Min-jae Kim

29 yrs · Centre-back · Korea, South
Bayern Munich · Bundesliga · Foot: right · Height: 190 cm
Risk: ModerateConfidence: MediumSample: MediumDevelopment: LowContract: until 30 Jun 2028
Market value
€20.0M
Updated 6 days ago
Estimated acquisition
€11.3M – €21.5M
Estimated · 20–80% range
Model value
€14.6M – €45.3M
Performance-implied · estimated
Projected (24m)
€7.2M – €15.6M
Resale range · estimated
Potential value gap
-€6.9M – €34.0M
Model value − acquisition

Value picture

Where the market, the likely price and the model estimates sit.

Estimated acquisition€11.3M – €21.5M
Model value (performance-implied)€14.6M – €45.3M
Projected value (24 months)€7.2M – €15.6M
Dashed line: current market value (€20.0M). Bars are 20–80% model ranges; tick = median.

The model rates Min-jae Kim's performance-implied value at EUR 14.6M-45.3M, above the estimated acquisition range of EUR 11.3M-21.5M. Its 24-month market value projection is EUR 7.2M-15.6M. Confidence is medium: no event-level data (xG, progressive actions) is available from the current free source, and injury history is unavailable.

Bargain Index: 8

What drives the score. Not a black box: each component and its level.

PerformanceElite97.1th pct among DEF peers (age-neutral)
AgeUnfavorable29.6 years
Acquisition costFavorableest. €16.5M vs market €20.0M
Expected appreciationVery low-47% over ~2 years (model)
Value gapLargeweak predictor on its own
DevelopmentLow
League translationBig-five leagueleague coefficient 0.94
Tactical fitNot evaluatedRequires event/role data not available from the current free source.
RiskModerate
Data confidenceMedium

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.

Highest recorded: €60.0M

Profile vs positional peers

Percentiles among 1268 evaluated DEF players with 900+ minutes.

Performance (last season, league)

Minutes
1605 (53% of available)
Goals + assists / 90
0.11
Context-adjusted G+A / 90
0.03
xG, xA, progression
Data unavailable
Defensive actions
Data unavailable
Performance percentile
97 (Elite)

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)
1871
Opponent strength
1437
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)
€7.2M – €15.6M

Trajectory drivers: minutes share down 22 pts; team strength up (+99 Elo); relative output down 29% vs positional peers.

Historical comparables (8): median 2-year value change -55% (IQR -71% to -38%).

Risk profile

Overall: Moderate

  • ⚠Limited sample: 1605 league minutes last season.
  • ⚠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).

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.

SeasonClubLeaguePosAppsMinMin shareGAG+A/90Adj. G+A/90Opp. EloTeam EloLeague str.
2025/26Bayern MunichBundesligaCB25160552%110.110.03143718710.94
2024/25Bayern MunichBundesligaCB27228975%200.080.04149717720.94
2023/24Bayern MunichBundesligaCB25197264%120.140.06150417151.01
2022/23SSC NapoliSerie ACB35305489%220.120.06146217390.94
2021/22FenerbahceSuper LigCB31267478%100.030.03123714220.42

Cups & continental competitions

SeasonCompetitionClubAppsMinGAAvg opp. Elo
2025/26Uefa Champions LeagueBayern Munich8336001633
2025/26World CupSouth Korea324600—
2025/26Dfb PokalBayern Munich310000—
2025/26Franz Beckenbauer SupercupBayern Munich110001531
2024/25Uefa Champions LeagueBayern Munich131072101650
2024/25Dfb PokalBayern Munich323200—
2023/24Uefa Champions LeagueBayern Munich9658001637
2023/24Dfb PokalBayern Munich19000—
2023/24Franz Beckenbauer SupercupBayern Munich145001685
2022/23Uefa Champions LeagueSSC Napoli9786001684
2022/23Italy CupSSC Napoli13800—
2021/22Uefa Europa LeagueFenerbahce6540001517

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)AgeValue then~2y laterOutcome
Giorgio Chiellini
Serie A · 2016
32€13.0M€10.0Mfell
Rafinha
Bundesliga · 2016
31€8.0M€3.5Mfell
Medhi Benatia
Bundesliga · 2015
28€26.0M€15.0Mfell
Gerard Piqué
Laliga · 2017
30€40.0M€35.0Mflat
Kyle Walker
Premier League · 2021
31€28.0M€13.0Mfell
Niklas Süle
Bundesliga · 2024
29€15.0M€4.0Mfell
Benjamin Pavard
Serie A · 2024
28€50.0M€12.0Mfell
Jérôme Boateng
Bundesliga · 2020
32€12.0M€3.5Mfell

Transfer history

Fees as reported by the source; undisclosed fees shown as unknown, never estimated.

DateFromToFeeMV at time
18 Jul 2023NapoliBayern Munich€50.0M€60.0M
27 Jul 2022FenerbahçeNapoli€19.0M€14.0M
16 Aug 2021BJ GuoanFenerbahçe€3.0M€2.0M
29 Jan 2019Jeonbuk HyundaiBJ Guoan€5.3M€1.2M
1 Jan 2017Gyeongju KHNPJeonbuk HyundaiFree / loanData unavailable
1 Jul 2016Yonsei Univ.Gyeongju KHNPFree / loanData unavailable
1 Jan 2015Suwon THSYonsei Univ.UnknownData unavailable
1 Jan 2012Yeoncho MSSuwon THSUnknownData unavailable
1 Jul 2010Nam. Haesung MSYeoncho MSUnknownData unavailable
1 Jan 2009Gimhae Gaya ESNam. Haesung MSUnknownData unavailable
1 Jan 2007Dooryong ESGimhae Gaya ESUnknownData 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.