Best Attacking Midfielders in the Bundesliga (Aug 2026)
Ranked by Analytical Strength Index
Use the search bar below to find specific players, or apply filters to narrow results by club, age range, or market value. Click the chart icon next to any player to view their historical value trajectory and forecast.
Player Rankings
Ranked by Analytical Strength Index. Click any player to view full profile, or click the chart icon to see value history.
Jamal Musiala
Bayern Munich • 23 years old
€86.5M
€100.0M
+15.6%
Expected: €94.6M
94.6
Lennart Karl
Bayern Munich • 18 years old
€51.9M
€60.0M
+15.6%
Expected: €63.3M
88.6
Ibrahim Maza
Bayer 04 Leverkusen • 20 years old
€38.9M
€45.0M
+15.6%
Expected: €47.5M
86.0
Can Uzun
Eintracht Frankfurt • 20 years old
€38.9M
€45.0M
+15.6%
Expected: €47.5M
86.0
Xavi Simons
RB Leipzig • 23 years old
€34.6M
€40.0M
+15.6%
Expected: €38.9M
85.8
Christoph Baumgartner
RB Leipzig • 27 years old
€34.6M
€40.0M
+15.6%
Expected: €39.9M
84.8
Bilal El Khannouss
VfB Stuttgart • 22 years old
€30.3M
€35.0M
+15.6%
Expected: €35.3M
83.7
Malik Tillman
Bayer 04 Leverkusen • 24 years old
€25.9M
€30.0M
+15.6%
Expected: €29.6M
79.1
Brajan Gruda
RB Leipzig • 22 years old
€24.2M
€28.0M
+15.6%
Expected: €28.4M
77.4
Paul Nebel
1.FSV Mainz 05 • 23 years old
€15.6M
€18.0M
+15.6%
Expected: €17.5M
72.3
Fábio Vieira
Hamburger SV • 26 years old
€15.6M
€18.0M
+15.6%
Expected: €17.1M
71.8
Farès Chaïbi
Eintracht Frankfurt • 23 years old
€13.0M
€15.0M
+15.6%
Expected: €14.6M
70.0
Romano Schmid
SV Werder Bremen • 26 years old
€13.0M
€15.0M
+15.6%
Expected: €14.2M
69.5
Julian Brandt
Borussia Dortmund • 30 years old
€19.4M
€15.0M
-22.6%
Expected: €9.9M
69.4
Alexis Claude-Maurice
FC Augsburg • 28 years old
€15.9M
€15.0M
-5.4%
Expected: €12.6M
69.1
Mert Kömür
FC Augsburg • 21 years old
€10.4M
€12.0M
+15.6%
Expected: €12.2M
66.4
Anton Kade
FC Augsburg • 22 years old
€8.6M
€10.0M
+15.6%
Expected: €10.1M
60.9
Arijon Ibrahimovic
1. Fußballclub Heidenheim 1846 • 20 years old
€8.6M
€10.0M
+15.6%
Expected: €11.0M
60.1
Léo Scienza
1. Fußballclub Heidenheim 1846 • 27 years old
€7.8M
€9.0M
+15.6%
Expected: €8.6M
59.1
Andrija Maksimovic
RB Leipzig • 19 years old
€7.8M
€9.0M
+15.6%
Expected: €9.9M
58.3
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Explore Market Size by Position in Bundesliga
Interactive bubble chart showing predicted 2-year growth vs current age for all Bundesliga Attacking Midfielders. Identify undervalued assets and track market momentum across 29 clubs with €679.1M combined value.
Age Distribution: Bundesliga Attacking Midfielders
The Bundesliga CAM market shows 5 distinct age segments, with the largest cohort in the 30+ bracket (48 players, 43% of market). The 21-23 age group holds the most value at €275.4M, averaging €13.1M per player.
Top Attacking Midfielders by Age Bracket
U21 Years (11 players)
21-23 Years (21 players)
24-26 Years (14 players)
27-29 Years (18 players)
Market Value Distribution
Elite Tier Concentration
The top 12 Attacking Midfielders (11% of players) control €474.0M
Market Tiers
Market structure shows distributed value with elite (€50m+) tier representing 2% of the Bundesliga CAM pool.
