Video summary

Once you see this, You’ll see Competitive Games Differently

Main summary

Key takeaways

Educational

Main ideas / lessons conveyed

  • Genre labels are misleading. It’s hard for people to answer what they like because genre categories don’t explain what the game is actually testing.
  • Every game separates good from bad players by using one or more kinds of challenge:
    • Skill / execution (reflexes, aim, timing)
    • Information / probability awareness (knowing what others don’t; reading what’s uncertain)
    • Hard-to-name mental adaptation (reading people, managing randomness, adapting mid-game)
  • These challenges correspond to three different gameplay “layers”:
    • Micro
    • Messo
    • Macro
  • The “cheat test” is the best way to understand what a game is testing:
    • Ask: If you wanted to completely break the game and make it trivial, what cheat would you use?
    • The type of cheat indicates which layer(s) the game is primarily testing.
  • Most competitive games blend layers in different proportions; the blend determines game identity and balance (not the genre name).
  • Player self-understanding: People tend to have natural strengths (“lean”) toward one layer; the most frustrating games usually demand skills your brain gives less naturally.
  • Stop blaming yourself for not “getting it”: some games train aspects you haven’t developed yet.

Methodology / framework (detailed)

1) Use the “cheat test” to identify what a game is testing

  • Pose the question:
    • “What cheat would I use to break this game and make it trivially easy?”
  • Interpret the cheat type:
    • Cheats that perfect execution beats the game → Micro-heavy
    • Cheats that reveal hidden/forbidden info or exploit uncertainty → Messo-heavy
    • Cheats that optimize the whole plan/system → Macro-heavy
  • Conclusion:
    • This tells you more about a game than genre labels.

2) Define the three game layers

Micro (execution layer)

  • What it tests:
    • Aim, timing, movement, muscle memory, mechanical precision
  • Micro cheat examples:
    • Aimbot / perfect targeting
    • OSU perfect cursor tracking
    • Geometry Dash perfect jumps
    • (Also referenced: aim trainers, Tetris)
  • Key idea:
    • If perfect execution makes the game trivial, it’s a micro game.

Messo (probability / mind-game layer)

  • What it tests:
    • Reacting to unpredictability
    • Reading habits
    • Exploiting randomness / limited information (what you can infer)
  • Messo cheat examples:
    • Stream sniping (seeing/hearing information you shouldn’t yet)
    • Knowing cooldowns/habits before they appear
    • Seeing something before it’s normally visible
  • Best “pure messo” example:
    • Rock-paper-scissors (no execution skill; mostly mental/probability)
  • Examples mentioned as messo-driven or similar:
    • Among Us (stream sniping to identify impostor)
    • Liar’s Bar
    • Stock market (insider trade as the “cheat” analogy)
  • Key idea:
    • Messo cheats don’t “execute better”—they know more than you should or exploit uncertainty.

Macro (systems layer)

  • What it tests:
    • Routing, resource management, win conditions
    • The mathematically correct way of winning (planning as a system)
  • Macro cheat examples:
    • Chess engine
    • Economy automation
    • A “perfect coach” that tells you exactly what to do
  • Examples of near-pure macro:
    • Factorio, Polybridge, Connect 4, tic-tac-toe
  • Key idea:
    • If there’s an optimal solution, once found the game can become essentially “solved.”

3) Classify games by layer combinations

Micro + Macro

  • Tends to be:
    • Single-player precision with optimal solutions + fast execution
  • Examples listed:
    • Rubik’s Cube speedrunning
    • Mario 64
    • Jump King
    • Getting Over It
  • Special notes:
    • Elden Ring: perfect micro → perfect dodges; perfect macro → optimal build.
    • 8-ball pool is a multiplayer outlier:
      • A mathematically optimal shot exists, but actual hitting still requires micro.
      • Mind games / messo are comparatively limited.

Micro + Messo

  • Tends to be:
    • PvP skill expression where mechanics + opponent reading matter
  • Examples listed:
    • Fighting games: Street Fighter, Tekken, Smash Bros, Brawlhalla
    • Mario Kart (driving micro + random items and unpredictability as messo)
    • Tetris 99 (micro from original Tetris; messo from sending garbage)
    • Physics-based PvP: Fall Guys, Gang Beasts

