A Little Breakthrough

This past March was zyxyvy’s best month in views up to date.


It also was one of the best months over at wywing, though in that case, the blog is still rather starved for views.


(In any case, thanks Lewis!)

One of the slow changes that have been occurring in the viewership of this blog is that it’s gradually getting more and more international.

Continue reading “A Little Breakthrough”


Answers: US Statistics-Identification Puzzles

This filler text is intended to make it harder to accidentally see answers in post preview applications.
This map charts number of syllables in the name.

This map charts number of distinct main interstate freeways (number <100).

3) [note the posted ERRATUM]
This map charts number of counties of at least a million people.

This map charts number of national parks.

This map charts number of years before 19th Amendment ratification when women were granted suffrage.

This map charts length of the road named after the state in Washington, DC (miles).

Pokémon Go: a 0xGG Journey to Level 40

This past February 20 at 1228 Eastern Time, I spun the MIT SIPB PokéStop (the first PokéStop I spun in Pokémon Go) to reach 20000025 XP, thus ending a 543-day journey to Level 40. During this journey, I walked 2690.1 km (as logged by Pokémon Go), caught 36291 Pokémon, won 11487 battles, and obtained the gold gym badge at 13 gyms.



Here’s what the top of my Pokémon page looked like:


Here’s what the top of my Gyms page looked like:


This will be a post saturated with charts of relevant statistics, but unlike my previous posts partway along this Pokémon Go adventure, I will first talk a bit about my goals in this Pokémon Go journey, and thoughts and decisions along the way.

Since this post is long, I will give each section a short string that could be used to navigate to the section using Ctrl+f.

I. My Goals and Play Style [gaps]
II. Choices and Thoughts [chat]
III. All the Stats [alts]

I. My Goals and Play Style [gaps]

These were my decisions as to how I’d play Pokémon Go.

  1. Play against the stereotype of Team Instinct as the team that just merrily hatches eggs and doesn’t bother to fight for gym territory; help spread a presence of Team Instinct in gyms. [pats]
  2. Spend $0.00 on the game. [0x$$]
  3. Play at not just a community-accepted standard of ethics, but Niantic’s prescribed standard of ethics; play such as to approximate the experience of an actual Pokémon trainer. [nomap]
  4. Treat my Pokémon as well as I could, as far as possible while still participating in the essentials of Pokémon. [<3<3]
  5. Record ample statistics along the way such that eventually when I reach Level 40, this post would be possible. [++++]

Here’s some elaborations on these.

1. Play against the stereotype of Team Instinct as the team that just merrily hatches eggs and doesn’t bother to fight for gym territory; help spread a presence of Team Instinct in gyms. [pats]

This one’s probably sufficiently self-contained and self-explanatory.

2. Spend $0.00 on the game. [0x$$]

In the spirit of zero-cost gaming, I didn’t spend money towards Pokémon Go that I wouldn’t have wanted to if Pokémon Go did not exist. I decided that that means:

  • I never purchase in-game items with real money. All Pokécoins I ever had I earn through in-game mechanisms.
  • I never purchase a GoPlus.
  • I never purchase a battery pack, as I wouldn’t have spent money on that if I didn’t play Pokémon Go.
  • I don’t increase my data plan from my original 1 GB/month plan I had before starting to play Pokémon Go, and that 1 GB was to be shared with Ingress, as well as the non-gaming functions I perform on my phone.

It turned out that over 50% of my data usage (and over 75% of my battery usage) in many months was Pokémon Go, even when including Ingress. Of course, even though I never purchased a battery pack, I’ve gotten offered one several times; I decided if I actually get offered one I’ll allow myself to use one. Thanks, all you kind trainers.

Until the new gym system, I consistently had barely any Pokécoins, due to the Mystic dominance of my play area. The new system was a great relief for me, after which I consistently earned the ceiling of 50 Pokécoins nearly every day. To be frugal with Pokécoins, I only ever bought the following items:

  • Premium Raid Pass
  • Lucky Egg
  • 8x Lucky Egg
  • 25x Lucky Egg
  • Bag Upgrade
  • Pokémon Storage Upgrade

I prioritized purchasing Pokémon Storage Upgrade for quite a while (see item 4 [<3<3]), and only made my first Bag Upgrade purchase at Level 38. I never spent any Pokécoins on style, although now that I’m Level 40, I plan on eventually purchasing some outfit components. In total, I only ever purchased about 5 Premium Raid Passes.

Luckily, I live in an urban area. Without this, I may have needed to spend Pokécoins on Poké Balls, and I shudder at that thought. I’m pretty sure in such a world my $0.00 run to Level 40 would have been severely hampered, in more ways than one.

Also luckily, as I’ve been a busy MIT student anyway, most of my playing occurred walking from place to place within MIT’s campus, which allowed me to utilize MIT’s WiFi networks instead of data, as much as it was tempting to use the more reliable option. Playing Pokémon Go on campus mainly on WiFi has helped me discover the locations of all the WiFi holes on campus. For instance, there’s a lot of holes around the Cosmic Ray Chandeliers gym. Along with the high drift around that gym, it makes playing there often quite frustrating. Also, Building 36 is nearly always a pain to play while walking through, due to the different main WiFi network, since RLE isn’t happy using the main MIT WiFi network for some reason.

