[Wolf Lord+ | Lore] Full Excerpt: Pick/Ban - The MMM Paper (Patreon)

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Hello everyone, LunaWolve here!

Welcome to the draft release of another Lore Chapter!

Once again with another Full-Length Excerpt!

It's effectively the un-shortened, un-edited version of the Excerpt, with it's full 1800~ Wordcount rather than the roughly 1000 Wordcount cut down version that will go into the chapter-proper.

As always: Thanks for supporting me in writing this novel and enjoy this little Lore Chapter!

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I highly recommend reading the Googledocs or PDF/epub version, as they have actual formatting, which Patreon does not offer.

Here's the link to the googledoc:

https://docs.google.com/document/d/1Pfthbnk41u_HhiXBipHGLSp9-CRAVT9NaaCiS_kS7TE/edit?usp=sharing

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Full Excerpt: Pick/Ban - The MMM Paper

VELDT: —aaaand we’re rolling! Welcome back to Pick/Ban, everybody. I’m Veldt, she’s Switchback—

SWITCHBACK: Hello, hello, helloooo!

VELDT: —and today we have an actual academic in the booth, which means both of us are going to have to pretend real hard that we finished secondary education at some point in our lives.

SWITCHBACK: Speak for yourself, Veldt. I finished mine. There’s even a certificate.

VELDT: Nobody’s ever seen this certificate, for the record. Anyway—joining us today is Dr. Enna Irinas of the—I’m gonna have to read this off the screen, one sec… Intoresita Academic Institute, Department of Competitive Systems Analysis—whose newest paper has been absolutely tearing through the scene for roughly the past week. Doctor, welcome.

DR. IRINAS: Haha, thank you for having me! I’ll confess up front, I’ve listened to the show for years, so this is mildly surreal to me to be here…

SWITCHBACK: Oh fuck, she’s a listener. We can’t swear anymore, Veldt!

VELDT: Nah, we can swear more, actually. Means she knows what she signed up for! Okay—Doctor… The paper. For the three listeners who haven’t seen it: The title is “The Generalist Anomaly,” and it is about—who else—MissyMoonlightMayhem. Everybody’s favourite ghost that recently showed a bit of a blip, which likely helped put more eyes on your paper, no doubt. But tell me, what made you pick MMM, of all the legends you could’ve studied? There’s no way you knew that there’d be a huge resurgence of interest in her case, after all—right?

DR. IRINAS: Hahaha, Emperor no! I promise, I have no more information about her whereabouts than any of you out there. To answer the question though… Honestly? I didn’t set out to study her at all. The original project was a comparative analysis of specialization patterns across Galactic Championship winners—going back about one-hundred and sixty years. Legends like NyxianFelzir, with his fourteen consecutive Championship titles. LykanthropyEmerged, with his twelve total. The great Dynastic players, and so many more. The goal was to map how dominant players dominate, then write about that. I found that every one of them fit a very clear model, every single one…

SWITCHBACK: —except her?

DR. IRINAS: [nodding] Except her. She broke the model so badly that I spent two entire months assuming my data pipeline was somehow corrupted… The paper now exists because I realized after all that work, that the pipeline was actually completely fine.

VELDT: Okay, okay, okay… Walk us through this, please? Because I think when most people hear “MMM broke the models,” they assume it means she was just better. Like bigger winrate, more titles, whatever, y’know?

DR. IRINAS: Right, of course. That’s also the first misconception the paper addresses, to be frank. In terms of raw winrate, MMM is not a statistical outlier among champions at all. She’s above average, considering her four—or five, counting the DQ incident—but that’s about it. She’s nowhere near Felzir’s peak or Lykanthropy’s dominant years. What sets her apart has never been the height of her performance, but the shape of it.

SWITCHBACK: The shape? What—What does that mean, exactly? Like, for people that haven’t finished secondary education like Veldt. I obviously know what that means, but it would be good to clue him and some of the listeners in.

DR. IRINAS: [laughing] The shape means… the breadth—the versatility. Actually, let me frame it this way—take a builder like GravewallSutra.

VELDT: [laughing] Oh, shit, Sutra’s gonna love being the example.

DR. IRINAS: [laughing] It’s an affectionate example! Sutra has focused on vanguard-style builds for thirty years straight. He dips a toe into other archetypes maybe once a season, publishes something competent, and returns to the vanguard pool. That’s the normal, expected pattern for any professional. That’s, ultimately, what mastery looks like in every dataset we have—deep in detail but narrow in breadth. Every great player or builder has a “home”, of sorts.

SWITCHBACK: [nodding] Absolutely! Mine’s skirmish-assault. Veldt’s is whatever the fuck’s broken this patch.

VELDT: [slow nod] Factually accurate, emotionally devastating… But carry on.

DR. IRINAS: [laughing] Which makes sense for a professional player, really. Yet MMM’s home—as far as eight years of recorded competitive data can establish—was “whatever the meta required.” Now, it’s not the same as for Veldt here, because she didn’t simply pivot to a Build that was particularly overpowered in a given meta, but she created new ones in the archetype that was favoured at the time. But the aspect that truly shocked me the most, was that I ran archetype-affinity scoring across her entire published catalogue and her tournament picks… There is no statistically meaningful “home” bias. None.

VELDT: Okay, but—hold on a second there, Doctor. I’m gonna push back here, because I played in two Championships she was at. Everybody knew MMM absolutely loved glass cannons. Ranged glass cannons, specifically. The joke was that her ideal build had the survivability of wet paper and the damage of the Emperor’s own flagship. That’s gotta be a bias, no?

