Jarryd Heasman

Training and game loads and injury risk in elite Australian footballers.

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.

Movement pattern comparisons in elite (AFL) and sub-elite (WAFL) Australian football games using GPS

This study examined differences in movement patterns between AFL (elite) and WAFL (sub-elite) players using Global Positioning System (GPS) devices. Maximum speed data and totals of high intensity efforts (>15 km h⁻¹), sprint efforts (>20 km h⁻¹) and distance covered were collected on 41 players during the 2008 season. Data were expressed per min of game time played, separated into first and second halves, and also into positions, for both elite and sub-elite players. Overall, elite players had higher movement demands, including 9% more distance covered/min (128±12 m min⁻¹ vs. 117±15 m min⁻¹; p<0.01, ES=0.84), and 21% more high intensity efforts/min (2.9±0.6 vs. 2.4±0.6; p<0.01, ES=0.83). Movement demands significantly declined (p<0.05-0.01) from first to second half, in both competition levels. For both leagues, Small Forwards/Small Backs and Midfield players covered significantly greater (p<0.05-0.01) total distances and completed more high intensity efforts than other positions. Ruckmen recorded significantly lower (p<0.05-0.01) movement demands than Small Forwards/Small Backs, Midfielders and Centre Half-Forwards/Centre Half-Backs over most variables. In conclusion, elite players recorded higher overall movement demands than sub-elite players. This information may be useful for coaches and conditioning staff in designing appropriate training drills for specific role requirements of individual players and assist in the progression of players from sub-elite to elite levels of competition.

Accelerometer and GPS-derived running loads and injury risk in elite Australian footballers

Abstract Colby, MJ, Dawson, B, Heasman, J, Rogalski, B, and Gabbett, TJ. Accelerometer and GPS-derived running loads and injury risk in elite Australian footballers. J Strength Cond Res 28(8): 2244–2252, 2014—The purpose of this study was to investigate the relationship between overall physical workload (global positioning systems [GPS]/accelerometer) measures and injury risk in elite Australian football players (n = 46) during a season. Workload data and (intrinsic) injury incidence were monitored across preseason and in-season (18 matches) phases. Multiple regression was used to compare cumulative (1-, 2-, 3-, and 4-weekly loads) and absolute change (from previous-to-current week) in workloads between injured and uninjured players for all GPS/accelerometer-derived variables: total distance, V1 distance (total distance above individuals aerobic threshold speed), sprint distance, force load, velocity load, and relative velocity change. Odds ratios (ORs) were calculated to determine the relative injury risk. Cumulative loads showed the strongest relationship with greater intrinsic injury risk. During preseason, 3-weekly distance (OR = 5.489, p = 0.008) and 3-weekly sprint distance (OR = 3.667, p = 0.074) were most indicative of greater injury risk. During in-season, 3-weekly force load (OR = 2.530, p = 0.031) and 4-weekly relative velocity change (OR = 2.244, p = 0.035) were associated with greater injury risk. No differences in injury risk between years of Australian Football League system experience and GPS/accelerometer data were seen. From an injury risk (prevention) perspective, these findings support consideration of several GPS/accelerometer running load variables in Australian football players. In particular, cumulative weekly loads should be closely monitored, with 3-weekly loads most indicative of a greater injury risk across both seasonal phases.

Game movements and player performance in the Australian Football League

This study examined the relationship between game movements and team and individual performance in Australian football. Movement data (GPS) was collected from 30 elite players from one club in 17 matches during the 2011 season. Selected movement variables were related to individual (possession number, Champion Data© player rankings and pressure points) and team [quarter points (scored) margin] performance indicators. Playing position (nomadic vs. key position), years of experience, game location (home/away), environmental conditions (wet/dry), time of day (day/night), break between games (6-12 days), quarter number (1-4) and quarter score (+/-) margin were also analysed. Overall, some small-moderate (but inconsistent) positive relationships between individual movement data and performance indicators were observed. Nomadic players had higher movement profiles and performance indicators than key position, whilst players with 7+ years’ experience recorded lower movement profiles than 1-3 and 4-6 years, but were only lower in performance in pressure points. min-1. Dry vs. wet (one exception), home vs. away and day vs. night, saw no differences in movements or performance. A 12 day turnaround saw higher movement profiles and performance indicators than for 6-8 days. For team performance, few moderate, inverse relationships were found between quarter points (scored) margin and movement profiles.

