Best Defensive Midfielders in the Superliga (Jul 2026)
Ranked by Analytical Strength Index
Market Overview: Superliga Defensive Midfielders 2025-26
Our database tracks 61 Superliga Defensive Midfielders in the 2025-26 season, representing 30 clubs with a combined market value of €33.4M. The average market value for Superliga Defensive Midfielders is €548K, with the average age at 31 years old.
The most valuable defensive midfielder in the Superliga is William Clem, worth €3.0M and playing for FC Copenhagen at 22 years old. The top 5 Defensive Midfielders average €2.5M in market value, including Tudor Băluță and Kasper Davidsen.
Age distribution shows the youngest tracked defensive midfielder is Kasper Davidsen (21 years, Aalborg BK, €2.5M), while the oldest is Erik Friberg (40 years, Esbjerg fB, €150K). Research shows Defensive Midfielders typically peak at age 26-27.
Our 1-year forecast model projects 6 Defensive Midfielders (10%) 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 Defensive 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 Defensive Midfielders. Identify undervalued assets and track market momentum across 30 clubs with €33.4M 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 Defensive Midfielders
The Superliga CDM market shows 4 distinct age segments, with the largest cohort in the 30+ bracket (38 players, 62% of market). The 30+ age group holds the most value at €13.0M, averaging €342K per player.
Top Defensive Midfielders by Age Bracket
21-23 Years (3 players)
24-26 Years (8 players)
27-29 Years (12 players)
30+ Years (38 players)
Market Value Distribution
Elite Tier Concentration
The top 7 Defensive Midfielders (11% of players) control €15.2M
Market Tiers
Market structure shows concentrated value with emerging (<€5m) tier representing 100% of the Superliga CDM pool.
Emerging (<€5M)
Club Distribution: Superliga Defensive Midfielders
Among 30 Superliga clubs, FC Copenhagen leads with 2 Defensive Midfielders worth €5.0M (averaging €2.5M per player). The top 10 clubs account for 43% of tracked Defensive Midfielders.
FC Copenhagen (2 Defensive Midfielders)
Universitatea Craiova (1 Defensive Midfielders)
Aalborg BK (2 Defensive Midfielders)
FC Rapid 1923 (3 Defensive Midfielders)
Player Rankings
Ranked by Analytical Strength Index. Click any player to view full profile, or click the chart icon to see value history.
William Clem
FC Copenhagen • 22 years old
€2.6M
€3.0M
+15.6%
Expected: €3.2M
42.3
Tudor Băluță
Universitatea Craiova • 27 years old
€3.0M
€2.8M
-5.4%
Expected: €2.5M
40.7
Kasper Davidsen
Aalborg BK • 21 years old
€2.2M
€2.5M
+15.6%
Expected: €2.8M
39.4
Mateo Tanlongo
FC Copenhagen • 22 years old
€1.7M
€2.0M
+15.6%
Expected: €2.1M
37.3
Mark Brink
FC Nordsjaelland • 28 years old
€2.6M
€2.0M
-22.6%
Expected: €1.8M
36.7
Jakub Hromada
FC Rapid 1923 • 30 years old
€1.9M
€1.5M
-22.6%
Expected: €1.2M
33.3
Karlo Muhar
CFR Cluj • 30 years old
€1.8M
€1.4M
-22.6%
Expected: €1.2M
32.5
Tobias Sommer
Sönderjyske Fodbold • 24 years old
€865K
€1.0M
+15.6%
Expected: €1.0M
28.8
Vlad Chiricheș
FCSB • 36 years old
€1.2M
€900K
-22.6%
Expected: €788K
27.6
Charalampos Kyriakou
FC Dinamo 1948 • 31 years old
€1.0M
€800K
-22.6%
Expected: €663K
25.6
Kader Keita
FC Rapid 1923 • 25 years old
€649K
€750K
+15.6%
Expected: €733K
24.7
Mees Hoedemakers
Viborg FF • 28 years old
€968K
€750K
-22.6%
Expected: €656K
24.5
Samuel Oum Gouet
ACSM Politehnica Iasi • 28 years old
€904K
€700K
-22.6%
Expected: €613K
23.6
Rasmus Vinderslev
Sönderjyske Fodbold • 28 years old
€904K
€700K
-22.6%
Expected: €613K
23.6
Andreas Pyndt
FC Fredericia • 25 years old
€519K
€600K
+15.6%
Expected: €587K
21.9
Eddy Gnahoré
FC Dinamo 1948 • 32 years old
€646K
€500K
-22.6%
Expected: €438K
19.9
Bruno Paz
SC Otelul Galati • 28 years old
€646K
€500K
-22.6%
Expected: €438K
19.4
Lundrim Hetemi
Vejle Boldklub • 26 years old
€432K
€500K
+15.6%
Expected: €515K
19.2
Jonas Gemmer
Hvidovre IF • 30 years old
€581K
€450K
-22.6%
Expected: €373K
18.3
Bjarke Jacobsen
AC Horsens • 32 years old
€517K
€400K
-22.6%
Expected: €350K
17.1
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)
Sönderjyske Fodbold's Tobias Sommer at 24 years old has the highest Pre-Peak Value Efficiency at 3.33×. That means Tobias Sommer is valued 3.33× higher than the median player in the 24-26 age bracket-representing exceptional value before reaching peak age.
