Best Midfielders in the Superliga (Jul 2026)
Ranked by Analytical Strength Index
Market Overview: Superliga Midfielders 2025-26
Our database tracks 132 Superliga Midfielders in the 2025-26 season, representing 30 clubs with a combined market value of €94.5M. The average market value for Superliga Midfielders is €716K, with the average age at 28 years old.
The most valuable midfielder in the Superliga is Oliver Sørensen, worth €7.5M and playing for FC Midtjylland at 24 years old. The top 5 Midfielders average €5.0M in market value, including Denil Castillo and Benjamin Tahirović.
Age distribution shows the youngest tracked midfielder is Hunor Németh (19 years, FC Copenhagen, €500K), while the oldest is Kasper Lorentzen (40 years, FC Nordsjaelland, €400K). Research shows Midfielders typically peak at age 26-27.
Our 1-year forecast model projects 52 Midfielders (39%) will increase in market value over the next 12 months based on age-curve trajectories, current performance trends, and playing time analysis. The Superliga market for Midfielders remains actively developing with emerging talent in the 2025-26 season.
Explore Market Size by Position in Superliga
Interactive bubble chart showing predicted 2-year growth vs current age for all Superliga Midfielders. Identify undervalued assets and track market momentum across 30 clubs with €94.5M combined value.
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.
Age Distribution: Superliga Midfielders
The Superliga CM market shows 5 distinct age segments, with the largest cohort in the 30+ bracket (59 players, 45% of market). The 21-23 age group holds the most value at €28.9M, averaging €1.0M per player.
Top Midfielders by Age Bracket
U21 Years (8 players)
21-23 Years (28 players)
24-26 Years (21 players)
27-29 Years (16 players)
Market Value Distribution
Elite Tier Concentration
The top 14 Midfielders (11% of players) control €45.7M
Market Tiers
Market structure shows distributed value with mid (€5-15m) tier representing 2% of the Superliga CM pool.
Mid (€5-15M)
Emerging (<€5M)
Club Distribution: Superliga Midfielders
Among 30 Superliga clubs, FC Midtjylland leads with 10 Midfielders worth €24.3M (averaging €2.4M per player). The top 10 clubs account for 64% of tracked Midfielders.
FC Midtjylland (10 Midfielders)
Silkeborg IF (13 Midfielders)
Viborg FF (9 Midfielders)
Bröndby IF (9 Midfielders)
Player Rankings
Ranked by Analytical Strength Index. Click any player to view full profile, or click the chart icon to see value history.
Oliver Sørensen
FC Midtjylland • 24 years old
€6.5M
€7.5M
+15.6%
Expected: €7.7M
57.4
Denil Castillo
FC Midtjylland • 22 years old
€4.8M
€5.5M
+15.6%
Expected: €5.8M
53.4
Benjamin Tahirović
Bröndby IF • 23 years old
€3.9M
€4.5M
+15.6%
Expected: €4.8M
51.6
Thomas Jørgensen
Viborg FF • 20 years old
€3.5M
€4.0M
+15.6%
Expected: €4.6M
48.3
Magnus Mattsson
FC Copenhagen • 27 years old
€3.7M
€3.5M
-5.4%
Expected: €3.1M
47.1
Pelle Mattsson
Silkeborg IF • 24 years old
€2.6M
€3.0M
+15.6%
Expected: €3.1M
42.4
Kristoffer Olsson
FC Midtjylland • 31 years old
€3.2M
€2.5M
-22.6%
Expected: €2.1M
39.8
Mads Emil Madsen
Aarhus GF • 28 years old
€3.2M
€2.5M
-22.6%
Expected: €2.2M
39.4
Pedro Bravo
FC Midtjylland • 21 years old
€2.2M
€2.5M
+15.6%
Expected: €2.8M
39.4
Dani Silva
FC Midtjylland • 26 years old
€2.2M
€2.5M
+15.6%
Expected: €2.6M
39.2
Magnus Knudsen
Aarhus GF • 25 years old
€1.7M
€2.0M
+15.6%
Expected: €2.0M
36.9
Valdemar Byskov
FC Midtjylland • 21 years old
€1.7M
€2.0M
+15.6%
Expected: €2.2M
36.6
John Björkengren
Randers FC • 27 years old
€2.1M
€2.0M
-5.4%
Expected: €1.8M
36.6
Mads Larsen
Silkeborg IF • 24 years old
€1.5M
€1.7M
+15.6%
Expected: €1.7M
35.4
Casper Winther
Lyngby Boldklub • 23 years old
€1.3M
€1.5M
+15.6%
Expected: €1.6M
34.3
Mads Freundlich
Silkeborg IF • 23 years old
€1.3M
€1.5M
+15.6%
Expected: €1.6M
34.3
Lauge Sandgrav
Lyngby Boldklub • 21 years old
€1.3M
€1.5M
+15.6%
Expected: €1.7M
33.1
Jean Manuel Mbom
Viborg FF • 26 years old
€1.3M
€1.5M
+15.6%
Expected: €1.5M
32.9
Mads Søndergaard
Viborg FF • 23 years old
€1.0M
€1.2M
+15.6%
Expected: €1.3M
31.5
Ján Gregus
FC Copenhagen • 35 years old
€1.5M
€1.2M
-22.6%
Expected: €1.1M
31.1
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)
FC Midtjylland's Oliver Sørensen at 24 years old has the highest Pre-Peak Value Efficiency at 12.50×. That means Oliver Sørensen is valued 12.50× higher than the median player in the 24-26 age bracket-representing exceptional value before reaching peak age.
