## A Chart of Friends’ Statistics in Pokémon Go

I made a chart of Pokémon caught, distance walked, and battles won (as given in friend profile stats) of 157 Pokémon Go trainers: my 156 Pokémon Go Friends of Ultra level or higher plus me.

I had 90040 Pokémon caught, 6661 kilometers walked, and 40959 battles won as of making this, so my bubble lies under a large stack of bubbles in the middle.

## US Representative Apportionment, but Performed upon the Countries of the World

What if the world had a representative body among the countries that operated on the apportionment rules of the US House of Representatives?

The following map shows the distribution of representatives to UN-recognized countries from running the Huntington-Hill algorithm for an assembly of 1650 representatives. (I arrived at 1650 via scaling extrapolation from the number of entities represented (not population) for the US House.) Each representative represents approximately 4.7 million people.

Slightly less than half the world’s countries get allotted 1 representative. Taiwan is not recognized by the UN, but Taiwan would have 5 representatives upon inclusion. Hong Kong, if included, would have 2 representatives.

These reduced numbers could be helpful for remembering ballpark relative populations of countries.

## A Geographical Puzzle Based on the Electoral College

Find a set of states of the US such that:

• No pair of them border each other.
• Their electoral votes combine to at least 270, the quantity needed to win a presidential election.

This is actually very difficult. I’d be extremely impressed if anyone can do this without looking at an electoral college map.

This puzzle somewhat demonstrates the unusually decentralized and relatively evenly distributed nature of the US’s population, as well as the dampening effect of voting power imbalance prescribed by the electoral college.

## Americans want Scientists more involved in Political Discourse, until Facts from Scientists make them Uncomfortable

Americans increasingly want scientists involved in discussion of policy, something that would be great if wanting them also involved listening to them. The American pastime that is still true today, though, is selectively endorsing what scientists have to say when the truth is convenient.

This phenomenon is recently well exemplified by public outrage over a statistics-delivering tweet by one of America’s finest voices in science, Neil deGrasse Tyson. An online tsunami involving multiple Twitter-verified people condemned Tyson’s tweet as insensitive and tone-deaf, many claiming Tyson does not deserve respect due to his tweet.

## Terrorism Panic Returned

One of the top tweets in response to Tyson’s post is this post relating the issue to America’s reactions to terrorism, speaking as if how America completely altered airport screening in response to terrorism was a good thing. This is incredible: I thought for sure America has learned from the TSA, the NSA, and their various unsavory paternalistic friends how incredibly costly it is (in both money and human dignity) to act on overreaction to the horror of well-media-covered terrorist acts when a nation has many more quiet problems claiming far more lives. Yet here comes this esteemed post asking that this reactive yoke on American wellbeing that hasn’t even left us yet be brought back. The sort of people that would’ve told the few congresspeople who opposed the Iraq War that they’re being insensitive towards the victims of 9/11 are still here.

## Too Soon?

Criticism that it was insensitive or tone-deaf for Tyson to post these statistics in the wake of the El Paso and Dayton mass shootings is very unfounded. Whereas Tyson went out of his way to make it clear he thinks this country should not have to deal with mass shootings (except to people who think use of the word “horrifically” sometimes applies to acts they’re okay with), most commenters are explicitly denouncing the bringing up of medical errors, disease, suicide, accidents, and other gun deaths. This reaction to the far more people who died of the other causes is vastly more cold than Tyson’s actual acknowledgment of the problematic nature of the commenters’ tragedy of choice. If anything, those criticizing Tyson along these lines have a shortage of empathy for the people who died of medical errors, disease, suicide, accidents, and other gun deaths as recently as those that died in the mass shootings did. Many that responded went so far as to silently remove “other gun deaths” from the list of death causes Tyson cited to claim that there’s an important difference between intentional tragedy and accidental tragedy, a doubly-faulted claim that was maliciously framed to cherry-pick the items from the list they could feasibly apply their ill-conceived argument towards.

This case demonstrates a frequent trait of the sort of people that cry tone-deaf: they only care about the tone of those speaking against their favorite issues. Their often-worse personal insensitivities are defects they choose to remain oblivious to. And unfortunately, the internet outrage machine enables these people to effectively carry out their anger, when the time is right.