In second is FC Rapid 1923's Kader Keita, who is 25 years old, with a 2.50× PPVE. Third is Andreas Pyndt of FC Fredericia, who is 25 years old with a 2.00× 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 3.33× means the player is worth 233% 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 | Tobias Sommer Sönderjyske Fodbold | 24 | 24-26 | €1.0M | €300K | 3.33× |
| #2 | Kader Keita FC Rapid 1923 | 25 | 24-26 | €750K | €300K | 2.50× |
| #3 | Andreas Pyndt FC Fredericia | 25 | 24-26 | €600K | €300K | 2.00× |
| #4 | William Clem FC Copenhagen | 22 | 21-23 | €3.0M | €2.5M | 1.20× |
| #5 | Hans Höllsberg Vejle Boldklub | 24 | 24-26 | €300K | €300K | 1.00× |
| #6 | Kasper Davidsen Aalborg BK | 21 | 21-23 | €2.5M | €2.5M | 1.00× |
| #7 | Mateo Tanlongo FC Copenhagen | 22 | 21-23 | €2.0M | €2.5M | 0.80× |
| #8 | Andrei Mărginean FC Dinamo 1948 | 25 | 24-26 | €225K | €300K | 0.75× |
| #9 | Noah Nurmi Esbjerg fB | 25 | 24-26 | €150K | €300K | 0.50× |
| #10 | André Seruca FCV Farul Constanta | 25 | 24-26 | €150K | €300K | 0.50× |
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)
Aalborg BK's Kasper Davidsen at 21 years old has the highest Return-to-Peak Potential at +30%. That means Kasper Davidsen is projected to appreciate 30% as they reach their peak age in 5 years-representing significant upside before entering their prime.
In second is FC Copenhagen's Mateo Tanlongo, who is 22 years old, with a +25% RPP (4 years to peak). Third is William Clem of FC Copenhagen, who is 22 years old with a +25% RPP (4 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 30% RPP means the player is expected to gain 30% 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 | Kasper Davidsen Aalborg BK | 21 | 5 | €2.5M | €3.6M | +30% |
| #2 | Mateo Tanlongo FC Copenhagen | 22 | 4 | €2.0M | €2.7M | +25% |
| #3 | William Clem FC Copenhagen | 22 | 4 | €3.0M | €4.0M | +25% |
| #4 | Hans Höllsberg Vejle Boldklub | 24 | 2 | €300K | €347K | +14% |
| #5 | Tobias Sommer Sönderjyske Fodbold | 24 | 2 | €1.0M | €1.2M | +14% |
| #6 | Andreas Pyndt FC Fredericia | 25 | 1 | €600K | €645K | +7% |
| #7 | Noah Nurmi Esbjerg fB | 25 | 1 | €150K | €161K | +7% |
| #8 | André Seruca FCV Farul Constanta | 25 | 1 | €150K | €161K | +7% |
| #9 | Kader Keita FC Rapid 1923 | 25 | 1 | €750K | €806K | +7% |
| #10 | Andrei Mărginean FC Dinamo 1948 | 25 | 1 | €225K | €242K | +7% |
Risk-Adjusted Upside (RAU)
Upside potential weighted against forecast uncertainty. Higher RAU = better risk-reward profile.
