
Ryan Yates
Value picture
Where the market, the likely price and the model estimates sit.
The model rates Ryan Yates's performance-implied value at EUR 8.9M-13.6M, above the estimated acquisition range of EUR 2.6M-7.3M. Its 24-month market value projection is EUR 2.4M-6.5M. Context-adjusted goal contributions are 1.17x the positional average in the league (603 minutes, league coefficient 1.30). 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 | High | 86.3th pct among MID peers (age-neutral) |
| Age | Unfavorable | 28.6 years |
| Acquisition cost | Favorable | est. €4.7M vs market €7.0M |
| Expected appreciation | Very low | -41% over ~2 years (model) |
| Value gap | Very large | weak predictor on its own |
| Development | Low | |
| League translation | Big-five league | league coefficient 1.3 |
| 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 1164 evaluated MID players with 900+ minutes.
Performance (last season, league)
- Minutes
- 603 (18% of available)
- Goals + assists / 90
- 0.30
- Context-adjusted G+A / 90
- 0.26
- xG, xA, progression
- Data unavailable
- Defensive actions
- Data unavailable
- Performance percentile
- 86 (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
- 1.30 (rank 1)
- Team strength (Elo)
- 1635
- Opponent strength
- 1588
- 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)
- €2.4M – €6.5M
Trajectory drivers: minutes share down 38 pts; team strength up (+63 Elo); relative output up 32% vs positional peers.
Historical comparables (8): median 2-year value change -55% (IQR -71% to -38%).
Risk profile
Overall: High
- ▲Small sample: 603 league minutes last season.
- ⚠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 | Nottingham Forest | Premier League | CM | 22 | 603 | 18% | 0 | 2 | 0.30 | 0.26 | 1588 | 1635 | 1.30 |
| 2024/25 | Nottingham Forest | Premier League | CM | 35 | 1904 | 56% | 2 | 1 | 0.14 | 0.19 | 1596 | 1573 | 1.23 |
| 2023/24 | Nottingham Forest | Premier League | CM | 35 | 1986 | 58% | 1 | 1 | 0.09 | 0.15 | 1567 | 1428 | 1.14 |
| 2022/23 | Nottingham Forest | Premier League | CM | 26 | 1839 | 54% | 0 | 2 | 0.10 | 0.19 | 1569 | 1433 | 1.23 |
Cups & continental competitions
| Season | Competition | Club | Apps | Min | G | A | Avg opp. Elo |
|---|---|---|---|---|---|---|---|
| 2025/26 | Uefa Europa League | Nottingham Forest | 12 | 690 | 2 | 2 | 1512 |
| 2024/25 | Fa Cup | Nottingham Forest | 4 | 385 | 2 | 0 | — |
| 2023/24 | Fa Cup | Nottingham Forest | 4 | 336 | 0 | 2 | — |
| 2022/23 | Fa Cup | Nottingham Forest | 1 | 25 | 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 |
|---|---|---|---|---|
| Ander Iturraspe Laliga · 2016 | 27 | €7.5M | €5.0M | fell |
| Mohamed Elneny Premier League · 2022 | 30 | €11.0M | €3.5M | fell |
| Morgan Schneiderlin Premier League · 2019 | 30 | €15.0M | €7.0M | fell |
| Francis Coquelin Laliga · 2018 | 27 | €12.0M | €19.0M | rose |
| Míchel Herrero Laliga · 2014 | 26 | €3.5M | €700k | fell |
| Manuel Lanzini Premier League · 2021 | 28 | €10.0M | €6.0M | fell |
| Isaac Hayden Premier League · 2022 | 27 | €8.0M | €1.8M | fell |
| Tom Cleverley Premier League · 2019 | 30 | €7.0M | €3.0M | fell |
Transfer history
Fees as reported by the source; undisclosed fees shown as unknown, never estimated.
| Date | From | To | Fee | MV at time |
|---|---|---|---|---|
| 31 May 2018 | Scunthorpe Utd. | Nott'm Forest | Free / loan | Data unavailable |
| 11 Jan 2018 | Nott'm Forest | Scunthorpe Utd. | Free / loan | Data unavailable |
| 10 Jan 2018 | Notts County | Nott'm Forest | Free / loan | Data unavailable |
| 4 Aug 2017 | Nott'm Forest | Notts County | Free / loan | Data unavailable |
| 31 May 2017 | Shrewsbury | Nott'm Forest | Free / loan | Data unavailable |
| 31 Jan 2017 | Nott'm Forest | Shrewsbury | Free / loan | Data unavailable |
| 25 Nov 2016 | Barrow | Nott'm Forest | Free / loan | Data unavailable |
| 26 Aug 2016 | Nott'm Forest | Barrow | Free / loan | Data unavailable |
| 1 Jul 2016 | Nottingham U18 | Nott'm Forest | 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.