1. bookVolumen 19 (2020): Heft 1 (July 2020)
16 Apr 2016
2 Hefte pro Jahr
Uneingeschränkter Zugang

A development framework for decision support systems in high-performance sport

Online veröffentlicht: 29 Jun 2020
Volumen & Heft: Volumen 19 (2020) - Heft 1 (July 2020)
Seitenbereich: 1 - 23
16 Apr 2016
2 Hefte pro Jahr

Abut, F., & Akay, M. F. (2015). Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances. Med Devices (Auckl), 8, 369-379. doi:10.2147/mder.S5728110.2147/MDER.S57281Search in Google Scholar

Adelman, L. (1992). Evaluating decision support and expert systems. New York, NY, USA: Wiley-Interscience.Search in Google Scholar

Baeza-Yates, R. (2016). Data and algorithmic bias in the web. Proceedings of the 8th ACM Conference on Web Science. Retrieved from https://cacm.acm.org/magazines/2018/6/228035-bias-on-the-web/fulltext10.1145/2908131.2908135Search in Google Scholar

Bartlett, J. D., O’Connor, F., Pitchford, N., Torres-Ronda, L., & Robertson, S. J. (2017). Relationships between internal and external training load in team-sport athletes: evidence for an individualized approach. Int J Sports Physiol Perform, 12(2), 230-234. doi:10.1123/ijspp.2015-079110.1123/ijspp.2015-0791Search in Google Scholar

Bate, L., Hutchinson, A., Underhill, J., & Maskrey, N. (2012). How clinical decisions are made. Br J Clin Pharmacol, 74(4), 614-620. doi:10.1111/j.1365-2125.2012.04366.x10.1111/j.1365-2125.2012.04366.xSearch in Google Scholar

Bennet, A., & Bennet, D. (2004). Organizational survival in the new world: the itelligent complex adaptive system. Boston: Elsevier.10.4324/9780080513331Search in Google Scholar

Bennet, A., & Bennet, D. (2008). The decision-making process in a complex situation. Berlin: Springer-Verlag Berlin Heidelberg.Search in Google Scholar

Bertani, A., Cappello, A., Benedetti, M. G., Simoncini, L., & Catani, F. (1999). Flat foot functional evaluation using pattern recognition of ground reaction data. Clin Biomech (Bristol, Avon), 14(7), 484-493.10.1016/S0268-0033(98)90099-7Search in Google Scholar

Blythe, D. A., & Kiraly, F. J. (2016). Prediction and quantification of individual athletic performance of runners. PLoS ONE, 11(6), e0157257. doi:10.1371/journal.pone.015725710.1371/journal.pone.0157257491909427336162Search in Google Scholar

Bourne, M., Neely, A., Mills, J., & Platts, K. (2003). Implementing performance measurement systems: a literature review. International Journal of Business Performance Management, 5(1), 1-24.10.1504/IJBPM.2003.002097Search in Google Scholar

Calder, J. M., & Durbach, I. N. (2015). Decision support for evaluating player performance in rugby union. Int J Sports Sci Coach, 10(1), 21-37. doi:https://doi.org/10.1260/1747-9541.10.1.2110.1260/1747-9541.10.1.21Search in Google Scholar

Carey, D. L., Ong, K., Whiteley, R., Crossley, K. M., Crow, J., & Morris, M. E. (2018). Predictive modelling of training loads and injury in australian football. International Journal of Computer Science in Sport, 17(1), 49-66. doi:10.2478/ijcss-2018-000210.2478/ijcss-2018-0002Search in Google Scholar

Chaudhry, S. S., Salchenberger, L., & Beheshtian, M. (1996). A small business inventory DSS: design development, and implementation issues. Computers & Operations Research, 23(1), 63-72. doi:https://doi.org/10.1016/0305-0548(95)00004-610.1016/0305-0548(95)00004-6Search in Google Scholar

Chengular-Smith, I. N., Ballou, D., & Pazer, H. L. (1999). The impact of data quality information on decision making: an exploratory analysis. IEEE Trans. Knowl Data Eng, 11(6).10.1109/69.824597Search in Google Scholar

