Discussion

Some athletes within the tested group are considered the world’s best rowers4 and the opportunity to collect such a data set from an elite sporting population is rare. Even though the planned number of athletes to be tested was reduced by illness and injury, this data set provides an invaluable insight into high per- formance rowing. Due to their elite nature, such populations will be small and therefore statistical significance is difficult to achieve although for such studies, the data points of interest may be outliers rather than group averages. Athletes who achieved exceptionally high results become the models for the rest of the training group to aspire towards while low results identify athletes with technical areas for improvement.

It also became clear how difficult it is to apply scientific controls to an elite group of athletes where the ultimate control of the session lies with the coach rather than the investigator. While mean boat velocities were being recorded, some athletes treated the session more like squad selection than others which changed how they approached the session. During the 1000 m interval, a range of stroke rates and race profiles were adopted making the resultant water times unreliable. Both high stroke rates and race profiling (using a range of stroke rates) led to faster mean boat velocity.

This research has succeeded in differentiating an elite population of athletes. Figures 3.9 and 3.10 provides visual differentiation of the four example athletes for all parameters except VD. VD is the difference between the maximum and minimum velocities within a stroke cycle and is likely to be correlated with ath- lete mass and power output rather than technical competence and therefore not considered so relevant to the aims of this study. The lack of correlation between VR, QoC and QoD, the component velocity parameters, justifies the adoption of all three parameters to describe different aspects of the stroke and the correlation between VR and the global parameter QoS suggests that VR is a key measure of efficiency. What is not clear from this study is what effect a change in one parameter has on other parameters.

Between the groups, the difference between women with and without gold medals was not significant. In elite homogenous populations where athlete num- bers are small and performance levels tend towards threshold values, lack of statist- ical differentiation is expected. However from this data set, normative (or model) velocity parameters for heavy-weight women can be concluded from the gold medal athletes (figures 3.11 and 3.12).

Significant differences were observed between the light-weight men and both

the light-weight and heavy-weight women although it is not clear whether athlete gender led to a difference in normative values or whether the light-weight men as a group row their boats differently. This finding warrants further research.

Coaches should be trained how to interpret this new data set. The coach ranking exercise highlighted the need to supplement information obtained by ob- servation with quantitative data while at the same time, it is not pertinent to rank an athlete on QoS alone. Taking athletes A to D as an example, table 3.4 provides a comparison of component and global velocity parameters and relative results at the subsequent GBRT long distance sculling trials. Results for most athletes correlate between velocity parameters and trial ranking except for athlete D who ranks highly for velocity parameters but doesn’t rank highly at the trial. Such a comparison assumes that all athletes have the same physiological output and that other key biomechanical determinants of performance are the same, e.g. effective stroke arc. DR is a measure of the amount of time the athlete spends driving the boat versus total stroke time and athletes C and D are outside the normative range of 33.5 % for gold medallist heavy-weight women (taken from fig- ure 3.11). This might explain the discrepancy between these results and suggest that a relevant intervention should be taken.

Table 3.4: Comparison of HW sculling trial rankings to velocity parameters.

GBRT trial QoS QoC QoD VR

A 1st 3rd 3rd 3rd 1st

B 2nd ₂nd ₂nd ₂nd ₂nd

C 4th ₄th ₄th ₄th ₄th

D 3rd 1st 1st 1st 3rd

The proposed flowchart in figure 3.13 could act as a guide for coaches to em- ploy when considering this new data set. The first step is to assess DR since it may be masking true findings for other parameters (as previously discussed). If it is outside the normative range for the group, actions should be taken including modifying the rowed arc through rigging or coaching interventions or changing mechanical loading. It is then advised in step two to focus on QoC before other velocity parameters since this is believed to be the most readily trainable aspect of the rowing stroke (from personal conversation with Klaus Mattes, Hamburg Uni- versity, Germany). The flowchart suggests the adoption of real-time feedback as interventions for modifying QoC and QoD, techniques that are trialled in chapter 6. If significant interventions have been made as a result of steps one or two, Klaus further suggests retesting athletes prior to drawing further conclusions around ne- cessary interventions due to potential associated changes to velocity parameters. I.e. it is unlikely to be possible to modify one velocity parameter in isolation.

At high extreme of range. Athlete spends too long with

oars in the water.

Assess Drive Ratio

At low extreme of range. Athlete spends too little time

with oars in the water.

Check effective stroke arc

Arc too large. Reduce through rigging change or rowing less slide.

Correct arc. Check for consistent pacing strategy.

Consider reducing mechanical loading.

Check effective stroke arc

Arc too short. Assess rigging or

retrain athlete.

Correct arc. Increase mechanical loading.

QoC below normative value for

boat type?

Retrain with real-time feedback

QoD below normative value for

boat type?

Retrain with real-time feedback

1

2

3