Data Analysis of Middle Distance Running Strategy Based on Binary Discrete Choice Model
- DOI
- 10.2991/978-94-6463-064-0_51How to use a DOI?
- Keywords
- middle-distance running; pace strategy; binary discrete selection model
- Abstract
Through video analysis, this paper establishes the database of the pace strategy of distance runners in the 2008–2019 World Championships and the final of the Olympic Games. Combined with the econometric binary discrete selection model, this paper analyzes the differences between the self pace strategy, team strategy, the pace strategy of award-winning athletes and non award-winning athletes, and the pace strategy of male and female athletes. The results show that: 1) In the middle distance running, high-level athletes prefer to adopt the leading running strategy, whether there is team cooperation or not. 2) In 1500 m, athletes with 4–2 tactics (the speed ratio of the last 400 m is the highest and the speed ratio of the second 400 m is the lowest) are more likely to win medals; but athletes who adopt 1–2 tactics (the speed ratio of the first 400 m is the highest and the speed ratio of the second 400 m is the lowest) are more likely to win the medals in 800 m; 3) In the 1500 m events, the overall pace of male athletes is more even than female athletes.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Tingting Zheng AU - Xi Yang AU - Shanwen Cao PY - 2022 DA - 2022/12/27 TI - Data Analysis of Middle Distance Running Strategy Based on Binary Discrete Choice Model BT - Proceedings of the 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022) PB - Atlantis Press SP - 496 EP - 504 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-064-0_51 DO - 10.2991/978-94-6463-064-0_51 ID - Zheng2022 ER -