Yegor Demchenko

29 yrs · Central midfielder (also AM) · Ukraine
Epicentr Kamyanets-Podilskyi · Premier Liga · Foot: right · Height: 182 cm
Risk: Very HighConfidence: LowSample: LowDevelopment: LowContract: Data unavailable
Market value
€250k
Updated 6 days ago
Estimated acquisition
€61k – €300k
Estimated · 20–80% range
Model value
€181k – €416k
Performance-implied · estimated
Projected (24m)
€166k – €265k
Resale range · estimated
Potential value gap
-€119k – €355k
Model value − acquisition

Value picture

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

Estimated acquisition€61k – €300k
Model value (performance-implied)€181k – €416k
Projected value (24 months)€166k – €265k
Dashed line: current market value (€250k). Bars are 20–80% model ranges; tick = median.

The model rates Yegor Demchenko's performance-implied value at EUR 0.2M-0.4M, above the estimated acquisition range of EUR 0.1M-0.3M. Its 24-month market value projection is EUR 0.2M-0.3M. Context-adjusted goal contributions are 1.07x the positional average in the league (617 minutes, league coefficient 0.25). Confidence is low: 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.

Insufficient sample (< 900 league minutes)

PerformanceBelow average0.1th pct among MID peers (age-neutral)
AgeUnfavorable28.9 years
Acquisition costVery favorableest. €151k vs market €250k
Expected appreciationNeutral-5% over ~2 years (model)
Value gapLargeweak predictor on its own
DevelopmentLow
League translationUncertainleague coefficient 0.25
Tactical fitNot evaluatedRequires event/role data not available from the current free source.
RiskVery High
Data confidenceLow

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: €300k

Profile vs positional peers

Percentiles among 1164 evaluated MID players with 900+ minutes.

Performance (last season, league)

Minutes
617 (23% of available)
Goals + assists / 90
0.15
Context-adjusted G+A / 90
0.23
xG, xA, progression
Data unavailable
Defensive actions
Data unavailable
Performance percentile
0 (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
0.25 (rank 30)
Team strength (Elo)
1091
Opponent strength
1171
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)
€166k – €265k

Historical comparables (8): median 2-year value change +0% (IQR +0% to +3%).

Risk profile

Overall: Very High

  • ▲Small sample: 617 league minutes last season.
  • ▲League level coefficient 0.25 (1.00 = big-five average): output may not translate.
  • ⚠No continental-competition minutes last season.
  • ⚠Played under 40% of available league minutes (injury data unavailable, cause unknown).
  • ⓘ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).

Historical movers
37 (all positions)
Relative output kept
87%–128%
Held a regular role (900+ min)
86%
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.
2025/26Epicentr Kamyanets-PodilskyiPremier LigaCM1761723%100.150.23117110910.25
2022/23Metalist KharkivPremier LigaCM22127647%130.280.32128710890.38
2019/20Kolos KovalivkaPremier LigaAM3975%000.000.45134812290.54
2015/16Metalurg Zaporizhya (-2016)Premier LigaCM632514%000.000.3714219970.54

Cups & continental competitions

SeasonCompetitionClubAppsMinGAAvg opp. Elo
2025/26Ukrainian CupEpicentr Kamyanets-Podilskyi14500—
2015/16Ukrainian CupMetalurg Zaporizhya (-2016)16200—

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
Vitaliy Grusha
Premier Liga · 2024
30€250k€250kflat
David Carson
Scottish Premiership · 2024
29€250k€250kflat
Kyle Turner
Scottish Premiership · 2024
27€375k€375kflat
Javier Mendoza
Super League 1 · 2021
29€250k€400krose
Valeriy Rogozynskyi
Premier Liga · 2024
29€300k€250kflat
Michal Skvarka
Super League 1 · 2023
31€300k€300kflat
Marko Bakic
Super League 1 · 2023
30€350k€400kflat
Bogdan Myshenko
Premier Liga · 2023
29€200k€200kflat

Transfer history

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

DateFromToFeeMV at time
1 Feb 2025FSC MariupolEpicentrFree / loan€250k
1 Aug 2024Karpaty LvivFSC MariupolFree / loan€300k
6 Jul 2023MetalistKarpaty LvivFree / loan€300k
20 Aug 2020Avangard K.FC MetalFree / loan€200k
2 Sept 2019Kolos KovalivkaAvangard K.Free / loan€250k
2 Jul 2019Avangard K.Kolos KovalivkaFree / loan€250k
11 Mar 2017Without ClubAvangard K.Unknown€200k
4 Jan 2017Olimpik DonetskWithout ClubUnknown€200k
28 Nov 2016Without ClubOlimpik DonetskUnknown€200k
1 Jul 2016BukovynaWithout ClubUnknown€200k
5 Apr 2016Zorya IIBukovynaFree / loan€200k
25 Jan 2016Metalurg Z.Zorya IIFree / loan€200k
1 Jul 2015Metalurg Z. IIMetalurg Z.UnknownData unavailable
1 Jan 2015Zaporizhya U19Metalurg Z. IIUnknownData unavailable
1 Aug 2014Metalurg Z. U17Zaporizhya U19UnknownData 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.