Wright, R., Slattery, K. and Howell, J.
Introduction
Netball at an elite level requires players to possess a high level of skill in combination with high levels of aerobic and anaerobic fitness. The major fitness components netballers need to possess include, speed, agility, strength, power and endurance. Netball has been described as a game reliant on rapid acceleration, sudden and rapid changes in direction in combination with jumping
or leaping movements [1]. The intermittent nature of netball is highlighted from a
number of studies into the movement patterns and work intensities of the players.
The average work to rest ratio is 1:3 [1, 2] with the work to rest ratio for centre court players being greater than those of goal keepers or goal shooters [2]. The majority of work periods are less than 10 s [1]. Players change activities approximately every 4.1 s [2] and the average sprint duration is approximately
1.4 s [3]. Moreover, centre court players cover ~ 8 km during a game compared to ~ 4.2 km for goal keepers or goal shooters [2].
Depending on the phase of the year, netball players have three to four strength and conditioning sessions and two to three on court training sessions per week.
Players often play with two different teams in regular weekly competition which can impact the ability to adequately develop and maintain all fitness components.
Netball players are usually involved in State League, Underage Nationals (17/U,
19/U and/or 21/U) and the Australian Netball League. These competitions can overlap and the State League competitive season is played over a demanding 20 week schedule. By having several different training programs with different coaches and a heavy competition schedule, players are at risk of excessive training fatigue, illness and/or injury. It is therefore important to accurately quantify the training load of netball players.

The relationship between training load and athletic performance is of critical importance for athletes and coaches who aim to quantitatively describe and reproduce an athlete’s training program. At present, the common methods for quantifying training load in team sports are heart rate (HR) and micro-technology,
including global positioning systems (GPS) and accelerometers [4, 5] For netball,
both of these methods have limitations in quantifying training load. The validity of heart rate analysis has been questioned when assessing the intensity of short sprints and quick high intensity work efforts that make up a large part of netball training and competition. The slow heart rate response to short duration high intensity bouts of activity is an acknowledged limitation of heart rate
measurement [4]. Other technology such as GPS is impractical for predominately
indoor sports such as netball and has been shown to underestimate the distance covered and speeds recorded during court based, confined movement patterns
[6]. Therefore, due to the intermittent nature of netball, HR analysis and GPS
would not accurately reflect the physical exertion of the athlete.
The session-RPE method has been proposed as an alternative, practical and
non-invasive method for evaluating training load in team sport athletes [7].
Research has shown that session-RPE compares favorably with other measures of training load (i.e. oxygen consumption and external work) and is valid across
numerous exercise intensities and activities [8, 9].
This research project is
designed to establish the effectiveness of using the Busso [10] mathematical
model to predict performance in netball players using session-RPE to quantify training load. It is anticipated that the results of the study will enable optimal training loads to be applied in netball players to create a state of peak physical condition during competition.

Methods
Experimental Design
The research project was designed as a pilot study to establish the effectiveness
of using the Busso [10] mathematical model to predict performance in netball
players. Players were monitored throughout a 12 week training block during the
competitive netball season. Session-RPE [11] was used to quantify training load
and measures of performance, fitness and fatigue were taken on a weekly basis.
As it is difficult to implement a performance test to mimick the specific demands of a competitive netball match measures of leg power (vertical jump height and counter movement jump velocity) and a 10 metre sprint were used to detect
changes in performance capacity. The 5’-5’ submaximal heart rate test (5’-5’) [12]
was used as a fitness measure. Fatigue was estimated by the Daily Analysis of
Life Demands for Athletes (DALDA) [13] and heart rate variability (HRV).
Following a standardised warm-up, tests for fitness and fatigue were completed at the same time of day, in the same order, each week. Familiarisation testing sessions were completed in the week prior to the study.
Subjects
Three female Australian 21/U National Team netball players volunteered to participate in the investigation. Participants were fully informed of the potential risks and benefits associated with participation and provided written informed consent. Ethical approval was granted by the AIS Ethics Committee and complied with the Declaration of Helsinki.

