A Walkthrough of RizzStats, From Upload to Rizz Score

You've got your Tinder or Hinge data export sitting in a folder somewhere. Now what?

This is the part nobody explains well. Dating apps hand you a JSON file full of timestamps and swipe logs and call it "transparency." It's not useful on its own. That's the gap RizzStats fills, and here's how to actually walk through it.

Step 1: Get the file uploaded

Head to /upload and drop in the export you downloaded from Tinder or Hinge. RizzStats reads that file directly — nothing gets pulled from your live account, and nothing you type gets sent anywhere except your own dashboard. If you haven't exported your data yet, do that first; it's the one manual step neither app makes obvious.

Step 2: Match rate and reply rate — read them separately

Once the file is processed, the dashboard splits into a few distinct numbers, and the first mistake people make is treating them as one metric.

Match rate is what percentage of your right-swipes turned into matches. Reply rate is what percentage of your matches turned into an actual back-and-forth conversation. These measure two completely different behaviors — one is about how you're perceived on first pass, the other is about what happens after someone already said yes. A high match rate with a low reply rate tells a different story than the reverse, and conflating them is how people end up chasing the wrong fix.

This distinction matters more than it sounds like. There's a documented pattern where people internalize every one of these numbers as personal judgment. One recent analysis put it bluntly: the constant, quantified rejection measured in match rates, response rates, and read receipts creates a feedback loop of inadequacy that follows people off the app entirely. The numbers on your dashboard are diagnostic, not a verdict — that's the mindset to bring in before you even look at them.

Step 3: The activity timeline — look for the shape, not the day

The activity timeline plots your swiping and messaging behavior over time. Most people scan it for a single bad week. That's not the point. Look for the overall shape — long gaps, sudden bursts right after installing the app, activity that dies off exactly when conversations start. The timeline is more useful as a pattern-finder than a highlight reel of any one date.

Step 4: Streaks — treat them as a behavior nudge, not a score

Streaks track how consistently you're actually engaging, day over day, rather than how well any single swipe performed. That distinction is intentional. Research on self-tracking tools consistently finds that the act of monitoring a habit changes the habit — one review of tracking-app studies found that software motivates and promotes the automatic repetition of behaviors once people can see it laid out. A streak isn't a grade. It's a mirror that happens to make consistency visible, which tends to be enough to nudge it.

Step 5: Rizz Score — last, not first

By the time you get to the Rizz Score, you should already have context for it: how your match rate compares to your reply rate, what the timeline actually looks like, whether your streaks are steady or erratic. The score is a composite of all of that. Reading it first, before the individual numbers, is how people end up over-indexing on a single figure instead of understanding what's driving it.

The actual point of the order

None of this works as well in reverse. Jumping straight to the Rizz Score is the digital equivalent of reading a restaurant's star rating before looking at what anyone actually said in the reviews. The individual metrics are the reviews. The score is just the summary.

If you're going to spend the ten minutes it takes to upload your export, spend the next five actually reading it in order. That's the whole workflow.