Add finer ranking levels

readme.md

@@ -108,7 +108,7 @@ Change the `?username=` value to your GitHub username.
 ```
 
 > **Note**
-> Available ranks are S+ (top 1%), S (top 25%), A++ (top 45%), A+ (top 60%), and B+ (everyone). The values are calculated by using the [cumulative distribution function](https://en.wikipedia.org/wiki/Cumulative_distribution_function) using commits, contributions, issues, stars, pull requests, followers, and owned repositories. The implementation can be investigated at [src/calculateRank.js](./src/calculateRank.js).
+> Available ranks are S (top 1%), A+ (12.5%), A (25%), A- (37.5%), B+ (50%), B (62.5%), B- (75%), C+ (87.5%) and C (everyone). This ranking scheme is based on the [Japanese academic grading](https://wikipedia.org/wiki/Academic_grading_in_Japan) system. The global percentile is calculated as a weighted sum of percentiles for each statistic (number of commits, pull requests, issues, stars and followers), based on the cumulative distribution function of the [exponential](https://wikipedia.org/wiki/exponential_distribution) and the [log-normal](https://wikipedia.org/wiki/Log-normal_distribution) distributions. The implementation can be investigated at [src/calculateRank.js](./src/calculateRank.js). The circle around the rank shows 100 minus the global percentile.
 
 ### Hiding individual stats
 

src/calculateRank.js

@@ -1,5 +1,10 @@
-function expsf(x, lambda = 1) {
-  return 2 ** (-lambda * x);
+function exponential_cdf(x) {
+  return 1 - 2 ** -x;
+}
+
+function log_normal_cdf(x) {
+  // approximation
+  return x / (1 + x);
 }
 
 /**
@@ -13,7 +18,7 @@ function expsf(x, lambda = 1) {
  * @param {number} params.repos Total number of repos.
  * @param {number} params.stars The number of stars.
  * @param {number} params.followers The number of followers.
- * @returns {{level: string, score: number}}} The users rank.
+ * @returns {{level: string, percentile: number}}} The users rank.
  */
 function calculateRank({
   all_commits,
@@ -24,15 +29,15 @@ function calculateRank({
   stars,
   followers,
 }) {
-  const COMMITS_MEAN = all_commits ? 1000 : 250,
+  const COMMITS_MEDIAN = all_commits ? 1000 : 250,
     COMMITS_WEIGHT = 2;
-  const PRS_MEAN = 50,
+  const PRS_MEDIAN = 50,
     PRS_WEIGHT = 3;
-  const ISSUES_MEAN = 25,
+  const ISSUES_MEDIAN = 25,
     ISSUES_WEIGHT = 1;
-  const STARS_MEAN = 250,
+  const STARS_MEDIAN = 50,
     STARS_WEIGHT = 4;
-  const FOLLOWERS_MEAN = 25,
+  const FOLLOWERS_MEDIAN = 10,
     FOLLOWERS_WEIGHT = 1;
 
   const TOTAL_WEIGHT =
@@ -42,30 +47,21 @@ function calculateRank({
     STARS_WEIGHT +
     FOLLOWERS_WEIGHT;
 
-  const rank =
-    (COMMITS_WEIGHT * expsf(commits, 1 / COMMITS_MEAN) +
-      PRS_WEIGHT * expsf(prs, 1 / PRS_MEAN) +
-      ISSUES_WEIGHT * expsf(issues, 1 / ISSUES_MEAN) +
-      STARS_WEIGHT * expsf(stars, 1 / STARS_MEAN) +
-      FOLLOWERS_WEIGHT * expsf(followers, 1 / FOLLOWERS_MEAN)) /
-    TOTAL_WEIGHT;
+  const THRESHOLDS = [1, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100];
+  const LEVELS = ["S", "A+", "A", "A-", "B+", "B", "B-", "C+", "C"];
 
-  const RANK_S_PLUS = 0.025;
-  const RANK_S = 0.1;
-  const RANK_A_PLUS = 0.25;
-  const RANK_A = 0.5;
-  const RANK_B_PLUS = 0.75;
+  const rank =
+    1 -
+    (COMMITS_WEIGHT * exponential_cdf(commits / COMMITS_MEDIAN) +
+      PRS_WEIGHT * exponential_cdf(prs / PRS_MEDIAN) +
+      ISSUES_WEIGHT * exponential_cdf(issues / ISSUES_MEDIAN) +
+      STARS_WEIGHT * log_normal_cdf(stars / STARS_MEDIAN) +
+      FOLLOWERS_WEIGHT * log_normal_cdf(followers / FOLLOWERS_MEDIAN)) /
+      TOTAL_WEIGHT;
 
-  const level = (() => {
-    if (rank <= RANK_S_PLUS) return "S+";
-    if (rank <= RANK_S) return "S";
-    if (rank <= RANK_A_PLUS) return "A+";
-    if (rank <= RANK_A) return "A";
-    if (rank <= RANK_B_PLUS) return "B+";
-    return "B";
-  })();
+  const level = LEVELS[THRESHOLDS.findIndex((t) => rank * 100 <= t)];
 
-  return { level, score: rank * 100 };
+  return { level: level, percentile: rank * 100 };
 }
 
 export { calculateRank };

src/cards/stats-card.js

@@ -209,9 +209,8 @@ const renderStatsCard = (stats = {}, options = { hide: [] }) => {
