
Senne Lynen
Value picture
Where the market, the likely price and the model estimates sit.
The model rates Senne Lynen's performance-implied value at EUR 3.1M-11.9M, below the estimated acquisition range of EUR 3.4M-10.1M. Its 24-month market value projection is EUR 3.4M-8.7M. Context-adjusted goal contributions are 0.46x the positional average in the league (2757 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.
Acquisition note: Contract end date unavailable: range widened by 15% each side.
Bargain Index: 20
What drives the score. Not a black box: each component and its level.
| Performance | Above average | 73.7th pct among MID peers (age-neutral) |
| Age | Neutral | 27.4 years |
| Acquisition cost | Neutral | est. €6.9M vs market €8.0M |
| Expected appreciation | Very low | -34% over ~2 years (model) |
| Value gap | 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 1163 evaluated MID players with 900+ minutes.
Performance (last season, league)
- Minutes
- 2757 (90% of available)
- Goals + assists / 90
- 0.03
- Context-adjusted G+A / 90
- 0.06
- xG, xA, progression
- Data unavailable
- Defensive actions
- Data unavailable
- Performance percentile
- 74 (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)
- 1378
- Opponent strength
- 1500
- 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)
- €3.4M – €8.7M
Trajectory drivers: team strength down (-104 Elo).
Historical comparables (8): median 2-year value change -13% (IQR -38% to +27%).
Risk profile
Overall: Low
- ⓘ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).
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 | SV Werder Bremen | Bundesliga | DM | 31 | 2757 | 90% | 0 | 1 | 0.03 | 0.06 | 1500 | 1378 | 0.94 |
| 2024/25 | SV Werder Bremen | Bundesliga | DM | 32 | 2748 | 90% | 0 | 1 | 0.03 | 0.06 | 1500 | 1483 | 0.94 |
| 2023/24 | SV Werder Bremen | Bundesliga | DM | 32 | 1793 | 59% | 0 | 3 | 0.15 | 0.16 | 1537 | 1459 | 1.01 |
| 2023/24 | Union Saint-Gilloise | Jupiler Pro League | CM | 2 | 180 | 7% | 0 | 0 | 0.00 | 0.13 | 1402 | 1589 | 0.63 |
| 2022/23 | Union Saint-Gilloise | Jupiler Pro League | CM | 32 | 2675 | 87% | 1 | 6 | 0.24 | 0.14 | 1312 | 1571 | 0.58 |
| 2021/22 | Union Saint-Gilloise | Jupiler Pro League | DM | 10 | 507 | 17% | 1 | 3 | 0.71 | 0.30 | 1369 | 1499 | 0.49 |
Cups & continental competitions
| Season | Competition | Club | Apps | Min | G | A | Avg opp. Elo |
|---|---|---|---|---|---|---|---|
| 2025/26 | Dfb Pokal | SV Werder Bremen | 1 | 90 | 0 | 0 | — |
| 2024/25 | Dfb Pokal | SV Werder Bremen | 4 | 360 | 0 | 0 | — |
| 2022/23 | Uefa Europa League | Union Saint-Gilloise | 10 | 890 | 1 | 0 | 1584 |
| 2022/23 | Uefa Champions League Qualifying | Union Saint-Gilloise | 2 | 177 | 0 | 0 | 1611 |
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 |
|---|---|---|---|---|
| Walace Serie A · 2023 | 28 | €7.0M | €4.5M | fell |
| Ylber Ramadani Serie A · 2024 | 28 | €4.0M | €1.8M | fell |
| Maximilian Eggestein Bundesliga · 2024 | 28 | €10.0M | €10.0M | flat |
| Benjamin André Ligue 1 · 2017 | 27 | €4.0M | €10.0M | rose |
| Tomás Pina Laliga · 2013 | 26 | €4.0M | €3.0M | fell |
| Thomas Monconduit Ligue 1 · 2019 | 28 | €3.5M | €2.0M | fell |
| Gary Medel Premier League · 2014 | 27 | €11.0M | €12.0M | flat |
| Mikel Rico Laliga · 2013 | 29 | €4.0M | €8.0M | rose |
Transfer history
Fees as reported by the source; undisclosed fees shown as unknown, never estimated.
| Date | From | To | Fee | MV at time |
|---|---|---|---|---|
| 8 Aug 2023 | Union SG | Werder Bremen | €2.5M | €4.0M |
| 1 Jul 2020 | Telstar | Union SG | Free / loan | €325k |
| 1 Jul 2018 | FC Brügge U21 | Telstar | Free / loan | €75k |
| 30 Jun 2018 | Telstar | FC Brügge U21 | Free / loan | €75k |
| 29 Jan 2018 | FC Brügge U21 | Telstar | Free / loan | Data unavailable |
| 1 Jul 2015 | Club Brugge Yth | FC Brügge U21 | Unknown | Data unavailable |
| 1 Jul 2014 | Lierse SK Yth. | Club Brugge Yth | Free / loan | Data unavailable |
| 1 Jul 2012 | Westerlo Yth. | Lierse SK Yth. | Free / loan | Data unavailable |
| 1 Jul 2009 | Antwerp Yth. | Westerlo 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.