Danila Vedernikov

25 yrs · Left-back (also LM) · Russia
FC Orenburg · Premier Liga · Foot: left · Height: 176 cm
Risk: Very HighConfidence: MediumSample: MediumDevelopment: LowContract: until 30 Jun 2027
Market value
€750k
Updated 6 days ago
Estimated acquisition
€219k – €678k
Estimated · 20–80% range
Model value
€410k – €879k
Performance-implied · estimated
Projected (24m)
€358k – €1.3M
Resale range · estimated
Potential value gap
-€269k – €661k
Model value − acquisition

Value picture

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

Estimated acquisition€219k – €678k
Model value (performance-implied)€410k – €879k
Projected value (24 months)€358k – €1.3M
Dashed line: current market value (€750k). Bars are 20–80% model ranges; tick = median.

The model rates Danila Vedernikov's performance-implied value at EUR 0.4M-0.9M, above the estimated acquisition range of EUR 0.2M-0.7M. Its 24-month market value projection is EUR 0.4M-1.3M. 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: 95

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

PerformanceBelow average10.6th pct among DEF peers (age-neutral)
AgeNeutral25.1 years
Acquisition costVery favorableest. €292k vs market €750k
Expected appreciationNeutral-9% over ~2 years (model)
Value gapLargeweak predictor on its own
DevelopmentLow
League translationModerateleague coefficient 0.56
Tactical fitNot evaluatedRequires event/role data not available from the current free source.
RiskVery High
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: €750k

Profile vs positional peers

Percentiles among 1268 evaluated DEF players with 900+ minutes.

Performance (last season, league)

Minutes
1658 (61% of available)
Goals + assists / 90
0.05
Context-adjusted G+A / 90
0.13
xG, xA, progression
Data unavailable
Defensive actions
Data unavailable
Performance percentile
11 (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
0.56 (rank 10)
Team strength (Elo)
1218
Opponent strength
1363
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)
€358k – €1.3M

Historical comparables (8): median 2-year value change -50% (IQR -56% to +10%).

Risk profile

Overall: Very High

  • ⚠Limited sample: 1658 league minutes last season.
  • ⚠League level coefficient 0.56 (1.00 = big-five average): output may not translate.
  • ⚠No continental-competition minutes last season.
  • ⚠League level estimated mainly from squad values (few matches vs other leagues).
  • ⚠Contract ends in 12 months: cheaper, but competition and wage demands likely.
  • ⚠Projection range wider than for most evaluated players.
  • ⓘ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
25 (DEF)
Relative output kept
48%–200%
Held a regular role (900+ min)
72%
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.
2025/26FC OrenburgPremier LigaLB22165861%100.050.13136312180.56
2024/25FK Nizhny NovgorodPremier LigaLM533012%000.000.23132311810.55
2023/24FK Nizhny NovgorodPremier LigaLB160%000.000.20139711640.49
2019/20FK RostovPremier LigaLB5452%000.000.13134513740.63

Cups & continental competitions

SeasonCompetitionClubAppsMinGAAvg opp. Elo
2025/26Russian CupFC Orenburg314700—
2024/25Russian CupFK Nizhny Novgorod649500—
2019/20Russian CupFK Rostov218000—

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
Bruno Nascimento
Liga Portugal · 2016
25€700k€200kfell
Roy Gelmi
Eredivisie · 2021
26€550k€200kfell
Zé Carlos
Liga Portugal · 2024
26€700k€2.0Mrose
Dimitrios Goutas
Jupiler Pro League · 2017
23€850k€400kfell
Pedro Pinto
Liga Portugal · 2018
24€600k€300kfell
Lucas Woudenberg
Eredivisie · 2018
24€650k€650kflat
Igor Tyshchenko
Premier Liga · 2015
26€600k€300kfell
Karim Hafez
Super Lig · 2020
24€675k€1.0Mrose

Transfer history

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

DateFromToFeeMV at time
11 Jul 2025AstrakhanOrenburgFree / loanData unavailable
20 Mar 2025Chaika Pes.AstrakhanFree / loanData unavailable
7 Mar 2025Without ClubChaika Pes.Unknown€400k
20 Feb 2025Pari NNWithout ClubUnknown€400k
17 Jan 2024MuromPari NNFree / loan€400k
14 Sept 2023Pari NNMuromFree / loan€450k
13 Sept 2023RostovPari NN€100k€450k
30 Jun 2023VolgarRostovFree / loan€450k
1 Jul 2022RostovVolgarFree / loan€350k
30 Jun 2022KubanRostovFree / loan€350k
28 Jan 2022RostovKubanFree / loan€400k
21 Jan 2022VolgarRostovFree / loan€400k
14 Aug 2020RostovVolgarFree / loan€250k
9 Aug 2019Krasnodar IIRostov€12k€125k
1 Jan 2018Krasnodar 2Krasnodar IIUnknownData unavailable
1 Jul 2017Krasnodar U17Krasnodar 2UnknownData 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.

Danila Vedernikov · MoneyballAI