{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T08:36:34Z","timestamp":1768811794508,"version":"3.49.0"},"reference-count":34,"publisher":"Walter de Gruyter GmbH","issue":"2","license":[{"start":{"date-parts":[[2017,11,27]],"date-time":"2017-11-27T00:00:00Z","timestamp":1511740800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,11,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p> Two different computational approaches were used to predict Olympic distance triathlon race time of German male elite triathletes. Anthropometric measurements and two treadmill running tests to collect physiological variables were repeatedly conducted on eleven male elite triathletes between 2008 and 2012. After race time normalization, exploratory factor analysis (EFA), as a mathematical preselection method, followed by multiple linear regression (MLR) and dominance paired comparison (DPC), as a preselection method considering professional expertise, followed by nonlinear artificial neural network (ANN) were conducted to predict overall race time. Both computational approaches yielded two prediction models. MLR provided R\u00b2 = 0.41 in case of anthropometric variables (predictive: pelvis width and shoulder width) and R\u00b2 = 0.67 in case of physiological variables (predictive: maximum respiratory rate, running pace at 3-mmol\u00b7L<jats:sup>-1<\/jats:sup> blood lactate and maximum blood lactate). ANNs using the five most important variables after DPC yielded R\u00b2 = 0.43 in case of anthropometric variables and R\u00b2 = 0.86 in case of physiological variables. The advantage of ANNs over MLRs was the possibility to take non-linear relationships into account. Overall, race time of male elite triathletes could be well predicted without interfering with individual training programs and season calendars.<\/jats:p>","DOI":"10.1515\/ijcss-2017-0009","type":"journal-article","created":{"date-parts":[[2017,12,6]],"date-time":"2017-12-06T22:16:34Z","timestamp":1512598594000},"page":"101-116","source":"Crossref","is-referenced-by-count":6,"title":["Predicting Elite Triathlon Performance: A Comparison of Multiple Regressions and Artificial Neural Networks"],"prefix":"10.1515","volume":"16","author":[{"given":"M.","family":"Hoffmann","sequence":"first","affiliation":[{"name":"BioMotion Center, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe , Germany"}]},{"given":"T.","family":"Moeller","sequence":"additional","affiliation":[{"name":"Institute for Applied Training Science, Leipzig , Germany"}]},{"given":"I.","family":"Seidel","sequence":"additional","affiliation":[{"name":"Center, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe , Germany"},{"name":"Olympic Training Centre Lower Saxony, Melle-Bruchm\u00fchlen , Germany"}]},{"given":"T.","family":"Stein","sequence":"additional","affiliation":[{"name":"BioMotion Center, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe , Germany"}]}],"member":"374","published-online":{"date-parts":[[2017,11,30]]},"reference":[{"key":"2021040703154994310_j_ijcss-2017-0009_ref_001_w2aab3b7b3b1b6b1ab1ab1Aa","unstructured":"Ackland, T. R., Blanksby, B. A., Landers, G., & Smith, D. (1998). Anthropometric profiles of elite triathletes. Journal of Science and Medicine in Sport, 1(1), 52-56. https:\/\/doi.org\/10.1016\/S1440-2440(98)80008-X10.1016\/S1440-2440(98)80008-X"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_002_w2aab3b7b3b1b6b1ab1ab2Aa","doi-asserted-by":"crossref","unstructured":"Anderson, T. (1996). Biomechanics and running economy. Sports Medicine, 22(2), 76-89.10.2165\/00007256-199622020-00003","DOI":"10.2165\/00007256-199622020-00003"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_003_w2aab3b7b3b1b6b1ab1ab3Aa","unstructured":"Barnes, K. R., & Kilding, A. E. (2015). Running economy: measurement, norms, and determining factors. Sports