Training Load Quantification
Individual training dose was measured using session-RPE [11]. 30 min following
each training session, athletes recorded a global perception of effort using a rating of perceived exertion (RPE) according to the category ratio scale (CR 10-
scale) of Borg et al. [14]. The RPE was then by multiplied by the training duration
in minutes to give an arbitrary measure of training load. Players were required to complete a training diary on a daily basis to record the RPE and duration of each training session performed.
Testing Protocols
All tests were completed in an indoor gymnasium. Upon arrival (~ 17:00 h) players completed the DALDA, 5’-5’ test, vertical jump (VJ), counter movement jump (CMJ) followed by a 10 metre sprint.
Performance Tests
Vertical jump height was assessed using a Vertec (Swift Yardstick, Qld,
Australia) and jump velocity was assessed during a counter movement jump on a
force plate (AMTI force plate, MA, USA) [15]. Players completed three trials of
each jump and the highest value was recorded. 10 metre sprint testing was conducted on an indoor synthetic track using electronic timing gates (Swift
Timing System, Qld, Australia) [15]. Participants completed three trials and the
average velocity of the fastest trial (Sprint vel
) was calculated.
5’–5’ Test
The
5’–5’ Test [16] consists of five min continuous running at nine km/h then five
min of resting in a supine position. Heart rate (HR) and R-R intervals (Suunto T6,

Vantaa, Finland) were continuously recorded throughout the test. For each test, the mean HR for the final 30 s of the effort was taken as the submaximal HR response to exercise (HR ex
). HR recovery post-exercise was assessed as both the time (s) taken for the HR to reach 100 bpm (HRR
100
) and the HR (bpm) at 60 s post-exercise (HR
60s
). HRV was assessed by exporting the R-R data into customized software (Kubios, version 2.0, Biosignal Analysis and Medical
Imaging Group, Finland) to calculate power frequency bands. From this data, the
HRV ratio (HF/LF) was determined as high frequency (HF) range (HF =
0.15

0.40 Hz) divided by the low frequency (LF) range (LF = 0.04

0.15 Hz). The standard deviation of instantaneous beat-to-beat R-R interval variability
measured from Poincaré plots (SD1) [17] was calculated during the last three
minutes of the five minute supine period as a vagal-related HRV index [18].
DALDA
The DALDA questionnaire was used to assess general stress levels and stress
reaction symptoms [13]
. The number of ‘worse than normal’ responses was used as the input to model.
Fitting the Model
The model used in the study has been previously described [10]. Briefly, this
model assumes that the gain term of the fatigue effect is mathematically related to the training dose using a first-order filter. Performance output can be described as:
̂ ∑ ( ) ∑ ( )

in which the value of k2 at day i is estimated by mathematical recursion using a first-order filter with a gain terms k3 and a time constant 3:
∑ ( )
The parameters for the model were determined by fitting the model performances with actual performances using the least squares method. The set of model parameters was determined by minimizing the residual sum of squares (RSS) between modeled performance and actual performances:
∑ [ ̂ ]
Where n takes the N value corresponding to the days of measurement of the actual performance. Successive minimisation of the RSS with a grid of values for each time constant gave the total set of model parameters.
Statistical Analyses
Models were developed for each player. The goodness of fit for the model was established by calculating the coefficient of determination (r
2
). Within-individual correlations between the actual and predicted measures of performance, fitness and fatigue were analysed using the Pearson’s correlation coefficient. The following criteria were adopted to interpret the magnitude of the correlation (r) between test measures: <0.1 trivial, 0.1
–0.3 small, 0.3–0.5 moderate, 0.5–0.7 large, 0.7
–0.9 very large, and 0.9–1.0 almost perfect.

