Jay-Roy Grot

28 yrs · Striker (also LW) · Suriname
Odense Boldklub · Superliga · Foot: right · Height: 193 cm
Risk: HighConfidence: MediumSample: MediumDevelopment: LowContract: until 30 Jun 2027
Market value
€750k
Updated 6 days ago
Estimated acquisition
€224k – €665k
Estimated · 20–80% range
Model value
€479k – €1.1M
Performance-implied · estimated
Projected (24m)
€378k – €945k
Resale range · estimated
Potential value gap
-€186k – €893k
Model value − acquisition

Value picture

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

Estimated acquisition€224k – €665k
Model value (performance-implied)€479k – €1.1M
Projected value (24 months)€378k – €945k
Dashed line: current market value (€750k). Bars are 20–80% model ranges; tick = median.

The model rates Jay-Roy Grot's performance-implied value at EUR 0.5M-1.1M, above the estimated acquisition range of EUR 0.2M-0.7M. Its 24-month market value projection is EUR 0.4M-0.9M. Context-adjusted goal contributions are 1.30x the positional average in the league (1621 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: 94

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

PerformanceBelow average15.6th pct among ATT peers (age-neutral)
AgeUnfavorable28.3 years
Acquisition costVery favorableest. €299k vs market €750k
Expected appreciationLow-12% over ~2 years (model)
Value gapLargeweak 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: €1.3M

Profile vs positional peers

Percentiles among 900 evaluated ATT players with 900+ minutes.

Performance (last season, league)

Minutes
1621 (82% of available)
Goals + assists / 90
0.56
Context-adjusted G+A / 90
0.66
xG, xA, progression
Data unavailable
Defensive actions
Data unavailable
Performance percentile
16 (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)
1215
Opponent strength
1272
Possession, role
Data unavailable

League strength 1.00 = average club of the big-five leagues. Elo includes continental matches.

Potential

Development
Low
Trajectory
insufficient
Breakout probability
Age > 23
Resale range (24m)
€378k – €945k

Historical comparables (8): median 2-year value change -39% (IQR -42% to +5%).

Risk profile

Overall: High

  • ⚠Limited sample: 1621 league minutes last season.
  • ⚠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
20 (ATT)
Relative output kept
78%–121%
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.
2025/26Odense BoldklubSuperligaST21162182%730.560.66127212150.47
2022/23Viborg FFSuperligaST17144173%930.750.65128513270.47
2021/22Viborg FFSuperligaST1697049%200.190.46129212340.45
2019/20Vitesse ArnhemEredivisieLW2284536%210.320.30135313930.62
2018/19VVV-VenloEredivisieLW33181259%630.450.49138312610.61
2016/17NEC NijmegenEredivisieST20135444%500.330.46133612230.54
2015/16NEC NijmegenEredivisieST102729%000.000.46133612730.58

Cups & continental competitions

SeasonCompetitionClubAppsMinGAAvg opp. Elo
2025/26Oddset PokalenOdense Boldklub16200—
2022/23Uefa Conference League QualifyingViborg FF5347001398
2022/23Oddset PokalenViborg FF16400—
2021/22Oddset PokalenViborg FF15910—
2019/20Knvb BekerVitesse Arnhem314000—
2018/19Knvb BekerVVV-Venlo19000—

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
Tony Watt
Scottish Premiership · 2022
29€800k€400kfell
Michalis Manias
Super League 1 · 2017
27€800k€500kfell
Christos Aravidis
Super League 1 · 2014
27€800k€1.0Mrose
David Torres
Super League 1 · 2014
28€500k€300kfell
Georgios Pamlidis
Super League 1 · 2021
28€600k€350kfell
Vitaliy Balashov
Premier Liga · 2019
28€600k€350kfell
Rasmus Festersen
Superliga · 2015
29€500k€500kflat
Junior Kabananga
Jupiler Pro League · 2015
26€1.0M€1.2Mrose

Transfer history

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

DateFromToFeeMV at time
4 Mar 2025Without ClubOdense BKUnknown€400k
1 Feb 2025Kashiwa ReysolWithout ClubUnknown€400k
2 Feb 2023Viborg FFKashiwa Reysol€1.8M€1.0M
6 Jul 2021VfL OsnabrückViborg FFFree / loan€400k
1 Feb 2021LeedsVfL OsnabrückFree / loan€800k
30 Jun 2020VitesseLeedsFree / loan€800k
1 Jul 2019LeedsVitesseFree / loan€1.3M
30 Jun 2019VVV-VenloLeedsFree / loan€1.3M
5 Jul 2018LeedsVVV-VenloFree / loan€1.3M
24 Aug 2017NEC NijmegenLeeds€1.6M€750k
1 Jul 2015NEC U19NEC NijmegenUnknownData unavailable
1 Jul 2014NEC U17NEC U19UnknownData unavailable
1 Jul 2013NEC YouthNEC U17UnknownData unavailable
1 Jul 2008ESA YouthNEC YouthFree / loanData 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.