Messo + Macro

  • Tends to be:
    • “Mind and math” games: probabilistic inference + strategic optimization
  • Examples listed:
    • Hearthstone, TFT, Pokémon VGC, Battleship
  • Chess note:
    • Theoretically macro-heavy, but in practice includes major messo at human level (opponent habit exploitation).
  • Weird / outlier examples:
    • Balatro: single-player but mezo + macro (RNG + maximizing hand value via choices)
    • Phasmophobia: co-op mezo + macro
      • Macro: deducing ghost using evidence
      • Messo: ghost behavior uncertainty/randomness

All three overlapping (Micro + Messo + Macro)

  • Claim:
    • Biggest competitive games live here.
  • Examples listed:
    • League of Legends, Dota 2, CS2, Valorant, Overwatch, Marvel Rivals, Rocket League, Apex, Rainbow Six, World of Warcraft

4) Compare specific games using the cheat test (how layer balance shifts)

Counter-Strike (CS2) example

  • Micro test:
    • Aimbot alone can likely win many games.
    • “Never miss” ends duels quickly → micro extremely high.
  • Messo test (x-ray through walls):
    • Experiments described: X-ray player vs teams without it.
    • X-ray can still win rounds it “shouldn’t,” but teams often still prevail → macro relatively low.
  • Macro conclusion:
    • CS emphasizes short rounds and execution more than long-term economy/macro optimization (unlike MOBAs).

Overwatch example (requires role-specific analysis)

  • Roles differ in layer emphasis:
    • Support: more macro-heavy / defense-heavy
    • DPS: more micro-heavy / offense-heavy
    • Tank: “messy,” with a mix leaning toward control
  • Concept framework used:
    • Offense / Defense / Control
    • Tanks rely more on control, supports on defense, DPS more on offense.
  • “If you gave each role one cheat” idea:
    • DPS: aimbot
    • Tank: perfect cooldown knowledge / seeing enemy cooldowns
    • Support: a “perfect coach” (engine)
  • Note:
    • 5v5 structure (only one tank) makes winning duels as tank especially impactful.

Apex example

  • Claim:
    • Apex is “remarkably balanced” across micro, messo, macro.
  • Reasoning via three hypothetical perfect cheats:
    • Perfect aimbot: can kill during rotations, but close-range fights still uncertain.
    • Perfect wall hacks: can use abilities/nades to take teams, but can still lose to strong ring positioning.
    • Perfect ring knowledge: still vulnerable to players who contest effectively.
  • Conclusion:
    • No single cheat layer guarantees dominance; all layers are meaningful.

Rocket League example (with a pro coach response)

  • Asked about perfect micro/macro/messo and which matters most.
  • Coach’s framing:
    • Perfect micro = taking ball from point A to B as quickly and efficiently as possible (mechanics dominate).
    • Smarts/macro can’t fully compensate for mechanics because mechanical players can get the ball to open nets far more reliably.
  • Layer priority conclusion:
    • Micro most dominant
    • Macro second
    • Messo last
  • Additional reasoning:
    • In Rocket League, information is largely knowable at all times.
    • A perfect coach (macro) can be better than hearing comms or seeing boosts (messo).
  • Supporting observation:
    • AI bot gameplay vs semi-pro players:
      • The AI bots reportedly win comfortably, aligning with micro dominance.

5) Apply the framework to player identity and motivation

  • Self-analysis observations:
    • The narrator enjoys games in:
      • the full micro + messo + macro middle cluster
      • and also games that are pure micro or pure macro
    • They don’t enjoy micro + macro games (speedrunning doesn’t match their “itch”).
  • Example of personal gap (Apex):
    • They claim:
      • aim and movement are decent
      • rotations and ring positioning are solid (macro reads exist)
      • but they tunnel vision in close quarters, missing messo reads under pressure
    • Interpretation:
      • Their biggest mismatch is underdeveloped messo.
  • Final lesson:
    • Players naturally lean toward one layer:
      • Micro players: obsess over mechanics; want skill gaps to feel fair
      • Messo players: love chaos, raiding, unpredictability; thrive on probabilistic PvP
      • Macro players: love efficiency, routes, planning between games
    • Most people are mixed, but not equally.
    • Frustrating games likely demand your weakest layer.
    • Games you return to likely reward your strongest layer.
  • Meta conclusion:
    • Genre labels never told you which skill layer you’re building.
    • Once you understand the 3 dimensions, you can interpret games differently and stop self-blame when they don’t “click.”

Speakers / sources featured

  • Unspecified narrator / main speaker (talking throughout and presenting the framework)
  • Overwatch expert (asked to compare three cheat versions) — quoted speaker discussing role differences (Support macro/defense, DPS micro/offense, Tank messy/control)
  • Luke (Pro Rocket League coach) — provides Rocket League cheat-test response

Original video