Methodology
The science behind StrideMatch
How we analyze 500+ shoes to find you the best one
Pipeline d'analyse
Our analysis pipeline
Each shoe is analyzed against more than 50 technical criteria before entering our catalog
01
Data Collection
Analysis of your running data from your connected apps
02
Biomechanical Analysis
Extraction of stride metrics via video and sensors
03
Smart Matching
Cross-referencing your profile with our shoe database
04
Personalized Recommendations
Tailored model suggestions based on your unique needs
Biomécanique
Our analysis capabilities
Machine learning, biomechanical data and human expertise combined
Cadence Analysis
Measures steps per minute to optimize running efficiency and prevent over-striding.
Foot Strike
Analyzes whether you land on your heel, midfoot, or forefoot to determine shoe drop requirements.
Pronation Control
Evaluates the inward rolling of your foot to recommend neutral or stability shoes.
Dynamic Stability
Assesses how well your body maintains balance during the stance phase.
Vertical Oscillation
Tracks the vertical movement of your torso to improve forward propulsion.
Ground Contact Symmetry
Compares left and right foot contact times to identify muscular imbalances.
Recherche
The expertise of our team
Specialists in biomechanics, podiatry and sports science
Maxime combines expertise in sports biomechanics and data science to develop StrideMatch's recommendation algorithms.
“Every runner is unique. Our mission is to translate that uniqueness into precise shoe recommendations.”
1,885+
100k+ Runners Analyzed
+500
500+ Shoe Models
4
50+ Biomechanical Datasets
15+
20+ Metrics Tracked
Datasets
Scientific datasets used
Running Injury Clinic Kinematic Dataset
2024Ferber R, Osis ST, Hettinga BA, Noehren B
University of Calgary · n=1 798 coureurs
CadenceBlessuresCinématique
Fukuchi Running Biomechanics Dataset
2017Fukuchi RK, Fukuchi CA, Duarte M
Universidade Federal do ABC · n=39 coureurs
GRFCinématique 3DTapis
Van Hooren Running Dataset
2023Van Hooren B, Fuller JT, Miller J, et al.
Maastricht University · n=19 coureurs
TerrainGRFIMU
Publications
Referenced biomechanical research
Increased vertical impact forces and altered running mechanics with softer midsole shoes
2015Baltich J, Maurer C, Nigg BM
Journal of Biomechanics
Biomechanical risk factors associated with running-related injuries: a systematic review
2019Ceyssens L, Vanelderen R, Barton C, et al.
Sports Medicine
Foot strike patterns and collision forces in habitually barefoot versus shod runners
2010Lieberman DE, Venkadesan M, et al.
Nature
A public dataset of running biomechanics and the effects of running speed on lower extremity kinematics and kinetics
2017Fukuchi RK, Fukuchi CA, Duarte M
PeerJ
The effect of running shoes on lower extremity joint torques
2009Kerrigan DC, Franz JR, et al.
PM&R
Machine Learning
Transparency & methodology
We publish our methodology so you can understand how we calculate our recommendations
Open Methodology
We believe in complete transparency. Our matching algorithms are based on peer-reviewed biomechanical research, not marketing claims.
Collaborative Filtering
Our system learns from runners with similar profiles to yours to refine its recommendations.
Feedback Loop
Every user feedback and new Strava run helps improve the overall accuracy of the algorithm.
