{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T23:06:25Z","timestamp":1772751985849,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,20]],"date-time":"2024-01-20T00:00:00Z","timestamp":1705708800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>This study utilized advanced data mining and machine learning to examine player injuries in the National Basketball Association (NBA) from 2000\u201301 to 2022\u201323. By analyzing a dataset of 2296 players, including sociodemographics, injury records, and financial data, this research investigated the relationships between injury types and player recovery durations, and their socioeconomic impacts. Our methodology involved data collection, engineering, and mining; the application of techniques such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), isolation forest, and the Z score for anomaly detection; and the application of the Apriori algorithm for association rule mining. Anomaly detection revealed 189 anomalies (1.04% of cases), highlighting unusual recovery durations and factors influencing recovery beyond physical healing. Association rule mining indicated shorter recovery times for lower extremity injuries and a 95% confidence level for quick returns from \u201cRest\u201d injuries, affirming the NBA\u2019s treatment and rest policies. Additionally, economic factors were observed, with players in lower salary brackets experiencing shorter recoveries, pointing to a financial influence on recovery decisions. This study offers critical insights into sports injuries and recovery, providing valuable information for sports professionals and league administrators. This study will impact player health management and team tactics, laying the groundwork for future research on long-term injury effects and technology integration in player health monitoring.<\/jats:p>","DOI":"10.3390\/info15010061","type":"journal-article","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T11:36:41Z","timestamp":1705923401000},"page":"61","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Unsupervised Learning in NBA Injury Recovery: Advanced Data Mining to Decode Recovery Durations and Economic Impacts"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9361-8621","authenticated-orcid":false,"given":"George","family":"Papageorgiou","sequence":"first","affiliation":[{"name":"School of Science and Technology, International Hellenic University, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8757-8969","authenticated-orcid":false,"given":"Vangelis","family":"Sarlis","sequence":"additional","affiliation":[{"name":"School of Science and Technology, International Hellenic University, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8263-9024","authenticated-orcid":false,"given":"Christos","family":"Tjortjis","sequence":"additional","affiliation":[{"name":"School of Science and Technology, International Hellenic University, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Brefeld, U., Davis, J., Van Haaren, J., and Zimmermann, A. (2022). Machine Learning and Data Mining for Sports Analytics, Springer.","DOI":"10.1007\/978-3-031-02044-5"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Rossi, A., Perri, E., Pappalardo, L., Cintia, P., and Iaia, F. (2019). Relationship between External and Internal Workloads in Elite Soccer Players: Comparison between Rate of Perceived Exertion and Training Load. Appl. Sci., 9.","DOI":"10.3390\/app9235174"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mehrotra, K.G., Mohan, C.K., and Huang, H. (2017). Anomaly Detection Principles and Algorithms, Springer.","DOI":"10.1007\/978-3-319-67526-8"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"755","DOI":"10.32604\/iasc.2020.010110","article-title":"Design of the Sports Training Decision Support System Based on Improved Association Rule, the Apriori Algorithm","volume":"26","author":"Wang","year":"2020","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.jsams.2012.05.008","article-title":"Association between post-game recovery protocols, physical and perceived recovery, and performance in elite Australian Football League players","volume":"16","author":"Bahnert","year":"2013","journal-title":"J. Sci. Med. Sport"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1093\/bmb\/ldq026","article-title":"Sport injuries: A review of outcomes","volume":"97","author":"Maffulli","year":"2011","journal-title":"Br. Med. Bull."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1080\/00913847.2017.1325313","article-title":"Return to play and performance after anterior cruciate ligament reconstruction in the National Basketball Association: Surgeon case series and literature review","volume":"45","author":"Nwachukwu","year":"2017","journal-title":"Physician Sportsmed."