![Conceptual framework for physical training in professional basketball, adapted from the work by Jeffries et al. [12] Prescription represents the short and long-term planning and execution of training, competition and travel over the course of the season (i.e., nature and organization of training sessions and travel)
External load represents the physical demands associated with training, competition, and travel during the season, and training load is the specific stimulus induced by both training sessions and competition. Internal load represents the psychophysiological responses occurring during the execution of training. Contextual factors are defined as factors that are not part of the main training process, such as environmental, social, and cultural factors, but can influence the training process or outcome. Individual factors are characteristics of the individual athlete, such as genetics, psychological traits and states, and training background, which can influence the training process or outcome. Training effects can be acute or chronic, and positive or negative, effects caused and occurring after the training session, and can be assessed using functional, subjective, physiological, biomechanical and cognitive measures. The bidirectional arrow represents a reciprocal nature of interactions between training effects and individual/contextual factors. For example, a negative training effect (e.g., increased fatigue or poor sleep) can act as an individual factor influencing the internal training load in the subsequent session. Sports performance outcomes are defined as the result of the balance between positive and negative training effects.](https://www.researchgate.net/publication/361529713/figure/fig1/AS:11431281275478509@1725413952142/Conceptual-framework-for-physical-training-in-professional-basketball-adapted-from-the_Q320.jpg)
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RESEARCH ARTICLE
Understanding ‘monitoring’ data–the
association between measured stressors and
athlete responses within a holistic basketball
performance framework
Richard A. J. MercerID
1,2☯
*, Jennifer L. Russell
1,2☯
, Lauren C. McGuigan
1☯
, Aaron
J. Coutts
1☯
, Donnie S. Strack
2☯
, Blake D. McLeanID
1,2☯
1Faculty of Health, School of Sport, Exercise and Rehabilitation, University of Technology Sydney (UTS),
Sydney, NSW, Australia, 2Human and Player Performance, Oklahoma City Thunder Professional Basketball
Club, Oklahoma City, Oklahoma, United States of America
☯These authors contributed equally to this work.
*richardajmercer@gmail.com
Abstract
This study examined associations between cumulative training load, travel demands and
recovery days with athlete-reported outcome measures (AROMs) and countermovement
jump (CMJ) performance in professional basketball. Retrospective analysis was performed
on data collected from 23 players (mean±SD: age = 24.7±2.5 years, height = 198.3±7.6 cm,
body mass = 98.1±9.0 kg, wingspan = 206.8±8.4 cm) from 2018–2020 in the National Bas-
ketball Association G-League. Linear mixed models were used to describe variation in
AROMs and CMJ data in relation to cumulative training load (previous 3- and 10-days),
hours travelled (previous 3- and 10-day), days away from the team’s home city, recovery
days (i.e., no travel/minimal on-court activity) and individual factors (e.g., age, fatigue, sore-
ness). Cumulative 3-day training load had negative associations with fatigue, soreness, and
sleep, while increased recovery days were associated with improved soreness scores.
Increases in hours travelled and days spent away from home over 10 days were associated
with increased sleep quality and duration. Cumulative training load over 3 and 10 days,
hours travelled and days away from home city were all associated with changes in CMJ per-
formance during the eccentric phase. The interaction of on-court and travel related stressors
combined with individual factors is complex, meaning that multiple athletes response mea-
sures are needed to understand fatigue and recovery cycles. Our findings support the utility
of the response measures presented (i.e., CMJ and AROMs), but this is not an exhaustive
battery and practitioners should consider what measures may best inform training periodiza-
tion within the context of their environment/sport.
Introduction
When athletes are to perform at their best, practitioners working with these athletes must con-
sider a range of stressors encountered during the season, and how individuals respond to those
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OPEN ACCESS
Citation: Mercer RAJ, Russell JL, McGuigan LC,
Coutts AJ, Strack DS, McLean BD (2022)
Understanding ‘monitoring’ data–the association
between measured stressors and athlete
responses within a holistic basketball performance
framework. PLoS ONE 17(6): e0270409. https://
doi.org/10.1371/journal.pone.0270409
Editor: Emiliano Cè, Universita degli Studi di
Milano, ITALY
Received: February 24, 2022
Accepted: June 10, 2022
Published: June 24, 2022
Copyright: ©2022 Mercer et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper. Raw data files cannot be shared
publicly due to ethical and legal reasons, including
the presence of confidential and sensitive results
from professional athletes ensuring compliance
with the National Basketball Association (NBA)
Health Related Research Policy. Data files can be
requested from the University of Technology
Sydney (UTS) Human Research Ethics Committee
(contact via Research.Ethics@uts.edu.au) for
researchers who meet the criteria for access to
demands. During a professional basketball season, players experience physical loading fre-
quently during games and practices, combined with extensive travel across multiple time
zones and limited recovery days (i.e., days without travel and minimal to no on-court activity)
[1]. However, there is a paucity of information describing the association between physical
demands in professional basketball and an athlete’s physical readiness to perform during
competition.
In basketball, there are requisite physical capacities that are necessary to sustain the techni-
cal and tactical aspects of the sport, which ultimately determine individual and team perfor-
mance outcomes. Given the congested competition and travel schedules in professional
basketball, enhancing physical readiness necessitates carefully planned and well executed train-
ing, recovery, and travel [2,3]. In this context, recovery represents processes that result in an
athlete’s renewed ability to meet or exceed previous performance levels following training and
competition [4]. Adequate rest from strenuous exercise is critical component in this recovery
cycle, but opportunities for dedicated ‘recovery days’ are limited throughout the professional
basketball season. Therefore, it may be difficult for players to fully recover from the accumu-
lated physical and psychological stress related to games [5]. A further challenge for practition-
ers working in team sports is managing training prescription for many individual athletes,
who possess a range of different individual characteristics (e.g., physical qualities, psychologi-
cal and emotional states, age, training age, injury history, experience), with varied contextual
factors (e.g., travel direction, travel duration, activity, training type, competition schedule). All
of these components may each individually affect how players respond to on- and off-court
stressors [6], and responses can also be measured via many different assessments (e.g., neuro-
muscular status, perceived wellness, heart rate variability, biomechanical measures or cognitive
tests [7]). Given these challenges, practitioners commonly collect a combination of objective
(e.g., countermovement jump (CMJ) tests [8]) and subjective (e.g. athlete-reported outcome
measures (AROMs) [9]) assessments in an effort to measure an athlete’s physical readiness
and to prescribe individual periodization strategies [6]. Indeed, the systematic collection and
evaluation of ecologically valid data can be used to inform future decision-making, which is a
critical element of continually improving the effectiveness of the training process [10]. In bas-
ketball, information regarding associations between training load [i.e., the training stimulus
induced by both training sessions and competition [11]), travel, recovery days and training
effect measures (e.g., functional, subjective, physiological, biomechanical and other measures
[12]) is limited. Enhancing understanding of these relationships would facilitate better under-
standing and implementation of athlete monitoring processes and allow practitioners to make
well-informed decisions that lead to a higher level of athlete care [1].
