Niklas Beste
Value picture
Where the market, the likely price and the model estimates sit.
The model rates Niklas Beste's performance-implied value at EUR 4.5M-11.0M, below the estimated acquisition range of EUR 6.0M-12.4M. Its 24-month market value projection is EUR 3.6M-10.3M. Context-adjusted goal contributions are 0.70x the positional average in the league (1710 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: 7
What drives the score. Not a black box: each component and its level.
| Performance | Above average | 70th pct among ATT peers (age-neutral) |
| Age | Neutral | 27.5 years |
| Acquisition cost | Neutral | est. €10.0M vs market €10.0M |
| Expected appreciation | Very low | -37% over ~2 years (model) |
| Value gap | Very negative | weak predictor on its own |
| Development | Low | |
| 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 900 evaluated ATT players with 900+ minutes.
Performance (last season, league)
- Minutes
- 1710 (56% of available)
- Goals + assists / 90
- 0.26
- Context-adjusted G+A / 90
- 0.27
- xG, xA, progression
- Data unavailable
- Defensive actions
- Data unavailable
- Performance percentile
- 70 (Above 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
- 0.94 (rank 3)
- Team strength (Elo)
- 1551
- Opponent strength
- 1487
- Possession, role
- Data unavailable
League strength 1.00 = average club of the big-five leagues. Elo includes continental matches.
Potential
- Development
- Low
- Trajectory
- improving
- Breakout probability
- Age > 23
- Resale range (24m)
- €3.6M – €10.3M
Trajectory drivers: minutes share up 33 pts; changed club; league level up (+114 Elo); role change LW -> RW.
Historical comparables (8): median 2-year value change -46% (IQR -64% to -9%).
Risk profile
Overall: Low
- ⚠Limited sample: 1710 league 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).
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 | SC Freiburg | Bundesliga | RW | 28 | 1710 | 56% | 2 | 3 | 0.26 | 0.27 | 1487 | 1551 | 0.94 |
| 2024/25 | SL Benfica | Liga Portugal | LW | 13 | 495 | 16% | 0 | 1 | 0.18 | 0.20 | 1299 | 1632 | 0.51 |
| 2024/25 | SC Freiburg | Bundesliga | LW | 12 | 200 | 7% | 0 | 1 | 0.45 | 0.38 | 1473 | 1505 | 0.94 |
| 2023/24 | 1. Fußballclub Heidenheim 1846 | Bundesliga | LW | 31 | 2629 | 86% | 8 | 13 | 0.72 | 0.73 | 1518 | 1424 | 1.01 |
| 2019/20 | FC Emmen | Eredivisie | LB | 6 | 410 | 18% | 0 | 1 | 0.22 | 0.17 | 1362 | 1285 | 0.62 |
Cups & continental competitions
| Season | Competition | Club | Apps | Min | G | A | Avg opp. Elo |
|---|---|---|---|---|---|---|---|
| 2025/26 | Uefa Europa League | SC Freiburg | 14 | 1030 | 1 | 4 | 1492 |
| 2025/26 | Dfb Pokal | SC Freiburg | 5 | 373 | 0 | 0 | — |
| 2024/25 | Uefa Champions League | SL Benfica | 5 | 109 | 0 | 1 | 1660 |
| 2023/24 | Dfb Pokal | 1. Fußballclub Heidenheim 1846 | 1 | 64 | 0 | 1 | — |
| 2017/18 | Dfb Pokal | Borussia Dortmund | 1 | 90 | 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 |
|---|---|---|---|---|
| Adnan Januzaj Laliga · 2022 | 27 | €12.0M | €2.0M | fell |
| Ibrahima Traoré Bundesliga · 2016 | 28 | €6.0M | €3.5M | fell |
| Roland Sallai Bundesliga · 2024 | 27 | €15.0M | €14.0M | flat |
| Loïs Diony Ligue 1 · 2019 | 27 | €5.0M | €2.5M | fell |
| Ihlas Bebou Bundesliga · 2020 | 26 | €8.0M | €16.0M | rose |
| Alan Kasaev Premier Liga · 2015 | 29 | €4.5M | €1.8M | fell |
| Loren Morón Laliga · 2019 | 26 | €10.0M | €9.0M | flat |
| Arkadiusz Milik Serie A · 2023 | 29 | €8.0M | €2.0M | fell |
Transfer history
Fees as reported by the source; undisclosed fees shown as unknown, never estimated.
| Date | From | To | Fee | MV at time |
|---|---|---|---|---|
| 31 Jan 2025 | Benfica | Freiburg | €8.0M | €14.0M |
| 11 Jul 2024 | Heidenheim | Benfica | €8.0M | €15.0M |
| 1 Jul 2022 | Werder Bremen | Heidenheim | €1.8M | €1.2M |
| 30 Jun 2022 | J. Regensburg | Werder Bremen | Free / loan | €1.2M |
| 23 Jul 2020 | Werder Bremen | J. Regensburg | Free / loan | €350k |
| 30 Jun 2020 | FC Emmen | Werder Bremen | Free / loan | €350k |
| 1 Jul 2019 | Werder Bremen | FC Emmen | Free / loan | €500k |
| 4 Jul 2018 | B. Dortmund U19 | Werder Bremen | €250k | Data unavailable |
| 1 Jul 2016 | B. Dortmund U17 | B. Dortmund U19 | Unknown | Data unavailable |
| 1 Oct 2014 | Dortmund Yth. | B. Dortmund U17 | Unknown | Data unavailable |
| 1 Jul 2007 | Hamm. SpVg Yth. | Dortmund Yth. | 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.