## Emotions Should Matter

Emotions should matter. Most of us, if not all, can agree that emotional wellbeing is important to the human. It’s good to be aware of how to phrase things in ways that don’t rub off badly.

But from reactions, it’s clear that there’s no way this information could’ve been phrased in a way the public would’ve been happy with. What was attacked was the information’s presence itself, when Tyson clearly put an effort into phrasing the statistics in a way that acknowledged recent tragedy. No one is forced to read Tyson’s tweets, but the bringing of facts letting people know of many more people that are often overlooked was considered just too much to even see the light of day.

And this is where emotional reaction oversteps its appropriate bounds: when emotion guides our actions and decisions in ways that turn out to be the more harmful choice, when it screams “say no to facts”. And this is precisely the problem Tyson was trying to bring attention to.

And in the end, Neil deGrasse Tyson ended up apologizing for bringing facts to the public. Why aren’t there more scientists involved in policy? Oh right, because it’s an environment hostile to scientists.

## American Presidential Primaries: Campaigns

The following is a juxtaposition of the current and the previous three US presidential primary seasons for the Democratic and Republican parties, in days relative to the Iowa caucus. Each + indicates a campaign’s announcement and each – indicates a campaign’s withdrawal.

 Day Obama/McCain election Obama/Romney election Trump/Clinton election This election Iowa-1100 Iowa-1090 Iowa-1081 +Donald Trump Iowa-1070 Iowa-1060 Iowa-1050 Iowa-1040 Iowa-1030 Iowa-1020 Iowa-1010 Iowa-1000 Iowa-990 Iowa-980 Iowa-970 Iowa-960 Iowa-950 Iowa-940 Iowa-930 Iowa-920 +John Delaney Iowa-910 Iowa-900 Iowa-890 Iowa-880 Iowa-870 Iowa-860 Iowa-850 Iowa-840 Iowa-830 Iowa-819 +Andrew Yang Iowa-810 Iowa-800 Iowa-790 Iowa-780 Iowa-790 Iowa-760 Iowa-750 Iowa-740 Iowa-730 Iowa-720 Iowa-710 Iowa-700 Iowa-690 Iowa-680 Iowa-670 Iowa-660 Iowa-650 Iowa-640 Iowa-626 +Mike Gravel Iowa-620 Iowa-610 Iowa-600 Iowa-590 Iowa-580 Iowa-570 Iowa-560 Iowa-550 Iowa-540 Iowa-530 Iowa-520 Iowa-510 Iowa-500 Iowa-490 Iowa-480 Iowa-470 Iowa-460 Iowa-449 +Richard Ojeda Iowa-440 Iowa-430 Iowa-420 Iowa-410 Iowa-400 Iowa-388 +Tulsi Gabbard Iowa-387 +Dennis Kucinich +Julian Castro Iowa-378 +Kamala Harris Iowa-374 -Richard Ojeda Iowa-373 +John Edwards Iowa-371 +Marianne Williamson Iowa-367 +Cory Booker Iowa-359 +Elizabeth Warren Iowa-358 +Amy Klobuchar Iowa-357 +Chris Dodd Iowa-353 +Bill Weld Iowa-349 +Bernie Sanders Iowa-348 +Sam Brownback Iowa-343 +Hillary Clinton +Duncan Hunter Iowa-340 +Mike Huckabee Iowa-339 +Jay Inslee Iowa-337 +Joe Biden Iowa-336 +John Hickenlooper Iowa-327 +Barack Obama Iowa-326 +Beto O’Rourke Iowa-324 +Mitt Romney Iowa-323 +Kirsten Gillibrand Iowa-322 +Rudy Giuliani Iowa-315 +Ted Cruz Iowa-312 +Wayne Messam