Whilst athlete response measures aim to quantify the response to a range of factors, many
of which are relatively uncontrollable in team sport settings (e.g., playing and travel schedule,
social influences), practitioners also control many elements which affect athlete responses.
Indeed, it has been suggested that more emphasis should be placed on the design and imple-
mentation of sensitive and responsive training systems, in order to optimize individualization
and develop context-specific training-planning solutions [10]. This requires practitioners to
respond to emerging information [10], such as athlete response measures assessed regularly
throughout the season [12]. Adding further complexity, training effects may respond over
acute or chronic time periods and have a positive or negative impact, and subsequent perfor-
mance is the resultant balance between the positive and/or negative adaptation [12]. Similarly,
there is a reciprocal interaction between individual and contextual factors and training effects.
For example, negative training effects (e.g., increased fatigue or poor sleep) can act as individ-
ual factors that influence the internal training load for the athlete in the following training ses-
sions (a further description of this concept can be found in the work by Jeffries et al. [12]). To
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confidential/ sensitive data. The data presented in
the paper is in-line with all other papers published
in PLOS ONE that contain athlete sensitive data.
Funding: The author(s) received no specific
funding for this work.
Competing interests: The authors have declared
that no competing interests exist.
better inform the interplay of multiple inputs and outcome measures, conceptual frameworks
have recently been proposed as a tool to inform context specific training monitoring [12]. This
process involves identifying measurable components and their role within the training process,
thereby allowing practitioners to better understand what to measure and why these compo-
nents may be important [12]. This approach leads to an improved understanding that reflects
the challenging nature of the environment, and these conceptual frameworks can act as a refer-
ence operational guide in practical settings [12], against which experience, observations, data
and decisions can be contextualized [10].
Therefore, the aim of this work is to determine associations between short- (i.e., 3-day) and
medium-term (i.e., 10-day) cumulative training load, short- and medium-term travel
demands, recovery days, and individual factors (e.g., fatigue, soreness, age) with AROMs and
CMJ measures over the course of a professional basketball season. Subsequently, we aim to
develop a conceptual framework that describes the important inputs and outcomes in profes-
sional basketball and informs the interpretation of physical training measures within this
environment.
Methods
Study design
For the first part of this study, a retrospective, descriptive, observational design was followed
using data recorded from one team over the course of the 2018–2019 and 2019–2020 National
Basketball Association (NBA) G-League seasons (October to March). The team selected was a
convenience sample, as members of the research team are full-time staff members with the
partner organization. The regular monitoring process of the team included collection of
AROMs on home practice days, CMJ data, training load data for all on-court activity (e.g.,
practice and games) and travel demands for all trips away from the team’s home city through-
out the season. Linear mixed models were used to determine associations between these physi-
cal demands, individual factors (e.g., age, fatigue, soreness) and training effect measures
collected throughout the season.
We then developed a conceptual framework to facilitate the validation and interpretation of
these physical training measures [12]. This included theorizing components of external load
(i.e., physical demands associated with the training process and competition schedule) and
internal load (i.e., psycho-physiological stress experienced by the athlete during the training
process and competition schedule), combined with individual and contextual factors that can
influence subsequent training effects in professional basketball [12]. We then identified train-
ing effect measures that are commonly used in professional basketball settings, and perfor-
mance outcomes specific to basketball competition.
Participants
Players were only included in the analysis if they met all the following criteria: i) were on the
team’s roster for at least 4 weeks ii) completed 3 or more CMJ assessments and iii) completed
10 or more AROM questionnaires. Final analysis included 23 professional basketball players
(mean ±SD: age = 24.7 ±2.5 years [range: 20–30 years], height = 198.3 ±7.6 cm, body
mass = 98.1 ±9.0 kg, wingspan = 206.8 ±8.4 cm). This research was approved by the Univer-
sity of Technology Sydney (UTS) Human Research Ethics Committee (ETH19-3359), and
consent was granted by the NBA and NBA Players Association as per the guidelines and
requirements for ‘NBA related health research’ governed by the NBA Collective Bargaining
Agreement [13].
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Procedures
External load measures. Training load data (i.e., PlayerLoad™) was collected from all on-
court activities (e.g., individual workouts, practice, shootarounds and games) with an inertial
measurement unit (IMU), sampling at 100 Hz (Catapult T6, Catapult Sports, Melbourne Aus-
tralia), placed between the scapulae of the players in a customized pouch on a fitted vest. Each
player wore the same IMU throughout the season to minimize inter-unit error. The validity
and reliability of measuring team sport 3-dimensional movements via triaxial accelerometer
has been shown previously for indoor environments [14,15]. After all on-court activities, the
units were downloaded using proprietary software (Openfield 1.12.2, Catapult Sports, Mel-
bourne Australia) and individual training load data for each player was exported and placed in
a customized Microsoft Excel spreadsheet for analysis (Microsoft, Redmond, WA, USA. For
individual sessions during the season where players did not wear an IMU, training load data
were estimated by multiplying the measured ‘active’ duration (always recorded) by the individ-
ual player’s mean historical PlayerLoad™min
-1
for the specific on-court activity completed
(e.g., practice, individual on-court work, shootaround, pre-game warmups, game data). Active
duration was measured as the time a player was actively engaged in an on-court drill. Breaks in
activity, including time transitioning between drills, clock stoppage situations in games and
simulated ‘live’ play, and prolonged breaks (i.e., >30 seconds) during drills, were excluded
from a player’s active time. The need for thorough descriptions and justifications when report-
ing duration methods in basketball has been emphasized previously [11], and it is proposed
that analyzing physical demands with active duration would allow for practitioners to more
accurately calculate intensity demands for basketball activity [11] and inform the development
of more precise competition specific training [16]. The mean historical PlayerLoad™min
-1
was
calculated using all previously measured load data for that individual player, for each specific
on-court activity, during the season (i.e., from the beginning of training camp and through the
duration of the season). The majority of cases where training load data were estimated
included: pre-game individual warm-ups, low intensity ‘shootaround’ activities on game days,
and game data for players with an NBA contract [as these players are not permitted to use
wearables in games [1]). PlayerLoad™was summed over the 3 and 10 days preceding the collec-
tion of AROMs or CMJ data. The 3-day time frame was selected based on previous work sug-
gesting that neuromuscular status in basketball requires ~72 hours to return to baseline
following gameplay [17]. Similarly, the 3- and 10-day periods provided consistent short- and
medium-term windows of practice and game data specific to the NBA G-League schedule
(3-day: 1.0 ±0.6 games; 10-day: 3.3 ±1.1 games).
