Yira Sor
Value picture
Where the market, the likely price and the model estimates sit.
The model rates Yira Sor's performance-implied value at EUR 1.7M-5.1M, above the estimated acquisition range of EUR 0.8M-2.1M. Its 24-month market value projection is EUR 0.9M-3.0M. Context-adjusted goal contributions are 0.77x the positional average in the league (1045 minutes, league coefficient 0.65). Confidence is medium: no event-level data (xG, progressive actions) is available from the current free source, and injury history is unavailable.
Acquisition note: Final year of contract: selling club's leverage is reduced.
Bargain Index: 81
What drives the score. Not a black box: each component and its level.
| Performance | Average | 46.5th pct among ATT peers (age-neutral) |
| Age | Neutral | 25.9 years |
| Acquisition cost | Very favorable | est. €1.0M vs market €2.5M |
| Expected appreciation | Very low | -31% over ~2 years (model) |
| Value gap | Very large | weak predictor on its own |
| Development | Low | |
| League translation | Moderate | league coefficient 0.65 |
| 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
- 1045 (39% of available)
- Goals + assists / 90
- 0.43
- Context-adjusted G+A / 90
- 0.31
- xG, xA, progression
- Data unavailable
- Defensive actions
- Data unavailable
- Performance percentile
- 47 (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
- 0.65 (rank 6)
- Team strength (Elo)
- 1471
- Opponent strength
- 1363
- Possession, role
- Data unavailable
League strength 1.00 = average club of the big-five leagues. Elo includes continental matches.
Potential
- Development
- Low
- Trajectory
- stable
- Breakout probability
- Age > 23
- Resale range (24m)
- €872k – €3.0M
Trajectory drivers: minutes share up 17 pts; team strength down (-64 Elo); relative output down 27% vs positional peers.
Historical comparables (8): median 2-year value change -21% (IQR -33% to +53%).
Risk profile
Overall: High
- ⚠Limited sample: 1045 league minutes last season.
- ⚠Contract ends in 12 months: cheaper, but competition and wage demands likely.
- ⚠Projection range wider than for most evaluated players.
- ⚠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
- 78 (ATT)
- Relative output kept
- 72%–126%
- Held a regular role (900+ min)
- 60%
- 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 | KRC Genk | Jupiler Pro League | LW | 24 | 1045 | 39% | 4 | 1 | 0.43 | 0.31 | 1363 | 1471 | 0.65 |
| 2024/25 | KRC Genk | Jupiler Pro League | LW | 14 | 597 | 22% | 1 | 4 | 0.75 | 0.44 | 1358 | 1535 | 0.63 |
| 2023/24 | KRC Genk | Jupiler Pro League | ST | 20 | 1073 | 40% | 6 | 1 | 0.59 | 0.48 | 1374 | 1459 | 0.63 |
| 2022/23 | KRC Genk | Jupiler Pro League | RW | 14 | 340 | 11% | 2 | 0 | 0.53 | 0.28 | 1276 | 1495 | 0.58 |
Cups & continental competitions
| Season | Competition | Club | Apps | Min | G | A | Avg opp. Elo |
|---|---|---|---|---|---|---|---|
| 2025/26 | Uefa Europa League | KRC Genk | 11 | 406 | 2 | 0 | 1445 |
| 2025/26 | Uefa Europa League Qualifying | KRC Genk | 2 | 93 | 0 | 1 | 1426 |
| 2025/26 | Jupiler Pro League Europe Play Offs | KRC Genk | 3 | 31 | 0 | 0 | 1333 |
| 2023/24 | Uefa Conference League Qualifying | KRC Genk | 2 | 139 | 0 | 0 | 1404 |
| 2023/24 | Uefa Europa League Qualifying | KRC Genk | 2 | 114 | 0 | 0 | 1454 |
| 2023/24 | Uefa Champions League Qualifying | KRC Genk | 1 | 4 | 0 | 0 | 1464 |
| 2022/23 | Uefa Conference League Qualifying | SK Slavia Prague | 1 | 54 | 2 | 0 | 1175 |
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 |
|---|---|---|---|---|
| Riad Bajic Super Lig · 2019 | 25 | €3.0M | €2.0M | fell |
| Petar Skuletic Premier Liga · 2016 | 26 | €2.8M | €1.0M | fell |
| Ahmed Hassan Liga Portugal · 2018 | 25 | €2.0M | €2.4M | rose |
| Vyacheslav Podberezkin Premier Liga · 2018 | 26 | €2.5M | €1.8M | fell |
| Raul Rusescu Liga Portugal · 2014 | 26 | €3.0M | €2.0M | fell |
| Dieumerci Ndongala Jupiler Pro League · 2018 | 27 | €1.5M | €1.3M | flat |
| Viktor Claesson Premier Liga · 2017 | 25 | €3.0M | €18.0M | rose |
| Oussama Tannane Eredivisie · 2019 | 25 | €1.3M | €4.0M | rose |
Transfer history
Fees as reported by the source; undisclosed fees shown as unknown, never estimated.
| Date | From | To | Fee | MV at time |
|---|---|---|---|---|
| 1 Jan 2023 | Slavia Praha | Genk | €6.5M | €4.0M |
| 21 Jan 2022 | Banik Ostrava | Slavia Praha | €1.2M | €500k |
| 9 Feb 2021 | 36 Lion FC | Banik Ostrava | Unknown | €50k |
| 1 Jan 2019 | Family Love | 36 Lion FC | 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.