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tahm-ken.ch's Introduction

challenges intersection league

trying to create an app which help to optimize the teamwork challenges in league of legends by finding compositions that contain the most challenges.

still wip, you can pr if you want.

todo

  • allow to remove champions (right click), idea from BlackDizzle
  • multi-language, adapt the code for this
  • (bugfix) show points for harmony and globetrotter
  • responsiveness for mobile
  • update table sorting and add visual clue of the current sort
  • create a batch file for windows install
  • User eXperience (help needed)

done

  • show challenges for each champions when hovering selecting them (act like you added them)
  • have a multisearch feature like op.gg
  • collect the data
  • parse the data
  • create a basic ui
  • add functions to intersect the sets
  • update the ui according to the challenge selections
  • copy the selected champion into the clipboard
  • update the ui according to the champion selections
  • add a page to good composition found for each challenges that require 5 specifics champions
  • add a filter for specifics champions in optimized compositions
  • show challenges for each champions when selecting them
  • find a way to implement Variety's Overrated
  • fetch from Riot API current challenges
  • in the compositions, update the filter to only show available champs
  • search for champions when typing letters
  • add tooltips for the challenges, idea from PureImplosion on Reddit
  • filter out champions in compositions, idea from DOOGLAK on Reddit
  • recompute the optimized composition for "Variety's Overrated", idea from Konstamonsta on Reddit
  • replace the space in the name of the compositions by underscores or dashes
  • use cdragon icon for the challenge
  • add a Q&A
  • add summoners icon and challenge progression when searching for summoner
  • block api route when too many request from same client (look into the flask_limiter package)
  • custom compositions (algo that find good comps depending on the masteries)
  • use role identifications for ordering the champions in the optimized compositions
  • move the "how to use" on the corresponding pages (modal dialog)
  • add more filters for optimized compositions (for champions role for example) -> became the stupidity level metric
  • select the 'correct' server by default depending on the ip address

install

you need python 3

make install

the makefile will download the current compositions.json which can be obtained by running compositions.ipynb with jupyter.

create config.json from config_sample.json and fill it in.

keys:

i'll try to make the riot api key not mandatory...

run

python app.py

thanks

people who directly helped the project (more than feedbacks):

  • thanks to @celiendonze and @Etiouse for helping me populate the initial challenges.json
  • thanks to @Pomarine for reviewing and correcting the challenges.json file
  • thanks to @Naralas for fixing paths in brute_force_compositions.ipynb
  • thanks to @DarkIntaqt for fixing a few typos and adding better meta tags
  • thanks to @DarkIntaqt for adding the share composition feature

people who gave feedback that were implemented/bug fixed:

  • u/PureImplosion
  • u/DOOGLAK
  • u/Konstamonsta
  • Scraf#2052
  • Amy#5664
  • NotDay#2927
  • (NA) Carbunkle#0740
  • DarkIntaqt#2858

tahm-ken.ch's People

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tahm-ken.ch's Issues

Idea: Use champion winrate data to determine whether a champion should be included for a nonstandard role

Overview

The current priority system and "Stupidity level score" use hardcoded lists to determine what position each champion should be valid for assignment in team comp generation.

A more effective method to handle this step would be to utilize champion winrate data when played in positions other than the hardcoded position. This way, champions who are very strong in less common positions can also be included in the calculations.

Examples Of Champions That Could Be Used If This Method Was Added

Champion Position Pickrate 13.24 All Rank Pickrate 13.24 Emerald + Winrate 13.24
Karthus Bottom 0.26% 0.48% (53.74 / 54.49)
Cassiopeia Top 0.29% 0.45% (51.39 / 53.21)
Camille Middle 0.11% 0.15% (52.35 / 55.10)
Brand Jungle 3.59% 4.93% (51.81 / 52.22)
Zac Support 0.45% 0.63% (52.56 / 53.11)

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