{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T23:05:14Z","timestamp":1772751914099,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,12,16]],"date-time":"2023-12-16T00:00:00Z","timestamp":1702684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Greece and the European Union (European Social Fund-SF)","award":["MIS 5154651"],"award-info":[{"award-number":["MIS 5154651"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Injuries are an unfortunate part of professional sports. This study aims to explore the multi-dimensional impact of injuries in professional basketball, focusing on player performance, team dynamics, and economic outcomes. Employing advanced machine learning and text mining techniques on suitably preprocessed NBA data, we examined the intricate interplay between injury and performance metrics. Our findings reveal that specific anatomical sub-areas, notably knees, ankles, and thighs, are crucial for athletic performance and injury prevention. The analysis revealed the significant economic burden that certain injuries impose on teams, necessitating comprehensive long-term strategies for injury management. The results provide valuable insights into the distribution of injuries and their varied effects, which are essential for developing effective prevention and economic strategies in basketball. By illuminating how injuries influence performance and recovery dynamics, this research offers comprehensive insights that are beneficial for NBA teams, healthcare professionals, medical staff, and trainers, paving the way for enhanced player care and optimized performance strategies.<\/jats:p>","DOI":"10.3390\/computers12120261","type":"journal-article","created":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T05:41:35Z","timestamp":1702878095000},"page":"261","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Sports Analytics and Text Mining NBA Data to Assess Recovery from Injuries and Their Economic Impact"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8757-8969","authenticated-orcid":false,"given":"Vangelis","family":"Sarlis","sequence":"first","affiliation":[{"name":"School of Science and Technology, International Hellenic University, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9361-8621","authenticated-orcid":false,"given":"George","family":"Papageorgiou","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":[[2023,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101562","DOI":"10.1016\/j.is.2020.101562","article-title":"Sports analytics\u2014Evaluation of basketball players and team performance","volume":"93","author":"Sarlis","year":"2020","journal-title":"Inf. Syst."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","unstructured":"Malamatinos, M.-C., Vrochidou, E., and Papakostas, G.A. (2022). On Predicting Soccer Outcomes in the Greek League Using Machine Learning. Computers, 11.","DOI":"10.3390\/computers11090133"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cortez, A., Trigo, A., and Loureiro, N. (2022). Football Match Line-Up Prediction Based on Physiological Variables: A Machine Learning Approach. Computers, 11.","DOI":"10.3390\/computers11030040"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"pgac176","DOI":"10.1093\/pnasnexus\/pgac176","article-title":"Return to performance following severe ankle, knee, and hip injuries in National Basketball Association players","volume":"1","author":"Bullock","year":"2022","journal-title":"PNAS Nexus"},{"key":"ref_6","unstructured":"Cole, B., Arundale, A.J.H., Bytomski, J., and Amendola, A. (2020). Basketball Sports Medicine and Science, Springer."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1177\/1747954120911308","article-title":"Clustering performance in the European Basketball according to players\u2019 characteristics and contextual variables","volume":"15","author":"Mateus","year":"2020","journal-title":"Int. J. Sport. Sci. Coach."},{"key":"ref_8","first-page":"34","article-title":"Risk factors for noncontact anterior cruciate ligament injury in female high school basketball and handball players: A prospective 3-year cohort study","volume":"22","author":"Nakase","year":"2020","journal-title":"Asia-Pacific J. Sport. Med. Arthrosc. Rehabil. Technol."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Kaplan, S. (2020). The Economic Value of Popularity: Evidence from Superstars in the National Basketball Association. SSRN Electron. J., 50.","DOI":"10.2139\/ssrn.3543686"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"276","DOI":"10.2106\/JBJS.17.01601","article-title":"Analytics in sports medicine: Implications and responsibilities that accompany the era of big data","volume":"101","author":"Sikka","year":"2019","journal-title":"J. Bone Jt. Surg. Am."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5787","DOI":"10.1063\/1.1829162","article-title":"Methods, systems and software programs for enhanced sports analytics and applications","volume":"85","author":"Marks","year":"2004","journal-title":"Appl. Phys. Lett."},{"key":"ref_14","unstructured":"McKeag, D.B. (2020). Handbook of Sports Medicine and Science, CRC Press."