Pokémon Go: Stats Upon Level 35

This post is in the format of and an extension of my Level 33 stats post from four months ago.

Around 1615 Eastern Time on 06.20, I spun a PokéStop to reach 6000005 XP, thus levelling up to Level 35.

I started playing Pokémon Go on August 26 last year. Using this baseline, this is when my level-ups occurred:

Day 87: Level 29
Day 92: Level 30
Day 102: Level 31
Day 138: Level 32
Day 181: Level 33
Day 235: Level 34
Day 298: Level 35

My buddy is currently a Tyranitar, which I have walked with for 399.5 km.

My Pokémon are currently at 377/400.
My eggs are currently at 9/9.
My items are currently at 423/350, specifically:
17x Potion
16x Super Potion
12x Hyper Potion
44x Max Potion
45x Revive
31x Max Revive
12x Lucky Egg
2x Incense
66x Poké Ball
1x Great Ball
42x Ultra Ball
1x Lure Module
20x Nanab Berry
80x Pinap Berry
Camera
Egg Incubator ∞
Egg Incubator
7x Sun Stone
7x King’s Rock
7x Metal Coat
3x Dragon Scale
5x Up-Grade

(Parentheticals in this section show change from last update. For comparison, the XP change from the previous update is +60.0%.)

Pokédex: 205 (+59)
Pokémon Caught: 13945 (+58.6%)
Evolutions: 1732 (+27.5%)
PokéStop Visits: 16815 (+45.0%)
Distance Walked: 1208.8 (+62.7%)
Eggs Hatched: 282 (+77.4%)
Gym Battles Won: 1859 (+42.5%)
Gym Trainings: 458 (+25.5%)

All 377 of my Pokémon are currently fully healed.

Strongest Pokémon:
Gyarados (CP 2992)
Gyarados (CP 2955)
Vaporeon (CP 2901)
Vaporeon (CP 2788)
Vaporeon (CP 2650)
Golem (CP 2556)

(Strongest Pokémon at last update)
Gyarados (CP 2945)
Vaporeon (CP 2766)
Vaporeon (CP 2650)
Flareon (CP 2499)
Snorlax (CP 2393)
Flareon (CP 2339)

Most Represented Pokémon by Total CP

cp_level35

Pokémon Caught by Type
(Parentheticals in this section show change from last update. For comparison, the XP change from the previous update is +60.0%, and the Pokémon caught change from the previous update is +58.6%.)
Normal: 8969 (+37.8%)
Flying: 6412 (+58.0%)
Poison: 2580 (+34.2%)
Bug: 2298 (+48.5)
Water: 1746 (+94.9%)
Psychic: 1089 (+233.0%)
Ground: 341 (+122.0%)
Fire: 330 (+283.7%)
Electric: 313 (+85.2%)
Fairy: 260 (+65.6%)
Grass: 233 (+113.8%)
Ghost: 227 (+12.4%)
Dark: 217 (+1346.7%)
Rock: 192 (+291.8%)
Steel: 159 (+72.8%)
Ice: 97 (+125.6%)
Fighting: 54 (+42.1%)
Dragon: 17 (+30.8%)

Continue reading “Pokémon Go: Stats Upon Level 35”

Orbs.it

About a month ago, someone on the generals.io discord introduced me to Orbs.it. At the time, I decided not to share it further because that would probably be very detrimental during finals season; now, though, at least MIT’s finals are over.

It’s quite a fun simple game; I’d recommend checking it out. During its early days, there was a noticeable rate of connection issues, but at least for me, things seemed to have gotten much better. Like generals.io, it is a browser-based comparatively-minimalist game with 8 players in the most common mode, so I could actually make a lot of my commentary of the game in comparison to generals.io. I’m not going to go over how playing Orbs.it works here; if you go to the game and play a round it’s really quickly to asborb how the game works. It’s probably a coincidence that both games support 8 players per game; 8’s a nice round number to choose for player quantity.

Orbs.it is arguably even more minimalist than generals.io; there’s just 24 orbs rather than a whole playing field, no orb has intrinsic attributes that make them different in capacity to other orbs (unlike generals.io’s mountain tiles and city tiles), and there’s just two power-ups a player has at disposal. It’s a really simple rule set, and yet the game still has interesting possibilities in strategy consideration (though probably not nearly as deep as in generals.io). I really like this combination of two properties in a game.

