Frequently asked questions

• What is Stockkarp?

  Stockkarp is a VGC learning tool that grades the decisions in your Pokémon Showdown! replays.

• How does Stockkarp work?

  Stockkarp uses a top-human-level VGC playing model to review Pokémon Showdown! replays and provide a turn-by-turn analysis. It combines board-state valuations and opponent prediction decisions to grade moves, suggest alternatives, and showcase what could have happened in other plausible scenarios.

  It works best on open team sheet games, where the model can more easily and efficiently understand the possible sets and game states. Closed team sheets are supported, with more variance.

• How accurate are Stockkarp reviews?

  While Stockkarp is powered by a neural network with top-level playing capabilities, the goal of the product is to illustrate the impact of every in-game decision so that reviews can be understood and trusted without having to put your faith in the bot.

• Why did my review miss something?

  Unlike chess, Pokémon is a game where information is hidden from the opponent. Stockkarp's model predicts and samples to fill in missing information in the most likely way, but this means reviews will always be limited by the model's understanding of opponent behavior and the stats and sets Pokémon typically use. Rare scenarios can catch the model off guard.

  Think you've found an issue with a review? Send feedback to [email protected].

• Can Stockkarp be used to cheat?

  Our goal is for the competitive integrity of online competitive Pokémon to be maintained. Stockkarp is a post-game learning tool. It does not and will never expose any custom-position analysis that can be maliciously used in live games. Only signed, Showdown-hosted replays get reviewed by our system.

• Are my reviews private?

  Of course! Your reviews, teams, and battle choices are private and secure. The Stockkarp model will never be trained on users' private battle reviews. Stockkarp was primarily trained through self-play, and only public human reviews were used to fine-tune.

Something not covered here? Write to [email protected].