Chenoweth, T., L. Dowling, K. L., & St Louis, R. (2004). Convincing DSS users that complex models are worth the effort. Decision Support Systems, 37(1), 71-82. doi:10.1016/S0167-9236(03)00005-810.1016/S0167-9236(03)00005-8Search in Google Scholar

Clermont, C. A., Osis, S. T., Phinyomark, A., & Ferber, R. (2017). Kinematic gait patterns in competitive and recreational runners. J Appl Biomech, 33(4), 268-276. doi:10.1123/jab.2016-021810.1123/jab.2016-021828253053Search in Google Scholar

Croskerry, P. (2005). The theory and practice of clinical decision-making. Canadian Journal of Anesthesia, 52(S1), R1–R8.10.1007/BF03023077Search in Google Scholar

Croskerry, P. (2009). Context is everything or how could I have been that stupid? Healthc Q, 12 Spec No Patient, e171-176.10.12927/hcq.2009.2094519667765Search in Google Scholar

Donabedian, A. (1980). Definition of quality and approaches to its assessment. Ann Arbor, MI: Health Administration Press.Search in Google Scholar

Donabedian, A. (1988). The quality of care. How can it be assessed? JAMA, 260(12), 1743-1748. doi:10.1001/jama.260.12.174310.1001/jama.260.12.17433045356Search in Google Scholar

Dutt-Mazumder, A., Button, C., Robins, A., & Bartlett, R. (2011). Neural network modelling and dynamical system theory: are they relevant to study the governing dynamics of association football players? Sports Med, 41(12), 1003-1017. doi:10.2165/11593950-000000000-0000010.2165/11593950-000000000-0000022060175Search in Google Scholar

Elragal, A., & Klischewski, R. (2017). Theory-driven or process-driven prediction? Epistemological challenges of big data analytics. J Big Data, 4(19). doi:10.1186/s40537-017-0079-210.1186/s40537-017-0079-2Search in Google Scholar

Ertelt, T., Solomonovs, I., & Gronwald, T. (2018). Enhancement of force patterns classification based on Gaussian distributions. J Biomech, 67, 144-149. doi:10.1016/j.jbiomech.2017.12.00610.1016/j.jbiomech.2017.12.00629276071Search in Google Scholar

Everitt, B. S., & Skrondal, A. (Eds.). (2010) Cambridge Dictionary of Statistics. Cambridge University Press.10.1017/CBO9780511779633Search in Google Scholar

Fisher, C. W., Chengalur-Smith, I., & Ballou, D. P. (2003). The impact of experience and time on the use of data quality information in decision making. Inform Syst Res, 14(2), ^ í ˙˙10.1287/isre. in Google Scholar

Fortmann-Roe, S. (2012a). Accurately measuring model prediction error. Retrieved from http://scott.fortmann-roe.com/docs/MeasuringError.htmlSearch in Google Scholar

Fortmann-Roe, S. (2012b). Understanding the bias-variance tradeoff. Retrieved from http://scott.fortmann-roe.com/docs/BiasVariance.htmlSearch in Google Scholar

Gönül, M. S., Önkal, D., & Lawrence, M. (2006). The effects of structural characteristics of explanations on use of a DSS. Decision Support Systems, 42(3), 1481–1493.10.1016/j.dss.2005.12.003Search in Google Scholar

Gregor, S., & Benbasat, I. (1999). Explanations from intelligent systems: theoretical foundations and implications for practice. MIS Quarterly, 23(4), 497–530.10.2307/249487Search in Google Scholar

Grehaigne, J., Godbout, P., & Bouthier, D. (1997). Performance assessment in team sports. Journal of Teaching in Physical Education, 16, 500-516.10.1123/jtpe.16.4.500Search in Google Scholar

Hoch, S. J., & Schkade, D. A. (1996). A psychological approach to decision support systems. Management Science, 42(1), 51-64. Retrieved from http://www.jstor.org/stable/263301510.1287/mnsc.42.1.51Search in Google Scholar

