McKinsey Solve: Sea Wolf Game and Redrock Study Guide (2026)

The McKinsey Solve is a 65-minute, game-based digital assessment with two modules — Redrock Study and Sea Wolf — taken by approximately 300,000 candidates per year, with only 20-30% advancing to first-round interviews. Most McKinsey Solve guides online are outdated: they still describe the Ecosystem Building game, which McKinsey phased out in 2023. If you're taking Solve in 2026, and especially if you're searching for "McKinsey Sea Wolf game" or "McKinsey Redrock," you'll face two entirely different modules. The strategies that worked for Ecosystem Building don't apply.

This guide covers the current format, the dual scoring system most candidates don't know about, and a day-by-day prep plan built around the specific skills each module tests.

What Is McKinsey Solve?

McKinsey Solve is an online, game-based assessment sent to candidates after resume screening and before live interviews, according to McKinsey's Solve page. It replaced the old Problem Solving Test (PST) starting around 2020, built on technology from Imbellus, a game-based assessment company McKinsey acquired — as described in McKinsey's game-based innovation lab announcement. McKinsey now administers Solve to approximately 300,000 early-career candidates per year globally.

Here's what you need to know upfront:

• Duration: approximately 65 minutes total across two modules
• Format: game-based simulations, not traditional multiple-choice
• When: after resume screening, before first-round case interviews
• Retake policy: one attempt per application cycle, with a 12-24 month waiting period before you can retake
• Pass-through rate: roughly 20-30% of candidates, based on community reports on Wall Street Oasis, PrepLounge forums, and MConsultingPrep's pass-rate analysis
• Result usage: McKinsey evaluates Solve results alongside your resume to decide who gets interviews, not a standalone pass/fail gate

McKinsey Redrock and Sea Wolf: The Two Modules

Module 1: McKinsey Redrock Study (~35 minutes)

Redrock Study presents you with a series of research scenarios. Each scenario contains a mix of data: charts, tables, written findings, and methodology descriptions. For each scenario, you answer questions about the data: identify patterns, assess evidence quality, and evaluate whether conclusions are supported.

Think of it as a scientific research evaluation exercise. You're not conducting the research. You're judging it. Typical question formats include:

• "Which conclusion is best supported by the data?"
• "What is the most significant limitation of this study?"
• "Which data point most contradicts the researcher's finding?"
• "Based on the evidence, which recommendation is strongest?"

You'll face 5-7 mini-cases during the module. Each one is self-contained with its own dataset and questions. The difficulty increases as you progress, and later scenarios involve more variables, messier data, and subtler distinctions between answer choices.

Module 2: McKinsey Sea Wolf Game (~30 minutes)

Sea Wolf is an ecosystem management simulation set in an ocean environment. You're given information about different ocean sites, each with specific conditions like water temperature, depth, current speed, and nutrient concentration. Your job: select the right combination of marine species for each site based on how species characteristics match site conditions.

This is fundamentally a pattern-matching and optimization problem. Each species has defined traits: preferred temperature range, depth tolerance, nutrient requirements, predator/prey relationships. Each site has a set of environmental parameters. Your task is to match species to sites so the ecosystem functions.

The module runs across multiple rounds with increasing complexity:

• Early rounds: Fewer species, fewer sites, clear matches between traits and conditions
• Middle rounds: More species options, overlapping trait profiles, and sites with mixed conditions
• Late rounds: Species interactions matter, and some species compete for resources, others have symbiotic relationships. Placing two competing species in the same site will hurt your score

The Dual Scoring System

This is the piece most candidates miss, and it changes how you should approach every minute of the assessment.

Product score measures whether you selected correct answers. Did you identify the right patterns? Did you place the right species? This is straightforward accuracy.

Process score measures your approach. McKinsey's platform tracks behavioral signals throughout both modules:

• Reading time: Do you spend time reading scenarios before answering, or do you jump straight to questions?
• Navigation patterns: Do you review all available information, or do you skip panels?
• Revision behavior: Do you change answers after reviewing additional data, or do you commit without checking?
• Decision consistency: Does your clicking pattern suggest systematic analysis or random guessing?
• Pacing: Do you spend proportional time across questions, or do you rush through some and stall on others?

Both scores contribute to your overall evaluation. A candidate who gets 80% of answers correct through careful analysis may outscore a candidate who gets 85% correct through rapid guessing, because the process score penalizes erratic behavior.

What Happened to the Old Games?

If you've been researching Solve, you may have encountered advice about "building an ecosystem with 8 species" or "protecting a plant from predators." That information is outdated. Here's the timeline:

• 2020: McKinsey replaces the Problem Solving Test (PST) with Solve, initially featuring Ecosystem Building and Plant Defense modules (per McKinsey's recruiting blog)
• 2021-2023: Format evolves, with Redrock Study and Sea Wolf gradually replacing older modules
• 2023-2024: Ecosystem Building (the food chain game) and Plant Defense fully phased out across offices
• 2025-2026: Redrock Study and Sea Wolf are the standard modules globally

If you find prep materials about building food chains or defending plants, skip them. The underlying cognitive skills overlap (pattern recognition, systems thinking), but the specific strategies, interfaces, and time management approaches are completely different.

10-Day Preparation Plan

Generic advice like "practice logic puzzles" wastes your limited prep time. Each day below targets the specific skills Redrock Study and Sea Wolf actually test.