Movement acuity probably matters more in Orbs.it, given that the Orbs actually move without player impulse. Neither Orbs.it nor generals.io is absolutely turn-based in nature or absolutely a dynamic game, but are somewhere in between, with Orbs.it being more towards the dynamic side.

One aspect I definitely appreciate better in Orbs.it is substantially greater fairness; players spawn in roughly evenly spaced orbs, and because the margin of interaction is rather finite, the original state has only that much variation in luck due to other players’ decisions. Free-for-all generals.io is quite plagued by the issue that most of the community agrees that happening to spawn in the center of the board is a massive disadvantage. Unlike generals.io, Orbs.it does not allow a supermajority of queueing players to decide to start a game with less than 8 players; fortunately, I have never found waiting for 8 players to be playing the game to take really long. (Which of course prompts the paranoid question: huh, are there planted bots?)

But the thing that I really like about generals.io is actually something not in the gameplay itself, but how generals.io maintains a copious public profile per player, in which all replays are publically accessible, which facilitates spectating for interest, learning for strategy, and easy outing of cheaters. Of course, generals.io does a truly exemplary job of this, so it’s quite a high bar for making comparison. Orbs.it also has a profile page:

orbsitprofile

, but it’s not public, and although results for each game are saved (the right column), just the results and some basic stats and not a replay is saved:

orbsit_firstgame

(This was my first game of Orbs.it.) It’s unclear to me whether saving a replay of an Orbs.it game may be substantially harder than saving a replay of a generals.io game. Supposedly, the trajectories of orbs are deterministic from certain starting values, so one would need to save those, starting locations, and times of shooting and deploying powerups (times of changing of ownership of orbs can be derived from this and trajectories).

Still, it’s quite an extensive profile, and I appreciate it. If I were to ask for something likely small to be added, it would be a set of all-time stats.

Continue reading “Orbs.it”

Lengths of Selected Countries

These calculations are performed with the assumption of nullification of Antarctic claims. These diagrams are generated using the daftlogic wrapper over Google Maps.

(And in case these maps constitute obscuring copyright, these maps are ©Gregorian 2017 Google, INEGI, ORION-ME.)

For reference, the equatorial circumference of the Earth is 40075 km, so 20037 km is the furthest apart two points on Earth can be.

France: 18168 km

france_18168

United Kingdom, according to British claims: 16139 km

united_kingdom

United States: 15326 km

united_states

Norge (Norway): 15098 km

norge

United Kingdom, according to Mauritian, but not Argentinian, claims: 13589 km

united_kingdom_mauritian_claims

United Kingdom, according to Argentinian and Mauritian claims: 10989 km

united_kingdom_mauritian_argentinian
(Sidenote: the longest distance from the Shetlands to Tristan da Cunha just barely edges out the longest distance from Grand Cayman to Dhekelia, that is, by about 60 km, so we will not have to also address British-Cypriot territorial disputes.)

Россия (Russia), according to Russian claims: 8188 km

russia_all_russian_claims

Nederland (Netherlands): 8057 km

nederland

Россия (Russia), according to Ukrainian, but not Japanese, claims: 7974 km

russia_russian_claims

Россия (Russia), according to Ukrainian and Japanese claims: 7941 km

russia_japanese_claims

Australia: 7869 km

australia

中國 (China), according to claims of the Republic: 5750 km

china_republic_claims

Aotearoa (New Zealand): 5721 km

aotearoa

中国 (China), according to claims of the People’s Republic: 5582 km

china_peoples_republic_claims

Canada: 5555 km

canada

Continue reading “Lengths of Selected Countries”

Business Insider Please

These states of the US are populated by American humans. They kill over 10000 humans each year in the US, far surpassing the counts for the species you’ve introduced combined. dangerous.png

In particular, you mentioned sharks. Sharks kill about 10 people worldwide each year. That’s so few people even terrorists kill more people than they do.

On the other hand, humans kill 100 million sharks each year, so sharks, watch out for the states in the map above, they contain humans.

A Season with LearnedLeague

I recently finished playing in my first (and probably only, the reason for which I’ll explain shortly later) season of LearnedLeague, an online trivia league. I’d say it was a fun experience, and that the ideas behind how competition in LearnedLeague works are generally good ideas.