Iowa-305 +Tim Ryan Iowa-301 +Mike Gravel +Eric Swalwell Iowa-300 +Rand Paul Iowa-298 TODAY Iowa-297 +Ron Paul Iowa-295 +Hillary Clinton Iowa-294 +Marco Rubio Iowa-277 +Tommy Thompson +Bernie Sanders Iowa-276 +Tom Tancredo Iowa-274 +Barack Obama +Ben Carson Iowa-273 +Carly Fiorina Iowa-272 +Mike Huckabee Iowa-257 +Gary Johnson Iowa-253 +John McCain Iowa-252 +Jim Gilmore Iowa-250 +Rick Santorum Iowa-249 +George Pataki Iowa-247 +Martin O’Malley Iowa-245 +Lindsey Graham Iowa-243 +Lincoln Chafee Iowa-242 +Rick Perry Iowa-239 +Newt Gingrich Iowa-237 +Ron Paul Iowa-235 +Tim Pawlenty Iowa-231 +Jeb Bush Iowa-230 +Donald Trump Iowa-227 +Bill Richardson +Herman Cain Iowa-222 +Bobby Jindal Iowa-216 +Chris Christie Iowa-215 +Mitt Romney Iowa-214 +Jim Webb Iowa-211 +Rick Santorum Iowa-204 +Michele Bachmann Iowa-203 +Scott Walker Iowa-196 +Jon Huntsman Iowa-195 +John Kasich Iowa-186 +Thaddeus McCotter +Jim Gilmore Iowa-180 Iowa-173 -Jim Gilmore Iowa-160 Iowa-148 +Lawrence Lessig Iowa-145 -Tommy Thompson Iowa-143 +Rick Perry -Rick Perry Iowa-142 -Tim Pawlenty Iowa-133 -Scott Walker Iowa-120 +Fred Thompson Iowa-111 +Alan Keyes Iowa-104 -Jim Webb Iowa-103 -Thaddeus McCotter Iowa-101 -Lincoln Chafee Iowa-91 -Lawrence Lessig Iowa-76 -Sam Brownback -Bobby Jindal Iowa-70 Iowa-60 Iowa-50 Iowa-42 -Lindsey Graham Iowa-34 -George Pataki Iowa-31 -Herman Cain Iowa-20 Iowa-14 -Tom Tancredo Iowa-6 -Gary Johnson Iowa Caucus -Chris Dodd -Joe Biden -Mike Huckabee -Martin O’Malley Iowa+1 -Michele Bachmann Iowa+2 -Rand Paul -Rick Santorum Iowa+7 -Bill Richardson Iowa+9 -Carly Fiorina -Chris Christie Iowa+11 -Jim Gilmore Iowa+13 -Jon Huntsman Iowa+13 -Rick Perry Iowa+16 -Duncan Hunter Iowa+19 -Fred Thompson -Jeb Bush Iowa+21 -Dennis Kucinich Iowa+27 -John Edwards -Rudy Giuliani Iowa+32 -Ben Carson Iowa+35 -Mitt Romney Iowa+43 -Marco Rubio Iowa+50 Iowa+61 -Mike Huckabee Iowa+70 Iowa+83 -Mike Gravel Iowa+92 -Ted Cruz Iowa+93 -John Kasich Iowa+98 -Rick Santorum Iowa+103 -Alan Keyes Iowa+110 Iowa+120 -Newt Gingrich Iowa+130 Iowa+140 Iowa+150 Iowa+156 -Hillary Clinton Iowa+161 -Ron Paul Iowa+170 Iowa+180

## Making a Metric for the Brightness of Constellations

Orion is often described as the brightest constellation. For a star, apparent magnitude tells its brightness as it appears from Earth, but what does it mean to consider a constellation bright?

One would clearly say Orion is brighter than bordering constellation Monoceros, with Orion’s brightest stars being that much brighter than Monoceros’ brightest stars, but could we develop a quantitative metric for a constellation’s brightness?

Since apparent magnitude is on a logarithmic scale, we can exponentiate apparent magnitudes of individual stars to get a relatively comparable brightness quantity, a relative flux. We can then sum this across the stars of a constellation to get a total brightness flux, how much light the stars that constitute a constellation beam towards us.