In second is FC Midtjylland's Denil Castillo, who is 22 years old, with a 9.17× PPVE. Third is Thomas Jørgensen of Viborg FF, who is 20 years old with a 8.00× 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 12.50× means the player is worth 1150% 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 | Oliver Sørensen FC Midtjylland | 24 | 24-26 | €7.5M | €600K | 12.50× |
| #2 | Denil Castillo FC Midtjylland | 22 | 21-23 | €5.5M | €600K | 9.17× |
| #3 | Thomas Jørgensen Viborg FF | 20 | U21 | €4.0M | €500K | 8.00× |
| #4 | Benjamin Tahirović Bröndby IF | 23 | 21-23 | €4.5M | €600K | 7.50× |
| #5 | Pelle Mattsson Silkeborg IF | 24 | 24-26 | €3.0M | €600K | 5.00× |
| #6 | Pedro Bravo FC Midtjylland | 21 | 21-23 | €2.5M | €600K | 4.17× |
| #7 | Magnus Knudsen Aarhus GF | 25 | 24-26 | €2.0M | €600K | 3.33× |
| #8 | Valdemar Byskov FC Midtjylland | 21 | 21-23 | €2.0M | €600K | 3.33× |
| #9 | Mads Larsen Silkeborg IF | 24 | 24-26 | €1.7M | €600K | 2.83× |
| #10 | Casper Winther Lyngby Boldklub | 23 | 21-23 | €1.5M | €600K | 2.50× |
| #11 | Lauge Sandgrav Lyngby Boldklub | 21 | 21-23 | €1.5M | €600K | 2.50× |
| #12 | Mads Freundlich Silkeborg IF | 23 | 21-23 | €1.5M | €600K | 2.50× |
| #13 | Mads Søndergaard Viborg FF | 23 | 21-23 | €1.2M | €600K | 2.00× |
| #14 | Julius Lorents Silkeborg IF | 20 | U21 | €800K | €500K | 1.60× |
| #15 | Jakob Vester Viborg FF | 21 | 21-23 | €900K | €600K | 1.50× |
| #16 | Lukas Björklund Sönderjyske Fodbold | 22 | 21-23 | €850K | €600K | 1.42× |
| #17 | Gustav Fraulo Lyngby Boldklub | 21 | 21-23 | €850K | €600K | 1.42× |
| #18 | Laurits Pedersen Randers FC | 20 | U21 | €700K | €500K | 1.40× |
| #19 | Tobias Lauritsen Vejle Boldklub | 22 | 21-23 | €800K | €600K | 1.33× |
| #20 | Mathias Jensen Bröndby IF | 21 | 21-23 | €750K | €600K | 1.25× |
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)
FC Midtjylland's Sofus Johannesen at 19 years old has the highest Return-to-Peak Potential at +40%. That means Hunor Németh is projected to appreciate 40% as they reach their peak age in 7 years-representing significant upside before entering their prime.