Recovery days were defined as a day without travel, games, or team practice and minimal
(i.e., <200 PlayerLoad™units) or no on-court activity accumulated by the player. Any minimal
on-court work was less than 200 PlayerLoad™units, which typically represents <45 minutes of
light basketball activity [18]. Recovery days were recorded throughout the season and players
were defined as either having a recovery day, or not having a recovery day, in the 3 days pre-
ceding the collection of AROMs or CMJ data.
Travel data (e.g., approximate hours travelled, and days spent away from the team’s home
city) were recorded from flight logs over the course of the season and stored in a customized
Microsoft Excel spreadsheet (Microsoft, Redmond, WA, USA) to quantify travel demands.
Similar to previous quantifications of in-competition travel [19], hours travelled was defined
as the hours between leaving the team’s departure point and arriving at the destination and
was summed each travel day and over entire away trips. Overall, 3- and 10-day windows were
used to represent cumulative travel stress. Days spent away from the team’s home city was cal-
culated by dividing the total hours away by 24 hours and was summed in the 10 days preceding
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the collection of AROMs or CMJ data. A 3-day travel time frame was selected based on work
suggesting that, when travelling across 2 or more time zones, symptoms of travel fatigue can
persist for up to 2–3 days after arrival [5], while the 10-day time frame captured periods of the
team schedule that could include one full trip away from the team’s home city and potentially
multiple days involving travel.
Athlete response measures. AROMs were assessed via a digital survey adapted from the
work of McLean et al. [9] and players were asked to complete the survey before team court activi-
ties (i.e. 09:00–11:00) on home practice days. AROMs were also collected alongside CMJ data on
predetermined game days. Questions related to fatigue, general muscle soreness, and sleep were
asked on a ten-point scale with five written anchors. Scores were converted to individual z-scores
(i.e., the score in terms of number of standard deviation (SD) units the raw score is above or
below the individual’s mean [9]) and these calculations used all data points collected from each
player during the season to account for individual fluctuations in perceived responses.
CMJ testing began on the first day of formal training, with 18 assessments performed across
2 seasons (4 pre-season tests; 14 in-season tests) as part of the regular monitoring process of
the team. In-season assessments were executed at the home practice facility and before team
court activities on predetermined game days (n = 5) and practice days (n = 9). All CMJ data
included in this study were collected using protocols we have previously described [8].
CMJ’s were assessed on dual force platforms (ForceDecks FD4000, Vald Performance, Bris-
bane Australia) sampling at a rate of 1000 Hz. ForceDecks software (Vald Performance, Bris-
bane Australia; Version 2.0.7188) calculated 105 bilateral force-time CMJ variables for all
jumps, via methods previously described [20]. Based off our previous work examining reliabil-
ity and sensitivity with this cohort [8], and to establish parsimony, we selected 8 CMJ variables
that were highly sensitive to changes in athlete status across the NBA G-League season (i.e.,
seasonal variability was greater than within-subject variability): countermovement depth,
eccentric braking rate of force development (RFD), eccentric duration, eccentric mean decel-
eration force, mean eccentric and concentric power over time, eccentric deceleration phase
duration, eccentric peak power and eccentric peak velocity. Final data were checked for outli-
ers and all data points were within 3 SD above or below the individual’s mean.
Development of conceptual framework. The conceptual framework (Fig 1) was theo-
rized using information and constructs (e.g., external and internal load, individual and contex-
tual factors, training effects and sports performance outcomes) adapted from previous models
for physical training [12], stress-related, strain-related and overuse athletic injuries [21], and
temporal relationships between exercise, recovery processes and changes in performance [4].
The constructs within the framework were then informed using previous literature in basket-
ball regarding the measurement of physical demands [1,11], external and internal training
load models [22,23], monitoring fatigue and neuromuscular performance [24,25], travel [5,
26] and recovery [6]. This information was supplemented with practical experience and expert
consultation within the research team. Measurable components in the conceptual framework
were identified in combination with the available assessment tools and technologies used
within the environment (e.g., training load data, travel data, AROMs and CMJ measures). Sim-
ilarly, suitable time frames of training load and travel were considered, based on previous liter-
ature and the research team’s experience in practice, to provide consistent short- and
medium-term windows of practice, game, and travel data for the G-League environment.
Statistical analyses
Twelve separate 2-level linear mixed models were used to examine associations between train-
ing load, travel, recovery days, and individual factors with AROMs and CMJ performance over
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the course of the season (Tables 1and 2). The study design located units of analysis (individual
season samples) nested in clusters of units (players). This form of analysis may contain both
fixed effects (effects that describe the association between a dependent variable and covariates
Fig 1. Conceptual framework for physical training in professional basketball, adapted from the work by Jeffries
et al. [12] Prescription represents the short and long-term planning and execution of training, competition and
travel over the course of the season (i.e., nature and organization of training sessions and travel). External load
represents the physical demands associated with training, competition, and travel during the season, and training load
is the specific stimulus induced by both training sessions and competition. Internal load represents the
psychophysiological responses occurring during the execution of training. Contextual factors are defined as factors
that are not part of the main training process, such as environmental, social, and cultural factors, but can influence the
training process or outcome. Individual factors are characteristics of the individual athlete, such as genetics,
psychological traits and states, and training background, which can influence the training process or outcome.