Results
The weekly training load throughout the investigation is shown in Figure 1.
Individual and mean (±SD) correlations between modeled and actual performance, fitness and fatigue using VJ, CMJ vel and Sprint vel
are shown in
Table 1, Table 2 and Table 3, respectively.
Figure 1: Mean (± SD) weekly training load during the 12 week investigation.
Table 1: Individual and mean (±SD) correlations between modelled and actual
VJ performance, fitness and fatigue.
Subject Performance
1
2
3
Mean ± SD
VJ
0.74
0.38
0.91
0.67 ± 0.27
Fitness Measures Fatigue Measures
HR ex
0.02
-0.44
HR
60
-0.76
-0.81
HR
100
-0.7
-0.9
SD1
0.19
0.14
LF/HF
-0.31
0.49
DALDA
-0.23
0.61
0.02 -0.34 -0.34 0.58 -0.74 0.35
-0.13 ± 0.27 -0.64 ± 0.26 -0.65 ± 0.28 0.30 ± 0.24 -0.18 ± 0.62 0.24 ± 0.43

Table 2: Individual and mean (±SD) correlations between modelled and actual
CMJ vel
performance, fitness and fatigue.
Subject Performance
1
2
3
Mean ± SD
CMJ vel
0.41
0.47
0.45
0.44 ± 0.03
Fitness Measures Fatigue Measures
HR ex
0.29
-0.08
-0.19
HR
60
-0.79
-0.5
-0.52
HR
100
-0.63
-0.52
-0.61
SD1
0.09
0.13
-0.02
LF/HF
-0.16
0.64
0.39
DALDA
0.72
0.61
0.39
0.01 ± 0.25 -0.60 ± 0.16 -0.58 ± 0.06 0.07 ± 0.06 0.27 ± 0.41 0.57 ± 0.17
Table 3: Individual and mean (±SD) correlations between modelled and actual
Sprint vel
performance, fitness and fatigue.
Subject Performance
1
2
Sprint vel
0.77
0.78
3
Mean ± SD
0.76
0.77 ± 0.01
HR ex
-0.3
-0.06
Fitness Measures
HR
60
-0.67
-0.36
HR
100
-0.37
-0.63
SD1
0.44
0.12
Fatigue Measures
LF/HF
-0.32
0.64
DALDA
0.23
0.2
-0.18 -0.43 -0.47 0.48 -0.41 0.71
-0.18 ± 0.12 -0.49 ± 0.16 -0.49 ± 0.13 0.34 ± 0.20 -0.03 ± 0.58 0.38 ± 0.29
Discussion
The purpose of this study was to establish the effectiveness of using a systems
model approach [10] to predict performance in netball players using session-RPE
as the method to quantify training load. To date, there is limited information regarding the typical training load completed by high level netball players and the resultant impact of manipulations in training stimulus on performance. The effect of training load on a performance can be described as Performance = Fitness -
Fatigue.
Whereby, on a given day, a player’s performance is dependent on their current level of fitness and amount of acute fatigue. For instance, a player undergoing heavy training periods is gaining fitness whilst also accumulating a large amount of training-induced fatigue. Therefore, despite an increase in fitness,

during this time their capacity to perform is reduced due to high levels of fatigue.
It is important to be able to assess a player’s relative level of fitness and fatigue throughout the competitive season to allow training prescription which will optimize performance in major competitive matches.
It is difficult to directly quantify and monitor changes in netball match performance. Currently there are no skill based performance targets to assess individual performances of players. Netball requires players to often work in combination with other players to achieve success as a team. It becomes difficult to objectively measure the value of individual efforts within that group. Therefore,
CMJ vel
, Sprint vel
and VJ were chosen as predictive measures of performance.
Leg power and speed are two components that are of importance in netball. The
Netball Australia Fitness Testing Protocols include both a vertical jump and 10 metre sprint as part of the standard battery of tests undertaken. The tests are easily administered and result in minimal fatigue for the player. Finally as the players are familiar with both tests there is a decreased learning effect leading to a reduced risk of erroneous data. The results showed strong correlations for both the Sprint vel
(r = 0.77) and VJ (r = 0.68). The CMJ vel
had a weaker correlation (r = 0.44) when compared to the other two performance measures.
Based on these findings it is more beneficial to use either Sprint vel
or VJ as performance inputs in the training model.
The predictive measures chosen for fitness were HR ex
, HR
60
and HR
100
. These measures were chosen as reduced submaximal HR and faster HR recovery have
been shown to be related to improved fitness and player performance [4, 16].
The results showed strong correlations for both the HR
60
and HR
100
when VJ was used as the performance measure. However, HR ex had a weaker correlation across all performance measures and individual subjects and may not be a good marker of acute changes in fitness. This weak correlation for HR ex
could be due

