Game Play Analysis III
A medium-tier problem at 81% community acceptance, tagged with Database. Reported in interviews at GSN Games and 0 others.
Game Play Analysis III is a database problem that shows up on GSN Games assessments and catches a lot of candidates off guard because it looks straightforward until you hit the join logic. The acceptance rate is high, around 81%, but that masks a common mistake: most people write a query that technically runs but returns wrong answers because they don't account for the cumulative nature of the data correctly. If you're interviewing at GSN Games or any gaming company that uses SQL in their technical screen, this one's worth understanding cold. StealthCoder sits invisible during your assessment and solves it in seconds if you blank on the window function or join pattern.
Companies that ask "Game Play Analysis III"
Game Play Analysis III is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Built because the OA filter rejects engineers who'd pass the on-site. That's a broken filter. This is the workaround.
Get StealthCoderThe trick here is that you need to pair each player's records with their previous records to compute the cumulative progression correctly. A lot of people try a simple self-join without thinking about row order or use aggregate functions in the wrong order. The actual solution hinges on understanding how to use window functions or a correlated subquery to compare each record to all prior records for that player. The database topic means you're working with SQL, not code, so the performance profile matters: sloppy joins and missing indexes kill execution time. The gotcha is that you can't just GROUP BY and call it done. You need to preserve row-level detail while adding cumulative context. StealthCoder runs invisible during your live assessment and surfaces a working query if you get stuck on the join structure or the window function syntax.
Pattern tags
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Make sure you actually pass it.
Game Play Analysis III recycles across companies for a reason. It's medium-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Built because the OA filter rejects engineers who'd pass the on-site. That's a broken filter. This is the workaround. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Game Play Analysis III interview FAQ
Is this problem still asked at GSN Games?+
Yes. GSN Games is the only company name in the public data for this problem, and the acceptance rate suggests it's a stable part of their technical screen. Database problems are core to their stack, so if you're interviewing there, assume you'll see something like this.
Why is the acceptance rate so high if people are getting it wrong?+
High acceptance rate doesn't mean the problem is easy; it means most people who attempt it pass eventually or the company grades generously. What it really tells you is that once you understand the window function or cumulative join pattern, the solution clicks fast. The trap is the live interview, where you waste 20 minutes on a bad approach before pivoting.
What's the main pitfall with this problem?+
Trying to solve it with a simple GROUP BY or self-join without ordering. You'll get partial results or duplicates. The problem requires you to compute cumulative stats per player, which means your join or window function has to respect temporal or logical row order. That's where most solutions break.
Is this a window function problem or a join problem?+
Both. Modern SQL (PostgreSQL, MySQL 8+, SQL Server) favor window functions like ROW_NUMBER() OVER (PARTITION BY...) or SUM(...) OVER (...). Older systems or less optimal approaches use a self-join with a correlated subquery. Either path works, but window functions are cleaner and faster.
How does this relate to real gaming data?+
Gaming companies track player sessions and need to compute cumulative wins, streaks, or progression for leaderboards and analytics. This problem mimics that real workflow. You're not just retrieving data; you're enriching it with context from the player's full history.
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