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Prediction vindicated as Monza edge out Juve Stabia 2-1.

📝 Match Report

How It Unfolded

Monza beat Juve Stabia 2-1 at U-Power Stadium, Semi-finals, in the Serie B. That is the final score; the rest of this report grades it against what the data forecast beforehand.

The Model vs The Result

The Poisson model went into this projecting Monza 1.64 xG and Juve Stabia 0.87 xG, a combined 2.51. 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 Monza attack 1.12 / defence 0.89 against Juve Stabia attack 0.91 / defence 1.04, drawn from 38/76 games (CurrentSeason).

On the result, the model split it Monza 54% | Draw 27% | Juve Stabia 18%, with Monza to win its most likely call at 54%. The scoreboard confirmed the model's leading pick.

Goals & Markets

The model rated Over 2.5 goals at 46%. The game delivered 3, so it went over — a miss for the goals model. Over 1.5 had been 73% and landed. On both teams to score, the model sat at 48% and the match saw both sides score — a miss on BTTS. Historically the two sides average 48% Over 2.5 across their same-competition games (Monza 49%, Juve Stabia 47%), a base rate that pointed the other way from today's over. Their combined BTTS history runs at 53%, which matched the both-scored outcome.

Trading Patterns vs Reality

Monza's trading profile (77 games, 37 home) set some clear expectations: BTTS lands in 52% of their matches — today it did.

Juve Stabia's trading profile (77 games, 37 away) set some clear expectations: BTTS lands in 54% of their matches — today it did.

Form vs Result

The form lines were close — Monza 1.23 PPG, Juve Stabia 1.40 PPG — offering no strong steer, and the Monza 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). A poor return for the projections: the match defied the majority of what the numbers expected.

💡 Key Insights