The New Era FitScore study built a 90-second, full-body reactive fitness test. Clubs can use it as a general profile, but role-specific decisions still need match data, individual baselines, and proof that changes exceed normal test error.

Deva, Berisha, Prieto-González and Sagat’s New Era FitScore study developed a comprehensive fitness test, the New Era FitScore (NEFS), integrating full-body movement with reactive agility and neuromotor components. It used a 90-second circuit that combines burpees with reactive sprints triggered by randomized Witty SEM lights. The sample was 96 participants, including 24 U15 male soccer players. The authors reported high overall test-retest reliability, ICC (2,1) = 0.940, 95% CI = 0.906-0.961, and, in the U15 soccer group, a correlation with Yo-Yo test distance and estimated VO2max of r = 0.742, p = 0.002.

The soccer subgroup result needs context. Reliability in that group was lower than the overall figure, ICC = 0.812, 95% CI = 0.441-0.937, and mean NEFS performance rose from 21.46 ± 1.846 laps on the first test to 22.53 ± 2.416 on the retest. That does not make the test unusable. It does mean clubs should be careful about treating small score changes as meaningful.

FitScore can screen a player profile, not define positional performance

The study makes a compact screening case. It does not make a position-specific model for elite football. The football sample was U15 male players, and the study did not split the soccer results by centre-back, full-back, midfielder, wide player, or forward.

A club can still use the global score as a flag. The next step is narrower: what physical quality might the test be pointing toward, does that quality matter for the player’s job in this team, and does the club’s match or training data show the same pattern?

Match demands change what the same score means

A full-back, central midfielder, and forward do not carry the same running and technical work through a match. A 2024 scoping review of 178 adult male soccer match-demand studies found that central midfielders, external midfielders, and external defenders covered greater total and high-speed distance than forwards or central defenders, while external midfielders covered greater sprint distance than all other positions. The same review found that defenders and central midfielders performed more passes than external midfielders and forwards.

A Major League Soccer study of 1,243 matches and 800 players, collected with Second Spectrum optical tracking, reported average match outputs of 9,950 ± 990 m total distance, 519 ± 171 m high-speed running, 166 ± 98 m sprint distance, and 10 ± 5 sprints. Central midfielders covered the most total distance at 10,510 ± 1,000 m, while full-backs and wide midfielders covered the most high-speed running and sprint distance. Venue and opposition quality also affected running loads.

Players also solve different movement problems inside those distances. Baptista, Johansen, Seabra and Pettersen studied 18 players from one Norwegian elite club across 23 home matches, using the ZXY Sport Tracking System and triaxial accelerometers for 138 observations. Central backs and central midfielders had lower sprint, deceleration, and acceleration work-rates than full-backs, wide midfielders, and central forwards, and full-backs and wide midfielders made more turns above 90 degrees than central backs.

Staff therefore need local benchmarks, not just published averages. Formation, tactical role, competition, tracking provider, and sample definition all change the numbers. A useful role benchmark includes accelerations, decelerations, turns, sprint exposure, recovery between efforts, and team style, not just total distance or a single fitness score.

One total score cannot rank players across roles

Lab testing can flatten differences that matter on the pitch. Altmann, Neumann, Woll and Härtel tested 136 male professional players from Germany’s first and second divisions on an incremental treadmill test with blood lactate sampling. Goalkeepers differed significantly from outfield players on all endurance parameters, but the study found no significant differences among outfield positions. The authors also noted that tactical systems were not accounted for.

A Bundesliga study of 25 players who completed at least four full matches in at least two positions helps explain why. Official tracking data from 163 matches showed that changes in physical match performance when switching position were explained 44-58% by normative positional data. The remaining variance reflected other factors, and the authors reported large individual differences in how players adapted or maintained output across positions.

That leaves clubs with an individual reading task. A centre-back, a wide full-back, and a pressing forward may need different combinations of repeated acceleration, sprint distance, turning load, aerobic repeatability, and recovery between efforts. A multidimensional task can show that a player’s general capacity has changed. It cannot, on its own, tell staff which player fits which role better.

Staff need the ingredients, not just the index

A composite is only useful if the coaching and performance staff can still see what sits underneath it. A LaLiga composite-index study used 24,980 match observations from 1,138 players and reduced tracking variables into three latent components that explained 95% of initial variability: acceleration-specific performance, high-intensity running variables, and medium-intensity actions.

Playing time correlated strongly with the composite index, r = 0.76 and R2 = 0.58, while position differences were statistically significant but small in effect size.

For club use, the practical move is to keep the total score visible but avoid making decisions from it alone. The tracking and composite-index evidence supports that inference, even if it does not validate one universal protocol. Staff need to separate aerobic repeatability, reactive agility, acceleration and deceleration ability, strength-endurance, and coordination where the test design allows it, then compare those qualities with the player’s match and training demands.

Ravé, Granacher, Boullosa, Hackney and Zouhal list total distance, high-speed running, sprint distance, maximal speed, accelerations, and decelerations as relevant GPS parameters for elite soccer monitoring, while warning that threshold-based GPS metrics have not reached full consensus.

Will Abbott’s description of Brighton’s GPS use shows the same idea in day-to-day practice: report two or three key metrics per player, often as percentages of a 90-minute match, and assess training load relative to positional or individual competition demands. He also said Brighton were not governed by GPS; it informed the coach’s decision-making.

Small changes should not move selection on their own

A change only helps if staff can trust that it is bigger than normal noise. Clubb, Towlson and Barrett studied 88 players from two English professional clubs over three seasons in standardized 11v11, 10v10, and 7v7+6 training games using Catapult Optimeye S5 GPS and accelerometers.

Total distance and PlayerLoad showed good group-level sensitivity, but within-player reliability varied widely. High-speed running was poorly reliable across formats, with CV% = 51-103% and ICC = 0.03-0.53, leading the authors to recommend individual-level reliability checks.

The device layer matters too. FIFA’s EPTS testing process says the FIFA Basic wearable test concerns safety and does not endorse the quality of data generated. FIFA Quality performance testing compares systems against Vicon motion capture and full-pitch Vision Kit tracking to quantify accuracy for football use.

Donough Holohan, former Manchester City Head of Physical Performance, made the staffing consequence plain: monitoring data should not be used solely to make player availability decisions, practitioners should avoid collecting data that cannot be actioned in time, and staff should say clearly where the science falls short.

The decision starts after the headline score

Before a multidimensional fitness test changes selection, training, or return-to-play planning, a club should ask five questions.

• Did the score move enough to clear normal test error?
• Which quality is the test most likely pointing toward?
• Is that quality important for this player’s role and the team’s game model?
• Does match or training tracking show the same strength, weakness, or risk?
• Do the coach, player, medical staff, and performance staff see the same issue?

If the answer is no, the result stays in the monitoring file. If the answer is yes, staff can use it to shape an individual training block, a return-to-play step, or a role-specific conditioning target.

The evidence base is strongest for adult male professional match demands and tracking data. The FitScore evidence includes U15 male soccer players and non-athlete groups. Clubs that want position-specific judgments at elite level still need testing in the players, roles, and match demands they are actually selecting for.