Validity and Reliability of a Novel AI-Based System in Athletic Performance Assessment: The Case of DeepSport

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dc.authorid0000-0003-3922-3693
dc.authorid0000-0003-1688-3410
dc.contributor.authorAydemir, Burakhan
dc.contributor.authorAydogan, Muhammed Talha
dc.contributor.authorBoz, Emre
dc.contributor.authorKul, Murat
dc.contributor.authorKirkbir, Fatih
dc.contributor.authorOzkara, Abdullah Bora
dc.date.accessioned2026-02-28T12:18:20Z
dc.date.available2026-02-28T12:18:20Z
dc.date.issued2025
dc.departmentBayburt Üniversitesi
dc.description.abstractThis study aimed to examine the validity and reliability of the AI-based DeepSport application by comparing its outcomes with those from the reference device, OptoJump. The primary dependent variables measured were jump height and anaerobic power during vertical jump assessments. Twelve elite male basketball players voluntarily participated in the study (age = 21.53 +/- 1.14 years; sports experience = 6.47 +/- 1.01 years). DeepSport uses AI-based image processing from standard cameras, while OptoJump uses optical sensor technology. Both DeepSport and OptoJump systems were utilized to assess participants' Countermovement Jump (CMJ) and Squat Jump (SJ) performances. A G*Power (version 3.1.9.7) analysis determined the required sample size, adopting a 95% confidence level, 90% test power, and an effect size of 0.25. Validity assessments were conducted using Bland-Altman plots and ordinary least products (OLP) regression analysis, while reliability was evaluated through intraclass correlation coefficient (ICC), coefficient of variation (CV), standard error of measurement (SEM), and smallest detectable change (SDC) analyses. DeepSport showed excellent reliability in CMJ and SJ tests with ICC values > 0.90, and CV ranged between 2.12% and 4.95%. Results were consistent with OptoJump, showing no significant differences according to t-test results (p > 0.05). Bland-Altman analyses indicated no systematic bias and random distribution. These findings confirm that both DeepSport and OptoJump devices demonstrate high reliability and consistency, suggesting their validity and reliability for use in athlete performance assessments by coaches and athletes.
dc.identifier.doi10.3390/s25175580
dc.identifier.issn1424-8220
dc.identifier.issue17
dc.identifier.pmid40943009
dc.identifier.scopus2-s2.0-105015894827
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/s25175580
dc.identifier.urihttps://hdl.handle.net/20.500.12403/6198
dc.identifier.volume25
dc.identifier.wosWOS:001571521400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSensors
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260218
dc.subjectDeepSport
dc.subjectartificial intelligence
dc.subjectCMJ
dc.subjectSJ
dc.subjectperformance
dc.titleValidity and Reliability of a Novel AI-Based System in Athletic Performance Assessment: The Case of DeepSport
dc.typeArticle

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