{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:31:06Z","timestamp":1772166666623,"version":"3.50.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"DOI":"10.1186\/s40537-025-01216-4","type":"journal-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T07:29:26Z","timestamp":1751959766000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting tennis match outcomes mid-game using machine learning on psychological and physical data"],"prefix":"10.1186","volume":"12","author":[{"given":"Boyuan","family":"Li","sequence":"first","affiliation":[]},{"given":"Zihui","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Gaurav","family":"Gupta","sequence":"additional","affiliation":[]},{"given":"Jinger","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yixuan","family":"Miao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,8]]},"reference":[{"key":"1216_CR1","unstructured":"Springer, S. Analytics in tennis has been an evolution, not a revolution. The New York Times; 2022. Available online: www.nytimes.com\/2022\/08\/27\/sports\/tennis\/us-open-analytics-data.html."},{"issue":"10","key":"1216_CR2","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1080\/026404102320675611","volume":"20","author":"D Liebermann","year":"2002","unstructured":"Liebermann D, Katz L, Hughes M, Bartlett R, McClements J, Franks I. Advances in the application of information technology to sport performance. J Sports Sci. 2002;20(10):755\u201369. https:\/\/doi.org\/10.1080\/026404102320675611.","journal-title":"J Sports Sci"},{"key":"1216_CR3","doi-asserted-by":"publisher","DOI":"10.30827\/digibug.80900","author":"H Takahashi","year":"2022","unstructured":"Takahashi H. Performance analysis in tennis since 2000: a systematic review focused on the methods of data collection. Int J Racket Sports Sci. 2022. https:\/\/doi.org\/10.30827\/digibug.80900.","journal-title":"Int J Racket Sports Sci"},{"issue":"3","key":"1216_CR4","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.smr.2014.10.003","volume":"18","author":"J Brouwers","year":"2015","unstructured":"Brouwers J, Sotiriadou P, De Bosscher V. Sport-specific policies and factors that influence international success: the case of tennis. Sport Manag Rev. 2015;18(3):343\u201358. https:\/\/doi.org\/10.1016\/j.smr.2014.10.003.","journal-title":"Sport Manag Rev"},{"issue":"3","key":"1216_CR5","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1080\/24748668.2008.11868449","volume":"8","author":"G O\u2019Donoghue","year":"2008","unstructured":"O\u2019Donoghue G, Brown E. The importance of service in grand slam singles tennis. Int J Perform Anal Sport. 2008;8(3):70\u20138. https:\/\/doi.org\/10.1080\/24748668.2008.11868449.","journal-title":"Int J Perform Anal Sport"},{"issue":"8","key":"1216_CR6","doi-asserted-by":"publisher","first-page":"1159","DOI":"10.1123\/ijspp.2022-0091","volume":"17","author":"J Colomar","year":"2022","unstructured":"Colomar J, Corbi F, Brich Q, Baiget E. Determinant physical factors of tennis serve velocity: a brief review. Int J Sports Physiol Perform. 2022;17(8):1159\u201369. https:\/\/doi.org\/10.1123\/ijspp.2022-0091.","journal-title":"Int J Sports Physiol Perform"},{"issue":"4","key":"1216_CR7","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1080\/02640414.2016.1165858","volume":"35","author":"T Pereira","year":"2016","unstructured":"Pereira T, Nakamura F, De Jesus M, et al. Analysis of the distances covered and technical actions performed by professional tennis players during official matches. J Sports Sci. 2016;35(4):361\u20138. https:\/\/doi.org\/10.1080\/02640414.2016.1165858.","journal-title":"J Sports Sci"},{"issue":"5","key":"1216_CR8","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1002\/sam.11316","volume":"9","author":"S