     hide_rank ? 0 : 150,
   );
 
-  // the better user's score the the rank will be closer to zero so
-  // subtracting 100 to get the progress in 100%
-  const progress = 100 - rank.score;
+  // the lower the user's percentile the better
+  const progress = 100 - rank.percentile;
   const cssStyles = getStyles({
     titleColor,
     ringColor,

src/fetchers/stats-fetcher.js

@@ -192,7 +192,7 @@ const fetchStats = async (
     totalIssues: 0,
     totalStars: 0,
     contributedTo: 0,
-    rank: { level: "B", score: 0 },
+    rank: { level: "C", percentile: 100 },
   };
 
   let res = await statsFetcher(username);

src/fetchers/types.d.ts

@@ -22,7 +22,7 @@ export type StatsData = {
   totalIssues: number;
   totalStars: number;
   contributedTo: number;
-  rank: { level: string; score: number };
+  rank: { level: string; percentile: number };
 };
 
 export type Lang = {

tests/calculateRank.test.js

@@ -2,7 +2,7 @@ import "@testing-library/jest-dom";
 import { calculateRank } from "../src/calculateRank.js";
 
 describe("Test calculateRank", () => {
-  it("new user gets B rank", () => {
+  it("new user gets C rank", () => {
     expect(
       calculateRank({
         all_commits: false,
@@ -13,76 +13,90 @@ describe("Test calculateRank", () => {
         stars: 0,
         followers: 0,
       }),
-    ).toStrictEqual({ level: "B", score: 100 });
+    ).toStrictEqual({ level: "C", percentile: 100 });
   });
 
-  it("average user gets A rank", () => {
+  it("beginner user gets B- rank", () => {
+    expect(
+      calculateRank({
+        all_commits: false,
+        commits: 125,
+        prs: 25,
+        issues: 10,
+        repos: 0,
+        stars: 25,
+        followers: 5,
+      }),
+    ).toStrictEqual({ level: "B-", percentile: 69.333868386557 });
+  });
+
+  it("median user gets B+ rank", () => {
     expect(
       calculateRank({
         all_commits: false,
         commits: 250,
         prs: 50,
         issues: 25,
         repos: 0,
-        stars: 250,
-        followers: 25,
+        stars: 50,
+        followers: 10,
       }),
-    ).toStrictEqual({ level: "A", score: 50 });
+    ).toStrictEqual({ level: "B+", percentile: 50 });
   });
 
-  it("average user gets A rank (include_all_commits)", () => {
+  it("average user gets B+ rank (include_all_commits)", () => {
     expect(
       calculateRank({
         all_commits: true,
         commits: 1000,
         prs: 50,
         issues: 25,
         repos: 0,
-        stars: 250,
-        followers: 25,
+        stars: 50,
+        followers: 10,
       }),
-    ).toStrictEqual({ level: "A", score: 50 });
+    ).toStrictEqual({ level: "B+", percentile: 50 });
   });
 
-  it("more than average user gets A+ rank", () => {
+  it("advanced user gets A rank", () => {
     expect(
       calculateRank({
         all_commits: false,
         commits: 500,
         prs: 100,
         issues: 50,
         repos: 0,
-        stars: 500,
-        followers: 50,
+        stars: 200,
+        followers: 40,
       }),
-    ).toStrictEqual({ level: "A+", score: 25 });
+    ).toStrictEqual({ level: "A", percentile: 22.72727272727273 });
   });
 
-  it("expert user gets S rank", () => {
+  it("expert user gets A+ rank", () => {
     expect(
       calculateRank({
         all_commits: false,
         commits: 1000,
         prs: 200,
         issues: 100,
         repos: 0,
-        stars: 1000,
-        followers: 100,
+        stars: 800,
+        followers: 160,
       }),
-    ).toStrictEqual({ level: "S", score: 6.25 });
+    ).toStrictEqual({ level: "A+", percentile: 6.082887700534744 });
   });
 
-  it("ezyang gets S+ rank", () => {
+  it("sindresorhus gets S rank", () => {
     expect(
       calculateRank({
         all_commits: false,
-        commits: 1000,
-        prs: 4000,
-        issues: 2000,
+        commits: 1300,
+        prs: 1500,
+        issues: 4500,
         repos: 0,
-        stars: 5000,
-        followers: 2000,
+        stars: 600000,
+        followers: 50000,
       }),
-    ).toStrictEqual({ level: "S+", score: 1.1363983154296875 });
+    ).toStrictEqual({ level: "S", percentile: 0.49947889605312934 });
   });
 });