Training and game loads and injury risk in elite Australian footballers

Abstract

Objectives:
To examine the relationship between combined training and game loads and injury risk in elite Australian footballers.

Design:
Prospective cohort study.

Methods:
Forty-six elite Australian footballers (mean±SD age of 22.2±2.9 y) from one club were involved in a one-season study. Training and game loads (session-RPE multiplied by duration in min) and injuries were recorded each time an athlete exerted an exercise load. Rolling weekly sums and week-to-week changes in load were then modelled against injury data using a logistic regression model. Odds ratios (OR) were reported against a reference group of the lowest training load range.

Results:
Larger 1 weekly (>1750 AU, OR=2.44-3.38), 2 weekly (>4000 AU, OR=4.74) and previous to current week changes in load (>1250 AU, OR=2.58) significantly related (p<0.05) to a larger injury risk throughout the in-season phase. Players with 2-3 and 4-6 years of experience had a significantly lower injury risk compared to 7+ years players (OR=0.22, OR=0.28) when the previous to current week change in load was more than 1000 AU. No significant relationships were found between all derived load values and injury risk during the pre-season phase.

Conclusions:
In-season, as the amount of 1-2 weekly load or previous to current week increment in load increases, so does the risk of injury in elite Australian footballers. To reduce the risk of injury, derived training and game load values of weekly loads and previous week-to-week load changes should be individually monitored in elite Australian footballers.

Full text

Journal of Science and Medicine in Sport 16 (2013) 499–503
Contents lists available at ScienceDirect
Journal of Science and Medicine in Sport
journal homepage: www.elsevier.com/locate/jsams
Original Research
Training and game loads and injury risk in elite Australian footballers
Brent Rogalskia,b,∗, Brian Dawsona,b, Jarryd Heasmanb, Tim J. Gabbettc
aSchool of Sport Science, Exercise and Health, The University of Western Australia, Perth, Australia
bWest Coast Eagles Football Club, Perth, Australia
cSchool of Exercise Science, Australian Catholic University, Brisbane, Australia
article info
Article history:
Received 25 May 2012
Received in revised form 7 December 2012
Accepted 13 December 2012
Keywords:
Injury prevention
Load monitoring
Team sport
Odds ratios
abstract
Objectives: To examine the relationship between combined training and game loads and injury risk in
elite Australian footballers.
Design: Prospective cohort study.
Methods: Forty-six elite Australian footballers (mean ±SD age of 22.2 ±2.9 y) from one club were involved
in a one-season study. Training and game loads (session-RPE multiplied by duration in min) and injuries
were recorded each time an athlete exerted an exercise load. Rolling weekly sums and week-to-week
changes in load were then modelled against injury data using a logistic regression model. Odds ratios
(OR) were reported against a reference group of the lowest training load range.
Results: Larger 1 weekly (>1750 AU, OR= 2.44–3.38), 2 weekly (>4000 AU, OR=4.74) and previous to cur-
rent week changes in load (>1250 AU, OR = 2.58) significantly related (p< 0.05) to a larger injury risk
throughout the in-season phase. Players with 2–3 and 4–6 years of experience had a significantly lower
injury risk compared to 7+ years players (OR = 0.22, OR= 0.28) when the previous to current week change
in load was more than 1000 AU. No significant relationships were found between all derived load values
and injury risk during the pre-season phase.
Conclusions: In-season, as the amount of 1–2 weekly load or previous to current week increment in
load increases, so does the risk of injury in elite Australian footballers. To reduce the risk of injury,
derived training and game load values of weekly loads and previous week-to-week load changes should
be individually monitored in elite Australian footballers.
© 2012 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
1. Introduction
Playing Australian football requires repeated physical contact
and movements involving endurance, speed and acceleration1
over match durations of 100+ min.2Recently, Australian Foot-
ball League (AFL) interchange rates have dramatically increased,
allowing players additional breaks throughout games, possibly
contributing to higher mean game speeds.3With greater player
physical demands,1,4 injury incidence and prevalence rates have
also increased.3During 2010, each AFL club (on average) expe-
rienced 38.6 new injuries, causing a player to miss one or more
games. Overall, player injuries resulted in an average of 153.8
missed games per club.3
Understanding potential mechanisms of sporting injuries is
important to AFL medical and conditioning staff, as they manage
their players to be fit for matches. Training and game overload is
one possible cause of injury, therefore monitoring these loads in
∗Corresponding author.