Elite (€50M+)
Premium (€30-50M)
High (€15-30M)
Club Distribution: Bundesliga Attacking Midfielders
Among 29 Bundesliga clubs, Bayern Munich leads with 4 Attacking Midfielders worth €163.8M (averaging €40.9M per player). The top 10 clubs account for 60% of tracked Attacking Midfielders.
Bayern Munich (4 Attacking Midfielders)
RB Leipzig (5 Attacking Midfielders)
Bayer 04 Leverkusen (6 Attacking Midfielders)
Eintracht Frankfurt (10 Attacking Midfielders)
Scout Tools
Advanced analytics for scouting and recruitment decisions. Each tool provides unique insights into player value, potential, and market dynamics.
Pre-Peak Value Efficiency (PPVE)
Identifies pre-peak players offering exceptional value relative to their age bracket. Higher PPVE = better value.
Understanding Pre-Peak Value Efficiency (PPVE)
Bayer 04 Leverkusen's Malik Tillman at 24 years old has the highest Pre-Peak Value Efficiency at 37.50×. That means Malik Tillman is valued 37.50× higher than the median player in the 24-26 age bracket-representing exceptional value before reaching peak age.
In second is Bayern Munich's Jamal Musiala, who is 23 years old, with a 28.57× PPVE. Third is Xavi Simons of RB Leipzig, who is 23 years old with a 11.43× PPVE.
How PPVE is calculated: PPVE compares a player's current market value to the median value of all players in their age bracket. A PPVE of 37.50× means the player is worth 3650% more than typical players their age-making them high-value targets before they reach peak value.
PPVE by Age Bracket
| Rank | Player | Age | Bracket | Current Value | Bracket Median | PPVE |
|---|---|---|---|---|---|---|
| #1 | Malik Tillman Bayer 04 Leverkusen | 24 | 24-26 | €30.0M | €800K | 37.50× |
| #2 | Jamal Musiala Bayern Munich | 23 | 21-23 | €100.0M | €3.5M | 28.57× |
| #3 | Xavi Simons RB Leipzig | 23 | 21-23 | €40.0M | €3.5M | 11.43× |
| #4 | Lennart Karl Bayern Munich | 18 | U21 | €60.0M | €6.0M | 10.00× |
| #5 | Bilal El Khannouss VfB Stuttgart | 22 | 21-23 | €35.0M | €3.5M | 10.00× |
| #6 | Brajan Gruda RB Leipzig | 22 | 21-23 | €28.0M | €3.5M | 8.00× |
| #7 | Can Uzun Eintracht Frankfurt | 20 | U21 | €45.0M | €6.0M | 7.50× |
| #8 | Ibrahim Maza Bayer 04 Leverkusen | 20 | U21 | €45.0M | €6.0M | 7.50× |
| #9 | Albert Grønbaek Hamburger SV | 25 | 24-26 | €5.0M | €800K | 6.25× |
| #10 | Paul Nebel 1.FSV Mainz 05 | 23 | 21-23 | €18.0M | €3.5M | 5.14× |
| #11 | Farès Chaïbi Eintracht Frankfurt | 23 | 21-23 | €15.0M | €3.5M | 4.29× |
| #12 | Shinta Appelkamp Fortuna Düsseldorf | 25 | 24-26 | €3.0M | €800K | 3.75× |
| #13 | Mert Kömür FC Augsburg | 21 | 21-23 | €12.0M | €3.5M | 3.43× |
| #14 | Immanuel Pherai Hamburger SV | 25 | 24-26 | €2.5M | €800K | 3.13× |
| #15 | Anton Kade FC Augsburg | 22 | 21-23 | €10.0M | €3.5M | 2.86× |
| #16 | Arijon Ibrahimovic 1. Fußballclub Heidenheim 1846 | 20 | U21 | €10.0M | €6.0M | 1.67× |
| #17 | Andrija Maksimovic RB Leipzig | 19 | U21 | €9.0M | €6.0M | 1.50× |
| #18 | Muhammed Damar TSG 1899 Hoffenheim | 22 | 21-23 | €5.0M | €3.5M | 1.43× |
| #19 | Giovanni Reyna Borussia Dortmund | 23 | 21-23 | €4.0M | €3.5M | 1.14× |
| #20 | Lilian Egloff VfB Stuttgart | 24 | 24-26 | €800K | €800K | 1.00× |
Return-to-Peak Potential (RPP)
Recovery potential from current value to forecasted peak. Shows how much upside remains for players approaching their prime.
