
Jae-sung Lee
Value picture
Where the market, the likely price and the model estimates sit.
The model rates Jae-sung Lee's performance-implied value at EUR 1.4M-3.3M, above the estimated acquisition range of EUR 0.6M-2.4M. Its 24-month market value projection is EUR 0.5M-1.5M. Context-adjusted goal contributions are 0.74x the positional average in the league (2201 minutes, league coefficient 0.94). Confidence is medium: no event-level data (xG, progressive actions) is available from the current free source, and injury history is unavailable.
Acquisition note: Contract end date unavailable: range widened by 15% each side.
Bargain Index: not assigned
What drives the score. Not a black box: each component and its level.
Age above 33 (outside validated pool)
| Performance | High | 85.4th pct among MID peers (age-neutral) |
| Age | Unfavorable | 33.9 years |
| Acquisition cost | Favorable | est. €1.2M vs market €2.0M |
| Expected appreciation | Very low | -54% over ~2 years (model) |
| Value gap | Very large | weak predictor on its own |
| Development | Low | |
| League translation | Big-five league | league coefficient 0.94 |
| 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
- 2201 (72% of available)
- Goals + assists / 90
- 0.24
- Context-adjusted G+A / 90
- 0.30
- 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
- 0.94 (rank 3)
- Team strength (Elo)
- 1485
- Opponent strength
- 1491
- 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)
- €484k – €1.5M
Trajectory drivers: minutes share down 15 pts; relative output down 34% vs positional peers.
Historical comparables (8): median 2-year value change -68% (IQR -76% to -60%).
Risk profile
Overall: Moderate
- ▲Age 34: resale value typically declines.
- ⚠Declining trajectory versus previous season.
- ⓘContract end date unavailable (or stale in source).
- ⓘ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 | 1.FSV Mainz 05 | Bundesliga | AM | 28 | 2201 | 72% | 4 | 2 | 0.25 | 0.30 | 1491 | 1485 | 0.94 |
| 2024/25 | 1.FSV Mainz 05 | Bundesliga | AM | 33 | 2666 | 87% | 7 | 8 | 0.51 | 0.44 | 1510 | 1515 | 0.94 |
| 2023/24 | 1.FSV Mainz 05 | Bundesliga | AM | 29 | 2128 | 70% | 6 | 4 | 0.42 | 0.43 | 1525 | 1489 | 1.01 |
| 2022/23 | 1.FSV Mainz 05 | Bundesliga | AM | 34 | 1904 | 62% | 7 | 4 | 0.52 | 0.40 | 1526 | 1496 | 1.04 |
| 2021/22 | 1.FSV Mainz 05 | Bundesliga | AM | 27 | 1449 | 47% | 4 | 3 | 0.43 | 0.39 | 1507 | 1507 | 1.05 |
Cups & continental competitions
| Season | Competition | Club | Apps | Min | G | A | Avg opp. Elo |
|---|---|---|---|---|---|---|---|
| 2025/26 | Uefa Conference League Qualifying | 1.FSV Mainz 05 | 2 | 172 | 1 | 0 | 1335 |
| 2025/26 | World Cup | South Korea | 2 | 119 | 0 | 0 | — |
| 2025/26 | Dfb Pokal | 1.FSV Mainz 05 | 2 | 75 | 0 | 0 | — |
| 2024/25 | Dfb Pokal | 1.FSV Mainz 05 | 1 | 111 | 0 | 1 | — |
| 2023/24 | Dfb Pokal | 1.FSV Mainz 05 | 2 | 139 | 0 | 0 | — |
| 2022/23 | Dfb Pokal | 1.FSV Mainz 05 | 2 | 110 | 0 | 0 | — |
| 2021/22 | Dfb Pokal | 1.FSV Mainz 05 | 3 | 178 | 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 |
|---|---|---|---|---|
| Nicolas Höfler Bundesliga · 2024 | 34 | €2.0M | €500k | fell |
| Florent Balmont Ligue 1 · 2013 | 33 | €2.0M | €500k | fell |
| Glenn Whelan Premier League · 2017 | 33 | €2.5M | €1.0M | fell |
| Blagoy Georgiev Premier Liga · 2014 | 33 | €1.5M | €300k | fell |
| Ugur Inceman Super Lig · 2013 | 32 | €1.3M | €200k | fell |
| Gareth Barry Premier League · 2016 | 35 | €2.5M | €1.0M | fell |
| Roberto Trashorras Laliga · 2013 | 32 | €2.0M | €1.5M | fell |
| Daniel Baier Bundesliga · 2018 | 34 | €1.0M | €400k | fell |
Transfer history
Fees as reported by the source; undisclosed fees shown as unknown, never estimated.
| Date | From | To | Fee | MV at time |
|---|---|---|---|---|
| 8 Jul 2021 | Holstein Kiel | Mainz | Free / loan | €3.0M |
| 26 Jul 2018 | Jeonbuk Hyundai | Holstein Kiel | €900k | €2.0M |
| 1 Jan 2014 | Korea University | Jeonbuk Hyundai | Free / loan | Data unavailable |
| 1 Jan 2011 | Haksung HS | Korea University | Unknown | Data unavailable |
| 1 Jan 2008 | U. Haksung MS | Haksung HS | Unknown | Data unavailable |
| 1 Jan 2005 | U. Okdong ES | U. Haksung MS | 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.