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Prediction vindicated as Blackburn edge out Preston 2-1.

📝 Match Report

How It Unfolded

Blackburn beat Preston 2-1 at Ewood Park, Regular Season - 30, in the Championship. 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 Blackburn 1.23 xG and Preston 0.95 xG, a combined 2.18. The scoreboard read 2-1 for 3 actual goals. Both sides finished within a goal of their individual projections, so the scoreline sat close to the model's expected shape. Those figures were built on strength ratings of Blackburn attack 0.85 / defence 0.95 against Preston attack 0.96 / defence 1.05, drawn from 75/75 games (CurrentSeason).

On the result, the model split it Blackburn 42% | Draw 31% | Preston 28%, with Blackburn to win its most likely call at 42%. That is exactly how it finished — the model's leading outcome was vindicated.

Goals & Markets

The model rated Over 2.5 goals at 37%. The game delivered 3, so it went over — the opposite of the projected direction. Over 1.5 had been 65% and landed. On both teams to score, the model sat at 44% and the match saw both sides score — a miss on BTTS. Historically the two sides average 53% Over 2.5 across their same-competition games (Blackburn 52%, Preston 53%), a base rate that agreed with today's over. Their combined BTTS history runs at 51%, which matched the both-scored outcome.

Trading Patterns vs Reality

Blackburn's trading profile (75 games, 37 home) set some clear expectations: BTTS lands in 48% of their matches — today it did.

Preston's trading profile (75 games, 37 away) set some clear expectations: BTTS lands in 53% of their matches — today it did.

Form vs Result

The form lines were close — Blackburn 1.27 PPG, Preston 1.33 PPG — offering no strong steer, and the Blackburn 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