We Graded 108,000 Player Props. Here's What the Data Actually Says.

Every sports betting service shows you a record. Almost none of them show you the record.

The difference is everything. A record is 12-2 last week, screenshotted and cropped. The record is every single pick a system has ever made, graded automatically, with the losses sitting right next to the wins where you can't hide them. One is marketing. The other is evidence.

We built PropzSniper to only ever show you the second kind.

The number that matters isn't the one you think

As of today, our models have graded 108,480 player props across MLB, NBA, WNBA, NHL, KBO, and NPB. Every projection gets logged the moment the game ends — HIT, MISS, or PUSH — with no human in the loop deciding what's flattering enough to publish.

Here's the uncomfortable truth most services will never tell you: no honest player-prop model hits 65%. If someone is advertising that number, they're either working with a sample so small it's meaningless, or they're quietly deleting the weeks that don't fit the story.

Real edges in player props are thin. They show up as a couple of percentage points over the break-even line, and they only become visible — provable, trustworthy, real — across thousands and thousands of graded outcomes. Ten picks tell you nothing. A hundred picks tell you a little. A hundred thousand picks start telling you the truth.

That's why we don't lead with a hit rate. We lead with the volume behind it, because volume is what makes any hit rate believable in the first place.

Why "our own models" is the whole point

There's a dirty secret in the betting content world: a huge share of "premium picks" are just the sportsbook's own numbers, handed back to you with a markup. If a service is pulling public odds and repackaging consensus projections that a thousand other sites also sell, there is no edge — there's just a subscription fee wrapped around information you could get for free.

Every projection on PropzSniper comes from our own algorithms. Not scraped lines. Not public consensus. Not third-party projection feeds. When our model's number disagrees with where the market has a prop set, that disagreement is the entire product. It's the only thing worth paying for, because it's the only thing you can't already see.

What the data taught us

Across six sports and six figures of graded props, a few things stopped being opinions and became measurable facts:

• Not all stat types are created equal. Some props are genuinely predictable. Others are close to coin flips dressed up as edges. The models treat them very differently, and so should you.
• Sample size humbles everyone. A stat type can look elite over 40 props and regress hard by prop 400. We only trust rates that have survived real volume.
• Transparency compounds. The longer we track publicly, the more the record speaks for itself — and the less we have to ask anyone to just take our word.

What you'll find on this blog

This is where we show the work. No hot takes, no "lock of the day," no manufactured urgency. Expect:

• Breakdowns of what the tracked data reveals — which stats are predictable, which aren't, and the numbers behind both.
• Honest post-mortems, including the picks and stretches that lost. Especially those.
• Methodology over noise — how the models actually weigh matchup, form, and situation to find an edge the market missed.

If you've been burned by cherry-picked records and you want to see real numbers before you trust anyone with your money, you're finally in the right place.

The data is the product. Everything else is just commentary on it — including this post.

Want to see what the models are flagging today? Take a look at the board.