🇭🇷 Dinamo vs NEC 🇳🇱 simulation card and correct score matrix BTTS hit in all of the top 5 scorelines

⚔️ Manager vs Manager H2H (domestic league only) Oliver Glasner has dominated Unai Emery across their five Premier League meetings: ▸ Goals per match: 2.80 vs 0.60 ▸ Win rate: 60% vs 0% ▸ Over 2.5 goals: 80% One of my favorite insights on DataGaffer.com I’d been looking for this type of data for years, it’s one of the reasons I created the site.

Champions League Round 1 projected goals after 10,000 simulations

Pace Score results since Matchweek 1: As the score rises from 70+ → 80+ → 90+: Avg goals: 4.34 → 5.09 → 5.60 Avg SOT: 10.78 → 11.32 → 13.40 Over 2.5: 88.1% → 95.5% → 100% Still an early sample, particularly at 90+, but the trend is clear: higher Pace Scores are identifying better goal environments and more chance heavy matchups.

Proceed with caution over the next couple of weeks. The 26/27 season has started strongly for goals, with the top five leagues averaging 2.97 per match. I’ve been noticing tons of goals in other leagues lately as well. Regression might be near 📉

Yeah we’re winning the league

🇩🇰 Safe to say the Danish Superliga hasn’t exactly been off to a flying start this season. The league is averaging just 2.25 goals per match, with the latest matchweek averaging only 1.25 goals. Surely due for some positive regression this weekend?

🇫🇷 PSG vs 🇩🇪 Bayern One of the most aggressive attacking matchups we’ve ever recorded 👀 Out of 410 teams (per90): • Attacks → #1 vs #2 • Dangerous Attacks → #3 vs #2 • Big Chances Created → #4 vs #1 • Goal Attempts → #5 vs #1 Constant pressure on both sides.

📑 Match Card There last 5 h2h average goals are basically the same as our projected score which is pretty interesting ⬇️ H2H Levante 1.20 vs Real Madrid 2.40 Sim: Levante 1.19 vs Real Madrid 2.37