One of the largest structured football betting datasets available for independent analysts. 100,000+ matches including: • Results • Bookmaker Odds • Implied Probabilities • Market Margins Built for: • Poisson Models • ML • Market Research https://t.co/u16fsWcIuI

Field Tilt vs PPDA no Brasileirão! ▶️Field Tilt é uma métrica que mede a posse de bola no terço de ataque de cada equipe, ou seja, quais times tem ficam mais com a bola em zonas perigosas para o adversário. ▶️PPDA é uma métrica que mede quantos passes cada equipe cede em seu

Pra mim é óbvio que muitos jornalistas e torcedores avaliam a ida do @neymarjr à Copa colocando um peso enorme nas decisões políticas do jogador. Existe uma má vontade clara por ele ter se declarado politicamente. Dito isso, a seleção precisa do Neymar. Nem que seja para ele

Most betting models don’t fail because they’re wrong. They fail because they’re used in the wrong leagues. Base & slope reveal where your edge is real—and where it’s just noise. Read: https://t.co/Yive01jJ8Y

https://t.co/9Pu0Lw1HiV

URGENTE! 🚨 A CBF comunicou aos clubes da Série e da Série B que haverá uma reunião na sede da entidade na primeira semana de abril. MOTIVO? A CBF VAI DISCUTIR COM OS CLUBES A CRIAÇÃO DE UMA LIGA UNIFICADA NO FUTEBOL BRASILEIRO. Após a FFU e LiBRA quase explodirem, a CBF quer

Most prediction tools stop at Poisson. We go further. Monte Carlo simulation = risk filter. That’s where edge lives. https://t.co/7Kx6pHcr7d

Villarreal - Real Sociedad

https://t.co/7Kx6pHcr7d

Bournemouth vs Man United according to the Football Hacking Web App. We use the Poisson distribution and Monte Carlo simulation to test robustness.

9/9 If you're serious about data-driven football… Start here 👇

8/9 It’s already being used inside: • Football Hacking app • League analysis pipelines • Strategy development Not theory. Production.

7/9 This is especially useful if you: • Build betting models • Trade markets • Run analytics dashboards • Work with football data

6/9 Because two identical probabilities can behave very differently. 50% ≠ 50% One is stable. The other is chaos.

5/9 Then comes the layer most people ignore: 👉 Monte Carlo simulation Thousands of simulated matches to test: • Variance • Stability • Outcome spread

4/9 The API doesn’t just output probabilities. It gives you the full probability matrix. So you can see: • Score distributions • BTTS probabilities • Over/Under dynamics • Where the game is actually “living”

3/9 Most models stop at: “Home win = 45%” But what does that actually mean? 👉 Is it stable? 👉 Is it fragile? 👉 Does it depend on a narrow score range?

2/9 This isn’t just Poisson slapped on top of match data. It combines: • Poisson goal modeling • Dixon-Coles adjustment • Monte Carlo simulations To go beyond “expected”… into robust.

1/9 Most football predictions APIs give you probabilities. Mine gives you structure behind the probabilities. https://t.co/5qSSKnNvpe

Football Hacking is moving to its own domain. No new features — just the foundation for faster development, better support, and long-term growth. Read more: https://t.co/UbGwNtgBoc

Football Hacking is moving to its own domain. No new features — just the step that makes everything else possible. 👉 https://t.co/7Kx6pHcr7d