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FRANSA BİSİKLET TURU MAYO KLASMANINDAKİ SPORCULARIN GENEL KLASMAN SIRALAMASIYLA İLİŞKİSİNİN ÇEŞİTLİ DEĞİŞKENLERLE KARŞILAŞTIRILMASI

Yıl 2023, Cilt: 12 Sayı: 2, 85 - 95, 30.04.2023
https://doi.org/10.22282/tojras.1253703

Öz

Fransa Bisiklet Turunu dünyanın en popüler yarışlarından birisi olması ve mayo klasmanlarında gözlemlenen literatür eksikliğinden bu çalışmanın yapılmasına ihtiyaç duyulmuştur. Bu çalışmanın amacı 2000-2022 yılları arasındaki Fransa Bisiklet Turunda beyaz mayo, kırmızı mayo ve yeşil mayo klasmanındaki ilk üç (toplam; altmış dokuz) sporcunun müsabaka sonuçlarının, genel klasman sıralamasıyla ilişkisinin çeşitli değişkenlere göre incelenmesidir. Çalışmadaki sporcuların yarış sonuçları Procyclingstats ve Tour de France sitelerinden alınmıştır. Her mayo klasmanı için ilk üç sırada yer alan 69 sporcunun genel klasmandaki sıraları yaş, sıra, hız değişkenleriyle karşılaştırılmıştır. Normal dağılım sergilemeyen verilerin karşılaştırılmasında Kruskal Wallis ve Spearman korelasyon testleri kullanılmıştır. Araştırma sonuçlarında beyaz mayo klasmanındaki ilk üç sporcunun genel klasmandaki ortalama sıraları arasında anlamlı farklılık tespit edilmiştir. Beyaz mayo klasmanındaki ilk üç sporcunun “genel klasman sıraları” ile arasında pozitif yönde yüksek düzeyde (rho=0,726), yeşil mayo klasmanındaki (puana dayalı) ilk üç sporcunun “genel klasman sıraları ” ve “ortalama hız” arasında negatif yönde orta düzeyde (rho=-0,477), kırmızı mayo klasmanındaki (puana dayalı) ilk üç sporcunun “genel klasman sıraları” ve “ortalama hız” arasında negatif yönde orta düzeyde (rho=-0,429) ve “genel klasman sırası” ile “genel klasman yaş” arasında pozitif yönde düşük düzeyde bir ilişki (rho=0,29), olduğu tespit edilmiştir. Kırmızı ve yeşil mayo klasman sıralamaları puanla ve beyaz mayo klasmanı sporcuların bitirme sürelerine göre belirlenmektedir. Kırmızı mayo klasmanındaki kişiler en iyi yokuş çıkan sporcular olduğu için bu kişilerin yaş faktörüyle düşük düzeyde ilişkili olduğu sonucuna varılmıştır.

