**SOLD** Guzz Series II

MattHenry

Includes pickup, original cable, box, carry bag, and Guzz card. It also has two tiny rubber shims on the underside that were cut and installed by luthier and DB regular Craig Bumgarner. These are handy to get the pickup close to the strings but they can also be removed if you don't need the extra height.

PayPal payment and CONUS only please since I'm covering the shipping costs.

Thanks!

MattHenry

$350. Message me if you're interested and thanks!

MattHenry

$340

rgrice

Interested. I've heard the balance may be tricky and pickup can't be moved much. Also some issue with clip and bracing?
Need to check for clearance.

rgrice

Sent pm

MattHenry

Reduced to $325 and still available!

MattHenry

Reduced to $300 and still available.

MattHenry

still available!

MattHenry

still available

wim

The price of Matt Henry's pickup is changing by $-1.35 per day. We can use linear regression to model the future prices. For any buyers still sitting on the fence, it should be at $190 by Christmas time, and the item is predicted to hit $0 on 14 May 2017. I'll update the analyses as new data becomes available.

from datetime import date, timedelta
import matplotlib.pyplot as plt
import numpy as np

data = {
    date(2016,  8, 30): 350,
    date(2016,  9,  2): 340,
    date(2016,  9, 19): 325,
    date(2016, 10,  3): 300,
    date(2016, 10, 11): 280,
}

dates = sorted(data)
prices = [data[k] for k in dates]

xs = [(d-dates[0]).days for d in dates]
ys = prices[:]

gradient, y_intercept = np.polyfit(xs, ys, 1)
x_intercept = -y_intercept/gradient

x_fit = [dates[0], dates[0] + timedelta(days=x_intercept)]
y_fit = [y_intercept, 0]

zero_day = x_fit[1]
print(zero_day)

plt.plot(dates, prices, 'bo-')
plt.plot(x_fit, y_fit, 'g-.')
plt.title("Price of Matt Henry's guzz")
plt.xlabel('Date')
plt.ylabel('$ USD')
plt.ylim([0, 370])
plt.xlim([date(2016, 8, 20), date(2017, 6, 1)])
plt.xticks(rotation=25)
plt.show()

MattHenry

$280 and still available!

I'm not sure how today's price drop affects the linear regression model. But I do pride myself on consistency.