E-mail addresses: brent.rogalski@live.com,brentr@westcoasteagles.com.au
(B. Rogalski).
players is important. Measuring training and game loads exerted
by athletes can be done by multiplying session rating of perceived
exertion5(Borg CR10 RPE) and duration (min). Previous studies
have analysed the relationship between load exerted and injury risk
in team sports including sub-elite6and professional rugby league,7
soccer,8basketball9and cricket.10
Gabbett and Domrow6analysed training loads and injuries
of 183 sub-elite rugby league players, finding increases in odds
of injury in pre-season (OR = 2.12, p= 0.01), early competition
(OR = 2.85, p= 0.01) and late competition (OR = 1.50, p= 0.04)
phases, for each increase in a log (150 arbitrary units) of training
load. Orchard et al.10 reported cricket bowlers completing more
than 50 overs in a match had a significantly increased risk (1.77
times) of injury in the next 14–21 days compared to bowlers com-
pleting less than 50 overs. The delayed effect of the load of previous
weeks is important to consider when analysing load and injury
relationships.
Piggott et al.11 analysed the relationship of injury and illness
with weekly training load in 16 AFL players across a 15-week pre-
season training phase. No significant relationships were reported
between injuries or illness and training load across this period.
However, studies using a larger sample and conducted over a longer
1440-2440/$ – see front matter © 2012 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.jsams.2012.12.004

500 B. Rogalski et al. / Journal of Science and Medicine in Sport 16 (2013) 499–503
time period may provide a more comprehensive understanding of
the relationship between training load and injury in AFL players.
Each year approximately 4–8 new rookie players are drafted into
AFL clubs, coming primarily from junior competitions and State
leagues. Senior AFL players have significantly more lean body mass
and bone mineral density than State junior players,12 which is likely
a result of physical maturation from previous training and game
loads within the elite system. Greater movement demands in the
AFL compared to State leagues13 further highlights the increased
physical demands required of junior or sub-elite players in making
the transition into the professional AFL game and training environ-
ment. Therefore, exploring the training and game load tolerance
of players with different years of experience at an elite level is
important.
To date, studies of the training load-injury relationship of AFL
players are limited, with the only study performed restricted to
a small sample of AFL players over a pre-season period.11 There-
fore, the aim of the present study was to examine the relationship
between training and game loads and injury risk in AFL players
from a full club squad, across an entire season. Identifying a rela-
tionship between load and injury may allow club staff to make
more objective decisions on when players are at increased risk of
injury.
2. Methods
Elite (n= 46) Australian footballers were involved in this
prospective study. Their mean ±SD age, stature and body mass
were 22.2 ±2.9 years, 187.7 ±7.5 cm and 85.4 ±8.9 kg, respectively.
All were from one AFL club and competed in matches in the AFL
or Western Australian Football League (WAFL) during 2010. The
AFL team played 22 competition matches but did not qualify for
finals. All players provided informed written consent prior to par-
ticipation and all data were obtained anonymously. Ethics approval
was obtained from the Human Research Ethics Committee of The
University of Western Australia.
The 2010 season was split into two main phases to match
the training and game demands required of each period. Dur-
ing pre-season (November to mid-March), players performed ∼3
field sessions, ∼3 weights sessions, and several cross training and
running conditioning sessions each week. Late pre-season saw a
gradual reduction in field training loads with the introduction of
pre-season practice games. In-season (mid-March to late-August)
consisted of two weight sessions, one main field training session,
with two lighter field sessions planned around the main session,
followed by a game at the end of the week.
Intensity of training sessions and games were estimated by
each player using the modified Borg CR-10 RPE scale5approx-
imately 30 min following each session. Training and game load
arbitrary units (AU) for each player were then derived by multiply-
ing session-RPE by session/game duration (min). Measurements of
blood lactate concentration and heart rate have correlated strongly
with session-RPE in rugby league6and Australian football14,15
training, suggesting that session-RPE is a valid method for quan-
tifying training loads in team sports.
All injuries were categorised by the club’s physiotherapist and
defined as incidents resulting in a modified training program,
missed training sessions or games. Injuries were classified as being
low severity, resulting in training modification or 1–2 missed train-
ing sessions; moderate severity, where a player was unavailable
for 1–2 games; or high severity, where a player missed 3+ games.