But some constellations are way larger than others, and this value would be larger for them not necessarily because their stars are actually brighter, but because they have more stars. So let’s do something more: like how with country populations one could divide by area to acquire a population density, let’s divide by the area occupied by the constellation (since there’s actually official boundaries) to get a brightness density. Let’s compute these numbers and see what they say the brightest constellations are.

(Note that resulting numbers are pretty much useful mostly as a linear relative quantity. Even though area below is in fraction of a hemisphere occupied [hemisphere simply because a visible field of view of the night sky is a hemisphere], brightness is a relative quantity, and so the numbers for density are mostly helpful only in the relative sense. I only included stars of magnitude brighter than 6 because around where human eyes stop perceiving the stars seems like a good cutoff.)

 Rank Constellation Brightness Area (Hemis) Density 1 Crux 1.349 0.0034 396.7 2 Canis Major 5.435 0.0184 295.4 3 Carina 3.892 0.0240 162.2 4 Orion 3.760 0.0288 130.6 5 Canis Minor 0.974 0.0088 110.7 6 Scorpius 2.602 0.0240 108.4 7 Lyra 1.386 0.0138 100.4 8 Triangulum Australe 0.496 0.0054 91.9 9 Centaurus 4.405 0.0514 85.7 10 Vela 1.862 0.0242 77.0 11 Gemini 1.679 0.0250 67.2 12 Musca 0.446 0.0068 65.6 13 Lupus 1.039 0.0162 64.1 14 Auriga 1.988 0.0318 62.5 15 Taurus 2.304 0.0386 59.7 16 Perseus 1.687 0.0298 56.6 17 Puppis 1.778 0.0326 54.5 18 Corvus 0.467 0.0090 51.8 19 Sagitta 0.197 0.0038 51.7 20 Ara 0.584 0.0114 51.2 21 Cygnus 1.964 0.0390 50.4 22 Boötes 2.208 0.0440 50.2 23 Lepus 0.695 0.0140 49.7 24 Piscis Austrinus 0.572 0.0118 48.5 25 Corona Borealis 0.404 0.0086 47.0 26 Ursa Minor 0.582 0.0124 46.9 27 Circinus 0.208 0.0046 45.3 28 Scutum 0.235 0.0052 45.2 29 Cassiopeia 1.288 0.0290 44.4 30 Sagittarius 1.842 0.0420 43.9 31 Corona Australis 0.272 0.0062 43.8 32 Grus 0.776 0.0178 43.6 33 Aquila 1.345 0.0316 42.6 34 Eridanus 2.284 0.0552 41.4 35 Leo 1.848 0.0460 40.2 36 Columba 0.497 0.0130 38.2 37 Andromeda 1.324 0.0350 37.8 38 Triangulum 0.239 0.0064 37.3 39 Reticulum 0.208 0.0056 37.1 40 Ursa Major 2.297 0.0620 37.0 41 Cepheus 1.046 0.0284 36.8 42 Volans 0.249 0.0068 36.6 43 Pavo 0.663 0.0184 36.0 44 Lacerta 0.345 0.0098 35.2 45 Ophiuchus 1.516 0.0460 33.0 46 Aries 0.677 0.0214 31.6 47 Capricornus 0.627 0.0200 31.3 48 Draco 1.610 0.0526 30.6 49 Chamaeleon 0.194 0.0064 30.3 50 Hydrus 0.349 0.0118 29.6 51 Equuleus 0.095 0.0034 28.1 52 Dorado 0.241 0.0086 28.0 53 Vulpecula 0.351 0.0130 27.0 54 Hercules 1.602 0.0594 27.0 55 Serpens 0.794 0.0308 25.8 56 Libra 0.657 0.0260 25.3 57 Hydra 1.588 0.0632 25.1 58 Monoceros 0.588 0.0234 25.1 59 Delphinus 0.229 0.0092 24.9 60 Phoenix 0.564 0.0228 24.7 61 Pegasus 1.338 0.0544 24.6 62 Virgo 1.537 0.0628 24.5 63 Aquarius 1.152 0.0476 24.2 64 Norma 0.182 0.0080 22.8 65 Tucana 0.323 0.0142 22.7 66 Cetus 1.312 0.0598 21.9 67 Apus 0.203 0.0100 20.3 68 Telescopium 0.247 0.0122 20.3 69 Lynx 0.527 0.0264 20.0 70 Pyxis 0.210 0.0108 19.5 71 Pictor 0.230 0.0120 19.2 72 Octans 0.269 0.0142 19.0 73 Leo Minor 0.207 0.0112 18.5 74 Coma Berenices 0.336 0.0188 17.9 75 Cancer 0.436 0.0246 17.7 76 Indus 0.245 0.0142 17.2 77 Pisces 0.743 0.0432 17.2 78 Microscopium 0.171 0.0102 16.7 79 Camelopardalis 0.589 0.0366 16.1 80 Crater 0.198 0.0136 14.5 81 Antlia 0.162 0.0116 14.0 82 Canes Venatici 0.311 0.0226 13.7 83 Horologium 0.156 0.0120 13.0 84 Mensa 0.093 0.0074 12.6 85 Caelum 0.065 0.0060 10.9 86 Sculptor 0.250 0.0230 10.9 87 Fornax 0.196 0.0192 10.2 88 Sextans 0.108 0.0152 7.1

