Bertuğ Yıldırım
Value picture
Where the market, the likely price and the model estimates sit.
The model rates Bertuğ Yıldırım's performance-implied value at EUR 1.0M-2.8M, below the estimated acquisition range of EUR 4.8M-11.7M. Its 24-month market value projection is EUR 2.6M-12.9M. Context-adjusted goal contributions are 1.58x the positional average in the league (556 minutes, league coefficient 0.40). Confidence is low: no event-level data (xG, progressive actions) is available from the current free source, and injury history is unavailable.
Bargain Index: not assigned
What drives the score. Not a black box: each component and its level.
Insufficient sample (< 900 league minutes)
| Performance | Average | 39.3th pct among ATT peers (age-neutral) |
| Age | Favorable | 24 years |
| Acquisition cost | Neutral | est. €7.3M vs market €7.0M |
| Expected appreciation | Low | -13% over ~2 years (model) |
| Value gap | Very negative | weak predictor on its own |
| Development | Low | |
| League translation | Uncertain | league coefficient 0.4 |
| 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
- 556 (18% of available)
- Goals + assists / 90
- 1.46
- Context-adjusted G+A / 90
- 0.81
- xG, xA, progression
- Data unavailable
- Defensive actions
- Data unavailable
- Performance percentile
- 39 (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
- 0.40 (rank 5)
- Team strength (Elo)
- 1357
- Opponent strength
- 1300
- 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)
- €2.6M – €12.9M
Trajectory drivers: changed club; league level down (-220 Elo); relative output up 159% vs positional peers.
Historical comparables (8): median 2-year value change -18% (IQR -35% to +3%).
Risk profile
Overall: Very High
- ▲Small sample: 556 league minutes last season.
- ▲Projection range among the widest 10% of evaluated players.
- ⚠League level coefficient 0.40 (1.00 = big-five average): output may not translate.
- ⚠No continental-competition minutes last season.
- ⚠Played under 40% of available league minutes (injury data unavailable, cause unknown).
- ⓘ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
- 53 (ATT)
- Relative output kept
- 79%–133%
- Held a regular role (900+ min)
- 58%
- 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 | Basaksehir FK | Super Lig | ST | 19 | 554 | 18% | 7 | 2 | 1.46 | 0.80 | 1300 | 1357 | 0.40 |
| 2025/26 | Stade Rennais FC | Ligue 1 | ST | 1 | 2 | 0% | 0 | 0 | 0.00 | 0.36 | 1290 | 1520 | 0.88 |
| 2024/25 | Getafe CF | Laliga | ST | 22 | 1087 | 32% | 1 | 0 | 0.08 | 0.32 | 1481 | 1395 | 0.95 |
| 2023/24 | Stade Rennais FC | Ligue 1 | ST | 21 | 453 | 15% | 0 | 2 | 0.40 | 0.40 | 1449 | 1507 | 0.87 |
| 2023/24 | Hatayspor | Super Lig | ST | 3 | 260 | 8% | 1 | 0 | 0.35 | 0.74 | 1261 | 1163 | 0.45 |
| 2022/23 | Hatayspor | Super Lig | ST | 16 | 747 | 23% | 4 | 2 | 0.72 | 0.68 | 1304 | 1076 | 0.50 |
| 2022/23 | Antalyaspor | Super Lig | ST | 13 | 361 | 11% | 2 | 1 | 0.75 | 0.53 | 1240 | 1274 | 0.50 |
| 2021/22 | Hatayspor | Super Lig | ST | 16 | 202 | 6% | 1 | 1 | 0.89 | 0.68 | 1315 | 1252 | 0.42 |
Cups & continental competitions
| Season | Competition | Club | Apps | Min | G | A | Avg opp. Elo |
|---|---|---|---|---|---|---|---|
| 2024/25 | Copa Del Rey | Getafe CF | 4 | 312 | 2 | 1 | — |
| 2023/24 | Uefa Europa League | Stade Rennais FC | 5 | 280 | 1 | 0 | 1500 |
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 |
|---|---|---|---|---|
| Wilson Isidor Premier Liga · 2022 | 22 | €6.0M | €4.0M | fell |
| Yassine El Ghanassy Eredivisie · 2013 | 23 | €2.5M | €1.5M | fell |
| Ronaldo Premier Liga · 2024 | 24 | €3.5M | €3.5M | flat |
| Trezeguet Super Lig · 2022 | 28 | €10.0M | €8.5M | flat |
| Henry Onyekuru Super Lig · 2021 | 24 | €7.0M | €5.5M | fell |
| Emrah Başsan Super Lig · 2016 | 24 | €4.6M | €1.3M | fell |
| Moses Simon Jupiler Pro League · 2015 | 20 | €4.0M | €4.5M | flat |
| Franculino Superliga · 2024 | 20 | €7.5M | €22.0M | rose |
Transfer history
Fees as reported by the source; undisclosed fees shown as unknown, never estimated.
| Date | From | To | Fee | MV at time |
|---|---|---|---|---|
| 12 Sept 2025 | Stade Rennais | Basaksehir | €2.2M | €2.0M |
| 30 Jun 2025 | Getafe | Stade Rennais | Free / loan | €2.0M |
| 23 Aug 2024 | Stade Rennais | Getafe | Free / loan | €3.5M |
| 29 Aug 2023 | Hatayspor | Stade Rennais | €5.4M | €2.7M |
| 30 Jun 2023 | Antalyaspor | Hatayspor | Free / loan | €2.7M |
| 17 Feb 2023 | Hatayspor | Antalyaspor | Free / loan | €700k |
| 1 Jul 2021 | Sariyer | Hatayspor | Free / loan | €150k |
| 4 Aug 2020 | Sariyer Yth | Sariyer | Unknown | €25k |
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.