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1002\/jor.25064","article-title":"Balance, reframe, and overcome: The attitudes, priorities, and perceptions of exercise-based activities in youth 12\u201324 months after a sport-related ACL injury","volume":"40","author":"Truong","year":"2022","journal-title":"J. Orthop. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1002\/jor.23414","article-title":"Mechanisms, prediction, and prevention of ACL injuries: Cut risk with three sharpened and validated tools","volume":"34","author":"Hewett","year":"2016","journal-title":"J. Orthop. Res."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Laver, L., Kocaoglu, B., Cole, B., Arundale, A.J.H., Bytomski, J., and Amendola, A. (2020). Basketball Sports Medicine and Science, Springer.","DOI":"10.1007\/978-3-662-61070-1"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1177\/0363546506293899","article-title":"Mechanisms of Anterior Cruciate Ligament Injury in Basketball","volume":"35","author":"Krosshaug","year":"2007","journal-title":"Am. J. Sports Med."},{"key":"ref_12","unstructured":"Kalaian, S.A., and Kasim, R. (2015). Handbook of Research on Organizational Transformations through Big Data Analytics, IGI Global."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1055\/a-1231-5304","article-title":"New Machine Learning Approach for Detection of Injury Risk Factors in Young Team Sport Athletes","volume":"42","author":"Jauhiainen","year":"2021","journal-title":"Int. J. Sports Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1146\/annurev-statistics-040720-015536","article-title":"Modeling Player and Team Performance in Basketball","volume":"8","author":"Terner","year":"2021","journal-title":"Annu. Rev. Stat. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101750","DOI":"10.1016\/j.is.2021.101750","article-title":"A Data Science approach analysing the Impact of Injuries on Basketball Player and Team Performance","volume":"99","author":"Sarlis","year":"2021","journal-title":"Inf. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"277","DOI":"10.3233\/JSA-200529","article-title":"A deep learning approach to injury forecasting in NBA basketball","volume":"7","author":"Cohan","year":"2021","journal-title":"J. Sports Anal."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3031","DOI":"10.1007\/s00167-016-4060-y","article-title":"Athletic performance and career longevity following anterior cruciate ligament reconstruction in the National Basketball Association","volume":"25","author":"Kester","year":"2017","journal-title":"Knee Surg. Sports Traumatol. Arthrosc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1007\/s11420-019-09736-5","article-title":"Impact of Knee Injuries on Post-retirement Pain and Quality of Life: A Cross-Sectional Survey of Professional Basketball Players","volume":"16","author":"Khan","year":"2020","journal-title":"HSS J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1177\/1941738113495788","article-title":"Return-to-Sport and Performance after Anterior Cruciate Ligament Reconstruction in National Basketball Association Players","volume":"5","author":"Harris","year":"2013","journal-title":"Sports Health"},{"key":"ref_20","first-page":"1","article-title":"Sex-specific differences in injury types among basketball players","volume":"6","author":"Iwamoto","year":"2015","journal-title":"Open Access J. Sports Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1177\/1941738115593441","article-title":"Prevention of Lower Extremity Injuries in Basketball: A Systematic Review and Meta-Analysis","volume":"7","author":"Taylor","year":"2015","journal-title":"Sports Health"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1097\/01.CSMR.0000434055.36042.cd","article-title":"Basketball injuries: Caring for a basketball team","volume":"12","author":"Trojian","year":"2013","journal-title":"Curr. Sports Med. Rep."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1177\/0363546515623028","article-title":"The Effect of an Orthopaedic Surgical Procedure in the National Basketball Association","volume":"44","author":"Minhas","year":"2016","journal-title":"Am. J. Sports Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/s12195-020-00612-5","article-title":"Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy","volume":"13","author":"Afara","year":"2020","journal-title":"Cell. Mol. Bioeng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"29","DOI":"10.3810\/psm.2010.06.1780","article-title":"Sports Injuries in Young Athletes: Long-Term Outcome and Prevention Strategies","volume":"38","author":"Maffulli","year":"2010","journal-title":"Phys. Sportsmed."