There are some incredibly questionable results in these numbers. Triangulum Australe manages to claim the #8 spot, managing to outrank, say, Gemini and Auriga. Musca, Lupus, and Ara are constellations an experienced stargazer may expect to rank in the lower half, yet are in the top quarter. And Ursa Major is as far down as #40, in fact lower than Ursa Minor? (I personally expected the bottom of the list to comprise of Caelum, Mensa, and Norma; well, two of them got quite close to the very bottom.)

Returning to an analogy with countries and population density, one could remember that many countries on Earth have absurdly high population densities mostly because it’s that much easier to crowd a small amount of space, and thus, for instance, Monaco and Bahrain have substantially higher population densities than Bangladesh despite Bangladesh’s crowdedness being very arguably more impressive. The constellation at the top of the list, Crux, is the area-wise smallest constellation on the night sky, and thus experiences benefits from this lack of need to make very much very bright, just happening to be defined as a notable small piece of terran night sky. Notably, in both of these cases, if a smaller region was indeed an independent unit, it is more likely to contain an interesting segment than a boring segment.

Another important contribution is the effect of the galactic plane of the Milky Way. Since we’re summing all stars of magnitude at least 6, there is a lot more background going into the numbers for constellations along the galactic plane. In fact, the top-ranked constellation in the list that doesn’t either intersect the galactic plane or come close enough to have background effects is…Corvus, at #18. In fact, this effect helps explain why constellations like Triangulum Australe and Musca are so far up in this list: they experience the significant bump of the background light of the Milky Way, which makes our statistic increase, but what causes this increase is what stargazers would view as noise, interfering with the process of recognizing the constellations. Thus, if wanted a metric that better captured the stargazer’s experience, perhaps raw brightness is not what we want to look for, but rather some sense of salience, and it’s very unclear how that would be measured.

More curious to me, it appears that both the bright and dim ends of the list are Southern-hemisphere dominated, whereas Northern-hemisphere constellations tend to more frequently fall in the center. I am rather curious as to what causes this. Since dividers in constellation space are artificial structures on a naturally continuous space, like country borders, we’d want to question effects in the decision-making of the constellation coiners. I wouldn’t be surprised if some explanation lies within the stages in which the constellations were decided.

## Pokémon Go: 60 Million XP (Level 40 a Third Time)

Around 1400 on 12-23, I passed 60000000 XP, through obtaining a best-friendship. I have thus now accumulated three Level 40-equivalents of XP. Paragraphs on thoughts in this post will not be anywhere as extensive as they were with the previous two multiples of Level 40 XP equivalence.

Thoughts first before the charts this time, since they’re short; after all, only 95 days have passed since 40 million XP.

Ironically, I apparently rank among the most prolific in friendship interactions despite being strongly critical of most friendship-related features in Pokémon Go. I’m not sure how that happened. I am still of the belief that much of what Niantic produced in terms of interactions with friends are very poorly conceived, particularly with the emptiness of the gifting action; see my 40 million XP post for expounding on that.

