Evaluation of the match external load in soccer

“The Evaluation of the Match External Load in Soccer: Methods Comparison” by Castagna C et al.

International Journal of Sports Physiology and Performance

© 2016 Human Kinetics, Inc.

Note. This article will be published in a forthcoming issue of the

International Journal of Sports Physiology and Performance. The

article appears here in its accepted, peer-reviewed form, as it was

provided by the submitting author. It has not been copyedited,

proofread, or formatted by the publisher.

Section: Original Investigation

Article Title: The Evaluation of the Match External Load in Soccer: Methods Comparison

Authors: Carlo Castagna1,2, Matthew Varley3, Susana Cristina Póvoas Araújo4 and Stefano

D’Ottavio2

Affiliations: 1Fitness training and biomechanics laboratory, Italian Football Federation

(FIGC), Technical Department, Coverciano (Florence), Italy; 2University of Rome Tor

Vergata, Rome, Italy; 3College of Sport and Exercise Science, Victoria University,

Melbourne VIC, Australia; 4Research Center in Sports Sciences, Health Sciences and Human

Development, CIDESD, University Institute of Maia, ISMAI, Maia, Portugal.

Journal: International Journal of Sports Physiology and Performance

Acceptance Date: August 2, 2016

©2016 Human Kinetics, Inc.

DOI: http://dx.doi.org/10.1123/ijspp.2016-0160

http://dx.doi.org/10.1123/ijspp.2016-0160




Title of the Article:

The Evaluation of the Match External Load in Soccer: Methods Comparison

Submission Type:

Original Investigation

Authors:

Carlo Castagna1,2, Matthew Varley3, Susana Cristina Póvoas Araújo4 and Stefano

D’Ottavio2

Authors’ Affiliations:

1) Fitness training and biomechanics laboratory, Italian Football Federation

(FIGC), Technical Department, Coverciano (Florence), Italy;

2) University of Rome Tor Vergata, Rome, Italy;

3) College of Sport and Exercise Science, Victoria University, Footscray PO Box

14428, Melbourne VIC 8001, Australia;

4) Research Center in Sports Sciences, Health Sciences and Human Development,

CIDESD, University Institute of Maia, ISMAI, Maia, Portugal.

Contact Details for the Corresponding Author:

Carlo Castagna PhD, via Sparapani 30, 60131, Ancona, Italy;

tel: +39 071-2866532, @mail: [email protected]

Preferred Running Head: Match External Load in Soccer

Abstract Word Count: 220 words

Text-Only Word Count: 2903 words

Number of Figures and Tables: 1 table and 2 figure

References number: 22 citations

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mailto:[email protected]




Abstract

Purpose: The aim of this study was to test the interchangeability of two match-analysis

approaches for external-load detection considering arbitrary selected speeds and metabolic

power (MP) thresholds in male top-class level soccer. Methods: Data analyses were

performed considering match physical performance of 120 team data (1200 player cases) of

randomly selected Spanish, German and English first division championship matches (2013-

14 season). Match analysis was performed with a validated semi-automated multi-camera

system operating at 25 Hz. Results: During a match players covered 10673±348m of which

1778±208m and 2759±241m were performed at High-Intensity using the speed (≥16 km·h-1,

HI) and metabolic power notations (≥20 watt·kg-1, MPHI). High-intensity notations were

nearly perfect associated (r=0.93, p<0.0001). A huge method bias (980.63± 87.82m. d=11.67)

was found when considering MPHI and HI. Very large correlations were found between

match total distance covered and MPHI (r=0.84, p<0.0001) and HI (r=0.74, p<0.0001).

Players high-intensity decelerations (≥-2 m·s2) coverage was very largely associated with

MPHI (r=0.73, p<0.0001). Conclusions: The results of this study showed that the speed and

MP methods are highly interchangeable at relative (magnitude rank) but not absolute

(measure magnitude) level. The two physical match analysis methods can be independently

used to track match external-load in elite level players. However match-analyst decisions

must be based on single method use in order to avoid bias in external-load determination.

