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A transparent football analytics engine powered by Poisson, Dixon-Coles & Monte Carlo simulations. We calculate exact probabilities across 8 variables. Key features: 1. Past Analyst: We log yesterday’s AI predictions vs actual results. Total transparency, no hidden misses. 2. Freemium: Get 2 Free Credits daily to fully analyze any 2 matches worldwide!
Hi Product Hunt! 👋 I'm Paul, the solo maker behind Statlyzer. A bit of background: I actually built this entire app and its backend architecture (PostgreSQL/Supabase) from the cabin of my truck during my driving breaks across Europe. 🚛💻 The Problem I wanted to solve: The sports analytics and prediction industry is broken. It's full of "experts" selling tips while completely hiding their lost bets. I wanted to build something based on pure, undeniable math, with absolute transparency. What Statlyzer does: It runs heavy Monte Carlo simulations, Poisson distribution, and Dixon-Coles models to calculate exact probabilities for football matches across 8 different variables (1X2, Over/Under, Cards, Corners, etc.). 🔥 Why I'm proud of it: The "Past Analyst" tab. It automatically logs yesterday's AI predictions against the actual real-world results. If the AI missed, you see it. No deleted data, no hidden stats. Just raw accuracy tracking. The app gives every user 2 Free Credits daily, which means you can run a full deep-dive analysis on any 2 matches on the globe, every single day, for free. I would absolutely love for this community to download it, test the UI, critique the math models, and give me your raw, honest feedback. I'll be here all day answering your questions!
P.S. A quick technical note: Statlyzer isn't just a static template; it's a living engine. Every night, after the final whistle, the system automatically ingests the new raw match data, learns from yesterday's variances, and recalibrates the Dixon-Coles parameters. The model you use today is mathematically smarter than it was yesterday! 📈
finally a football stats tool that doesn't hide its misses, the past analyst log is a nice touch.Used my two free credits on a derby and the numbers were surprisingly detailed.
Love the transparency angle with the past analyst tracker. One thing that would really help me as a user is being able to filter those logged predictions by league, bet type, or odds range so I can quickly see how the model performs in the specific markets I actually care about.
Finally tried Statlyzer for a Premier League match and the transparent log of yesterday's picks vs actual results is genuinely refreshing, you can see exactly where the model missed without digging. The daily free credits are a nice touch for casual testing.
Love that you log yesterday's predictions against actual results publicly. That kind of transparency is rare in sports analytics and it immediately sets Statlyzer apart from every other tipster site out there.
love that the past analyst feature logs yesterday's AI predictions against actual results, that kind of transparency is honestly rare in football analytics tools
honestly love that you show yesterday's predictions vs actual results right on the homepage. most analytics tools hide their misses so that kind of transparency is genuinely refreshing, and it tells me the team actually trusts their model
love that you actually log yesterday's AI picks against real results instead of just showing the wins. that kind of transparency is rare in this space and takes real guts to build.
About Statlyzer on Product Hunt
“Pure mathematical football analytics.”
Statlyzer was submitted on Product Hunt and earned 17 upvotes and 15 comments, placing #37 on the daily leaderboard. A transparent football analytics engine powered by Poisson, Dixon-Coles & Monte Carlo simulations. We calculate exact probabilities across 8 variables. Key features: 1. Past Analyst: We log yesterday’s AI predictions vs actual results. Total transparency, no hidden misses. 2. Freemium: Get 2 Free Credits daily to fully analyze any 2 matches worldwide!
Statlyzer was featured in Android (57.4k followers), Sports (11.3k followers), Artificial Intelligence (473.7k followers) and Data & Analytics (5.7k followers) on Product Hunt. Together, these topics include over 159k products, making this a competitive space to launch in.
Who hunted Statlyzer?
Statlyzer was hunted by Iroveanu Paul. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Iroveanu Paul
Hi Product Hunt! 👋 I'm Paul, the solo maker behind Statlyzer.
A bit of background: I actually built this entire app and its backend architecture (PostgreSQL/Supabase) from the cabin of my truck during my driving breaks across Europe. 🚛💻
The Problem I wanted to solve: The sports analytics and prediction industry is broken. It's full of "experts" selling tips while completely hiding their lost bets. I wanted to build something based on pure, undeniable math, with absolute transparency.
What Statlyzer does:
It runs heavy Monte Carlo simulations, Poisson distribution, and Dixon-Coles models to calculate exact probabilities for football matches across 8 different variables (1X2, Over/Under, Cards, Corners, etc.).
🔥 Why I'm proud of it:
The "Past Analyst" tab. It automatically logs yesterday's AI predictions against the actual real-world results. If the AI missed, you see it. No deleted data, no hidden stats. Just raw accuracy tracking.
The app gives every user 2 Free Credits daily, which means you can run a full deep-dive analysis on any 2 matches on the globe, every single day, for free.
I would absolutely love for this community to download it, test the UI, critique the math models, and give me your raw, honest feedback. I'll be here all day answering your questions!
P.S. A quick technical note: Statlyzer isn't just a static template; it's a living engine. Every night, after the final whistle, the system automatically ingests the new raw match data, learns from yesterday's variances, and recalibrates the Dixon-Coles parameters. The model you use today is mathematically smarter than it was yesterday! 📈