Sleep patterns and injury occurrence in elite Australian footballers

OBJECTIVES To examine the potential relationship between sleep duration and efficiency and injury incidence in elite Australian footballers. DESIGN Prospective cohort study. METHODS Australian footballers (n=22) from one AFL club were studied across the 2013 competitive season. In each week sleep duration and efficiency were recorded via actigraphy for 5 nights (the 3 nights preceding a game, the night of the game and the night after the game). Injury incidence was monitored and matched with sleep data: n=9 players suffered an injury that caused them to miss a game. Sleep in the week of the injury (T2) was compared to the average of the previous 2 weeks (T1). A two-way repeated measures ANOVA was used to determine any effect of sleep duration and efficiency on injury. Significance was accepted at p<0.05. RESULTS Injury incidence was not significantly affected by sleep duration, sleep efficiency or a combination of these factors. Analysis of individual nights for T2 versus T1 also showed no differences in sleep quality or efficiency. However, a main effect for time was found for sleep duration and efficiency, with these being slightly, but significantly greater (p<0.05) at T2 (437±61min and 82±7%) than T1 (414±64min and 79±7%). CONCLUSIONS No significant effect of sleep duration and efficiency on injury occurrence was found in elite Australian footballers.

Development and validation of a player impact ranking system in Australian football

This study aimed to develop and validate player performance impact rankings for Australian football, considering players’ time on ground and game situation. Player performance data was collected from an Australian Football League (AFL) club and their opponents in each game during the 2006 season. Individual player and team impact scores were generated by multiplying the frequency of selected game actions by allocated positive or negative numerical values. The study was divided into three phases. In phase 1 higher team impact scores were shown to have a significant correlation with winning (r=−0.69, p<0.01). A greater final points margin between the teams was also correlated with an increased impact score margin (r=0.85, p<0.001). In phase 2 one-way ANOVAs revealed individual player impact scores were significantly higher in the midfield than in the forward and defensive positional zones (p<0.001), suggesting that impact score comparisons should only be made within positional zones. In phase 3 a chi-square analysis revealed significant differences between individual players within each of the positional zones. It was concluded that the impact ranking scores provided a valid method of assessing game performance for players (within positional zones) and teams, allowing performance profiles to be created for coaching purposes.

Preseason workload volume and high-risk periods for noncontact injury across multiple Australian Football League seasons

Abstract Colby, MJ, Dawson, B, Heasman, J, Rogalski, B, Rosenberg, M, Lester, L, and Peeling, P. Preseason workload volume and high-risk periods for noncontact injury across multiple Australian Football League seasons. J Strength Cond Res 31(7): 1821–1829, 2017—The purpose of this study was to assess the association between preseason workloads and noncontact injury risk in Australian football players. Individual player injury data were recorded over 4 full seasons (2012–15) from one professional club. Noncontact injury incidence (per 1,000 “on legs” field training and game hours) was compared across the preseason, precompetition, and in-season phases to determine relative noncontact injury risk. Preseason workloads (global positioning system–derived total distance run and sprint distance) and individual (fixed) injury risk factors (age, previous injury history) were incorporated into the analysis. A generalized estimating equation with a binary logistic function modeled potential risk factors with noncontact injury for selected periods across the annual cycle. Odds ratios were calculated to determine the relative injury risk. The (preseason) precompetition phase (19.1 injuries per 1,000 hours) and (in-season) rounds 12–17 (16.0 injuries per 1,000 hours) resulted in the highest injury incidence. Low cumulative total distances in late preseason (<108 km) and precompetition (76–88 km) periods were associated with significantly (p ⩽ 0.05) greater injury risk during the in-season phase. In conclusion, these results suggest players are at the greatest injury risk during the precompetition period, with low preseason cumulative workloads associated with increased in-season injury risk. Therefore, strength and conditioning staff should place particular emphasis on achieving at least moderate training loads during and leading into this phase, where competitive game play is first introduced.