Mecheri","year":"2016","unstructured":"Mecheri S, Rioult F, Mantel B, Kauffmann F, Benguigui N. The serve impact in tennis: first large-scale study of big hawk-eye data. Stat Anal Data Min. 2016;9(5):310\u201325. https:\/\/doi.org\/10.1002\/sam.11316.","journal-title":"Stat Anal Data Min"},{"issue":"18","key":"1216_CR9","doi-asserted-by":"publisher","first-page":"2096","DOI":"10.1080\/02640414.2018.1438094","volume":"36","author":"S Kovalchik","year":"2018","unstructured":"Kovalchik S, Reid M. A shot taxonomy in the era of tracking data in professional tennis. J Sports Sci. 2018;36(18):2096\u2013104. https:\/\/doi.org\/10.1080\/02640414.2018.1438094.","journal-title":"J Sports Sci"},{"issue":"1","key":"1216_CR10","doi-asserted-by":"publisher","first-page":"49","DOI":"10.6063\/motricidade.16370","volume":"15","author":"Y Cui","year":"2019","unstructured":"Cui Y, Liu H, Liu H, Gomez M. Data-driven analysis of point-by-point performance for male tennis player in grand slams. Motricidade. 2019;15(1):49\u201361. https:\/\/doi.org\/10.6063\/motricidade.16370.","journal-title":"Motricidade"},{"key":"1216_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.joep.2020.102269","volume":"78","author":"P Meier","year":"2020","unstructured":"Meier P, Flepp R, Ruedisser M, Franck E. Separating psychological momentum from strategic momentum: evidence from men\u2019s professional tennis. J Econ Psychol. 2020;78: 102269. https:\/\/doi.org\/10.1016\/j.joep.2020.102269.","journal-title":"J Econ Psychol"},{"key":"1216_CR12","doi-asserted-by":"publisher","unstructured":"Jackson D, Mosurski K. Heavy defeats in tennis: Psychological momentum or random effect? In: Society for Industrial and Applied Mathematics eBooks, 2005:303\u2013310. https:\/\/doi.org\/10.1137\/1.9780898718386.ch42.","DOI":"10.1137\/1.9780898718386.ch42"},{"issue":"5","key":"1216_CR13","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1177\/15270025221085715","volume":"23","author":"CA Depken","year":"2022","unstructured":"Depken CA, et al. Set-level strategic and psychological momentum in best-of-three-set professional tennis matches. J Sports Econ. 2022;23(5):598\u2013623. https:\/\/doi.org\/10.1177\/15270025221085715.","journal-title":"J Sports Econ"},{"key":"1216_CR14","unstructured":"Nesseler C, Dietl H. Momentum in Tennis: Controlling the Match. ideas.repec.org. Available online: ideas.repec.org\/p\/zrh\/wpaper\/365.html 2017."},{"issue":"1","key":"1216_CR15","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1123\/jsep.10.1.92","volume":"10","author":"R Vallerand","year":"1988","unstructured":"Vallerand R, Colavecchio P, Pelletier L. Psychological momentum and performance inferences: a preliminary test of the antecedents-consequences psychological momentum model. J Sport Exerc Psychol. 1988;10(1):92\u2013108. https:\/\/doi.org\/10.1123\/jsep.10.1.92.","journal-title":"J Sport Exerc Psychol"},{"key":"1216_CR16","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3438123","author":"P Meier","year":"2019","unstructured":"Meier P, Flepp R, R\u00fcdisser M, Franck E. Investigating the conditions for psychological momentum in the field: evidence from men\u2019s professional tennis. Soc Sci Res Netw. 2019. https:\/\/doi.org\/10.2139\/ssrn.3438123.","journal-title":"Soc Sci Res Netw"},{"issue":"4","key":"1216_CR17","doi-asserted-by":"publisher","first-page":"1329","DOI":"10.1016\/j.ijforecast.2020.01.006","volume":"36","author":"S Kovalchik","year":"2020","unstructured":"Kovalchik S. Extension of the elo rating system to margin of victory. Int J Forecast. 2020;36(4):1329\u201341. https:\/\/doi.org\/10.1016\/j.ijforecast.2020.01.006.","journal-title":"Int J Forecast"},{"key":"1216_CR18","unstructured":"Lerner S, ICME, Badri D, Monogue K. Electrical\u00a0Engineering, S.U.: DeepTennis: Mid-Match Tennis Predictions. https:\/\/cs230.stanford.edu\/projects_fall_2019\/reports\/26249098.pdf 2019."