Understanding Return-to-Peak Potential (RPP)
RB Leipzig's Viggo Gebel at 18 years old has the highest Return-to-Peak Potential at +48%. That means Viggo Gebel is projected to appreciate 48% as they reach their peak age in 8 years-representing significant upside before entering their prime.
In second is Bayern Munich's Lennart Karl, who is 18 years old, with a +48% RPP (8 years to peak). Third is Love Arrhov of Eintracht Frankfurt, who is 18 years old with a +48% RPP (8 years to peak).
How RPP is calculated: RPP compares a player's current market value to their forecasted peak value, calculating the percentage appreciation potential. A 48% RPP means the player is expected to gain 48% value as they enter their prime-making them excellent growth investments.
Recovery Potential by Player
| Rank | Player | Age | Years to Peak | Current | Peak Forecast | RPP % |
|---|---|---|---|---|---|---|
| #1 | Viggo Gebel RB Leipzig | 18 | 8 | €350K | €673K | +48% |
| #2 | Lennart Karl Bayern Munich | 18 | 8 | €60.0M | €115.3M | +48% |
| #3 | Love Arrhov Eintracht Frankfurt | 18 | 8 | €5.0M | €9.6M | +48% |
| #4 | Francis Onyeka Bayer 04 Leverkusen | 19 | 7 | €6.0M | €10.7M | +44% |
| #5 | Andrija Maksimovic RB Leipzig | 19 | 7 | €9.0M | €16.1M | +44% |
| #6 | Patrice Covic SV Werder Bremen | 19 | 7 | €4.0M | €7.1M | +44% |
| #7 | Noah Darvich VfB Stuttgart | 19 | 7 | €4.0M | €7.1M | +44% |
| #8 | Can Uzun Eintracht Frankfurt | 20 | 6 | €45.0M | €74.8M | +40% |
| #9 | Ibrahim Maza Bayer 04 Leverkusen | 20 | 6 | €45.0M | €74.8M | +40% |
| #10 | Arijon Ibrahimovic 1. Fußballclub Heidenheim 1846 | 20 | 6 | €10.0M | €16.6M | +40% |
| #11 | Bence Dárdai VfL Wolfsburg | 20 | 6 | €5.0M | €8.3M | +40% |
| #12 | Mert Kömür FC Augsburg | 21 | 5 | €12.0M | €18.5M | +35% |
| #13 | Laurin Ulrich VfB Stuttgart | 21 | 5 | €500K | €773K | +35% |
| #14 | Florian Micheler TSG 1899 Hoffenheim | 21 | 5 | €500K | €773K | +35% |
| #15 | Maurice Krattenmacher Bayern Munich | 21 | 5 | €3.5M | €5.4M | +35% |
| #16 | Ayman Aourir Bayer 04 Leverkusen | 21 | 5 | €300K | €464K | +35% |
| #17 | Leon Opitz SV Werder Bremen | 21 | 5 | €600K | €927K | +35% |
| #18 | Omar Megeed Hamburger SV | 21 | 5 | €150K | €232K | +35% |
| #19 | Krisztián Lisztes Eintracht Frankfurt | 21 | 5 | €1.7M | €2.6M | +35% |
| #20 | Anton Kade FC Augsburg | 22 | 4 | €10.0M | €14.4M | +30% |
Risk-Adjusted Upside (RAU)
Upside potential weighted against forecast uncertainty. Higher RAU = better risk-reward profile.
Understanding Risk-Adjusted Upside (RAU)
RB Leipzig's Viggo Gebel has the highest Risk-Adjusted Upside at 31.1. That means Viggo Gebel has 10% upside potential with only 0% forecast uncertainty-representing excellent risk-reward for value appreciation.
In second is Bayer 04 Leverkusen's Francis Onyeka with a 31.1 RAU (10% upside, 0% uncertainty). Third is Patrice Covic of SV Werder Bremen with a 31.1 RAU (10% upside, 0% uncertainty).
How RAU is calculated: RAU divides upside potential by forecast uncertainty (RAU = Upside % ÷ Uncertainty %). A RAU of 31.1 means the upside is 31.1× greater than the uncertainty-making it a high-confidence growth opportunity. Target RAU ≥2.0 for balanced risk-reward.