DR. IRINAS: It’s technically a preference, and the paper spends a whole section on exactly this difference. A preference is an active choice, while a bias is a subconscious thing. You are correct in that a majority of her builds skewed towards high-damage, low-durability, ranged Builds. But when you actually go in and control for skill ceiling, which I have done plenty of in my collation of data for this, the bias evaporates entirely, because it was always merely a preference that she could turn off at any moment. Glass-cannon ranged builds are, mechanically, the archetypes with the highest ceiling in Wildmaws—the most power available to someone actually capable of piloting them at that limit. She wasn’t simply choosing them because she loved them—even if she might have enjoyed them the most. What truly drove her to choosing them was that they were the correct choice for a player of her caliber. It’s merely a conscious efficiency signature, not a subconscious stylistic one. The moment the meta punished those builds, however? She’d immediately throw down in a vanguard role, full-tank, assault, support hybrid—whatever was optimal at the time—without even an inkling of hesitation and, most importantly, without any measurable performance drop.

SWITCHBACK: [breathes out heavily and leans forward] Which, as someone who has had to learn dozens of off-role picks at Championship level—that drop is insanely brutal to overcome. And it always exists, trust me on this. There’s a saying in professional play that goes as follows: “Your comfort picks carry you, your flex picks feed you.” Meaning you’d always be better at your comfort picks, but contractual obligations require you to win, so you need flex options to counter counters, even if you’re worse at them.

DR. IRINAS: That nicely brings me to the finding that made me rerun the entire analysis four times, with different parameters each time, as I figured something had to be broken somewhere... Her matchup statistics.

VELDT: The part of the paper that’s been melting the forums, for the record.

DR. IRINAS: So. The developers of Wildmaws—and Archion, I actually included her statistics for that game too, as she’d earned most of her Galactic Championships in it—publish expected matchup statistics—build-versus-build win probabilities under equal skill conditions. Every champion in history deviates from those expectations in a distinctly patterned way. They overperform against archetypes they love practicing against, and underperform against the ones they find tedious or rare. Felzir, for all his dominance, had a documented soft spot against summoner-type Builds his entire career—he under-prepared for them, because he personally abhorred them in every facet of the game, fourteen titles or not. Lykanthropy famously over-trained mirror matches. All players have favourites. Favourite things to study, to counter, to remember. That bias is about as human as a heartbeat, and it shows up in every dataset.

Except hers.

Across eight years of recorded tournament play, MMM’s wins and losses track the developer-published matchup expectations almost exactly. She won when the matchups said she should win. She lost—at almost precisely the expected rate—against Builds designed to counter whatever she was using. Upsets existed in both directions, of course, but only at the frequencies skill variance predicted. The bias term—the fingerprint of human preference in preparation—however, is statistically absent. She appears to somehow be prepared for everything, equally, all the time.

SWITCHBACK: [blinks several times] … I—I need the listeners to understand just how insane what the Doctor is saying really is, here... I keep a literal spreadsheet of matchups I fucking hate. Almost everyone does. You mostly prepare for the statistically most likely opponents to face, and the ones you hate the most, that have the lowest appearance rate in a given tournament, you simply hope you don’t meet—that’s it. Preparing for everything—that’s impossible. There’s only so many hours in a day, so much mental capacity to learn the matchups…

DR. IRINAS: Which is why I state in the paper, very carefully, that I cannot explain the statistics that exist. I can only document that they exist for her, and that they exist nowhere else. Not in one-hundred and sixty years of champions. She is the only player in recorded competitive history with no measurable preparation bias. Terra’s own literature department, apparently coined in the times before the Galactic Expansion even, has a term for players with wide competencies—”jack of all trades, master of none.” The paper I’ve written, or rather, the data I looked at while writing said paper, forced me to coin a variant.

VELDT: [leans into the mic] The line everybody’s quoting in the forums.

DR. IRINAS: “MMM is a true jack of all trades—a master of all.”

VELDT: [exhales] And then she just—vanished, huh? No farewell, no final build, nothing. Only to reappear recently, if the forum rumors are to be believed. You spend a year of your life in the guts of this woman’s data, Doctor. I have to ask... What’s your theory on all this?

DR. IRINAS: [pause] My professional answer is that I study performance records, not people, and speculation about a private individual’s whereabouts is outside the scope of my expertise.

SWITCHBACK: [leans in] And your unprofessional answer?

DR. IRINAS: [longer pause] My unprofessional answer is that somewhere out there is a person who prepares for every possible opponent, equally, all the time, without bias and without rest—and that I find it very hard to imagine a person like that ever actually stopping. So… Whatever she’s doing right now? I’d wager she’s doing exactly the same thing she’s always done: Preparing for everything.

VELDT: ...Yeah. Okay, fuck. Straight chills, Doctor. We’re gonna take a break here and when we come back, Dr. Irinas is gonna grade Switchback’s ladder builds live on air.

SWITCHBACK: [almost falls off chair] What?! We didn’t talk about any of this, Veldt! What the fuck do you mean—absolutely not—

[AD BREAK]

—

[Excerpt from “The Pick/Ban Podcast”, Episode 312: ‘The MMM Paper, with Dr. Enna Irinas’” Hosted by Wildmaws professionals “Veldt” (Tonni Veldt, Hallion Hawks) and “Switchback” (Miira Oszeyk, Vespera Royals). Broadcast on the GalNet, PFC 943.]

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