Destekleyen Kurum

Destekleyen Kurum Yoktur

Kaynakça

  • Cherchye, L., & Vermeulen, F. (2006). Robust rankings of multidimensional performances an application to tour de france racing cyclists. Journal of Sports Economics, 7(4), 359–373. https://doi.org/10.1177/1527002505275092
  • Cohen, C., Brunet, E., Roy, J., & Clanet, C. (2021). Physics of road cycling and the three jerseys problem*. Journal of Fluid Mechanics, 914, 1–22. https://doi.org/10.1017/jfm.2020.1022
  • Dauncey, H., & Hare, G. (2003). The Tour de France: A pre-modern contest in a post-modern context. The International Journal of the History of Sport, 20(2), 1–29. https://doi.org/10.1080/09523360412331305613
  • Earnest, C. P., Foster, C., Hoyos, J., Muniesa, C. A., Santalla, A., & Lucia, A. (2009). Time trial exertion traits of cycling’s Grand Tours. International journal of sports medicine, 30(4), 240–244. https://doi.org/10.1055/s-0028-1105948
  • El Helou, N., Berthelot, G., Thibault, V., Tafflet, M., Nassif, H., Campion, F., Hermine, O., & Toussaint, J. F. (2010). Tour de france, giro, vuelta, and classic european races show a unique progression of road cycling speed in the last 20 years. Journal of Sports Sciences, 28(7), 789–796. https://doi.org/10.1080/02640411003739654
  • Fernandez-Garcia, B., Perez-Landaluce, J., Rodriguez-Alonso, M., & Terrados, N. (2000). Intensity of exercise during road racepro-cycling competition. Medicine & Science in Sports & Exercise, 32, 1002–1006. https://doi.org/10.1097/00005768-200005000-00019
  • Janssens, B., Bogaert, M., & Maton, M. (2022). Predicting the next pogačar: a data analytical approach to detect young professional cycling talents. Annals of Operations Research. https://doi.org/10.1007/s10479-021-04476-4
  • Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. https://doi.org/10.2307/2529310
  • Leo, P., Giorgi, A., Lorang, D., Spragg, J., & Mujika, I. (2020). Workload characteristics and race performance of u23 and elite cyclists during an uci 2. pro multistage race (tour of the alps). Journal of Science & Cycling, 9(02), 4–6.
  • Lucía, A., Hoyos, J., Carvajal, A., & Chicharro, J. L. (1999). Heart rate response to professional road cycling: The tour de france. International Journal of Sports Medicine, 20(3), 167–172. https://doi.org/10.1055/s-1999-970284
  • Lucia, A., Hoyos, J., & Chicharro, J. L. (2001). Physiology of professional road cycling. Sports Medicine, 31(5), 325–337. https://doi.org/10.2165/00007256-200131050-00004
  • Menaspà, P., Abbiss, C. R., & Martin, D. T. (2013). Performance analysis of a world-class sprinter during cycling Grand Tours. International Journal of Sports Physiology and Performance, 8(3), 336–340. https://doi.org/10.1123/ijspp.8.3.336
  • Morley, B., & Thomas, D. (2005). An investigation of home advantage and other factors affecting outcomes in English one-day cricket matches. Journal of Sports Sciences, 23(3), 261–268. https://doi.org/10.1080/02640410410001730133
  • Padilla, S., Mujika, I., Cuesta, G., & Goiriena, J. J. (1999). Level ground and uphill cycling ability in professional road cycling. Medicine and Science in Sports and Exercise, 31(6), 878–885. https://doi.org/10.1097/00005768-199906000-00017
  • Procyclingstats. (2022). Tour de France Results. https://www.procyclingstats.com/race/tour-de-france/2022
  • Rogge, N., Reeth, D. Van, & Puyenbroeck, T. Van. (2012). Performance evaluation of Tour de France cycling teams using Data Envelopment Analysis.
  • Schumacher, Y. O., Mroz, R., Mueller, P., Schmid, A., & Ruecker, G. (2006). Success in elite cycling: A prospective and retrospective analysis of race results. Journal of Sports Sciences, 24(11), 1149–1156. https://doi.org/10.1080/02640410500457299
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6. baskı). Pearson Education Limited.
  • TourdeFrance. (2022). The history of the tour de france. https://www.letour.fr/en/rankings
  • UCI. (2022). Uci cycling regulations part 2: road races. http://www.uci.ch/mm/Document/News/Rulesandregulation/18/23/94/2-ROA-20180101-E_English.PDF
  • Vikipedi. (2022). Fransa bisiklet turu. https://tr.wikipedia.org/wiki/Fransa_Bisiklet_Turu
  • Vogt, S., Roecker, K., Schumacher, Y. O., Pottgiesser, T., Dickhuth, H. H., Schmid, A., & Heinrich, L. (2008). Cadence-power-relationship during decisive mountain ascents at the tour de france. International Journal of Sports Medicine, 29(3), 244–250. https://doi.org/10.1055/s-2007-965353

COMPARISON OF THE RELATIONSHIP OF THE ATHLETES VYING FOR JERSEY IN THE TOUR DE FRANCE WITH THEIR GENERAL CLASSIFICATION RANKINGS IN TERMS OF VARIOUS VARIABLES

Yıl 2023, Cilt: 12 Sayı: 2, 85 - 95, 30.04.2023
https://doi.org/10.22282/tojras.1253703

Öz

This study was needed due to the fact that the Tour de France is one of the most popular races in the world and the lack of literature observed in jersey classifications. The aim of this study is to examine the correlation between the race results of the first three (total; sixty-nine) athletes vying for the white jersey, red jersey and green jersey in the Tour de France between the years 2000-2022 in terms of various variables. The race results of the athletes in the study were obtained through the web sites of Procyclingstats and Tour de France. The ranks of the 69 athletes in the top three for each jersey classification were compared with age, position and speed variables in the general classification. Kruskal Wallis and Spearman correlation tests were used to compare nonparametric data. In the results of the research, a significant difference was found between the average rankings of the first three athletes vying for white jersey in the general classification. It was found that there is a high-degree positive correlation (rho=0,726) between the first three athletes vying for the white jersey and general classification rankings; that there is a negative moderate degree correlation (rho=-0,477) between the "general classification rankings" (based on points) and "average speed" of the top three athletes vying for the green jersey; that there is a negative moderate degree correlation (rho=-0,429) between the "general classification rankings" (based on points) and "average speed" of the top three athletes vying for the red jersey; and that there is a low-degree positive correlation (rho=0.29) between “general classification rankings” and “ general classification by age”. Classifications for red and green jerseys are based on the points, and the white jersey classification is based on the finish times of the athletes. As the contenders who wore the red jersey are the best climbers, a low degree correlation was found with the age factor of these athletes.