to the relatively short length of this study. Previous research has shown that significant changes in heart rate measures are only likely to occur after multiple
weeks of training [19, 20]. This study was conducted over 12 weeks, in
previously well-trained athletes and therefore there may not have been sufficient time for changes in significant changes submaximal heart rate to be observed.
Based on the findings of this project measures of HR recovery appear to be better measures of small variations in fitness
The measures chosen for fatigue were HRV (HF/LF and SD1) and the DALDA, a psychometric questionnaire. The results showed DALDA to have a moderate correlation with the performance measures. Both of the other fatigue measures
SD1 and heart rate variability had trivial or small correlations with predicted performance. The stronger correlation with DALDA supports several investigations of overreaching whom have shown increases in the number of
‘worse than normal’ responses in part B of the DALDA during overload training
periods [21, 22].
The high fitness levels of the participants may explain the moderate correlation of
HRV and performance. All players in the current research were mid-court players and play at a National Level (19/U Age group and 21/U Age group). Mid-court players at this level need to possess high levels of cardio-respiratory fitness. It is possible that due to their fitness levels, these players had greater exercise tolerance resulting in smaller daily changes in HRV. This supports previous studies that have found high cardio-respiratory fitness levels were associated
with reduced day to day HRV, but do not affect other HR derived indices [23].
It is acknowledged that there are limitations to this investigation. As a pilot study the results are based on the responses on a small number of participants which

may be altered if the model was applied to a larger cohort. This study was completed using three players. Although, most modeling research has been a
case studies on a single athlete or have investigated less than ten subjects [24-
26]. In addition, the duration of the current project was relatively short in
comparison to previous modeling research. Modelling studies are commonly
conducted over periods of greater than 12 weeks [10, 26, 27]. For example,
Busso [10] investigated the use of a nonlinear model of the effects of training on
performance. This model added a variable to account for changes in fatigue. The
study was undertaken using six subjects over a period of 12 weeks. Busso [10]
found that his model described the response to training more precisely than previous models. The data also suggested that there was an inverted U relationship between daily amounts of training and performance. There are also a number of modeling studies that have undertaken over periods between 40-52
weeks [24, 25]. For example, Suzuki et al [24] using the same model investigated
the use of Rating of Perceived Exertion (RPE) on the training program of a 400 m runner. This study had one subject and was conducted over 52 weeks. Suzuki et
al [24] found that the RPE mathematical model was able to predict changes in
performance. In practical terms the model highlights how optimal performance can be obtained by adjusting training time, intensity and frequency. It also suggests that the accuracy of the model improves the longer the study is conducted.
There is limited data available regarding training load for netballers. The findings of this investigation have demonstrated promising results regarding the usefulness of model to track performance changes in netballers. Robust correlations were found when Sprint vel
, VJ, HR
60
, HR
100
and the DALDA were utilised as inputs in the model. However, further research is required to establish the usefulness of the model and selected performance / fitness / fatigue measures in a larger sample size across an entire yearly training cycle.

