
Leonardo Bittencourt
Value picture
Where the market, the likely price and the model estimates sit.
The model rates Leonardo Bittencourt's performance-implied value at EUR 0.8M-1.9M, above the estimated acquisition range of EUR 0.2M-1.0M. Its 24-month market value projection is EUR 0.4M-0.9M. Context-adjusted goal contributions are 0.77x the positional average in the league (610 minutes, league coefficient 0.94). 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 | Above average | 55th pct among MID peers (age-neutral) |
| Age | Unfavorable | 32.5 years |
| Acquisition cost | Very favorable | est. €511k vs market €900k |
| Expected appreciation | Very low | -35% over ~2 years (model) |
| Value gap | Very large | weak predictor on its own |
| Development | Low | |
| League translation | Big-five league | league coefficient 0.94 |
| 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
- 610 (20% of available)
- Goals + assists / 90
- 0.15
- Context-adjusted G+A / 90
- 0.17
- xG, xA, progression
- Data unavailable
- Defensive actions
- Data unavailable
- Performance percentile
- 55 (Above 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
- 0.94 (rank 3)
- Team strength (Elo)
- 1378
- Opponent strength
- 1461
- 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)
- €362k – €902k
Trajectory drivers: team strength down (-104 Elo); relative output down 36% vs positional peers.
Historical comparables (8): median 2-year value change -33% (IQR -45% to -20%).
Risk profile
Overall: Very High
- ▲Small sample: 610 league minutes last season.
- ▲Age 33: resale value typically declines.
- ⚠Played under 40% of available league minutes (injury data unavailable, cause unknown).
- ⚠Declining trajectory versus previous 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).
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 | SV Werder Bremen | Bundesliga | CM | 17 | 610 | 20% | 0 | 1 | 0.15 | 0.17 | 1461 | 1378 | 0.94 |
| 2024/25 | SV Werder Bremen | Bundesliga | CM | 27 | 974 | 32% | 2 | 1 | 0.28 | 0.26 | 1493 | 1483 | 0.94 |
| 2023/24 | SV Werder Bremen | Bundesliga | CM | 29 | 1584 | 52% | 1 | 2 | 0.17 | 0.23 | 1560 | 1459 | 1.01 |
| 2022/23 | SV Werder Bremen | Bundesliga | AM | 25 | 1641 | 54% | 3 | 2 | 0.27 | 0.32 | 1532 | 1393 | 1.04 |
| 2020/21 | SV Werder Bremen | Bundesliga | AM | 27 | 1522 | 50% | 4 | 2 | 0.35 | 0.42 | 1520 | 1384 | 1.06 |
| 2019/20 | SV Werder Bremen | Bundesliga | AM | 28 | 1898 | 62% | 4 | 2 | 0.28 | 0.36 | 1550 | 1428 | 1.06 |
| 2019/20 | TSG 1899 Hoffenheim | Bundesliga | AM | 1 | 1 | 0% | 0 | 0 | 0.00 | 0.38 | 1542 | 1558 | 1.06 |
| 2018/19 | TSG 1899 Hoffenheim | Bundesliga | AM | 21 | 1069 | 35% | 1 | 3 | 0.34 | 0.28 | 1567 | 1573 | 1.05 |
| 2017/18 | 1.FC Köln | Bundesliga | LW | 22 | 1612 | 53% | 5 | 3 | 0.45 | 0.49 | 1489 | 1387 | 1.04 |
| 2016/17 | 1.FC Köln | Bundesliga | LW | 16 | 977 | 32% | 3 | 7 | 0.92 | 0.63 | 1505 | 1522 | 1.06 |
