has at least 10 ratings at specialty running shops or 25 at mass-market retailers. A shoe with fewer shows the heading and a note that we are waiting for more ratings.
Converting model estimates into published numbers
The model's raw outputs are statistical estimates on a scale that would be unintuitive for most readers, so we convert them for display. Both published numbers — the rating and its confidence score — are placed on the 0 to 100 scale runners already understand, where a mid-90s shoe is among the best we have rated and a shoe in the 60s is ordinary.
The published rating is deliberately cautious. It is the rating we are 75% sure the shoe clears, placed on a scale set by shoes with at least five of our reviews. A shoe with little evidence therefore publishes below a proven shoe with the same most likely quality, and its rating usually rises as reviews confirm it. Ratings can also fall when reviews sour. We publish this way so that a new shoe cannot debut near the top on its family's reputation alone.
Each rating also carries a label. From the top down, they are Exceptional, Excellent, Very good, Good, Decent, Below average, and Bottom-tier.
An evidence-based shoe-buying guide
How will you know whether any specific shoe will work for you? The most reliable way to end up in a shoe that works is to buy a well-reviewed shoe, ideally one similar to a shoe you already trust. Our ratings pick that shortlist and tell you how sure we are about each name on it. We will not claim to be able to pinpoint a single shoe that is perfect for you. We tested whether knowing a runner's body and habits lets us match shoes to people, and it does not. What you know about your own feet, and about the shoes you have already run in, is evidence we do not hold. Our ratings are the part of the decision that can be measured across thousands of runners. We hope it helps you find a good shoe.
We have explained how we rate shoes. How you should buy them is its own question, and it is the subject of our evidence-based buying guide for running shoes.
Frequently asked questions
Is the overall Rating Score an average of the five sub-ratings?
No. Comfort, cushioning, durability, appearance, and value each get their own model, with the same brand, product line, and shoe structure behind them. The Overall Rating Score is modeled separately, from the overall rating runners give directly. We do however consider the sub-ratings in our overall model. The impact is very modest, but it has two advantages: it helps reconcile overall ratings that differ substantially from subratings. Moreover, it helps shoes without reviews move away from their baseline score a little more aggressively.
For this reason, a shoe can rate above or below its sub-ratings, and these deviations are often insightful when judging a shoe's strengths and weaknesses.
How do you know the model works?
We use cross-validation, the standard way to test a predictive model. We build the model on one part of our data, then check its predictions against reviews it has never seen. It predicts those unseen reviews better than a simple average does. Every change to the model faces the same test: if the predictions get worse, the change was bad, and it does not ship.
Who are your reviewers?
Our reviewers are mostly LetsRun.com readers. These readers tend to be more experienced and more knowledgeable than the average runner. For example, the median reviewer runs 40 miles a week and has been running for 12 years.
How often do the lists update? Do you edit the numbers yourself?
The models rerun daily, and the ratings and lists they produce are published without a human step in between. Price lists follow the retailer feeds, which we fetch nightly. No one raises or lowers a shoe for a sponsorship or a commission, and the code has no mechanism for doing it. We do change the data the models read. Reviews that look fraudulent are held back or removed, and wrong specs are corrected at their source, which moves the ratings that rest on them.
How do you make money?
We have no sponsorships, no paid placements, and no per-shoe incentive of any kind. We earn money when you buy through our links, and the commission pays the same whichever shoe you pick, so the only thing the ratings can be optimized for is being right. Detail in our affiliate disclosure and editorial policy.
Is this AI slop?
No. The written reviews you read are all written by humans. We do use AI tools to help find and purge non-genuine reviews, pick out informative quotes among the dozens in each review, suggest pros and cons, and improve shoe categorizations. Our statistical models were built and are maintained by a professional data scientist/applied statistician. Every suggestion made by an LLM is tagged and reviewable by a human.
Data are accurate as of October 13, 2026. Numbers may differ slightly as new reviews come in. We periodically update these articles to stay in sync.