Injuries were also categorised for injury type (description), body
site (injury location) and intrinsic (internal; overuse, overexer-
tion) or extrinsic (external; collision, contact) factors. The club’s
injury definition differs slightly from that of Orchard and Seward’s,3
in that all injuries, including those that limited a player’s capac-
ity to complete training, were taken into account in assessing a
load/injury relationship.
Each day a player was involved in a training session or game,
their previous 1, 2, 3 and 4 weekly individual loads were calcu-
lated. Relationships between training and game loads and injury
were investigated in two ways. Firstly, the likelihood that an accu-
mulation of load could contribute to an injury at a later date was
considered by examining the link between 1, 2, 3 and 4 weekly
cumulative loads and subsequent injury. Secondly, whether a large
increment in load between weeks contributed to an injury was also
explored. This involved analysing week-to-week change between
the current and previous week’s totals. Cumulative and absolute
changes in load are further explained in Supplementary figs. A, B,
C and D (online supplementary data). Load exposure values and
injury data (injury vs. no injury) were then modelled in a logistic
regression analysis. Data were divided into four groups, with the
lowest training and game load range being the reference group.
When an odds ratio (OR) was greater than 1, an increased odds of
injury was reported. Conversely, when an OR was less than 1, a
decreased odds of injury was reported. For an OR to be significant,
95% confidence intervals (CI) would not contain the null OR of 1.00.
Injury incidence was calculated by dividing total number of
injuries by exposure time and reported as rates per 1000 train-
ing and game hours. Chi square analysis compared the frequency
of injuries between pre-season and in-season periods. Differences
in training and game loads between players of different AFL expe-
rience (1, 2–3, 4–6 and 7+ years) were analysed using a one-way
ANOVA and group means compared using a Scheffé post hoc test.
Data were analysed using IBM SPSS Statistics 20.0 and reported as
means and 95% CI. Significance was accepted at p< 0.05.
3. Results
Additional data pertaining to classifications of pre-season and
in-season injuries are provided in (online) Supplementary Table A.
Injury incidence increased (2= 9.37, df = 1, p= 0.002) from
pre-season (21.9 per 1000 h) to in-season (32.8 per 1000 h)
(Supplementary Table B). The thigh (7.3 per 1000 h, 22.2%)
and hip/groin (5.9 per 1000 h, 18.1%) were the most com-
mon sites of injuries in-season, with the most common types
of injuries being muscle strains (10.7 per 1000 h, 32.6%) and
haematomas/contusions (9.3 per 1000 h, 28.5%). Extrinsic injuries
(18.9 per 1000 h, 57.6%) and intrinsic injuries (13.9 per 1000 h,
42.4%) were not different in rate of occurrence in-season.
Average field session loads decreased (p< 0.001) from pre-
season (∼151 AU) to in-season (∼97 AU) (Table 1). Game loads
from late pre-season practice matches (∼642 AU) were signif-
icantly lower (p< 0.001) than in-season competition matches
(∼912 AU). Similarly, session-RPE intensity measures were lower
in pre-season practice matches (∼8.5) than in-season competi-
tion matches (∼8.9), whereas field training intensities were lower
in-season (∼4.7) compared to pre-season (∼5.3) sessions. Aver-
age individual weekly loads were greater (p< 0.001) in pre-season
(∼2027 AU) than in-season (∼1651AU). Injury incidence in-season
was lowest for 1 year players (28.2 per 1000 h) and highest for
7+ year players (45.4 per 1000 h), however, no significant dif-
ferences between groups were found (Table 1). Players with 7+
years of AFL experience completed significantly (p< 0.01) less load
(∼29,371 AU) in-season, compared to 2–3 (∼40,788 AU) and 4–6
year (∼40,238 AU) players.
Players who exerted 1 weekly loads in-season of >1750 AU were
at significantly higher risk of injury compared to the reference
group of <1250 AU (OR =2.44, 95% CI 1.28–4.66, p= 0.007) (Table 2).
Similarly, players who had completed a 2 weekly load in-season

B. Rogalski et al. / Journal of Science and Medicine in Sport 16 (2013) 499–503 501
Table 1
Load type per session for season phases, total training and game loads (arbitrary units) and injury incidence for different years of AFL experience. Data are mean (95%
Confidence Intervals).