Medicine - Open, 1(1), 357. https:\/\/doi.org\/10.1186\/s40798-015-0007-y10.1186\/s40798-015-0007-y"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_004_w2aab3b7b3b1b6b1ab1ab4Aa","unstructured":"Bassett, D. R. (2000). Limiting factors for maximum oxygen uptake and determinants ofendurance performance. Medicine & Science in Sports & Exercise, 32(1), 70-84. https:\/\/doi.org\/10.1097\/00005768-200001000-0001210.1097\/00005768-200001000-00012"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_005_w2aab3b7b3b1b6b1ab1ab5Aa","unstructured":"Butts, N. K., Henry, B. A., & Mclean, D. (1991). Correlations between VO2max and performance times of recreational triathletes. Journal of Sports Medicine and Physical Fitness, 31(3), 339-344."},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_006_w2aab3b7b3b1b6b1ab1ab6Aa","unstructured":"Edelmann-Nusser, J., Hohmann, A., & Henneberg, B. (2002). Modeling and prediction of competitive performance in swimming upon neural networks. European Journal of Sport Science, 2(2), 1-10. https:\/\/doi.org\/10.1080\/1746139020007220110.1080\/17461390200072201"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_007_w2aab3b7b3b1b6b1ab1ab7Aa","unstructured":"Fr\u00f6hlich, M., Klein, M., Pieter, A., Emrich, E., & Gie\u00dfling, J. (2008). Consequences of the Three Disciplines on the Overall Result in Olympic-distance Triathlon. International Journal of Sports Science and Engineering, 2(4), 204-210."},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_008_w2aab3b7b3b1b6b1ab1ab8Aa","unstructured":"Gilinsky, N., Hawkins, K. R., Tokar, T. N., & Cooper, J. A. (2014). Predictive variables for half-Ironman triathlon performance. Journal of Science and Medicine in Sport, 17(3), 300-305. https:\/\/doi.org\/10.1016\/j.jsams.2013.04.01410.1016\/j.jsams.2013.04.014"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_009_w2aab3b7b3b1b6b1ab1ab9Aa","unstructured":"Hair, J. F. (1995). Multivariate data analysis with readings (4th ed). Englewood Cliffs, N.J.: Prentice Hall."},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_010_w2aab3b7b3b1b6b1ab1ac10Aa","unstructured":"Heiberger, R. M., & Holland, B. (2004). Statistical analysis and data display: An intermediate course with examples in S-plus, R, and SAS. New York: Springer.10.1007\/978-1-4757-4284-8"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_011_w2aab3b7b3b1b6b1ab1ac11Aa","unstructured":"Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359-366. https:\/\/doi.org\/10.1016\/0893-6080(89)90020-810.1016\/0893-6080(89)90020-8"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_012_w2aab3b7b3b1b6b1ab1ac12Aa","unstructured":"Hue, O., Le Gallais, D., Boussana, A., Chollet, D., & Prefaut, C. (2000). Performance level and cardiopulmonary responses during a cycle-run trial. International Journal of Sports Medicine, 21(4), 250-255. https:\/\/doi.org\/10.1055\/s-2000-888310.1055\/s-2000-8883"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_013_w2aab3b7b3b1b6b1ab1ac13Aa","doi-asserted-by":"crossref","unstructured":"Hue, O., Le Gallais, D., Chollet, D., & Pr\u00e9faut, C. (2000). Ventilatory threshold and maximal oxygen uptake in present triathletes. Canadian Journal of Applied Physiology, 25(2), 102-113.10.1139\/h00-007","DOI":"10.1139\/h00-007"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_014_w2aab3b7b3b1b6b1ab1ac14Aa","unstructured":"Hue, O. (2003). Prediction of Drafted-Triathlon Race Time From Submaximal Laboratory Testing in Elite Triathletes. Canadian Journal of Applied Physiology, 28(4), 547-560. https:\/\/doi.org\/10.1139\/h03-04210.1139\/h03-042"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_015_w2aab3b7b3b1b6b1ab1ac15Aa","unstructured":"Kaiser, H. F., & Rice, J. (1974). Little Jiffy, Mark Iv. Educational and Psychological Measurement, 34(1), 