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Prediction vindicated as Charlton edge out Wigan 2-1.

📝 Match Report

How It Unfolded

Charlton beat Wigan 2-1 at The Valley, Regular Season - 37, in the League One. 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 Charlton 0.99 xG and Wigan 0.59 xG, a combined 1.58. The scoreboard read 2-1 for 3 actual goals. Charlton beat their projection by 1.0 — clinical relative to the chances the model priced in. Those figures were built on strength ratings of Charlton attack 1.07 / defence 0.67 against Wigan attack 0.80 / defence 0.75, drawn from 82/81 games (CurrentSeason).

On the result, the model split it Charlton 44% | Draw 35% | Wigan 21%, with Charlton to win its most likely call at 44%. That is exactly how it finished — the model's leading outcome was vindicated.

Goals & Markets

The model rated Over 2.5 goals at 21%. The game delivered 3, so it went over — the opposite of the projected direction. Over 1.5 had been 47% and landed. On both teams to score, the model sat at 28% and the match saw both sides score — a miss on BTTS. Historically the two sides average 45% Over 2.5 across their same-competition games (Charlton 48%, Wigan 42%), a base rate that pointed the other way from today's over. Their combined BTTS history runs at 54%, which matched the both-scored outcome.

Trading Patterns vs Reality

Charlton's trading profile (81 games, 40 home) set some clear expectations: BTTS lands in 60% of their matches — today it did.

Wigan's trading profile (81 games, 40 away) set some clear expectations: BTTS lands in 47% of their matches — today it did; they keep a clean sheet 36% of the time, and conceded here.

Form vs Result

The form lines were close — Charlton 1.40 PPG, Wigan 1.40 PPG — offering no strong steer, and the Charlton 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