Hey guys,
I'm sure we've all seen the bots around here recently. It may be kind of annoying to see bots on the ladder, but that's not all they can do. Pokémon bots, or machine learning models, can be used for a variety of tools now. The first and most obvious next step is a matchup analyzer. A bot can play games hundreds, thousands, tens of thousands of times faster than a human can. Thanks to that, a bot can test a matchup between two teams by simply playing the game out thousands of times to get a more precise evaluation of how good or bad a matchup is and how good each Pokémon is.
To that end, I have created trickroom.dev, a website where you can go and run matchup checks right now. The server will take any two teams you paste in and calculate the matchup. It will show you:
Uh, in case you're wondering why there's a bunch of grayed out navigation options at the top... I designed this site a while ago and was quietly working on it by myself for months with my own ML model. But then a lot of other people went public with their bots which forced my hand early. I have other features that I plan on releasing soon in the future on the site. The next one is the natural progression of the matchup analyzer: the meta analyzer, as I call it. Basically instead of just analyzing the matchup between just two teams, how about analyzing how well your team performs into the "meta"? I'll probably make another post when I finish with that one. There's even more than just that too.
And to other bot devs, if you're reading this: a matchup analyzer's accuracy is derived from the strength of the bot playing the matchup. If the bot cannot properly play the teams, then it is failing to represent the matchup faithfully. I've refrained from testing my model on the ladder, so I don't have an objective evaluation of its strength. It's decent, at least. But I have no doubt it is not the strongest model out there. If you have a very strong model and want to work together, send me a message.
If you have any feedback or questions, let me know. You can send me feedback through the box at the bottom of the site.
I'm sure we've all seen the bots around here recently. It may be kind of annoying to see bots on the ladder, but that's not all they can do. Pokémon bots, or machine learning models, can be used for a variety of tools now. The first and most obvious next step is a matchup analyzer. A bot can play games hundreds, thousands, tens of thousands of times faster than a human can. Thanks to that, a bot can test a matchup between two teams by simply playing the game out thousands of times to get a more precise evaluation of how good or bad a matchup is and how good each Pokémon is.
To that end, I have created trickroom.dev, a website where you can go and run matchup checks right now. The server will take any two teams you paste in and calculate the matchup. It will show you:
- Who's favoured, with a confidence range
- Which four to bring and who to lead, for each side
- Which Pokémon actually matter in the matchup, and how much each is worth
- How each Pokémon does against each of the opponent's likely fours
Uh, in case you're wondering why there's a bunch of grayed out navigation options at the top... I designed this site a while ago and was quietly working on it by myself for months with my own ML model. But then a lot of other people went public with their bots which forced my hand early. I have other features that I plan on releasing soon in the future on the site. The next one is the natural progression of the matchup analyzer: the meta analyzer, as I call it. Basically instead of just analyzing the matchup between just two teams, how about analyzing how well your team performs into the "meta"? I'll probably make another post when I finish with that one. There's even more than just that too.
And to other bot devs, if you're reading this: a matchup analyzer's accuracy is derived from the strength of the bot playing the matchup. If the bot cannot properly play the teams, then it is failing to represent the matchup faithfully. I've refrained from testing my model on the ladder, so I don't have an objective evaluation of its strength. It's decent, at least. But I have no doubt it is not the strongest model out there. If you have a very strong model and want to work together, send me a message.
If you have any feedback or questions, let me know. You can send me feedback through the box at the bottom of the site.
















