Best Players (All Positions) in the Superliga (Jul 2026)
Ranked by Analytical Strength Index
Market Overview: Superliga Players (All Positions) 2025-26
Our database tracks 916 Superliga Players (All Positions) in the 2025-26 season, representing 49 clubs with a combined market value of €693.6M. The average market value for Superliga Players (All Positions) is €757K, with the average age at 30 years old.
The most valuable player in the Superliga is Kurt Zouma, worth €25.0M and playing for CFR Cluj at 31 years old. The top 5 Players (All Positions) average €17.8M in market value, including Franculino and Youssoufa Moukoko.
Age distribution shows the youngest tracked player is Jacob Ambæk (18 years, Bröndby IF, €1.0M), while the oldest is Jim Larsen (40 years, FC Midtjylland, €800K). Research shows Players (All Positions) typically peak at age 26-27.
Our 1-year forecast model projects 257 Players (All Positions) (28%) 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 Players (All Positions) remains highly competitive with significant transfer activity expected 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 Players (All Positions). Identify undervalued assets and track market momentum across 49 clubs with €693.6M 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 Players (All Positions)
The Superliga ALL market shows 5 distinct age segments, with the largest cohort in the 30+ bracket (467 players, 51% of market). The 30+ age group holds the most value at €211.0M, averaging €452K per player.
Top Players (All Positions) by Age Bracket
U21 Years (32 players)
21-23 Years (95 players)
24-26 Years (147 players)
27-29 Years (175 players)
Market Value Distribution
Elite Tier Concentration
The top 92 Players (All Positions) (10% of players) control €354.7M
Market Tiers
Market structure shows distributed value with high (€15-30m) tier representing 0% of the Superliga ALL pool.
High (€15-30M)
Mid (€5-15M)
Emerging (<€5M)
Club Distribution: Superliga Players (All Positions)
Among 49 Superliga clubs, FC Midtjylland leads with 55 Players (All Positions) worth €117.7M (averaging €2.1M per player). The top 10 clubs account for 49% of tracked Players (All Positions).
FC Midtjylland (55 Players (All Positions))
FC Copenhagen (44 Players (All Positions))
Bröndby IF (45 Players (All Positions))
Aarhus GF (49 Players (All Positions))
Player Rankings
Ranked by Analytical Strength Index. Click any player to view full profile, or click the chart icon to see value history.
Kurt Zouma
CFR Cluj • 31 years old
€32.3M
€25.0M
-22.6%
Expected: €20.9M
77.0
Franculino
FC Midtjylland • 22 years old
€19.0M
€22.0M
+15.6%
Expected: €24.2M
74.8
Youssoufa Moukoko
FC Copenhagen • 21 years old
€19.0M
€22.0M
+15.6%
Expected: €25.2M
73.9
Darío Osorio
FC Midtjylland • 22 years old
€8.6M
€10.0M
+15.6%
Expected: €10.6M
61.5
Ben Godfrey
Bröndby IF • 28 years old
€10.6M
€10.0M
-5.4%
Expected: €8.8M
61.0
Ousmane Diao
FC Midtjylland • 22 years old
€6.9M
€8.0M
+15.6%
Expected: €8.8M
58.5
Oliver Sørensen
FC Midtjylland • 24 years old
€6.5M
€7.5M
+15.6%
Expected: €7.7M
57.4
Dominik Kotarski
FC Copenhagen • 26 years old
€5.2M
€6.0M
+15.6%
Expected: €6.4M
55.6
Rodrigo Huescas
FC Copenhagen • 22 years old
€5.2M
€6.0M
+15.6%
Expected: €6.6M
54.9
Aral Şimşir
FC Midtjylland • 24 years old