Pre-season In-season Whole-season
Playing experience (y) Injury incidence (per 1000 h)
1 year (n= 7) 12.4 (0.0–27.8) 28.2 (17.3–39.1) 22.0 (15.1–28.8)
2–3 years (n= 15) 23.3 (13.9–32.7) 34.6 (24.2–44.9) 28.7 (21.8–35.6)
4–6 years (n= 13) 24.8 (13.9–35.8) 33.9 (22.9–45.0) 29.2 (19.9–38.5)
7+ years (n= 11) 25.4 (8.3–42.6) 45.4 (7.7–83.1) 31.8 (11.2–52.3)
Training and game load (sum)
1 year 23,475 (20,811–26,140)*35,212 (32,201–38,223) 58,688 (53,399–63,976)‡
2–3 years 40,986 (38,541–43,430) 40,788 (37,705–43,872) 81,774 (76,693–86,855)
4–6 years 38,303 (34,714–41,891) 40,238 (37,603–42,873) 78,540 (73,522–83,559)
7+ years 33,611 (30,309–36,914)** 29,371 (23,764–34,978)†62,982 (54,866–71,098)‡
Load type Mean load per session
Cross training 294 (286–301)a241 (231–250) 276 (270–282)
Field 151 (147–155)a97 (94–100) 125 (122–128)
Game 642 (612–617) 912 (898–925)b856 (842–871)
Running conditioning 113 (110–117) 111 (104–117) 113 (110–116)
Weights 268 (266–271)a231 (229–233) 250 (248–252)
Note: No significant differences were found between injury incidence in AFL years experience groups.
*1 year (p< 0.001) significantly lower than 2–3, 4–6 and 7+ years.
** 7+ years (p< 0.01) significantly lower than 2–3 years.
†7+ years (p< 0.01) significantly lower than 2–3 and 4–6 years.
‡1 year and 7+ years (p< 0.01) significantly lower than 2–3 and 4–6 years.
aPre-season (p< 0.001) significantly greater load than in-season.
bIn-season (p< 0.001) significantly greater load than pre-season.
of >4000 AU were at significantly higher risk of injury compared
to the reference group of <2000 AU (OR = 4.74, 95% CI 1.14–19.76,
p= 0.033). Injury occurrence in-season was also higher for play-
ers who experienced a previous to current week change in load of
>1250 AU (OR = 2.58, 95% CI 1.43–4.66, p= 0.002) compared to the
reference group of <250 AU.
Players with 2–3 (OR = 0.22, 95% CI 0.07–0.68, p= 0.009) and 4–6
(OR = 0.28, 95% CI 0.10–0.82, p= 0.020) years of AFL experience were
found to have a significantly lower risk of injury compared to 7+
year players when a previous to current week change in load was
>1000 AU (Table 3). Interestingly, 1 year players had a significantly
lower injury risk (OR = 0.39, 95% CI 0.16–0.93, p= 0.035) when com-
pared to the 7+ year reference group when experiencing a 1 week
load of >1650 AU.
Table 2
In-season training and game load risk factors for injury in elite Australian footballers.
Load calculation In-season
OR 95% CI p-Value
Exp(B) Lower Upper Sign.
Cumulative load (sum)
1 week
<1250 AU (reference) 1.00
1250 AU to <1750 AU 1.95 0.98 3.85 0.056
1750 AU to <2250 AU 2.44 1.28 4.66 0.007
>2250 AU 3.38 1.69 6.75 0.001
2 weeks
<2000 AU (reference) 1.00
2000 AU to <3000 AU 2.98 0.70 12.66 0.138
3000 AU to <4000 AU 4.03 0.98 16.53 0.053
>4000 AU 4.74 1.14 19.76 0.033
Absolute change (±)
Previous to current week
<250 AU (reference) 1.00
250 AU to <750 AU 1.34 0.90 2.01 0.148
750 AU to <1250 AU 0.89 0.50 1.58 0.680
>1250 AU 2.58 1.43 4.66 0.002
Note: No significant odds ratios were calculated in the pre-season phase.
OR, odds ratio; CI, confidence intervals.
4. Discussion
The purpose of this study was to examine whether a relationship
existed between training and game loads and injury in AFL players.