111-117. https:\/\/doi.org\/10.1177\/00131644740340011510.1177\/001316447403400115"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_016_w2aab3b7b3b1b6b1ab1ac16Aa","doi-asserted-by":"crossref","unstructured":"Knechtle, B., Wirth, A., R\u00fcst, C. A., & Rosemann, T. (2011). The Relationship between Anthropometry and Split Performance in Recreational Male Ironman Triathletes. Asian Journal of Sports Medicine, 2(1), 23-30.","DOI":"10.5812\/asjsm.34823"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_017_w2aab3b7b3b1b6b1ab1ac17Aa","unstructured":"Knussmann, R., & Barlett, H. L. (1988). Anthropologie : Handbuch der vergleichenden Biologie des Menschen [Anthropology : Handbook of comparative biology of humans] (2nd ed). Stuttgart: Fischer"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_018_w2aab3b7b3b1b6b1ab1ac18Aa","doi-asserted-by":"crossref","unstructured":"Kohrt, W. M., Morgan, D. W., Bates, B., & Skinner, J. S. (1987). Physiological responses of triathletes to maximal swimming, cycling, and running. Medicine & Science in Sports & Exercise, 19(1), 51-55.","DOI":"10.1249\/00005768-198702000-00011"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_019_w2aab3b7b3b1b6b1ab1ac19Aa","doi-asserted-by":"crossref","unstructured":"Landers, G. J., Blanksby, B. A., Ackland, T. R., & Smith, D. (2000). Morphology and performance of world championship triathletes. Annals of Human Biology, 27(4), 387-400.10.1080\/03014460050044865","DOI":"10.1080\/03014460050044865"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_020_w2aab3b7b3b1b6b1ab1ac20Aa","unstructured":"Marquardt, D. W. (1963). An Algorithm for Least-Squares Estimation of Nonlinear Parameters. Journal of the Society for Industrial and Applied Mathematics, 11(2), 431-441. https:\/\/doi.org\/10.1137\/011103010.1137\/0111030"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_021_w2aab3b7b3b1b6b1ab1ac21Aa","unstructured":"McLaughlin, J. E., Howley, E. T., Bassett, D. R., Thompson, D. L., & Fitzhugh, E. C. (2010). Test of the classic model for predicting endurance running performance. Medicine & Science in Sports & Exercise, 42(5), 991-997. https:\/\/doi.org\/10.1249\/MSS.0b013e3181c0669d10.1249\/MSS.0b013e3181c0669d"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_022_w2aab3b7b3b1b6b1ab1ac22Aa","unstructured":"Millet, G. P., Vleck, V. E., & Bentley, D. J. (2009). Physiological differences between cycling and running: lessons from triathletes. Sports Medicine, 39(3), 179-206. https:\/\/doi.org\/10.2165\/00007256-200939030-0000210.2165\/00007256-200939030-00002"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_023_w2aab3b7b3b1b6b1ab1ac23Aa","unstructured":"Millet, G. P., Vleck, V. E., & Bentley, D. J. (2011). Physiological requirements in triathlon. Journal of Human Sport and Exercise, 6(2 Suppl.), 184-204. https:\/\/doi.org\/10.4100\/jhse.2011.62.0110.4100\/jhse.2011.62.01"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_024_w2aab3b7b3b1b6b1ab1ac24Aa","unstructured":"Miura, H., Kitagawa, K., & Ishiko, T. (1997). Economy during a simulated laboratory test triathlon is highly related to Olympic distance triathlon. Journal of Human Sport and Exercise, 18(4), 276-280. https:\/\/doi.org\/10.1055\/s-2007-97263310.1055\/s-2007-972633"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_025_w2aab3b7b3b1b6b1ab1ac25Aa","doi-asserted-by":"crossref","unstructured":"Schabort, E. J., Killian, S. C., St Clair Gibson, Hawley, J. A., & Noakes, T. D. (2000). Prediction of triathlon race time from laboratory testing in national triathletes. Medicine & Science in Sports & Exercise, 32(4), 844-849.","DOI":"10.1097\/00005768-200004000-00018"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_026_w2aab3b7b3b1b6b1ab1ac26Aa","unstructured":"Silva, A. J., Costa, A. M., Oliveira, P. M., Reis, V. M., Saavedra, J., Perl, J., & Marinho, D. A. (2007). The Use of Neural Network Technology to Model Swimming Performance. Journal of Sports Science & Medicine, 6(1), 117-125."