Now that PvP battling has been introduced, I have yet another friendship interaction for which I have to gripe about mechanical decisions. There’s already a sort of battling in the game, gym battling; why couldn’t the two sorts of battling be more consistent, and have more shared features? The dodge mechanic in gym battling is, to me, a really great feature; it makes it so that this game has some aspect of tight-timing strategy, which normal Pokémon games do not provide. As such, dodging gives gym battles a feeling of involved action, as much as the feature has been somewhat glitchy. In PvP battles, instead of dodging, there’s these newfangled protect shields? What’s the deal with that? They do add some element of strategizing, but why couldn’t we still have the dodging mechanic and also the protect shields? And why does nearly everything else in the interface have to be different from the gym battle interface? We don’t get health bars in the same place, the charge meter for the charge move operates differently, and notifications of attacks occur in different locations. Shouldn’t it even be easier for Niantic to make if elements were reused?

Voltorb was for a long while my most-frequently-caught non-Ditto Pokémon for which I had a perfect catch rate, but this ended when I finally missed one between 50 million and 55 million XP. My perfect streak for catching Grimer also ended between 45 million and 50 million XP, but due to a glitch. I have since then also legitimately missed a Grimer. Remoraid took Voltorb’s spot as most-frequently-caught non-Ditto Pokémon for which I have a perfect catch rate, until my total catches for Snorunt overtook it.

That’s all for thought; charts now ensue.

Here’s XP versus time; the large dots denote multiples of 20 million XP.

Here’s a general statistics table. I lost data on the size of my Pokédex upon reaching 45M XP.

Here’s these statistics as a chart in multiples of amount for the gold badge.

And here’s that in multiples of quantity at Level 30.

At first, I thought the Distance Walked line disappeared. Then, I realized it tracked along PokéStop Visits that closely. It makes sense that they’re close together, but it’s even still incredible that they manage to be that close.

Here’s a table of catches by type.

Here’s that as a chart.

My catch totals from types other than the six most common types (Normal, Flying, Water, Poison, Bug, and Psychic) are only finally catching up to my catch totals in Normal and Flying when I was Level 30.

Here’s the gyms chart. The columns from left to right are, as usual, battles won, hours defended, and berries fed.

Here’s my progression of gold gyms, with days starting with the beginning of the new gym system.

#1: Alchemist at MIT, on Day 28

#2: Transparent Horizon, on Day 53
#3: Kresge Auditorium, on Day 54

#4: Vine Wall Art At No 6, on Day 82
#5: In Memory of Dorothy P. Simmons, on Day 94
#6: Cosmic Ray Chandeliers, on Day 102

#7: Community of Cambridge Super Awesome Mural!, on Day 137 [removed gym]

#8: Officer Sean Collier Memorial and Plaque, on Day 173
#9: DeWolfe Boathouse, on Day 190
#10: Martin Annis Crossing, on Day 191

#11: Smoot Plaques on Mass Ave Bridge, on Day 213
#12: Miracle of Science, on Day 227
#13: Fort Washington, on Day 241

#14: This is Where We Live Work Create Mural, Day 259
#15: Column of Faces, Day 265

#16: Jimmy Johnson Street Hockey Court, Day 288
#17: Not Art, Day 295
#18: The Apple Tree at MIT, Day 295

#19: Police Memorial, Day 342
#20: Putnam School, Day 351

#21: Danny Lewin Park, Day 387
#22: Cambridge Public Library – O’C, Day 387
#23: Flapper Mermaid, Day 388
#24: Dewey Library, Day 394
#25: Polygon Rope Gym, Day 398
#26: Vellucci Fountain at Cambridgeside Galleria, Day 399

#27: Skating Rink, Day 419
#29: Sloan School Spring Sculpture, Day 439

#30: Galaxy: Earth Sphere, Day 451

#31: Cow Mascot, [data lost: Day 452-468]
#32: Bao Nation Mural, [data lost: Day 468-494]

#33: Branch Hair Mural, Day 495 [renamed Wall Mural]

#34: The Big 186, Day 519

#35: Statue of Dante Alighieri, Day 541

Here’s a chart of Pokémon species by CP.

