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Shock result as Boulogne defy the odds to beat PAU 1-2.

📝 Match Report

How It Unfolded

Boulogne beat PAU 1-2 at Nouste Camp, Regular Season - 23, in the Ligue 2. 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 PAU 1.40 xG and Boulogne 1.25 xG, a combined 2.65. The scoreboard read 1-2 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 PAU attack 1.12 / defence 1.28 against Boulogne attack 0.83 / defence 1.08, drawn from 56/22 games (CurrentSeason).

On the result, the model split it PAU 40% | Draw 26% | Boulogne 34%, with PAU to win its most likely call at 40%. The actual Boulogne win had been the model's second-ranked read at 34%, so the result diverged from the top call without being a shock.

Goals & Markets

The model rated Over 2.5 goals at 49%. The game delivered 3, so it went over — the opposite of the projected direction. Over 1.5 had been 74% and landed. On both teams to score, the model sat at 54% and the match saw both sides score — another call in the model's favour. Historically the two sides average 60% Over 2.5 across their same-competition games (PAU 67%, Boulogne 54%), a base rate that agreed with today's over. Their combined BTTS history runs at 58%, which matched the both-scored outcome.

Trading Patterns vs Reality

PAU's trading profile (24 games, 12 home) set some clear expectations: BTTS lands in 58% of their matches — today it did.

Boulogne's trading profile (24 games, 12 away) set some clear expectations: BTTS lands in 58% of their matches — today it did.

Form vs Result

The form lines were close — PAU 1.42 PPG, Boulogne 1.08 PPG — offering no strong steer, and the Boulogne win broke the near-deadlock. Boulogne (home/away splits) scored 2 against a 1.00 average — above their attacking norm.

Verdict

Grading the result against the stored data, 1 of 3 pre-match signals came in: result (miss), Over 2.5 (miss), BTTS (hit). 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