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https://doi.org/10.1186/s40798‑022‑00408‑z
REVIEW ARTICLE
Tracking Systems inTeam Sports: ANarrative
Review ofApplications oftheData andSport
Specic Analysis
Lorena Torres‑Ronda1,2* , Emma Beanland3, Sarah Whitehead4,5, Alice Sweeting1 and Jo Clubb6
Abstract
Seeking to obtain a competitive advantage and manage the risk of injury, team sport organisations are investing in
tracking systems that can quantify training and competition characteristics. It is expected that such information can
support objective decision‑making for the prescription and manipulation of training load. This narrative review aims
to summarise, and critically evaluate, different tracking systems and their use within team sports. The selection of
systems should be dependent upon the context of the sport and needs careful consideration by practitioners. The
selection of metrics requires a critical process to be able to describe, plan, monitor and evaluate training and competi‑
tion characteristics of each sport. An emerging consideration for tracking systems data is the selection of suitable time
analysis, such as temporal durations, peak demands or time series segmentation, whose best use depends on the
temporal characteristics of the sport. Finally, examples of characteristics and the application of tracking data across
seven popular team sports are presented. Practitioners working in specific team sports are advised to follow a critical
thinking process, with a healthy dose of scepticism and awareness of appropriate theoretical frameworks, where pos‑
sible, when creating new or selecting an existing metric to profile team sport athletes.
Keywords: Global position, Optical tracking, Radio frequency systems, Technology, Performance
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Key Points
• Data from tracking systems can be used across
a myriad of applications, which can be broadly
grouped into describing, planning, and monitoring
external loads, all with a view to supporting objec-
tive decision-making pertaining to performance and
injury risk.
• It is advisable to be critical by considering precision
(validity and reliability) and ecological validity when
selecting from the multitude of metrics available in
such systems, and when analysing time series derived
from the data; selecting the most suitable informa-
tion to a specific team sport, environment and play-
ing position is also critical.
• Considering tracking data through the lens of a spe-
cific team sport reveals how the context and con-
straints (e.g., playing dimensions, player density,
position characteristics, game rules, timing structure,
physical demands, among others) of a sport influence
how such information can be applied. e alignment
of technical-tactical and physical data provides prac-
titioners with greater context for the physical charac-
teristics, and perhaps greater application of tracking
data to training practices.
Introduction
Athlete tracking systems have become commonplace in
professional team sports. Seeking to obtain a competi-
tive advantage, organisations are investing financial and
Open Access
*Correspondence: lorenatorres07@yahoo.es
1 Institute for Health and Sport, Victoria University, Melbourne, Australia
Full list of author information is available at the end of the article
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Torres‑Rondaetal. Sports Medicine - Open (2022) 8:15
time resources in technologies that can quantify train-
ing and competition characteristics in a valid and reliable
manner. It is expected that such information can sup-
port decision-making processes for the prescription and
manipulation of training load [1].
External load has been described as the foundation of
a monitoring system [2], and is represented by the activi-
ties performed by an athlete [1]. As part of the monitor-
ing process, tracking data are used to quantify external
load. Tracking data can be combined with other streams
of information to determine readiness for competi-
tion, analyse the load-performance relationship, support
appropriate planning for training and competition load,
as well as to minimise risk of injury, illness and non-func-
tional overreaching [2]. To enable such applications, it is
advised to integrate external load in a multivariate moni-
toring system that may include internal load—the physio-
logical stress imposed by the external load—and training
response mechanisms [3]. erefore, the appropriate col-
lection and interpretation of tracking data is vital for this
process.
e description, planning, and monitoring of external
load provides valuable information for understanding
the training and competition characteristics of various
team sports. As interest in this information has garnered
greater attention, manufacturers also attempt to improve
their filtering systems and algorithms by incorporating
new variables to satisfy the needs of their consumers.
However, this rapid improvement and rollout may lead
to confusion between different sports whereby, with dif-
fering needs, the user has to carry out a critical thinking
process to guarantee the optimal use of tracking systems
and subsequent data, within their own context. As out-
lined by Torres and Schelling [4], the implementation
of technology in the applied setting should be driven by
recognising a suitable solution to a problem in the spe-
cific environment. With a plethora of external load met-
rics and methodologies constantly emerging [5–7], it is
paramount to understand how the data can be analysed
in order to add objectivity to decision making and to ulti-
mately support the athletic training process.
Once the characteristics and limitations of different
tracking systems used in team sports [Global Position-
ing Systems (GPS); Optical Tracking; Local Position-
ing Systems (LPS); and Inertial Measurement Units
(IMU)] are understood, there is a need for a pragmatic
and systematic approach to data collection, analysis and
interpretation.
In this narrative review, we present practical applica-
tions for data provided by tracking systems. Reviewing
all aspects of a monitoring system is beyond the scope of
this narrative review, which focuses on tracking systems
only. Whilst research has typically focussed on specific
methods of analysing such data, the literature lacks an
overview of the various purposes of tracking data in rela-
tion to the context of specific team sports. erefore, the
objective of this review is to examine the critical thinking
required to select the most suitable metrics and describe
the varying evidence-based applications of tracking data
in the applied setting. We then aim to demonstrate this
critical process by discussing the specific considerations
and contexts of analysing tracking data within the con-
text of seven different team sports.
Tracking Metrics
e metrics provided by tracking technologies may vary
between systems. For example, optical tracking deter-
mines 2-dimensional coordinates that can be extrapo-
lated into distance and speed measures, whereas IMU
combine data from multiple sources (e.g., accelerometer,
magnetometer, and gyroscope) to measure acceleration
of the body or body segment. Some technologies also
combine multiple tracking systems, such as GPS, LPS
and IMU, within a single device [8]. Certain professional
sports, and relevant governing bodies, do not permit the
use of certain systems within competition (e.g., National
Basketball Association; NBA). Hence, practitioners may
be required to combine metrics from different systems to
integrate data across training and competition. As such,
understanding which metrics are provided by the dif-
ferent systems, their definitions, calculations, ecological
validity and specificity use to a sport (or team, athlete), is
paramount to the practitioner.
Considerations forMetric Selection
Practitioners are besieged with a multitude of metrics
from tracking systems (Table1). Categorising these met-
rics according to their similarities may be adequate in
appraising their usefulness. Distances covered at vari-
ous speeds and the occurrences of high-speed move-
ment, accelerations and decelerations (Levels 1 and 2;
Table1) appear to be those most commonly reported by
practitioners in team sports [9, 10]. However, this does
not mean they are necessarily appropriate in all sports.
Selecting the most pertinent metrics, given each sport’s
unique constraints, is vital to ensure metrics are appro-
priate for the context of a specific sport.
In the applied sport environment, there is a need to
distil the myriad of metrics into concise, meaningful
information. Decision makers (e.g., coaches and per-
formance staff) require simple, accurate, and coherent
feedback, including succinct conclusions from data in
a timely manner [11]. It is advisable to include stake-
holders in the metric selection process, as coaches
have expressed an interest in tracking data pertaining
to ‘high-intensity’ actions and ‘intensity’ [12]. Indeed,
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greater involvement and improved communication
in this process could help to avoid a detrimental gap
between information and its impact [13].
A variety of considerations for selecting metrics are
shown in Figs.1 and 2. ese include, but are not lim-
ited to: playing dimensions, player density, position
characteristics, game rules, and timing structure. e
combination of these factors demonstrates the impor-
tance of context both between and within sports. As
an illustration, it could be questioned if tracking an
athlete’s maximum velocity is pertinent in basketball,
given the limitation of court size. Similarly, absolute
high-speed running (HSR) in American football may
be much less meaningful to linemen than wide receiv-
ers due to their unique positional characteristics (see
Sect. 4). However, this process presents somewhat of
a paradox; the practitioner may first have to measure
the characteristics to determine its meaningfulness and
then be able to apply critical thinking in order to deter-
mine the most appropriate measures to the sport.
