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      Machine Learning Outperforms Logistic Regression Analysis to Predict Next-Season NHL Player Injury: An Analysis of 2322 Players From 2007 to 2017

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          Abstract

          Background:

          The 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 Design:

          Descriptive epidemiology study.

          Methods:

          Professional 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.

          Results:

          Player 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 ( P < .0001). For models predicting next-season injury risk for goalies, XGBoost had the highest AUC with 0.956, compared with an AUC of 0.947 for LR ( P < .0001).

          Conclusion:

          Advanced 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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            Collinearity and Causal Diagrams: A Lesson on the Importance of Model Specification.

            Correlated data are ubiquitous in epidemiologic research, particularly in nutritional and environmental epidemiology where mixtures of factors are often studied. Our objectives are to demonstrate how highly correlated data arise in epidemiologic research and provide guidance, using a directed acyclic graph approach, on how to proceed analytically when faced with highly correlated data.
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              Major and Minor League Baseball Hamstring Injuries: Epidemiologic Findings From the Major League Baseball Injury Surveillance System.

              Hamstring strains are a recognized cause of disability for athletes in many sports, but no study exists that reports the incidence and circumstances surrounding these injuries in professional baseball.
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                Author and article information

                Journal
                Orthop J Sports Med
                OJS
                spojs
                Orthopaedic Journal of Sports Medicine
                SAGE Publications (Sage CA: Los Angeles, CA )
                2325-9671
                25 September 2020
                September 2020
                : 8
                : 9
                : 2325967120953404
                Affiliations
                [* ]Department of Orthopedic Surgery, Baylor College of Medicine, Houston, Texas, USA.
                []Machine Learning Orthopaedics Lab, Cleveland Clinic, Cleveland, Ohio, USA.
                []Hospital for Special Surgery, New York, New York, USA.
                [§ ]Department of Orthopaedics, Henry Ford Health System, West Bloomfield, Michigan, USA.
                [5-2325967120953404] Investigation performed at the Cleveland Clinic, Cleveland, Ohio, USA
                Author notes
                [*] []Prem N. Ramkumar, MD, MBA, 9500 Euclid Ave, Cleveland, OH, USA (email: premramkumar@ 123456gmail.com ) (Twitter: @prem_ramkumar).
                Article
                10.1177_2325967120953404
                10.1177/2325967120953404
                7522848
                33029545
                a3d2c923-8982-4fbf-a66b-6a1c5e973d7c
                © The Author(s) 2020

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License ( https://creativecommons.org/licenses/by-nc-nd/4.0/) which permits non-commercial use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 9 April 2020
                : 22 April 2020
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                nhl,machine learning,regression,injury prediction
                nhl, machine learning, regression, injury prediction

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