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1177\/0363546506293899","article-title":"Mechanisms of anterior cruciate ligament injury in basketball: Video analysis of 39 cases","volume":"35","author":"Krosshaug","year":"2007","journal-title":"Am. J. Sports Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"54","DOI":"10.12775\/JEHS.2021.11.07.005","article-title":"Application of Artificial Intelligence in Basketball Sport","volume":"11","author":"Li","year":"2021","journal-title":"J. Educ. Health Sport"},{"key":"ref_17","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. Its Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Torres-Ronda, L., G\u00e1mez, I., Robertson, S., and Fern\u00e1ndez, J. (2022). Epidemiology and injury trends in the National Basketball Association: Pre- and perCOVID-19 (2017\u20132021). PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0263354"},{"key":"ref_19","first-page":"1","article-title":"Economic and Performance Impact of Anterior Cruciate Ligament Injury in National Basketball Association Players","volume":"9","author":"NVaudreuil","year":"2021","journal-title":"Orthop. J. Sports Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s11587-007-0017-2","article-title":"Sports Performance Measurement and Analytics: The Science of Assessing Performance, Predicting Future Outcomes, Interpreting Statistical Models, and Evaluating the Market Value of Athletes. Pearson Education LTD","volume":"56","author":"Franco","year":"2007","journal-title":"Ric. di Mat."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Nagarajan, R., and Li, L. (2017, January 6\u201310). Optimizing NBA player selection strategies based on salary and statistics analysis. Proceedings of the 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC\/PiCom\/DataCom\/CyberSciTech), Orlando, FL, USA.","DOI":"10.1109\/DASC-PICom-DataCom-CyberSciTec.2017.175"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Louw, Q., Grimmer, K., and Vaughan, C. (2006). Knee movement patterns of injured and uninjured adolescent basketball players when landing from a jump: A case-control study. BMC Musculoskelet. Disord., 7.","DOI":"10.1186\/1471-2474-7-22"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.ptsp.2019.07.004","article-title":"Effects of augmented feedback on training jump landing tasks for ACL injury prevention: A systematic review and meta-analysis","volume":"39","author":"Neilson","year":"2019","journal-title":"Phys. Ther. Sport"},{"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","unstructured":"Aljunid, M.F., and Manjaiah, D.H. (2019). Data Management, Analytics and Innovation, Springer."},{"key":"ref_26","unstructured":"McClusky, M. (2014). Faster, Higher, Stronger: How Sports Science Is Creating a New Generation of Superathletes and What We Can Learn from Them, Cambridge University Press. Available online: https:\/\/www.cambridge.org\/core\/product\/identifier\/CBO9781107415324A009\/type\/book_part."},{"key":"ref_27","unstructured":"WNBA Basketball Reference (2023, August 20). NBA Basketball Reference. Available online: https:\/\/www.basketball-reference.com\/."},{"key":"ref_28","unstructured":"ESPN Enterprises Inc. Website\u2014NBA Stats (2023, September 01). ESPN NBA Stats. Available online: https:\/\/www.espn.com\/nba\/stats."},{"key":"ref_29","unstructured":"(2023, September 01). NBA.com Website. NBA.com. Available online: https:\/\/stats.nba.com."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.ipm.2012.06.001","article-title":"Evaluating the performance of demographic targeting using gender in sponsored search","volume":"49","author":"Jansen","year":"2013","journal-title":"Inf. Process. Manag."},{"key":"ref_31","unstructured":"Oliver, D. (2004). Basketball on Paper: Rules and Tools for Performance Analysis, University of Nebraska Press."},{"key":"ref_32","first-page":"0123456789","article-title":"Editorial special issue: Statistics in sports","volume":"107","author":"Groll","year":"2022","journal-title":"AStA Adv. Stat. Anal."},{"key":"ref_33","unstructured":"(2023, September 01). Investopedia. U.S. Inflation Rate by Year: 1929\u20132023. Available online: https:\/\/www.investopedia.com\/inflation-rate-by-year-7253832."},{"key":"ref_34","unstructured":"CoinNews Media Group Company (2023, September 01). US Inflation Calculator. Available online: https:\/\/www.usinflationcalculator.com\/inflation\/current-inflation-rates\/."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1249\/MSS.0b013e31816c4807","article-title":"Functional data analysis of running kinematics in Chronic Achilles tendon injury","volume":"40","author":"Donoghue","year":"2008","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1123\/ijspp.2018-0169","article-title":"Developing athlete monitoring systems in team sports: Data analysis and visualization","volume":"14","author":"Thornton","year":"2019","journal-title":"Int. J. Sports Physiol. Perform."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1177\/1747954118772485","article-title":"Crunch time in the NBA\u2014The effectiveness of different play types in the endgame of close matches in professional basketball","volume":"13","author":"Christmann","year":"2018","journal-title":"Int. J. Sports Sci. Coach."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/12\/12\/261\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:40:10Z","timestamp":1760132410000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/12\/12\/261"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,16]]},"references-count":37,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["computers12120261"],"URL":"https:\/\/doi.org\/10.3390\/computers12120261","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,16]]}}}