Subjectivity is important in the critical selection of
metrics, based on the sport and/or position character-
istics. However, despite a more subjective process based
on observation, it remains that such metrics should still
demonstrate face validity (perceived to be relevant) and
high content validity (representative of the construct, in
this case external load) —for further information, inter-
ested readers are directed to [14]. ere may be scope to
further support this process with objectivity via statistical
analysis of the existing suite of metrics available. Weaving
and colleagues (2019) demonstrated how principal com-
ponent analysis, a linear algebra technique, can reduce
and visualise complex tracking data into meaningful
components [15]. is approach can help the practitioner
to understand multi-collinearity between metrics and
therefore, filter out redundancies [15]. For example, in
professional rugby league, such analysis suggested some
Table 1 Definitions of common tracking metrics
Level 1: distances covered in dierent velocity zones; Level 2: events related to changes in velocity (i.e. acceleration, deceleration, and changes in direction); Level 3:
events derived from the inertial sensors; Hybrid = combination of levels (28)
2D, 2‑dimensional; 3D, 3‑dimensional; AU, arbitrary units; g, g force; kN, kilonewton; m, metre; AMF, American Football; IH, Ice Hockey; GK, Goalkeeper; Cal, calorie; kg,
kilogram; ml, milliliter; min, minute
Level Metric Denition Common Measures
1 Distance Cumulative distance Total, Relative, Distances in speed/acceleration/decel‑
eration zones
2 Acceleration (2D) Instantaneous peak rate of positive change in velocity Maximal/Peak, Average, Distance/Efforts/Time in accel‑
eration zones
Deceleration (2D) Instantaneous peak rate of negative change in veloc‑
ity Maximal/Peak, Average, Distance/Efforts/Time in decel‑
eration zones
Change of Direction (2D) Count and intensity of changes of direction derived
from positional data Total, Percentage Difference Left vs Right, Count in
intensity zones
3 Accelerometry‑derived load A manufacturer‑specific, modified vector magnitude
of 3D acceleration values (expressed in AU) Total, Relative to time, Relative to distance, 2D (excludes
vertical axis), 1D (absolute or relative contribution of
individual axes)
Change of Direction (3D) Count and magnitude (g) of changes of direction
derived from inertial sensors Total, Percentage Left v Right, Count in intensity zones
Impacts A manufacturer‑specific metric that provides a count
of 3D acceleration values (g) over a threshold Count and Magnitude of Impacts
Collisions/ Tackles A manufacturer‑specific metric that classifies collisions
specific to the sport Count and Magnitude of Collisions
Stride Variables Accelerometry‑derived metrics estimating ground
contact time Contact Time, Flying Time, Vertical Stiffness (KN·m)
Stride Imbalances Accelerometry‑derived metrics split by left and right
side Percentage Left v Right
Hybrid Speed Instantaneous peak rate of position change Maximal/Peak, Average
Sport‑specific Metrics Specific machine learning algorithms designed to
quantifying movement demands per sport AMF QB throws, Basketball court transition, IH skating
strides, Rugby scrum detection, Soccer GK Left v Right
Dive Count
Metabolic Power Estimates the energetic demands of high‑intensity
Level 1 and 2 actions via GPS or LPS data Metabolic Energy (Cal·kg), Equivalent Distance (distance
covered running at constant speed on flat terrain, for
a given energy expenditure), Total Metabolic Power
(ml·kg·min), Distance/Efforts/Time in Metabolic Power
bands
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Australian Football Rugby League Rugby UnionSoccerAmerican FootballIce Hockey NetballBasketball
135-185 m
109 m
90-100 m
80 m
60 m
30.5 m
28 m
x18x13x11
x11
x15x11x5
x7
Fig. 1 A comparison of field size across different team sports. The number of players per team is represented by the figure above each field. The
numbers of players, team and opponents number, are also represented by the dots shown on the field (not to scale). Orange and black colours
represent opposing teams
020406080100 120 140 160 180 200
AMERICAN FOOTBALL
AUSTRALIAN FOOTBALL
BASKETBALL
ICE HOCKEY
NETBALL
RUGBY LEAGUE
RUGBY UNION
RUGBY SEVENS
SOCCER
ACTUAL TIME (MINS)
CONTINUOUS PLAY PERIOD BREAKS INTERMITTENT PLAY
Fig. 2 A bar chart visualising the difference in playing time and actual time during match‑play across different team sports. Continuous play is
shown in solid blue. These sports may have pauses in play for substitutions but do not have pauses in game‑play periods for commercial breaks or
time‑outs. Sports of a play‑by‑play nature (clock stop), with intermittent breaks, for example for faults or commercial reason, are shown in striped
blue and grey
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Torres‑Rondaetal. Sports Medicine - Open (2022) 8:15
measures of training load (e.g., training impulse, session
rating of perceived exertion, body load, high-speed dis-
tance, and total impacts) may be used interchangeably to
describe small-sided game and conditioning loads, but
not for other modes such as skills, wrestling, and speed
training [16]. Similarly, by using feature selection in the
time and frequency domain, team sport related activi-
ties can be accurately classified through a single track-
ing device. is approach may allow for the generation
of more sport and activity specific algorithms, from one
device [17]. Such approaches may be especially worth-
while, given that a multivariate approach to monitor-
ing has been recommended for team sports [16]. Focus
should therefore be given to developing and improving
metrics already in use, ideally through the integration of
physical and tactical insights into combined metrics [18].
e onus is thus on the practitioner, to determine the
most relevant metrics to interpret and communicate with
key stakeholders.
e workflow of selecting the most pertinent tracking
metrics is an ongoing process, requiring current knowl-
edge on the validity and application of tracking tech-
nologies, based on research and technological advances.
Recently, the measurement and management of decel-
erations have been declared important to capture, given
their distinct demands and potential as a critical media-
tor of neuromuscular fatigue and tissue damage [19].
However, concerns have been raised regarding the pre-
cision of tracking devices to capture accelerations and
decelerations [20]. Whilst sport and tracking technolo-
gies are constantly evolving, practitioners need to bal-
ance innovation with an understanding of the precision
and sensitivity of technology.
Applications ofTracking Data
Once the precision and accuracy of a tracking system
are quantified, attention can turn to the analysis process.
Tracking data can be used to identify key competition
characteristics, including the most demanding situations,
in order to objectively manage physical preparation,
readiness, and return-to-play. Buchheit and Simpson
(2016) proposed three main objectives for tracking data
to: i) better understand locomotor characteristics and
external load; ii) assist the programming of team training
external load; and iii) help with decisions pertaining to
performance and injury risk as they relate to an individ-
ual’s programme [11]. ese objectives can be condensed
into the following overarching and overlapping purposes:
Describing, Planning, and Monitoring (Fig.3).
Describing
Descriptive studies are an essential first step in epidemio-
logical research [21]. is is reflected in applied sport sci-
ence, whereby the initial application of a tracking system
is to quantify locomotor characteristics across different
contexts. e first notational systems in the 1970s were
used to describe the differences in external load across
playing position in football match-play [22]. Since then,
Describing
PlanningMonitoring
Long Term (e.g., multi-season, season)
Macro Blocks (e.g., preseason, monthly)
Weekly
Daily
Drill/Period
Level
Detailed
Epochs
(e.g., rolling
1-minute)
Use monitoring data to
update descriptions of
sport and/or position Use descriptions to
plan physical outputs
Compare resulting
physical outcomes
with planned
Fig. 3 Applications of tracking systems data. The overlapping purposes of Describing, Planning, and Monitoring are shown. The inverted Reuleaux
triangle in the centre of the Venn diagram represents the varied time analysis approaches, drilling down by time, that can be applied
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training and competition outputs have been described
by playing position in a variety of team sports [23–26].
Given the growing availability of tracking systems in
youth environments, such descriptions now also extend
across age groups [27] and bio-banding, according to
maturity status [28].
Given that tracking systems have existed for more than
four decades, calls have been made to further descriptive
analysis. One proposal is to adopt an integrated approach
to competition tracking data that contextualises char-
acteristics by combining physical and tactical data [18].
is alignment of different data sources may allow for
an improved understanding and translation of training
to performance in team sport [18]. Spatiotemporal data,
integrated with tactical context, have allowed the explo-
ration of new concepts, including space occupation [29],
off-the-ball scoring opportunities [30], the risk-reward
of passing [31], and team pace of play [32]. ese ana-
lytical approaches have started to delve into the com-
plex problems that sport scientists face, and go beyond
simply describing aggregate external load data. With the
rise in data availability, affordability and accessibility,
sport scientists now have the opportunity to apply many
analytical techniques to the same tracking dataset, thus
expanding upon descriptive reporting.
Planning andtheIntersection withDescribing
Practitioners use descriptive data to aid in the planning of
training. eoretical frameworks of the training process
depict how external load, as determined by the training
plan, is prescribed to elicit the desired training outcomes
[33, 34]. Training plan development involves combining
both an objective (e.g., external load) and subjective (e.g.,
coach experience) understanding of the sport’s charac-
teristics. In team sports, performance is complex and the
training process involves more consideration than physi-
cal inputs alone. However, it remains that fundamental
training principles, such as overload and progression,
should form the basis of physical preparation and train-
ing design [34].
A sliding scale of timeframes can be considered when
planning the training process; long-term, seasonal, and
day-to-day planning may all incorporate objective infor-
mation provided by tracking systems [35]. is may be
especially pertinent during periods of congested fix-
tures. For example, netball at the elite level is played
across tournament style competition during World Cups
and Commonwealth Games. is congested schedule,
with matches often played twice per day, can result in
reduced wellbeing markers, sleep quality and neuro-
muscular function [36]. Similarly, in field hockey, daily
wellbeing markers were accompanied by a reduction in
HSR, despite rest days [37]. Collectively, these results
demonstrate the importance of a multivariate monitoring
system whereby, both dose and response are tracked.
