
Kamryn Hellman - How She Moved From 0 to 2000 ELO in Two Years 🎙️ [No Board Needed]
The Chess Cognition PodcastShow Notes
In this special year-end episode, I had the pleasure of hosting Kamryn Helman, an extraordinary adult improver who achieved a chesscom rapid rating of 2000 in just two years, starting from absolute zero. Kamryn shared her inspirational journey, which began in 2021 after watching The Queen’s Gambit. Despite balancing her undergraduate studies and later a full-time job, she devoted around 20 hours per week to chess and made remarkable progress. Kamryn's disciplined approach combined playing, tactical exercises, and joining a chess club, where she set goals to compete with stronger players. Her story is a testament to how dedication and a structured approach can yield extraordinary results in a short time.During our conversation, Kamryn offered valuable insights into her improvement methods. She talked about her "Why I Am Losing" document, a weekly habit of analyzing losses to identify patterns and guide her studies. We also discussed her thought process, including a mental checklist to avoid blunders, and the critical role of breaks in consolidating knowledge. Kamryn's ability to stay intentional and disciplined about her chess journey is both inspiring and instructional for players at all levels.Toward the end, we explored her educational contributions to the chess community, including her Chessable course, 50 Essential Chess Concepts: An Improver's Blueprint. She shared the challenges of designing clean, instructive puzzles that minimize cognitive overload and maximize learning. Kamryn's passion for chess and commitment to improvement was evident throughout our discussion, leaving me—and hopefully you—with fresh ideas and inspiration to tackle our own chess journeys. I’m thrilled to see what Kamryn accomplishes next and I look forward to another conversation as her chess story unfolds.
01:54 The Chess Bug and the Very Short Summary of the Journey to 2000 ELO08:57 The Thought Process10:47 Blunder-Check15:16 'Why Am I Losing?' Document23:07 How Blitz Helped 26:00 Study Advice for 0-1500 ELO29:16 Study Advice for 1500 - 2000 ELO37:18 Her Chessable Course41:40 Her New Openings44:47 Final Advice for Beginners45:40 Final Advice for Intermediate Players46:34 The Weekly Training Schedule
More from The Chess Cognition Podcast
00:00 Intro01:19 The Recognition Trap02:29 The PER Method: Preview, Explain, Retrieve02:48 How to Choose the Right Master Games04:18 Step 1: Preview the Game05:43 Previewing Tal–Larsen 196507:49 Step 2: Explain the Master’s Move (Only if it is Annotated)09:15 Tal’s Famous Nd5 Sacrifice12:21 Compare Your Explanation With Tal’s14:40 Your Blind Spots Are the Lesson16:43 Another Example: Explain Qf419:04 Step 3: Retrieve21:11 The One-Sentence Game Summary22:23 Why This Method Works24:23 Using the Method With Chessable CoursesMost chess players are told to study master games, but simply clicking through the moves and reading annotations can create the illusion of learning. You recognize good chess when you see it, but that does not mean you can reproduce those ideas in your own games. In this video, I introduce the PER Method — Preview, Explain, Retrieve — a practical way to turn master games from passive entertainment into active chess training without overwhelming yourself by trying to guess every single move.Using the famous Tal–Larsen game as an example, I show how to first preview a game to reduce novelty, then explain the master's important decisions before reading the annotations, and finally return a few days later to retrieve the critical moves and ideas from memory. The method combines active generation, feedback, reduced cognitive load, spaced retrieval, and carefully selected model games to help you actually transfer what you learn from great players into your own chess.
0:00 You Understand It But Can’t Apply It0:43 The Real Problem: Retrieval1:03 The Encoding Specificity Principle2:02 Why Chess Study and Real Games Feel Different3:39 Recognition Is Not Retrieval5:17 How to Make Chess Knowledge Transfer5:33 Guess the Move Training6:27 Mixed Practice and Interleaving7:00 Train Under Game-Like Conditions7:32 Botvinnik Method8:17 Solve Puzzles Like You Play Games8:44 Why Active Retrieval Matters9:36 The Key to Applying What You LearnHave you ever watched a chess lesson, understood everything perfectly, and then completely failed to use the idea in your own games? The problem may not be your chess knowledge at all. It may be a retrieval problem. In this video, I explain how the encoding specificity principle, recognition vs. retrieval, and context-dependent memory can help us understand why chess concepts often seem obvious during study but disappear when the clock is ticking.More importantly, I show how to make your chess training transfer better to real games. We look at guess-the-move training, mixed practice and interleaving, game-like training conditions, active retrieval, and spaced retrieval. If you want to improve your chess decision-making and actually apply what you study over the board, your training needs to involve more than simply watching videos and recognizing familiar ideas.Godden, D. R., & Baddeley, A. D. (1975). Context-dependent memory in two natural environments: On land and underwater. British Journal of Psychology, 66(3), 325–331.Tulving, E., & Thomson, D. M. (1973). Encoding specificity and retrieval processes in episodic memory. Psychological Review, 80(5), 352–373.