€5.2M
€6.0M
+15.6%
Expected: €6.1M
54.4
Aaron Boupendza
FC Rapid 1923 • 29 years old
€8.4M
€6.5M
-22.6%
Expected: €5.3M
54.3
Mads Bech Sørensen
FC Midtjylland • 27 years old
€5.2M
€6.0M
+15.6%
Expected: €6.2M
54.3
Denil Castillo
FC Midtjylland • 22 years old
€4.8M
€5.5M
+15.6%
Expected: €5.8M
53.4
Kristian Arnstad
Aarhus GF • 22 years old
€4.3M
€5.0M
+15.6%
Expected: €5.3M
53.1
Benjamin Tahirović
Bröndby IF • 23 years old
€3.9M
€4.5M
+15.6%
Expected: €4.8M
51.6
Gue-sung Cho
FC Midtjylland • 28 years old
€5.8M
€4.5M
-22.6%
Expected: €3.9M
49.9
Nicolai Vallys
Bröndby IF • 29 years old
€5.8M
€4.5M
-22.6%
Expected: €3.7M
49.9
Mohamed Elyounoussi
FC Copenhagen • 31 years old
€5.8M
€4.5M
-22.6%
Expected: €3.7M
49.8
Gabriel Pereira
FC Copenhagen • 26 years old
€3.5M
€4.0M
+15.6%
Expected: €3.9M
49.6
Tobias Bech
Aarhus GF • 24 years old
€3.5M
€4.0M
+15.6%
Expected: €4.1M
49.4
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 Copenhagen's Youssoufa Moukoko at 21 years old has the highest Pre-Peak Value Efficiency at 55.00×. That means Franculino is valued 55.00× higher than the median player in the 21-23 age bracket-representing exceptional value before reaching peak age.
In second is FC Midtjylland's Franculino, who is 22 years old, with a 55.00× PPVE. Third is Darío Osorio of FC Midtjylland, who is 22 years old with a 25.00× 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 55.00× means the player is worth 5400% 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 | Youssoufa Moukoko FC Copenhagen | 21 | 21-23 | €22.0M | €400K | 55.00× |
| #2 | Franculino FC Midtjylland | 22 | 21-23 | €22.0M | €400K | 55.00× |
| #3 | Darío Osorio FC Midtjylland | 22 | 21-23 | €10.0M | €400K | 25.00× |
| #4 | Ousmane Diao FC Midtjylland | 22 | 21-23 | €8.0M | €400K | 20.00× |
| #5 | Rodrigo Huescas FC Copenhagen | 22 | 21-23 | €6.0M | €400K | 15.00× |
| #6 | Denil Castillo FC Midtjylland | 22 | 21-23 | €5.5M | €400K | 13.75× |
| #7 | Oliver Sørensen FC Midtjylland | 24 | 24-26 | €7.5M | €600K | 12.50× |
| #8 | Kristian Arnstad Aarhus GF | 22 | 21-23 | €5.0M | €400K | 12.50× |
| #9 | Benjamin Tahirović Bröndby IF | 23 | 21-23 | €4.5M | €400K | 11.25× |
| #10 | Aral Şimşir FC Midtjylland | 24 | 24-26 | €6.0M | €600K | 10.00× |
| #11 | Victor Bak FC Midtjylland | 22 | 21-23 | €3.0M | €400K | 7.50× |
| #12 | William Clem FC Copenhagen | 22 | 21-23 | €3.0M | €400K | 7.50× |
| #13 | Tobias Bech Aarhus GF | 24 | 24-26 | €4.0M | €600K | 6.67× |
| #14 | Thomas Jørgensen Viborg FF | 20 | U21 | €4.0M | €600K | 6.67× |
| #15 | Wahid Faghir Vejle Boldklub | 22 | 21-23 | €2.5M | €400K | 6.25× |
| #16 | Filip Bundgaard Bröndby IF | 22 | 21-23 | €2.5M | €400K | 6.25× |
| #17 | Pedro Bravo FC Midtjylland | 21 | 21-23 | €2.5M | €400K | 6.25× |
| #18 | Kasper Davidsen Aalborg BK | 21 | 21-23 | €2.5M | €400K | 6.25× |
| #19 | Oscar Schwartau Bröndby IF | 20 | U21 | €3.5M | €600K | 5.83× |
| #20 | Robert FC Copenhagen | 21 | 21-23 | €2.2M | €400K | 5.50× |
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)
FC Copenhagen's William Glindtvad at 19 years old has the highest Return-to-Peak Potential at +44%. That means Nóel Atli Arnórsson is projected to appreciate 44% as they reach their peak age in 7 years-representing significant upside before entering their prime.
