266
Views
1
CrossRef citations to date
0
Altmetric
Sports Performance

Evaluating the predictability of distance race performance in NCAA cross country and track and field from high school race times in the United States

Pages 1808-1815 | Accepted 24 Dec 2017, Published online: 30 Dec 2017

References

  • Barreiros, A., Côté, J., & Fonesca, A. M. (2014). From early to adult sport success: Analyzing athletes’ progression in national squads. European Journal of Sport Science, 14, 178–182.
  • Brenner, J. S. (2007). Overuse injuries, overtraining, and burnout in child and adolescent athletes. Pediatrics, 119(6), 1242–1245.
  • Brodersen, K. H., Ong, C. S., Stephan, K. E., & Buhmann, J. M. (2010). The balanced accuracy and its posterior distribution. Proceedings of the 20th international conference on pattern recognition (pp. 3121–3124). IEEE computer society. doi:10.1109/ICPR.2010.764
  • Bussman, G., & Alfermann, D. (1994). Drop-out and the female athlete: A study with track-and-field athletes. In D. Hackfort (Ed.), Psycho-social issues and interventions in elite sport (pp. 89–128). Frankfurt: Lang.
  • Caro, C. A. (2012). College football success: The relationship between recruiting and winning. International Journal of Sports & Coaching, 7, 139–152.
  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37–46.
  • Davies, C. T. M., & Thompson, M. W. (1979). Aerobic performance of female marathon and male ultramarathon athletes. European Journal of Applied Physiology and Occupational Physiology, 41, 233–245.
  • De’ath, G., & Fabricius, K. E. (2000). Classification and regression trees: A powerful yet simple technique for ecological data analysis. Ecology, 81, 3178–3192.
  • Deaner, R. O. (2006). More males run fast a stable sex difference in competitiveness in U.S. distance runners. Evolution and Human Behavior, 27, 63–84.
  • Deaner, R. O., Lowen, A., Rogers, W., & Saksa, E. (2015). Does the sex difference in competitiveness decrease in selective sub-populations? A test with intercollegiate distance runners. PeerJ, 3, e884.
  • Depken, C. A., & Haglund, L. E. (2011). Peer effects in team sports: Empirical evidence from NCAA relay teams. Journal of Sports Economics, 12, 3–19.
  • DiFiori, J. P., Benjamin, H. J., Brenner, J. S., Gregory, A., Jayanthi, N., Landry, G., & Luke, A. (2014). Overuse injuries and burnout in youth sports: A position statement from the American Medical Society for Sports Medicine. British Journal of Sports Medicine, 48, 287–288.
  • Gabrielli, E., Fulle, S., Fanò-Illic, G., & Pietrangelo, T. (2015). Analysis of training load and competition during the PhD course of a 3000-m steeplechase female master athlete: An autobiography. European Journal of Translational Myology, 25, 195–202.
  • García, V., Mollineda, R. A., & Sánchez, J. S. (2009). Index of balanced accuracy: A performance measure for skewed class distributions. In Proceedings of the Fourth IbPRIA, Póvoa de Varzim, Portugal (pp. 441–448). Heidelberg: Springer.
  • Geisser, S. (1975). The predictive sample reuse method with applications. Journal of the American Statistical Association, 70(350), 320–328.
  • Goose, M., & Winter, S. (2012). The coach’s impact on long distance runners’ training and competition motivation. International Journal of Sports Science & Coaching, 7, 383–398.
  • Herda, T. J., Ryan, E. D., DeFreitas, J. M., Costa, P. B., Walter, A. A., Hodge, K. M., ... Cramer, J. T. (2009). Can recruiting rankings predict the success of NCAA Division I football teams? An examination of the relationships among rivals and scouts recruiting rankings and Jeff Sagarin end-of-season ratings in collegiate football. Journal of Quanitative Analysis in Sports, 5, 4.
  • Hothorn, T., Hornik, K., & Zeileis, A. (2006). Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical Statistics, 15, 651–674.
  • Hothorn, T., & Zeileis, A. (2015). Partykit: A modular toolkit for recursive partytioning in R. Journal of Machine Learning Research, 16, 3905–3909. Retrieved from http://www.jmlr.org