For the first time, Machamp leads this chart.

Here’s a chart of catch rates per species for species without a prior evolution earlier in the Pokédex.

And here’s the contents of my bag upon reaching milestones. I lost my data on quantities at 45 million XP and for Metal Coat at 50 million XP.

Here’s that as a chart.

## The News Reports Science: Bingo Board

Take a news outlet, and read its science articles over time. Mark the relevant invoked squares on the board.

## Pokémon Go: a 0xGG Journey to Level 40 Twice Over

Still zero money spent. Still zero third-party apps. Still 0xGG.

## 0. Contents [topp]

0. Contents [topp]
1. Introduction and General Statistics [ints]
1a. Preface [egbd]
1b. Level 40 Again [eity]
1c. General Statistics [gens]
2. Gym Statistics [dazz]
2a. Gold Gym Timeline [psyc]
2b. Stats per Gym [hype]
3. Notes [gbdf]
3a. Maintenance of Established Goals and Play Style [zer0]
3b. Thoughts on the Current Gym Coin Reward System [\$\$\$\$]
3c. MILOTIC IS SO BEAUTIFULLLLLLLLL [^_/~]
3d. Raid Optimization [3553]
3e. Espeon [4eon]
3f. Cars [4to5]
3g. Squirtle Community Day at Tufts University [shvl]
3h. Screenshots of Notable and Funny Moments [pics]
4. Thoughts on New Gameplay Elements [argh]
4a. Intricacies and Curiosities in Weather [boom]
4b. Nothing Good About It: Utterly Perverting the Concept of Friendship [xxxp]
4c. Devaluation of Rarity [unon]
5. Other Statistics [##%%]
5a. Bag Contents [brry]
5b. Most Represented Pokémon Species by Total CP [++cp]
5c. Catch Rates [%cch]

## 1. Introduction and General Statistics [ints]

1a. Preface [egbd]

Due to a variety of factors, this is a ridiculously long post, and also took me a ridiculously long time to write (even more the latter, due to factors like getting sick over the course of writing this post). In fact, while writing this post, I’ve earned another over 4.4 million XP above two Level 40s worth of XP, or in other words, almost as much as where I’d make the next post given my pattern (every 5 million XP) so far.

As such, I will not make a 45 million XP update, and I might not even make a 50 million XP update. If I decide to opt out of the latter, see you again at Level 40 times 3, at 60 million XP.

Like my Level 40 post, this post will contain not only the usual deluge of tables and charts, but also plenty of thoughts and opinions on the game (sections 3 and 4).

Use the bracketed entities in section headers with Ctrl+f to quickly navigate to the section you wish to read.

1b. Level 40 Again [eity]

At 1858 on 09-19 (2 minutes before ESP worksession!), I reached 40000000 XP, via spinning the PokéStop for the Alchemist at MIT gym, just southeast of the MIT SIPB PokéStop, the first Stop I spun and the Stop that took me to Level 40. Since Level 40 comes at 20000000 XP, I’ve now accumulated two Level 40 equivalents in XP.

Since that was Day 754 of me playing Pokémon Go, and I reached Level 40 on Day 543, the second 20 million XP took me 211 days.

Here’s a chart of XP over time.

1c. General Statistics [gens]

Here’s a table of general statistics at 12 snapshots in time so far.

Here’s a comparison of the change in quantity of these statistics over the second 20 million XP versus over the first 20 million XP, both in total and normalized per day. Note that Berries Fed at Gyms, Hours Defended at Gyms, Raids Won, and Legendary Raids Won are statistics not available for increasing for much of the timespan of the first 20 million XP.

Here’s this data as a couple of charts.

Here’s a table of total catches per type at these snapshots.

Here’s that as a chart.

Upon level up, all of my Pokémon were fully healed. My six strongest by CP:

Kyogre (CP 3760)
Groudon (CP 3696)
Slaking (CP 3682)
Tyranitar (CP 3670)
Mewtwo (CP 3622)
Ho-Oh (CP 3613)

## Pokémon Go: Stats at 35 Million XP

This August 11 (Day 715 of playing), I won a gym battle to reach 35 million XP.