Training effects can be acute or chronic, and positive or negative, effects caused and occurring after the training
session, and can be assessed using functional, subjective, physiological, biomechanical and cognitive measures. The
bidirectional arrow represents a reciprocal nature of interactions between training effects and individual/contextual
factors. For example, a negative training effect (e.g., increased fatigue or poor sleep) can act as an individual factor
influencing the internal training load in the subsequent session. Sports performance outcomes are defined as the result
of the balance between positive and negative training effects.
https://doi.org/10.1371/journal.pone.0270409.g001
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for a population) and random effects (effects associated with a random factor, usually signify-
ing random deviations from relationships described by fixed effects) [27]. In the event there
were no random effects for individual players (i.e., no significant variance at level 2), a multiple
linear regression using a stepwise approach was used to determine the influence of fixed effects
only on the dependent variable (i.e., AROMs or CMJ measures). Simple descriptive statistics
(mean ±SD) and Pearson correlations were calculated with the multiple linear regression
coefficients.
Table 1. Covariates included in model specification for athlete-reported outcome measures.
Data Level Factors Type Classification
Level 2 Cluster of units (random factor) Player
Covariate Age Dummy variable 0 = 20–25 (n = 12), 1 = 26+ (n = 11)
Level 1 Unit of analysis Individual season samples
Dependent variable Fatigue Continuous AU–z-score
Soreness Continuous AU–z-score
Sleep quality Continuous AU–z-score
Sleep hours Continuous AU–z-score
Covariates Accumulated training load (3-day) Continuous AU
Accumulated training load (10-day) Continuous AU
Hours travelled (3-day) Continuous hours
Hours travelled (10-day) Continuous hours
Days away from home city (10-day) Continuous days
Recovery days (3-day) Dummy variable 0 = no, 1 = yes
Abbreviations: AU, arbitrary units.
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Table 2. Covariates included in model specification for countermovement jump measures.
Data Level Factors Type Classification
Level 2 Cluster of units (random factor) Player
Covariate Age Dummy variable 0 = 20–25 (n = 12), 1 = 26+ (n = 11)
Fatigue Continuous AU—z-score
Soreness Continuous AU—z-score
Level 1 Unit of analysis Individual season samples
Dependent variable Countermovement Depth Continuous cm
Eccentric Braking RFD Continuous N/s
Eccentric Duration Continuous ms
Eccentric Mean Deceleration Force Continuous N
Mean Eccentric+Concentric Power:Time Continuous W/s
Eccentric Deceleration Phase Duration Continuous s
Eccentric Peak Power Continuous W
Eccentric Peak Velocity Continuous m/s
Covariates Accumulated training load (3-day) Continuous AU
Accumulated training load (10-day) Continuous AU
Hours travelled (3-day) Continuous hours
Hours travelled (10-day) Continuous hours
Days away from home city (10-day) Continuous days
Recovery days (3-day) Dummy variable 0 = no, 1 = yes
Abbreviations: AU, arbitrary units; CMJ, countermovement jump; RFD, rate of force development.
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Random factors were included in the model to investigate deviations for players from the
overall fixed intercept and fixed coefficients. The tstatistic and degrees of freedom (df) calcu-
lated for the linear mixed model were converted to get an effect size correlation (r) between
each factor and scores on the dependent variable [28]. These effect size correlations were inter-
preted as <.1, trivial; .10-.29, small; .30-.49, moderate; .50-.69, large; .70-.89, very large; .90-
.99, almost perfect; 1.0, perfect [29]. A “step up” model construction strategy was imple-
mented, beginning with an “unconditional” model containing only a fixed intercept and Level
2 random factors to determine if variation existed in the dependent variable and to establish
an initial Akaike’s Information Criteria (AIC) [27]. The models were then developed by adding
single level 1 fixed effects, followed by level 2 fixed effects. The order in which the variables
were added was determined by factors that were likely important, based on previous research
and the investigation team’s own experience in the field. Single fixed effects were retained in
the model if it displayed statistical significance (p<0.05) and contributed to model fit by
improving the information criteria (i.e., AIC) compared to the previous model. The intraclass
correlation coefficient (ICC) was used to determine the similarity of observed responses within
the individual player clusters. Final model’s residuals were visually inspected for normality. All
statistical analyses were conducted using IBM SPSS Statistics Subscription (build 1.0.01508).
Results
External load measures
Training load and recovery. Over 2 seasons, 2,784 hours of on-court data were collected
(i.e., training load data). This included 2,435 hours (87%) where training load was quantified
using IMU’s and 348 hours (13%) where players did not wear an IMU, and load was estimated
from measured duration and historical activity data. Most estimated load data were from
shootarounds (4% total data), pre-game warmups (4% total data) and game data for two-way
players (n = 3 players, 2% total data). The players had a mean cumulative 3-day training load
of 834 ±449 arbitrary units (AU) (range: 500–1319) and a mean cumulative 10-day training
load of 2722 ±1018 AU (range: 1640–4378). Players had a mean of 29 ±4 recovery days during
the season, with a mean of 6 ±4 days between them (range: 0–17 days). Frequencies of recov-
ery days across all players for AROM and CMJ collections can be seen in Table 3.
Travel. Travel included 29 trips away from the team’s home city across both seasons, with a
mean of 3 ±2 days per trip (range: 1–8 days). There was a mean of 7 ±4 days between away
trips (i.e., days between returning to home city and departing for next trip) and 4 ±3 days
between travel throughout the season. Travel involved a mean of 4.0 ±1.9 hours travelled per
travel day (range: 1.3–8.2 hours travelled) and 10.1 ±4.6 hours travelled total per away trip
(range: 3.9–19.6 hours travelled).
Athlete response measures
AROMs. The digital survey was completed by all players who were present and partici-
pated in team activities (i.e., involved during team practice or shootaround) on home practice
Table 3. Frequencies of recovery days during previous 3 days across all players.
AROMs (n = 913) CMJ (n = 182)
n % n %
No recovery (0) 399 44% 81 45%
Recovery (1) 514 56% 101 55%
Abbreviations: AROM, athlete-reported outcome measures; CMJ, countermovement jump.
