4 ms·

Makes me think you could have the character stats and game design decisions as parameters, and have it select characters based on attributes rather than hit enc

Makes me think you could have the character stats and game design decisions as parameters, and have it select characters based on attributes rather than hit encoding of their simple identities (names) but from what I know of LoL the unique thing is character abilities aren't really differentiated in that modular way and the exact values it aren't given (like a spin attack movie's exact radius, though I bet that could be derived).

jiggawatts 5y ago
The parameters I trained the model for were things like "greed", "preferred game duration for winning", "effect on game duration", and several 3-way tables of win rates for things like ally-ally-oppose and ally-oppose-oppose. An interesting effect I noticed was that the pub games have all sorts of weird second-order effects. E.g.: Some characters are unpopular because pub players play them ineffectively, and/or they're hard to play well because they're so niche, so few people get enough practice with them in the right kind of scenario. Dota's Jakiro is a great example. It's a lumbering support hero with very slow spells and a glacial turn rate. It's like trying to do acrobatics with a jumbo jet. He's frustrating to play and when most people pick him, the effect on the overall win rate is about -15%, which is insanely bad for 1 out of 10 players in a game! My win rate with him was something like 25%, which is absolutely atrocious. That's "throwing the game" bad. My picker app often recommended Jakiro when the enemy team had over-represented heroes that typically commit to a fight, such as Legion Commander or Axe. Those heroes are nailed to the ground in a fight and can't escape Jakiro's devastating-but-slow attacks. I ended up playing Jakiro a lot, and eventually I got about a 55% overall win rate with him, which is an amazing swing if you think about it. It took practice though, figuring out how to best utilise him. If I had played him in random games, I never would have gotten anywhere. The picker app however made this possible, by providing these hints of when and how to use the hero.

jiggawatts 5y ago

The parameters I trained the model for were things like "greed", "preferred game duration for winning", "effect on game duration", and several 3-way tables of win rates for things like ally-ally-oppose and ally-oppose-oppose. An interesting effect I noticed was that the pub games have all sorts of weird second-order effects. E.g.: Some characters are unpopular because pub players play them ineffectively, and/or they're hard to play well because they're so niche, so few people get enough practice with them in the right kind of scenario. Dota's Jakiro is a great example. It's a lumbering support hero with very slow spells and a glacial turn rate. It's like trying to do acrobatics with a jumbo jet. He's frustrating to play and when most people pick him, the effect on the overall win rate is about -15%, which is insanely bad for 1 out of 10 players in a game! My win rate with him was something like 25%, which is absolutely atrocious. That's "throwing the game" bad. My picker app often recommended Jakiro when the enemy team had over-represented heroes that typically commit to a fight, such as Legion Commander or Axe. Those heroes are nailed to the ground in a fight and can't escape Jakiro's devastating-but-slow attacks. I ended up playing Jakiro a lot, and eventually I got about a 55% overall win rate with him, which is an amazing swing if you think about it. It took practice though, figuring out how to best utilise him. If I had played him in random games, I never would have gotten anywhere. The picker app however made this possible, by providing these hints of when and how to use the hero.