Jeppe Grønning

35 yrs · Defensive midfielder (also CM) · Denmark
Viborg FF · Superliga · Foot: right · Height: 182 cm
Risk: HighConfidence: MediumSample: HighDevelopment: LowContract: until 30 Jun 2027
Market value
€100k
Updated 7 days ago
Estimated acquisition
€36k – €132k
Estimated · 20–80% range
Model value
€162k – €277k
Performance-implied · estimated
Projected (24m)
€80k – €101k
Resale range · estimated
Potential value gap
€29k – €241k
Model value − acquisition

Value picture

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

Estimated acquisition€36k – €132k
Model value (performance-implied)€162k – €277k
Projected value (24 months)€80k – €101k
Dashed line: current market value (€100k). Bars are 20–80% model ranges; tick = median.

The model rates Jeppe Grønning's performance-implied value at EUR 0.2M-0.3M, above the estimated acquisition range of EUR 0.0M-0.1M. Its 24-month market value projection is EUR 0.1M-0.1M. Context-adjusted goal contributions are 0.92x the positional average in the league (1876 minutes, league coefficient 0.47). 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: not assigned

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

Market value below EUR 250k (outside validated pool)

PerformanceBelow average23.5th pct among MID peers (age-neutral)
AgeUnfavorable35.1 years
Acquisition costVery favorableest. €48k vs market €100k
Expected appreciationNeutral-0% over ~2 years (model)
Value gapVery largeweak predictor on its own
DevelopmentLow
League translationUncertainleague coefficient 0.47
Tactical fitNot evaluatedRequires event/role data not available from the current free source.
RiskHigh
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: €400k

Profile vs positional peers

Percentiles among 1163 evaluated MID players with 900+ minutes.

Performance (last season, league)

Minutes
1876 (95% of available)
Goals + assists / 90
0.10
Context-adjusted G+A / 90
0.11
xG, xA, progression
Data unavailable
Defensive actions
Data unavailable
Performance percentile
24 (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
0.47 (rank 12)
Team strength (Elo)
1286
Opponent strength
1268
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)
€80k – €101k

Trajectory drivers: minutes share up 17 pts; team strength up (+64 Elo); league level up (+45 Elo); relative output down 32% vs positional peers.

Historical comparables (8): median 2-year value change -7% (IQR -39% to +0%).

Risk profile

Overall: High

  • ▲Age 35: resale value typically declines.
  • ⚠League level coefficient 0.47 (1.00 = big-five average): output may not translate.
  • ⚠No continental-competition minutes last season.
  • ⚠Contract ends in 12 months: cheaper, but competition and wage demands likely.
  • ⓘ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
23 (MID)
Relative output kept
68%–122%
Held a regular role (900+ min)
65%
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.

SeasonClubLeaguePosAppsMinMin shareGAG+A/90Adj. G+A/90Opp. EloTeam EloLeague str.
2025/26Viborg FFSuperligaDM22187695%110.100.11126812860.47
2024/25Viborg FFSuperligaDM19153277%300.180.18133012220.38
2023/24Viborg FFSuperligaDM17133167%010.070.09130012610.48
2022/23Viborg FFSuperligaDM21186894%110.100.12128913270.47
2021/22Viborg FFSuperligaDM18146474%110.120.17129512340.45
2016/17Viborg FFSuperligaCM25209890%000.000.08132212210.48
2015/16Viborg FFSuperligaDM28206970%000.000.06132112510.53
2013/14Viborg FFSuperligaDM51204%000.000.12135812360.63

Cups & continental competitions

SeasonCompetitionClubAppsMinGAAvg opp. Elo
2025/26Oddset PokalenViborg FF644010—
2024/25Oddset PokalenViborg FF322310—
2023/24Oddset PokalenViborg FF214821—
2022/23Uefa Conference League QualifyingViborg FF5435001393
2022/23Oddset PokalenViborg FF325701—
2016/17Oddset PokalenViborg FF210300—
2013/14Oddset PokalenViborg FF19000—

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
Kevin Geudens
Jupiler Pro League · 2015
35€100k€100kflat
Vadym Milko
Premier Liga · 2020
34€100k€100kflat
Rasmus Würtz
Superliga · 2017
34€350k€300kflat
Luíz Carlos
Liga Portugal · 2021
36€100k€100kflat
Marvin Bartley
Scottish Premiership · 2021
35€300k€100kfell
Braga
Liga Portugal · 2017
34€300k€100kfell
Marcel Rømer
Superliga · 2024
33€250k€250kflat
Stéphane Sessègnon
Super Lig · 2020
36€300k€225kfell

Transfer history

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

DateFromToFeeMV at time
30 Jun 2014Hobro IKViborg FFFree / loan€250k
31 Jan 2014Viborg FFHobro IKFree / loan€300k
1 Jul 2012FC FynViborg FFFree / loan€75k
1 Jan 2009FC Fyn U19FC FynUnknownData 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.

Jeppe Grønning · MoneyballAI