In second is Aalborg BK's Nóel Atli Arnórsson, who is 19 years old, with a +44% RPP (7 years to peak). Third is Gavin Beavers of Bröndby IF, who is 21 years old with a +44% RPP (5 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 44% RPP means the player is expected to gain 44% 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 | William Glindtvad FC Copenhagen | 19 | 7 | €400K | €715K | +44% |
| #2 | Nóel Atli Arnórsson Aalborg BK | 19 | 7 | €700K | €1.3M | +44% |
| #3 | Gavin Beavers Bröndby IF | 21 | 5 | €400K | €715K | +44% |
| #4 | Jacob Ambæk Bröndby IF | 18 | 8 | €1.0M | €1.8M | +44% |
| #5 | Noah Markmann FC Nordsjaelland | 19 | 7 | €2.5M | €4.5M | +44% |
| #6 | Aske Andrésen Silkeborg IF | 21 | 5 | €250K | €447K | +44% |
| #7 | William Lykke FC Nordsjaelland | 21 | 5 | €500K | €894K | +44% |
| #8 | Frederik Emmery Aarhus GF | 19 | 7 | €350K | €582K | +40% |
| #9 | Sofus Johannesen FC Midtjylland | 19 | 7 | €250K | €415K | +40% |
| #10 | Hunor Németh FC Copenhagen | 19 | 7 | €500K | €831K | +40% |
| #11 | Valdemar Møller Aalborg BK | 19 | 7 | €300K | €499K | +40% |
| #12 | Luka Callø Aarhus GF | 20 | 6 | €500K | €831K | +40% |
| #13 | Sabil Hansen Randers FC | 20 | 6 | €600K | €997K | +40% |
| #14 | Lukas Larsen Bröndby IF | 20 | 6 | €300K | €499K | +40% |
| #15 | Lasse Flø Vejle Boldklub | 20 | 6 | €400K | €665K | +40% |
| #16 | Lucas Høgsberg FC Nordsjaelland | 20 | 6 | €200K | €332K | +40% |
| #17 | Darius Fălcușan Universitatea Craiova | 20 | 6 | €200K | €332K | +40% |
| #18 | Mikkel Kannegaard Aarhus GF | 20 | 6 | €150K | €249K | +40% |
| #19 | Mark Ugboh FC Midtjylland | 22 | 4 | €150K | €249K | +40% |
| #20 | Pavlo Isenko Universitatea Craiova | 22 | 4 | €1.3M | €2.2M | +40% |
Risk-Adjusted Upside (RAU)
Upside potential weighted against forecast uncertainty. Higher RAU = better risk-reward profile.
Understanding Risk-Adjusted Upside (RAU)
Silkeborg IF's Aske Andrésen has the highest Risk-Adjusted Upside at 103.3. That means William Lykke has 23% upside potential with only 0% forecast uncertainty-representing excellent risk-reward for value appreciation.
In second is FC Nordsjaelland's William Lykke with a 103.3 RAU (23% upside, 0% uncertainty). Third is Gavin Beavers of Bröndby IF with a 103.3 RAU (23% upside, 0% uncertainty).
