ArticleOrthopaedic journal of sports medicine2020
Machine Learning Outperforms Logistic Regression Analysis to Predict Next-Season NHL Player Injury: An Analysis of 2322 Players From 2007 to 2017.
Article in Orthopaedic journal of sports medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The application of machine learning in the prediction of sports injuries: systematic review and meta-analysis.Scientific reports · 2026Pooled it
- Application of Artificial Intelligence for Predicting Sports Injuries and Customizing Personalized Prevention Strategies: A Scoping Review.Bioengineering (Basel, Switzerland) · 2026Review
- Predicting self-reported injury status among runners training for the New York City Marathon.PM & R : the journal of injury, function, and rehabilitation · 2026Observational
- Machine learning models to predict postoperative incontinence after endoscopic enucleation of the prostate for benign prostatic hyperplasia: An EAU-Endourology study.Prostate cancer and prostatic diseases · 2026Article
- Strength, Jump Height, Landing, and Mobility Metrics Predict High and Low Performers of a Fire and Move Assessment: A Machine Learning Approach.Medicine and science in sports and exercise · 2025Article
- Hamstring Strain Injury Risk in Soccer: An Exploratory, Hypothesis-Generating Prediction Model.Muscles (Basel, Switzerland) · 2025Article
- A practical guide to the implementation of artificial intelligence in orthopaedic research-Part 3: How orthopaedic research benefits from the implementation of artificial intelligence.Journal of experimental orthopaedics · 2025Review
- Large Language Model-Based Writing in Published Sports Medicine Research: Uncovering a Growing Influence.Orthopaedic journal of sports medicine · 2025Article
- Artificial intelligence in orthopedics: fundamentals, current applications, and future perspectives.Military Medical Research · 2025Review
- A practical guide to the implementation of AI in orthopaedic research-Part 7: Risks, limitations, safety and verification of medical AI systems.Journal of experimental orthopaedics · 2025Review
- Machine learning approaches to injury risk prediction in sport: a scoping review with evidence synthesis.British journal of sports medicine · 2025Article
- Prediction of football injuries using GPS-based data in Iranian professional football players: a machine learning approach.Frontiers in sports and active living · 2025Article
- Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods.Diagnostics (Basel, Switzerland) · 2024Review
- Hip Muscle Strength Ratios Predicting Groin Injury in Male Soccer Players Using Machine Learning and Multivariate Analysis-A Prospective Cohort Study.Muscles (Basel, Switzerland) · 2024Article
- Non-contact lower limb injuries in Rugby Union: A two-year pattern recognition analysis of injury risk factors.PloS one · 2024Article
- Review
- Application of artificial intelligence in trauma orthopedics: Limitation and prospects.World journal of clinical cases · 2023Review
- Demographic Factors and Instantaneous Lower Extremity Injury Occurrence in a National Collegiate Athletic Association Division I Population.Journal of athletic training · 2023Article
- Supervised machine learning and associated algorithms: applications in orthopedic surgery.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2023Review
- The impact of sport-specific physical fitness change patterns on lower limb non-contact injury risk in youth female basketball players: a pilot study based on field testing and machine learning.Frontiers in physiology · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe opportunity to quantitatively predict next-season injury risk in the National Hockey League (NHL) has become a reality with the advent of advanced computational processors and machine learning (ML) architecture. Unlike static regression analyses that provide a momentary prediction, ML algorithms are dynamic in that they are readily capable of imbibing historical data to build a framework that improves with additive data. PURPOSE: To (1) characterize the epidemiology of publicly reported NHL injuries from 2007 to 2017, (2) determine the validity of a machine learning model in predicting next-season injury risk for both goalies and position players, and (3) compare the performance of modern ML algorithms versus logistic regression (LR) analyses. STUDY
designDescriptive epidemiology study.
methodsProfessional NHL player data were compiled for the years 2007 to 2017 from 2 publicly reported databases in the absence of an official NHL-approved database. Attributes acquired from each NHL player from each professional year included age, 85 performance metrics, and injury history. A total of 5 ML algorithms were created for both position player and goalie data: random forest, K Nearest Neighbors, Naïve Bayes, XGBoost, and Top 3 Ensemble. LR was also performed for both position player and goalie data. Area under the receiver operating characteristic curve (AUC) primarily determined validation.
resultsPlayer data were generated from 2109 position players and 213 goalies. For models predicting next-season injury risk for position players, XGBoost performed the best with an AUC of 0.948, compared with an AUC of 0.937 for LR (
conclusionAdvanced ML models such as XGBoost outperformed LR and demonstrated good to excellent capability of predicting whether a publicly reportable injury is likely to occur the next season.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.