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2015-12-10

Stephen Meschke

5 balls, 4 clubs, 5 clubs, runfinder, handstand, 90 minutes

# 5 ball - 645, 744, (6x4)*
# 6 ball - async/sync transitions
# 7 ball - [61, 90, 9, 9, 97, 37, 34, 117, 91, 14, 16, 10, 28, 23, 55, 50, 23, 9, 19, 75, 7, 102, 54, 9, 10, 133, 70, 14, 68, 99, 21, 37, 30, 21, 21, 30, 43, 52, 14, 52, 12, 61, 19, 46, 18, 18, 23, 9, 63, 36, 30, 12, 109, 61, 90, 9, 9, 97, 37, 34, 117, 91, 14, 16, 10, 28, 23, 55, 50, 23, 9, 19, 75, 7, 102, 54, 9, 10, 133, 70, 14, 68, 99, 21, 37, 30, 21, 21, 30, 43, 52, 14, 52, 12, 61, 19, 46, 18, 18, 23, 9, 63, 36, 30, 12, 109]
Density graph: http://i.imgur.com/IFIcUhu.png
The average for 100 runs was 41. The optimal time for working on 7b endurance is eight minutes.
# 4 clubs - 534
# 5 clubs - no progress
# handstand - tucking knees into chest makes it easier to balance

#newfeaturerequest. It would be great if I could render that graph by entering, 'Pattern_name:[list of catch counts].' This is the Python code that I used to generate the density function:


import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde

data = [61, 90, 9, 9, 97, 37, 34, 117, 91, 14, 16, 10, 28, 23, 55, 50, 23, 9, 19, 75, 7, 102, 54, 9, 10, 133, 70, 14, 68, 99, 21, 37, 30, 21, 21, 30, 43, 52, 14, 52, 12, 61, 19, 46, 18, 18, 23, 9, 63, 36, 30, 12, 109, 61, 90, 9, 9, 97, 37, 34, 117, 91, 14, 16, 10, 28, 23, 55, 50, 23, 9, 19, 75, 7, 102, 54, 9, 10, 133, 70, 14, 68, 99, 21, 37, 30, 21, 21, 30, 43, 52, 14, 52, 12, 61, 19, 46, 18, 18, 23, 9, 63, 36, 30, 12, 109]

density = gaussian_kde(data)
xs = np.linspace(0,200,200)
density.covariance_factor = lambda : .25
density._compute_covariance()

plt.plot(xs,density(xs))

plt.show()

Total practice time: 90 minutes

Location: Racquetball Court

Comments (2)
7b_wizard - How did you find optimal time from a graph with density and catches (but no time in it) ?
Stephen Meschke - It's more of a guess. I can see that there are lots of runs that are <average/2 in the second half of the data.