Hogarth, L., Payton, C., Van de Vliet, P., Connick, M., & Burkett, B. (2018). A novel method to guide classification of para swimmers with limb deficiency. Scand J Med Sci Sports, 28(11), 2397-2406. doi:10.1111/sms.1322910.1111/sms.13229Search in Google Scholar

Hogue, J. T., & Hugh, J. W. (1984). Current practices in the development of decision support systems. Information and Management, 205-212. Retrieved from http://aisel.aisnet.org/icis1984/1610.1016/0378-7206(85)90017-5Search in Google Scholar

Holsapple, C. W. (2008). Decisions and knowledge. Berlin: Springer-Verlag Berlin Heidelberg.Search in Google Scholar

Hooshyar, D., Yousefi, M., & Lim, H. (2017). A systematic review of data-driven approaches in player modeling of educational games. Artificial Intelligence Review, 4(19), 1-20. doi:doi:10.1007/s10462-017-9609-810.1007/s10462-017-9609-8Search in Google Scholar

Hunt, D. L., Haynes, R. B., Hanna, S. E., & Smith, K. (1998). Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review. JAMA, 280(15), 1339-1346.10.1001/jama.280.15.13399794315Search in Google Scholar

IBM. (2012). IBM SPSS Modeler CRISP-DM Guide. Retrieved from https://www.ibm.com/support/knowledgecenter/en/SS3RA7_15.0.0/com.ibm.spss.crispdm.help/crisp_overview.htmSearch in Google Scholar

Janssen, D., Schollhorn, W. I., Newell, K. M., Jager, J. M., Rost, F., & Vehof, K. (2011). Diagnosing fatigue in gait patterns by support vector machines and self-organizing maps. Hum Mov Sci, 30(5), 966-975. doi:10.1016/j.humov.2010.08.01010.1016/j.humov.2010.08.01021195495Search in Google Scholar

Jaspers, A., De Beeck, T. O., Brink, M. S., Frencken, W. G. P., Staes, F., Davis, J. J., & Helsen, W. F. (2018). Relationships between the external and internal training load in professional soccer: what can we learn from machine learning? Int J Sports Physiol Perform, 13(5), 625-630. doi:10.1123/ijspp.2017-029910.1123/ijspp.2017-029929283691Search in Google Scholar

Kahneman, D., & Klein, G. (2009). Conditions for intuitive expertise: a failure to disagree. Am Psychol, 64(6), 515-526. doi:10.1037/a001675510.1037/a001675519739881Search in Google Scholar

Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47(2), 263-292.10.2307/1914185Search in Google Scholar

Kawamoto, K., Houlihan, C. A., Balas, E. A., & Lobach, D. F. (2005). Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ, 330(7494), 765. doi:10.1136/bmj.38398.500764.8F10.1136/bmj.38398.500764.8F55588115767266Search in Google Scholar

Kayande, U., De Bruyn, A., Lilien, G. L., Rangaswamy, A., & van Bruggen, G. H. (2009). How incorporating feedback mechanisms in a DSS affects DSS evaluations. Information Systems Research, 20(4), 527-546.10.1287/isre.1080.0198Search in Google Scholar

Kenett, R., & Shmueli, G. (2016). Dimensions of information quality and InfoQ assessment. In R. Kenett & G. Shmueli (Eds.), The Potential of Data and Analytics to Generate Knowledge. Chichester, West Sussex: WILEY.10.1002/9781118890622Search in Google Scholar

Kenrose, S. (2015). Parsimonious model: definition, ways to compare models. Statistics How To. Retrieved from https://www.statisticshowto.datasciencecentral.com/parsimonious-model/Search in Google Scholar

Khazanchi, D. (1991). Evaluating decision support systems: a dialectical perspective. Paper presented at the 24th Annual Hawaii International Conference on Systems Sciences (HICSS-24), Hawaii.10.1109/HICSS.1991.184131Search in Google Scholar