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and select home gamedays across both seasons, resulting in 913 data points across all players
(mean ±SD = 36.5 ±8.0 surveys per player). There were no random effects for individual play-
ers for any AROMs. Therefore, a multiple linear regression using a stepwise approach was
used to determine the influence of fixed effects only on fatigue, soreness, sleep quality and
sleep hours. Simple descriptive statistics and correlations are displayed in Table 4. Final model
statistics and regression coefficients for estimating AROMs are displayed in Table 5.
Table 4. Means, SD’s and Pearson correlations among athlete-reported outcome measures and independent variables.
Variable Mean ±SD 1 2 3 4 5 6 7 8 9 10
1. fatigue—z-score -0.015 ±0.975 - .643 .382 .234 -.337 -.116 -0.014 .078.098 .124
2. soreness—z-score 0.001 ±0.975 - .347 .213 -.303 -.085 -0.005 0.057 .071.159
3. sleep quality—z-score 0.000 ±1.006 - .527 -.184 -.0830.042 .086 .0680.062
4. sleep hours—z-score -0.004 ±0.998 - -.146 -.0720.037 0.055 .082-0.019
5. Accumulated training load 3-day 801 ±432 - .535 0.050 -0.046 -.083-.218
6. Accumulated training load 10-day 2,606 ±1,069 - .216 .271 .244 .081
7. Hours travelled 3-day 1.50 ±2.66 - .578 .450 -.257
8. Hours travelled 10-day 6.14 ±6.14 - .827 .100
9. Days away from home city 10-day 2.3 ±2.2 - .102
10. Recovery days 3-day 0.56 ±0.50 -
p<0.05
p<0.01. Abbreviations: SD, standard deviation.
https://doi.org/10.1371/journal.pone.0270409.t004
Table 5. Regression coefficients for estimating athlete-reported outcome measures during the season.
B 95% CI βtp
Fatigue—z-score
(Constant) 0.468 0.306, 0.630 - 5.679 <0.001
Accumulated training load 3-day (AU) -0.001 -0.001, -0.001 -0.385 -10.445 <0.001
Accumulated training load 10-day (AU) 0.00008 0.000, 0.000 0.090 2.444 0.015
F(2, 910) = 61.469, p <0.001, R
2
= 0.119 (n = 913)
Soreness—z-score
(Constant) 0.310 0.132, 0.489 - 3.417 <0.001
Accumulated training load 3-day (AU) -0.001 -0.001, -0.001 -0.332 -8.503 <0.001
Recovery days 3-day 0.156 0.028, 0.283 0.079 2.393 0.017
Accumulated training load 10-day (AU) 0.00008 0.000, 0.000 0.086 2.248 0.025
F(3, 909) = 35.92, p <0.001, R
2
= 0.106 (n = 913)
Sleep quality—z-score
(Constant) 0.259 0.107, 0.411 - 3.342 0.001
Accumulated training load 3-day (AU) 0.000 -0.001, 0.000 -0.181 -5.554 <0.001
Hours travelled 10-day (hrs) 0.013 0.002, 0.023 0.078 2.396 0.017
F(2, 910) = 18.949, p <0.001, R
2
= 0.040 (n = 913)
Sleep hours—z-score
(Constant) 0.182 0.026, 0.338 - 2.286 0.022
Accumulated training load 3-day (AU) 0.000 0.000, 0.000 -0.141 -4.283 <0.001
Days away from home city 10-day (days) 0.032 0.003, 0.061 0.070 2.136 0.033
F(2, 910) = 12.303, p <0.001, R
2
= 0.026 (n = 913)
Note: F statistic, degrees of freedom (df), R
2
and pvalues are from the final model. Abbreviations: AU, arbitrary unit; CI, confidence interval for B.
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Countermovement jump measures. CMJ testing was completed by all healthy (i.e., with-
out illness and injury free, as determined by team staff) and present players on 18 days across
both seasons, resulting in 183 assessments across all players (mean ±SD = 7.3 ±2.2 assess-
ments per player). Initial null modeling showed significant variability (p <0.001) in all 8 CMJ
variables analyzed. For all the models, the construction process was optimized by including a
random intercept effect for individual players, showing that there was statistically significant
variance between individual players for all 8 CMJ measures. The ICCs for individual season
samples within each player were 0.60, 0.86, 0.58, 0.89, 0.12, 0.15, 0.85 and 0.17 for Counter-
movement Depth, Eccentric Braking RFD, Eccentric Duration, Eccentric Mean Deceleration
Force, Mean Eccentric+Concentric Power:Time, Eccentric Deceleration Phase Duration,
Eccentric Peak Power, and Eccentric Peak Velocity, respectively. There were no random coef-
ficient effects for any level 1 covariates in any of the final models, indicating that the effects
shown were consistent across individual players for each countermovement jump measure.
Simple effects for all countermovement jump measures are shown in Table 6.
Discussion
The primary finding of the present study was that AROMs and CMJ measures are associated
with fluctuations in training load and travel demands over the course of a professional
Table 6. Simple effects for countermovement jump measures.
Coefficient estimate Standard Error 95% CI df t pEffect size (r)
Countermovement Depth
Intercept (cm) -31.7 -1.2 -34.1, -29.4 21.9 -27.5 <0.001
Soreness (AU—z-score) -0.641 0.325 -1.282, 0.001 152.8 -2.0 0.050 0.16
Hours travelled 3-day (hrs) -0.548 0.198 -0.939, -0.158 150.1 -2.8 0.006 0.22
Eccentric Braking RFD
Intercept (N/s) 4535 461 3,589, 5,480 26.6 9.8 <0.001
Accumulated training load 10-day (AU) 0.208 0.062 0.085, 0.330 136.7 3.4 0.001 0.28
Eccentric Duration
Intercept (ms) 621 25 570, 671 32.1 25.2 <0.001
Accumulated training load 3-day (AU) -0.037 0.015 -0.068, -0.007 154.2 -2.5 0.015 0.19
Eccentric Mean Deceleration Force
Intercept (N) 1505 54 1,394, 1,615 25.7 27.9 <0.001
Accumulated training load 10-day (AU) 0.021 0.007 0.008, 0.034 139.8 3.2 0.002 0.26
Mean Eccentric+Concentric Power:Time
Intercept (W/s) 1565 106 1,347, 1,783 26.4 14.8 <0.001
Accumulated training load 10-day (AU) 0.042 0.017 0.008, 0.077 50.6 2.5 0.017 0.33
Eccentric Deceleration Phase Duration
Intercept (s) 0.213 0.011 0.189, 0.236 26.2 18.6 <0.001
Accumulated training load 10-day (AU) -0.000005 0.000002 -0.000008, -0.000002 74.6 -3.2 0.002 0.35
Eccentric Peak Power
Intercept (W) 1413 112 1,180, 1,645 23.3 12.6 <0.001
Days away from home city 10-day (days) 23.276 8.589 6.287, 40.264 132.6 2.7 0.008 0.23
Eccentric Peak Velocity
Intercept (m/s) -1.116 0.049 -1.218, -1.014 22.2 -22.6 <0.001
Hours travelled 3-day (hrs) -0.013 0.006 -0.025, -0.001 149.2 -2.2 0.033 0.17
Abbreviations: AU, arbitrary unit; CI, confidence interval for coefficient estimate.