Higher external loads have been demonstrated during
preseason training and yet, preseason participation may
help to protect players from injury in the regular season
[38]. According to training theory this can be expected,
given it is the systematic repetition of a stimulus and its
associated response that are necessary to elicit chronic
adaptations [33]. Training plans, attempting to optimise
this systematic repetition of stimulus, can be generated
from tracking data using a constrained optimisation
framework, to optimise physical performance and reduce
injury-risk. Specifically, practitioners can use machine-
generated algorithms to hone-in on how much risk is
associated with a particular external load, by adjust-
ing metrics or levers including total distance or HSR,
for example. As an example, in Australian Football [39],
Banister’s impulse-response was utilised for the training
load—risk model [39], however, the same conclusions
can be delivered from other studies in other sports using
the most pertinent parameters to their environment
[38]. Whatever method utilised, clear communication
between practitioners and coaching staff is advised, to
align and iteratively review physical and tactical objec-
tives throughout the planning process, given that com-
munication quality between coaching and medical staffs
has been shown to be associated with injury burden and
player availability [40].
Training goals within cycles are varied in an attempt
to balance physical preparation and readiness for com-
petition. For instance, it is commonplace for team sport
training one day prior to competition to be substantially
lower in external load than others within the microcy-
cle [41, 42]. As the days between competitions increase,
training load will also increase [43]. erefore, the
opportunity to apply tracking data analysis to influence
planning may vary across and within sports, depending
on fixture congestion. For example, American football
schedules one game per week whilst soccer can have one
or two, and basketball or ice hockey face three or four
games per week during the in-season phase (see Sect.4).
Competition characteristics are used as a benchmark
to understand the most intense periods of play, from
which the design of appropriate drills replicating or sur-
passing the intensity of the game can be planned [44].
Indeed, drill design can have a substantial impact on the
external load elicited and thus is a vital piece of the plan-
ning process [45]. is is illustrated through research
into small-sided games in soccer, whereby the number
of players, floating players, pitch size, rules, goalkeepers,
duration of bouts, and coach encouragement each impact
external load [45–47]. By quantifying and storing drills
in a systematic way, a database can be utilised to analyse
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Torres‑Rondaetal. Sports Medicine - Open (2022) 8:15
and subsequently plan drill rules, duration, sets and rep-
etitions structure, amongst other considerations. Armed
with this objective information, practitioners could sup-
port coaches with a training design that elicits physi-
cal outputs in line with the training goals, whilst also
respecting other training objectives outside of the physi-
cal realm (e.g., tactical, technical, psycho-social, cultural).
Utilising descriptions of training and game characteris-
tics also assists with planning for the rehabilitation pro-
cess. Returning a player to competition after injury is a
challenging and complex process, which involves balanc-
ing risk and objective criteria with subjective experience
[48]. Tracking data can therefore assist in the planning
of rehabilitation from control to chaos, designed to meet
the individual needs of the sport, playing position, indi-
vidual athlete, and the specific injury in question [49].
Monitoring andtheIntersection withPlanning
Planning is an essential part of the training process,
whereby tracking system data can play a vital role. How-
ever, for plans to be successful, it is advisable for stake-
holders to be aligned and communicate physical training
goals, before, during and post, the monitoring of train-
ing and rehabilitation. is is typically conducted as part
of an ongoing review process across the multidiscipli-
nary performance staff. e monitoring process has two
main purposes: i) to assess the interaction between the
resulting external loads compared with those that were
planned [34], and ii) to analyse the dose–response of said
training loads on a team and individual basis [35]. e
data derived from tracking systems can be key for this
process, providing practitioners with vital information on
an athlete’s external load.
Performance staff plan the external load for desired
training adaptations and responses from a drill to macro-
cycle level (Fig.3) to assist in performance, development
and injury risk reduction [11]. As discussed, practition-
ers put plans in place to target an appropriate volume
and intensity of training, at the right time in the train-
ing cycle, to either increase or decrease fatigue [2, 50].
In team sports, one of the goals of tracking systems data
is to assess whether athletes have been subjected to the
planned training load [34]. is can be accomplished
through live monitoring and retrospective session analy-
sis. e first, live monitoring, enables in-session adjust-
ments to assist with trying to achieve the planned load
during the training session [35], and ultimately contrib-
ute to the chronic fatigue-recovery response.
Training load management approaches have been
widely researched and utilised in an attempt to reduce
the risk of injury [51]. However, the ability to control the
risk of injury, through the manipulation of training load,
has recently come under scrutiny [34]. is is due to
methodological concerns in the analysis of training load
data [34]. Whilst this specific topic is beyond the scope
of the present review, it remains that understanding the
external load quantified through tracking system data is
a useful tool as part of training load planning and moni-
toring processes [35]. An understanding of the external
load placed on the athlete(s) can assist in titrating the
fatigue response [2]. Further, it has been recommended
that practitioners should not focus on external load alone
[33]. e quantification of internal load, and the response
to training, should be considered as part of a multivari-
ate system alongside the external load data, in order to
understand the dose–response relationship to training
[16]. Including measures of the athletes’ individual char-
acteristics, such as fitness [52]or maturation [28], into
such a system also merits consideration. e cycle of
planning and monitoring is an ongoing, iterative process
in which the quantification and evaluation of planned
and implemented load alongside training responses
and outcomes can be beneficial to practitioners and the
coaches and athletes they strive to support.
The Intersection ofMonitoring‑Describing andtheSport
Evolution
e physical characteristics of sport evolve over time
and thus, another application of tracking is to use ongo-
ing monitoring data to update the objective description
of the sport. For example, HSR has evolved in the Pre-
mier League (soccer), with a 35% increase in sprint dis-
tance over a seven-season period [53], a trend that has an
even greater impact on full backs (36–63% increase) [54].
Evolving physical characteristics have also been dem-
onstrated across other team sports [55, 56]. Similarly,
longitudinal changes in physiological profiles may be
representative of changes in competition characteristics,
which could be explored with the use of tracking tech-
nology [57]. Such findings have implications for physical
preparation and highlight the need to update objective
descriptions of physical characteristics with ongoing data
collection.
e evolution in physical characteristics within a sport
may, in part, be influenced by changes to the rules of the
game, which may be captured by ongoing monitoring of
tracking data. For instance, rule changes that reduce the
time taken to restart play in men’s professional Austral-
ian football have led to an increased flow and speed [55].
Changes to the kickoff portion of an NFL game, including
a stationary start for coverage players, may have resulted
in a change to physical outputs that contributed to a nota-
ble decrease in concussion injuries according to league
medical officials [58]. e combination of IMU, pres-
sure sensors and video cameras have been used to assess
biomechanical loading in multiple scrum engagement
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Torres‑Rondaetal. Sports Medicine - Open (2022) 8:15
techniques in rugby, with a pre-binding technique shown
to reduce the stresses acting on the players [59]. e use
of mouthguards instrumented with accelerometers and
gyroscopes is also being explored in collision sports for
measuring head kinematics, with a view to assisting with
the detection and monitoring of concussion [60]. Such
examples highlight the need to update the quantita-
tive description of a sport as new means of monitoring
become available. Clearly, sport is a dynamic ecosystem
that changes over time. erefore, tracking data allows
the ability to recursively describe, plan, and monitor
external load in line with a specific sport’s characteristics.
Between‑System Interchangeability
In order to provide complete athlete monitoring, practi-
tioners often need to combine tracking data from multi-
ple systems [61]. Understanding the agreement between
systems is important for practitioners, in order to track
meaningful changes in profiles [62]. Comparison of
optical tracking to GPS has shown slight-to-moderate
and moderate-to-large differences for total distance
and HSR distance (> 18 km∙h), respectively [63]. Buch-
heit (2014) found trivial-to-small overestimation of dis-
tance (5.4%) and slight-to-moderate overestimation of
HSR (> 19.8km∙h: 26.5%). ese differences highlight the
importance of considering GPS sampling rate, the num-
ber of visible satellites connected, satellite signal strength,
and software filtering when reviewing system compari-
sons [64]. Recent advances in GPS hardware technology
have resulted in a stronger correlation with an opti-
cal tracking system [61, 65]. Given that such differences
remain, a recent area of interest is the use of predictive
equations to account for system differences and enhance
accuracy of the interchangeability of data [60, 61]. A
number of techniques can be used to assess interchange-
ability between systems, including regression analysis.
Time Series Analysis
ere are many applications and approaches to time
series analysis of tracking data throughout the processes
of Describing, Planning, and Monitoring, as indicated in
Fig.3. To determine an appropriate approach, practition-
ers and researchers can consider the most relevant time
analysis approach(es) according to their specific sport,
setting and intended application of the data. Reporting
metrics derived from tracking systems can be done in a
variety of ways, including by absolute values, temporal
durations, moving averages or as normalised data (e.g.,
per 100h played, per 100 possessions). Absolute values
often describe metrics per the whole match, halves/quar-
ters and training periods. Such aggregated approaches
are commonly used across the literature in a range of
sports, providing an indication of the external load
encountered by athletes [66, 67]. is information can be
useful in practice, providing total volumes and averages
to assist in training planning and periodisation, especially
when combined with internal load measures [68].