},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_027_w2aab3b7b3b1b6b1ab1ac27Aa","doi-asserted-by":"crossref","unstructured":"Sleivert, G. G., & Rowlands, D. S. (1996). Physical and physiological factors associated with success in the triathlon. Sports Medicine, 22(1), 8-18.10.2165\/00007256-199622010-00002","DOI":"10.2165\/00007256-199622010-00002"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_028_w2aab3b7b3b1b6b1ab1ac28Aa","doi-asserted-by":"crossref","unstructured":"Sleivert, G. G., & Wenger, H. A. (1993). Physiological predictors of short-course triathlon performance. Medicine & Science in Sports & Exercise, 25(7), 871-876.","DOI":"10.1249\/00005768-199307000-00017"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_029_w2aab3b7b3b1b6b1ab1ac29Aa","doi-asserted-by":"crossref","unstructured":"Stratton, E., O'Brien, B. J., Harvey, J., Blitvich, J., McNicol, A. J., Janissen, D.,. . . Knez, W. (2009). Treadmill Velocity Best Predicts 5000-m Run Performance. International Journal of Sports Medicine, 30(1), 40-45.","DOI":"10.1055\/s-2008-1038761"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_030_w2aab3b7b3b1b6b1ab1ac30Aa","unstructured":"Tittel, K., & Wutscherk, H. (1972). Sportanthropometrie : Aufgaben, Bedeutung, Methodik und Ergebnisse biotypologischer Erhebungen [Sports Anthropology : tasks, meanings, methodology and results of biotypological surveys]. Leipzig: Barth."},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_031_w2aab3b7b3b1b6b1ab1ac31Aa","unstructured":"Van Schuylenbergh, R., Eynde, B. V., & Hespel, P. (2004). Prediction of sprint triathlon performance from laboratory tests. European Journal of Applied Physiology, 91(1), 94-99. https:\/\/doi.org\/10.1007\/s00421-003-0911-610.1007\/s00421-003-0911-6"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_032_w2aab3b7b3b1b6b1ab1ac32Aa","unstructured":"Vleck, V. E., Burgi, A., & Bentley, D. J. (2006). The consequences of swim, cycle, and run performance on overall result in elite olympic distance triathlon. International Journal of Sports Medicine, 27(1), 43-48. https:\/\/doi.org\/10.1055\/s-2005-83750210.1055\/s-2005-837502"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_033_w2aab3b7b3b1b6b1ab1ac33Aa","doi-asserted-by":"crossref","unstructured":"Williams, K. R., Cavanagh, P. R., & Ziff, J. L. (1987). Biomechanical studies of elite female distance runners. International Journal of Sports Medicine, 8 Suppl 2, 107-118.10.1055\/s-2008-1025715","DOI":"10.1055\/s-2008-1025715"},{"key":"2021040703154994310_j_ijcss-2017-0009_ref_034_w2aab3b7b3b1b6b1ab1ac34Aa","unstructured":"Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998). Forecasting with artificial neural networks. International Journal of Forecasting, 14(1), 35-62. https:\/\/doi.org\/10.1016\/S0169-2070(97)00044-710.1016\/S0169-2070(97)00044-7"}],"container-title":["International Journal of Computer Science in Sport"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/content.sciendo.com\/view\/journals\/ijcss\/16\/2\/article-p101.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.sciendo.com\/article\/10.1515\/ijcss-2017-0009","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T06:29:41Z","timestamp":1617776981000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.sciendo.com\/article\/10.1515\/ijcss-2017-0009"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,11,27]]},"references-count":34,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2017,11,30]]},"published-print":{"date-parts":[[2017,11,27]]}},"alternative-id":["10.1515\/ijcss-2017-0009"],"URL":"https:\/\/doi.org\/10.1515\/ijcss-2017-0009","relation":{},"ISSN":["1684-4769"],"issn-type":[{"value":"1684-4769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,11,27]]}}}