T-Mobile vs AT&T 7 day Test Drive

Originally, this blog wasn't going to have any code in it. I was just going to tell you the result, but what fun would that be. Also, it wouldn't really fit the theme of the blog.

Disclaimer: I've been an AT&T cell subscriber since 1999 when they were still called Cingular Wireless.

That said, you might think I'm biased, right? But which way?

Anyhow, T-Mobile was offering an opportunity to test drive their network. Basically, they send you an iPhone 5S in the mail, and you get to try it out for a week. Luckily, for the sake of science, my personal phone is also an iPhone 5S, but on the AT&T network.

The plan was fairly simple, I'd run Ookla's speed test app at a number of different locations that I commonly travel to. My daily commute takes me from the Far East Bay to downtown Oakland, and I also went to Berkeley for soccer practice. I was expecting to just eyeball it, and if the conclusion was, "Hmm, well, it's pretty close" I'd be tempted to switch from AT&T to T-mobile.

Then I discovered that the speedtest app will let you email a csv file containing all your test results, so of course I had to take advantage of that and put the results under further scrutiny.

So, I wrote up about 60 lines of throw away python code to evaluate the results. The way I graded the results was in three parts:

If one network was faster in both download and upload speeds, we'd call that a win. If one network won upload and the other download we'd call that mixed results.

The second criteria I looked at was simply average upload and download speeds across all the tests.

The third criteria was how many dead-zones with little to no network speed I found. I played around with the thresholds for what I considered to be a "dead-zone".

There are probably a lot more interesting things I could do with the data, and I may yet do so, as I'm only on day 4 of the test.

Here is the throwaway python code I used:

import csv
import os

with open('data' + os.sep + 'att.csv', 'rb') as a:
    a_data = csv.reader(a)
    att_data = []
    for row in a_data:
        att_data.append(row)

with open('data' + os.sep + 'tmobile.csv', 'rb') as t:
    t_data = csv.reader(t)
    tmobile_data = []
    for row in t_data:
        tmobile_data.append(row)

att_wins = 0
tmobile_wins = 0
mixed = 0

att_sum_dl = 0
tmobile_sum_dl = 0
att_sum_ul = 0
tmobile_sum_ul = 0

total_comparisons = 0

att_dead_zones = 0
tmobile_dead_zones = 0
both_dead_zones = 0
dl_dead_threshold = 1000
ul_dead_threshold = 500

for a_row in att_data:
    for t_row in tmobile_data:
        if a_row and t_row:  # Ignore blank lines
            if a_row[0] == t_row[0] and a_row[0] != 'Date':  # if times match exactly
                if int(a_row[4]) > int(t_row[4]) and int(a_row[5]) > int(t_row[5]):
                    att_wins += 1
                elif int(a_row[4]) < int(t_row[4]) and int(a_row[5]) < int(t_row[5]):
                    tmobile_wins += 1
                if int(a_row[4]) > int(t_row[4]) and int(a_row[5]) < int(t_row[5]):
                    mixed += 1
                if int(a_row[4]) < dl_dead_threshold or int(a_row[5]) < ul_dead_threshold:
                    if int(t_row[4]) < dl_dead_threshold or int(t_row[5]) < ul_dead_threshold:
                        both_dead_zones += 1
                    else:
                        att_dead_zones += 1
                if int(t_row[4]) < dl_dead_threshold or int(t_row[5]) < ul_dead_threshold:
                    if int(a_row[4]) < dl_dead_threshold or int(a_row[5]) < ul_dead_threshold:
                        pass
                    else:
                        tmobile_dead_zones += 1

                att_sum_dl += int(a_row[4])
                tmobile_sum_dl += int(t_row[4])
                att_sum_ul += int(a_row[5])
                tmobile_sum_ul += int(t_row[5])
                total_comparisons += 1

print 'AT&T wins:     ' + str(att_wins)
print 'T-mobile wins: ' + str(tmobile_wins)
print 'mixed results: ' + str(mixed)
print 'AT&T average download:     ' + str(att_sum_dl // total_comparisons)
print 'T-mobile average download: ' + str(tmobile_sum_dl // total_comparisons)
print 'AT&T average upload:       ' + str(att_sum_ul // total_comparisons)
print 'T-mobile average upload    ' + str(tmobile_sum_ul // total_comparisons)
print 'AT&T deadzones:     ' + str(att_dead_zones)
print 'T-mobile deadzones: ' + str(tmobile_dead_zones)
print 'both deadzones:     ' + str(both_dead_zones)

And here are the results:

AT&T wins: 13
T-mobile wins: 39
mixed results: 22


AT&T average download: 14926
T-mobile average download: 17747
AT&T average upload: 6495
T-mobile average upload 10466

AT&T deadzones: 7
T-mobile deadzones: 6
both deadzones: 4

As you can see, it was pretty much a slaughter.

One thing to note was that Berkeley seemed to have better AT&T coverage than T-mobile.
Also, the only place along the BART line from Dublin/Pleasanton to 12th Street Oakland that AT&T wins is at the Oakland Coliseum stop.