0:00 Why Chess Overloads Your Brain1:09 The Intelligence Illusion1:59 Your Working Memory Has a Problem3:10 Why Obvious Blunders Happen4:02 How Strong Players Make Chess Simpler4:37 Schemas: The Secret of Chess Expertise6:10 What Masters See That Beginners Don’t6:38 Why Random Chess Training Often Fails7:20 How to Build Chess Patterns Properly8:08 Why Beginners Need Worked Examples8:44 Gradually Increase the Complexity9:16 How to Reduce Cognitive Load During Games9:59 Build Your Chess Pattern Vocabulary10:36 How to Study for Transfer11:37 Why Memorable Analogies Help12:06 The Real Meaning of Chess Mastery13:05 How I Apply This to Chess TrainingWhy does chess feel so overwhelming, and why do strong players seem to understand a position almost instantly? In this video, I explain how **working memory, cognitive load, schemas, and pattern recognition** influence chess improvement. The real limitation is often not intelligence or calculation ability, but the amount of information your brain is trying to process at once. Strong chess players overcome this by compressing complicated positions into meaningful patterns or “chunks.”You’ll learn why simply calculating more can sometimes make things worse, how masters reduce cognitive load through chess patterns, and how to structure your training so that important ideas actually stick. I also explain how clean examples, worked examples, gradual increases in difficulty, and a strong chess vocabulary can help you recognize patterns faster, avoid blunders, improve chess vision, and make better decisions during your games.
0:00 Mistakes Look Obvious Afterwards01:00 The Hindsight Bias2:10 Why Your Brain Rewrites the Past2:41 How Engines Contaminate Your Analysis3:47 A Painful Example From My Own Game6:02 What to Do Before Turning On the Engine7:22 Introducing the SCAN Method10:25 Finding the Real Cause of a Mistake11:53 The True Purpose of Game AnalysisWhy do your chess mistakes suddenly look obvious the moment the engine reveals the best move? The answer may be hindsight bias—a cognitive bias that causes us to see past events as more predictable than they really were. When you turn on Stockfish immediately after a game, the engine gives you the evaluation, the correct move, and the tactical solution within seconds. But once you know the answer, it becomes much harder to remember what you actually saw, considered, calculated, and understood during the game.In this video, I explain how hindsight bias can undermine chess improvement and show you a better way to analyze your games with an engine. You will learn how to reconstruct your original thinking before checking the computer and how to use the SCAN method to diagnose the real cause of a mistake: Did you see the critical feature, consider the right candidate move, analyze it correctly, and understand the resulting position? The goal is not simply to discover the best move, but to uncover the blind spots, calculation errors, evaluation mistakes, and knowledge gaps that prevented you from finding it.
00:00 Why Puzzle Skill Disappears in Real Games00:59 What Is Transfer in Chess Training?01:50 The Study That Changed How We Understand Practice03:18 Why Harder Practice Produces Better Learning03:45 The Hidden Problem With Themed Chess Puzzles04:24 Advantages of Blocked Practice04:49 A Three-Phase Method for Training Tactics05:19 Why Mixed Puzzles Still Aren’t Game-Like Enough05:55 Training Recognition Under Real-Game Conditions06:30 Courses Involving Interleaving07:42 How Would You Help Shaquille O'Neal Improve His Free Throws? Why do some chess players achieve extremely high puzzle ratings but still struggle to find tactics in real games? In this video, I explain the learning science concepts of blocked practice, interleaved practice, and transfer, and show why repeatedly solving the same type of chess puzzle can yield impressive training results without necessarily improving over-the-board performance. Using the famous Rohrer and Taylor study, we explore why difficult, mixed practice may feel less effective in the moment, but produce stronger long-term learning and better pattern recognition.You’ll learn a practical three-phase approach to chess tactics training: first build new tactical patterns through blocked practice, then mix forks, pins, skewers, and discovered attacks through interleaving, and finally practise with game-like positions where you do not know whether a tactic exists at all. This method can help you recognise what kind of position you are facing before you begin calculating—the skill that real chess actually demands. If your chess puzzle rating is much higher than your online or OTB rating, this video may explain why.Scientific Reference: Rohrer, D., & Taylor, K. (2007). The shuffling of mathematics problems improves learning. Instructional Science, 35, 481–498.