Kianifar, R., Lee, A., Raina, S., & Kulic, D. (2016). Classification of squat quality with inertial measurement units in the single leg squat mobility test. Conf Proc IEEE Eng Med Biol Soc, 2016, 6273-6276. doi:10.1109/embc.2016.759216210.1109/EMBC.2016.759216228269683Search in Google Scholar

Kipp, K., Giordanelli, M., & Geiser, C. (2018). Predicting net joint moments during a weightlifting exercise with a neural network model. J Biomech, 74, 225-229. doi:10.1016/j.jbiomech.2018.04.02110.1016/j.jbiomech.2018.04.02129706383Search in Google Scholar

Kovalchik, S., & Reid, M. (2018). A shot taxonomy in the era of tracking data in professional tennis. J Sports Sci, 36(18), 2096-2104. doi:10.1080/02640414.2018.143809410.1080/02640414.2018.143809429419342Search in Google Scholar

Lai, F., Macmillan, J., Daudelin, D. H., & Kent, D. M. (2006). The potential of training to increase acceptance and use of computerized decision support systems for medical diagnosis. Hum Factors, 48(1), 95-108. doi:10.1518/00187200677641230610.1518/00187200677641230616696260Search in Google Scholar

Lai, M., Meo, R., Schifanella, R., & Sulis, E. (2018). The role of the network of matches on predicting success in table tennis. J Sports Sci, 1-8. doi:10.1080/02640414.2018.148281310.1080/02640414.2018.148281329897306Search in Google Scholar

Leicht, A. S., Gomez, M. A., & Woods, C. T. (2017). Explaining match outcome during the men’s basketball tournament at the olympic games. J Sports Sci Med, 16(4), 468-473.Search in Google Scholar

Li, X., Huang, H., Wang, J., Yu, Y., & Ao, Y. (2016). The analysis of plantar pressure data based on multimodel method in patients with anterior cruciate ligament deficiency during walking. Biomed Res Int, 2016, 7891407. doi:10.1155/2016/789140710.1155/2016/7891407516855128050565Search in Google Scholar

Limayem, M., & DeSanctis, G. (2000). Providing decisional guidance for multicriteria decision making in groups. Information Systems Research, 11(4), 386-401. Retrieved from http://www.jstor.org/stable/2301104410.1287/isre.11.4.386.11874Search in Google Scholar

Link, D., & Hoernig, M. (2017). Individual ball possession in soccer. PLoS ONE, 12(7), e0179953. doi:10.1371/journal.pone.017995310.1371/journal.pone.0179953550322528692649Search in Google Scholar

Lopez-Valenciano, A., Ayala, F., Puerta, J. M., MBA, D. E. S. C., Vera-Garcia, F. J., Hernandez-Sanchez, S., . . . Myer, G. D. (2018). A preventive model for muscle injuries: a novel approach based on learning algorithms. Med Sci Sports Exerc, 50(5), 915-927. doi:10.1249/mss.000000000000153510.1249/MSS.0000000000001535658236329283933Search in Google Scholar

MacMahon, C., & McPherson, S. L. (2009). Knowledge base as a mechanism for perceptual-cognitive tasks: skill is in the details! International Journal of Sport Psychology, 40, 565–579.Search in Google Scholar

Maier, T., Meister, D., Trosch, S., & Wehrlin, J. P. (2018). Predicting biathlon shooting performance using machine learning. J Sports Sci, 1-7. doi:10.1080/02640414.2018.145526110.1080/02640414.2018.145526129565223Search in Google Scholar

Makridakis, S., Kirkham, R., Wakefield, A., Papadaki, M., Kirkham, J., & Long, L. (2019). Forecasting, uncertainty and risk; perspectives on clinical decision-making in preventive and curative medicine. International Journal of Forecasting, 35(2), 659-666. doi:https://doi.org/10.1016/j.ijforecast.2017.11.00310.1016/j.ijforecast.2017.11.003Search in Google Scholar

