Identifying Pacing Profiles in 2000 Metre World Championship Rowing

The pacing strategy adopted by athletes is a major determinants of success during timed competition. Various pacing profiles are reported in the literature and its importance depends on the mode of sport. However, in 2000 metre rowing, the definition of these pacing profiles has been limited by the minimal availability of data. PURPOSE: Our aim is to objectively identify pacing profiles used in World Championship 2000 metre rowing races using reproducible methods. METHODS: We use the average speed for each 50 metre split for each available boat in every race of the Rowing World Championships from 2010-2017. This data was scraped from www.worldrowing.com. This data set is publicly available (https://github.com/danichusfu/rowing pacing profiles) to help the field of rowing research. Pacing profiles are determined by using k-shape clustering, a time series clustering method. A multinomial logistic regression is then fit to test whether variables such as boat size, gender, round, or rank are associated with pacing profiles. RESULTS: Four pacing strategies (Even, Positive, Reverse J-Shaped, and U-Shaped) are identified from the clustering process. Boat size, round (Heat vs Finals), rank, gender, and weight class are all found to affect pacing profiles. CONCLUSION: We use an objective methodology with more granular data to identify four pacing strategies. We identify important associations between these pacing profiles and race factors. Finally, we make the full data set public to further rowing research and to replicate our results.

Automated summary

The pacing strategy used by athletes is a crucial factor for success in timed competitions. This study aims to objectively identify pacing profiles used in World Championship 2000 metre rowing races using reproducible methods. By analyzing data from 2010-2017 Rowing World Championships and using k-shape clustering and multinomial logistic regression, four pacing strategies are identified, and factors such as boat size, round, rank, gender, and weight class are found to affect pacing profiles.