Key word: High-Intensity, Association Football, Match Analysis, High-intensity, Metabolic

Power

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Introduction

In modern soccer training control and regulation is regarded as a relevant

methodological procedure to optimize training adaptations to maximize match performance 1-

3. Training progress is the result of the interplay of external and internal loads imposed on

players during training sessions 2. Although physiological adaptations are mediated by

internal load functional variation the doses necessary for obtaining them are practically seized

monitoring training external load 4. The recent exponential advancement of match analysis

systems such as multi-camera and Global Position System Technology has enabled the

evaluation of player’s external load during specific training in elite and sub-elite competitive

and recreational soccer 5,6. Besides the replicability and accuracy of match analysis hardware

of vital importance is the validity and reliability of the variables used to describe player’

activities constituting the back bone of external load evaluation. External training load is

usually assessed evaluating distances and time performed in arbitrary selected speed

categories 1,4. This method approach has been used for practical issues as the data

interpretation is straightforward for training (i.e. sprinting, speed-endurance training) and not

requiring for definition consideration of acceleration calculations that would need the use of

often not sustainable devices 7,8. Despite the interest in information obtained through the

speed method, if acceleration is not considered the actual nature of soccer specific training

results is underestimated 1,4. Recently a metabolic approach (MP) was proposed to provide an

instantaneous picture of soccer specific activities7. This method considers acceleration and

speed to profile individual distances and time spent by players at arbitrary chosen estimated

power thresholds 4,7.

The metabolic approach assumes that the energy produced by a player during actual

match-play is a direct result of the product of the running cost from acceleration and the

corresponding instantaneous speed 7. The relative cost of accelerations is evidenced from the

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steady state O2 of running at an inclination that corresponds to the supposed body inclination

of the player during the acceleration bout. The theoretical framework of the MP relies on the

assumed constant energetic cost of running across players and on the estimation of the

energetic cost of acceleration from a mainly aerobic exercise performed in a stationary to

quasi stationary status 7,9. Furthermore, a strong relationship between acceleration and body

inclination is postulated when player can variate acceleration ratio with no significant

variation on body posture 7. Additionally, MP estimates the energetic cost of accelerations

from incline running performed by endurance up-hill runners in an laboratory set-up

questioning the contextual validity of this assumption for soccer 9,10. Despite these theoretical

and practical incongruences that pose questions about the internal validity of this novel

approach, several papers were recently published a-critically considering this issue 10.

Furthermore match-analysis systems software provide by defaults MP variables with the aim

to profile player game performance during soccer-specific training. However the actual

superiority of the MP versus the classic arbitrary speed zone approach has yet to be examined

in detail, posing doubt about the effectiveness of this interesting novel approach.

While the MP approach may provide a more detailed tracking of player’s game

activity, there is limited research regarding the validity of this method 4,7. Furthermore in the

paper that firstly proposed the MP approach no objective evaluation was provided by authors

regarding the actual superiority (i.e. statistical verification) and interchangeability of the

metabolic approach with the classic speed method. Indeed in the Osgnach et al.7 study only

descriptive statistics were reported (i.e. means and standard deviations). Thus, information

regarding the objective difference and or association of the MP approach over the speed

threshold method is unknown. Additionally being the MP approach based on acceleration

calculation the associated error using common video-tracking systems may provide a large

measurement bias 7. This information has huge practical implications as a growing number of

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match analysis systems are reporting players activity with the MP variables in the attempt to

characterise match and training external-load.

Therefore the aim of this descriptive study was to examine the association between

classic speed and MP approach in tracking the external-load of elite level players during

highly competitive official matches. It was hypothesized that there would be a large

association between the two match analysis approaches.

Methods

Subjects

Match physical performance was assessed in 1200 male outfield soccer-players (age

24.5±0.8, height 176.4±4.5 cm, body mass 74.6 kg) playing in the first division

Championships of Germany (Bundesliga 1), England (English Premier League) and Spain

(Liga BBVA) during the 2013-14 season. Written informed consent was obtained from

players organisation to treat anonymously the collected data for research purpose. This study

design was approved by the Institutional Research Board before commencement of this

study.

Design

With the aim to examine methods interchangeability a descriptive correlation design

was considered. Match physical-performance was evaluated in professional top-class players

competing during European first-division championships matches. This provided population

specificity and internal validity of this research design. Association between selected

arbitrary speeds and MP zones was performed tracking players match activities with a

validated multi-camera semi-automatic system (operating at 25 hz) 11,12. Games were

randomly selected from a proprietary match database in order to warrant external validity.

