Did you know that Oar Angles showed the lowest variation (1-2%) and were the most consistent indicators? Force Curve indicators had the highest variation (10-15%), while Timing, Force-Power, and Rowing Style indicators fell in the mid-range (2-6%).

After the creation of a new type of information, it is essential to understand its significance. The first step involves quantitative evaluation, i.e., determining the average values within population groups and assessing data consistency. This allows us to determine whether each individual data point is typical or deviates significantly from the norm. The next step is qualitative evaluation, where we assess whether deviations from the norm have positive or negative effects on performance. To achieve this, we should correlate the new data with previously known information, in this case, criteria for rowing efficiency. The final step involves developing benchmarks or "gold standards" for the new data and, if necessary, creating training methods to improve it.

Here, we take the first step in evaluating new data on the variability of rowing technique and crew synchronization, obtained using the recently developed BioRow method (RBN 2024/10). Data collected in 2024 from 70 boats and 195 rowers (113 males and 82 females) were reprocessed using this method producing 1,464 data samples analyzed. Twenty defined variables were grouped into six categories:

1. Timing (stroke rate, drive time, and rhythm),
2. Oar Angles (catch, finish, and total angles),
3. Force-Power (maximal and average forces, work per stroke [WpS]),
4. Force Curve (ratio of average to maximal force, position of peak force, catch and finish gradients),
5. Rowing Factors (Catch Factor and Rowing Style Factor),
6. Synchronization (n.16-20 in RBN 2024/10).

In the first Timing group, the variation in drive time - and the related rowing rhythm - was nearly twice as high in sweep rowers as in scullers, indicating greater consistency among scullers. Scullers also exhibited higher rhythm values, with relatively longer drive phase, and lower SD, indicating reduced rhythm variation.

Scullers in singles and doubles displayed lower variation in catch angles than sweep rowers, especially at lower stroke rates. Similarly, finish angle variation was lower for single scullers and stroke rowers compared to other rowers, which may serve as a criterion for selection of stroke rowers. At higher stroke rates, sweep rowers were slightly more consistent in finish angles.

Similar patterns emerged in the Force-Power group, where scullers in singles and doubles showed roughly half the variation of other rowers.

In the Force Curve group, scullers in doubles and quads exhibited higher variation in the position of peak force and in catch and finish force gradients. Variation in the Rowing Style Factor was lower for singles, doubles, and surprisingly, fours. The difference diminished at higher rates.

For the Catch Factor, sweep rowers exhibited significantly higher variation. At 20 spm, the variation was over three times higher for sweep rowers (29 ms) than for scullers (8 ms); at 40 spm, it was twice as high (8 ms vs. 4 ms).

Lastly, variation in key Synchronization indicators was significantly higher in large boats (4x, 8+) compared to small boats (2x, 2-). At catch, the variation of timing from the stroke rower at the handle and seat, decreased 3-4 times at higher stroke rates, supporting our earlier hypothesis that rowing at high stroke rates is an effective drill for synchronization.

As always, feedback, questions, and suggestions are welcome

©2024 Dr. Valery Kleshnev

This is a short version of the Newsletter. To access the full text, subscribe to BioRow membership here: https://biorow.com/membership/