An introduction to the theory and practice of poker (2020)
Advances in game-theory-based “solver” tools have dramatically changed how serious players study poker, making much traditional material – including classic books still used in university courses – feel outdated to many. Commenters debate how far a beginner should go into modern GTO-style theory versus simpler tight-aggressive fundamentals, and emphasize that game selection, emotional control, and understanding rake often matter more for making money than perfectly “optimal” play. Others highlight the growing role of AI in both solving and cheating at online poker, and question how realistic it is today to reach semi-professional earnings compared to more conventional careers.
Perceptions of the Hopkins Poker Course
- Seen as a “cool idea” and good for taking a total beginner to basic competence.
- Several commenters consider the reading list and theory somewhat outdated (boom-era / pre-solver mindset).
- Critiques focus on lack of emphasis on modern concepts like solver-based GTO, polarization, MDF, and range-vs-range analysis.
Modern Poker Theory: GTO and Solvers
- Current high-level play is heavily solver-driven; players study equilibrium-style (“GTO”) outputs and reverse-engineer principles.
- Debate on importance for non-pros:
- One side: solvers are essential to understand theory and to maximize exploitation (e.g., via node locking).
- Other side: insights from solvers trickle down; most learners are better off with distilled educational content and simple charts.
- Specific strategic shifts mentioned: donk-betting on certain boards, small blind completing, occasional open limping, more nuanced bluff selection using card removal.
How to Learn Poker / From Noob to Semi‑Pro
- Recommended resources: training sites (Upswing, RaiseYourEdge, pokerstrategy.com), modern books (Modern Poker Theory, Play Optimal Poker, Grinder’s Manual), forums, Discord communities, YouTube solver walkthroughs.
- Practical advice: memorize solid preflop charts, learn basic odds, start very tight in live low stakes, and gradually layer postflop skills.
- Some argue thousands of hours plus extensive solver work are needed to approach semi-pro; $50/hr is cited as an achievable but non-trivial online win rate.
Variance, Psychology, and Game Selection
- Emotional regulation and tilt control emphasized; exercises like folding every hand for hours used to test discipline.
- Game selection repeatedly called the single biggest money-maker: seek soft games with clearly weaker opponents and avoid tough ones.
- Opportunity cost noted: even if winning, low hourly rates may not justify full-time play.
AI, Bots, and Online Cheating
- Strong AI already beats humans in heads-up and 6-max NLHE; modern solvers approximate Nash equilibrium strategies.
- Real-time assistance and bots are acknowledged problems in online poker, though claims of near-ubiquity are disputed.
- Sites can detect solver-like patterns to some degree; still an arms race.
Nature and Complexity of Poker
- One view: basic optimal moves are simple, so at high levels outcomes are mostly luck plus “reading people.”
- Counterview: high-level theory is extremely complex: huge game trees, hidden information, multi-street mixed strategies, stack-depth effects.
- Detailed arguments over whether GTO guarantees non-negative EV against “clowns,” especially in multiway pots with implicit collusion; heads-up vs multi-player distinctions raised.
CS / Poker Tools and Related Courses
- Multiple Rust-based poker libraries, servers, and learning tools described (hand evaluation, simulations, CFR bots, training arenas).
- Mention of other academic poker courses (e.g., MIT OCW).
Ethics and Personal Fit
- Some commenters lost interest after realizing that the best “strategy” is often just targeting weaker, sometimes vulnerable, players.
- For such people, the theory remains interesting, but making money that way feels ethically uncomfortable.