The Relationship of Training Methods in NCAA Division I Cross-Country Runners and 10,000-Meter Performance

For the past several decades, machine learning has played an important role in sports science with regard to player performance and result prediction. However, it is still challenging to quantify team-level game performance because there is no strong ground truth. Thus, a team cannot receive feedback in a standardized way. The aim of this study was twofold. First, we designed a metric called LAX-Score to quantify a collegiate lacrosse team's athletic performance. Next, we explored the relationship between our proposed metric and practice sensing features for performance enhancement. To derive the metric, we utilized feature selection and weighted regression. Then, the proposed metric was statistically validated on over 700 games from the last three seasons of NCAA Division I women's lacrosse. We also explored our biometric sensing dataset obtained from a collegiate team's athletes over the course of a season. We then identified the practice features that are most correlated with high-performance games. Our results indicate that LAX-Score provides insight into athletic performance beyond wins and losses. Moreover, though COVID-19 has stalled implementation, the collegiate team studied applied our feature outcomes to their practices, and the initial results look promising with regard to better performance.

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Collegiate Cross Country Coaches’ Knowledge of Eating Disorders

Women in Sport and Physical Activity Journal ◽

10.1123/wspaj.12.1.53 ◽

2003 ◽

Vol 12 (1) ◽

pp. 53-65 ◽

Cited By ~ 5

Author(s):

Cheryl Govero ◽

Barbara A. Bushman

Keyword(s):

Eating Disorders ◽

High Risk ◽

Ncaa Division I ◽

Confidence Level ◽

Division I ◽

Knowledge Level ◽

Sources Of Information ◽

Early Morning Training Impacts Previous Night’s Sleep in NCAA Division I Cross Country Runners

Women in Sport and Physical Activity Journal ◽

10.1123/wspaj.2018-0050 ◽

2020 ◽

Vol 28 (1) ◽

pp. 27-33

Author(s):

Courteney L. Benjamin ◽

William M. Adams ◽

Ryan M. Curtis ◽

Yasuki Sekiguchi ◽

Gabrielle E.W. Giersch ◽

...

Keyword(s):

Sleep Quality ◽

Ncaa Division I ◽

Early Morning ◽

Sleep Diary ◽

Training Time ◽

Sleep Quantity ◽

The Hours ◽

Cross Country ◽

Cross Country Runners ◽

Study Participants

The effects of training time on sleep has been previously studied; however, the influence on sleep in female collegiate cross-country runners is unknown. The aim of this study was to investigate the influence of training time on self-reported sleep metrics. Eleven female collegiate cross-country runners (mean [M] age = 19 years, standard deviation [SD] age = 1 year; M [SD] body mass = 58.8 [9.6] kg; M [SD] height = 168.4 [7.7] cm; M [SD] VO2max = 53.6 [5.6] mL·kg−1·min−1) competing in the 2016 NCAA cross-country season were included in this study. Participants completed a sleep diary daily to assess perceived measures of sleep on days when training took place between the hours of 5:00–8:00 a.m. (AM), and when training did not take place during this time (NAM). Sleep quality questions utilized a 5-point Likert scale, in which a score of 1 is associated with the worst outcomes and a score of 5 is associated with the best outcomes. Sleep duration was significantly higher on NAM (M [SD] = 8.26 [1.43] h) compared to AM (M [SD] = 7.97 [1.09] h, p < .001). Sleep quality was significantly higher on NAM (M [SD] = 3.30 [1.01]) compared to AM (M [SD] = 3.02 [1.06], p < .001). The impairment of sleep quantity and quality the night prior to early morning training suggests that future considerations should be made to sleep schedules and/or training times to optimize perceived sleep quality.

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