Abu Francis
Value picture
Where the market, the likely price and the model estimates sit.
The model rates Abu Francis's performance-implied value at EUR 1.4M-4.0M, above the estimated acquisition range of EUR 0.9M-2.8M. Its 24-month market value projection is EUR 0.9M-3.2M. Context-adjusted goal contributions are 0.62x the positional average in the league (532 minutes, league coefficient 0.88). Confidence is low: no event-level data (xG, progressive actions) is available from the current free source, and injury history is unavailable.
Bargain Index: not assigned
What drives the score. Not a black box: each component and its level.
Insufficient sample (< 900 league minutes)
| Performance | Average | 47th pct among MID peers (age-neutral) |
| Age | Neutral | 25.2 years |
| Acquisition cost | Favorable | est. €1.7M vs market €2.0M |
| Expected appreciation | Low | -15% over ~2 years (model) |
| Value gap | Large | weak predictor on its own |
| Development | Low | |
| League translation | Big-five league | league coefficient 0.88 |
| Tactical fit | Not evaluated | Requires event/role data not available from the current free source. |
| Risk | Very 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 1164 evaluated MID players with 900+ minutes.
Performance (last season, league)
- Minutes
- 532 (17% of available)
- Goals + assists / 90
- 0.00
- Context-adjusted G+A / 90
- 0.14
- xG, xA, progression
- Data unavailable
- Defensive actions
- Data unavailable
- Performance percentile
- 47 (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
- 0.88 (rank 6)
- Team strength (Elo)
- 1442
- Opponent strength
- 1430
- Possession, role
- Data unavailable
League strength 1.00 = average club of the big-five leagues. Elo includes continental matches.
Potential
- Development
- Low
- Trajectory
- declining
- Breakout probability
- Age > 23
- Resale range (24m)
- €894k – €3.2M
Trajectory drivers: minutes share down 44 pts; changed club; team strength up (+81 Elo); league level up (+90 Elo); relative output down 44% vs positional peers.
Historical comparables (8): median 2-year value change -33% (IQR -50% to -5%).
Risk profile
Overall: Very High
- ▲Small sample: 532 league minutes last season.
- ⚠No continental-competition minutes last season.
- ⚠Projection range wider than for most evaluated players.
- ⚠Played under 40% of available league minutes (injury data unavailable, cause unknown).
- ⚠Declining trajectory versus previous season.
- ⓘ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).
Already playing in a big-five league.
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 Toulouse | Ligue 1 | CM | 8 | 532 | 17% | 0 | 0 | 0.00 | 0.14 | 1430 | 1442 | 0.88 |
| 2024/25 | Cercle Brugge | Jupiler Pro League | CM | 29 | 1653 | 61% | 0 | 4 | 0.22 | 0.23 | 1364 | 1361 | 0.63 |
| 2023/24 | Cercle Brugge | Jupiler Pro League | CM | 8 | 409 | 15% | 0 | 0 | 0.00 | 0.15 | 1391 | 1394 | 0.63 |
| 2022/23 | Cercle Brugge | Jupiler Pro League | CM | 26 | 1643 | 54% | 1 | 3 | 0.22 | 0.21 | 1330 | 1373 | 0.58 |
| 2021/22 | FC Nordsjaelland | Superliga | CM | 15 | 1013 | 51% | 0 | 0 | 0.00 | 0.10 | 1251 | 1231 | 0.45 |
| 2020/21 | FC Nordsjaelland | Superliga | CM | 14 | 599 | 30% | 4 | 1 | 0.75 | 0.38 | 1331 | 1308 | 0.43 |
| 2019/20 | FC Nordsjaelland | Superliga | CM | 14 | 689 | 29% | 2 | 3 | 0.65 | 0.37 | 1289 | 1329 | 0.41 |
Cups & continental competitions
| Season | Competition | Club | Apps | Min | G | A | Avg opp. Elo |
|---|---|---|---|---|---|---|---|
| 2024/25 | Uefa Europa League Qualifying | Cercle Brugge | 4 | 299 | 0 | 0 | 1356 |
| 2024/25 | Uefa Conference League Qualifying | Cercle Brugge | 1 | 90 | 0 | 0 | 1320 |
| 2021/22 | Oddset Pokalen | FC Nordsjaelland | 1 | 120 | 0 | 0 | — |
| 2020/21 | Oddset Pokalen | FC Nordsjaelland | 2 | 112 | 0 | 0 | — |
| 2019/20 | Oddset Pokalen | FC Nordsjaelland | 2 | 124 | 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 |
|---|---|---|---|---|
| Gilles Sunu Ligue 1 · 2017 | 26 | €1.5M | €750k | fell |
| Sergi Palencia Ligue 1 · 2020 | 24 | €2.8M | €2.8M | flat |
| Christoph Moritz Bundesliga · 2015 | 25 | €1.5M | €900k | fell |
| Samuel Moutoussamy Ligue 1 · 2020 | 24 | €3.2M | €3.0M | flat |
| Paul Lasne Ligue 1 · 2016 | 27 | €1.5M | €2.5M | rose |
| Valentin Eysseric Serie A · 2018 | 26 | €5.0M | €1.2M | fell |
| Marco Richter Bundesliga · 2024 | 27 | €2.0M | €1.5M | fell |
| Pierre Kunde Bundesliga · 2021 | 26 | €3.5M | €1.7M | fell |
Transfer history
Fees as reported by the source; undisclosed fees shown as unknown, never estimated.
| Date | From | To | Fee | MV at time |
|---|---|---|---|---|
| 6 Aug 2025 | Cercle Brugge | Toulouse | €2.1M | €1.8M |
| 19 Aug 2022 | Nordsjaelland | Cercle Brugge | €500k | €1.0M |
| 1 Jul 2019 | Right to Dream | Nordsjaelland | 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.