However, aggregated values are limited in the prescrip-
tion of specific training practices given the intermittent
nature of team sports. erefore, practitioners may also
consider other time -analysis approaches. Match-play
and training can be stratified into periods based on differ-
ent temporal durations (e.g., 5-min or 10-min) to capture
the fluctuation in external load throughout match-play
[69–71] and the peak characteristics, sometimes known
as the “worst-case scenario” [72]. A segmental approach
(e.g., the match file is split from zero according to
the duration: 0–5 min, 5–10 min), a moving averages
approach (i.e., a rolling average of the raw instantaneous
data) [72], or time series segmentation (i.e., the compu-
tation of non-uniform segments from a time series) can
also be used [73]. Additionally, the physical characteris-
tics of match-play can be stratified per phases-of-play or
by match-activities through the alignment of video and
tracking systems data. Examples of such approaches in
the current literature include: stratification per attack and
defense [74, 75] or per possession of the ball [76, 77]. e
alignment of technical-tactical and physical data provides
practitioners with greater context to the physical char-
acteristics, and perhaps greater application to training
practices [18, 78].
Period Selection
e selection of the period for analysis (e.g., whole-
match, temporal duration or phase-of-play) should be
determined by the primary use of the data. When strati-
fying match-play based on temporal durations, a range
of durations have been utilised [72]. It is important to
consider that when using temporal durations, the inten-
sities will differ depending on the epoch analysed [25,
79] and data cannot purely be extrapolated for different
epochs. If the intended use of the data is to aid in the
prescription of training drills, this should drive the dura-
tion window analysed, however this is often not known
when the analysis of match-play is carried out. e use
of the power-law relationship with moving averages was
therefore proposed, providing an equation to predict
the peak intensities as a function of time [80], which has
been utilised in research in a number of team sports [26,
80–83]. Similar approaches have recently been applied to
model the decrement in peak acceleration magnitudes in
basketball [84]. Such approaches can be used as a simple
monitoring tool, in practice, without the pre-determined
selection of an epoch. For example, the unique power-law
relationship can be determined for a sport, team, squad
and/ or competition, and then the peak intensity for the
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Torres‑Rondaetal. Sports Medicine - Open (2022) 8:15
desired time frame can be predicted from the equation.
is approach can also allow practitioners to further
investigate the peak periods of activity during training
and matches, from a continuous data trace.
Peak Demands
e quantification of the peak passages of match-play
has gained popularity in recent years, due to the practical
utility of the data over whole match/ training aggregated
values [25, 44, 85] and the availability of raw trace data.
Research has quantified the peak locomotor demands
(sometimes described as the “worst case scenario”) of
match-play across the football codes [72], and other field-
based team sports (e.g., lacrosse [85], field hockey [26]
and court-based team sports [e.g., netball [81], basketball
[84, 86]). Whilst different methodologies have been uti-
lised—including segmental or moving averages and ball-
in-play [44]—the moving averages approach is the most
commonly used [87–90], given its ability to capture the
subtle fluctuations in the intensity of match play, as well
as the functionality of the power-law relationship. An
example of its use is the monitoring of the intensity of
small-sided games, to attempt to replicate for the inten-
sity of peak periods of match-play [45]. rough the use
of live monitoring, the intensity can be monitored and
manipulated via feedback to coaches and consequent
alterations to the match-play for example rules, pitch size
and player numbers [35].
However, further considerations should be made
regarding the depth in analysis of the peak demands. To
shift the focus from one metric in isolation and enhance
training application, the quantification of the concur-
rent demands within the most demanding physical pas-
sages of play can be investigated. For example, in collision
sports (e.g., the rugby codes), the number of collisions
that occur within the peak running periods, or the run-
ning that occurs within the peak collision periods, can
be identified [3, 45, 91]. Additionally, it is important to
understand the technical and tactical requirements
alongside the physical data provided by tracking systems
[18, 78], as well as any influence of contextual factors [92],
to provide greater context to the data. Changes in the
peak movement demands in relation to skill involvement
have been investigated in Australian football, highlight-
ing reductions in the movement profile as the number of
involvements increases [91]. Additionally, in soccer, the
“worst-case scenario” has been found to be impacted by
contextual factors (i.e., match-location, match-outcome)
[93], with greater peak characteristics in away games
compared to home games, or the effect of ball possession
within these worst-case scenario periods [18]. Moreover,
it has been recently shown that the “worst-case scenario”
produces unstable metrics that lack context, with high
variability, and therefore, training drills targeting this
metric may not have representative designs and so may
underprepare athletes for future match demands [94].
Time Series Segmentation
Examining the physical output of team sport athletes
via aggregate parameters has many challenges. Periods
where physical output changes over time are unlikely
to be detected when examining only the total distance
covered or percentage of time spent performing high-
intensity running. As described above, team sport ath-
letes often execute periods of physical output whereby
intensity is far greater than that of an averaged total game
[25]. Given the volatility of team sport matches to iden-
tify meaningful changes on a per-second basis, moving
minute intervals have been used to detect these periods
[95]. However, the length of these moving intervals is
often decided arbitrarily and typically only focuses on the
rolling average (or peak average) of a metric [80]. Alter-
natively, time series segmentation involves detection of
the mean and variance of a metric, over segments of non-
uniform size, without the need for a priori defined inter-
vals [73].
Time series data, including raw GPS and LPS traces,
are characterised by their continuous nature, as opposed
to match events which are transactional and discrete.
Sport scientists are often faced with a difficult problem
in how to analyse this continuous data, in order to derive
meaningful information. A method which may be useful,
when dealing with continuous data, is time series seg-
mentation. is is an analysis technique that comprises
of algorithms which search for change points within tem-
poral data [96]. ese change points designate that the
pattern of subsequent data points is characteristically
different to those prior [97]. Segments are automatically
detected, based on a given number of change points,
within an underlying time series, for example a raw GPS
trace. is trace could be analysed via time-series seg-
mentation, to detect how athlete physical output changes
during a match, as a function of time. In team sport, time
series analysis has been utilised in Australian football
to identify and describe the segments of physical (and
skilled) output during matches [73]. Similarly, time series
analysis has been utilised to profile the skilled output in
team sport matches [98] and predict team success in the
English Premier League [99]. e visualisation of met-
rics from athlete tracking systems, including raw trace
data that can be analysed via time series segmentation,
requires the visual encoding of thousands of data points.
Sport scientists thus need to decide whether to aggregate
specific time periods (e.g., distance covered during an
on-field rotation or stint), or all data points that are con-
tained within the time period. erefore, communicating
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how data are selected and analysed before visualisation
occurs is an important skillset for the modern sport
scientist.
Sport Specic Analysis
Context will drive what technology (and in turn, met-
rics) should be selected to capture the characteristics of
team sport athletes during training and competition. For
example, in basketball and netball, the use of GPS is ren-
dered inoperable, given at the elite level both sports play
and train indoors. erefore, LPS, IMU and optical track-
ing are more appropriate. Similarly, the use of optical
tracking to monitor athletes during Australian football
and rugby codes may be limited, given the large (and var-
ying) field sizes, whereby many cameras would need to
be installed at height around the ground. erefore, the
tracking technologies and derived metrics used for spe-
cific sports and playing positions need careful considera-
tion. Below we have arbitrarily selected team sports and
introduced sport-specific considerations that practition-
ers should be mindful of, when selecting the technology
and corresponding metrics to profile the physical charac-
teristics of athletes during training and matches.
American Football
American football is an intermittent, contact sport char-
acterised by physical demands that include HSR, acceler-
ations, decelerations, and changes of direction [100]. e
game is play-by-play in nature across four 15-min quar-
ters, with multiple stoppages and commercial breaks,
extending the game length, in actual time, to upwards of
three hours (Fig.2). Players are selected from a roster of
53 to 120, depending on the time of the season and the
level (i.e. collegiate vs professional) with specialist posi-
tions across defense, offense and special teams [101].
Factors that set this sport apart includes the vast differ-
ences in positional characteristics, the mandatory inclu-
sion of personal equipment (i.e., helmets and pads) that
in turn likely influences the magnitude of collisions, and
the prolonged time course over which the game is played.
As such, there are nuanced considerations for applying
tracking data in this sport.
e wide disparity of positional characteristics in this
sport provides practitioners with challenges related to
both physical preparation and tracking itself. e pro-
cess for selecting metrics may be especially pertinent
given that the notable difference in positional character-
istics may lead to the focus of different metrics for differ-
ent positions. Differences in running, assessed via HSR,
and non-running, assessed via total inertial movement
analysis (IMA) from the IMU, characteristics were nota-
ble across position groups during a professional training
camp [102]. Similar differences have been illustrated in
training and competition characteristics at the collegiate
level [100, 103, 104]. While the use of IMU data may help
to capture sport-specific actions (e.g., throwing, contact,
and collisions) and be developed into position-specific
metrics [105], this technology may still be unable to fully
quantify some characteristics that rely less on movement
tracking, such as the high isometric demands of grap-
pling and blocking. Further, IMU technology is not per-
mitted in competition at the professional level, wherein
Radio Frequency Identification technology is currently
employed [106].