Maselli, A., Dhawan, A., Cesqui, B., Russo, M., Lacquaniti, F., & d’Avella, A. (2017). Where are you throwing the ball? I better watch your body, not just your arm! Front Hum Neurosci, 11, 505. doi:10.3389/fnhum.2017.0050510.3389/fnhum.2017.00505567493329163094Search in Google Scholar

Mason, R. O., & Mitroff, I. I. (1973). A program for research on management information systems. Manage Sci, 19(5), 475–487.10.1287/mnsc.19.5.475Search in Google Scholar

Mawhinney, C. H., & Lederer, A. L. (1990). A study of personal computer utilization by managers. Information & mangement, 18(5), 243-253.10.1016/0378-7206(90)90026-ESearch in Google Scholar

McNichol, D. (2018). On average, you’re using the wrong average: geometric & harmonic means in data analysis. Retrieved from https://towardsdatascience.com/on-average-youre-using-the-wrong-average-geometric-harmonic-means-in-data-analysis-2a703e21ea0Search in Google Scholar

Montazemi, A. R., Wang, F., Khalid Nainara, S. M., & Barta, C. K. (1996). On the effectiveness of decisional guidance. Decision Support Systems, 18(2), 181-198. doi:10.1016/0167-9236(96)00038-310.1016/0167-9236(96)00038-3Search in Google Scholar

Montgomery, A. (2005). The implementation challenge of pricing decision support systems for retail managers. Appl Stochastic Models Bus Indust, 27(4-5), 367-378.10.1002/asmb.572Search in Google Scholar

Montoliu, R., Martin-Felez, R., Torres-Sospedra, J., & Martinez-Uso, A. (2015). Team activity recognition in association football using a bag-of-words-based method. Hum Mov Sci, 41, 165-178. doi:10.1016/j.humov.2015.03.00710.1016/j.humov.2015.03.00725816795Search in Google Scholar

Morana, S., Schacht, S., Scherp, A., & Maedche, A. (2014). Conceptualization and typology of guidance in information systems. Working Paper Series in Information Systems. University of Mannheim., 7, 1-13.Search in Google Scholar

Morgulev, E., & Galily, Y. (2018). Choking or delivering under pressure? the case of elimination games in NBA playoffs. Front Psychol, 9(979). doi:10.3389/fpsyg.2018.0097910.3389/fpsyg.2018.00979600651929946290Search in Google Scholar

Myung, I. J. (2000). The importance of complexity in model selection. J Math Psychol, 44(1), 190-204. doi:10.1006/jmps.1999.128310.1006/jmps.1999.128310733864Search in Google Scholar

Nagata, T., Nakamura, N., Miyatake, M., Yuuki, A., Yomo, H., Kawabata, T., & Hara, S. (2016). VO2 estimation using 6-axis motion sensor with sports activity classification. Conf Proc IEEE Eng Med Biol Soc, 2016, 4735-4738. doi:10.1109/embc.2016.759178510.1109/EMBC.2016.759178528269329Search in Google Scholar

Novatchkov, H., & Baca, A. (2013). Artificial intelligence in sports on the example of weight training. J Sports Sci Med, 12(1), 27-37.Search in Google Scholar

Ofoghi, B., Zeleznikow, J., Macmahon, C., & Dwyer, D. (2013). Supporting athlete selection and strategic planning in track cycling omnium: A statistical and machine learning approach. Inf. Sci., 233, 200-213. doi:10.1016/j.ins.2012.12.05010.1016/j.ins.2012.12.050Search in Google Scholar

Ofoghi, B., Zeleznikow, J., Macmahon, C., Rehula, J., & Dwyer, D. B. (2016). Performance analysis and prediction in triathlon. J Sports Sci, 34(7), 607-612. doi:10.1080/02640414.2015.106534110.1080/02640414.2015.106534126177783Search in Google Scholar

Olade, R. A. (2004). Strategic collaborative model for evidence-based nursing practice. Worldviews Evid Based Nurs, 1(1), 60-68. doi:10.1111/j.1741-6787.2004.04003.x10.1111/j.1741-6787.2004.04003.x17147759Search in Google Scholar