Team movement patterns with and without ball possession in Australian Football League players

This study assessed the relationship of GPS derived movement patterns (integrated with match footage), separated into time with and without ball possession, and team performance in elite Australian football. Time spent running <14 km. h-1, >14 km. h-1, >19 km. h-1 and >24 km. h-1 were used, plus when the ball was in dispute or dead. Games and quarters were separated by win/loss, and further categorized by player position. In wins compared to losses, no significant difference existed between time spent with and without ball possession across full games. Within quarters, time spent with possession was significantly greater than without possession in winning quarters. In game and quarter wins, % time spent at >14 km. h-1 with possession was significantly lower than in losses, whilst % time spent >19 km. h-1 and >14 km. h-1 without possession was significantly greater in quarter wins than losses. Forwards had a greater % of time spent at >14 km. h-1 with possession, defenders a greater % without possession, and midfielders had the most balanced profile. Overall, in winning quarters, teams had a greater amount of possession and time spent at >14 km. h-1 without ball possession, which was a significant predictor of success.

Relationship between pre-season strength and power measures and performance in elite Australian football

This study investigated the relationship between various measures of muscular strength and power and individual player performance in elite Australian football (AF). Strength and power data was collected from 30 players from one Australian Football League club at various time points over the 2014 pre-season period. Upper and lower body strength and power were assessed using a one repetition maximum bench press, isometric mid-thigh pull and loaded countermovement jumps respectively. These variables were related to individual performance indicators (Champion Data ranking and descriptive match statistics) averaged over 22 games of the 2014 regular season. A secondary analysis also correlated strength and power measures with high speed running data derived from GPS units worn during games. For all players combined, no significant relationships for strength measures were found with Champion data ranking, but some power measures recorded moderate-large inverse relationships (r = -0.38 to -0.61) with certain match statistics. When separated by position, strong positive associations (r = 0.51 to 0.73) between upper body strength and performance (Champion Data© ranking and match statistics) were recorded for nomadic (midfield) players, but not for non-nomadic (taller, set position) players. Lower body power was also moderately-strongly associated (r = 0.42-0.69) with (GPS) maximum game acceleration and speed for the nomadic and combined player groups. These findings suggest that strength and power are significantly related to some common AF performance indicators and that particular consideration should be given to the positional requirements of players in planning strength and power programmes.

How much is enough in rehabilitation? High running workloads following lower limb muscle injury delay return to play but protect against subsequent injury

OBJECTIVES Examine the influence of rehabilitation training loads on return to play (RTP) time and subsequent injury in elite Australian footballers. DESIGN Prospective cohort study. METHODS Internal (sessional rating of perceived exertion: sRPE) and external (distance, sprint distance) workload and lower limb non-contact muscle injury data was collected from 58 players over 5 seasons. Rehabilitation periods were analysed for running workloads and time spent in 3 rehabilitation stages (1: off-legs training, 2: non-football running, 3: group football training) was calculated. Multi-level survival analyses with random effects accounting for player and season were performed. Hazard ratios (HR) and 95% confidence intervals (CI) for each variable were produced for RTP time and time to subsequent injury. RESULTS Of 85 lower limb muscle injuries, 70 were rehabilitated to RTP, with 30 cases of subsequent injury recorded (recurrence rate=11.8%, new site injury rate=31.4%). Completion of high rehabilitation workloads delayed RTP (distance: >49,775m [reference: 34,613-49,775m]: HR 0.12, 95%CI 0.04-0.36, sRPE: >1266AU [reference: 852-1266AU]: HR 0.09, 95%CI 0.03-0.32). Return to running within 4days increased subsequent injury risk (3-4days [reference: 5-6 days]: HR 25.88, 95%CI 2.06-324.4). Attaining moderate-high sprint distance (427-710m) was protective against subsequent injury (154-426m: [reference: 427-710m]: HR 37.41, 95%CI 2.70-518.64). CONCLUSIONS Training load monitoring can inform player rehabilitation programs. Higher rehabilitation training loads delayed RTP; however, moderate-high sprint running loads can protect against subsequent injury. Shared-decision making regarding RTP should include accumulated training loads and consider the trade-off between expedited RTP and lower subsequent injury risk.