Felix Nmecha
Value picture
Where the market, the likely price and the model estimates sit.
The model rates Felix Nmecha's performance-implied value at EUR 14.1M-51.6M, below the estimated acquisition range of EUR 35.9M-63.8M. Its 24-month market value projection is EUR 27.1M-67.2M. Context-adjusted goal contributions are 0.54x the positional average in the league (2190 minutes, league coefficient 0.94). Confidence is medium: no event-level data (xG, progressive actions) is available from the current free source, and injury history is unavailable.
Bargain Index: 15
What drives the score. Not a black box: each component and its level.
| Performance | High | 89th pct among MID peers (age-neutral) |
| Age | Neutral | 25.7 years |
| Acquisition cost | Neutral | est. €53.0M vs market €50.0M |
| Expected appreciation | Low | -24% over ~2 years (model) |
| Value gap | Very negative | weak predictor on its own |
| Development | Moderate | |
| League translation | Big-five league | league coefficient 0.94 |
| Tactical fit | Not evaluated | Requires event/role data not available from the current free source. |
| Risk | Low | |
| 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 1163 evaluated MID players with 900+ minutes.
Performance (last season, league)
- Minutes
- 2190 (72% of available)
- Goals + assists / 90
- 0.20
- Context-adjusted G+A / 90
- 0.12
- xG, xA, progression
- Data unavailable
- Defensive actions
- Data unavailable
- Performance percentile
- 89 (High)
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
- 0.94 (rank 3)
- Team strength (Elo)
- 1656
- Opponent strength
- 1474
- Possession, role
- Data unavailable
League strength 1.00 = average club of the big-five leagues. Elo includes continental matches.
Potential
- Development
- Moderate
- Trajectory
- declining
- Breakout probability
- Age > 23
- Resale range (24m)
- €27.1M – €67.2M
Trajectory drivers: minutes share up 22 pts; role change DM -> CM; relative output down 66% vs positional peers.
Historical comparables (8): median 2-year value change +7% (IQR -17% to +27%).
Risk profile
Overall: Low
- ⚠Declining trajectory versus previous 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).
Already playing in a big-five league.
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 | Borussia Dortmund | Bundesliga | CM | 29 | 2190 | 72% | 2 | 3 | 0.21 | 0.12 | 1474 | 1656 | 0.94 |
| 2024/25 | Borussia Dortmund | Bundesliga | DM | 26 | 1512 | 49% | 4 | 2 | 0.36 | 0.20 | 1509 | 1680 | 0.94 |
| 2023/24 | Borussia Dortmund | Bundesliga | CM | 20 | 1017 | 33% | 1 | 2 | 0.27 | 0.16 | 1529 | 1716 | 1.01 |
| 2022/23 | VfL Wolfsburg | Bundesliga | AM | 30 | 1836 | 60% | 3 | 6 | 0.44 | 0.37 | 1555 | 1525 | 1.04 |
| 2021/22 | VfL Wolfsburg | Bundesliga | CM | 16 | 310 | 10% | 0 | 1 | 0.29 | 0.26 | 1534 | 1495 | 1.05 |
Cups & continental competitions
| Season | Competition | Club | Apps | Min | G | A | Avg opp. Elo |
|---|---|---|---|---|---|---|---|
| 2025/26 | Uefa Champions League | Borussia Dortmund | 10 | 753 | 3 | 0 | 1615 |
| 2025/26 | World Cup | Germany | 3 | 227 | 1 | 2 | — |
| 2025/26 | Dfb Pokal | Borussia Dortmund | 3 | 193 | 0 | 0 | — |
| 2024/25 | Uefa Champions League | Borussia Dortmund | 9 | 618 | 1 | 1 | 1675 |
| 2024/25 | Dfb Pokal | Borussia Dortmund | 1 | 120 | 0 | 0 | — |
| 2023/24 | Uefa Champions League | Borussia Dortmund | 8 | 348 | 1 | 0 | 1707 |
| 2023/24 | Dfb Pokal | Borussia Dortmund | 1 | 20 | 0 | 0 | — |
| 2022/23 | Dfb Pokal | VfL Wolfsburg | 2 | 48 | 0 | 0 | — |
| 2021/22 | Uefa Champions League | VfL Wolfsburg | 2 | 35 | 0 | 0 | 1619 |
| 2020/21 | Fa Cup | Manchester City | 1 | 45 | 0 | 0 | — |
| 2020/21 | Uefa Champions League | Manchester City | 1 | 5 | 0 | 1 | 1569 |
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 |
|---|---|---|---|---|
| Adrien Rabiot Serie A · 2021 | 26 | €30.0M | €40.0M | rose |
| Manuel Locatelli Serie A · 2023 | 25 | €30.0M | €30.0M | flat |
| Xaver Schlager Bundesliga · 2024 | 27 | €28.0M | €10.0M | fell |
| İlkay Gündoğan Bundesliga · 2016 | 26 | €30.0M | €40.0M | rose |
| Rodrigo Bentancur Serie A · 2021 | 24 | €35.0M | €40.0M | flat |
| Raphaël Guerreiro Bundesliga · 2020 | 27 | €28.0M | €25.0M | flat |
| Piotr Zielinski Serie A · 2019 | 25 | €40.0M | €50.0M | rose |
| Morgan Schneiderlin Premier League · 2016 | 27 | €30.0M | €20.0M | fell |
Transfer history
Fees as reported by the source; undisclosed fees shown as unknown, never estimated.
| Date | From | To | Fee | MV at time |
|---|---|---|---|---|
| 3 Jul 2023 | Wolfsburg | Dortmund | €30.0M | €15.0M |
| 21 Jul 2021 | Man City U23 | Wolfsburg | Free / loan | €450k |
| 1 Jul 2018 | Man City U18 | Man City U23 | Unknown | Data unavailable |
| 1 Jul 2017 | Man City Youth | Man City U18 | 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.