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Methodology

Sixty official matches were considered for calculations, they were randomly selected

in reason of 20 from each national league (Germany, England and Spain) database. Players

were tracked for the entire duration of the game using a multicamera semi-automatic system

(Prozone Sport, Leeds, UK) working at 25 hz 11,12. Home and away teams data were available

for each of the 60 games considered. Players were tracked for match physical-performance

using either speed and MP arbitrary selected intensity zones. In order to examine match

tempo of the most competitive leagues of the world only physical match variables tracking

high-intensity activities were considered. They were as follows;

Distance covered at High-Intensity (speed ≥ than 16 km·h-1, HI);

Distance covered at High-Intensity Running (speed ≥18.97≤21.99 km·h-1, HIR);

Distance covered at Very High-Intensity Running (speed>21.99 km·h-1, VHIR);

Distance covered with High-Intensity Accelerations (acceleration ≥2 m·s2 , DAcHI);

Distance covered with High-Intensity Decelerations (deceleration ≤-2 m·s2 , HIDec);

Distance covered with Very High-Intensity Deceleration (deceleration ≤ -3 m·s2,

VHIDec);

Distance covered with Very High-Intensity Acceleration (acceleration ≥ 3 m·s2,

VHIAcc);

Distance covered at High-Intensity (MP≥20 watt·kg-1, MPHI).

To characterize global match coverage the total distance (TD) covered and the

average MP (AMP) were calculated. Data were processed with proprietary software (K-

SportOnline, K-Sport, Montelabbate, PU, Italy) and then analysed with commercially

available spreadsheets (Excel, Microsoft, USA) and with a dedicated statistical package

(Statistica 10, Statsoft, USA).

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Statistical Analysis

Data are presented as mean ± standard deviation and confidence interval (95%CI).

Assumption of normality was verified using the Shapiro-Wilk W-test. Variables association

was assessed using Pearson’s product-moment correlation coefficients. Qualitative magnitude

of associations was reported according to Hopkins (2002) as follows: trivial r < 0.1, small 0.1

< r < 0.3, moderate 0.3 < r < 0.5, large 0.5 < r < 0.7, very large 0.7 < r < 0.9, nearly perfect r

> 0.9 and perfect r= 1. Partial correlations were used for path analysis when necessary.

Differences between variables were assessed with paired t-tests using a Bonferroni correction

to account for comparison number. The Cohen’s d was used to assess effect-size (ES) 13.

According to Hopkins et al. 14 ES of above 4, between 4 and 2, between 2 and 1.2, between

1.2 and 0.6, between 0.6 and 0.2 and 0.2 and 0 were considered as huge, very large, large,

moderate, small, and trivial respectively. In order to provide normative cues for metrics

changes the Smallest Worthwhile Change (SWC) was considered according to Hopkins et al.

14 Measure agreement was assessed with Bland and Altman plots with bias test against the

zero difference hypothesis for significance. Significance was set at p 0.05. Preliminary

power calculation showed that to obtain the a statistical power of 80% would be necessary

400 cases. The final power obtained with this study design was higher than 90% (1200

cases).

Results

Descriptive statistics and SWC for the considered variables depicting match physical-

performance are reported in table 1. Data showed high inter-match (CV>10%) variations for

all the considered variables but not for TD, AMP and MPHI. Bland and Altman plot statistic

showed a significant measurement bias of 980.63± 87.82m (95%CI 964.75 996.51,

p<0.0001) between MPHI and HI distances. Upper and lower 95% limits of agreement were

1152.77 (95%CI 1125.56 1179.97) and 808.50m (95%CI 781.29 835.70) respectively

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(Figure 1). Plot of MPHI and HI difference vs mean showed the existence of a moderate data

heteroscedasticity (r= 0.38, p<0.0001, 95%CI 0.22 0.53) suggesting the likelihood of a

systematic measurement error as variables magnitude increased.

A nearly perfect association was found between MPHI and HI (r=0.93, p<0.0001,

95%CI 0.91 0.95, Fig. 2). The MPHI was very largely associated with TD (r=0.84,

p<0.0001, 95%CI 0.78 0.89) HIDec (r=0.73, p<0.0001, 95%CI 0.63 0.80) and HIR

(r=0.87, p<0.0001 95%CI 0.82 0.91). The HI showed very large correlation with TD

(r=0.74, p<0.0001, CI95% 0.64 0.81) and AMP (r=0.73, p<0.0001, 95%CI 0.63 0.80).