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"232596712211117","DOI":"10.1177\/23259671221111742","article-title":"Machine Learning for Predicting Lower Extremity Muscle Strain in National Basketball Association Athletes","volume":"10","author":"Lu","year":"2022","journal-title":"Orthop. J. Sports Med."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2917","DOI":"10.1177\/03635465221112095","article-title":"Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes","volume":"50","author":"Jauhiainen","year":"2022","journal-title":"Am. J. Sports Med."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sarlis, V., George, P., and Christos, T. (2023). Sports Analytics and Text Mining NBA Data to Assess Recovery from Injuries and Their Economic Impact. Computers, 12.","DOI":"10.3390\/computers12120261"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Rehman, S.U., Asghar, S., Fong, S., and Sarasvady, S. (2014, January 17\u201319). DBSCAN: Past, present and future. Proceedings of the Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014), Bangalore, India.","DOI":"10.1109\/ICADIWT.2014.6814687"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., and Zhou, Z.-H. (2008, January 15\u201319). Isolation Forest. Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy.","DOI":"10.1109\/ICDM.2008.17"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e1236","DOI":"10.1002\/widm.1236","article-title":"Anomaly detection by robust statistics","volume":"8","author":"Rousseeuw","year":"2018","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_32","unstructured":"Borgelt, C., and Kruse, R. (2002). Compstat, Physica-Verlag HD."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, C., and Zhang, S. (2002). Association Rule Mining, Springer.","DOI":"10.1007\/3-540-46027-6"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chom\u0105tek, \u0141., and Sierakowska, K. (2021). Automation of Basketball Match Data Management. Information, 12.","DOI":"10.3390\/info12110461"},{"key":"ref_35","unstructured":"(2023, November 15). swar. nba_api. Available online: https:\/\/github.com\/swar\/nba_api."},{"key":"ref_36","unstructured":"ESPN (2023, November 15). NBA Stats. Available online: https:\/\/www.espn.com\/nba\/stats."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1093\/bib\/bbt026","article-title":"Web scraping technologies in an API world","volume":"15","year":"2014","journal-title":"Brief Bioinform."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ochieng, P.J., London, A., and Kr\u00e9sz, M. (2022). A Forward-Looking Approach to Compare Ranking Methods for Sports. Information, 13.","DOI":"10.3390\/info13050232"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/2.53","article-title":"Fuzzy logic","volume":"21","author":"Zadeh","year":"1988","journal-title":"Computer"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Alexandridis, G., Varlamis, I., Korovesis, K., Caridakis, G., and Tsantilas, P. (2021). A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media. Information, 12.","DOI":"10.3390\/info12080331"},{"key":"ref_41","unstructured":"Li, L., Pratap, A., Lin, H.-T., and Abu-Mostafa, Y.S. (2005). Knowledge Discovery in Databases: PKDD 2005, Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, 3\u20137 October 2005, Springer."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Vatsalan, D., Bhaskar, R., Gkoulalas-Divanis, A., and Karapiperis, D. (2021, January 15\u201318). Privacy Preserving Text Data Encoding and Topic Modelling. Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA.","DOI":"10.1109\/BigData52589.2021.9671552"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1257\/aer.p20161076","article-title":"On the Optimal Inflation Rate","volume":"106","author":"Brunnermeier","year":"2016","journal-title":"Am. Econ. Rev."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ali, T., Asghar, S., and Sajid, N.A. (2010, January 14\u201316). Critical analysis of DBSCAN variations. Proceedings of the 2010 International Conference on Information and Emerging Technologies, Karachi, Pakistan.","DOI":"10.1109\/ICIET.2010.5625720"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.datak.2006.01.013","article-title":"ST-DBSCAN: An algorithm for clustering spatial\u2013temporal data","volume":"60","author":"Birant","year":"2007","journal-title":"Data Knowl. Eng."