In second is FC Copenhagen's Hunor Németh, who is 19 years old, with a +40% RPP (7 years to peak). Third is Valdemar Møller of Aalborg BK, who is 19 years old with a +40% RPP (7 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 40% RPP means the player is expected to gain 40% 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 | Sofus Johannesen FC Midtjylland | 19 | 7 | €250K | €415K | +40% |
| #2 | Hunor Németh FC Copenhagen | 19 | 7 | €500K | €831K | +40% |
| #3 | Valdemar Møller Aalborg BK | 19 | 7 | €300K | €499K | +40% |
| #4 | Laurits Pedersen Randers FC | 20 | 6 | €700K | €1.1M | +35% |
| #5 | Mohamed Cherif Haidara Sönderjyske Fodbold | 20 | 6 | €500K | €773K | +35% |
| #6 | Thomas Jørgensen Viborg FF | 20 | 6 | €4.0M | €6.2M | +35% |
| #7 | Julius Lorents Silkeborg IF | 20 | 6 | €800K | €1.2M | +35% |
| #8 | Adam Claridge Bröndby IF | 20 | 6 | €300K | €464K | +35% |
| #9 | Oskar Boesen Silkeborg IF | 21 | 5 | €500K | €719K | +30% |
| #10 | Valdemar Byskov FC Midtjylland | 21 | 5 | €2.0M | €2.9M | +30% |
| #11 | Max Ejdum Odense Boldklub | 21 | 5 | €300K | €431K | +30% |
| #12 | Pedro Bravo FC Midtjylland | 21 | 5 | €2.5M | €3.6M | +30% |
| #13 | Gustav Fraulo Lyngby Boldklub | 21 | 5 | €850K | €1.2M | +30% |
| #14 | Magnus Isager AC Horsens | 21 | 5 | €150K | €216K | +30% |
| #15 | Jakob Vester Viborg FF | 21 | 5 | €900K | €1.3M | +30% |
| #16 | Lauge Sandgrav Lyngby Boldklub | 21 | 5 | €1.5M | €2.2M | +30% |
| #17 | Mathias Jensen Bröndby IF | 21 | 5 | €750K | €1.1M | +30% |
| #18 | Magnus Munck FC Nordsjaelland | 21 | 5 | €200K | €287K | +30% |
| #19 | Marius Papuga Hvidovre IF | 21 | 5 | €200K | €287K | +30% |
| #20 | Marinus Larsen Bröndby IF | 22 | 4 | €150K | €201K | +25% |
Risk-Adjusted Upside (RAU)
Upside potential weighted against forecast uncertainty. Higher RAU = better risk-reward profile.
Understanding Risk-Adjusted Upside (RAU)
Aalborg BK's Valdemar Møller has the highest Risk-Adjusted Upside at 53.6. That means Valdemar Møller has 19% upside potential with only 0% forecast uncertainty-representing excellent risk-reward for value appreciation.
In second is FC Midtjylland's Sofus Johannesen with a 53.6 RAU (19% upside, 0% uncertainty). Third is Hunor Németh of FC Copenhagen with a 53.6 RAU (19% upside, 0% uncertainty).
How RAU is calculated: RAU divides upside potential by forecast uncertainty (RAU = Upside % ÷ Uncertainty %). A RAU of 53.6 means the upside is 53.6× 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 | Valdemar Møller Aalborg BK | €357K | €304K-411K | +19% | 53.6 |
| #2 | Sofus Johannesen FC Midtjylland | €298K | €253K-342K | +19% | 53.6 |
| #3 | Hunor Németh FC Copenhagen | €595K | €506K-684K | +19% | 53.6 |
| #4 | Mohamed Cherif Haidara Sönderjyske Fodbold | €573K | €488K-659K | +15% | 42.8 |
| #5 | Adam Claridge Bröndby IF | €344K | €293K-395K | +15% | 42.8 |
| #6 | Laurits Pedersen Randers FC | €803K | €683K-923K | +15% | 42.8 |
| #7 | Thomas Jørgensen Viborg FF | €4.6M | €3.9M-5.3M | +15% | 42.8 |
| #8 | Julius Lorents Silkeborg IF | €917K | €780K-1.1M | +15% | 42.8 |
| #9 | Lauge Sandgrav Lyngby Boldklub | €1.7M | €1.4M-1.9M | +10% | 31.1 |
| #10 | Mathias Jensen Bröndby IF | €827K | €703K-950K | +10% | 31.1 |
| #11 | Oskar Boesen Silkeborg IF | €551K | €469K-634K | +10% | 31.1 |
| #12 | Pedro Bravo FC Midtjylland | €2.8M | €2.3M-3.2M | +10% | 31.1 |
| #13 | Valdemar Byskov FC Midtjylland | €2.2M | €1.9M-2.5M | +10% | 31.1 |
| #14 | Max Ejdum Odense Boldklub | €331K | €281K-380K | +10% | 31.1 |
| #15 | Gustav Fraulo Lyngby Boldklub | €937K | €797K-1.1M | +10% | 31.1 |
| #16 | Magnus Isager AC Horsens | €165K | €141K-190K | +10% | 31.1 |
| #17 | Jakob Vester Viborg FF | €992K | €844K-1.1M | +10% | 31.1 |
| #18 | Magnus Munck FC Nordsjaelland | €221K | €188K-253K | +10% | 31.1 |
| #19 | Marius Papuga Hvidovre IF | €221K | €188K-253K | +10% | 31.1 |
| #20 | Sherzod Esanov FC Buxoro | €321K | €283K-360K | +7% | 27.5 |
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: midfielder position shows weak 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)
Odense Boldklub's Troels Klöve in the 30+ age bracket has the highest Age-Share Concentration at +-20.4%. That means Kristoffer Olsson captures 24.3% of total market value while representing only 44.7% of players in their age group-showing dominant elite status.