Understanding Risk-Adjusted Upside (RAU)
Aalborg BK's Kasper Davidsen has the highest Risk-Adjusted Upside at 31.1. That means Kasper Davidsen has 10% upside potential with only 0% forecast uncertainty-representing excellent risk-reward for value appreciation.
In second is FC Copenhagen's Mateo Tanlongo with a 21.2 RAU (6% upside, 0% uncertainty). Third is William Clem of FC Copenhagen with a 21.2 RAU (6% 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 | Kasper Davidsen Aalborg BK | €2.8M | €2.3M-3.2M | +10% | 31.1 |
| #2 | Mateo Tanlongo FC Copenhagen | €2.1M | €1.8M-2.4M | +6% | 21.2 |
| #3 | William Clem FC Copenhagen | €3.2M | €2.8M-3.6M | +6% | 21.2 |
| #4 | Lundrim Hetemi Vejle Boldklub | €515K | €448K-582K | +3% | 11.1 |
| #5 | Hans Höllsberg Vejle Boldklub | €307K | €267K-347K | +2% | 9.1 |
| #6 | Tobias Sommer Sönderjyske Fodbold | €1.0M | €891K-1.2M | +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: defensive 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)
Odense Boldklub's Izunna Uzochukwu in the 30+ age bracket has the highest Age-Share Concentration at +-23.4%. That means Jakub Hromada captures 38.9% of total market value while representing only 62.3% of players in their age group-showing dominant elite status.
In second is Viborg FF's Mathias Wichmann with a +-23.4% ASC (38.9% value share vs 62.3% player share in 30+ bracket). Third is Azer Busuladzic of Vejle Boldklub with a +-23.4% ASC (38.9% value vs 62.3% players in 30+ bracket).
How ASC is calculated: ASC = (% of total value) - (% of total players) in age bracket. A +-23.4% ASC means the player captures -23.4% 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 | Izunna Uzochukwu Odense Boldklub | 30+ | 38.9% | 62.3% | -23.4% |
| #2 | Mathias Wichmann Viborg FF | 30+ | 38.9% | 62.3% | -23.4% |
| #3 | Azer Busuladzic Vejle Boldklub | 30+ | 38.9% | 62.3% | -23.4% |
| #4 | Vlad Chiricheș FCSB | 30+ | 38.9% | 62.3% | -23.4% |
| #5 | Perry Kitchen Randers FC | 30+ | 38.9% | 62.3% | -23.4% |
| #6 | Damjan Djokovic CFR Cluj | 30+ | 38.9% | 62.3% | -23.4% |
| #7 | Matti Steinmann Vendsyssel FF | 30+ | 38.9% | 62.3% | -23.4% |
| #8 | Lasha Parunashvili Esbjerg fB | 30+ | 38.9% | 62.3% | -23.4% |
| #9 | Conor O'Brien AC Horsens | 30+ | 38.9% | 62.3% | -23.4% |
| #10 | Eddy Gnahoré FC Dinamo 1948 | 30+ | 38.9% | 62.3% | -23.4% |
| #11 | Jakub Hromada FC Rapid 1923 | 30+ | 38.9% | 62.3% | -23.4% |
| #12 | Filip Lesniak Silkeborg IF | 30+ | 38.9% | 62.3% | -23.4% |
| #13 | Daniel Pedersen Aarhus GF | 30+ | 38.9% | 62.3% | -23.4% |
| #14 | Dario Canadjija ASFC Buzau (2016-2025) | 30+ | 38.9% | 62.3% | -23.4% |
| #15 | Charalampos Kyriakou FC Dinamo 1948 | 30+ | 38.9% | 62.3% | -23.4% |
| #16 | André Rømer Randers FC | 30+ | 38.9% | 62.3% | -23.4% |
| #17 | Mikkel Wohlgemuth Vendsyssel FF | 30+ | 38.9% | 62.3% | -23.4% |
| #18 | Martin Örnskov Lyngby Boldklub | 30+ | 38.9% | 62.3% | -23.4% |
| #19 | Bjarke Jacobsen AC Horsens | 30+ | 38.9% | 62.3% | -23.4% |
| #20 | Jonas Gemmer Hvidovre IF | 30+ | 38.9% | 62.3% | -23.4% |
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: 0 immediate targets, 3 standard acquisitions, 0 watch-list prospects, 18 at peak.