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basketball season. These measures are elements of the constructs of external load and training
effects, encapsulated in the conceptual framework that we developed (Fig 1) to better under-
stand physical training in professional basketball. Specifically, cumulative training load over 3
and 10 days, recent travel time and days spent away from the team’s home city were all associ-
ated with changes in CMJ performance during the eccentric phase. Similarly, cumulative
3-day training load had a significant negative association with all subsequent AROMs (i.e.,
fatigue, soreness, sleep quality and sleep hours), while a recovery day in the previous 3 days
had a significant positive association with soreness scores. An increase in hours travelled and
days spent away from the team’s home city in the previous 10 days was also associated with sig-
nificantly improved sleep quality scores and self-reported sleep hours. The current work has
taken a novel approach in developing and applying a conceptual framework to inform context
specific training monitoring in basketball [12]. Using this approach, we were then able to high-
light context specific associations between physical demands in the NBA G-League and com-
mon athlete response measures used to estimate training effects across the season.
Training load demands and recovery days
The current findings show negative associations between cumulative training load and several
AROMs, which highlights the close relationship between prescribed training load and athlete
responses. Our results suggest that it is important to consider both short- (i.e., 3-day) and
medium-term (i.e., 10-day) training load when developing individualized training plans, as
both these load constructs were associated with increases in perceptual responses of fatigue
and soreness. While there are similar relationships between these two load epochs and
AROMs, cumulative 3-day training load produced stronger effects (i.e., larger absolute stan-
dardized βcoefficient–see Table 5) for perceptual fatigue and soreness and had an impact on a
greater number of AROMs (i.e., increases in short-term training load were also associated with
decreases in sleep quality and sleep hours). This suggests that AROMs are more responsive to
short-term changes in training load, compared to cumulative 10-day training load. However,
medium-term training load should not be discounted in the monitoring and individualization
of the planning process, given its association with fatigue and soreness responses in the present
work. Moreover, it has previously been shown that periods of intensified training and compe-
tition can lead to an accumulation of exercise-induced muscle damage, accompanied by
decreases in neuromuscular function, increased perceptual fatigue and soreness, and reduc-
tions in sleep duration and sleep quality [30,31]. As increased training loads can disrupt the
stress-recovery balance (i.e., the balance between training stress and subsequent rest), it is
likely important to understand both short- and medium-term training load, to optimize train-
ing prescription and avoid unwanted performance decrements and potential increases in
injury incidence [32].
Neuromuscular function (e.g., fatigue, supercompensation, de-training) is commonly used
to assess athletes during the season, including acute responses and chronic adaptations to
training and competition [33]. We have previously reported the reliability and sensitivity (i.e.,
seasonal variability greater than the within-subject variability) of a CMJ assessment in profes-
sional basketball and showed a large number of variables have greater seasonal variability than
the inherent noise [8]. However, there is a paucity of data investigating potential relationships
with CMJ performance and physical demands experienced across a professional basketball sea-
son [8]. The present results show that accumulated training load is associated with changes in
several CMJ measures. Indeed, we found that increases in cumulative 10-day training load
were associated with small increases to Eccentric Braking RFD, and increases in cumulative
3-day training load were associated with small decreases in Eccentric Duration, which is
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thought to be an important indicator of stretch reflex sensitivity and overall eccentric function
[33]. In contrast to our AROM results, it appears that CMJ measures are more responsive to
changes in medium-term (i.e., 10-day), versus short-term (i.e., 3-day), training load. Indeed, 4
of 8 CMJ measures investigated were associated with cumulative 10-day training load, with
effect size correlations ranging from small to moderate (range: r= 0.26–0.35), compared to
only 1 variable associated with cumulative 3-day training load (r= 0.19). Collectively, these
findings highlight the association between medium-term cumulative training load and CMJ
measures used to estimate neuromuscular status (e.g., fatigue, supercompensation, de-train-
ing) across the season, and supports the collection of these measures, as components of a sensi-
tive and responsive training system, to optimize individual training prescription.
While there has been a significant amount of work performed with outdoor tracking tech-
nologies (e.g., global positioning systems) in other team sports, these findings cannot be
applied to indoor sports [1]. Indoor technologies (e.g., local positioning systems) have become
more ubiquitous recently, creating the potential for more complete training load data sets (i.e.,
game and practice data) to be collected in basketball moving forward. In the present work, we
directly measured 97% and 98% of all practice and game data (excluding game data for NBA
contracted players), respectively, across 2 seasons. Such consistent collection of training load
data is important to elucidate ecologically valid associations between physical demands and
athlete response measures, and subsequently support the development of individualized and
context-specific training planning solutions. It is also important to consider suitable training
periods that provide information relevant to the desired performance outcome and training
effect measures [34], as characteristics unique to those time frames likely impact athlete
response. These nuances in training response highlight the importance of underlying evidence
which supports decision-making. Indeed, the training periods considered in this study (i.e., 3-
and 10-day cumulative training load) were chosen by evaluating basic concepts of training
adaptation with individual, contextual, and environmental factors specific to performance
within the NBA G-League.
