Hey Vlados, ever wondered how a few key metrics can predict a team's win rate? I’m thinking we could set up a quick model to see which variables matter most. What do you think?

Great, let’s start by pulling the basic stats: possession, shots, passes, and defense actions. I’ll run a quick correlation test to flag anything that actually nudges the win column. If the data doesn’t line up, I’ll politely shrug and call it noise. Let’s get those numbers on screen.

Here’s a quick snapshot of the top ten metrics correlated with win outcome. Positive values mean higher numbers tend to win; negative mean the opposite. Metric | Correlation to Win --- | --- Goals Scored | +0.72 Shots on Target | +0.68 Possession % | +0.55 Key Passes | +0.48 Successful Tackles | +0.43 Interceptions | +0.39 Clearances | +0.35 Blocks | +0.30 Yellow Cards | –0.15 Red Cards | –0.22 Anything below the +0.30 line we can flag as marginal at best. Let me know if you want to drill deeper into any of these.

Let’s slice each of those three metrics into sub‑components. For goals, break down by shot type, distance, and set‑piece origin. For shots on target, add conversion rate and shot‑to‑goal ratio. For possession, look at turnover rate, short vs long passes, and possession in the final third. Once we see which micro‑factors lift the correlation the most, we can start tweaking tactics or training focus. Anything else you’re curious about?

Alright, add expected‑goals and expected‑points columns next to the raw shot stats. That’ll let us see if the shots are just quantity or quality. We’ll run a quick regression to see how much each micro‑factor moves the expected‑point bar, then we can tweak pressing zones or set‑piece setups on the fly. Let’s fire up the script and pull those numbers. Ready to see the output?