Lechi Sadulaev
Value picture
Where the market, the likely price and the model estimates sit.
The model rates Lechi Sadulaev's performance-implied value at EUR 0.8M-2.2M, below the estimated acquisition range of EUR 1.0M-2.9M. Its 24-month market value projection is EUR 1.4M-3.4M. Context-adjusted goal contributions are 0.68x the positional average in the league (2076 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: 73
What drives the score. Not a black box: each component and its level.
| Performance | Below average | 29.9th pct among ATT peers (age-neutral) |
| Age | Neutral | 26.5 years |
| Acquisition cost | Very favorable | est. €1.7M vs market €3.0M |
| Expected appreciation | Very low | -31% over ~2 years (model) |
| Value gap | Very 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 | 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
- 2076 (77% of available)
- Goals + assists / 90
- 0.13
- Context-adjusted G+A / 90
- 0.28
- xG, xA, progression
- Data unavailable
- Defensive actions
- Data unavailable
- Performance percentile
- 30 (Below 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 10)
- Team strength (Elo)
- 1279
- Opponent strength
- 1346
- 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)
- €1.4M – €3.4M
Trajectory drivers: relative output down 23% vs positional peers.
Historical comparables (8): median 2-year value change -1% (IQR -24% to +9%).
Risk profile
Overall: High
- ⚠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 | RFK Akhmat Grozny | Premier Liga | LW | 28 | 2076 | 77% | 1 | 2 | 0.13 | 0.28 | 1346 | 1279 | 0.56 |
| 2024/25 | RFK Akhmat Grozny | Premier Liga | LW | 28 | 2296 | 85% | 3 | 4 | 0.27 | 0.37 | 1300 | 1248 | 0.55 |
| 2023/24 | RFK Akhmat Grozny | Premier Liga | LW | 14 | 767 | 28% | 2 | 0 | 0.23 | 0.41 | 1347 | 1273 | 0.49 |
| 2022/23 | RFK Akhmat Grozny | Premier Liga | RM | 22 | 1396 | 52% | 2 | 4 | 0.39 | 0.41 | 1322 | 1364 | 0.53 |
| 2021/22 | RFK Akhmat Grozny | Premier Liga | LM | 16 | 547 | 20% | 1 | 0 | 0.16 | 0.29 | 1347 | 1300 | 0.52 |
| 2020/21 | RFK Akhmat Grozny | Premier Liga | AM | 18 | 373 | 14% | 1 | 1 | 0.48 | 0.43 | 1316 | 1307 | 0.55 |
| 2019/20 | RFK Akhmat Grozny | Premier Liga | LM | 6 | 210 | 8% | 0 | 0 | 0.00 | 0.29 | 1393 | 1312 | 0.63 |
Cups & continental competitions
| Season | Competition | Club | Apps | Min | G | A | Avg opp. Elo |
|---|---|---|---|---|---|---|---|
| 2025/26 | Russian Cup | RFK Akhmat Grozny | 5 | 285 | 1 | 1 | — |
| 2024/25 | Russian Cup | RFK Akhmat Grozny | 4 | 178 | 0 | 0 | — |
| 2023/24 | Russian Cup | RFK Akhmat Grozny | 1 | 45 | 0 | 0 | — |
| 2022/23 | Russian Cup | RFK Akhmat Grozny | 6 | 349 | 3 | 2 | — |
| 2021/22 | Russian Cup | RFK Akhmat Grozny | 2 | 122 | 0 | 0 | — |
| 2020/21 | Russian Cup | RFK Akhmat Grozny | 2 | 124 | 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 |
|---|---|---|---|---|
| Kevin Nisbet Scottish Premiership · 2022 | 25 | €2.0M | €2.5M | rose |
| Kerim Mrabti Jupiler Pro League · 2021 | 27 | €2.4M | €2.5M | flat |
| Ömer Ali Şahiner Super Lig · 2017 | 25 | €4.0M | €2.0M | fell |
| Nikola Storm Jupiler Pro League · 2023 | 29 | €2.5M | €2.0M | fell |
| Saviour Godwin Liga Portugal · 2023 | 27 | €1.5M | €2.3M | rose |
| Deni Milosevic Super Lig · 2019 | 24 | €2.0M | €1.9M | flat |
| Louka Prip Superliga · 2023 | 26 | €1.5M | €1.0M | fell |
| Søren Rieks Eredivisie · 2013 | 26 | €1.3M | €1.3M | flat |
Transfer history
Fees as reported by the source; undisclosed fees shown as unknown, never estimated.
| Date | From | To | Fee | MV at time |
|---|---|---|---|---|
| 1 Jul 2019 | Akhmat II | Akhmat Grozny | Unknown | €175k |
| 1 Jul 2017 | Akademia Ramzan | Akhmat 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.