Given the heterogeneity of the physical characteristics
by position, relative velocity thresholds may be pertinent.
Ward and colleagues (2017) used a HSR threshold above
70% of the maximum speed for the respective position
group, derived from training sessions within the previ-
ous year. Absolute speed zones for the entire team, which
may over- and under-estimate demands for faster and
slower athletes respectively [100], have also been uti-
lised. However, it is also important to note that research
in other sports (soccer) found the use of relative speed
thresholds did not better quantify the dose–response
and, in fact, the application of a player’s peak speed to
establish speed zones may result in erroneous interpre-
tations [107]. More research is required in American
football to determine the most suitable approach for
quantifying the dose–response relationship, especially
given the wide heterogeneity of characteristics by posi-
tions and also the variation of intensities within position-
specific periods in a training session [102].
e heterogeneity of American football characteris-
tics is exacerbated by the special teams element. During
these passages of play, a mixture of offensive and defen-
sive players (generally non-starters) combine to perform
roles in support of specialist kickers, who are attempting
punts, kickoffs, and/ or field goals. us, practitioners are
challenged to prepare these players for the physical char-
acteristics of both their primary and special teams roles
concurrently. For example, a Linebacker who is also a
special teams specialist, may play across all four phases
of Punt, Punt Return, Kickoff, and Kickoff Return. If an
injury occurs, the planned roles may be further influ-
enced. ese passages of play may often be the most
physically demanding with regards to HSR (unpublished
observations), and so there are repercussions for tracking
the physical outputs of these passages, both in terms of
understanding the specific characteristics and monitor-
ing the external load each individual player is subjected
to.
ere may be further disparity in the physical require-
ments for players within numerous periods of a training
session. Whilst a session may be divided into five key
periods (i.e., warm up, position-specific training drills,
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Torres‑Rondaetal. Sports Medicine - Open (2022) 8:15
special teams drills, preparatory plays, and team periods
[102]), players may be required to work on different char-
acteristics during those time periods, based on their role.
For instance, starters not involved with special teams
may be training separately according to their position
role on offense and defense during such periods. is is
an important contextual note for practitioners attempt-
ing to categorise, analyse, store, and plan periods/drills
using a database.
Considering the physical characteristics of a session,
period or individual play level, is worthwhile in the plan-
ning process, as American football is a sport character-
ised by a high tactical demand. With an intermittent
play-by-play structure (Fig. 2), players are expected to
learn set movement demands outlined in a playbook,
more akin to set pieces in other sports. As such, certain
time epoch analysis, including segmental analysis, roll-
ing averages or game speed approaches, may be less rel-
evant to track in this setting. Rather, tracking outputs on
a specific play level may be more pertinent. Given the
prominence of the integrated combination of physical,
tactical, and technical characteristics of the game, there
may be benefit in aligning tracking data with video and
play/scheme notations to understand the physical out-
puts within the game context. Indeed, machine learning
techniques are exploring the ability to classify route com-
binations, blocking assignments or coverage type from
tracking data [108].
Australian Football
Australian football is an invasion-sport contested
between two teams of 22 players, 18 permitted on the
field and four on the interchange bench. A unique con-
straint of the sport is the non-uniformity of field size.
e dimensions of fields used within the professional
competition, the Australian Football League (AFL) vary
from 175m in length and 145m in width (University of
Tasmania Stadium) to 155m by 136m (Sydney Cricket
Ground). e average length and width of AFL grounds
are 163.6 ± 5.9 m and 132.1 ± 6.9 m, respectively. One
AFL field (Marvel Stadium) is indoors. Collectively, field
size and stadia constrain the type of tracking systems
used. In Australian football, GPS is commonly utilised
during matches and training [109–111]. Given their suit-
ability across outdoor and indoor stadia, inertial sen-
sors including accelerometers, are also used [112]. Only
recently have LPS been utilised during elite competition
[113]. Using optical tracking is unsuitable for this sport,
given the vast ground sizes that require a large number
of cameras be used [114]. Athlete tracking systems are
therefore, largely confined to accelerometers, GPS and
LPS, and their derived metrics. e selection of which
metrics to use, for the purpose of profiling Australian
football training and match play, from these different sys-
tems is an important consideration.
e physical characteristics of these athletes is com-
plex, part of interacting sub-systems and often reactive
to a stimulus, including the ball, umpires, opponents or
teammates. Understanding how these stimuli impact
physical output is useful, to decide which metrics are
meaningful. Features including anthropometric (e.g.,
height) and physiological (e.g., aerobic capacity) may
impact external load. For example, aerobic fitness has a
large effect on relative total and HSR distances covered
during AFL matches [109]. Rucks in Australian football
are typically taller than their teammates but cover up
to 45% less distance at high-speed [109]. Environmen-
tal factors also impact metrics obtained. ese results
demonstrate that sport scientists should be mindful of
the performer constraints during training and matches,
which can impact the metrics.
A number of contextual factors influence the external
load of Australian football athletes during training and
matches. e number of rotations, margin, opposition
quality and stoppages all impact the direction and magni-
tude of physical output in men’s matches [109]. In wom-
en’s matches, physical output is influenced by on-field
rotation stint, opposition quality and margin [115]. Other
contextual factors, including stoppages or brief breaks
in play, also impact Australian football athlete external
load. In elite men’s matches, increased stoppages result in
less relative total distance covered [116]. Sport scientists
should therefore be mindful of these contextual factors
when analysing men’s and women’s tracking data.
e relationship between physical and skilled char-
acteristics has been examined in Australian football, in
an attempt to give further context to physical metrics.
Trivial and weak relationships exist between aggregated
physical (e.g., absolute total high-intensity running) and
skilled (number of involvements, including handballs
and tackles) characteristics, when analysed via general-
ised linear models and conditional inference trees [113,
116]. Linear mixed models had low explanatory power
whilst the conditional inference trees also had poor
accuracy [113]. is is likely due to subtle changes in
athlete physical and skilled output not detected in aggre-
gate parameters. Moving averages have been utilised in
Australian football, with men’s match intensities peak-
ing at 223 ± 35 m.min across one-minute moving aver-
ages [117]. However, time-series analysis as described
in 3.6.3 above, removes the need for manually selecting
pre-defined time windows and can utilise the mean and
variance of a metric. Athlete velocity data can then be
examined, without having to rely on fixed duration win-
dows, allowing for the detection of precisely when a peak
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Torres‑Rondaetal. Sports Medicine - Open (2022) 8:15
match intensity occurs at a specific point in time [73]. By
utilising time series and data mining techniques, sport
scientists can therefore delve beyond aggregate param-
eters and extract features from raw GPS or LPS data. e
specificity of Australian football training drills to matches
could be examined by visualising the distribution of fea-
tures from raw velocity traces, identifying when players
obtain match intensities and how often this happens.
Given the abundant data available from athlete tracking
systems and the dynamic, non-linear nature of the sport,
sport scientists should look to move beyond reporting
aggregate parameters, including total distance covered
per drill, on-field rotation, quarter or match. Instead,
sport scientists could utilise the raw velocity (or acceler-
ometer) trace data to identify where, when and how Aus-
tralian football athletes’ external load alters as a function
of time. When combined with an underlying theoretical
framework, for example ecological dynamics [118], phys-
ical and skilled characteristics could together be analysed
to potentially provide rich insights into Australian foot-
ball training and matches.
Basketball
While there are data describing physical characteristics
from different basketball leagues [119], the description
of external load at the highest professional level (NBA),
is limited [62, 120]. Game positional data is only acces-
sible through the NBA’s official optical tracking provider,
Second Spectrum (Los Angeles, U.S), and there are strict
rules for the use of data for publication [62]. Commonly,
other tracking systems are used during practices (those
pre-approved by the NBA and the National Basketball
Players Association), which implies a lack of homogene-
ity and compromises the ability of practitioners to build
complete external load profiles across practice and com-
petition [121]. As such, basketball sport scientists, per-
formance and medical staff face numerous challenges on
a day-to-day basis when it comes to using tracking sys-
tems and how to best use the information.
Basketball is an intermittent sport that, due to the court
dimensions, number of players, and the rules [e.g., ball
possession time (24 s)], requires the player to perform
repeated high-intensity actions, such as rapid changes of
direction and cutting actions, changes of speed in short
distances, contacts (e.g., post-ups, screens, box-out), or
run-to-jump actions, occurring between different loco-
motor demands (e.g., standing, walking, running, sprint-
ing). Likely heavily influenced by pre-existing research
from other team sports, the most common tracking met-
rics studied in basketball have been total distance, rela-
tive distance (distance/duration), distance and/or time in
speed zones (total, relative and percentages), high-inten-
sity actions (usually referred as distance, time and/or
counts of accelerations, decelerations, jumps) and peak
velocity [119, 122]. Moreover, as in other team sports,
the analysis of describing the most demanding scenarios,
both through discreet or fixed-length time epochs and
rolling average time epochs, is emerging [86]. However,
the mentioned influence from other team sports reflects
a certain lack of critical thinking in the analysis of basket-
ball specifically.
