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Prediction vindicated as Rubin edge out Fakel 2-1.

📝 Match Report

How It Unfolded

Rubin beat Fakel 2-1 at Ak Bars Arena, Regular Season - 26, in the Premier League. 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 Rubin 1.59 xG and Fakel 0.56 xG, a combined 2.14. 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 Rubin attack 0.92 / defence 0.85 against Fakel attack 0.59 / defence 1.15, drawn from 55/55 games (CurrentSeason).

On the result, the model split it Rubin 60% | Draw 29% | Fakel 10%, with Rubin to win its most likely call at 60%. That is exactly how it finished — the model's leading outcome was vindicated.

Goals & Markets

The model rated Over 2.5 goals at 36%. 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 36% and the match saw both sides score — a miss on BTTS. Historically the two sides average 36% Over 2.5 across their same-competition games (Rubin 46%, Fakel 26%), a base rate that pointed the other way from today's over. Their combined BTTS history runs at 41%, which did not match the both-scored outcome.

Trading Patterns vs Reality

Rubin's trading profile (55 games, 27 home) set some clear expectations: BTTS lands in 49% of their matches — today it did.

Fakel's trading profile (55 games, 27 away) set some clear expectations: BTTS lands in 33% of their matches — today it did; they keep a clean sheet 34% of the time, and conceded here.

Form vs Result

On form, Rubin arrived the stronger side — 1.42 PPG against 0.85. That form edge translated into the three points. Rubin (home/away splits) scored 2 against a 1.15 average — above their attacking norm.

Verdict

Grading the result against the stored data, 2 of 4 pre-match signals came in: result (hit), Over 2.5 (miss), BTTS (miss), form (hit). The model got the gist right while missing on the margins.

💡 Key Insights