Kaynakça

  • Cherchye, L., & Vermeulen, F. (2006). Robust rankings of multidimensional performances an application to tour de france racing cyclists. Journal of Sports Economics, 7(4), 359–373. https://doi.org/10.1177/1527002505275092
  • Cohen, C., Brunet, E., Roy, J., & Clanet, C. (2021). Physics of road cycling and the three jerseys problem*. Journal of Fluid Mechanics, 914, 1–22. https://doi.org/10.1017/jfm.2020.1022
  • Dauncey, H., & Hare, G. (2003). The Tour de France: A pre-modern contest in a post-modern context. The International Journal of the History of Sport, 20(2), 1–29. https://doi.org/10.1080/09523360412331305613
  • Earnest, C. P., Foster, C., Hoyos, J., Muniesa, C. A., Santalla, A., & Lucia, A. (2009). Time trial exertion traits of cycling’s Grand Tours. International journal of sports medicine, 30(4), 240–244. https://doi.org/10.1055/s-0028-1105948
  • El Helou, N., Berthelot, G., Thibault, V., Tafflet, M., Nassif, H., Campion, F., Hermine, O., & Toussaint, J. F. (2010). Tour de france, giro, vuelta, and classic european races show a unique progression of road cycling speed in the last 20 years. Journal of Sports Sciences, 28(7), 789–796. https://doi.org/10.1080/02640411003739654
  • Fernandez-Garcia, B., Perez-Landaluce, J., Rodriguez-Alonso, M., & Terrados, N. (2000). Intensity of exercise during road racepro-cycling competition. Medicine & Science in Sports & Exercise, 32, 1002–1006. https://doi.org/10.1097/00005768-200005000-00019
  • Janssens, B., Bogaert, M., & Maton, M. (2022). Predicting the next pogačar: a data analytical approach to detect young professional cycling talents. Annals of Operations Research. https://doi.org/10.1007/s10479-021-04476-4
  • Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. https://doi.org/10.2307/2529310
  • Leo, P., Giorgi, A., Lorang, D., Spragg, J., & Mujika, I. (2020). Workload characteristics and race performance of u23 and elite cyclists during an uci 2. pro multistage race (tour of the alps). Journal of Science & Cycling, 9(02), 4–6.
  • Lucía, A., Hoyos, J., Carvajal, A., & Chicharro, J. L. (1999). Heart rate response to professional road cycling: The tour de france. International Journal of Sports Medicine, 20(3), 167–172. https://doi.org/10.1055/s-1999-970284
  • Lucia, A., Hoyos, J., & Chicharro, J. L. (2001). Physiology of professional road cycling. Sports Medicine, 31(5), 325–337. https://doi.org/10.2165/00007256-200131050-00004
  • Menaspà, P., Abbiss, C. R., & Martin, D. T. (2013). Performance analysis of a world-class sprinter during cycling Grand Tours. International Journal of Sports Physiology and Performance, 8(3), 336–340. https://doi.org/10.1123/ijspp.8.3.336
  • Morley, B., & Thomas, D. (2005). An investigation of home advantage and other factors affecting outcomes in English one-day cricket matches. Journal of Sports Sciences, 23(3), 261–268. https://doi.org/10.1080/02640410410001730133
  • Padilla, S., Mujika, I., Cuesta, G., & Goiriena, J. J. (1999). Level ground and uphill cycling ability in professional road cycling. Medicine and Science in Sports and Exercise, 31(6), 878–885. https://doi.org/10.1097/00005768-199906000-00017
  • Procyclingstats. (2022). Tour de France Results. https://www.procyclingstats.com/race/tour-de-france/2022
  • Rogge, N., Reeth, D. Van, & Puyenbroeck, T. Van. (2012). Performance evaluation of Tour de France cycling teams using Data Envelopment Analysis.
  • Schumacher, Y. O., Mroz, R., Mueller, P., Schmid, A., & Ruecker, G. (2006). Success in elite cycling: A prospective and retrospective analysis of race results. Journal of Sports Sciences, 24(11), 1149–1156. https://doi.org/10.1080/02640410500457299
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6. baskı). Pearson Education Limited.
  • TourdeFrance. (2022). The history of the tour de france. https://www.letour.fr/en/rankings
  • UCI. (2022). Uci cycling regulations part 2: road races. http://www.uci.ch/mm/Document/News/Rulesandregulation/18/23/94/2-ROA-20180101-E_English.PDF
  • Vikipedi. (2022). Fransa bisiklet turu. https://tr.wikipedia.org/wiki/Fransa_Bisiklet_Turu
  • Vogt, S., Roecker, K., Schumacher, Y. O., Pottgiesser, T., Dickhuth, H. H., Schmid, A., & Heinrich, L. (2008). Cadence-power-relationship during decisive mountain ascents at the tour de france. International Journal of Sports Medicine, 29(3), 244–250. https://doi.org/10.1055/s-2007-965353
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Spor Hekimliği
Bölüm Araştırma Makaleleri
Yazarlar

Nuray Satılmış 0000-0002-2086-1436

Duran Akbaş 0000-0002-4030-0935

Akan Bayrakdar 0000-0002-3217-0253

Işık Bayraktar 0000-0003-1001-5348

Erken Görünüm Tarihi 31 Mart 2023
Yayımlanma Tarihi 30 Nisan 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 12 Sayı: 2

Kaynak Göster

Vancouver Satılmış N, Akbaş D, Bayrakdar A, Bayraktar I. FRANSA BİSİKLET TURU MAYO KLASMANINDAKİ SPORCULARIN GENEL KLASMAN SIRALAMASIYLA İLİŞKİSİNİN ÇEŞİTLİ DEĞİŞKENLERLE KARŞILAŞTIRILMASI. TOJRAS. 2023;12(2):85-9.