High-speed, very high-speed running and sprinting dis-
tance are commonly reported at > 10 km.h−1, 18 km.h−1
[123], and > 24 km.h−1 [86, 122], respectively, in the liter-
ature; whereas, top speed reached by players reported in
the literature is ~ 20 km.h−1 [119, 124]. However, different
results in peak speeds have been shown at the elite level
(e.g., NBA; unpublished data). Based on the limitation of
court size and the subsequent shorter lengths of explo-
sive efforts in basketball, practitioners should reconsider
the selection of peak speed as a key metric for planning
and/or monitoring in the decision-making processes.
e lack of consensus, and the actual requirements of
distances at different intensities, requires that the prac-
titioners consider reviewing speed thresholds for sprint-
ing and high- and very-high speed running in basketball,
independently of references from other team sports. Data
mining techniques have been used to determine sport-
specific thresholds, including fitting Gaussian curves
[125], k-means clustering [126], and spectral clustering
[127]. Such methods warrant consideration in basketball.
Given the difference in the size of the playing area, it is
likely that speed thresholds lower than other sports may
be more suitable for analysing tracking data in the con-
text of basketball.
Measures of velocity change (i.e., accelerations and
decelerations) are other commonly used metrics, how-
ever, there is a lack of clarification and consensus across
different tracking systems and manufacturers on how
signals are filtered, calculations performed, or which are
the suitable thresholds for this sport. Regarding the lat-
ter, thresholds for LPS vary from < > 2 m.s−2 [86, 123],
while research utilising IMU has used < > 3 and 3.5 m.s−2
for total and ‘at high-intensity’, respectively [119]. Simi-
larly, there are differences across tracking systems as to
whether acceleration and deceleration data are reported
in counts, distances or time spent changing velocity.
Alternatively, a simple method for averaging the accel-
eration and/or deceleration profile of a team sport has
been proposed to overcome issues with using predefined
thresholds with time-series data [128]. While this analy-
sis was conducted on rugby league athletes, the authors
discuss the importance of such movements to physical
preparation and performance across a variety of team
sports. While sport-specific research should be under-
taken for basketball, given the court size, the nature of
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Torres‑Rondaetal. Sports Medicine - Open (2022) 8:15
the rapid movements required and the importance of
actions such as turnovers, cuts, close outs, or defensive
shuffles, it appears these movements are vital for manag-
ing injury risk, planning and monitoring the training pro-
cess, and quantifying competition characteristics. Which
calculation to use will depend upon the use of the infor-
mation by the practitioner; for example, the summated
acceleration profile may be most relevant for description
and monitoring; whereas, the count and distance covered
accelerating may be useful in programming individual
workouts in a rehabilitation process.
Quantifying overall external load using accelerom-
etery technology has been become a key metric in bas-
ketball. Many manufacturers have their own version of
accelerometer-derived load, although PlayerLoad™ may
be the original and the most commonly used [7]. It is
recommended that practitioners seek to understand how
manufacturer-specific “load” metrics are calculated, not
only in basketball but all sports using this metric, since
the measurement, filtering processes, and threshold
rules differ. For example, some manufacturers calcu-
late “load” from three-dimensional accelerometery data,
while others use two-dimensional LPS for the calcula-
tion. Additionally, Schelling and Torres (2016) showed
that constraints such as number of players, opponents,
and court dimensions (i.e., half-, full-court) influence
the external load [124]. Such studies are relevant for the
practitioners to understand how the manipulation of
constraints affect external load. Another pertinent aspect
in basketball is the impact of the vertical load (z-axis) in
the total count of ‘load’. e nature of this sport implies
vertical actions (e.g., shooting, blocks), an aspect that has
not been commonly reported in the literature, probably
due to the lack of studies validating the quantification
of jumps (and landing impact) across different tracking
systems.
e evolution of tracking systems and machine learning
techniques is allowing greater precision in the detection
of basketball-specific movements. At present, techni-
cal aspects such as types of shots (e.g., driving layup or
floater, pull-up jumper, step back, catch and shoot), picks,
posts, isolations, off ball screens, among others, are
recorded during matches with optical tracking. is, in
combination with the metrics quantifying physical char-
acteristics, can become a powerful tool for the generation
of new and powerful insights in the description, planning
and monitoring of external load in basketball.
Ice Hockey
Ice hockey is an intermittent, collision sport played on
ice, characterised by high-intensity bouts of skating with
rapid changes in speed and direction [129] and high tech-
nical demands, such as puck control, evading defenders,
and body checking [130]. Players rotate on and off the
rink in shifts, each lasting approximately 30 to 80s, gen-
erally between 20 and 35 times across 60 min of game
time [131].
At the highest professional level, the National Hockey
League (NHL), the 82-game regular season is played
with a game approximately every 2.25 days, prior to a
post-season that can include an additional 28 games over
60days [132]. Due to shift rotations, there is a wide range
of individual game-time per player, with the total time on
ice potentially varying from approximately 5 to 28min
for skaters (excludes Goaltenders). Given this variation in
game participation, compounded by the rate of competi-
tion, monitoring individual external load with a team is a
worthwhile application of athlete tracking.
In order to monitor external load, tracking technology
should be validated for the distinctive requirements of
this sport. Notably, describing the unique biomechanical
challenges of ice-skating reveals different characteristics
to running [132]. Recent research has deemed an accel-
erometer-derived measure a reliable quantification of
on-ice external load in a closed-roof hockey arena [133].
is measure can also reliably distinguish between cer-
tain ice hockey-specific movements including: accelera-
tion, top speed, shooting, and repeated shift timing [133].
Describing external load, stratified into sport-specific
categories and/or metrics, can further the understanding
of technical and physical characteristics of training and
competition. However, such microsensor technology may
not be permitted in official competition and therefore,
practitioners may be required to integrate such systems
from the training environment with the different solu-
tions permitted in competition.
Describing the high-intensity characteristics of skat-
ing with validated tracking technology is useful for the
physical preparation of such athletes. One study of 36
NHL players demonstrated an average of seven high-
intensity bouts per minute required, with high-intensity
(> 17 km.h−1) skating accounting for approximately 45%
of total skating distance [131]. However, this distribu-
tion of skating intensity is different according to position.
Defensemen and forwards accumulate a similar distance
across a game but in a different manner; with defense-
men skating significantly higher distances at lower veloc-
ity skating speeds and forwards covering more in higher
velocity bands [131, 134, 135].
Given these positional differences, there is opportu-
nity for tracking data to assist with planning appropri-
ate training drills and sessions, both on a positional and
individual level. e selection of suitable temporal dura-
tions for analysis and in turn, planning, should be con-
sidered by the practitioner based on training objectives.
While game-time is structured by shifts with varying
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Torres‑Rondaetal. Sports Medicine - Open (2022) 8:15
work-to-rest ratios, training drills may at times be more
continuous in nature with all skaters participating on
the ice. is warrants a critical appraisal to consider the
most appropriate time-series analysis. For instance, given
a forward could spend ~ 22.7s of a shift in maximal or
near-maximal skating [135], a similar time epoch (nota-
bly less than one minute) may be used to better under-
stand skating intensity. In addition to understanding the
intensity across positions, there is also a “special teams”
component in ice hockey with “power play” and “penalty
kill” periods. ese passages may have implications for
physical preparation, given the difference in the number
of skaters permitted on the ice.
Using the playing environment to assess fitness param-
eters, rather than requiring additional time for isolated
testing, is an appealing solution that tracking technol-
ogy may assist with. Positioning systems may be capable
of measuring on-ice sprint times in place of timing gates,
although the context of the sprint should be considered,
with the duration and movement complexity influencing
the reliability of the measure [136]. e ability to repeat-
edly produce power is important to success and tracking
systems may be able to objectively capture this ability
[137]. is may be particularly valuable given that simi-
lar off-ice (i.e. land-based) measures do not necessarily
relate to on-ice performance [137].
Tracking systems are still a relatively recent addition
to ice hockey, with competition player and puck train-
ing introduced to the NHL in the 2019–20 regular sea-
son [138]. As such, there may currently be a paucity of
tracking research within this sport, but numerous poten-
tial avenues to explore going forward. ese include;
the indication of fatigue based on drop-off in tracking
outputs [136], assessing team pace of play using spatio-
temporal possession data [32], quantifying the unique
characteristics of the Goaltending position, and continu-
ing to describe the characteristics of the game across dif-
ferent competitions, age groups, and genders.
Netball
Netball is a dynamic, high-intensity intermittent court-
based team sport [139, 140]. Netball has unique physical
[141], technical [142] and tactical [143] characteristics
due to rules restricting players to specific areas of the
court based on seven distinct playing positions, moving
only one step when in possession of the ball and releasing
the ball within three-seconds of receiving it [144]. Unlike
other team sports, netball is capped at 15min quarters,
unless an injury occurs and play is halted, whilst the clock
is stopped. Profiling the physical characteristics of netball
athletes has largely been confined to video analysis and
wearable IMU, due to training and matches being held
indoors at the elite level [139, 141]. Recent advancements
in LPS have allowed the physical characteristics of elite
netballers to be profiled.