  • Humphreys, B. R., Paul, R. J., & Weinbach, A. P. (2016). Performance expectations and the tenure of head coaches: Evidence from NCAA football. Research in Economics, 70, 482–492.
  • Judson, K. M., James, J. D., & Aurand, T. W. (2004). Marketing the university to student-athletes: Understanding university selection criteria. Journal of Marketing Higher Education, 14, 23–40.
  • Kelly, B. C., & Baghurst, T. (2009). Development of the coaching issues survey (CIS). The Sport Psychologist, 23, 367–387.
  • Kuhn, M. (2016). Caret: classification and regression training. Contributions from Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., and the R Core Team. Retrieved from http://CRAN.R-project.org/package=caret. R package version 6.0-73
  • Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174.
  • Langelett, G. (2003). The relationship between recruiting and team performance in Division 1A college football. Journal of Sports Economics, 4, 240–245.
  • Louncks, A. B., Manore, M. M., Sanborn, C. F., Sundgot-Borgen, J., & Warren, M. P. (2007). The female athlete triad. Medicine & Science in Sports & Exercise, 39, 1867–1882.
  • Lynch, K. E., Thomas, A., & Gibson, B. (2016). Baseline ability makes a larger contribution to race performance in higher school sprinters than race experience or training exposure. Pediatric Exercise Science, 28(4), 565–571.
  • Marx, J., Huffmon, S., & Doyle, A. (2008). The student-athlete model and the socialization of intercollegiate athletes. Athletic Insight, 10(1). Retrieved from http://www.athleticinsight.com/Vol10Iss1/StudentAthleteModel.htm
  • Meinhausen, N. (2006). Quantile regression forests. Journal of Machine Learning Research, 7, 983–999.
  • NCAA Division I Manual. (2014-2015). Indianapolis, Indiana: National Collegiate Athletic Association. Retrieved from https://www.ncaapublications.com/productdownloads/D115.pdf
  • R Core Team. (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from http://www.R-project.org/.
  • Rauh, M. J., Margherita, A. J., Rice, S. G., Koepsell, T. D., & Rivara, F. P. (2000). High school cross country running injuries: A longitudinal study. Clinical Journal of Sport Medicine, 10, 110–116. Retrieved from http://journals.lww.com
  • Rees, T., Hardy, L., Güllich, A., Abernethy, B., Côté, J., Woodman, T., … Warr, C. (2016). The great British medalists project: A review of current knowledge on the development of the world’s best sporting talent. Sports Medicine, 46, 1041–1058.
  • Saunders, P. U., Pyne, D. B., Telford, R. D., & Hawley, J. A. (2004). Factors affecting running economy in trained distance runners. Sports Medicine, 34, 465–485.
  • Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society B, 36, 111–147.
  • Strobl, C., Boulesteix, A., Zeileis, A., & Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics, 8, 25.
  • Tenforde, A. S., Sayres, L. C., McCurdy, M. L., Collado, H., Sainani, K. L., & Fredericson, M. (2009). Overuse injuries in high school runners: Lifetime prevalence and prevention strategies. Pm&R, 3, 125–131.
  • Thompson, S. H., Smith, P., & DiGioacchino, R. (2004). Performance-related injuries and exercise orientation of National Collegiate Athletic Association Division I, II, and III female collegiate cross country runners. Women in Sport & Physical Activity Journal, 13, 17–26.
  • Treadway, D. C., Adams, G., Hanes, T. J., Perrewé, P. L., Magnusen, M. J., & Ferris, G. R. (2012). The roles of recruiter political skill and performance resource leveraging in NCAA football recruitment effectiveness. Journal of Management, 40, 1607–1626.
  • Vaeyens, R., Güllich, A., Warr, C. R., & Philippaerts, R. (2009). Talent identification and promotion programmes of Olympic athletes. Journal of Sports Sciences, 27, 1367–1380.
  • Wilmore, H. J. (1991). Eating and weight disorders in the female athlete. International Journal of Sport Nutrition, 1, 104–117.
  • Wooten, H. R. (1994). Cutting losses for student-athletes in transition: An integrative transition model. Journal of Employment Counseling, 31, 2–9.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.