返回榜单
StarSnap: Sports Card Scanner

StarSnap: Sports Card Scanner

Next Vision Limited

Tools免费v1.0.4Android
Google Play
评分

4.7

9,379 条评分

星级

★★★★★

最近更新

2026年3月31日

发布日期

2026年1月28日

更新内容

v1.0.4

StarSnap is now on Android! Identify sports cards, get accurate market values, and organize your collection with ease. We’ve also made performance improvements and bug fixes for a smoother experience.

应用信息

开发者
Next Vision Limited
分类
Tools
价格
免费
版本
1.0.4
安装量
50,000+
包名
com.sportscardsnap.identifier

简介

Discover the true value of your sports cards with StarSnap – the smart app to identify, value, and manage your collection. Snap a photo to instantly recognize players, series, card types, and get market price estimates along with grading insights. Whether you’re a rookie collector, seasoned trader, or investor, StarSnap is your all-in-one sports card companion. What can StarSnap do? - Accurate Market Valuation – Quickly assess if your card is a common collectible or a valuable rare piece before buying or selling. - Instant Card Identification – Identify player, sport, issuing year, series, card number, and special attributes like autographs or limited editions with one scan. - Grading & Condition Guidance – Receive condition grading suggestions (Mint, Near Mint, Excellent, etc.) to price cards confidently. - Collection Management – Save scans, track total collection value, and monitor investment growth. - Learn & Explore – Access articles on collecting strategies, player legacies, and market trends. Why Choose StarSnap? Accurate — Reliable valuations in seconds Effortless — Simply scan, no manual lookup Insightful — Detailed card and market information at your fingertips Organized — Manage and grow your entire sports card collection Privacy Policy: https://app-service.sportscardworth.com/static/privacy_policy.html Terms of Use: https://app-service.sportscardworth.com/static/user_agreement.html

下载量预测

专业 · 预览

预估总下载量

695K469K1.3M
保守估计乐观估计

117K

低 / 月

174K

预估 / 月

335K

高 / 月

基于9,379 条评分
假设评分率1.4%
应用年龄4 个月

基于评分数量 ÷ 类别评分率估算,实际下载量误差可达 ±50%,与 Sensor Tower 方法一致。