Parasuraman, R., & Riley, V. (1997). Humans and automation: use, misuse, disuse, abuse. Human Factors: The Journal of the Human Factors and Ergonomics Society, 39(2), 230-253.10.1518/001872097778543886Search in Google Scholar

Parikh, M., Fazlollahi, B., & Verma, S. (2001). The effectiveness of decisional guidance: an empirical evaluation. Decision Sciences, 32(2), 303-332. doi:10.1111/j.1540-5915.2001.tb00962.x10.1111/j.1540-5915.2001.tb00962.xSearch in Google Scholar

Pernek, I., Kurillo, G., Stiglic, G., & Bajcsy, R. (2015). Recognizing the intensity of strength training exercises with wearable sensors. J Biomed Inform, 58, 145-155. doi:10.1016/j.jbi.2015.09.02010.1016/j.jbi.2015.09.02026453822Search in Google Scholar

Pidun, T., & Felden, C. (2011). Limitations of performance measurement systems based on key performance indicators. Paper presented at the AMCIS.Search in Google Scholar

Plous, S. (1993). The psychology of judgment and decision Making. New York: McGraw-Hill.Search in Google Scholar

Price, R., & Shanks, G. (2005). A semiotic information quality framework fevelopment and comparative analysis. Journal of Information Technology, 20(2), 88-102. doi:https://doi.org/10.1057/palgrave.jit.200003810.1057/palgrave.jit.2000038Search in Google Scholar

Price, R., & Shanks, G. (2008). Data quality and decision making. In F. Burstein & C. W. Holsapple (Eds.), Handbook on Decision Support Systems: Springer-Verlag Berlin Heidelberg.Search in Google Scholar

Redman, T. (1997). Improve data quality for competitive advantage. Sloan Manag Rev, 36(2), 99–107Search in Google Scholar

Rein, R., & Memmert, D. (2016). Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. Springerplus, 5(1), 1410. doi:10.1186/s40064-016-3108-210.1186/s40064-016-3108-2499680527610328Search in Google Scholar

Rhee, C., & Rao, H. R. (2008). Evaluation of decision support systems. In F. Burstein & C. W. Holsapple (Eds.), Handbook on Decision Support Systems: Springer-Verlag Berlin Heidelberg.Search in Google Scholar

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why should i trust you?: explaining the predictions of any classifier. Paper presented at the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.10.1145/2939672.2939778Search in Google Scholar

Richter, C., King, E., Falvey, E., & Franklyn-Miller, A. (2018). Supervised learning techniques and their ability to classify a change of direction task strategy using kinematic and kinetic features. J Biomech, 66, 1-9. doi:10.1016/j.jbiomech.2017.10.02510.1016/j.jbiomech.2017.10.02529146284Search in Google Scholar

Rindal, O. M. H., Seeberg, T. M., Tjonnas, J., Haugnes, P., & Sandbakk, O. (2017). Automatic classification of sub-techniques in classical cross-country skiing using a machine learning algorithm on micro-sensor data. Sensors (Basel), 18(1). doi:10.3390/s1801007510.3390/s18010075579594529283421Search in Google Scholar

Robertson, S., Bartlett, J. D., & Gastin, P. B. (2016). Red, amber or green? athlete monitoring in team sport: the need for decision support systems. Int J Sports Physiol Perform, 1-24. doi:10.1123/ijspp.2016-054110.1123/ijspp.2016-054127967289Search in Google Scholar

Robertson, S., & Joyce, D. (2018). Evaluating strategic periodisation in team sport. J Sports Sci, 36(3), 279-285. doi:10.1080/02640414.2017.130031510.1080/02640414.2017.130031528266908Search in Google Scholar

Robertson, S. J., & Joyce, D. G. (2015). Informing in-season tactical periodisation in team sport: development of a match difficulty index for Super Rugby. J Sports Sci, 33(1), 99-107. doi:10.1080/02640414.2014.92557210.1080/02640414.2014.92557224977714Search in Google Scholar