| 2015/16 | 1.FC Köln | Bundesliga | LW | 29 | 2229 | 73% | 3 | 4 | 0.28 | 0.33 | 1517 | 1478 | 1.02 |
| 2014/15 | Hannover 96 | Bundesliga | RW | 26 | 1377 | 45% | 1 | 3 | 0.26 | 0.36 | 1550 | 1433 | 1.03 |
| 2013/14 | Hannover 96 | Bundesliga | RW | 31 | 1823 | 60% | 4 | 5 | 0.44 | 0.41 | 1562 | 1473 | 1.03 |
| 2012/13 | Borussia Dortmund | Bundesliga | CM | 5 | 214 | 7% | 1 | 1 | 0.84 | 0.18 | 1360 | 1746 | 1.11 |
Cups & continental competitions
| Season | Competition | Club | Apps | Min | G | A | Avg opp. Elo |
|---|---|---|---|---|---|---|---|
| 2025/26 | Dfb Pokal | SV Werder Bremen | 1 | 54 | 0 | 0 | — |
| 2024/25 | Dfb Pokal | SV Werder Bremen | 4 | 174 | 0 | 1 | — |
| 2023/24 | Dfb Pokal | SV Werder Bremen | 1 | 71 | 0 | 0 | — |
| 2022/23 | Dfb Pokal | SV Werder Bremen | 2 | 126 | 1 | 1 | — |
| 2020/21 | Dfb Pokal | SV Werder Bremen | 2 | 121 | 1 | 0 | — |
| 2019/20 | Dfb Pokal | SV Werder Bremen | 3 | 201 | 2 | 0 | — |
| 2018/19 | Uefa Champions League | TSG 1899 Hoffenheim | 3 | 142 | 0 | 1 | 1776 |
| 2018/19 | Dfb Pokal | TSG 1899 Hoffenheim | 2 | 113 | 0 | 0 | — |
| 2017/18 | Uefa Europa League | 1.FC Köln | 4 | 245 | 0 | 2 | 1525 |
| 2017/18 | Dfb Pokal | 1.FC Köln | 2 | 113 | 1 | 0 | — |
| 2016/17 | Dfb Pokal | 1.FC Köln | 2 | 126 | 0 | 1 | — |
| 2015/16 | Dfb Pokal | 1.FC Köln | 1 | 90 | 0 | 0 | — |
| 2014/15 | Dfb Pokal | Hannover 96 | 2 | 148 | 0 | 1 | — |
| 2013/14 | Dfb Pokal | Hannover 96 | 2 | 157 | 0 | 0 | — |
| 2012/13 | Dfb Pokal | Borussia Dortmund | 1 | 3 | 0 | 1 | — |
| 2012/13 | Uefa Champions League | Borussia Dortmund | 1 | 2 | 0 | 0 | 1650 |
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 |
|---|---|---|---|---|
| Rodrigo Battaglia Laliga · 2023 | 32 | €1.5M | €900k | fell |
| Daniele Dessena Serie A · 2018 | 31 | €700k | €400k | fell |
| Jean-Daniel Akpa Akpro Serie A · 2024 | 32 | €1.0M | €500k | fell |
| Mitar Novakovic Premier Liga · 2013 | 32 | €800k | €800k | flat |
| Kevin Lejeune Ligue 1 · 2017 | 32 | €500k | €400k | fell |
| Paulo Azzi Serie A · 2024 | 30 | €1.0M | €750k | fell |
| Youssouf M'Changama Ligue 1 · 2023 | 33 | €900k | €200k | fell |
| Borja García Laliga · 2023 | 33 | €1.0M | €800k | fell |
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 | Werder Bremen | Energie Cottbus | Free / loan | €900k |
| 15 Jul 2020 | Hoffenheim | Werder Bremen | €7.0M | €4.8M |
| 7 Jul 2020 | Werder Bremen | Hoffenheim | Free / loan | €4.8M |
| 2 Sept 2019 | Hoffenheim | Werder Bremen | Free / loan | €7.0M |
| 1 Jul 2018 | 1.FC Köln | Hoffenheim | €6.0M | €6.0M |
| 14 Jul 2015 | Hannover 96 | 1.FC Köln | €2.5M | €4.0M |
| 1 Jul 2013 | Dortmund | Hannover 96 | €2.8M | €2.8M |
| 1 Jul 2012 | Energie Cottbus | Dortmund | €2.7M | €2.5M |
| 1 Jul 2011 | E. Cottbus U19 | Energie Cottbus | Unknown | €250k |
| 1 Jul 2010 | E. Cottbus U17 | E. Cottbus U19 | Unknown | Data unavailable |
| 1 Jul 2008 | E. Cottbus Yth. | E. Cottbus U17 | 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.