The AMP was very largely associated with TD (r=0.85, p<0.0001, 95%CI 0.79 0.89) and

deceleration categories VHIDec (r=0.72, p<0.0001, 95%CI 0.62 0.79) and , HIDec (r=0.76,

p<0.0001, 95%CI 0.68 0.83).

Discussion

This is the first study to assess the associations between two data analysis methods for

assessing external load in soccer, specifically; speed and metabolic power of arbitrarily

chosen activity categories. The main finding of this descriptive comparative study was the

almost perfect association (r=0.93) between the distance covered at a high-intensity speed

(≥16 km·h-1) with that accumulated at high-intensity using the MP approach (≥20 watt·kg-1).

However, practical very large absolute differences (i.e. 52%) in variable magnitude were

detected between HI and MPHI distance covered. These results confirm the original work

hypothesis for relative but not absolute interchangeability of the two external load approaches

here considered.

Match and training high-intensity is considered as a relevant construct in modern

soccer with a number of papers providing direct or indirect evidence 1,15. This study used two

methods considering arbitrary categories to depict match high-intensity activity according to

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the methods of Osgnach et al. 7. However, in the original paper the authors failed to report

any quantitative basic statistic to compare methods and as a result information about measure

interchangeability was not provided. The speed (≥16 km·h-1) and MP (≥20 watt·kg-1)

thresholds used in this study were assumed to represent the average speed and corresponding

relative metabolic power at the estimated maximal aerobic-speed in professional soccer

players 7. This enabled direct comparison between the two considered methods for external-

load detection to evaluate their concurrent (i.e. magnitude association) and convergent

construct (i.e. measure agreement) validity. The results of this study showed that speed and

MP approach possess an almost perfect relative (i.e. r= 0.93) but a poor absolute

interchangeability (measurement bias of 981m). This suggest that the two external-load

methods are sensible in depicting player’s high-intensity activity reported as distance covered

in arbitrary match high-intensity categories thresholds. However, the reported huge difference

in absolute values between the HI and MPHI underlines the difference in the informing

criteria of the two methods.

The lower HI distance compared to MPHI was likely the result of the inability of the

speed threshold method to consider the accelerative phases of high-intensity efforts thus

underestimating actual match HI demands. Indeed using the speed approach HI distance

accumulates only when players exceed the set velocity threshold (≥16 km·h-1), thus

neglecting the preparatory phase involving high-intensity accelerative efforts 4. Given these

limitations in the speed approach the metabolic power approach was introduced in the

attempt to account for instantaneous acceleration gradients during actual match-play 7.

Despite the theoretical interest provided by the MP approach its essential use of

acceleration data provide some concern about the validity of this novel method 4,10. Indeed

the acceleration data per se have an inherent systematic error consequence of filtering and

sampling rate 7. The reported error in acceleration data collection and the required technology

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for valid measurement limits the practical interest of the MP approach 10. Additionally the

reported iso-power phenomenon considered as the likelihood of producing the same MP

values with an unpredictable variation in instantaneous acceleration and speed data, may

produce construct derived artefacts 7. Indeed consideration for high-intensity bouts can be

done when actually they are in a lower intensity phenomenological domain.

Data difference of supposed convergent constructs was reported as a practical

representative of measurement variability (causal and random changes) 16. Explorative data

analysis showed large association between MPHI and HI absolute difference with distance

covered (r=-0.62, p<0.0001, from -0.72 to -0.50,) and time spent at DAcHI (r= -0.70,

p<0.0001, from -0.78 to -0.60). This finding may represent an effect of speed maintenance

over the MPHI production during the highly competitive matches considered in this study.

The very large (r= 0.87, p<0.0001, 95%CI 0.82 0.91) association between HIR and MPHI

may partially confirm this assumption. Furthermore when controlling for HIR the association

between MPHI and HI changed from almost perfect into the lower range of the very large

correlation categories (i.e. from 0.93 to 0.74). Additionally HI showed to be only moderately

correlated with acceleration and deceleration performed during the match by players (r from

0.44 to 0.51, p<0.0001) confirming the lack of sensitivity of the speed threshold approach in

accurately tracking player’s match high-intensity activities. Although more thorough analyses

are necessary, the results of this study support the idea that the effect of movement speed on

MP is more pronounced than the supposed added advantage of considering acceleration as a

variable informing energy cost. Further studies providing more detailed information about the

real genesis of MP are warranted.