Days 1-2: Understand the Format

• Read this guide thoroughly and take notes on the question types in each module
• Watch 2-3 YouTube walkthroughs of the current Solve format (search "McKinsey Solve 2025 2026 walkthrough", filter for videos showing Redrock Study and Sea Wolf, not the old Ecosystem Building)
• Read 5-10 candidate experience posts on Wall Street Oasis and PrepLounge to understand what the real test interface looks like
• Write down the specific skills each module targets: data interpretation for Redrock, pattern-matching and optimization for Sea Wolf

Days 3-5: Build Data Interpretation Skills (for Redrock Study)

Redrock Study tests your ability to evaluate research evidence. The fastest way to build this skill:

• Practice reading scientific study summaries and identifying flaws. Khan Academy's statistics and probability unit is free and directly relevant, covering percentages, mean/median/mode, and probability — the exact math Redrock requires
• Do 15-20 GMAT-style data sufficiency problems per day. Focus on: identifying sample size issues, distinguishing correlation from causation, and reading charts with precision
• Practice evaluating conclusions against evidence: given a chart and a written claim, determine whether the chart supports, contradicts, or is irrelevant to that claim
• Read two research abstracts per day (from any field) and write one sentence on the strongest limitation of each study

Days 6-8: Build Systems Thinking Skills (for Sea Wolf)

Sea Wolf tests whether you can match multiple variables simultaneously under constraints. Train this:

• Practice constraint satisfaction problems: "If species X needs warm water and high nutrients, and species Y needs cold water and low nutrients, which site accommodates both?"
• Play optimization games that involve multi-variable matching. Sudoku variants, logic grid puzzles, and resource allocation exercises build the right mental patterns
• Practice if-then reasoning chains: "If I place species A here, it competes with species B. If I move species B to site 3, it conflicts with the temperature range. So species A must go to site 2."
• Focus on systematic approaches: practice completing one site fully before moving to the next, checking your work, then proceeding

Days 9-10: Simulate Test Conditions

• Find a quiet space with no distractions. Close all other browser tabs and applications. Set a 65-minute timer
• Run through a practice session focusing on process: read before answering, work systematically, don't rush
• After your simulation, review: Where did you spend too much time? Where did you rush? Did you read all available information before answering?
• Adjust your pacing strategy based on what you learned. A second timed simulation on Day 10 should feel noticeably smoother

Pre-Assessment Checklist

Complete this checklist before starting your Solve session. Once you begin, you cannot pause or restart.

Five Mistakes That Tank Solve Scores

These aren't hypothetical. They come from candidate reports on Wall Street Oasis and PrepLounge forums, patterns from candidates who felt confident during the assessment but received rejections afterward.

1. Clicking answers before reading the full scenario. In Redrock Study, the first answer option often looks plausible in isolation. But the correct answer depends on the full data context. Candidates who jump to questions before reading the scenario consistently pick the "obvious" wrong answer. The process score also penalizes this. The platform tracks how long you spend on the information panels before selecting an answer.

2. Matching species on a single trait in Sea Wolf. A species that prefers cold water seems like a fit for a cold-water site. But if that species also needs shallow depth and high nutrients, and the site is deep with low nutrients, it's a bad placement. Always check every trait against every site parameter. One-variable matching is the most common Sea Wolf error.

3. Using external tools. Opening a calculator app, taking a screenshot to study offline, or switching to a browser tab to look something up, all of these are detectable. Management Consulted reports that McKinsey's platform tracks focus state and application switching. Don't risk it.

4. Treating process score as secondary. Many candidates focus entirely on getting right answers and ignore how they arrive at them. But the process score captures whether you're engaging with information systematically. Skipping data panels, clicking rapidly between questions, and never revisiting answers all generate negative behavioral signals, even if your accuracy is high.

5. Running out of time on the last 2-3 scenarios. Poor pacing in Redrock Study means the final mini-cases get rushed or skipped entirely. Unanswered questions are worse than imperfect answers. Track your time: if you've spent more than 7 minutes on a single scenario and still aren't sure, pick your best option and move forward.

Bridge from Solve to Case Interviews

Passing Solve gets you to the interview round. It doesn't get you the offer. Once you clear the digital assessment, you'll face McKinsey's candidate-led case interviews and Personal Experience Interview (PEI).

The good news: the analytical skills you build for Solve, data interpretation, structured reasoning, pattern recognition, transfer directly to case performance. The transition is about applying those skills in a live conversation rather than a digital interface.

Start your case prep in parallel with Solve prep, not after it:

• How to practice case interviews, build the live format skill set
• McKinsey PEI guide, prepare the behavioral interview that accompanies every McKinsey case round
• Mental math for case interviews, the quantitative fluency Solve doesn't directly test, but McKinsey cases demand
• Consulting interview prep timeline, map out your full preparation schedule across Solve, case, and fit prep

Test Your Knowledge

Interactive Solve Prep Drills

Sources and Further Reading (checked February 7, 2026)

• McKinsey interviewing resources: https://www.mckinsey.com/careers/interviewing
• McKinsey Imbellus acquisition announcement: https://www.mckinsey.com/about-us/new-at-mckinsey-blog/mckinsey-acquires-imbellus
• Wall Street Oasis McKinsey Solve discussion threads: https://www.wallstreetoasis.com/forum/consulting/mckinsey-solve-game-2024
• PrepLounge McKinsey Solve forum: https://www.preplounge.com/en/consulting-forum/mckinsey-problem-solving-game
• Management Consulted McKinsey digital assessment guide: https://managementconsulted.com/mckinsey-digital-assessment/
• Khan Academy statistics and probability: https://www.khanacademy.org/math/statistics-probability
• CaseInterview.com McKinsey prep resources: https://www.caseinterview.com/
• McKinsey careers students page: https://www.mckinsey.com/careers/students

Related Guides

• McKinsey PEI Guide
• BCG Case Interview Guide
• Bain Case Interview Guide
• Consulting Interview Prep Timeline
• How to Practice Case Interviews