References
1. Otago, L., A game analysis of the activity of netball players.
Sports Coach,
1983. 7 (1): p. 24-28.
2. Davidson, A. and G. Trewartha, Understanding the physiological demands of netball: a time-motion investigation.
International Journal of
Performance Analysis in Sport 2008. 8 (3): p. 1-17.
3. Allison, B., A practical application of specificity in netball training.
Sports
Coach, 1978. 2 (2): p. 9-13.
4. Achten, J. and A.E. Jeukendrup, Heart rate monitoring: applications and limitations.
Sports Medicine, 2003. 33 (7): p. 517-38.
5. Dwyer, D.B. and T.J. Gabbett, Global positioning system data analysis: velocity ranges and a new definition of sprinting for field sport athletes.
Journal of Strength and Conditioning Research, 2012. 26 (3): p. 818-24.
6. Duffield, R., et al., Accuracy and reliability of GPS devices for measurement of movement patterns in confined spaces for court-based sports.
Journal of Science and Medicine in Sport, 2010. 13 (5): p. 523-5.
7. Impellizzeri, F.M., et al., Use of RPE-based training load in soccer.
Medicine and Science in Sports and Exercise, 2004. 36 (6): p. 1042-7.
8. Wallace, L.K., Slattery, K.M. and Coutts, A.J., Validity and reliability of common methods to quantify training load.
European Journal of Applied
Physiology, 2013. in review .
9. Lockie, R.G., et al., Quantifying session ratings of perceived exertion for field-based speed training methods in team sport athletes.
Journal of
Strength and Conditioning Research, 2012. 26 (10): p. 2721-8.
10. Busso, T., Variable dose-response relationship between exercise training and performance.
Medicine and Science in Sports and Exercise, 2003.
35 (7): p. 1188-95.
11. Foster, C., et al., A new approach to monitoring exercise training.
Journal of Strength and Conditioning Research, 2001. 15 (1): p. 109-15.
12. Buchheit, M., et al., Physiological and performance adaptations to an inseason soccer camp in the heat: associations with heart rate and heart rate variability.
Scandinavian Journal of Medicine and Science in Sports,
2011. 21 (6): p. e477-85.
13. Rushall, B.S., A tool for measuring stress tolerance in elite athletes.
Applied Sport Psychology, 1990. 2 (1): p. 51-66.
14. Borg, G.A.V., P. Hassmen, and M. Langerstrom, Perceived exertion in relation to heart rate and blood lactate during arm and leg exercise.
European Journal of Applied Physiology, 1985. 65 : p. 679-685.
15. Tanner, R.K. and C.J. Gore, Physiological tests for elite athletes . 2nd ed2012, Champaign, IL: Human Kinetics.
16. Buchheit, M., et al., Determinants of the variability of heart rate measures during a competitive period in young soccer players.
European Journal of
Applied Physiology, 2010. 109 (5): p. 869-78.
17. Huikuri, H.V., et al., Abnormalities in beat-to-beat dynamics of heart rate before the spontaneous onset of life-threatening ventricular

tachyarrhythmias in patients with prior myocardial infarction.
Circulation,
1996. 93 (10): p. 1836-44.
18. Tulppo, M.P., et al., Quantitative beat-to-beat analysis of heart rate dynamics during exercise.
American Journal of Physiology, 1996. 271 (1 Pt
2): p. H244-52.
19. Buchheit, M., et al., Exercise-induced plasma volume expansion and postexercise parasympathetic reactivation.
European Journal of Applied
Physiology, 2009. 105 (3): p. 471-81.
20. Sandercock, G.R. and D.A. Brodie, The use of heart rate variability measures to assess autonomic control during exercise.
Scandinavian
Journal of Medicine and Science in Sports, 2006. 16 (5): p. 302-13.
21. Coutts, A.J., K.M. Slattery, and L.K. Wallace, Practical tests for monitoring performance, fatigue and recovery in triathletes.
Journal of Science and
Medicine in Sport, 2007. 10 (6): p. 372-81.
22. Halson, S.L., et al., Time course of performance changes and fatigue markers during intensified training in trained cyclists.
Journal of Applied
Physiology, 2002. 93 (3): p. 947-56.
23. Hautala, A., et al., Changes in cardiac autonomic regulation after prolonged maximal exercise.
Clinical Physiology, 2001. 21 (2): p. 238-45.
24. Suzuki, S., et al., Program design based on a mathematical model using rating of perceived exertion for an elite Japanese sprinter: a case study.
Journal of Strength and Conditioning Research, 2006. 20 (1): p. 36-42.
25. Millet, G.P., et al., Modelling the transfers of training effects on performance in elite triathletes.
International Journal of Sports Medicine,
2002. 23 (1): p. 55-63.
26. Thomas, L. and T. Busso, A theoretical study of taper characteristics to optimize performance.
Medicine and Science in Sports and Exercise,
2005. 37 (9): p. 1615-21.
27. Zarkadas, P.C., J.B. Carter, and E.W. Banister, Modelling the effect of taper on performance, maxiaml oxygen uptake, and the anaerobic threshold in endurance triathletes , in Modeling and Control of Ventilation ,
S.J. Semple, L. Adams, and B.J. Whipp, Editors. 1995, Plenum Press:
New York.