BUY NOW - High Upside
No players in this category
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 €2.5M. 0 undervalued, 6 premium.
Value Positioning vs Peers
| Player | Market Value | Position Median | Z-Score | Assessment |
|---|---|---|---|---|
Mouhamadou Drammeh FC Universitatea Cluj | €200K | €300K | -0.86 | Good Value |
Victor Mpindi Sönderjyske Fodbold | €200K | €300K | -0.86 | Good Value |
Parfait Bizoza Lyngby Boldklub | €200K | €300K | -0.86 | Good Value |
Noah Nurmi Esbjerg fB | €150K | €300K | -0.67 | Good Value |
André Seruca FCV Farul Constanta | €150K | €300K | -0.67 | Good Value |
Andrei Mărginean FC Dinamo 1948 | €225K | €300K | -0.52 | Good Value |
Lasha Parunashvili Esbjerg fB | €150K | €300K | -0.50 | Good Value |
Daniel Pedersen Aarhus GF | €150K | €300K | -0.50 | Good Value |
Martin Örnskov Lyngby Boldklub | €150K | €300K | -0.50 | Good Value |
Ebenezer Ofori Vejle Boldklub | €150K | €300K | -0.50 | Good Value |
Sakari Mattila Sönderjyske Fodbold | €150K | €300K | -0.50 | Good Value |
Erik Friberg Esbjerg fB | €150K | €300K | -0.50 | Good Value |
Sebastian Avanzini AC Horsens | €150K | €300K | -0.50 | Good Value |
Daniel Norouzi Bröndby IF | €150K | €300K | -0.50 | Good Value |
David Löfquist Odense Boldklub | €150K | €300K | -0.50 | Good Value |
Jeppe Grønning Viborg FF | €150K | €300K | -0.50 | Good Value |
Takayuki Seto ACSC FC Arges | €150K | €300K | -0.50 | Good Value |
Mateo Tanlongo FC Copenhagen | €2.0M | €300K | -0.50 | Fair Value |
Hans Höllsberg Vejle Boldklub | €300K | €300K | -0.38 | Fair Value |
João Lameira SC Otelul Galati | €400K | €300K | -0.29 | Fair Value |
How We Rank Superliga Defensive Midfielders
Our Analytical Strength Index is calibrated specifically for defensive 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 CDM
Historical Achievement Index (35%)
Peak career market value for Superliga defensive 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 defensive 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.
CDM 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 defensive 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 Defensive Midfielders in the 2025-26 season
Who are the most valuable Defensive Midfielders in the Superliga in 2025-26?
The most valuable defensive midfielder in the Superliga in 2025-26 is William Clem, who is worth €3.0M and plays for FC Copenhagen. The second most valuable is Tudor Băluță (€2.8M, Universitatea Craiova), followed by Kasper Davidsen (€2.5M, Aalborg BK). Our database tracks 61 Superliga Defensive Midfielders with comprehensive market valuations updated for the 2025-26 season.
How are Superliga Defensive Midfielders ranked?
Superliga Defensive Midfielders are ranked by our proprietary Analytical Strength Index, which is specifically calibrated for Defensive 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 Defensive 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 defensive midfielder from the Superliga?
Transfer fees for Superliga Defensive Midfielders vary significantly based on market value, contract length, and club bargaining position. For the top-ranked defensive midfielder William Clem (market value: €3.0M), estimated transfer fees would range from €2.4M to €4.2M 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 Defensive Midfielders?
Our 1-year forecast model projects market value changes for Superliga Defensive 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 defensive midfielder data come from?
Our Superliga defensive 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.