Matvey Safonov
Value picture
Where the market, the likely price and the model estimates sit.
The model rates Matvey Safonov's performance-implied value at EUR 13.5M-21.6M, below the estimated acquisition range of EUR 22.7M-38.8M. Its 24-month market value projection is EUR 15.0M-35.5M. Confidence is medium: no event-level data (xG, progressive actions) is available from the current free source, and injury history is unavailable.
Bargain Index: 5
What drives the score. Not a black box: each component and its level.
| Performance | Elite | 90.7th pct among GK peers (age-neutral) |
| Age | Neutral | 27.3 years |
| Acquisition cost | Neutral | est. €31.2M vs market €30.0M |
| Expected appreciation | Very low | -38% over ~2 years (model) |
| Value gap | Very negative | weak predictor on its own |
| Development | Low | |
| League translation | Big-five league | league coefficient 0.88 |
| 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 284 evaluated GK players with 900+ minutes.
Performance (last season, league)
- Minutes
- 1350 (44% of available)
- Goals + assists / 90
- 0.00
- Context-adjusted G+A / 90
- 0.00
- xG, xA, progression
- Data unavailable
- Defensive actions
- Data unavailable
- Performance percentile
- 91 (Elite)
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
- 0.88 (rank 5)
- Team strength (Elo)
- 1818
- Opponent strength
- 1436
- 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)
- €15.0M – €35.5M
Trajectory drivers: minutes share up 15 pts.
Historical comparables (8): median 2-year value change -34% (IQR -59% to -15%).
Risk profile
Overall: Low
- ⚠Limited sample: 1350 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 | Paris Saint-Germain | Ligue 1 | GK | 15 | 1350 | 44% | 0 | 0 | 0.00 | 0.00 | 1436 | 1817 | 0.88 |
| 2024/25 | Paris Saint-Germain | Ligue 1 | GK | 10 | 878 | 29% | 0 | 0 | 0.00 | 0.00 | 1446 | 1812 | 0.88 |
| 2023/24 | FK Krasnodar | Premier Liga | GK | 30 | 2655 | 98% | 0 | 1 | 0.03 | 0.01 | 1329 | 1405 | 0.49 |
| 2022/23 | FK Krasnodar | Premier Liga | GK | 27 | 2430 | 90% | 0 | 0 | 0.00 | 0.00 | 1306 | 1405 | 0.53 |
| 2021/22 | FK Krasnodar | Premier Liga | GK | 27 | 2430 | 90% | 0 | 0 | 0.00 | 0.00 | 1355 | 1371 | 0.52 |
| 2020/21 | FK Krasnodar | Premier Liga | GK | 21 | 1890 | 70% | 0 | 0 | 0.00 | 0.00 | 1354 | 1357 | 0.55 |
| 2019/20 | FK Krasnodar | Premier Liga | GK | 27 | 2358 | 87% | 0 | 0 | 0.00 | 0.00 | 1391 | 1465 | 0.63 |
| 2018/19 | FK Krasnodar | Premier Liga | GK | 12 | 1080 | 40% | 0 | 0 | 0.00 | 0.00 | 1412 | 1543 | 0.69 |
| 2017/18 | FK Krasnodar | Premier Liga | GK | 1 | 90 | 3% | 0 | 0 | 0.00 | 0.00 | 1289 | 1522 | 0.75 |
Cups & continental competitions
| Season | Competition | Club | Apps | Min | G | A | Avg opp. Elo |
|---|---|---|---|---|---|---|---|
| 2025/26 | Uefa Champions League | Paris Saint-Germain | 10 | 900 | 0 | 1 | 1689 |
| 2024/25 | Uefa Champions League | Paris Saint-Germain | 2 | 180 | 0 | 0 | 1676 |
| 2022/23 | Russian Cup | FK Krasnodar | 6 | 540 | 0 | 0 | — |
| 2020/21 | Uefa Champions League | FK Krasnodar | 4 | 360 | 0 | 0 | 1589 |
| 2020/21 | Uefa Champions League Qualifying | FK Krasnodar | 2 | 180 | 0 | 0 | 1519 |
| 2019/20 | Uefa Champions League Qualifying | FK Krasnodar | 3 | 270 | 0 | 0 | 1650 |
| 2019/20 | Uefa Europa League | FK Krasnodar | 3 | 270 | 0 | 0 | 1547 |
| 2018/19 | Uefa Europa League | FK Krasnodar | 3 | 270 | 0 | 0 | 1638 |
| 2018/19 | Russian Cup | FK Krasnodar | 1 | 90 | 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 |
|---|---|---|---|---|
| Andriy Lunin Laliga · 2024 | 25 | €25.0M | €12.0M | fell |
| André Onana Serie A · 2023 | 27 | €35.0M | €25.0M | fell |
| Loris Karius Premier League · 2018 | 25 | €12.0M | €3.2M | fell |
| Alphonse Areola Laliga · 2020 | 27 | €13.0M | €8.0M | fell |
| Iker Casillas Laliga · 2013 | 32 | €30.0M | €6.0M | fell |
| Wojciech Szczesny Serie A · 2019 | 29 | €35.0M | €25.0M | fell |
| Caoimhín Kelleher Premier League · 2024 | 26 | €20.0M | €28.0M | rose |
| Neto Serie A · 2017 | 28 | €6.0M | €18.0M | rose |
Transfer history
Fees as reported by the source; undisclosed fees shown as unknown, never estimated.
| Date | From | To | Fee | MV at time |
|---|---|---|---|---|
| 1 Jul 2024 | Krasnodar | PSG | €20.0M | €20.0M |
| 1 Jan 2019 | Krasnodar 2 | Krasnodar | Unknown | €900k |
| 1 Jul 2018 | Krasnodar II | Krasnodar 2 | Unknown | €300k |
| 1 Jul 2015 | Krasnodar U17 | Krasnodar II | 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.