A key consideration for practitioners working within
netball is the positional differences. Playing position
defines where the players can move on court; Goal Keep-
ers (GK) and Goal Shooters (GS) are restricted to only
one third of the court, compared to Centres, who can
play in all thirds (except for the shooting circles). ese
large discrepancies in the space available for players to
move within greatly impacts the physical characteristics
of match-play. Centre court players (centres, wing attack
[WA] and wing defence [WD]) consistently have greater
external loads compared to GK and GS [139, 140, 145].
Positions also differ in the contribution of locomotor
(e.g., jogging, walking, shuffling, running) and non-loco-
motor (i.e., catch, jump, rebound, guarding) activities to
total match load [146]. Sweeting etal. (2017) found that
the movement sequences of GD, GA and WA are the
most closely related, with GS being highly dissimilar to
all other positions [110]. erefore, it is imperative that
practitioners working with tracking systems in netball
acknowledge the positional differences when considering
metric selection and analysis.
e distinct movement patterns of netball is another
consideration. e tracking system used will determine
the metrics utilised by practitioners; ongoing develop-
ments and increasing availability of technologies, includ-
ing LPS, allow for tracking locomotor characteristics
indoors [126, 140]. Whilst total distance and average
speed can be examined with these systems [140], practi-
tioners should consider how this is accumulated and the
non-locomotor movements that are unique to the sport.
Walking with straight movement and neutral accelera-
tion have been found to be the most prevalent movement
features in international match-play [126], and change-
of-direction has been identified as an important external
load metric in professional netballers [140]. When con-
sidering accelerometer-derived ‘load’ metrics, off-ball
guarding has the greatest amount of PlayerLoad™ per
minute, compared to other non-locomotor movements
[146]. Further, IMA-derived metrics can investigate the
non-locomotor movements and are particularly impor-
tant for specific positions. For example, GS covers the
lowest total distance, but performs the greatest num-
ber of total jumps [140]. Despite their use in research,
IMA-derived metrics are yet to be validated during
netball match-play, which must be acknowledged and
considered.
The interchangeability of different manufactur-
ers and providers is an issue for practitioners work-
ing in netball. During competitive matches, LPS could
be used but teams may not have access to the data or
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Page 15 of 22
Torres‑Rondaetal. Sports Medicine - Open (2022) 8:15
system during training and subsequently, rely on iner-
tial sensors to capture external load. Whilst wear-
able technologies such as IMU may have suitability to
detect the “off-ball” movements in netball, as described
above, clarity is needed on how to utilise the aggregate
outputs of these metrics, including PlayerLoad™ per
minute, for the design of training [146]. For example,
many actions, off and on ball, can comprise the same
PlayerLoad™ per minute. Therefore, practitioners may
look to a systems approach to examine the key perfor-
mance characteristics (physical and skilled) that exist
within netball [56].
Given the high frequency of skilled actions and
scoring in the complex and dynamic sport of netball,
opportunity exists for sport scientists to place physical
characteristics into context by overlying rich technical
and tactical data. For example, a work domain analy-
sis method, as part of a systems approach, for netball
was recently investigated [56]. Specifically, 19 values
and priority measures of elite netball were identified,
including; passing, scoring, cognitive measures of psy-
chological flow, team structure and the use of tactical
timeouts, alongside physical characteristics [56], high-
lighting the need to integrate data. For example, meas-
uring the acceleration or angular velocity of a netballer,
when coupled with applying defensive pressure on an
opponent could be a rich source of information [126].
Similarly, netballers use a variety of coordination strat-
egies to shape tactical and physical behaviours during
turnovers [147]. Together, these studies present a com-
plex systems approach to analysing netball athlete per-
formance that could potentially give further context to
existing metrics, on how and why activities take place.
For example, rather than presenting data on total dis-
tance covered or the number of accelerations that take
place in netball, practitioners could complement this
physical data with tactical and technical data to give
richer insights.
Rugby Codes
All three rugby codes (league, union and sevens) are
played on the same field dimensions and are character-
ised as high-intensity intermittent contact sports [148].
Yet despite these similarities, distinct differences exist
between codes. Rugby union and sevens are played
under similar laws, with different playing numbers (15
vs. 7), whereas rugby league is played with 13 players
per team and extensively different laws [148]. e dif-
ferent laws and playing numbers of the rugby codes
result in unique characteristics that should be con-
sidered by practitioners when determining the use of
tracking systems data in each code. Here we will focus
on some key considerations for rugby league.
Rugby League
Games of rugby league are played over two 30 to 40min
halves (depending on the level of competition), sepa-
rated by a 10-min rest interval. At the professional level
players cover between ~ 5367 to 7064m, with ~ 335 to
563 m HSR distance, whilst also carrying out ~ 21 to
34 collisions, depending on playing position, within a
match [149]. Given these physical characteristics, and
specifically the associated physiological, biomechanical,
and energetic cost of such contact elements on players
[1], quantifying both locomotor and contact demands
as part of the external load is vital.
Following the validation of a collision detection algo-
rithm [150], the use of tracking systems data to quan-
tify collisions has increased [151]. Hulin etal. (2017)
found Catapult Optimeye S5 devices to be sensitive
(97.6 ± 1.5%) to detecting collisions, and the over-
all accuracy to increase when low intensity (< 1 Play-
erLoad™ AU) and short duration (< 1s) collisions are
removed [150]). e use of tracking systems to detect
collisions in rugby league enhances the ability to con-
sider the locomotor and collision characteristics con-
currently as opposed to separate entities [152]. For
example, when quantifying and monitoring the ‘peak
demands’ of rugby league competition, practitioners
should consider: (1) the concurrent collision count,
during the duration specific peak locomotive periods,
and (2) the concurrent average running speed of the
duration of the specific peak collision periods, to appro-
priately prepare players for the periods of competition.
Due to the collision nature of the sport, and the spa-
tial confinements, players regularly accelerate and
decelerate at high intensities; given the metabolic cost
of these movements [128], it is important that they are
also quantified and monitored. A range of accelera-
tion metrics are utilised in rugby league, with average
absolute acceleration becoming increasingly popular,
especially for the analysis of the peak characteristics
[82, 128]. is is an important trend given that accel-
eration has been shown to occur separately to peak
periods of speed and yet, are equally important to the
match outcome [81]. e use of PlayerLoad™, and its
variants, has also been proposed to capture the acceler-
ation, deceleration and change-of-direction, as well as
the contact load [150] and are widely used in practice
[16, 153]. Interestingly, the variant capturing the slow
component (< 2m/s) of PlayerLoad™, known as Player-
Load™ Slow, has been used in rugby codes as a measure
of sport-specific low-speed activity (e.g., rucking) [154].
Such accelerometry-derived metrics are also useful to
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Page 16 of 22
Torres‑Rondaetal. Sports Medicine - Open (2022) 8:15
capture external load in indoor training environments,
given it is common practice for rugby league teams to
carry out contact training in specific ‘padded’ rooms,
and GPS derived acceleration metrics cannot be used in
such environments.
Additionally, the unique technical and tactical require-
ments of the different positions within a rugby league
team [155] result in differences in the physical charac-
teristics of match-play. A main difference between posi-
tions is in the playing time of the match; forwards have
lower playing times than backs [149, 156], which conse-
quently influences the physical characteristics and should
be taken into consideration. Backs are reported to cover
greater total distance than forwards [152], however stud-
ies report no differences in the average running speed of
match-play, given the differences in playing time between
positions [156, 157]. erefore, practitioners are encour-
aged to not only utilise total distances, but also consider
the intensity of the work given the differing playing time,
via analysis of the average running speeds of match-play
and the peak characteristics for position specific training
practices. Importantly, differences in HSR, very HSR, and
collisions are present between positions. Forwards cover
less HSR distance compared to adjustable and backs, but
carry out more collisions [149]. As such, tracking metrics
may therefore be of differing value to practitioners when
seeking to monitor the external load, and subsequent
dose–response, across positions.
Finally, the tactical characteristics of rugby league
should be considered alongside the physical, to enhance
the application of tracking systems data and aid in train-
ing practices. Whilst competition is 80-min in duration,
the match can be broken up into distinct phases of play
given the rule of the set of six tackles: attack, defence and
the attack-defence transition, as well as ball-in-play peri-
ods. By considering the physical characteristics within
these periods of play, practitioners can work with coaches
when planning and evaluating technical-tactical training.
Distinct locomotor characteristics exist during attack-
ing and defending phases, with greater average running
speeds during defense, but greater HSR distance per min-
ute during attack [74]. Further positional differences are
likely due to the unique positional requirements such as
‘backs’ leading the kick chase or challenging for the ball
during the attack-defence transition. erefore, prac-
titioners working with tracking systems data in rugby
league should consider the nature of the sport (e.g., con-
tact) and positional differences, alongside the tactical
characteristics when collecting, analysing and interpret-
ing data.