Risk-Adjusted Upside by Player
| Rank | Player | Expected | Range | Upside % | RAU |
|---|---|---|---|---|---|
| #1 | Viggo Gebel RB Leipzig | €386K | €328K-444K | +10% | 31.1 |
| #2 | Francis Onyeka Bayer 04 Leverkusen | €6.6M | €5.6M-7.6M | +10% | 31.1 |
| #3 | Patrice Covic SV Werder Bremen | €4.4M | €3.8M-5.1M | +10% | 31.1 |
| #4 | Love Arrhov Eintracht Frankfurt | €5.5M | €4.7M-6.3M | +10% | 31.1 |
| #5 | Arijon Ibrahimovic 1. Fußballclub Heidenheim 1846 | €11.0M | €9.4M-12.7M | +10% | 31.1 |
| #6 | Bence Dárdai VfL Wolfsburg | €5.5M | €4.7M-6.3M | +10% | 31.1 |
| #7 | Noah Darvich VfB Stuttgart | €4.4M | €3.8M-5.1M | +10% | 31.1 |
| #8 | Andrija Maksimovic RB Leipzig | €9.9M | €8.4M-11.4M | +10% | 31.1 |
| #9 | Can Uzun Eintracht Frankfurt | €47.5M | €40.4M-54.6M | +6% | 17.6 |
| #10 | Ibrahim Maza Bayer 04 Leverkusen | €47.5M | €40.4M-54.6M | +6% | 17.6 |
| #11 | Lennart Karl Bayern Munich | €63.3M | €53.9M-72.8M | +6% | 17.6 |
| #12 | Patrick Finger Eintracht Frankfurt | €154K | €134K-174K | +2% | 9.1 |
| #13 | Immanuel Pherai Hamburger SV | €2.6M | €2.2M-2.9M | +2% | 9.1 |
| #14 | Albert Grønbaek Hamburger SV | €5.1M | €4.5M-5.8M | +2% | 9.1 |
| #15 | Daniel Halfar 1.FC Köln | €410K | €356K-463K | +2% | 9.1 |
| #16 | Alaa Bakir Borussia Dortmund | €205K | €178K-231K | +2% | 9.1 |
| #17 | Lilian Egloff VfB Stuttgart | €819K | €713K-926K | +2% | 9.1 |
| #18 | Joshua Schwirten 1.FC Köln | €205K | €178K-231K | +2% | 9.1 |
| #19 | Kelvin Ofori Fortuna Düsseldorf | €410K | €356K-463K | +2% | 9.1 |
| #20 | Shinta Appelkamp Fortuna Düsseldorf | €3.1M | €2.7M-3.5M | +2% | 9.1 |
Roster Pressure Index (RPI)
Squad depth pressure based on Z-score distribution. Negative RPI = thin depth, positive = deep roster.
What This Shows
Z-Score explained: Measures how many standard deviations a player's strength is from the position average. Z-Score = 0 means average, +1.0 is one standard deviation above average, -1.0 is below average.
How to use: RPI < -1.0 indicates critical depth shortage. These positions need immediate reinforcement. RPI > +1.0 suggests strong depth, allowing selective, high-value additions only.
Current market: attacking midfielder position shows strong depth (avg Z-score: 0.00). RPI: 0.00.
Position Depth Analysis
Highest Z-Scores
Lowest Z-Scores
Age-Share Concentration (ASC)
Identifies players capturing disproportionate value relative to age group representation. Positive ASC = value concentration.
Understanding Age-Share Concentration (ASC)
1.FC Köln's Florian Kainz in the 30+ age bracket has the highest Age-Share Concentration at +-36.2%. That means Julian Brandt captures 6.7% of total market value while representing only 42.9% of players in their age group-showing dominant elite status.
In second is Borussia Mönchengladbach's Kevin Stöger with a +-36.2% ASC (6.7% value share vs 42.9% player share in 30+ bracket). Third is Sebastian Ernst of Hannover 96 with a +-36.2% ASC (6.7% value vs 42.9% players in 30+ bracket).