These results indicate injury risk is significantly higher for players
who exert larger 1 (>1750 AU) and 2 weekly loads (>4000 AU) or a
large previous to current week increment (>1250 AU) in compari-
son to lower training and game load ranges (<1250 AU, <2000 AU,
<250 AU), respectively. These findings suggest that the training and
game loads of elite Australian football players should be individu-
ally monitored on a weekly basis.
Non-contact and soft tissue intrinsic injuries are considered
largely preventable, whereas contact and collision extrinsic injuries
are considered generally unavoidable.7A range of intrinsic (42.4%)
and extrinsic injuries (57.6%) were found during in-season. The
inclusion of extrinsic injuries within this study is consistent with
previous research6,8 as Gabbett and Jenkins16 reported training and
game loads in professional rugby league to be strongly correlated
Table 3
AFL years experience risk factors for injury above certain training and game load
values.
Load calculation In-season
OR 95% CI p-Value
Exp(B) Lower Upper Sign.
Cumulative load (sum)
1 week
>1650 AU
7+ years (reference) 1.00
1 year 0.39 0.16 0.93 0.035
2–3 years 0.74 0.43 1.25 0.258
4–6 years 0.67 0.38 1.17 0.160
Absolute change (±)
Previous to current week
>1000 AU
7+ years (reference) 1.00
1 year 0.14 0.02 1.13 0.065
2–3 years 0.22 0.07 0.68 0.009
4–6 years 0.28 0.10 0.82 0.020
Note: No significant odds ratios were calculated in the pre-season phase.
OR, odds ratio; CI, confidence intervals.

502 B. Rogalski et al. / Journal of Science and Medicine in Sport 16 (2013) 499–503
with contact injuries (r= 0.80, p< 0.01). However, intrinsic injuries
are thought to be more directly linked with training and game
loads.7A limitation of the injury classification within the present
study was that recurrent or new injuries were not documented.
The risk of injury in-season for elite Australian footballers
increased as the amount of 1 weekly load increased from the
range of 1750 AU to <2250 AU (OR = 2.44) and >2250 AU (OR = 3.38)
when compared to the reference group of <1250 AU. Gabbett and
Domrow6also found significant relationships between 1-weekly
training loads and injury risk in sub-elite rugby league players in
early (OR =2.85) and late (OR = 1.50) in-season periods.
As this study completed a rolling day-by-day analysis of train-
ing and game loads and injuries, it accounted for instances where
two games were played within a 6-day period (e.g. Sunday game
followed by a Saturday game). Injury rates of elite soccer players
who played 2 matches within a week were significantly higher
when compared to players involved in only 1 match.17 Although
this study analysed elite soccer players playing 2 matches within 4-
days, our results suggest that AFL players participating in 2 matches
within 6-days may be at an elevated risk of injury. However, more
specific research on the turnaround time between matches and
injury risk using a larger sample of teams is required.
After recovering from an injury, a player does not always have
sufficient time to gradually increase their week-to-week training
load prior to returning to large game loads. To our knowledge, this is
the first study to highlight the importance of monitoring the week-
to-week change in training and game loads. Players who exerted a
previous to current week change in load of >1250 AU were found to
be 2.58 times more likely to be injured in comparison to the refer-
ence group of <250 AU. Players returning from previous hamstring
injuries have been shown to have a 9% chance of re-injury within
the week of returning to matches.18 A more conservative approach
by gradually increasing the week-to-week training loads of previ-
ously injured AFL players, before large game loads occur, may result
in a reduced chance of re-injury. Potentially, players returning from
injury may benefit by being used as a substitute as a method to limit
their initial game load and reduce injury risk.
During pre-season, no significant relationships between weekly
or week-to-week changes of load and odds of injury were found.
Similarly, no relationships were reported between average weekly
pre-season training load and team injury incidence in elite rugby
league19 and AFL11 players. However, in a study analysing the pre-
season training loads and odds of injury of sub-elite rugby league
players, an increase in log of training load (∼150 AU) per week was
found to significantly increase the odds of injury (OR = 2.12).6There
is greater perceived control over the load exerted by elite play-
ers during pre-season, as session durations are usually planned
by experienced conditioning staff and intensities are predicted by
selecting activity/drill types (based on session-RPE averages). Play-
ers involved in this study would have had their pre-season training
loads closely monitored and modified, which may have influenced
the insignificant pre-season results found. Furthermore, the spe-
cific details of the training program of the AFL club involved within
the study are currently unavailable for publication, as they are con-
sidered highly confidential.
