Знания ≠ Навык. Вот в чём ошибка почти всех курсов

Storyline / Purpose of the Video

• The speaker (a poker trainer) explains why “knowledge ≠ skill”: simply understanding GTO/math isn’t enough—players must convert study into repeatable, practiced decisions at the table.
• He shares his personal progress becoming a profitable NL500 regular and argues that success depends heavily on:
  • Selectivity (choosing the right games/opponents)
  • A systematic training method
• The main focus is how he studies specific flop/turn/river “textures” and then uses a custom simulator to train decisions until they become automatic (via spaced repetition / active recall).

Gameplay / Training Gameplay Highlights

1) Using Solver Research on Key “Textures”

• He starts with a site for analyzing poker spots using math/solver outputs (mentions analyzing BvB and “Sin and GTO Wizard” spots).
• He emphasizes picking high-frequency textures first, because they cover a lot of boards.
• Example texture: “Big Big Low”
  • Often involves very high cards like A/K plus lows.
  • Different connectedness states depend on ranks/suits.
• He argues this texture appears about 10–20% of boards, making it a high-priority study target.

2) Studying Betting Lines and Overbets (EV-Based Adjustments)

• He examines three-barrel lines (betting turn + river after earlier aggression) and solver results on:
  • How frequently the solver uses certain turn sizes (e.g., a common line like 3/4 out of position)
  • Whether opponents are overbluffing or overfolding versus specific sizings
• Overbet finding example (from his research):
  • He claims the overbet is not under-polished (i.e., it’s not as weak as some people assume in practice).
  • When the overbet “hits” (opponent uses it), calling is often not profitable.
  • Instead, he suggests using other turn sizes (example given: adjusting turn size like over 100, then continuing appropriately on the river).
• He also compares how different river sizes perform:
  • Different sizings can be more profitable because real opponents bet inconsistently (some bluff different sizes, others value different sizes).

3) Practical Exploit Idea: “GTO First, Then Adjust”

• His philosophy:
  1. Analyze with solver (GTO baseline).
  2. Then adjust based on how real players deviate—especially targeted regulars.
• Core exploit theme:
  • Some opponents don’t follow through with certain bet-bet lines, so your bluffs/calls must reflect the actual bluff frequency in real games.

4) Building Hand-Group Rules (Blockers, Turn/River Behavior)

• He shows how to turn solver output into “rules” by grouping hands, including:
  • How to treat top two pairs vs blanks
  • How to split decisions based on blockers (e.g., raising more with backdoor/blocker cards)
• Example concept:
  • Two pairs with certain blockers (e.g., backdoor / suit-connected blocker ideas) can justify aggression later, especially in multi-barrel lines.
  • Two pairs without blockers may perform worse and shift more toward calling than raising (in the described line).

Key Strategies / Key Tips Extracted

• Selectivity matters: it’s harder to win without choosing the right games/opponents.
• Prioritize study on high-frequency textures (e.g., “Big Big Low”).
• Use solver research to:
  • Determine which sizes work best in theory and which work best versus real opponents
  • Evaluate EV of calls across runouts, not just single boards
• Apply exploits:
  • If opponents are short on bluffs (or don’t “bet-bet” enough), adjust bluff-catching and bluff frequencies.
• During training:
  • Convert “conceptual knowledge” into automatic table decisions via practice and recall—not passive reading.
• Use spaced repetition:
  • Repeatedly revisit specific hands/decision types until you remember them correctly.

His Training System (Step-by-Step)

A) Solver → Notes / Strategy Writing (His Standard Workflow)

• Choose a major texture (example: “Big Big Low”).
• In the solver/analyzer:
  • Identify relevant board regions
  • Identify key lines (e.g., flop three-barrel continuation)
• Extract:
  • Preferred sizing frequencies
  • EV implications for calls/folds
  • Who overbluffs/underbluffs and how often
• Write a short cheat sheet:
  • What to do with certain hand classes
  • Why (logic)
  • What to do on flop/turn/river

B) Custom Simulator + Decision Drills

• His group members use his simulator to:
  • Practice 20 hands per session
  • Receive prompts tied to a specific concept (e.g., Big Big Low, three-barrel decisions, bluff categories)
• For each decision:
  • Choose an action/sizing button (e.g., fold/check/bet sizes)
  • If wrong, the correct answer is shown
  • The hand type is scheduled again for future repetition
• Spaced repetition scheduling:
  • The same concept reappears after increasing delays (e.g., “in 2 days… in 5 days…”).
• He logs performance through a “line of mastery”:
  • Correct answer: +1
  • Mistake: -3
  • Then you can filter which spots you’re weak at and train them more

C) After Training: Analyze Weak Spots

• After workouts, he emphasizes reviewing:
  • Mistake spots
  • Weak decision boundaries (flop/turn/river boundaries)
  • Areas to tighten

Sources / Gamers Featured (As Mentioned)

• Real Edge GG (mentioned as a reference point for sizings/field behavior)
• Telegram (used to share links to recordings/notes; not a specific gamer)
• No other specific named gamers/players are clearly and consistently identified in the provided subtitles.