How ASC is calculated: ASC = (% of total value) - (% of total players) in age bracket. A +-36.2% ASC means the player captures -36.2% more market value than their numerical representation-indicating marquee status. ASC > +15% = elite dominance, ASC < -15% = potential value targets.
Value Concentration by Player
| Rank | Player | Age Bracket | Value Share | Player Share | ASC |
|---|---|---|---|---|---|
| #1 | Florian Kainz 1.FC Köln | 30+ | 6.7% | 42.9% | -36.2% |
| #2 | Kevin Stöger Borussia Mönchengladbach | 30+ | 6.7% | 42.9% | -36.2% |
| #3 | Sebastian Ernst Hannover 96 | 30+ | 6.7% | 42.9% | -36.2% |
| #4 | Gianluca Korte Eintracht Braunschweig | 30+ | 6.7% | 42.9% | -36.2% |
| #5 | Robert Zulj TSG 1899 Hoffenheim | 30+ | 6.7% | 42.9% | -36.2% |
| #6 | Patrick Weihrauch Bayern Munich | 30+ | 6.7% | 42.9% | -36.2% |
| #7 | Sebastian Maier Hannover 96 | 30+ | 6.7% | 42.9% | -36.2% |
| #8 | Federico Palacios 1.FC Nuremberg | 30+ | 6.7% | 42.9% | -36.2% |
| #9 | Xizhe Zhang VfL Wolfsburg | 30+ | 6.7% | 42.9% | -36.2% |
| #10 | Julian Günther-Schmidt FC Augsburg | 30+ | 6.7% | 42.9% | -36.2% |
| #11 | Kevin-Prince Boateng Hertha BSC | 30+ | 6.7% | 42.9% | -36.2% |
| #12 | Philipp Förster VfL Bochum | 30+ | 6.7% | 42.9% | -36.2% |
| #13 | Jean-Paul Boëtius Hertha BSC | 30+ | 6.7% | 42.9% | -36.2% |
| #14 | Julian Brandt Borussia Dortmund | 30+ | 6.7% | 42.9% | -36.2% |
| #15 | Kerem Bülbül FC Ingolstadt 04 | 30+ | 6.7% | 42.9% | -36.2% |
| #16 | Tolcay Cigerci Hamburger SV | 30+ | 6.7% | 42.9% | -36.2% |
| #17 | Joel Gerezgiher Eintracht Frankfurt | 30+ | 6.7% | 42.9% | -36.2% |
| #18 | Todor Nedelev 1.FSV Mainz 05 | 30+ | 6.7% | 42.9% | -36.2% |
| #19 | Bruno Nazário TSG 1899 Hoffenheim | 30+ | 6.7% | 42.9% | -36.2% |
| #20 | Nicolas Sessa VfB Stuttgart | 30+ | 6.7% | 42.9% | -36.2% |
Buy-Now vs Wait-List Map
Categorizes players by age position and upside potential to guide timing of acquisition.
What This Shows
How to use:"Buy Now - High Upside" = immediate priority targets."Watch List" = monitor for 6-12 months."Peak" = pay premium for proven performers."Aging" = short-term depth only.
Current market: 0 immediate targets, 11 standard acquisitions, 21 watch-list prospects, 30 at peak.
BUY NOW - High Upside
No players in this category
WATCH LIST - High Upside
BUY NOW - Medium Upside
PEAK Players
Price vs Peer Z-Score
IQR-based pricing analysis relative to position peers. Identifies over/undervalued players vs market.
What This Shows
How to use: Z-score < -1.5 = significantly undervalued (potential bargain). Z-score > +1.5 = premium pricing (requires strong justification). Within ±1.0 = fair market value.
Current market: Position median is €225K. 0 undervalued, 10 premium.