The data reported in this study were captured with a semi-automated multi-camera

system previously tested for validity and reliability and used for disseminating match

performance in most of the relevant reports published in soccer performance profiling

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5,11,12,17. Methods comparison was carried-out studying match physical performance in

players participating in the most important championships of the world such us English

Premier League (United Kingdom), Liga BBVA (Spain) and Bundesliga 1 (Germany). Given

the number of matches considered and the number of cases processed, this data possesses a

great internal validity as it is representative of elite level soccer. Match analysis data showed

to be in line with those provided by previous descriptive studies using similar or the same

video capture system11,18-21. Indeed Osgnach et al.7 introducing the MP approach studied a

population of Italian professional soccer players that were reported to cover a match total

distance of 10,950 ± 1044 m with 1996m and 2839m covered at HI and MPHI respectively.

This study was the first to report random match data pooled from different European top class

leagues. Given that, the data reported may be considered as reference for match-analysts and

coaches interested in physical match activities (Table 1).

Data variability is a key factor in match analysis to determine the probability of causal

match to match changes in physical performance and to detect team or individual fitness and

or technical-tactical differences. In this study absolute inter-teams variability (i.e. CV%) were

below 5% for TD, AMP showing high measurement stability for these global match-activity

variables 22 (Table 1).

Higher CV% values were found in this study for the variables addressing match high-

intensity (i.e. C>10%) with MPHI showing a CV lower than 10%. These findings are in line

with those reported by other authors for English Premier League championship matches

confirming the high variability of high-intensity activities in top class soccer 18,19. In this

context acceleration and decelerations showed the highest variability with CV above 20 and

25% respectively. In order to detect casual changes the SWC may result of great practical

interest. 14 According to this study data changes in the team mean from 2.3 and 1.8% may be

regarded as causal match performance variations for HI and MPHI respectively.

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Practical Applications

Match and training external-load may provide key information for training

individualisation. Particularly at the elite level absolute performance variables may better

depict the intra individual and team differences. Thus, the use of valid and reliable methods

to profile match and training internal load result are vital. Given this study data the speed and

metabolic approaches showed similar sensitivity in depicting player and team profiles of the

supposed high-intensity constructs. Indeed the reported almost perfect association between

HI and MPHI provided evidence of construct converge (i.e concurrent validity). However

despite relative method interchangeability the reported very large absolute measure

differences (d > 4, ES) suggest accurate a priori choice of the data analysis method. The use

of the MP approach to provide concurrent consideration of speed and acceleration requires

further examination into the issue of population validity of the equation used for acceleration

derived energy-cost calculation and criterion validity. Additionally the provided equation

should be refined in order to account for collisions, tackles, jumps and non-orthodox

exercise-modes like sideward and backward running 1,10.

Conclusions

The scientific limitations of the MP approach should be acknowledged by

practitioners when using this measure for reporting purposes. Moreover, recent validation

studies suggest caution in using MP when evaluating the external load in soccer players

during pre-planned shuttle running10. Finally, the mismatch between the considerable higher

estimated anaerobic contribution to soccer match physical-performance and actual

physiological measures question the internal validity of the MP approach7,10.

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Acknowledgments

No financial support was provided for the completion of this study. The authors declare no

conflict of interest with the finding reported in this study.

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Figure 1. Bland and Altman Plot of the MPHI and HI variables.

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Figure 2. Relationship (r=0.93) between match distances performed at high-intensity using

the metabolic power (MPHI) and speed threshold (HI) notations.

R² = 0.8734

2000

2200

2400

2600

2800

3000

3200

3400

3600

1200 1400 1600 1800 2000 2200 2400

MP

HI (

m)

HI (m)

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Table 1 Values of match activities considered in this study.

Variable TD AMP HI HIR VHIR MPHI HIAcc HIDec VHIDec VHIAcc

Mean 10672.79 10.65 1778.34 482.04 239.81 2758.97 635.93 611.91 209.30 214.81

SD 347.74 0.49 208.00 67.46 48.22 240.99 118.21 97.68 47.75 55.90

CV% 3.26 4.61 11.70 13.99 20.11 8.73 18.59 15.96 22.81 26.02

Min 9417.76 9.14 1156.22 298.91 127.86 2028.14 397.10 437.58 120.24 106.44

Max 11595.77 12.09 2310.62 656.80 335.05 3344.14 911.68 889.97 380.48 370.11

SWC 69.55 0.10 41.6 13.49 9.64 48.20 23.64 19.54 9.55 11.18

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