https://github.com/kvbc/lol-alistar-matchups

LOL Alistar AI Matchup Analysis
https://github.com/kvbc/lol-alistar-matchups

ai analysis chartjs league-of-legends lol tomselect visualization

Last synced: 20 days ago
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LOL Alistar AI Matchup Analysis

• Host: GitHub
• URL: https://github.com/kvbc/lol-alistar-matchups
• Owner: kvbc
• Created: 2024-12-08T12:08:44.000Z (30 days ago)
• Default Branch: main
• Last Pushed: 2024-12-08T12:41:49.000Z (30 days ago)
• Last Synced: 2024-12-08T13:19:08.213Z (30 days ago)
• Topics: ai, analysis, chartjs, league-of-legends, lol, tomselect, visualization
• Language: JavaScript
• Homepage: https://kvbc.github.io/lol-alistar-matchups/
• Size: 728 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# LoL Alistar AI Matchup Analysis 

https://kvbc.github.io/lol-alistar-matchups/

![](./screenshot.PNG)

Prompts used (GPT-4):

```

What sums for alistar:

- ghost: stick to enemy

- flash: surprise, dodge, instant

- ignite: ranged squishy

- heal: peel, close distance

- exhaust: all-ins

determine for all 140 champions in league, for each of them, in JSON format, give values from 0.0 to 1.0 (0% to 100%) for these categories:

- "ghost": how much do u need to "stick" to these champions over time, chase them, etc rather than instant flash

- "flash": how much u rather instant dodge surprise them with flash

- ignite: how ranged and squishy they are to rather use ignire on them

- heal: maybe ignite and exhaust no good then use heal how much?

- exhaust: how all-in are they

```

```

add a property that describes in text how to play against that champ

```

```

also rate the favouriness of winning the matchup from 0.0 to 1.0

```

For newer champions (2023+), such as Hwei, Smolder, and Ambessa, I've copy and pasted their wiki description.