📊 Download League Data

Access the complete Summer Swing League 2025 dataset

Perfect for analytics, research, and personal insights

💾

Complete League Data Export

Download all league data from the 2025 season in multiple formats for easy analysis and offline access.

🗄️ Database Dump (PostgreSQL)

latest.dump

Complete PostgreSQL database dump (26.7 KB)

Download Database

📄 JSON Data Files

members.json

All league members (17.6 KB)

Download

scores.json

All golf scores (205 KB)

Download

player_stats.json

Player statistics (39.5 KB)

Download

season_summary.json

Season overview (458 bytes)

Download

📊 CSV Data Files

members.csv

All league members (3.1 KB)

Download

scores.csv

All golf scores (39.5 KB)

Download

📋 Data Index

data-index.json

Complete index of all available data files

Download Index

📋 What's Included

  • • All member registrations and handicaps
  • • Complete score history for all rounds
  • • Course information and difficulty ratings
  • • Group play data and bonus points
  • • Round dates and hole counts
  • • All calculated points and adjustments
  • • Player statistics and standings
  • • Season summary and analytics

🔧 How to Use

  • • JSON: Direct import into JavaScript/Node.js
  • • CSV: Excel, Google Sheets, Python pandas
  • • Database: Restore to PostgreSQL
  • • Create custom visualizations
  • • Perform statistical analysis
  • • Build personal dashboards
  • • Research golf performance trends

⚙️ Technical Details

Formats Available:

PostgreSQL, JSON, CSV

Total Data Size:

~270 KB (all formats)

Season:

May 1 - August 30, 2025

Records:

31 members, 190 scores

Export Date:

September 2, 2025

Offline Ready:

✅ No database required

🔄 Usage Examples

PostgreSQL Database:

# Create database and restore
createdb summer_swing_league_2025
pg_restore -d summer_swing_league_2025 summer-swing-league-2025-database.dump

JavaScript/Node.js:

// Load JSON data
const members = require('./members.json');
const scores = require('./scores.json');

Python pandas:

import pandas as pd
members = pd.read_csv('members.csv')
scores = pd.read_csv('scores.csv')

💡 Analytics Ideas

📈 Performance Trends

Analyze how players improved over the season

🏌️ Course Analysis

Compare performance across different courses

👥 Group Dynamics

Study the impact of group play on scores

📊 Handicap Analysis

Examine handicap accuracy and adjustments

🎯 Scoring Patterns

Identify scoring trends and consistency

📅 Seasonal Analysis

Track performance changes throughout the season

Happy analyzing! Share your insights with the league community.

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