Continue reading “Pokémon Go: a 0xGG Journey to Level 40”

Totals over Time

once a month for an average year: 12 times
once an hour for an average day: 24 times
once a day for an average month: 30 times
once a second for an average minute: 60 times
once a minute for an average hour: 60 times
once a year for an average American lifespan: 79 times
once a day for an average year: 365 times
once an hour for an average month: 730 times
once a month for an average American lifespan: 945 times
once a minute for an average day: 1440 times
once a second for an average hour: 3600 times
once an hour for an average year: 8766 times
once a day for an average American lifespan: 28760 times
once a minute for an average month: 43830 times
once a second for an average day: 86400 times
once a minute for an average year: 525960 times
once an hour for an average American lifespan: 690235 times
once a second for an average month: 2 629 800 times
once a second for an average year: 31 557 600 times
once a minute for an average American lifespan: 41 414 090 times
once a second for an average American lifespan: 2 484 845 424 times

Dual Frontier Analysis

I. Introduction, with Example in Population and Area of Countries and Country-Like Entities

In this post, I introduce a way of looking at correlated data I will term “dual frontier analysis”.

What motivates this idea? Often, we like to compare entities via a certain “rate”, how much of one quantity there is for a unit amount of another quantity, across a set of entities. One example of this is population density. But if you, like me, have glanced at a population density chart of, say, the countries, you may have had one of the same first reactions as I have had: “the top of the chart is pretty much just a listing of city-states!” You might then proceed with questioning whether it really makes sense to compare this quantity for city-states versus for “more normal” countries. Maybe we want a way of looking at this data that better captures what our prior idea of what an “impressively high” or “impressively low” population density is: Bangladesh’s population density definitely “feels” more impressive, even if it’s not as numerically high as Bahrain’s.

There are probably solutions to this problem involving designing a prior distribution of likeliness of one variable in terms of the other, and then comparing percentiles along respective distributions, but going down this path requires crunching a lot of numbers and, more importantly, extensive knowledge in the ideas being analyzed already.

Here is another solution: output the data on the dual frontiers. If two attributes are somewhat correlated, a scatterplot for entities in these attributes probably looks something like this.


What we’re outputting is this.


That is, we’re outputting entities for which no other entity has both more of one attribute and less of the other attribute than this entity.

In this way, we would capture, for instance, the country with the highest population density among countries of similar size. (We could even extend this to become a quantitative metric for entities not on this frontier: the percentage of the way an entity is from one frontier to the other.)

One could also look at an entity in this data and compare it to neighboring entities and see how much larger in one attribute another entity must be to be larger in the other attribute as well (as otherwise, this entity would also be in the frontier), which shows how prominently impressive a particular entity is in the ratio.

Continue reading “Dual Frontier Analysis”

500 Facts

Huh, a video of 500 facts.

Let’s see how many I’ve previously known.

1. No.
2. No.
3. No.  Who is John Cleese?
4. No.
5. No.
6. Yes.
7. No.
8. No.
9. No.
10. No.
11. Yes.
12. No.
13. No.
14. No.
15. No.
16. No.
17. No.
18. No. What is PEZ?
19. No.
20. No.
21. No.
22. No.
23. No.
24. No.
25. No.
26. No.
27. No. Who is Neil Diamond?
28. No.
29. No. Oh, that’s who Zachary Quinto is.
30. No.
31. No.
32. No.
33. No.
34. No.
35. No. Who is Orson Wel…oh.
36. No. And of course, fuck sportsball.
37. No.
38. No. And didn’t know who Deion Sanders was.
39. Yes.
40. No.
41. No.
42. Yes.
43. No. And who is Jack Benny?
44. Yes.
45. No. And I think I’m just going to stop pointing out people I haven’t even heard of.
46. No.
47. No.
48. Yes.
49. Yes.
50. No.
51. No.
52. No.
53. No.
54. No.
55. No.
56. No.
57. No.
58. No.
59. No.
60. Yes.
61. No.
62. No.
63. No.
64. No.
65. No.
66. No.
67. No.
68. No.
69. No.
70. Yes.
71. Yes.
72. Yes.
73. No.
74. Yes.
75. No.
76. No.
77. No. I actually thought the number would be substantially larger.
78. Yes.
79. No.
80. No.
81. Yes.
82. Yes. In fact it’s on http://web.mit.edu/~dzaefn/Public/best_of/w-best_wiki_articles.html.
83. No.
84. No.
85. No.
86. No.
87. No.
88. No.
89. No.
90. No.
91. No.
92. No.
93. No.
94. No.
95. No.
96. No.
97. No.
98. No.
99. No.
100. No.

Actually, fuck it, I’m bored. Let’s settle with just 15.