},{"issue":"7","key":"1216_CR19","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1080\/02640414.2016.1183805","volume":"35","author":"D Whiteside","year":"2016","unstructured":"Whiteside D, Reid M. Spatial characteristics of professional tennis serves with implications for serving aces: a machine learning approach. J Sports Sci. 2016;35(7):648\u201354. https:\/\/doi.org\/10.1080\/02640414.2016.1183805.","journal-title":"J Sports Sci"},{"issue":"9","key":"1216_CR20","doi-asserted-by":"publisher","first-page":"1212","DOI":"10.1123\/ijspp.2016-0683","volume":"12","author":"D Whiteside","year":"2017","unstructured":"Whiteside D, Cant O, Connolly M, Reid M. Monitoring hitting load in tennis using inertial sensors and machine learning. Int J Sports Physiol Perform. 2017;12(9):1212\u20137. https:\/\/doi.org\/10.1123\/ijspp.2016-0683.","journal-title":"Int J Sports Physiol Perform"},{"issue":"11","key":"1216_CR21","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.1080\/02640414.2013.775472","volume":"31","author":"M Shang","year":"2013","unstructured":"Shang M, Liu C, Tan Y, Chun S. Winning matches in grand slam men\u2019s singles: an analysis of player performance-related variables from 1991 to 2008. J Sports Sci. 2013;31(11):1147\u201355. https:\/\/doi.org\/10.1080\/02640414.2013.775472.","journal-title":"J Sports Sci"},{"issue":"3","key":"1216_CR22","first-page":"86","volume":"2","author":"M De Araujo Fernandes","year":"2017","unstructured":"De Araujo Fernandes M. Using soft computing techniques for prediction of winners in tennis matches. Mach Learn Res. 2017;2(3):86\u201398.","journal-title":"Mach Learn Res"},{"issue":"6","key":"1216_CR23","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1111\/j.1475-3995.2000.tb00218.x","volume":"7","author":"S Clarke","year":"2000","unstructured":"Clarke S, Dyte D. Using official ratings to simulate major tennis tournaments. Int Trans Oper Res. 2000;7(6):585\u201394. https:\/\/doi.org\/10.1111\/j.1475-3995.2000.tb00218.x.","journal-title":"Int Trans Oper Res"},{"key":"1216_CR24","doi-asserted-by":"publisher","unstructured":"Gao Z, Kowalczyk A. Random Forest Model Identifies Serve Strength as a Key Predictor of Tennis Match Outcome. ArXiv.org. Accessed April 11, 2024, 2019. https:\/\/doi.org\/10.48550\/arXiv.1910.03203.","DOI":"10.48550\/arXiv.1910.03203"},{"issue":"4","key":"1216_CR25","doi-asserted-by":"publisher","first-page":"0266838","DOI":"10.1371\/journal.pone.0266838","volume":"17","author":"J Yue","year":"2022","unstructured":"Yue J, Chou E, Hsieh M, Hsiao L. A study of forecasting tennis matches via the glicko model. PLoS ONE. 2022;17(4):0266838. https:\/\/doi.org\/10.1371\/journal.pone.0266838.","journal-title":"PLoS ONE"},{"key":"1216_CR26","doi-asserted-by":"publisher","unstructured":"Kr\u00e4mer O. K-Nearest neighbors, pp. 13\u201323 (2013). https:\/\/doi.org\/10.1007\/978-3-642-38652-7_2","DOI":"10.1007\/978-3-642-38652-7_2"},{"issue":"1","key":"1216_CR27","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1186\/s40537-024-00892-y","volume":"11","author":"J Li","year":"2024","unstructured":"Li J, Othman M, Chen H, Yusuf L. Optimizing iot intrusion detection system: feature selection versus feature extraction in machine learning. J Big Data. 2024;11(1):36. https:\/\/doi.org\/10.1186\/s40537-024-00892-y.","journal-title":"J Big Data"},{"key":"1216_CR28","unstructured":"Xing Y, Song Q, Cheng G. Benefit of interpolation in nearest neighbor algorithms. arxiv:https:\/\/arxiv.org\/abs\/1909.11720 2019."