How RAU is calculated: RAU divides upside potential by forecast uncertainty (RAU = Upside % ÷ Uncertainty %). A RAU of 103.3 means the upside is 103.3× 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 | Aske Andrésen Silkeborg IF | €309K | €280K-337K | +23% | 103.3 |
| #2 | William Lykke FC Nordsjaelland | €617K | €561K-674K | +23% | 103.3 |
| #3 | Gavin Beavers Bröndby IF | €494K | €448K-539K | +23% | 103.3 |
| #4 | Mark Ugboh FC Midtjylland | €179K | €164K-193K | +19% | 100.1 |
| #5 | Pavlo Isenko Universitatea Craiova | €1.5M | €1.4M-1.7M | +19% | 100.1 |
| #6 | Noah Markmann FC Nordsjaelland | €3.1M | €2.7M-3.4M | +23% | 82.7 |
| #7 | William Glindtvad FC Copenhagen | €494K | €437K-551K | +23% | 82.7 |
| #8 | Nóel Atli Arnórsson Aalborg BK | €864K | €765K-964K | +23% | 82.7 |
| #9 | Andreas Gülstorff FC Nordsjaelland | €287K | €264K-310K | +15% | 79.9 |
| #10 | Luka Callø Aarhus GF | €595K | €527K-664K | +19% | 69.6 |
| #11 | Hjalte Bidstrup Viborg FF | €893K | €790K-996K | +19% | 69.6 |
| #12 | Sabil Hansen Randers FC | €714K | €632K-797K | +19% | 69.6 |
| #13 | Lukas Larsen Bröndby IF | €357K | €316K-398K | +19% | 69.6 |
| #14 | Mikkel Kannegaard Aarhus GF | €179K | €158K-199K | +19% | 69.6 |
| #15 | Yoram Zague FC Copenhagen | €3.0M | €2.6M-3.3M | +19% | 69.6 |
| #16 | Lasse Flø Vejle Boldklub | €476K | €422K-531K | +19% | 69.6 |
| #17 | Lucas Høgsberg FC Nordsjaelland | €238K | €211K-266K | +19% | 69.6 |
| #18 | Darius Fălcușan Universitatea Craiova | €238K | €211K-266K | +19% | 69.6 |
| #19 | Jonathan Ægidius Lyngby Boldklub | €551K | €507K-595K | +10% | 58.1 |
| #20 | Marcus Bundgaard Sönderjyske Fodbold | €937K | €862K-1.0M | +10% | 58.1 |
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: player 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)
FC Copenhagen's Nicolai Jörgensen in the 30+ age bracket has the highest Age-Share Concentration at +-20.6%. That means Kurt Zouma captures 30.4% of total market value while representing only 51.0% of players in their age group-showing dominant elite status.
In second is Odense Boldklub's Izunna Uzochukwu with a +-20.6% ASC (30.4% value share vs 51.0% player share in 30+ bracket). Third is Kasper Kusk of Silkeborg IF with a +-20.6% ASC (30.4% value vs 51.0% players in 30+ bracket).
How ASC is calculated: ASC = (% of total value) - (% of total players) in age bracket. A +-20.6% ASC means the player captures -20.6% 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 | Nicolai Jörgensen FC Copenhagen | 30+ | 30.4% | 51.0% | -20.6% |
| #2 | Izunna Uzochukwu Odense Boldklub | 30+ | 30.4% | 51.0% | -20.6% |
| #3 | Kasper Kusk Silkeborg IF | 30+ | 30.4% | 51.0% | -20.6% |
| #4 | Mathias Wichmann Viborg FF | 30+ | 30.4% | 51.0% | -20.6% |
| #5 | Matthias Maak Sönderjyske Fodbold | 30+ | 30.4% | 51.0% | -20.6% |
| #6 | Bjørn Paulsen Odense Boldklub | 30+ | 30.4% | 51.0% | -20.6% |
| #7 | Troels Klöve Odense Boldklub | 30+ | 30.4% | 51.0% | -20.6% |
| #8 | Fredrik Berge Bröndby IF | 30+ | 30.4% | 51.0% | -20.6% |
| #9 | Mos Odense Boldklub | 30+ | 30.4% | 51.0% | -20.6% |
| #10 | Mike Grella Viborg FF | 30+ | 30.4% | 51.0% | -20.6% |
| #11 | Wilfried Domoraud Hobro IK | 30+ | 30.4% | 51.0% | -20.6% |
| #12 | Christian Sørensen Vejle Boldklub | 30+ | 30.4% | 51.0% | -20.6% |
| #13 | Jakub Vojtus FCV Farul Constanta | 30+ | 30.4% | 51.0% | -20.6% |
| #14 | Azer Busuladzic Vejle Boldklub | 30+ | 30.4% | 51.0% | -20.6% |
| #15 | Niklas Dannevang AC Horsens | 30+ | 30.4% | 51.0% | -20.6% |
| #16 | Mathias Kristensen Lyngby Boldklub | 30+ | 30.4% | 51.0% | -20.6% |
| #17 | Marcel Rømer Lyngby Boldklub | 30+ | 30.4% | 51.0% | -20.6% |
| #18 | Simon Jakobsen Hobro IK | 30+ | 30.4% | 51.0% | -20.6% |
| #19 | Dmytro Pospelov UTA Arad | 30+ | 30.4% | 51.0% | -20.6% |
| #20 | Márton Eppel FK Csikszereda Miercurea Ciuc | 30+ | 30.4% | 51.0% | -20.6% |
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: 24 immediate targets, 125 standard acquisitions, 0 watch-list prospects, 240 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 €350K. 0 undervalued, 159 premium.