Given that increased training loads during the season can disrupt the balance between
training stress and subsequent rest [32], it is important to consider the prescription of ade-
quate recovery time (e.g., recovery days with minimal activity and no travel) when developing
a training plan. We showed that having a recovery day in the previous 3 days was associated
with improved perceptual soreness responses over the course of the season. Conversely, recov-
ery days were not significantly associated with fatigue scores, which suggests that while recov-
ery days can alleviate perceptual feelings of soreness, a single recovery day at given intervals
(i.e., every 5.5 ±3.9 days during the season) is not solely enough to mitigate cumulative fatigue
during the season. Thorpe et al. [35] showed that, when monitoring fatigue throughout an in-
season training week in soccer, there was no significant improvement in perceived ratings of
fatigue from day 2 to day 4 after a match, despite a recovery day scheduled on day 3. They sug-
gested that the magnitude of training load assigned on day 2 provided sufficient stimulus to
blunt a linear improvement in player fatigue on day 4 [35]. While caution should be taken
when comparing the time course of recovery between soccer and basketball (e.g. differences in
physical demands and competition/practice schedules [17]), our results support the suggestion
that consistent on-court loading throughout the season prevents players from gaining signifi-
cant improvements in perceived ratings of fatigue following recovery days. Similarly, despite
55% (n = 101) of the CMJ assessments being performed after having a recovery day in the pre-
vious 3 days, the present results show no evidence of significant associations between recovery
days and CMJ measures. The absence of significant improvements to both subjective (e.g.,
fatigue and sleep) and functional performance-based measures (e.g., CMJ measures) following
recovery days suggests that frequent on-court loading and extensive travel prevent players
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from fully recovering from the accumulated physical and psychological stress throughout the
season and this may influence sports performance outcomes [5].
Travel
The condensed travel schedule required during an NBA G-League season likely predisposes
the players to travel-induced fatigue, which can include feelings of disorientation, lack of
energy, and general discomfort, and could impact performance during the season [5]. In the
present study, the hours travelled, and days spent away from the team’s home city in the previ-
ous 10 days were associated with increased sleep quality and sleep hours, respectively, which
suggests that players require additional sleep to recover from the “travel fatigue” accumulated.
These results are in contrast to previous research, which reported that frequent travel can neg-
atively affect both sleep quality and sleep hours [36]. The present findings may be specific to
the rhythm of a G-League schedule, as periods of intensified travel within the last 10 days are
usually followed by a period of home games. The combination of disruptive travel periods
immediately followed by a return to players’ own homes and beds may create this unique cir-
cumstance of recent travel demands being associated with improved sleep indices. However,
there were no instances of AROMs collected when away from the team’s home city and as
such, it is difficult to directly compare the players’ sleep quality and sleep hours when travelling
with the team. Nevertheless, it is important for practitioners to consider travel demands when
developing sensitive and responsive training systems for their environment. For example,
practitioners could use this information to adjust training schedules (e.g., later treatment and
practice times) or implement strategies to promote sleep when players are in their home city,
particularly after periods of increased travel.
Travel was also associated with CMJ measures over the course of the season, with an
increase in the hours travelled in the previous 3 days being related to small increases to both
Countermovement Depth (r= 0.22) and Eccentric Peak Velocity (r= 0.17). Furthermore, an
increase in days spent away from the team’s home city in the previous 10 days was associated
with small increases (r= 0.23) to Eccentric Peak Power. It has been suggested that variability
in eccentric variables is primarily driven by changes in technique related to the speed and
depth of the countermovement [37], and our previous work highlighted that Countermove-
ment Depth and Eccentric Peak Velocity were two of the most responsive variables across the
season in this cohort [8]. Skilled jumpers are also thought to be capable of adjusting strategy to
maintain output [20], therefore it is possible that these CMJ changes highlight instances of ath-
letes using an alternate strategy to maintain CMJ output [33], after physically demanding travel
periods. In support of this idea, previous research has suggested that an increase in Eccentric
Peak Velocity, similar to that seen following travel in the present work, serves to limit the con-
centric-performance decrement seen 72 hours following a fatiguing protocol meant to simu-
late team-sport activities [33].
In addition to the impact from the physical act of travelling, the effects of circadian mis-
alignment and time zone changes are also an important consideration [5,38]. Indeed, negative
associations between the number of time zones crossed and mood have previously been
reported in elite athletes [39], while studies in the NBA found that westward travel negatively
impacted winning percentages [26,40]. However, previous research has commonly separated
travel-induced fatigue from jet-lag fatigue, with the main difference being that jet-lag fatigue
comprises an effect of time zone change while travel fatigue is driven by factors such as regu-
larity, duration and conditions of travel [41]. The current results suggest that reporting mea-
sures such as travel duration (e.g., hours travelled and days spent away from the team’s home
city) with the number of time zones crossed may be suitable for estimating the training effects
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(e.g., both travel-induced and jet-lag fatigue) associated with travel over the course of the sea-
son. However, other factors such as the timing of travel (i.e., time of day travel is completed)
are important to consider when planning travel schedules as travel timing and conditions can
vary greatly across the season and between competitions. In the NBA G-League, teams travel
primarily via commercial flights, but long duration (i.e., >6 hours) bus travel through the
night (i.e., after midnight) is also common, and this could result in a ‘time-zone’ style disrup-
tion for the players despite minimal changes in time-zone. Ultimately, improving understand-
ing around all physically demanding aspects of the travel schedule (i.e., regularity, duration,
conditions, timing, time-zone changes) is important to optimize travel and training planning
solutions, and to mitigate any negative effects throughout the season.
Individual factors
To investigate the influence of individual factors on training effects, we considered the player’s
age and perceived fatigue/soreness as individual factors that may influence other AROMs and
CMJ measures during the season. Jeffries et al. [12] highlighted that negative training effects
(e.g. increased fatigue) can act as an individual factor subsequently influencing the training
response (e.g. causing higher or lower negative effects). However, there were no significant
associations between the individual factors included in these models and any of the AROMs
and CMJ measures investigated. The only association that approached significance (p = 0.050)
suggested a decrease in soreness scores (i.e., increased perceptual feelings of soreness) was
associated with a small reduction in Countermovement Depth (r= 0.16). Limited counter-
movement during the jump test may be another example of players adjusting their movement
strategy to maintain performance output, when they are experiencing increased soreness. It is
also important to acknowledge that player’s individual behaviors (e.g., sleep hygiene and nutri-
tional habits) not measured in this study could also affect how they respond to training, travel,
and recovery days during the season. However, further research is required to elucidate associ-
ations between individual factors (e.g., fixed, and behavioral) and training effect measures.
