Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Kongresse
Themenseiten
Künstliche Intelligenz Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Veranstaltungskalender Awards MyNewsletter Firmensuche
Kunden Login
Über Einzelzugang informieren
Über Unternehmenszugang informieren
Referenzkunden
Zukunftswerkstatt Sales Excellence 2025 | 08. Mai 2025

KI als Booster im Vertrieb

Lernen Sie von Digital-Experten, wie der gezielte Einsatz von KI-Unterstützung Ihrem Unternehmen in vielen Bereichen hilft, im Vertrieb einen Schritt voraus zu sein. Jetzt wieder live vor Ort teilnehmen in Frankfurt am Main!
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Mining Marathon Training Data to Generate Useful User Profiles

verfasst von : Jakim Berndsen, Barry Smyth, Aonghus Lawlor

Erschienen in: Machine Learning and Data Mining for Sports Analytics

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In this work we generate user profiles from the raw activity data of over 12000 marathon runners. We demonstrate that these user profiles capture accurate representations of the fitness and training of a runner, and show that they are comparable to current methods used to predict marathon performance – many of which require many years of prior experience or expensive laboratory testing. We also briefly investigate how these user profiles can be used to help marathon runners in their training and race preparation when combined with current recommender systems approaches.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel A Machine Learning Approach for Road Cycling Race Performance Prediction
Nächstes Kapitel Learning from Partially Labeled Sequences for Behavioral Signal Annotation
Fußnoten
Literatur
1.
Banister, E., Banister, E., Banister, E., Banister, E.: Modeling elite athletic performance. Human Kinetics (1991)
2.
Barandun, U., et al.: Running speed during training and percent body fat predict race time in recreational male marathoners. Open Access J. Sports Med. 3, 51 (2012)
3.
Berndsen, J., Lawlor, A., Smyth, B.: Running with recommendation. In: HealthRecSys@ RecSys. pp. 18–21 (2017)
4.
Berndsen, J., Smyth, B., Lawlor, A.: Pace my race: recommendations for marathon running. In: Proceedings of the 13th ACM Conference on Recommender Systems. pp. 246–250 (2019)
5.
Billat, V., Bernard, O., Pinoteau, J., Petit, B., Koralsztein, J.: Time to exhaustion at vo2max and lactate steady state velocity in sub elite long-distance runners. Archives internationales de physiologie, de biochimie et de biophysique 102(3), 215–219 (1994)CrossRef
6.
Billat, V.L., Demarle, A., Slawinski, J., Paiva, M., Koralsztein, J.P.: Physical and training characteristics of top-class marathon runners. Med. Sci. Sports Exercise 33(12), 2089–2097 (2001)CrossRef
7.
Billat, V., Hill, D., Pinoteau, J., Petit, B., Koralsztein, J.P.: Effect of protocol on determination of velocity at vo2 max and on its time to exhaustion. Arch. Physiol. Biochem. 104(3), 313–321 (1996)CrossRef
8.
Blythe, D.A., Király, F.J.: Prediction and quantification of individual athletic performance. arXiv preprint arXiv:​1505.​01147 (2015)
9.
Blythe, D.A., Király, F.J.: Prediction and quantification of individual athletic performance of runners. PLoS ONE 11(6), e0157257 (2016)CrossRef
10.
Burke, E.: Precision heart rate training. Human Kinetics (1998)
11.
Chen, T., Guestrin, C.: XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 785–794. KDD 2016, ACM, New York, USA (2016)
12.
Daniels, J.: Daniels’ running formula. Human Kinetics (2013)
13.
Di Prampero, P., Atchou, G., Brückner, J.C., Moia, C.: The energetics of endurance running. Eur. J. Appl. Physiol. 55(3), 259–266 (1986)CrossRef
14.
Doherty, C., Keogh, A., Davenport, J., Lawlor, A., Smyth, B., Caulfield, B.: An evaluation of the training determinants of marathon performance: a meta-analysis with meta-regression. J. Sci. Med. Sport 23(2), 182–188 (2020)CrossRef
15.
16.
17.
18.
Florence, S.I., Weir, J.P.: Relationship of critical velocity to marathon running performance. Euro. J. Appl. Physiol. Occup. Physiol. 75(3), 274–278 (1997)CrossRef