In second is Lyngby Boldklub's Marcel Rømer with a +-20.4% ASC (24.3% value share vs 44.7% player share in 30+ bracket). Third is Ján Gregus of FC Copenhagen with a +-20.4% ASC (24.3% value vs 44.7% players in 30+ bracket).
How ASC is calculated: ASC = (% of total value) - (% of total players) in age bracket. A +-20.4% ASC means the player captures -20.4% 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 | Troels Klöve Odense Boldklub | 30+ | 24.3% | 44.7% | -20.4% |
| #2 | Marcel Rømer Lyngby Boldklub | 30+ | 24.3% | 44.7% | -20.4% |
| #3 | Ján Gregus FC Copenhagen | 30+ | 24.3% | 44.7% | -20.4% |
| #4 | Mathias Schlie Hobro IK | 30+ | 24.3% | 44.7% | -20.4% |
| #5 | Jeff Mensah Viborg FF | 30+ | 24.3% | 44.7% | -20.4% |
| #6 | Mustafa Amini Aarhus GF | 30+ | 24.3% | 44.7% | -20.4% |
| #7 | Lucas Ohlander FC Helsingör | 30+ | 24.3% | 44.7% | -20.4% |
| #8 | Mads Aaquist Viborg FF | 30+ | 24.3% | 44.7% | -20.4% |
| #9 | Mads Pedersen FC Midtjylland | 30+ | 24.3% | 44.7% | -20.4% |
| #10 | Lasse Vigen Silkeborg IF | 30+ | 24.3% | 44.7% | -20.4% |
| #11 | Max Power Aarhus GF | 30+ | 24.3% | 44.7% | -20.4% |
| #12 | Oscar Hiljemark Aalborg BK | 30+ | 24.3% | 44.7% | -20.4% |
| #13 | Marc Rochester Sörensen Silkeborg IF | 30+ | 24.3% | 44.7% | -20.4% |
| #14 | Juri Cisotti FCSB | 30+ | 24.3% | 44.7% | -20.4% |
| #15 | Andrew Hjulsager Vejle Boldklub | 30+ | 24.3% | 44.7% | -20.4% |
| #16 | Nana Welbeck Odense Boldklub | 30+ | 24.3% | 44.7% | -20.4% |
| #17 | Ayo Simon Okosun Odense Boldklub | 30+ | 24.3% | 44.7% | -20.4% |
| #18 | Hamza Barry Vejle Boldklub | 30+ | 24.3% | 44.7% | -20.4% |
| #19 | Aron Elís Thrándarson Odense Boldklub | 30+ | 24.3% | 44.7% | -20.4% |
| #20 | Jeppe Illum Vendsyssel FF | 30+ | 24.3% | 44.7% | -20.4% |
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: 3 immediate targets, 33 standard acquisitions, 0 watch-list prospects, 32 at peak.
BUY NOW - High Upside
WATCH LIST - High Upside
No players in this category
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, 17 premium.
