Nicolas Moumi Ngamaleu
Value picture
Where the market, the likely price and the model estimates sit.
The model rates Nicolas Moumi Ngamaleu's performance-implied value at EUR 0.8M-1.9M, below the estimated acquisition range of EUR 0.5M-2.6M. Its 24-month market value projection is EUR 0.5M-1.5M. Context-adjusted goal contributions are 1.04x the positional average in the league (1180 minutes, league coefficient 0.56). Confidence is medium: no event-level data (xG, progressive actions) is available from the current free source, and injury history is unavailable.
Acquisition note: Contract end date unavailable: range widened by 15% each side.
Bargain Index: 2
What drives the score. Not a black box: each component and its level.
| Performance | Average | 42th pct among ATT peers (age-neutral) |
| Age | Unfavorable | 32 years |
| Acquisition cost | Very favorable | est. €1.4M vs market €2.5M |
| Expected appreciation | Very low | -70% over ~2 years (model) |
| Value gap | Negative | weak predictor on its own |
| Development | Low | |
| League translation | Moderate | league coefficient 0.56 |
| Tactical fit | Not evaluated | Requires event/role data not available from the current free source. |
| Risk | Very High | |
| 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
- 1180 (44% of available)
- Goals + assists / 90
- 0.46
- Context-adjusted G+A / 90
- 0.42
- xG, xA, progression
- Data unavailable
- Defensive actions
- Data unavailable
- Performance percentile
- 42 (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
- 0.56 (rank 10)
- Team strength (Elo)
- 1423
- Opponent strength
- 1349
- Possession, role
- Data unavailable
League strength 1.00 = average club of the big-five leagues. Elo includes continental matches.
Potential
- Development
- Low
- Trajectory
- declining
- Breakout probability
- Age > 23
- Resale range (24m)
- €528k – €1.5M
Trajectory drivers: minutes share down 17 pts.
Historical comparables (8): median 2-year value change -68% (IQR -76% to -58%).
Risk profile
Overall: Very High
- ⚠Limited sample: 1180 league minutes last season.
- ⚠League level coefficient 0.56 (1.00 = big-five average): output may not translate.
- ⚠No continental-competition minutes last season.
- ⚠League level estimated mainly from squad values (few matches vs other leagues).
- ⚠Age 32: resale value typically declines.
- ⚠Declining trajectory versus previous season.
- ⓘContract end date unavailable (or stale in source).
- ⓘ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
- 20 (ATT)
- Relative output kept
- 76%–104%
- Held a regular role (900+ min)
- 50%
- 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 | FK Dinamo Moskva | Premier Liga | LW | 21 | 1180 | 44% | 4 | 2 | 0.46 | 0.42 | 1349 | 1423 | 0.56 |
| 2024/25 | FK Dinamo Moskva | Premier Liga | LW | 26 | 1628 | 60% | 6 | 5 | 0.61 | 0.47 | 1300 | 1445 | 0.55 |
| 2023/24 | FK Dinamo Moskva | Premier Liga | LW | 26 | 1831 | 68% | 6 | 5 | 0.54 | 0.55 | 1332 | 1374 | 0.49 |
| 2022/23 | FK Dinamo Moskva | Premier Liga | LW | 19 | 1171 | 43% | 2 | 2 | 0.31 | 0.30 | 1341 | 1325 | 0.53 |
Cups & continental competitions
| Season | Competition | Club | Apps | Min | G | A | Avg opp. Elo |
|---|---|---|---|---|---|---|---|
| 2025/26 | Russian Cup | FK Dinamo Moskva | 8 | 602 | 2 | 1 | — |
| 2024/25 | Russian Cup | FK Dinamo Moskva | 4 | 173 | 0 | 0 | — |
| 2023/24 | Russian Cup | FK Dinamo Moskva | 6 | 233 | 1 | 1 | — |
| 2022/23 | Uefa Conference League Qualifying | BSC Young Boys | 5 | 340 | 0 | 0 | 1392 |
| 2022/23 | Russian Cup | FK Dinamo Moskva | 5 | 310 | 0 | 1 | — |
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 |
|---|---|---|---|---|
| Jérémy Perbet Jupiler Pro League · 2017 | 33 | €2.0M | €600k | fell |
| Fedor Smolov Premier Liga · 2024 | 34 | €2.0M | €2.0M | flat |
| Jeremain Lens Super Lig · 2020 | 33 | €1.6M | €50k | fell |
| Marcus Berg Premier Liga · 2020 | 34 | €1.6M | €1.0M | fell |
| Steven Berghuis Eredivisie · 2024 | 33 | €6.0M | €2.0M | fell |
| Ivelin Popov Premier Liga · 2019 | 32 | €2.8M | €1.0M | fell |
| Olcay Sahan Super Lig · 2018 | 31 | €1.8M | €400k | fell |
| Bas Dost Jupiler Pro League · 2022 | 33 | €2.5M | €600k | fell |
Transfer history
Fees as reported by the source; undisclosed fees shown as unknown, never estimated.
| Date | From | To | Fee | MV at time |
|---|---|---|---|---|
| 8 Sept 2022 | Young Boys | Dynamo Moscow | €2.0M | €4.7M |
| 26 Aug 2017 | SCR Altach | Young Boys | €2.5M | €800k |
| 3 Aug 2016 | Coton Sport FC | SCR Altach | Unknown | €25k |
| 1 Jul 2013 | Canon Yaoundé | Coton Sport FC | Unknown | Data unavailable |
| 1 Jul 2011 | Musango | Canon Yaoundé | Unknown | 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.