Each round (of which there are 25 in a season, at least in the one I participated in) consists of a head-to-head match between two league players, that involves answering trivia questions (called “offense”), and assigning numbers of points each question is worth for your opponent (called “defense”), for which one is allowed to consult their history to know what one’s opponent tends to be good at.

And this gets to why I don’t think I’ll be back for another season. LearnedLeague has failed to deviate from a problem I find in most trivia competitions: an excessive favoring of generalists. Frankly, there are four categories of LearnedLeague trivia (Film, Lifestyle, Pop Music, and Television) than not only am I terrible at but also I honestly have less than zero interest in getting better at (really, four and half, given the Games/Sport category; it’s a really easy exercise for the reader to figure out which half of that I don’t care about). But there are categories of trivia I am definitely extremely interested in. Overall, my trivia knowledge is terribly category-polar, and this makes me ridiculously easy to defend against. As much as I like the assigning-points-for-opponent mechanic, the resulting effects of the such for highly non-generalist people like me make it something I’m not willing to give money for. A second factor lies in excessive references to alcoholic drinks, which many may know I find elevated discomfort in. There’s quite a few questions for which it looks like the writer specifically wanted to force an association with alcohol. Yes, it turns out unfortunately trivia is often associated with bars. *Sigh*.

Anyway, my category stats:

ll_categories

Below I’ll make some notes on specific items in some categories.

Some of these questions have images that are part of the item when you click ‘Click here’. I’m too lazy to copy those over, so I’ll leave it to your imagination what those items were.

Art

art

MD15Q3 was the least-correctly-answered item in the entire season, with only 5% answering correctly.

MD06Q2 was a guess. They all looked like portraits. Might as well guess that they were of the self variety.

Business/Economics

bus_econ

I only got MD13Q1 because of knowing where Qaanaaq is and knowing there’s a military base named Thule nearby, and thus just guessing “Thule” off of only this information, possibly the most distant answer derivation I’ve underwent this entire season. Does this count as love towards Sweden via proxy?

I entered “Hand of God” instead of “Invisible Hand” for MH08Q5. Oops.

Current Events

curr_events

I’m fairly ashamed of having not been able to name the new UN Secretary-General. Apparently, neither could most of LearnedLeague, so shame on everyone else too.

Games/Sport

games_sport

MD12Q5: Booo Othello. I can’t believe the world accepted the game of Reversi getting a second name.

What is sportsball.

Geography

geography

Apparently the most frequently incorrectly guessed answer to MD20Q2 was “Washington”. I guess that’s what “Bellevue” tipped people off to?

I was not actually sure of the answer to MD12Q6; I based my guess off of understanding the Central American isthmus as gradually less inhabitable as one moves south and east.

American History

amer_hist

I don’t really have many comments to make here.

World History

world_hist

I really should’ve gotten MD24Q5, but didn’t answer “Peloponnesian League” because of the inclusion of Corinth, which I recalled as not-all-that-Peloponnesian, although maybe I should’ve still considered the isthmus Pelopennesian. I guessed “Dorian League”.

For MD15Q4, I knew where relatively chronologically the House of Saxe-Coburg and Gotha was; I just couldn’t exactly remember where it was delimited and wasn’t exactly sure which monarchs came between Victoria and Edward $LARGE_NUM. I ended up deciding I had the best chances with guessing Victoria, which it turns out was too early.

For MD12Q3, I made the rather hilarious-in-retrospect mistake of calling the Ostrogoths the “Orthogoths”. Herpity derp.

MD07Q5 was the most-correctly-answered item in the entire season, with 88% answering correctly. Fortunately, I was in the 88% for this case.

I was wondering whether MD01Q2 wanted the real (legal) name of Kim Il-Sung, since that is not it, but ended up deciding they probably just want “Kim Il-Sung” for the purposes of the question. It turns out that was a good assumption.

Language

language

I consider the FANBOYS question a bad question.

MD02Q4 was an item I really should’ve gotten, but I mind-blanked a bit much and put “al-“.

Literature

literature

Maybe if I thought a little harder on MD18Q4 I would’ve gotten it.

Math

math

*snicker*

MD08Q1 was the second-most-correctly-answered item in the entire season, with 87% answering correctly.

Classical Music

class_music

MD19Q4 was the second-least-correctly-answered item in the entire season, with only 14% answering correctly.

The Classical Music section consists of an excess of…I guess I’ll call it “applied” music and way not enough “pure” music, grump, grump. I’m saying “applied”, I guess because terms like “incidental” and “operatic” don’t actually capture the set I’m trying to describe.