Rossi, A., Pappalardo, L., Cintia, P., Iaia, F. M., Fernandez, J., & Medina, D. (2018). Effective injury forecasting in soccer with GPS training data and machine learning. PLoS ONE, 13(7), e0201264. doi:10.1371/journal.pone.020126410.1371/journal.pone.0201264605946030044858Search in Google Scholar

Rouse, M. (2018). Machine learning bias (algorithm bias or AI bias). Retrieved from https://searchenterpriseai.techtarget.com/definition/machine-learning-bias-algorithm-bias-or-AI-biasSearch in Google Scholar

Ruddy, J. D., Shield, A. J., Maniar, N., Williams, M. D., Duhig, S., Timmins, R. G., . . . Opar, D. A. (2018). Predictive modeling of hamstring strain injuries in elite australian footballers. Med Sci Sports Exerc, 50(5), 906-914. doi:10.1249/mss.000000000000152710.1249/MSS.000000000000152729266094Search in Google Scholar

Safdar, S., Zafar, S., Zafar, N., & Khan, N. F. (2017). Machine learning based decision support systems (DSS) for heart disease diagnosis: a review. Artificial Intelligence Review, 1-27.Search in Google Scholar

Sampaio, J., McGarry, T., Calleja-Gonzalez, J., Jimenez Saiz, S., Schelling, X., & Balciunas, M. (2015). Exploring game performance in the National Basketball Association using player tracking data. PLoS ONE, 10(7), e0132894. doi:10.1371/journal.pone.013289410.1371/journal.pone.0132894450183526171606Search in Google Scholar

Sanders, N. R., & Manrodt, K. B. (2003). Forecasting software in practice: use, satisfaction, and performance. Interfaces, 33(5), 90-93. Retrieved from http://www.jstor.org/stable/2014128910.1287/inte. in Google Scholar

Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289-310. doi:10.1214/10-sts33010.1214/10-STS330Search in Google Scholar

Siddall, M. E. (2002). Parsimony analysis. In R. DeSalle, G. Giribet, & W. Wheeler (Eds.), Techniques in Molecular Systematics and Evolution. Methods and Tools in Biosciences and Medicine. Basel: Birkhäuser.Search in Google Scholar

Silver, M. (2006). Decisional guidance. Broadening the scope. Advances in Management Information Systems, 6, 90–119.Search in Google Scholar

Silver, M. S. (1991). Decision guidance for computer based decision support. MIS Quart, 15(105-122).10.2307/249441Search in Google Scholar

Silver, M. S. (2008). On the design features of decision support systems: the role of system restrictiveness and decisional guidance. In F. Burstein & C. W. Holsapple (Eds.), Handbook on Decision Support Systems: Springer-Verlag Berlin Heidelberg.Search in Google Scholar

Simon, H. A. (1956). Rational Choice and the Structure of the Environment. Psychological Review, 63(2), 129–138.10.1037/h004276913310708Search in Google Scholar

Simon, H. A. (1978). Rational decision-making in business organizations. Nobel memorial lecture. Retrieved from http://nobelprize.org/nobel_prizes/economics/laureates/1978/simon-lecture.pdfSearch in Google Scholar

Sprague, R. H. (1980). A framework for the development of decision support systems. MIS Quarterly, 4(4), 1-26.10.2307/248957Search in Google Scholar

Springer, A., Garcia-Gathright, J., & Cramer, H. (2018). Assessing and addressing algorithmic bias — but before we get there. Paper presented at the 2018 AAAI Spring Symposium Series, Stanford University. https://www.aaai.org/ocs/index.php/SSS/SSS18/paper/viewFile/17542/15470Search in Google Scholar

Swalin, A. (2018). Choosing the right metric for evaluating machine learning models—Part 1. Retrieved from https://medium.com/usf-msds/choosing-the-right-metric-for-machine-learning-models-part-1-a99d7d7414e4Search in Google Scholar