},{"issue":"8","key":"1216_CR29","doi-asserted-by":"publisher","first-page":"1539","DOI":"10.1049\/iet-ipr.2019.1591","volume":"14","author":"J Zheng","year":"2020","unstructured":"Zheng J, Song W, Wu Y, Liu F. Image interpolation with adaptive k-nearest neighbours search and random non-linear regression. IET Image Proc. 2020;14(8):1539\u201348. https:\/\/doi.org\/10.1049\/iet-ipr.2019.1591.","journal-title":"IET Image Proc"},{"issue":"1","key":"1216_CR30","doi-asserted-by":"publisher","first-page":"91","DOI":"10.2307\/27757770","volume":"27","author":"H Kragh","year":"1996","unstructured":"Kragh H, Weininger SJ. Sooner silence than confusion: the tortuous entry of entropy into chemistry. Hist Stud Phys Biol Sci. 1996;27(1):91\u2013130.","journal-title":"Hist Stud Phys Biol Sci"},{"issue":"3","key":"1216_CR31","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","volume":"27","author":"CE Shannon","year":"1948","unstructured":"Shannon CE. A mathematical theory of communication. Bell Syst Tech J. 1948;27(3):379\u2013423.","journal-title":"Bell Syst Tech J"},{"issue":"4","key":"1216_CR32","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/j.trecan.2020.12.013","volume":"7","author":"A Karolak","year":"2021","unstructured":"Karolak A, Branciamore S, McCune JS, Lee PP, Rodin AS, Rockne RC. Concepts and applications of information theory to immuno-oncology. Trends in Cancer. 2021;7(4):335\u201346. https:\/\/doi.org\/10.1016\/j.trecan.2020.12.013.","journal-title":"Trends in Cancer"},{"key":"1216_CR33","unstructured":"R\u00e9nyi A. On measures of entropy and information. In: Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics, vol. 4, pp. 547\u2013562. University of California Press; 1961."},{"key":"1216_CR34","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.dsp.2015.08.001","volume":"46","author":"F Benedetto","year":"2015","unstructured":"Benedetto F, Giunta G, Mastroeni L. A maximum entropy method to assess the predictability of financial and commodity prices. Digit Signal Process. 2015;46:19\u201331.","journal-title":"Digit Signal Process"},{"key":"1216_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.neubiorev.2023.105070","volume":"146","author":"ED Fagerholm","year":"2023","unstructured":"Fagerholm ED, Dezhina Z, Moran RJ, Turkheimer FE, Leech R. A primer on entropy in neuroscience. Neurosci Biobehav Rev. 2023;146: 105070.","journal-title":"Neurosci Biobehav Rev"},{"key":"1216_CR36","first-page":"36","volume":"3","author":"M Wibral","year":"2014","unstructured":"Wibral M, Vicente R, Lindner M. Transfer entropy in neuroscience. Direct Inf Meas Neurosci. 2014;3:36.","journal-title":"Direct Inf Meas Neurosci"},{"issue":"1","key":"1216_CR37","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1186\/s12942-023-00349-0","volume":"22","author":"L Fox","year":"2023","unstructured":"Fox L, Peter BG, Frake AN, Messina JP. A bayesian maximum entropy model for predicting tsetse ecological distributions. Int J Health Geogr. 2023;22(1):31.","journal-title":"Int J Health Geogr"},{"issue":"3","key":"1216_CR38","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1016\/j.jcrc.2007.08.001","volume":"23","author":"PR Norris","year":"2008","unstructured":"Norris PR, Stein PK, Morris JA Jr. Reduced heart rate multiscale entropy predicts death in critical illness: a study of physiologic complexity in 285 trauma patients. J Crit Care. 2008;23(3):399\u2013405.","journal-title":"J Crit Care"},{"issue":"6","key":"1216_CR39","doi-asserted-by":"publisher","first-page":"2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","volume":"278","author":"JS Richman","year":"2000","unstructured":"Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol-Heart Circ Physiol. 