Value Positioning vs Peers
| Player | Market Value | Position Median | Z-Score | Assessment |
|---|---|---|---|---|
Viggo Gebel RB Leipzig | €350K | €700K | -1.00 | Good Value |
Romano Schmid SV Werder Bremen | €15.0M | €700K | -1.00 | Good Value |
Lovro Majer VfL Wolfsburg | €8.0M | €700K | -1.00 | Good Value |
Bilal El Khannouss VfB Stuttgart | €35.0M | €700K | -1.00 | Good Value |
Muhammed Damar TSG 1899 Hoffenheim | €5.0M | €700K | -0.71 | Good Value |
Maurice Covic Hertha BSC | €125K | €700K | -0.29 | Fair Value |
Vladislav Cherny Arminia Bielefeld | €125K | €700K | -0.27 | Fair Value |
Jakob Löpping SV Werder Bremen | €125K | €700K | -0.27 | Fair Value |
Maximilian Pronichev Hertha BSC | €175K | €700K | -0.25 | Fair Value |
Romario Rösch 1.FSV Mainz 05 | €175K | €700K | -0.25 | Fair Value |
Love Arrhov Eintracht Frankfurt | €5.0M | €700K | -0.25 | Fair Value |
Bence Dárdai VfL Wolfsburg | €5.0M | €700K | -0.25 | Fair Value |
Omar Megeed Hamburger SV | €150K | €700K | -0.25 | Fair Value |
Farès Chaïbi Eintracht Frankfurt | €15.0M | €700K | -0.23 | Fair Value |
Gianluca Korte Eintracht Braunschweig | €125K | €700K | -0.22 | Fair Value |
Kerem Bülbül FC Ingolstadt 04 | €125K | €700K | -0.22 | Fair Value |
Joel Gerezgiher Eintracht Frankfurt | €125K | €700K | -0.22 | Fair Value |
Florian Trinks SpVgg Greuther Fürth | €125K | €700K | -0.22 | Fair Value |
Torben Müsel Borussia Mönchengladbach | €225K | €700K | -0.22 | Fair Value |
Sercan Sararer VfB Stuttgart | €150K | €700K | -0.19 | Fair Value |
Market Overview: Bundesliga Attacking Midfielders 2022-23
Our database tracked 112 Bundesliga Attacking Midfielders in the 2022-23 season, representing 29 clubs with a combined market value of €679.1M. The average market value for Bundesliga Attacking Midfielders was €6.1M, with the average age at 28 years old.
The most valuable attacking midfielder in the Bundesliga was Jamal Musiala, worth €100.0M and played for Bayern Munich at 23 years old. The top 5 Attacking Midfielders averaged €58.0M in market value, including Lennart Karl and Ibrahim Maza.
Age distribution showed the youngest tracked attacking midfielder was Lennart Karl (18 years, Bayern Munich, €60.0M), while the oldest was Ronny (40 years, Hertha BSC, €300K). Research shows Attacking Midfielders typically peak at age 27.
Historical analysis showed 33 Attacking Midfielders (29%) increased in market value over the following 12 months based on age-curve trajectories, then-current performance trends, and playing time analysis. The Bundesliga market for Attacking Midfielders remained highly competitive with significant transfer activity in the 2022-23 season.
How We Rank Bundesliga Attacking Midfielders
Our Analytical Strength Index is calibrated specifically for attacking midfielders, using position-specific age curves and playing time benchmarks. The model draws from academic research on player valuation (Franck & Nüesch, 2012) and age-performance curves (Dendir, 2016).
Scoring Components for CAM
Historical Achievement Index (35%)
Peak career market value for Bundesliga attacking midfielders, reflecting proven track record and reputation. Uses log-scale to account for exponential value distribution at elite level.
Current Performance Proxy (30%)
Present market value for Bundesliga attacking midfielders, capturing recent form, injuries, and current performance level. Weighted to reflect age-related depreciation patterns.
Playing Time Utilization (18%)
Midfielders with 2,400+ minutes score highest, indicating regular starting role and sustained performance.
Age-Adjusted Performance Curve (12%)
Midfielders peak at 27 with 6.0%/year decline. Pre-peak players score higher on development trajectory.
Competition Level Adjustment (3%)
Bundesliga receives Top 5 European league premium for competitive intensity and quality of opposition.
Performance Expectations Multiplier (2%)
Players at clubs with Champions League pedigree face higher performance standards and tactical complexity, contributing to development and market validation.
CAM Performance Benchmarks
Peak Age: 27 years (technical skill and tactical awareness)
Decline Rate: Widening steps, not a flat rate: about -5% at 28, -10% at 29, steeper from 30 (technical skills age better than physical attributes)
Optimal Minutes: 2,400-2,500 per season (balance of involvement and recovery)
1-Year Market Value Forecast
Probabilistic model combining age-curve depreciation, value momentum, and playing time factors:
• Age Factor: Compounding and banded around a peak of 27: +25%/year through age 21, +15% at 22-23, +10% up to the year before peak, then -5%, -10% and steeper with each later band. Growth is damped above €30M.