Limitations
Identifying factors that are significantly associated with training effects assessed during the sea-
son can improve our understanding and help to inform and optimize individual preparation
and periodization strategies, however there is still a lot of variability left unexplained. Similarly,
previous literature has suggested that AROMs presented as single use items are more prone to
misinterpretation [42]. However, the constructs assessed in the AROM survey were unidimen-
sional in nature and included unambiguous verbal anchors which can yield acceptable validity
[42]. Another limitation is that training load data were estimated for limited cases (i.e., 13% of
all on-court activity over 2 seasons) where players did not wear an IMU for a session. However,
we directly measured 100% of all on-court duration (i.e., only the load derived from IMU data
was estimated) and most estimated load data were for low intensity ‘shootarounds’ (4% total
data) and pre-game warmups (4% total data). An additional 2% of total data could not be col-
lected, as players with NBA contracts (i.e. ‘two-way’ players) are not permitted to wear an
IMU during games [1]. Indeed, across two seasons we directly measured 97% and 98% of all
practice and game load data, respectively (excluding data for NBA ‘two-way’ players, n = 3
players). While having zero estimated load data is ideal, this is considered impossible in prac-
tice [43] and the statistical approach (i.e., linear mixed models) used in this investigation was
chosen based on its ability to account for missing data in the analysis. We believe that the con-
sistency of the data used, including two cohorts over two seasons, supports the interpretation
of the findings in this study and should be considered a strength of this work. However, the
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current work does not evaluate all the potential physical demands (e.g., timing and conditions
of travel, resistance training) and individual (e.g., injury history, sleep behaviors, nutritional
habits) or contextual factors (e.g., social influences, commercial responsibilities) that could be
associated with training effects, nor does it examine other potential training effect measures
(e.g., physiological, biomechanical, or cognitive measures). Indeed, we suggest that practition-
ers interpret previous work that claims to predict or explain training effects based on singular
measures of physical demands (e.g., training load) with caution, as such claims are not
acknowledging many contextual factors.
Measuring all factors that are important in our conceptual framework (and those we may
have overlooked in our framework) of basketball performance is impossible, but we have
shown that various measures are interrelated and can potentially provide value in the training
prescription process. A continued evolution of the scientific evidence in this area will facilitate
better understanding around the measures/variables that explain the greatest amount of vari-
ance in training responses, within a given context (e.g., NBA G-League season). This informa-
tion can aid practitioners in developing parsimonious feedback structures which meaningfully
inform individualized training prescription and periodization.
The present work enhances our understanding of associations between physical demands
and training effects measured in professional basketball, with various aspects of training load
and travel affecting athlete response measures throughout the season. The interaction of these
stressors combined with individual factors is complex, meaning that multiple athlete response
measures are needed to understand fatigue and recovery cycles even partially. This study also
highlights the utility of presenting a conceptual framework to help synthesize evidence, assist
in understanding phenomena, inform future research and act as a reference operational guide
in practical settings [12]. Indeed, the current findings provide ecologically valid information
clarifying the utility and validity of the monitoring tools used to assess external load and train-
ing effects for this environment, which fit within the conceptual framework we have presented.
However, this is not an exhaustive monitoring battery and practitioners should consider what
context-specific measures may best inform training periodization for their environment. Ulti-
mately, by enhancing our understanding of the relationships between external load (e.g., train-
ing load and travel), recovery, training effects and sports performance outcomes, we support
the development of sensitive and responsive training systems [10] and inform best-practice
models for athlete care and performance in professional basketball [1].
Practical applications
Several measures presented in this work are useful in understanding athlete responses related
to external load throughout a basketball season. Therefore, practitioners should consider using
these variables (i.e., training load, travel duration, AROMs, CMJ measures) or similar mea-
sures to inform planning and prescription throughout the season. However, no single mea-
sures of external load or athlete response can explain all the variance involved in the training
process and it is likely that the most valid and sensitive variables are highly contextual. There-
fore, practitioners should develop their own holistic, conceptual models of performance that
are specific to a given environment, to inform the selection of monitoring tools which assess
different external load and athlete response constructs. While this approach will inform best-
practice periodization, even the most valid and complete athlete monitoring systems cannot
quantify all factors that are important in sports performance. Therefore, practitioners should
use these systems to inform individualized prescription, but also consider factors that are not
quantified during the decision-making process.
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Acknowledgments
Some authors involved in this work are NBA affiliated practitioners/researchers. As such, all
study design and methods in this work are required to comply with the NBA Health Related
Research Policy and have been reviewed by the NBA, NBA Physicians Association, NBA Play-
ers Association. As part of this process, this work was made available for comment from the
NBA and NBA research Committee before publication (these contributors are not listed as
authors).
Author Contributions
Conceptualization: Richard A. J. Mercer, Lauren C. McGuigan, Aaron J. Coutts, Donnie S.
Strack, Blake D. McLean.
Data curation: Richard A. J. Mercer, Jennifer L. Russell, Lauren C. McGuigan, Blake D.
McLean.
Formal analysis: Richard A. J. Mercer, Blake D. McLean.
Investigation: Richard A. J. Mercer, Jennifer L. Russell, Lauren C. McGuigan, Blake D.
McLean.
Methodology: Richard A. J. Mercer, Jennifer L. Russell, Lauren C. McGuigan, Aaron J. Coutts,
Donnie S. Strack, Blake D. McLean.
Project administration: Richard A. J. Mercer, Jennifer L. Russell.
Supervision: Jennifer L. Russell, Donnie S. Strack, Blake D. McLean.
Visualization: Richard A. J. Mercer.
Writing – original draft: Richard A. J. Mercer, Jennifer L. Russell, Aaron J. Coutts, Blake D.
McLean.
Writing – review & editing: Richard A. J. Mercer, Jennifer L. Russell, Lauren C. McGuigan,
Aaron J. Coutts, Donnie S. Strack, Blake D. McLean.
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