
Eduard Spertsyan
Value picture
Where the market, the likely price and the model estimates sit.
The model rates Eduard Spertsyan's performance-implied value at EUR 8.7M-29.2M, below the estimated acquisition range of EUR 14.6M-26.6M. Its 24-month market value projection is EUR 15.1M-31.2M. Context-adjusted goal contributions are 1.48x the positional average in the league (2545 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: 63
What drives the score. Not a black box: each component and its level.
| Performance | High | 85.4th pct among MID peers (age-neutral) |
| Age | Neutral | 26.1 years |
| Acquisition cost | Favorable | est. €20.1M vs market €25.0M |
| Expected appreciation | Neutral | -9% over ~2 years (model) |
| Value gap | Negative | 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 | Moderate | |
| 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
- 2545 (94% of available)
- Goals + assists / 90
- 1.06
- Context-adjusted G+A / 90
- 0.60
- xG, xA, progression
- Data unavailable
- Defensive actions
- Data unavailable
- Performance percentile
- 85 (High)
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 10)
- Team strength (Elo)
- 1536
- Opponent strength
- 1334
- 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)
- €15.1M – €31.2M
Historical comparables (8): median 2-year value change +18% (IQR -7% to +59%).
Risk profile
Overall: Moderate
- ⚠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).
- ⓘ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
- 18 (MID)
- Relative output kept
- 54%–153%
- Held a regular role (900+ min)
- 56%
- Confidence
- Low
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 | AM | 30 | 2545 | 94% | 13 | 17 | 1.06 | 0.60 | 1334 | 1536 | 0.56 |
| 2024/25 | FK Krasnodar | Premier Liga | AM | 28 | 2296 | 85% | 11 | 7 | 0.71 | 0.46 | 1301 | 1511 | 0.55 |
| 2023/24 | FK Krasnodar | Premier Liga | AM | 29 | 2526 | 94% | 11 | 7 | 0.64 | 0.49 | 1327 | 1405 | 0.49 |
| 2022/23 | FK Krasnodar | Premier Liga | AM | 28 | 2369 | 88% | 10 | 12 | 0.84 | 0.53 | 1310 | 1405 | 0.53 |
| 2021/22 | FK Krasnodar | Premier Liga | AM | 25 | 2004 | 74% | 8 | 4 | 0.54 | 0.44 | 1345 | 1371 | 0.52 |
| 2020/21 | FK Krasnodar | Premier Liga | AM | 5 | 139 | 5% | 0 | 0 | 0.00 | 0.28 | 1351 | 1357 | 0.55 |
Cups & continental competitions
| Season | Competition | Club | Apps | Min | G | A | Avg opp. Elo |
|---|---|---|---|---|---|---|---|
| 2025/26 | Russian Cup | FK Krasnodar | 10 | 530 | 1 | 1 | — |
| 2025/26 | Russian Super Cup | FK Krasnodar | 1 | 90 | 0 | 0 | 1478 |
| 2024/25 | Russian Cup | FK Krasnodar | 6 | 171 | 0 | 0 | — |
| 2024/25 | Russian Super Cup | FK Krasnodar | 1 | 90 | 0 | 0 | 1491 |
| 2023/24 | Russian Cup | FK Krasnodar | 4 | 198 | 0 | 0 | — |
| 2022/23 | Russian Cup | FK Krasnodar | 10 | 804 | 3 | 1 | — |
| 2021/22 | Russian Cup | FK Krasnodar | 2 | 90 | 0 | 0 | — |
| 2020/21 | Uefa Champions League | FK Krasnodar | 2 | 39 | 0 | 0 | 1680 |
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 |
|---|---|---|---|---|
| Bryan Heynen Jupiler Pro League · 2023 | 26 | €11.0M | €11.0M | flat |
| Davy Klaassen Eredivisie · 2017 | 24 | €18.0M | €22.0M | rose |
| Danilo Pereira Liga Portugal · 2017 | 26 | €20.0M | €30.0M | rose |
| Hans Vanaken Jupiler Pro League · 2019 | 27 | €11.5M | €13.0M | flat |
| Ruslan Malinovskyi Jupiler Pro League · 2019 | 26 | €12.5M | €30.0M | rose |
| Wendel Premier Liga · 2024 | 27 | €20.0M | €15.0M | fell |
| Lucas Biglia Jupiler Pro League · 2013 | 27 | €8.0M | €15.0M | rose |
| Tonny Vilhena Eredivisie · 2019 | 24 | €14.0M | €8.0M | fell |
Transfer history
Fees as reported by the source; undisclosed fees shown as unknown, never estimated.
| Date | From | To | Fee | MV at time |
|---|---|---|---|---|
| 1 Jul 2021 | Krasnodar 2 | Krasnodar | Unknown | €800k |
| 1 Jul 2019 | Krasnodar 3 | Krasnodar 2 | Unknown | €175k |
| 1 Jul 2018 | Krasnodar II | Krasnodar 3 | Unknown | €50k |
| 5 Feb 2018 | 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.