• Value Trajectory: Near career peak (>95% of peak value): +3% momentum | Moderate decline: -5%
• Playing Time Factor: Regular starters (+2%), Squad rotation (-2%)
• Forecast Range: ±12-15% confidence interval
Research Foundation
• Dendir (2016): Age-performance curves for attacking midfielders
• Carmichael et al. (2011): Player depreciation in top leagues
• Franck & Nüesch (2012): Hedonic pricing models for talent valuation
• Szymanski, S. (2015). Money and Soccer: A Soccernomics Guide
Frequently Asked Questions
Common questions about Bundesliga Attacking Midfielders in the 2022-23 season
Who are the most valuable Attacking Midfielders in the Bundesliga in 2022-23?
The most valuable attacking midfielder in the Bundesliga in 2022-23 is Jamal Musiala, who is worth €100.0M and plays for Bayern Munich. The second most valuable is Lennart Karl (€60.0M, Bayern Munich), followed by Ibrahim Maza (€45.0M, Bayer 04 Leverkusen). Our database tracks 112 Bundesliga Attacking Midfielders with comprehensive market valuations updated for the 2022-23 season.
How are Bundesliga Attacking Midfielders ranked?
Bundesliga Attacking Midfielders are ranked by our proprietary Analytical Strength Index, which is specifically calibrated for Attacking Midfielders. The score combines six factors: Historical Achievement Index (35%) measuring peak career value, Current Performance Proxy (30%) reflecting recent market signals, Playing Time Utilization (18%) tracking minutes played, Age-Adjusted Performance Curve (12%) using position-specific peak ages, League Quality Coefficient (3%) for Bundesliga competition level, and Club Tier Multiplier (2%) accounting for club prestige. This methodology is grounded in academic research including work by Dendir (2016) on age-performance curves and Franck & Nüesch (2012) on hedonic pricing models.
What age do Attacking Midfielders peak?
Attacking midfielders typically peak at age 27, in line with other midfielders. In our valuation model, value then falls in widening steps rather than at a flat annual rate: around -5% at 28, -10% at 29, and steeper from 30. This position demands high technical ability, creativity, and burst acceleration. The optimal playing time is around 2,400 minutes per season.
How much does it cost to sign a top attacking midfielder from the Bundesliga?
Transfer fees for Bundesliga Attacking Midfielders vary significantly based on market value, contract length, and club bargaining position. For the top-ranked attacking midfielder Jamal Musiala (market value: €100.0M), estimated transfer fees would range from €80.0M to €140.0M depending on contract situation. Players with longer contracts (3+ years) command premium fees (1.2-1.4× market value), while those in the final year may be available for 0.8-1.1× market value. Our fee estimates are derived from historical transfer patterns and contract-clock modifiers validated against actual Bundesliga transactions.
What is the value forecast for Bundesliga Attacking Midfielders?
Our 1-year forecast model projects market value changes for Bundesliga Attacking Midfielders based on age-curve depreciation, historical trajectory, and playing time adjustments. The forecast combines three factors: age-based appreciation/depreciation (pre-peak players compound gains toward their position's peak age, up to +25% a year through age 21 and easing as they approach it, while post-peak players fall in widening steps), market trajectory momentum (comparing current to peak value), and playing time confidence (regular starters receive +2% boost). Forecast confidence intervals account for position-specific volatility-midfielders have ±12-15% volatility. Young players (under 22) and older players (over 32) receive 1.15× uncertainty multipliers due to unpredictable development or decline patterns.
Where does the Bundesliga attacking midfielder data come from?
Our Bundesliga attacking midfielder data is sourced from Football Analytics AI's proprietary Transfer Intelligence Database, which aggregates market valuations, player statistics, contract information, and transfer histories from multiple industry sources. Market values are updated regularly based on player performance, injuries, contract negotiations, and transfer market activity. We enhance this data with our proprietary analytics including position-specific scoring algorithms, age-performance curves calibrated to academic research, and statistical forecast models. All data is validated against official Bundesliga sources and updated monthly for the 2022-23 season to ensure accuracy for recruitment and investment decisions.