Comments (4)
if we let the score is zero, then
// TOTAL_VALUES = RANK_S_VALUE + RANK_A2_VALUE + RANK_A3_VALUE + RANK_B_VALUE = 206
// ALL_OFFSETS = CONTRIBS_OFFSET + ISSUES_OFFSET + STARS_OFFSET + PRS_OFFSET + FOLLOWERS_OFFSET + REPO_OFFSET = 5.35
normalcdf(0, 206, 5.35) * 100 = 51.03597635931825
but A+ rank requires the result between [0.45, 0.65), and the score will never be a negative. So any rank below it is useless.
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Yes @Dentrax the ranking algorithm isn't really consistent throughout,
@b1f6c1c4 did a pr which includes h-index #76 which might also work maybe.
I'm not good at maths so i don't have any idea which algorithm will result in good balance. so any help would be appreciated.
if anyone wanted to implement this PR feel free to do so.
from github-readme-stats.
- There is no ultimate and universal way to merge multiple metrics into one metric. Weighted arithmetic average is the closest one. However metrics needs to be scaled before they are averaged.
- The best way to determine how to scale the metric, as well as how to divide the continuous combined metrics into discrete ranks, is through statistics. It's definitely necessary to sample a moderate amount of GitHub users (not just famous users), collect their metrics, and do statistics.
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This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
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