Data Spaghetti (Thursday session)

On Thursday I did a few sessions experimenting with the RIM software. I found that the Stroke Efficiency metric is really sensitive to the calibration.

I did an experiment where I recorded the entire session in 4 parts. Between parts I took the phone out of the holder while not running RIM, waved with it, and put it back in the holder. I believe the only difference was the time I waited while not rowing but RIM already running.

I saw quite big differences in the Stroke Efficiency metric. In the first 2 “parts”, I saw values around 0.4 maximum, and even negative values. In the last two “parts” I saw values above 2 at 20spm. I think these were the “real” values.

RIM Stroke Analysis
RIM Stroke Analysis
RIM stroke metrics
RIM stroke metrics

This morning I managed to grab the analysis data from the analytics.rowinginmotion.com website using jsfiddle.net and read them into pylab. Here’s an example:

RIM data imported into python
RIM data imported into python

This is just a raw plot of the acceleration data of a few strokes at 30spm. Now it will be easy to compare my rowing model with real data!

The other application is CrewNerd. Also CrewNerd can export acceleration data, but I have yet to experiment with it. Will do in the near future.

But I took a different look at some of my 4x1km data this morning. I exported all 4 intervals into CSV tables, dumped all the data in one table and started playing with pivot charts:

Pivot data of Speed vs Stroke Rate
Pivot data of Speed vs Stroke Rate
Pivot data of Check vs Stroke Rate
Pivot data of Check vs Stroke Rate
Meters per Stroke vs Speed
Meters per Stroke vs Speed

I still have to find a useful plot that enables to draw conclusions from the data. Here is Check vs Speed for different stroke rates:

speedcheckspm

Data Spaghetti!

No training today. I feel a cold is coming on me, and I will race tomorrow.

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