Samu Aghehowa
Porto·ForwardScoring skill
What the number means: if Samu Aghehowa plays 90 minutes at a neutral venue against an average Liga defence, the model expects 0.43 more goals than if that place were taken by a replacement-level player — the kind of signing any club can make for free. Goals are capped (winsorized) before the estimate, so one four-goal afternoon is not read as permanent skill.
The model puts 90% probability on the true value lying between 0.29 and 0.59. Fewer minutes, wider interval.
Attacking contribution
#3 of 40 published
The same structure as the finishing metric, but adding goals and assists together. It sees what the goals-only metric could not: who creates.
Few minutes pull the estimate toward replacement level and widen the interval.
Season by season
Goals go up and down; the skill estimate barely moves. That is by design: the model only admits a change in skill when the data demand one, and across three Liga Portugal seasons they never have.
| Season | Minutes | Goals | Skill (with margin) |
|---|---|---|---|
| 2024-25 | 2,263 | 19 | 0.44 |
| 2025-26 | 1,414 | 13 | 0.45 |
Did he improve? Can't say
Between 2024-25 and 2025-26 the estimate moved +0.005 — but the margin of error on that difference is ±0.129, many times larger. In other words: indistinguishable from zero. A big goal season is usually natural variation, not new skill.
Recent appearances
Match-by-match data (SofaScore). The rating is the provider's, not the model's — the model only uses goals, minutes and opponent.
Bayesian player model fitted on 29,019 individual appearances since 2023-24, with a 600-minute minimum to qualify. Club = the player's most recent club in the data (2023-24 to 2026-27); recent transfers may not be reflected.