19.
Föhrenbach, R., Mader, A., Hollmann, W.: Determination of endurance capacity and prediction of exercise intensities for training and competition in marathon runners. Int. J. Sports Med. 8(01), 11–18 (1987)CrossRef
20.
Fry, R.W., Morton, A.R., Keast, D.: Periodisation and the prevention of overtraining. Canad. J. Sport Sci.= J. canadien des sciences du sport 17(3), 241–248 (1992)
21.
Fry, R.W., Morton, A.R., Keast, D.: Periodisation of training stress-a review. Canad. J. Sport Sci. = Journal canadien des sciences du sport 17(3), 234–240 (1992)
22.
García-Manso, J., Martín-González, J., Vaamonde, D., Da Silva-Grigoletto, M.: The limitations of scaling laws in the prediction of performance in endurance events. J. Theor. Biol. 300, 324–329 (2012)CrossRef
23.
Hagan, R., Upton, S., Duncan, J., Gettman, L.: Marathon performance in relation to maximal aerobic power and training indices in female distance runners. Br. J. Sports Med. 21(1), 3–7 (1987)CrossRef
24.
Hubble, C., Zhao, J.: Gender differences in marathon pacing and performance prediction. J. Sports Anal. 2(1), 19–36 (2016)CrossRef
25.
Jones, A.M., Burnley, M., Black, M.I., Poole, D.C., Vanhatalo, A.: The maximal metabolic steady state: redefining the ‘gold standard’. Physiological Reports 7(10), e14098 (2019)CrossRef
26.
Keogh, A., Smyth, B., Caulfield, B., Lawlor, A., Berndsen, J., Doherty, C.: Prediction equations for marathon performance: a systematic review. Int. J. Sports Physiol. Perform. 14(9), 1159–1169 (2019)CrossRef
27.
Millett, M., Melanson, T.: Predicting running times from race history (2015)
28.
Mohan, S., Venkatakrishnan, A., Silva, M., Pirolli, P.: On designing a social coach to promote regular aerobic exercise. In: Twenty-Ninth IAAI Conference (2017)
29.
Noakes, T.: Lore of running. Human Kinetics (2003)
30.
Poole, D.C., Burnley, M., Vanhatalo, A., Rossiter, H.B., Jones, A.M.: Critical power: an important fatigue threshold in exercise physiology. Med. Sci. Sports Exerc. 48(11), 2320 (2016)CrossRef
31.
Riegel, P.S.: Athletic records and human endurance: A time vs. distance equation describing world-record performances may be used to compare the relative endurance capabilities of various groups of people. Am. Sci. 69(3), 285–290 (1981)
32.
Schneider, H.: Adapting at run-time: Exploring the design space of personalized fitness coaches. In: Proceedings of the 22nd International Conference on Intelligent User Interfaces Companion, pp. 173–176 (2017)
33.
Smyth, B.: Marathon race planning: A case-based reasoning approach. In: The 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), Stockholm, Sweden, 13–19 July (2018)
34.
Smyth, B., Cunningham, P.: A novel recommender system for helping marathoners to achieve a new personal-best. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 116–120 (2017)
35.
36.
37.
Smyth, B., Muniz-Pumares, D.: Calculation of critical speed from raw training data in recreational marathon runners. Medicine and Science in Sports and Exercise (2020)
38.
Stöggl, T.L., Sperlich, B.: The training intensity distribution among well-trained and elite endurance athletes. Front. Physiol. 6, 295 (2015)CrossRef
39.
Tanda, G.: Prediction of marathon performance time on the basis of training indices. J. Human Sport Exerc., 511–520 (2011)
40.
Vickers, A.J., Vertosick, E.A.: An empirical study of race times in recreational endurance runners. BMC Sports Sci. Med. Rehab. 8(1), 26 (2016)CrossRef
41.
Zillmann, T., Knechtle, B., Rüst, C.A., Knechtle, P., Rosemann, T., Lepers, R.: Comparison of training and anthropometric characteristics between recreational male half-marathoners and marathoners. Chin. J. Physiol. 56(3), 138–146 (2013)
Metadaten
Titel
Mining Marathon Training Data to Generate Useful User Profiles
verfasst von
Jakim Berndsen
Barry Smyth
Aonghus Lawlor
Copyright-Jahr
2020
Buch
Machine Learning and Data Mining for Sports Analytics

Print ISBN: 978-3-030-64911-1

Electronic ISBN: 978-3-030-64912-8

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-64912-8_10

Premium Partner

    Bildnachweise