2000;278(6):2039\u201349.","journal-title":"Am J Physiol-Heart Circ Physiol"},{"issue":"6","key":"1216_CR40","doi-asserted-by":"publisher","first-page":"168781401985735","DOI":"10.1177\/1687814019857350","volume":"11","author":"A Namdari","year":"2019","unstructured":"Namdari A, Li Z. A review of entropy measures for uncertainty quantification of stochastic processes. Adv Mech Eng. 2019;11(6):168781401985735. https:\/\/doi.org\/10.1177\/1687814019857350.","journal-title":"Adv Mech Eng"},{"key":"1216_CR41","unstructured":"Entropy-based Grey Correlation Fault Diagnosis Prediction model. IEEE Conference Publication | IEEE Xplore. Available online: https:\/\/ieeexplore.ieee.org\/abstract\/document\/6305731 2012."},{"issue":"1","key":"1216_CR42","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1186\/s40537-024-00891-z","volume":"11","author":"K Shi","year":"2024","unstructured":"Shi K, Chen Z, Sun W, Hu W. Measuring regularity of human physical activities with entropy models. J Big Data. 2024;11(1):37. https:\/\/doi.org\/10.1186\/s40537-024-00891-z.","journal-title":"J Big Data"},{"issue":"1","key":"1216_CR43","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1186\/s40537-021-00472-4","volume":"8","author":"M Prasetiyowati","year":"2021","unstructured":"Prasetiyowati M, Maulidevi N, Surendro K. Determining threshold value on information gain feature selection to increase speed and prediction accuracy of random forest. J Big Data. 2021;8(1):84. https:\/\/doi.org\/10.1186\/s40537-021-00472-4.","journal-title":"J Big Data"},{"issue":"7","key":"1216_CR44","doi-asserted-by":"publisher","first-page":"849","DOI":"10.3390\/e24070849","volume":"24","author":"W Qu","year":"2022","unstructured":"Qu W, Li J, Song W, et al. Entropy-weight-method-based integrated models for short-term intersection traffic flow prediction. Entropy. 2022;24(7):849. https:\/\/doi.org\/10.3390\/e24070849.","journal-title":"Entropy"},{"key":"1216_CR45","unstructured":"Sackmann J. Tennis Slam Point-by-Point Data. GitHub. Accessed 1 May 2024, https:\/\/github.com\/JeffSackmann\/tennis_slam_pointbypoint 2023."},{"issue":"1","key":"1216_CR46","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1186\/s40537-023-00720-9","volume":"10","author":"I Taskin","year":"2023","unstructured":"Taskin I, Kasirga Z, Aladag CH. An enhanced random forest approach using coclust clustering: Mimic-iii and SMS spam collection application. J Big Data. 2023;10(1):38. https:\/\/doi.org\/10.1186\/s40537-023-00720-9.","journal-title":"J Big Data"},{"issue":"1","key":"1216_CR47","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1186\/s40537-024-00886-w","volume":"11","author":"M Talukder","year":"2024","unstructured":"Talukder M, Islam M, Uddin M, et al. Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction. J Big Data. 2024;11(1):33. https:\/\/doi.org\/10.1186\/s40537-024-00886-w.","journal-title":"J Big Data"},{"key":"1216_CR48","doi-asserted-by":"publisher","first-page":"216","DOI":"10.3390\/f12020216","volume":"2","author":"M Luo","year":"2021","unstructured":"Luo M, et al. Combination of feature selection and catboost for prediction: The first application to the estimation of aboveground biomass. Forests. 2021;2:216\u2013216. https:\/\/doi.org\/10.3390\/f12020216.","journal-title":"Forests"},{"issue":"11","key":"1216_CR49","doi-asserted-by":"publisher","first-page":"738","DOI":"10.14569\/ijacsa.2020.0111190","volume":"11","author":"AA Ibrahim","year":"2020","unstructured":"Ibrahim AA, et al. Comparison of the catboost classifier with other machine learning methods. Int J Adv Comput Sci Appl. 2020;11(11):738\u201348. https:\/\/doi.org\/10.14569\/ijacsa.2020.0111190.","journal-title":"Int J Adv Comput Sci Appl"},{"issue":"1","key":"1216_CR50","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1186\/s40537-020-00299-5","volume":"7","author":"I