Moses Opondo
Value picture
Where the market, the likely price and the model estimates sit.
The model rates Moses Opondo's performance-implied value at EUR 0.2M-0.5M, above the estimated acquisition range of EUR 0.1M-0.3M. Its 24-month market value projection is EUR 0.2M-0.4M. Context-adjusted goal contributions are 0.79x the positional average in the league (803 minutes, league coefficient 0.47). 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)
| Performance | Below average | 0th pct among ATT peers (age-neutral) |
| Age | Unfavorable | 28.7 years |
| Acquisition cost | Very favorable | est. €177k vs market €300k |
| Expected appreciation | Neutral | -2% over ~2 years (model) |
| Value gap | Large | weak predictor on its own |
| Development | Low | |
| League translation | Uncertain | league coefficient 0.47 |
| Tactical fit | Not evaluated | Requires event/role data not available from the current free source. |
| Risk | High | |
| Data confidence | Low |
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.
Profile vs positional peers
Percentiles among 901 evaluated ATT players with 900+ minutes.
Performance (last season, league)
- Minutes
- 803 (41% of available)
- Goals + assists / 90
- 0.11
- Context-adjusted G+A / 90
- 0.31
- 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
- 0.47 (rank 12)
- Team strength (Elo)
- 1183
- Opponent strength
- 1334
- 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)
- €213k – €384k
Historical comparables (8): median 2-year value change +22% (IQR -3% to +74%).
Risk profile
Overall: High
- ▲Small sample: 803 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 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
- 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.
| Season | Club | League | Pos | Apps | Min | Min share | G | A | G+A/90 | Adj. G+A/90 | Opp. Elo | Team Elo | League str. |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2025/26 | FC Fredericia | Superliga | RW | 13 | 803 | 41% | 0 | 1 | 0.11 | 0.31 | 1334 | 1183 | 0.47 |
| 2022/23 | AC Horsens | Superliga | CM | 22 | 1766 | 89% | 1 | 4 | 0.25 | 0.37 | 1308 | 1157 | 0.47 |
| 2020/21 | Odense Boldklub | Superliga | CM | 17 | 1052 | 53% | 1 | 0 | 0.09 | 0.13 | 1291 | 1252 | 0.43 |
| 2019/20 | Odense Boldklub | Superliga | CM | 17 | 785 | 34% | 1 | 0 | 0.11 | 0.15 | 1256 | 1264 | 0.41 |
| 2018/19 | Vendsyssel FF | Superliga | CM | 22 | 1735 | 74% | 4 | 3 | 0.36 | 0.37 | 1333 | 1160 | 0.44 |
Cups & continental competitions
| Season | Competition | Club | Apps | Min | G | A | Avg opp. Elo |
|---|---|---|---|---|---|---|---|
| 2025/26 | Oddset Pokalen | FC Fredericia | 1 | 74 | 0 | 0 | — |
| 2022/23 | Oddset Pokalen | AC Horsens | 1 | 19 | 0 | 0 | — |
| 2020/21 | Oddset Pokalen | Odense Boldklub | 2 | 96 | 1 | 0 | — |
| 2019/20 | Oddset Pokalen | Odense Boldklub | 2 | 150 | 0 | 0 | — |
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) | Age | Value then | ~2y later | Outcome |
|---|---|---|---|---|
| Vasilios Mantzis Super League 1 · 2020 | 29 | €250k | €500k | rose |
| Robert Cullen Eredivisie · 2013 | 28 | €300k | €300k | flat |
| Chris Kane Scottish Premiership · 2022 | 28 | €400k | €350k | flat |
| Aminu Umar Super Lig · 2022 | 27 | €500k | €175k | fell |
| Lonsana Doumbouya Scottish Premiership · 2017 | 27 | €300k | €450k | rose |
| Dani Schahin Eredivisie · 2017 | 28 | €300k | €500k | rose |
| Jordan Jones Scottish Premiership · 2023 | 29 | €400k | €400k | flat |
| Guilherme Schettine Premier Liga · 2024 | 29 | €700k | €1.5M | rose |
Transfer history
Fees as reported by the source; undisclosed fees shown as unknown, never estimated.
| Date | From | To | Fee | MV at time |
|---|---|---|---|---|
| 1 Jul 2026 | FC Fredericia | Without Club | Unknown | €300k |
| 2 Sept 2024 | AC Horsens | FC Fredericia | Free / loan | €350k |
| 1 Jul 2022 | Odense BK | AC Horsens | Unknown | €300k |
| 30 Jun 2022 | AC Horsens | Odense BK | Free / loan | €300k |
| 24 Jan 2022 | Odense BK | AC Horsens | Free / loan | €350k |
| 1 Jul 2019 | Vendsyssel FF | Odense BK | €135k | €350k |
| 16 Jan 2017 | Aalborg BK | Vendsyssel FF | Unknown | €150k |
| 31 Dec 2016 | Vendsyssel FF | Aalborg BK | Free / loan | €150k |
| 6 Jul 2016 | Aalborg BK | Vendsyssel FF | Free / loan | Data unavailable |
| 1 Jul 2016 | Aalborg BK U19 | Aalborg BK | Unknown | Data unavailable |
| 1 Jul 2014 | Aalborg BK Yth. | Aalborg BK U19 | Unknown | Data 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.