Musculoskeletal immaturity of 1 year AFL players12 was hypo-
thesised here to cause an increased injury risk per training and
game load, in comparison to 2–3 and 4–6 year players. However, no
significant relationships were found between load derived values
and injury risk. These results differ from a study reporting 1-weekly
training loads to significantly relate to traumatic injuries in elite
youth soccer players.8The in-season loads of the 1 year players
within our study were highly monitored and modified, resulting in
∼5000 AU lower loads compared to 2–3 and 4–6 year players. Due
to the strict load modification strategy of 1 year players, they were
not exposed to high weekly or week-to-week load changes and
recorded the lowest in-season injury incidence (28.2/1000 h). Con-
sequently, training and game load modification of immature first
year players entering the elite system may be useful for preventing
injuries. Reductions in pre-season training loads in sub-elite rugby
league players have also been found to reduce injury rates.20 The
influence of pre-season training load on in-season injury risk in
elite Australian footballers is an interesting concept which is yet to
be examined.
The best starting 22 AFL players are generally older and
more experienced21 and frequently have accelerated returns from
injury to full training and game loads, in an attempt to enhance
team performance. Consequently, they are generally exposed to
high week-to-week load increments. The 7+ year group had the
largest in-season injury incidence (45.4 per 1000 h) and therefore
completed significantly lower in-season loads (∼6000–11,000 AU)
compared to the less experienced groups. The injury risk of 2–3
(OR = 0.22) and 4–6 year players (OR= 0.28) was significantly lower
than 7+ year players when experiencing a previous to current week
change in load of >1000 AU. The body’s ability to respond to rapid
force changes or recover from fatigue has been speculated to slowly
diminish as age and experience increases.22 Therefore, care should
be taken when exposing 7+ year players to a large previous to cur-
rent week change in load. A confounding variable in the elevated
odds of injury in more experienced AFL players is possibly previous
injury history, as it has been reported as an independent predictor
of subsequent hamstring injuries.23 Future studies should analyse
multiple seasons of data to more thoroughly investigate the effect
of increasing experience on training and game loads and injury risk
in AFL players.
Minimising injury risk is vitally important in elite team sports,
as low injury rates can be critical to team performance.24 How-
ever, training programs must elicit fitness improvements so that
players are adequately prepared to endure the demands of compet-
itive games. No fitness observations were made to analyse whether
reductions in training load were detrimental to performance. Mon-
itoring of global positioning system information (distance, sprint
and accelerometer loads) and psychological data such as perceived
muscle soreness, fatigue, mood, and sleep ratings,18 may provide
extra insight into injury risk relationships in elite Australian foot-
ballers.
5. Conclusion
During an elite Australian football in-season, larger 1 (>1750 AU)
and 2 weekly loads (>4000 AU) and substantial previous to cur-
rent week change in load (>1250 AU) were found to significantly
increase injury risk when compared to lower training and game
load ranges (<1250 AU, <2000 AU, <250 AU), respectively. As a
method to reduce the risk of injury, derived training and game load
values of weekly loads and previous week-to-week load changes
should be monitored individually in elite Australian footballers.
Practical applications:
•The non-invasive and simple session-RPE method is useful for
tracking training and game loads in respect to injury risk in elite
Australian footballers.
•Weekly load sums and previous week-to-week changes in load
should be monitored in-season for individual elite Australian
footballers, as they are significantly related to injury risk.
•Training and game load modification strategies for first year AFL
players may be important in achieving low injury incidence in
their first season.
•Future in-season load management modifications could include
planned reduction in training or game loads (especially by being
the designated substitute, or player subbed out of game).

B. Rogalski et al. / Journal of Science and Medicine in Sport 16 (2013) 499–503 503
Acknowledgement
No external financial support was received for this study.
Appendix A. Supplementary data
Supplementary data associated with this article can be
found, in the online version, at http://dx.doi.org/10.1016/j.jsams.
2012.12.004.
References
[1]. Wisbey B, Montgomery PG, Pyne DB et al. Quantifying movement demands of
AFL football using GPS tracking. J Sci Med Sport 2010; 13(5):531–536.
