Prizma Analytics

A defensive metric with a fixed baseline isn’t fair. DDI averages two things: — the opponent’s attacking xG — the team’s xG allowed That single average becomes a dynamic baseline that adjusts for both who you’re facing and how good your defense already is.

Raw xG numbers aren’t fair to defenders. A player who played 45 minutes is being judged against one who played 90. DDI fixes that by scaling expected xG by minutes played ÷ 90 — the theoretical xG your opponent could’ve had while that defender was actually on the pitch.

Defenses don’t just “hold the line” — they disrupt. DDI measures that gap: Expected xG − Actual xG When it’s positive, the defense didn’t just survive the attack. It broke it. Most metrics count goals. DDI counts damage prevented 🛡️⚽

No Opta Needed! I built my own defensive metric using public data. DDI measures how much a player suppresses realized opposition xG vs. pre-match expectations — plus an interactive app to explore it yourself. Check it out below 👇 ddi-app.streamlit.app

K-means clustering groups players by playing style.
This cluster highlights wingers elite at box attacking and finishing — a powerful profile for uncovering hidden talent.

K-means clustering groups players by playing style. This cluster highlights wingers elite at progression and crossing — a profile that can be used to uncover hidden talent in smaller leagues.