george/bibdle
1
0
Fork 0
mirror of https://github.com/pupperpowell/bibdle.git synced 2026-02-04 02:44:43 -05:00
Code Issues Packages Projects Releases Wiki Activity

added function to measure correlation between ease of solving and number of players

Browse Source
This commit is contained in:
George Powell
2026-01-26 23:33:22 -05:00
parent 8c488d27df
commit b80c18c2aa
1 changed files with 60 additions and 7 deletions
Download Patch File Download Diff File Expand all files Collapse all files

67
scripts/average-guesses.ts
View File

@@ -2,18 +2,15 @@ import Database from 'bun:sqlite';
// Database path - adjust if your database is located elsewhere // Database path - adjust if your database is located elsewhere
const dbPath = process.env.DATABASE_URL || './local.db'; const dbPath = process.env.DATABASE_URL || './local.db';
console.log(`Connecting to database: ${dbPath}`); console.log(`Connecting to database: ${dbPath}`);
const db = new Database(dbPath); const db = new Database(dbPath);
// Query all rows from daily_completions // Query all rows from daily_completions
const query = db.query(` const query = db.query(`
SELECT date, guess_count SELECT date, guess_count
FROM daily_completions FROM daily_completions
ORDER BY date ORDER BY date
`); `);
const rows = query.all() as { date: string; guess_count: number }[]; const rows = query.all() as { date: string; guess_count: number }[];
if (rows.length === 0) { if (rows.length === 0) {
@@ -50,4 +47,60 @@ const overallAvg = (totalGuesses / totalCompletions).toFixed(2);
console.log('--------------|-------------|-------------------'); console.log('--------------|-------------|-------------------');
console.log(`Overall Average: ${overallAvg} guesses across ${totalCompletions} completions`); console.log(`Overall Average: ${overallAvg} guesses across ${totalCompletions} completions`);
db.close(); // Calculate correlation between avg_guesses and completions
function calculateCorrelation(data: { avgGuesses: number; completions: number }[]): number {
const n = data.length;
if (n < 2) return 0;
const avgX = data.reduce((sum, d) => sum + d.avgGuesses, 0) / n;
const avgY = data.reduce((sum, d) => sum + d.completions, 0) / n;
let numerator = 0;
let sumXSquared = 0;
let sumYSquared = 0;
for (const d of data) {
const xDiff = d.avgGuesses - avgX;
const yDiff = d.completions - avgY;
numerator += xDiff * yDiff;
sumXSquared += xDiff * xDiff;
sumYSquared += yDiff * yDiff;
}
const denominator = Math.sqrt(sumXSquared * sumYSquared);
return denominator === 0 ? 0 : numerator / denominator;
}
// Prepare data for correlation analysis
const allData = Array.from(dateStats.entries()).map(([date, stats]) => ({
date,
avgGuesses: stats.total / stats.count,
completions: stats.count
}));
// Split into pre and post marketing periods
const marketingStartDate = '2026-01-08';
const preMarketing = allData.filter(d => d.date < marketingStartDate);
const postMarketing = allData.filter(d => d.date >= marketingStartDate);
console.log('\n=== Correlation Analysis ===\n');
const allCorrelation = calculateCorrelation(allData);
console.log(`Overall correlation (avg_guesses vs completions): ${allCorrelation.toFixed(3)}`);
if (preMarketing.length >= 2) {
const preCorrelation = calculateCorrelation(preMarketing);
console.log(`Pre-marketing correlation (before ${marketingStartDate}): ${preCorrelation.toFixed(3)} (n=${preMarketing.length} days)`);
}
if (postMarketing.length >= 2) {
const postCorrelation = calculateCorrelation(postMarketing);
console.log(`Post-marketing correlation (${marketingStartDate} onward): ${postCorrelation.toFixed(3)} (n=${postMarketing.length} days)`);
}
console.log('\nInterpretation:');
console.log(' r close to -1: Strong negative correlation (easier verses → more completions)');
console.log(' r close to 0: No correlation');
console.log(' r close to +1: Strong positive correlation (harder verses → more completions)');
db.close();