Data Analytics: Best Pace.
I calculated my distance versus pace for all my races this year [blue] and plotted them versus some of my best times [grey]. I didn't include this year's marathon or the 10K [too hilly]. I then fitted the 'best pace' line with and exponential function. If you use the fit - I should've run the marathon in 3:57 - so I actually came out a little ahead!!! You can really tell the races in which I was sick or not trying very hard - they're way off the best-fit.
The y0 = 355.77 - apparently this would be my fastest sprint speed. This is probably a little high, as I'm certain I can do sub-6 min/mile paces for 50 yards or so. "a" is 274.3 and "b" is equal to 0.0461. The R^2 value is 0.9688. The fit is much worse removing the y0 - which makes physical sense. The polynomial fit as good - but would really miss the marathon time by a lot and would also suggest I can't ever, no matter how short the distance, run less than a 6 minute/mile pace.
The y0 = 355.77 - apparently this would be my fastest sprint speed. This is probably a little high, as I'm certain I can do sub-6 min/mile paces for 50 yards or so. "a" is 274.3 and "b" is equal to 0.0461. The R^2 value is 0.9688. The fit is much worse removing the y0 - which makes physical sense. The polynomial fit as good - but would really miss the marathon time by a lot and would also suggest I can't ever, no matter how short the distance, run less than a 6 minute/mile pace.
If only I could channel my energies toward real work today...
Labels: data analysis, pace
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