},{"key":"ref_46","first-page":"1","article-title":"Similarity-Measured Isolation Forest: Anomaly Detection Method for Machine Monitoring Data","volume":"70","author":"Li","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ferragut, E.M., Laska, J., and Bridges, R.A. (2012, January 12\u201315). A New, Principled Approach to Anomaly Detection. Proceedings of the 2012 11th International Conference on Machine Learning and Applications, Boca Raton, FL, USA.","DOI":"10.1109\/ICMLA.2012.151"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"e1307","DOI":"10.1002\/widm.1307","article-title":"A survey on association rules mining using heuristics","volume":"9","author":"Ghafari","year":"2019","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_49","unstructured":"Agrawal, R., and Srikant, R. (1994, January 12\u201315). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, Santiago de Chile, Chile."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Du, J., Zhang, X., Zhang, H., and Chen, L. (2016, January 6\u20138). Research and improvement of Apriori algorithm. Proceedings of the 2016 Sixth International Conference on Information Science and Technology (ICIST), Dalian, China.","DOI":"10.1109\/ICIST.2016.7483396"},{"key":"ref_51","unstructured":"Dasseni, E., Verykios, V.S., Elmagarmid, A.K., and Bertino, E. (2001). Information Hiding, Proceedings of the 4th International Workshop, IH 2001, Pittsburgh, PA, USA, 25\u201327 April 2001, Springer."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Scheffer, T. (2001, January 3\u20135). Finding Association Rules That Trade Support Optimally against Confidence. Proceedings of the European Conference on Principles of Data Mining and Knowledge Discovery, Freiburg, Germany.","DOI":"10.1007\/3-540-44794-6_35"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4712","DOI":"10.1016\/j.csda.2008.03.013","article-title":"Standardising the lift of an association rule","volume":"52","author":"McNicholas","year":"2008","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1023\/A:1008647823331","article-title":"Multi-Terminal Binary Decision Diagrams: An Efficient Data Structure for Matrix Representation","volume":"10","author":"Fujita","year":"1997","journal-title":"Form Methods Syst. Des."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1080\/00913847.2021.1896957","article-title":"The underpinning factors of NBA game-play performance: A systematic review (2001\u20132020)","volume":"50","author":"Huyghe","year":"2022","journal-title":"Phys. Sportsmed."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1177\/03635465211014506","article-title":"Systematic Review of Injuries in the Men\u2019s and Women\u2019s National Basketball Association","volume":"50","author":"Lian","year":"2022","journal-title":"Am. J. Sports Med."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Matthew, B. (2016). Financial Management in the Sport Industry, Routledge.","DOI":"10.4324\/9781315213064"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Mihajlovic, M., Cabarkapa, D., Cabarkapa, D.V., Philipp, N.M., and Fry, A.C. (2023). Recovery Methods in Basketball: A Systematic Review. Sports, 11.","DOI":"10.3390\/sports11110230"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1214\/09-SS057","article-title":"Causal inference in statistics: An overview","volume":"3","author":"Pearl","year":"2009","journal-title":"Stat Surv."},{"key":"ref_60","unstructured":"Yakhchi, S., Ghafari, S.M., Tjortjis, C., and Fazeli, M. (2017). Knowledge Science, Engineering and Management, Proceedings of the 10th International Conference, KSEM 2017, Melbourne, VIC, Australia, 19\u201320 August 2017, Springer."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Ren, B., Wang, Z., Ma, K., Zhou, Y., and Liu, M. (2023). An Improved Method of Heart Rate Extraction Algorithm Based on Photoplethysmography for Sports Bracelet. Information, 14.","DOI":"10.3390\/info14050297"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Xiao, J., Tian, W., and Ding, L. (2022). Basketball Action Recognition Method of Deep Neural Network Based on Dynamic Residual Attention Mechanism. Information, 14.","DOI":"10.3390\/info14010013"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Pint\u00e9r, G., and Felde, I. (2021). Analyzing the Behavior and Financial Status of Soccer Fans from a Mobile Phone Network Perspective: Euro 2016, a Case Study. Information, 12.","DOI":"10.3390\/info12110468"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/1\/61\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:46:20Z","timestamp":1760103980000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/1\/61"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,20]]},"references-count":63,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["info15010061"],"URL":"https:\/\/doi.org\/10.3390\/info15010061","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,20]]}}}