Here’s a chart of XP versus time at level-ups starting with Level 29 and every 5 million XP after level cap.

My buddy at the moment was Chansey.

Gold Gyms

Day numbers in this section reflect days since the motivation-based gym system began.

#1: Alchemist at MIT, on Day 28

#2: Transparent Horizon, on Day 53
#3: Kresge Auditorium, on Day 54

#4: Vine Wall Art At No 6, on Day 82
#5: In Memory of Dorothy P. Simmons, on Day 94
#6: Cosmic Ray Chandeliers, on Day 102

#7: Community of Cambridge Super Awesome Mural!, on Day 137 [removed gym]

#8: Officer Sean Collier Memorial and Plaque, on Day 173
#9: DeWolfe Boathouse, on Day 190
#10: Martin Annis Crossing, on Day 191

#11: Smoot Plaques on Mass Ave Bridge, on Day 213
#12: Miracle of Science, on Day 227
#13: Fort Washington, on Day 241

#14: This is Where We Live Work Create Mural, Day 259
#15: Column of Faces, Day 265

#16: Jimmy Johnson Street Hockey Court, Day 288
#17: Not Art, Day 295
#18: The Apple Tree at MIT, Day 295

#19: Police Memorial, Day 342
#20: Putnam School, Day 351

#21: Danny Lewin Park, Day 387
#22: Cambridge Public Library – O’C, Day 387
#23: Flapper Mermaid, Day 388
#24: Dewey Library, Day 394
#25: Polygon Rope Gym, Day 398
#26: Vellucci Fountain at Cambridgeside Galleria, Day 399

Gym Stats

Here’s a table of my stats at my top gyms. The three columns from left to right are battles won, hours defended, and berries fed.

Orange highlighting indicates where hours defended alone is enough for a gold badge (≥500).
Magenta highlighting indicates where berries fed alone is enough for a gold badge (≥3000).
Tan highlighting indicates where hours defended and berries fed together are enough for a gold badge.

General Statistics

Catch Statistics

Here are my catch statistics for all non-legendary species for which there is no earlier evolution with an earlier Pokédex number. Apparently last time I did this I left out Machop, Bellsprout, and Lapras. My apologies to them; please don’t hurt me. They’re included this time.

Here’s my catch statistics for legendary Pokémon.

Mewtwo: 5/5=100.0%
Mew: 1/1=100.0%
Registeel: 1/1=100.0%

Moltres: 5/6≈83.3%

Articuno: 9/13≈69.2%
Zapdos: 10/15≈66.7%
Entei: 3/5=60.0%

Ho-Oh: 7/16≈43.8%
Kyogre: 9/21≈42.9%
Rayquaza: 10/24≈41.7%
Regice: 5/12≈41.7%
Raikou: 4/11≈36.4%
Latias: 1/3≈33.3%
Groudon: 3/10=10.0%

Latios: 2/11≈22.2%
Lugia: 3/16≈18.8%

Suicune: 0/5=0.0%

Here’s a table and chart over time for common species.

Pokémon

Here’s my strongest Pokémon by CP upon reaching 35 million XP.
Groudon (CP 3696)
Slaking (CP 3682)
Tyranitar (CP 3670)
Mewtwo (CP 3622)
Ho-Oh (CP 3613)
Tyranitar (CP 3612)

(Strongest Pokémon upon reaching 30 million XP)
Tyranitar (CP 3670)
Groudon (CP 3668)
Tyranitar (CP 3604)
Tyranitar (CP 3587)
Ho-Oh (CP 3585)
Rayquaza (CP 3582)

This time, like last time, I actually had quite a few Pokémon not at full health (or even fainted) when I reached this milestone, but this time, it was because I just finished a gym battle.

Bag Contents

Finally, how’s the state of my bag as I reach XP milestones?

Between reaching 30 million XP and reaching 35 million XP, I’ve peaked above 400 of each of Max Potions and Golden Razz Berries briefly.