Nti","year":"2020","unstructured":"Nti I, Adekoya A, Weyori B. A comprehensive evaluation of ensemble learning for stock-market prediction. J Big Data. 2020;7(1):20. https:\/\/doi.org\/10.1186\/s40537-020-00299-5.","journal-title":"J Big Data"},{"issue":"6","key":"1216_CR51","doi-asserted-by":"publisher","first-page":"0249338","DOI":"10.1371\/journal.pone.0249338","volume":"16","author":"S Sherazi","year":"2021","unstructured":"Sherazi S, Bae J, Lee J. A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for stemi and nstemi during 2-year follow-up in patients with acute coronary syndrome. PLoS ONE. 2021;16(6):0249338. https:\/\/doi.org\/10.1371\/journal.pone.0249338.","journal-title":"PLoS ONE"},{"key":"1216_CR52","unstructured":"Scikit-learn: Comparison of calibration of classifiers. Scikit-learn website. Available online: https:\/\/scikit-learn.org\/stable\/auto_examples\/calibration\/plot_compare_calibration.html 2023."},{"issue":"1","key":"1216_CR53","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1186\/s40537-023-00814-4","volume":"10","author":"K Ren","year":"2023","unstructured":"Ren K, Zeng Y, Zhong Y, Sheng B, Zhang Y. Mafsids: a reinforcement learning-based intrusion detection model for multi-agent feature selection networks. J Big Data. 2023;10(1):137. https:\/\/doi.org\/10.1186\/s40537-023-00814-4.","journal-title":"J Big Data"},{"issue":"1","key":"1216_CR54","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1186\/s40537-024-00882-0","volume":"11","author":"I Emmanuel","year":"2024","unstructured":"Emmanuel I, Sun Y, Wang Z. A machine learning-based credit risk prediction engine system using a stacked classifier and a filter-based feature selection method. J Big Data. 2024;11(1):23. https:\/\/doi.org\/10.1186\/s40537-024-00882-0.","journal-title":"J Big Data"},{"key":"1216_CR55","unstructured":"Somboonphokkaphan A, Phimoltares S, Lursinsap C. Tennis winner prediction based on time-series history with neural modeling. In: IMECS 2009: International Multi-Conference of Engineers and Computer Scientists, pp. 127\u2013132 2009."},{"key":"1216_CR56","unstructured":"Sipko M. Machine Learning for the Prediction of Professional Tennis Matches. Imperial College London, Stratagem. Available online: https:\/\/www.doc.ic.ac.uk\/teaching\/distinguished-projects\/2015\/m.sipko.pdf 2015."},{"key":"1216_CR57","unstructured":"Gollub J. Producing Win Probabilities for Professional Tennis Matches from any Score. Available online: https:\/\/dash.harvard.edu\/bitstream\/handle\/1\/41024787\/my_thesis_11_6_final.pdf?sequence=5 2017."},{"issue":"3","key":"1216_CR58","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1515\/jqas-2015-0059","volume":"12","author":"S Kovalchik","year":"2016","unstructured":"Kovalchik S. Searching for the goat of tennis win prediction. J Quant Anal Sports. 2016;12(3):127\u201338. https:\/\/doi.org\/10.1515\/jqas-2015-0059.","journal-title":"J Quant Anal Sports"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01216-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-025-01216-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01216-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T09:21:01Z","timestamp":1752139261000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-025-01216-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,8]]},"references-count":58,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1216"],"URL":"https:\/\/doi.org\/10.1186\/s40537-025-01216-4","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4597222\/v1","asserted-by":"object"}]},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,8]]},"assertion":[{"value":"18 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no Competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"159"}}