Konan N’Dri
Value picture
Where the market, the likely price and the model estimates sit.
The model rates Konan N’Dri's performance-implied value at EUR 1.8M-2.7M, above the estimated acquisition range of EUR 0.5M-1.5M. Its 24-month market value projection is EUR 0.6M-2.1M. Context-adjusted goal contributions are 1.48x the positional average in the league (521 minutes, league coefficient 0.93). Confidence is low: no event-level data (xG, progressive actions) is available from the current free source, and injury history is unavailable.
Acquisition note: Final year of contract: selling club's leverage is reduced.
Bargain Index: not assigned
What drives the score. Not a black box: each component and its level.
Insufficient sample (< 900 league minutes)
| Performance | Average | 45.7th pct among ATT peers (age-neutral) |
| Age | Neutral | 25.7 years |
| Acquisition cost | Very favorable | est. €712k vs market €1.5M |
| Expected appreciation | Low | -18% over ~2 years (model) |
| Value gap | Very large | weak predictor on its own |
| Development | Low | |
| League translation | Big-five league | league coefficient 0.93 |
| Tactical fit | Not evaluated | Requires event/role data not available from the current free source. |
| Risk | Very High | |
| Data confidence | Low |
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 901 evaluated ATT players with 900+ minutes.
Performance (last season, league)
- Minutes
- 521 (15% of available)
- Goals + assists / 90
- 0.34
- Context-adjusted G+A / 90
- 0.58
- xG, xA, progression
- Data unavailable
- Defensive actions
- Data unavailable
- Performance percentile
- 46 (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
- 0.93 (rank 4)
- Team strength (Elo)
- 1352
- Opponent strength
- 1528
- 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)
- €627k – €2.1M
Trajectory drivers: minutes share down 40 pts; changed club; league level up (+87 Elo); relative output up 52% vs positional peers.
Historical comparables (8): median 2-year value change +10% (IQR -31% to +61%).
Risk profile
Overall: Very High
- ▲Small sample: 521 league minutes last season.
- ⚠Contract ends in 12 months: cheaper, but competition and wage demands likely.
- ⚠Projection range wider than for most evaluated players.
- ⚠Played under 40% of available league minutes (injury data unavailable, cause unknown).
- ⚠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 | US Lecce | Serie A | RW | 27 | 521 | 15% | 2 | 0 | 0.35 | 0.58 | 1528 | 1352 | 0.93 |
| 2024/25 | Oud-Heverlee Leuven | Jupiler Pro League | RW | 24 | 1593 | 59% | 3 | 2 | 0.28 | 0.41 | 1374 | 1323 | 0.63 |
| 2024/25 | US Lecce | Serie A | RW | 11 | 299 | 9% | 0 | 0 | 0.00 | 0.46 | 1459 | 1351 | 0.99 |
| 2023/24 | Oud-Heverlee Leuven | Jupiler Pro League | RW | 19 | 497 | 18% | 0 | 1 | 0.18 | 0.39 | 1396 | 1302 | 0.63 |
| 2022/23 | KAS Eupen | Jupiler Pro League | RW | 33 | 2098 | 69% | 7 | 5 | 0.51 | 0.60 | 1308 | 1171 | 0.58 |
| 2021/22 | KAS Eupen | Jupiler Pro League | RW | 31 | 1514 | 49% | 1 | 2 | 0.18 | 0.26 | 1350 | 1174 | 0.49 |
| 2020/21 | KAS Eupen | Jupiler Pro League | RW | 33 | 2130 | 70% | 1 | 4 | 0.21 | 0.29 | 1367 | 1285 | 0.55 |
| 2019/20 | KAS Eupen | Jupiler Pro League | RW | 3 | 35 | 1% | 0 | 0 | 0.00 | 0.55 | 1384 | 1247 | 0.63 |
| 2018/19 | KAS Eupen | Jupiler Pro League | RW | 1 | 14 | 1% | 0 | 0 | 0.00 | 0.54 | 1393 | 1253 | 0.65 |
Cups & continental competitions
| Season | Competition | Club | Apps | Min | G | A | Avg opp. Elo |
|---|---|---|---|---|---|---|---|
| 2025/26 | Italy Cup | US Lecce | 1 | 22 | 0 | 0 | — |
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 |
|---|---|---|---|---|
| Casimir Ninga Ligue 1 · 2017 | 24 | €2.5M | €3.0M | rose |
| Daniele Verde Serie A · 2021 | 25 | €1.5M | €3.0M | rose |
| Luis Suárez Laliga · 2024 | 27 | €3.5M | €30.0M | rose |
| Uffe Bech Bundesliga · 2016 | 23 | €1.8M | €300k | fell |
| Mariusz Stępiński Serie A · 2020 | 25 | €3.0M | €2.5M | flat |
| Hervé Bazile Ligue 1 · 2017 | 27 | €1.5M | €600k | fell |
| Irvin Cardona Bundesliga · 2023 | 26 | €2.5M | €2.5M | flat |
| Nils Petersen Bundesliga · 2015 | 27 | €3.0M | €4.5M | rose |
Transfer history
Fees as reported by the source; undisclosed fees shown as unknown, never estimated.
| Date | From | To | Fee | MV at time |
|---|---|---|---|---|
| 2 Feb 2025 | OH Leuven | Lecce | €1.5M | €1.5M |
| 21 Jul 2023 | KAS Eupen | OH Leuven | €1.3M | €2.0M |
| 31 Jan 2019 | ASPIRE FD | KAS Eupen | 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.