Science

science

I was actually quite surprised with how long it took me to recall what Newton’s Second Law was. It turned out my physical knowledge had progressed to a state where I only know if I’m supposed to know things. Fortunately, I did eventually remember the Law. I wonder if I would’ve forgotten it if I hadn’t taken an olympiad qualification test whose name is the equation of the Law.

Anyway, that’s all; ’twas a fun season. I’m going to stay around for some MiniLeagues, but then vanish from LearnedLeague. I did refer a friend to LearnedLeague, though, so maybe he will stay.

Pokémon Go: Most Represented Species by CP

I decided to add up the CPs of each individual for each species as a measure of how represented the species is in my set of Pokémon in Pokémon Go. The chart of the 27 most represented species is below.

pokemon_cp_distr

The total CP of all Pokémon I have is about 353000, so Jolteon represents about 5% of all CP in my Pokémon. I was quite surprised to find out how close the CP shares of my top three species (Jolteon, Vaporeon, and Pidgeot) were.

Pokémon Go: Stats Upon Level 33

This post is in the format of and an extension of my Level 30 stats post from three months ago.

Around 0100 Eastern Time today, I evolved a Pidgey to reach 3750334 XP, thus levelling up to Level 33.

I started playing Pokémon Go on August 26 last year. Using this baseline, this is when my level-ups occurred:

Day 87: Level 29
Day 92: Level 30
Day 102: Level 31
Day 138: Level 32
Day 181: Level 33

My buddy is currently a Lapras. My buddy has previously been a Dratini, a Bellsprout, and two different Charmanders.

My Pokémon are currently at 383/400.
My eggs are currently at 9/9.
My items are currently at 308/350, specifically:
3x Super Potion
68x Hyper Potion
87x Max Potion
40x Max Revive
2x Incense
11x Great Ball
20x Ultra Ball
1x Lure Module
16x Razz Berry
5x Nanab Berry
39x Pinap Berry
Camera
Egg Incubator ∞
1x Metal Coat
1x Dragon Scale
1x Up-Grade

(I have still only used Max Potions where Hyper Potions would also work. I now have several Pokémon with over 200 HP, but the situation still hasn’t arisen for the two to have different effects from singular use yet.)

(At some point since the Level 30 stats update, I actually accumulated 272=0xGG max potions, and then decided I’m done hoarding them and letting them hog space.)

(Parentheticals in this section show change from last update. For comparison, the XP change from the previous update is +87.5%.)

Pokédex: 146 (+33)
Pokémon Caught: 8794 (+88.3%)
Evolutions: 1358 (+85.0%)
Pokéstop Visits: 11593 (+69.9%)
Distance Walked: 742.9 (+73.3%)
Eggs Hatched: 159 (+43.2%)
Gym Battles Won: 1305 (+35.7%)
Gym Trainings: 365 (+45.4%)

All 383 of my Pokémon are currently fully healed.

Strongest Pokémon:
Gyarados (CP 2945)
Vaporeon (CP 2766)
Vaporeon (CP 2650)
Flareon (CP 2499)
Snorlax (CP 2393)
Flareon (CP 2339)

(Strongest Pokémon at last update)
Gyarados (CP 2805)
Vaporeon (CP 2351)
Vaporeon (CP 2288)
Flareon (CP 2177)
Vaporeon (CP 2158)
Vaporeon (CP 2138)

Pokémon Caught by Type
(Parentheticals in this section show change from last update. For comparison, the XP change from the previous update is +87.5%, and the Pokémon caught change from the previous update is +88.3%.)
Normal: 6510 (+102.2%)
Flying: 4059 (+104.2%)
Poison: 1922 (+70.8%)
Bug: 1547 (+69.4%)
Water: 896 (+56.1%)
Psychic: 327 (+52.1%)
Ghost: 202 (+32.0%)
Electric: 169 (+116.7%)
Fairy: 157 (+241.3%)
Ground: 155 (+40.9%)
Grass: 109 (+73.0%)
Steel: 92 (+130.0%)
Fire: 86 (+100.0%)
Rock: 49 (+88.5%)
Ice: 43 (+79.2%)
Fighting: 38 (+26.7%)
Dark: 15 (+∞%)
Dragon: 13 (+8.3%)

Continue reading “Pokémon Go: Stats Upon Level 33”