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Prediction vindicated as Ried edge out Grazer AK 2-1.

📝 Match Report

How It Unfolded

Ried beat Grazer AK 2-1 at Innviertel Arena, Relegation Group - 28, in the Bundesliga. With the result on the board, the sections below measure it against everything the model and the numbers projected in advance.

The Model vs The Result

The Poisson model went into this projecting Ried 1.20 xG and Grazer AK 1.04 xG, a combined 2.24. The scoreboard read 2-1 for 3 actual goals. Ried beat their projection by 0.8 — clinical relative to the chances the model priced in. Those figures were built on strength ratings of Ried attack 0.80 / defence 1.08 against Grazer AK attack 0.84 / defence 1.12, drawn from 22/44 games (CurrentSeason).

On the result, the model split it Ried 39% | Draw 30% | Grazer AK 31%, with Ried to win its most likely call at 39%. That is exactly how it finished — the model's leading outcome was vindicated.

Goals & Markets

The model rated Over 2.5 goals at 39%. The game delivered 3, so it went over — the opposite of the projected direction. Over 1.5 had been 66% and landed. On both teams to score, the model sat at 46% and the match saw both sides score — a miss on BTTS. Historically the two sides average 52% Over 2.5 across their same-competition games (Ried 48%, Grazer AK 56%), a base rate that agreed with today's over. Their combined BTTS history runs at 59%, which matched the both-scored outcome.

Trading Patterns vs Reality

Ried's trading profile (27 games, 13 home) set some clear expectations: BTTS lands in 56% of their matches — today it did.

Grazer AK's trading profile (27 games, 13 away) set some clear expectations: BTTS lands in 63% of their matches — today it did.

Form vs Result

The form lines were close — Ried 1.33 PPG, Grazer AK 1.11 PPG — offering no strong steer, and the Ried win broke the near-deadlock.

Verdict

Grading the result against the stored data, 1 of 3 pre-match signals came in: result (hit), Over 2.5 (miss), BTTS (miss). The data was on the wrong side of this result more often than not — a reminder that a single match sits well inside the model's variance.

💡 Key Insights