Soccer (Association Football)
Soccer is an intermittent field sport played by two teams
of 11 players (10 outfield plus a goalkeeper) over two con-
tinuous 45-min halves, separated by a 15-min half time
period [158]. e sport may be viewed as an early adopter
of tracking systems, with much of the early research con-
ducted in the 1990s stemming from optical tracking (pre-
dominantly semi-automated camera systems) and GPS
use, in competition and training environments respec-
tively, in the men’s professional game [158]. Despite being
permitted in professional competition from 2015, many
teams prefer to restrict GPS use to training only, poten-
tially due to the less invasive nature of optical tracking.
us, practitioners in these environments often face the
challenge of integrating tracking data in order to con-
sistently describe, plan, and monitor across the season.
While integrative equations have been proposed, these
are tracking system-specific, as well as dependent on the
pitch size of the data collection [64].
Physical competition characteristics vary by playing
position, depending on a number of situational factors,
including tactical decisions, team formation, opponents
style of play and level of competition [159, 160]. Total
distance during a professional men’s match ranges from
10 to 12km, with central midfielders covering the highest
distance (11,885m) while central defenders and strikers
cover the lowest (10,671m and 10,790 m, respectively)
[161]. High-speed running (> 5.5 m.s) constitutes on
average 12% of the total distance, however wide play-
ers are seen to cover a greater contribution of their total
distance at high speeds [162, 163]. Similarly, wide play-
ers compared to central also produce the highest accel-
eration efforts, which is important due to the greater
energetic demand of these movements [164]. e unique
demands of the goalkeeping position result in 50% less
total distance than outfield athletes (4–6km), with 98%
of match time spent in low intensity movement [165].
However, tracking systems have recently aimed to quan-
tify goalkeeper-specific movement demands that include
the number of dives, jumps, and overall forward and lat-
eral explosive movements.
While absolute totals are necessary to describe the
sport’s characteristics and monitor individual exter-
nal load, practitioners are encouraged to think beyond
absolute values to help guide physical preparation. e
most simplistic use of relative (per minute) metrics ena-
bles identification of athletes’ ‘pacing strategies’ [70].
For example, elite outfield male soccer players will range
between 102 and 118m/min depending on playing posi-
tion [166]. Similarly, Fereday and colleagues (2020) iden-
tified the relative total distance between 120 and 190m/
min across a range of rolling average durations in pro-
fessional male soccer players [90]. Rolling averages have
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Page 17 of 22
Torres‑Rondaetal. Sports Medicine - Open (2022) 8:15
been suggested as a superior analysis method compared
to discrete 5–15min epochs, due to a 12–25% underes-
timation in peak running demands [87]. Utilising peak
locomotor demands to design position-specific training
drills is common in applied soccer practice in an attempt
to simulate game intensity [18]. However, concern has
been raised over the validity of this concept, given that
peak demands do not occur concurrently across metrics
and players [94]. Practitioners are therefore reminded
that the training process is complex and no single metric
or calculation can surmise the external load an athlete is
subjected to.
Drills in soccer are often manipulated in their design
via field size, the number of players, or work-to-rest
ratios, in order to elicit particular intensities. Practi-
tioners using tracking systems in soccer can analyse the
data to quantify the effects of such manipulation. For
instance, larger pitch size provide greater opportunity to
sprint whereas, smaller pitch sizes may allow less expo-
sure to high velocities but greater exposure to changes
of direction, accelerations and decelerations [5]. In par-
ticular, small-sided games have garnered popularity as a
training methodology in soccer, however they should be
combined with other forms of drills due to limitations on
reaching higher velocities. High-speed exposure has been
particularly highlighted as important for performance
and injury risk perspective in soccer and therefore, many
practitioners use the objective characteristics of soccer
drills in comparison to competition characteristics to
monitor athlete speed exposure over time [167]. How-
ever, despite the widespread adoption of regular high-
speed exposure in practice, along with experts’ opinion
behind the concept [168], further work is required to
establish stronger evidence in support of injury protec-
tion properties against regular high-speed exposure.
While this focus on quantifying and planning training
drill design from a physical perspective is important, the
technical and tactical components of soccer are also key
contributors to success. erefore, coaches and practi-
tioners strive to combine physical preparation with the
tactical element. One method, tactical periodization, has
become a popular training strategy [169]. is method-
ology stresses different physical and tactical elements in
turn across the microcycle, whereby the main focus is
soccer-specific training [170]. Furthermore, the coach’s
style of play heavily influences physical characteristics
in soccer, as in other team sports, with tactical periodi-
zation assisting in training exercise selection that rep-
resent the specific coach’s principles of play [170]. With
many professional leagues competing multiple times a
week, along with congested schedules at other levels of
play, the taxing schedule adds an element of complexity
regarding preparation and recovery. As such, combining
physical and tactical goals into training drills and sessions
provides a time-efficient approach that tracking systems
can support.
While soccer research has historically focussed on
male athletes, there has recently been an increase in the
women’s game [171]. Whilst the volume and intensity
of total distance in the women is similar to that of males
(8–11km total; 108–119m/min) [171–173], male players
perform on average 30% more high-intensity movements
during matches [171]. erefore, to ensure appropriate
application of tracking data to inform training and match
preparation, an understanding of the physical character-
istics specific to the female athletes is required. Particu-
larly important to practitioners working with tracking
data in women’s soccer is the consideration of suitable
speed thresholds, given that most research has been con-
ducted on men. A Gaussian curve fitting approach was
used with instantaneous velocity data from women’s
soccer and other team sports, with the intersections
between curves used to determine sport-specific speed
thresholds [125]. However, concerns have been raised,
including the appropriateness of the technique itself—as
there is no evidence to suggest that the velocities within
each zone follow a Gaussian distribution—as well as the
dataset used, which was not from an elite female popula-
tion [127]. Consequently, another group used the spectral
clustering technique on a dataset from 27 female play-
ers across 52 international matches, which determined
thresholds of 12.5, 19.0, and 22.5km/h most suitable to
denote high-speed, very-high-speed, and sprint catego-
ries, respectively, for elite women’s soccer [127].
Conclusion
We have attempted to summarise and critically evaluate
the different tracking systems used within team sports,
along with the suitability of their derived metrics for spe-
cific team sports. In summary, tracking systems provide
the collection of athlete external load data, whereby prac-
titioners can use derived metrics to describe, plan, moni-
tor and evaluate training and competition characteristics.
e selection of these metrics, and the systems from
which they are obtained, are dependent upon the context
of the sport and will need careful consideration by prac-
titioners. Similarly, given the increasing amount of data
generated, the accessibility and affordability of technolo-
gies to capture athlete external load, practitioners need to
be critical in considering the validity, accuracy and preci-
sion of each system, along with metrics that provide eco-
logical validity. Given the speed at which new metrics are
introduced and developed by manufacturers, practition-
ers are encouraged to critically evaluate the suitability
of those, within their chosen sport and attempt to “peak
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Page 18 of 22
Torres‑Rondaetal. Sports Medicine - Open (2022) 8:15
under the hood” of what is happening within algorithms
and how data are being processed.
With the rise in popularity of open-source program-
ming languages, spatiotemporal data can now not only
be aggregated into drills, rotation stints, quarters, halves
or matches, but also into time sequences such as roll-
ing averages, frequency domain analysis and time series
approaches. e speed and access of these approaches
can now allow practitioners to sync vision with external
load data, examine tactical or collective behaviour, merge
skilled actions into a time series and quantify the specific
movements of acceleration and angular velocity. How-
ever, practitioners are encouraged to maintain critical
thinking, with a healthy dose of scepticism and awareness
of appropriate theoretical frameworks, where possible,
when creating a new or selecting an existing metric to
profile team sport athletes.
Abbreviations
GPS: Global positioning systems; LPS: Local positioning systems; IMU: Inertial
measurement units; NBA: National Basketball Association; NFL: National foot‑
ball league; HSR: High‑speed running; IMA: Total inertial movement analysis;
NHL: National hockey league; GK: Goal keepers; GS: Goal shooters; WA: Wing
attack; WD: Wing defence; AU: Arbitrary units.
Acknowledgements
Not applicable.
Authors’ Contributions
All authors read and approved the final manuscript.
Funding
No sources of funding were used to assist in the preparation of this
manuscript.
Availability of Data and Materials
Not applicable.
Declarations
Ethics Approval and Consent to Participate
Not applicable.
Consent for Publication
Not applicable.
Competing interests
Lorena Torres‑Ronda, Emma Beanland, Sarah Whitehead, Alice Sweeting and
Jo Clubb declare that they have no conflicts of interest relevant to the content
of this review.
Author details
1 Institute for Health and Sport, Victoria University, Melbourne, Australia. 2 Span‑
ish Basketball Federation, Madrid, Spain. 3 Sports Performance, Buffalo Bills,
Buffalo, USA. 4 Carnegie School of Sport, Leeds Beckett University, Leeds, UK.
5 Leeds Rhinos Netball, Leeds, UK. 6 School of Sport, Exercise and Rehabilitation,
University of Technology Sydney, Sydney, Australia.
Received: 23 February 2021 Accepted: 2 January 2022
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