Victor Sá
Value picture
Where the market, the likely price and the model estimates sit.
The model rates Victor Sá's performance-implied value at EUR 2.0M-3.1M, above the estimated acquisition range of EUR 0.7M-2.2M. Its 24-month market value projection is EUR 0.6M-1.4M. Context-adjusted goal contributions are 0.68x the positional average in the league (1643 minutes, league coefficient 0.56). Confidence is medium: no event-level data (xG, progressive actions) is available from the current free source, and injury history is unavailable.
Bargain Index: 9
What drives the score. Not a black box: each component and its level.
| Performance | Above average | 62th pct among ATT peers (age-neutral) |
| Age | Unfavorable | 32.3 years |
| Acquisition cost | Very favorable | est. €1.4M vs market €2.5M |
| Expected appreciation | Very low | -63% over ~2 years (model) |
| Value gap | Large | weak predictor on its own |
| Development | Low | |
| League translation | Moderate | league coefficient 0.56 |
| Tactical fit | Not evaluated | Requires event/role data not available from the current free source. |
| Risk | Very High | |
| 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
- 1643 (61% of available)
- Goals + assists / 90
- 0.33
- Context-adjusted G+A / 90
- 0.28
- xG, xA, progression
- Data unavailable
- Defensive actions
- Data unavailable
- Performance percentile
- 62 (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
- 0.56 (rank 7)
- Team strength (Elo)
- 1536
- Opponent strength
- 1335
- 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)
- €593k – €1.4M
Historical comparables (8): median 2-year value change -69% (IQR -81% to -58%).
Risk profile
Overall: Very High
- ▲Age 32: resale value typically declines.
- ⚠Limited sample: 1643 league minutes last season.
- ⚠League level coefficient 0.56 (1.00 = big-five average): output may not translate.
- ⚠No continental-competition minutes last season.
- ⚠League level estimated mainly from squad values (few matches vs other leagues).
- ⚠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).
- Historical movers
- 20 (ATT)
- Relative output kept
- 76%–104%
- Held a regular role (900+ min)
- 50%
- 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 | FK Krasnodar | Premier Liga | LW | 22 | 1643 | 61% | 5 | 1 | 0.33 | 0.28 | 1335 | 1536 | 0.56 |
| 2024/25 | FK Krasnodar | Premier Liga | LW | 25 | 1901 | 70% | 4 | 4 | 0.38 | 0.30 | 1293 | 1511 | 0.55 |
| 2023/24 | FK Krasnodar | Premier Liga | LW | 12 | 809 | 30% | 1 | 2 | 0.33 | 0.34 | 1318 | 1405 | 0.49 |
| 2020/21 | VfL Wolfsburg | Bundesliga | LW | 21 | 458 | 15% | 1 | 1 | 0.39 | 0.36 | 1586 | 1613 | 1.06 |
| 2019/20 | VfL Wolfsburg | Bundesliga | LW | 32 | 1631 | 53% | 2 | 2 | 0.22 | 0.28 | 1552 | 1562 | 1.06 |
Cups & continental competitions
| Season | Competition | Club | Apps | Min | G | A | Avg opp. Elo |
|---|---|---|---|---|---|---|---|
| 2025/26 | Russian Cup | FK Krasnodar | 8 | 270 | 0 | 0 | — |
| 2025/26 | Russian Super Cup | FK Krasnodar | 1 | 75 | 0 | 0 | 1478 |
| 2024/25 | Russian Super Cup | FK Krasnodar | 1 | 90 | 0 | 1 | 1491 |
| 2024/25 | Russian Cup | FK Krasnodar | 3 | 89 | 0 | 0 | — |
| 2023/24 | Russian Cup | FK Krasnodar | 1 | 45 | 0 | 0 | — |
| 2020/21 | Dfb Pokal | VfL Wolfsburg | 4 | 220 | 3 | 2 | — |
| 2020/21 | Uefa Europa League Qualifying | VfL Wolfsburg | 2 | 106 | 0 | 1 | 1378 |
| 2019/20 | Uefa Europa League | VfL Wolfsburg | 7 | 397 | 3 | 0 | 1533 |
| 2019/20 | Dfb Pokal | VfL Wolfsburg | 2 | 180 | 0 | 2 | — |
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 |
|---|---|---|---|---|
| Eran Zahavi Eredivisie · 2021 | 34 | €2.5M | €1.2M | fell |
| Ruben Schaken Eredivisie · 2014 | 32 | €2.0M | €800k | fell |
| Djalma Super League 1 · 2018 | 31 | €2.0M | €650k | fell |
| Mehmet Batdal Super Lig · 2017 | 31 | €1.5M | €200k | fell |
| Oleg Gusev Premier Liga · 2014 | 31 | €4.0M | €2.0M | fell |
| Júnior Moraes Premier Liga · 2021 | 34 | €2.0M | €400k | fell |
| Bryan Ruiz Liga Portugal · 2017 | 32 | €5.0M | €1.5M | fell |
| Ryan Babel Super Lig · 2020 | 34 | €2.0M | €350k | fell |
Transfer history
Fees as reported by the source; undisclosed fees shown as unknown, never estimated.
| Date | From | To | Fee | MV at time |
|---|---|---|---|---|
| 22 Feb 2024 | Botafogo | Krasnodar | €1.5M | €1.8M |
| 25 Mar 2022 | Al-Jazira | Botafogo | €2.3M | €2.5M |
| 16 Aug 2021 | Wolfsburg | Al-Jazira | €2.9M | €3.0M |
| 1 Jul 2019 | LASK | Wolfsburg | €3.5M | €3.0M |
| 21 Jul 2017 | SV Kapfenberg | LASK | Free / loan | €400k |
| 15 Jul 2015 | São José-SP | SV Kapfenberg | Unknown | Data unavailable |
| 1 Mar 2014 | Palmeiras U20 | São José-SP | 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.