Allahyar Sayyadmanesh
Value picture
Where the market, the likely price and the model estimates sit.
The model rates Allahyar Sayyadmanesh's performance-implied value at EUR 0.9M-3.7M, below the estimated acquisition range of EUR 1.6M-4.0M. Its 24-month market value projection is EUR 1.5M-4.0M. Context-adjusted goal contributions are 1.44x the positional average in the league (1776 minutes, league coefficient 0.65). Confidence is medium: no event-level data (xG, progressive actions) is available from the current free source, and injury history is unavailable.
Bargain Index: 46
What drives the score. Not a black box: each component and its level.
| Performance | Average | 40.3th pct among ATT peers (age-neutral) |
| Age | Neutral | 25 years |
| Acquisition cost | Favorable | est. €2.8M vs market €3.5M |
| Expected appreciation | Low | -24% over ~2 years (model) |
| Value gap | Very negative | weak predictor on its own |
| Development | Low | |
| League translation | Moderate | league coefficient 0.65 |
| Tactical fit | Not evaluated | Requires event/role data not available from the current free source. |
| Risk | Moderate | |
| Data confidence | Medium |
Bargain Index = percentile (among all evaluated players) of expected 2-year value relative to estimated cost. See methodology and backtests.
Market value history
Market value ≠ transfer fee ≠ acquisition cost.
Profile vs positional peers
Percentiles among 900 evaluated ATT players with 900+ minutes.
Performance (last season, league)
- Minutes
- 1776 (66% of available)
- Goals + assists / 90
- 0.46
- Context-adjusted G+A / 90
- 0.57
- xG, xA, progression
- Data unavailable
- Defensive actions
- Data unavailable
- Performance percentile
- 40 (Average)
Adjusted = shrunk toward positional baseline for small samples, corrected for team dominance over opponents, expressed relative to positional peers in the same league.
Context
- League strength
- 0.65 (rank 6)
- Team strength (Elo)
- 1348
- Opponent strength
- 1385
- Possession, role
- Data unavailable
League strength 1.00 = average club of the big-five leagues. Elo includes continental matches.
Potential
- Development
- Low
- Trajectory
- stable
- Breakout probability
- Age > 23
- Resale range (24m)
- €1.5M – €4.0M
Trajectory drivers: role change LW -> RW.
Historical comparables (8): median 2-year value change +0% (IQR -40% to +22%).
Risk profile
Overall: Moderate
- ⚠Limited sample: 1776 league minutes last season.
- ⚠No continental-competition minutes last season.
- ⓘInjury history unavailable from current data source.
League translation
What happened to players who moved from this league to the big-five leagues (observed, not a forecast).
- Historical movers
- 78 (ATT)
- Relative output kept
- 72%–126%
- Held a regular role (900+ min)
- 60%
- Confidence
- Medium
Output is relative to positional peers in each league. Movers are a selected group (clubs buy players who fit), so treat this as context, not a guarantee.
Season-by-season (domestic league)
Context-adjusted values are model outputs.
| Season | Club | League | Pos | Apps | Min | Min share | G | A | G+A/90 | Adj. G+A/90 | Opp. Elo | Team Elo | League str. |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2025/26 | KVC Westerlo | Jupiler Pro League | RW | 24 | 1776 | 66% | 5 | 4 | 0.46 | 0.57 | 1385 | 1348 | 0.65 |
| 2024/25 | KVC Westerlo | Jupiler Pro League | LW | 26 | 1817 | 67% | 6 | 6 | 0.59 | 0.59 | 1381 | 1322 | 0.63 |
| 2023/24 | KVC Westerlo | Jupiler Pro League | LW | 7 | 392 | 15% | 0 | 0 | 0.00 | 0.31 | 1416 | 1293 | 0.63 |
| 2020/21 | Zorya Lugansk | Premier Liga | ST | 19 | 1020 | 44% | 5 | 3 | 0.71 | 0.50 | 1303 | 1450 | 0.49 |
| 2019/20 | Fenerbahce | Super Lig | ST | 2 | 92 | 3% | 0 | 0 | 0.00 | 0.39 | 1273 | 1330 | 0.45 |
Cups & continental competitions
| Season | Competition | Club | Apps | Min | G | A | Avg opp. Elo |
|---|---|---|---|---|---|---|---|
| 2025/26 | Jupiler Pro League Europe Play Offs | KVC Westerlo | 6 | 458 | 2 | 1 | 1383 |
| 2023/24 | Fa Cup | Hull City | 2 | 150 | 0 | 0 | — |
| 2020/21 | Ukrainian Cup | Zorya Lugansk | 3 | 243 | 0 | 0 | — |
| 2020/21 | Uefa Europa League | Zorya Lugansk | 4 | 66 | 1 | 0 | 1482 |
Small continental samples are shown for context only and are not over-weighted.
Historical comparables
Most similar profiles 2+ years ago (same position group) and what happened next. Chosen by similarity only — failures included.
| Player (then) | Age | Value then | ~2y later | Outcome |
|---|---|---|---|---|
| Jón Dagur Thorsteinsson Jupiler Pro League · 2023 | 25 | €3.5M | €800k | fell |
| Yonathan Del Valle Super Lig · 2016 | 26 | €2.4M | €450k | fell |
| Hyun-jun Suk Liga Portugal · 2016 | 25 | €4.0M | €3.5M | flat |
| Óscar Estupiñán Liga Portugal · 2022 | 26 | €3.5M | €4.0M | flat |
| Mama Baldé Ligue 1 · 2020 | 25 | €3.2M | €4.5M | rose |
| Mario González Liga Portugal · 2021 | 25 | €3.0M | €3.5M | flat |
| Arbër Zeneli Eredivisie · 2019 | 24 | €6.0M | €5.0M | flat |
| Diogo Gonçalves Liga Portugal · 2020 | 23 | €1.6M | €5.0M | rose |
Transfer history
Fees as reported by the source; undisclosed fees shown as unknown, never estimated.
| Date | From | To | Fee | MV at time |
|---|---|---|---|---|
| 31 Jan 2024 | Hull City | KVC Westerlo | €2.3M | €2.5M |
| 9 Jul 2022 | Fenerbahçe | Hull City | €4.5M | €2.5M |
| 30 Jun 2022 | Hull City | Fenerbahçe | Free / loan | €2.5M |
| 31 Jan 2022 | Fenerbahçe | Hull City | Free / loan | €3.0M |
| 31 Dec 2021 | Zorya Lugansk | Fenerbahçe | Free / loan | €3.0M |
| 4 Oct 2020 | Fenerbahçe | Zorya Lugansk | Free / loan | €300k |
| 30 Jan 2020 | Istanbulspor | Fenerbahçe | Free / loan | €500k |
| 2 Sept 2019 | Fenerbahçe | Istanbulspor | Free / loan | €500k |
| 1 Jul 2019 | Esteghlal FC | Fenerbahçe | €753k | €500k |
| 1 Jul 2018 | Saipa FC U19 | Esteghlal FC | Free / loan | €300k |
| 1 Jul 2017 | Padideh S U17 | Saipa FC U19 | Free / loan | Data unavailable |
MoneyballAI identifies statistical opportunities from available data. A player flagged as a potential bargain is not guaranteed to succeed, to transfer, to rise in value, or to generate resale profit. Market values, fees and contracts come from third-party sources; ranges are model estimates with stated confidence.