Value Positioning vs Peers
| Player | Market Value | Position Median | Z-Score | Assessment |
|---|---|---|---|---|
Stefen Tchamche Aarhus GF | €150K | €300K | -0.64 | Good Value |
Mikkel Kannegaard Aarhus GF | €150K | €300K | -0.64 | Good Value |
Musa Touré Randers FC | €200K | €300K | -0.57 | Good Value |
Lucas Høgsberg FC Nordsjaelland | €200K | €300K | -0.57 | Good Value |
Darius Fălcușan Universitatea Craiova | €200K | €300K | -0.57 | Good Value |
Sofus Johannesen FC Midtjylland | €250K | €300K | -0.50 | Fair Value |
Wilfried Domoraud Hobro IK | €150K | €300K | -0.50 | Fair Value |
Simon Jakobsen Hobro IK | €150K | €300K | -0.50 | Fair Value |
Márton Eppel FK Csikszereda Miercurea Ciuc | €150K | €300K | -0.50 | Fair Value |
Nicolas Sandberg FC Vestsjaelland | €150K | €300K | -0.50 | Fair Value |
Mathias Schlie Hobro IK | €150K | €300K | -0.50 | Fair Value |
Danny Amankwaa Sönderjyske Fodbold | €150K | €300K | -0.50 | Fair Value |
Alex Silkeborg IF | €150K | €300K | -0.50 | Fair Value |
Peter Ankersen FC Nordsjaelland | €150K | €300K | -0.50 | Fair Value |
Mads Aaquist Viborg FF | €150K | €300K | -0.50 | Fair Value |
Nicolai Johannesen FC Nordsjaelland | €150K | €300K | -0.50 | Fair Value |
Artem Potapov Surkhon Termiz | €150K | €300K | -0.50 | Fair Value |
Lasha Parunashvili Esbjerg fB | €150K | €300K | -0.50 | Fair Value |
Eddi Gomes Esbjerg fB | €150K | €300K | -0.50 | Fair Value |
Marc Rochester Sörensen Silkeborg IF | €150K | €300K | -0.50 | Fair Value |
How We Rank Superliga Players (All Positions)
Our Analytical Strength Index is calibrated specifically for players (all positions), 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 ALL
Historical Achievement Index (35%)
Peak career market value for Superliga players (all positions), 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 players (all positions), 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.
ALL 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 players (all positions)
• 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 Players (All Positions) in the 2025-26 season
Who are the most valuable Players (All Positions) in the Superliga in 2025-26?
The most valuable player in the Superliga in 2025-26 is Kurt Zouma, who is worth €25.0M and plays for CFR Cluj. The second most valuable is Franculino (€22.0M, FC Midtjylland), followed by Youssoufa Moukoko (€22.0M, FC Copenhagen). Our database tracks 916 Superliga Players (All Positions) with comprehensive market valuations updated for the 2025-26 season.
How are Superliga Players (All Positions) ranked?
Superliga Players (All Positions) are ranked by our proprietary Analytical Strength Index, which is specifically calibrated for Players (All Positions). 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 Players (All Positions) peak?
How much does it cost to sign a top player from the Superliga?
Transfer fees for Superliga Players (All Positions) vary significantly based on market value, contract length, and club bargaining position. For the top-ranked player Kurt Zouma (market value: €25.0M), estimated transfer fees would range from €20.0M to €35.0M 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 Superliga transactions.
What is the value forecast for Superliga Players (All Positions)?
Our 1-year forecast model projects market value changes for Superliga Players (All Positions) 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 player data come from?
Our Superliga player 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.