Value Positioning vs Peers
| Player | Market Value | Position Median | Z-Score | Assessment |
|---|---|---|---|---|
Mikkel Basse FC Helsingör | €150K | €300K | -0.75 | Good Value |
Valance Nambishi Silkeborg IF | €150K | €300K | -0.75 | Good Value |
Sofus Johannesen FC Midtjylland | €250K | €300K | -0.50 | Fair Value |
Jesper Christjansen Lyngby Boldklub | €125K | €300K | -0.50 | Fair Value |
Asger Bust Aalborg BK | €200K | €300K | -0.50 | Fair Value |
Frederik Mortensen AC Horsens | €200K | €300K | -0.50 | Fair Value |
Valdemar Møller Aalborg BK | €300K | €300K | -0.40 | Fair Value |
Mathias Schlie Hobro IK | €150K | €300K | -0.40 | Fair Value |
Mads Aaquist Viborg FF | €150K | €300K | -0.40 | Fair Value |
Marc Rochester Sörensen Silkeborg IF | €150K | €300K | -0.40 | Fair Value |
Jeppe Illum Vendsyssel FF | €150K | €300K | -0.40 | Fair Value |
Andreas Blomqvist Aalborg BK | €150K | €300K | -0.40 | Fair Value |
Christian Rye Aalborg BK | €150K | €300K | -0.40 | Fair Value |
Mathias Thrane Odense Boldklub | €150K | €300K | -0.40 | Fair Value |
Ibrahim Moro Silkeborg IF | €150K | €300K | -0.40 | Fair Value |
Mike Jensen Bröndby IF | €150K | €300K | -0.40 | Fair Value |
Jacob Schoop Vejle Boldklub | €150K | €300K | -0.40 | Fair Value |
Markus Gustafsson Viborg FF | €150K | €300K | -0.40 | Fair Value |
Adam Claridge Bröndby IF | €300K | €300K | -0.40 | Fair Value |
Björn Daníel Sverrisson Aarhus GF | €150K | €300K | -0.40 | Fair Value |
How We Rank Superliga Midfielders
Our Analytical Strength Index is calibrated specifically for 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 CM
Historical Achievement Index (35%)
Peak career market value for Superliga 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 Superliga 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 26-27 with 6.0%/year decline. Pre-peak players score higher on development trajectory.
Competition Level Adjustment (3%)
Superliga competition level factored into comparative strength assessment.
Performance Expectations Multiplier (2%)
Players at clubs with Champions League pedigree face higher performance standards and tactical complexity, contributing to development and market validation.
CM Performance Benchmarks
Peak Age: 26-27 years (technical skill and tactical awareness)
Decline Rate: 6.0% per year (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: Midfielder -6.0%/year post-peak, +5%/year pre-peak
• 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 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 Superliga Midfielders in the 2025-26 season
Who are the most valuable Midfielders in the Superliga in 2025-26?
The most valuable midfielder in the Superliga in 2025-26 is Oliver Sørensen, who is worth €7.5M and plays for FC Midtjylland. The second most valuable is Denil Castillo (€5.5M, FC Midtjylland), followed by Benjamin Tahirović (€4.5M, Bröndby IF). Our database tracks 132 Superliga Midfielders with comprehensive market valuations updated for the 2025-26 season.
How are Superliga Midfielders ranked?
Superliga Midfielders are ranked by our proprietary Analytical Strength Index, which is specifically calibrated for 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 Superliga 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 Midfielders peak?
Midfielders typically peak at age 26-27, with a decline rate of 6.0% per year after peak. Central midfielders require a blend of physicality, technical skill, and tactical awareness. The optimal playing time for peak performance is around 2,400-2,500 minutes per season.
How much does it cost to sign a top midfielder from the Superliga?
Transfer fees for Superliga Midfielders vary significantly based on market value, contract length, and club bargaining position. For the top-ranked midfielder Oliver Sørensen (market value: €7.5M), estimated transfer fees would range from €6.0M to €10.5M 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 Superliga transactions.
What is the value forecast for Superliga Midfielders?
Our 1-year forecast model projects market value changes for Superliga Midfielders based on age-curve depreciation, historical trajectory, and playing time adjustments. The forecast combines three factors: age-based appreciation/depreciation (pre-peak players gain ~5% per year toward peak age, post-peak players decline at position-specific rates), 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 Superliga midfielder data come from?
Our Superliga 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 Superliga sources and updated monthly for the 2025-26 season to ensure accuracy for recruitment and investment decisions.