diff --git a/scripts/average-guesses.ts b/scripts/average-guesses.ts
index 2a07b08..3eed0ac 100644
--- a/scripts/average-guesses.ts
+++ b/scripts/average-guesses.ts
@@ -2,18 +2,15 @@ import Database from 'bun:sqlite';
 
 // Database path - adjust if your database is located elsewhere
 const dbPath = process.env.DATABASE_URL || './local.db';
-
 console.log(`Connecting to database: ${dbPath}`);
-
 const db = new Database(dbPath);
 
 // Query all rows from daily_completions
 const query = db.query(`
-	SELECT date, guess_count
-	FROM daily_completions
-	ORDER BY date
+    SELECT date, guess_count
+    FROM daily_completions
+    ORDER BY date
 `);
-
 const rows = query.all() as { date: string; guess_count: number }[];
 
 if (rows.length === 0) {
@@ -50,4 +47,60 @@ const overallAvg = (totalGuesses / totalCompletions).toFixed(2);
 console.log('--------------|-------------|-------------------');
 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();
\ No newline at end of file