Igor Paixão
Value picture
Where the market, the likely price and the model estimates sit.
The model rates Igor Paixão's performance-implied value at EUR 7.5M-25.2M, below the estimated acquisition range of EUR 21.6M-37.5M. Its 24-month market value projection is EUR 19.8M-41.5M. Context-adjusted goal contributions are 1.03x the positional average in the league (1985 minutes, league coefficient 0.88). Confidence is medium: no event-level data (xG, progressive actions) is available from the current free source, and injury history is unavailable.
Bargain Index: 22
What drives the score. Not a black box: each component and its level.
| Performance | High | 85.2th pct among ATT peers (age-neutral) |
| Age | Neutral | 26 years |
| Acquisition cost | Neutral | est. €30.7M vs market €35.0M |
| Expected appreciation | Very low | -31% over ~2 years (model) |
| Value gap | Very negative | 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 | Low | |
| Data confidence | Medium |
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 900 evaluated ATT players with 900+ minutes.
Performance (last season, league)
- Minutes
- 1985 (65% of available)
- Goals + assists / 90
- 0.54
- Context-adjusted G+A / 90
- 0.42
- xG, xA, progression
- Data unavailable
- Defensive actions
- Data unavailable
- Performance percentile
- 85 (High)
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 5)
- Team strength (Elo)
- 1504
- Opponent strength
- 1458
- 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)
- €19.8M – €41.5M
Trajectory drivers: minutes share down 29 pts; changed club; team strength down (-61 Elo); league level up (+147 Elo).
Historical comparables (8): median 2-year value change +13% (IQR +4% to +26%).
Risk profile
Overall: Low
- ⚠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 | Olympique Marseille | Ligue 1 | LW | 30 | 1985 | 65% | 6 | 6 | 0.54 | 0.42 | 1458 | 1504 | 0.88 |
| 2024/25 | Feyenoord Rotterdam | Eredivisie | LW | 34 | 2882 | 94% | 16 | 14 | 0.94 | 0.46 | 1290 | 1565 | 0.50 |
| 2023/24 | Feyenoord Rotterdam | Eredivisie | LW | 31 | 1939 | 63% | 9 | 4 | 0.60 | 0.30 | 1326 | 1593 | 0.48 |
| 2022/23 | Feyenoord Rotterdam | Eredivisie | LW | 28 | 1264 | 41% | 7 | 5 | 0.85 | 0.39 | 1329 | 1639 | 0.58 |
Cups & continental competitions
| Season | Competition | Club | Apps | Min | G | A | Avg opp. Elo |
|---|---|---|---|---|---|---|---|
| 2025/26 | Uefa Champions League | Olympique Marseille | 8 | 471 | 4 | 1 | 1612 |
| 2025/26 | Trophee Des Champions | Olympique Marseille | 1 | 67 | 0 | 0 | 1832 |
| 2024/25 | Uefa Champions League | Feyenoord Rotterdam | 11 | 921 | 2 | 5 | 1666 |
| 2024/25 | Knvb Beker | Feyenoord Rotterdam | 1 | 90 | 0 | 0 | — |
| 2024/25 | Johan Cruijff Schaal | Feyenoord Rotterdam | 1 | 77 | 0 | 0 | 1659 |
| 2023/24 | Uefa Champions League | Feyenoord Rotterdam | 6 | 401 | 0 | 0 | 1612 |
| 2023/24 | Knvb Beker | Feyenoord Rotterdam | 5 | 379 | 2 | 0 | — |
| 2023/24 | Uefa Europa League | Feyenoord Rotterdam | 2 | 138 | 1 | 0 | 1582 |
| 2023/24 | Johan Cruijff Schaal | Feyenoord Rotterdam | 1 | 72 | 0 | 0 | 1649 |
| 2022/23 | Uefa Europa League | Feyenoord Rotterdam | 6 | 205 | 1 | 1 | 1583 |
| 2022/23 | Knvb Beker | Feyenoord Rotterdam | 3 | 140 | 1 | 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 |
|---|---|---|---|---|
| Terem Moffi Ligue 1 · 2024 | 25 | €25.0M | €8.0M | fell |
| Amine Gouiri Ligue 1 · 2024 | 24 | €25.0M | €28.0M | flat |
| André Ayew Ligue 1 · 2014 | 25 | €12.0M | €15.0M | rose |
| Mattia Zaccagni Serie A · 2022 | 27 | €18.0M | €20.0M | flat |
| Andrea Belotti Serie A · 2018 | 25 | €38.0M | €32.0M | flat |
| Mauro Icardi Serie A · 2016 | 23 | €32.0M | €95.0M | rose |
| Dušan Tadić Premier League · 2015 | 27 | €15.0M | €17.0M | flat |
| Marcel Sabitzer Bundesliga · 2018 | 24 | €25.0M | €32.0M | rose |
Transfer history
Fees as reported by the source; undisclosed fees shown as unknown, never estimated.
| Date | From | To | Fee | MV at time |
|---|---|---|---|---|
| 1 Aug 2025 | Feyenoord | Marseille | €30.0M | €35.0M |
| 17 Aug 2022 | Coritiba FC | Feyenoord | €4.5M | €800k |
| 1 Mar 2021 | Coritiba U20 | Coritiba FC | Unknown | Data unavailable |
| 31 Jan 2021 | Londrina-PR | Coritiba U20 | Free / loan | Data unavailable |
| 6 Jan 2020 | Coritiba U20 | Londrina-PR | Free / loan | 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.