[2]. Gray AJ, Jenkins DG. Match analysis and the physiological demands of Australian
football. Sports Med 2010; 40(4):347–360.
[3]. Orchard JW, Seward H. Injury report 2010: Australian Football League. Sport
Health 2011; 29(2):15–29.
[4]. Norton KI, Craig NP, Olds TS. The evolution of Australian football. J Sci Med Sport
1999; 2(4):389–404.
[5]. Foster C, Florhaug JA, Franklin J et al. A new approach to monitoring exercise
training. J Strength Cond Res 2001; 15(1):109–115.
[6]. Gabbett TJ, Domrow N. Relationships between training load, injury, and fitness
in sub-elite collision sport athletes. J Sports Sci 2007; 25(13):1507–1519.
[7]. Gabbett TJ. The development and application of an injury prediction model for
noncontact, soft-tissue injuries in elite collision sport athletes. J Strength Cond
Res 2010; 24(10):2593–2603.
[8]. Brink MS, Visscher C, Arends S et al. Monitoring stress and recovery: new insights
for the prevention of injuries and illnesses in elite youth soccer players. Br J Sports
Med 2010; 44(11):809–815.
[9]. Anderson L, Triplett-McBride T, Foster C et al. Impact of training patterns on
incidence of illness and injury during a women’s Collegiate basketball season. J
Strength Cond Res 2003; 17(4):734–738.
[10]. Orchard JW, James T, Portus M et al. Fast bowlers in cricket demonstrate up to
3- to 4-week delay between high workloads and increased risk of injury. Am J
Sports Med 2009; 37(6):1186–1192.
[11]. Piggott B, Newton MJ, McGuigan MR. The relationship between training load
and incidence of injury and illness over a pre-season at an Australian Football
League club. J Aust Strength Cond 2009; 17(3):4–17.
[12]. Veale JP, Pearce AJ, Buttifant D et al. Anthropometric profiling of elite junior
and senior Australian football players. Int J Sports Physiol Perform 2010;
5(4):509–520.
[13]. Brewer C, Dawson B, Heasman J et al. Movement pattern comparisons in elite
(AFL) and sub-elite (WAFL) Australian football games using GPS. J Sci Med Sport
2010; 13(6):618–623.
[14]. Middleton P. Relationship of training intensity measures to player rating of
perceived exertion during Australian rules football training drills [Honours The-
sis (unpublished)]. Perth, The University of Western Australia, 2007.
[15]. Scott TJ, Black C, Quinn J et al. Validity and reliability of the session
RPE method for quantifying training in Australian Football: a com-
parison of the CR10 and CR100 scales. J Strength Cond Res 2012.
http://dx.doi.org/10.1519/JSC.0b013e3182541d2e.
[16]. Gabbett TJ, Jenkins DG. Relationship between training load and injury in profes-
sional rugby league players. J Sci Med Sport 2011; 14(3):204–209.
[17]. Dupont G, Nedelec M, McCall A et al. Effects of 2 soccer matches in a
week on physical performance and injury rate. Am J Sports Med 2010; 38(9):
1752–1758.
[18]. Orchard JW. Recurrent hamstring injury in Australian football. Med Sci Sports
Exerc 1998; 30(5):S52.
[19]. Killen NM, Gabbett TJ, Jenkins DG. Training loads and incidence of injury during
the preseason in professional rugby league players. J Strength Cond Res 2010;
24(8):2079–2084.
[20]. Gabbett TJ. Reductions in pre-season training loads reduce training injury rates
in rugby league players. Br J Sports Med 2004; 38(6):743–749.
[21]. Young WB, Newton RU, Doyle TLA et al. Physiological and anthropometric char-
acteristics of starters and non-starters and playing positions in elite Australian
Rules football: a case study. J Sci Med Sport 2005; 8(3):333–345.
[22]. Maffey L, Emery C. What are the risk factors for groin strain injury in
sport? A systematic review of the literature. Sports Med 2007; 37(10):
881–894.
[23]. Gabbe BJ, Bennell KL, Finch CF et al. Predictors of hamstring injury at the elite
level of Australian football. Scand J Med Sci Sports 2006; 16(1):7–13.
[24]. Eirale C, Tol JL, Farooq A et al. Low injury rate strongly correlates
with team success in Qatari professional football. Br J Sports Med 2012.
http://dx.doi.org/10.1136/bjsports-2012-091040.