Taha, Z., Musa, R. M., Abdul Majeed, A., Alim, M. M., & Abdullah, M. R. (2018). The identification of high potential archers based on fitness and motor ability variables: A Support Vector Machine approach. Hum Mov Sci, 57, 184-193. doi:10.1016/j.humov.2017.12.00810.1016/j.humov.2017.12.00829248809Search in Google Scholar

Thornton, H. R., Delaney, J. A., Duthie, G. M., & Dascombe, B. J. (2017). Importance of various training-load measures in injury incidence of professional rugby league athletes. Int J Sports Physiol Perform, 12(6), 819-824. doi:10.1123/ijspp.2016-032610.1123/ijspp.2016-032627918659Search in Google Scholar

Torres-Ronda, L., & Schelling, X. (2017). Critical process for the implementation of technology in sport organizations. Strength and Conditioning Journal, 39(6), 54-59. doi:10.1519/ssc.000000000000033910.1519/SSC.0000000000000339Search in Google Scholar

Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: heuristics and biases. Science, 185(4157), 1124-1131. doi:10.1126/science.185.4157.112410.1126/science.185.4157.112417835457Search in Google Scholar

Valatavičius, A., & Gudas, S. (2017). Towards the deep, knowledge-based interoperability of applications. Informacijos Mokslai, 79, 83-113.10.15388/Im.2017.79.11400Search in Google Scholar

VV.AA. (2018). OxfordDictionaries.com. Retrieved from https://en.oxforddictionaries.com/definition/overfittingSearch in Google Scholar

Wali Van Lohuizen, C. W. (1986). Knowledge management and policymaking. Knowledge, 8(1), 12-38. doi:10.1177/10755470860080010210.1177/107554708600800102Search in Google Scholar

Walther, B. A., & Moore, J. L. (2005). The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance. Ecography, 28, 815-829.10.1111/j.2005.0906-7590.04112.xSearch in Google Scholar

Whiteside, D., Cant, O., Connolly, M., & Reid, M. (2017). Monitoring hitting load in tennis using inertial sensors and machine learning. Int J Sports Physiol Perform, 12(9), 1212-1217. doi:10.1123/ijspp.2016-068310.1123/ijspp.2016-068328182523Search in Google Scholar

Whiteside, D., & Reid, M. (2017). Spatial characteristics of professional tennis serves with implications for serving aces: A machine learning approach. J Sports Sci, 35(7), 648-654. doi:10.1080/02640414.2016.118380510.1080/02640414.2016.118380527189847Search in Google Scholar

Witten, I. A., Frank, E., & Hall, M. A. (2011). Data mining. Practical machine learning tools and techniques (Third ed.). Burlington, Massachussetts, USA.: Elsevier.Search in Google Scholar

Woods, C. T., Veale, J., Fransen, J., Robertson, S., & Collier, N. F. (2018). Classification of playing position in elite junior Australian football using technical skill indicators. J Sports Sci, 36(1), 97-103. doi:10.1080/02640414.2017.128262110.1080/02640414.2017.128262128125339Search in Google Scholar

Wundersitz, D. W., Josman, C., Gupta, R., Netto, K. J., Gastin, P. B., & Robertson, S. (2015). Classification of team sport activities using a single wearable tracking device. J Biomech, 48(15), 3975-3981. doi:10.1016/j.jbiomech.2015.09.01510.1016/j.jbiomech.2015.09.01526472301Search in Google Scholar

Xie, J., Xu, J., Nie, C., & Nie, Q. (2017). Machine learning of swimming data via wisdom of crowd and regression analysis. Math Biosci Eng, 14(2), 511-527. doi:10.3934/mbe.201703110.3934/mbe.201703127879112Search in Google Scholar

Zhang, J., Lockhart, T. E., & Soangra, R. (2014). Classifying lower extremity muscle fatigue during walking using machine learning and inertial sensors. Ann Biomed Eng, 42(3), 600-612. doi:10.1007/s10439-013-0917-010.1007/s10439-013-0917-0394349724081829Search in Google Scholar

Empfohlene Artikel von Trend MD

Planen Sie Ihre Fernkonferenz mit Scienceendo