In-Game Workload Demands of Position Players in Major League Baseball

BACKGROUND

Athletes who are well prepared for the physical demands of competition are less susceptible to injury. Defining and then preparing athletes for these in-game demands is critical to athlete health and performance. The injury burden within Major League Baseball (MLB) is significant and differs by position. Despite its importance, the workload demands have not been described for position players in MLB.

HYPOTHESIS

That running demands would be significantly higher for outfielders, followed by infielders, and catchers, respectively, while batting and baserunning metrics would be similar across positions.

STUDY DESIGN

Cohort study.

LEVEL OF EVIDENCE

Level 3.

METHODS

Total and high-speed running distance (>75% Vmax), high-speed running count, hard accelerations (>2.78 m/s/s), defensive and baserunning minutes, total and hard throws (>75% max), and bat swing counts were calculated from Statcast data. Players with 100 games or more in the 2018 season (n = 126) were included for analysis.

RESULTS

All offensive and baserunning metrics were similar across positions; however, significant positional differences were observed for defensive and overall workload metrics. High-speed running was highest among outfielders (F1,7 = 27.1, P < 0.01), followed by infielders, then catchers. Hard accelerations (F1,7 = 12.9, P < 0.01) were highest among first basemen, then outfielders, remaining infielders, and catchers. Total throws (F1,7 = 17.7, P < 0.01) were highest among middle infielders. Hard throws (P < 0.01) were highest among shortstops and third basemen.

CONCLUSION

In-game workloads differ significantly by defensive position in MLB. These differences in running, throwing, and hitting volumes have significant implications for physical preparation and injury return-to-play progressions to optimize performance and minimize injury and reinjury risk for these athletes.

CLINICAL RELEVANCE

These data provide insight into how best to prepare athletes of different positions for the demands of the game both in terms of preseason preparation as well as return-to-play benchmarks following injury. These data should also serve as a platform for future research into the relationship between workload and injury among professional baseball players.

The effect of high-intensity plyometric training on anaerobic performance parameters: a pilot study in U17 elite A league

The aim of this study was to examine the effects of high-intensity plyometric training (HIPT) on some parameters in elite soccer players in the U17. Volunteer soccer players were randomly divided into two groups according to their positions: the regular training (RT) group (age: 16.71 ± 0.47 years; stature: 163.46 ± 3.31 cm; body weight: 61.04 ± 1.59 kg) and the HIPT group (age: 16.64 ± 0.50 years; stature: 165.60 ± 3.03 cm; body weight: 59.76 ± 1.92 kg), and each group included five defenders, five midfielders, and four attackers. While the RT group did only routine soccer training, the HIPT group performed high-intensity plyometric training three days a week in addition to routine soccer training. In the study, body weight, stature, sprint (10 m, 30 m, and 40 m), jump (standing long jump, right and left-limb triple hop) and 5-0-5 change of direction speed test measurements of the soccer players were taken. Skewness and Kurtosis values obtained from the pre-test and post-test measurements were calculated to test whether the normality assumption of the study was met. A mixed measure two-way ANOVA test was used to determine the difference between the pre-test and post-test. The significance was set as p < 0.05. The results indicated that 8-week high- intensity plyometric training provided more improvement in measured performance parameters than routine soccer training. As a result, when the data obtained is examined, it can be said that HIPT had better values in sprint, jump, and change of direction parameters, so plyometric training was more beneficial for anaerobic parameters than routine soccer training in children. It is recommended that the trainers make their plans considering these results.

Noncontact Injury Distribution and Relationship With Preseason Training Load and Nonmodifiable Risk Factors in Rugby Union Players Across Multiple Seasons

ABSTRACT

Evans, SL, Whittaker, G, Elphinstone Davis, E, Jones, ES, Hardy, J, and Owen, JA. Noncontact injury distribution and relationship with preseason training load and non-modifiable risk factors in Rugby Union players across multiple seasons. J Strength Cond Res 37(7): 1456-1462, 2023-This study examined the distribution of noncontact injury during phases of the competitive season and the association between preseason training load (TL) and nonmodifiable risk factors on injury risk during these phases. Injury data were recorded from 1 senior academy team over 3 seasons (2017-2020) and analyzed across early-season, midseason, and late-season phases. A generalized estimating equation was used to model risk factors with noncontact injury for selected phases. The highest noncontact injury incidence occurred in the late-season phase (22.2 per 1,000 hours) compared with early (13.7 per 1,000 hours, p < 0.001) and midseason phases (15.5 per 1,000 hours, p = 0.001). Low preseason TL (8,949-12,589 arbitrary units; odds ratio [OR], 95% confidence interval [CI] = 4.7, 1.0-21.6; p = 0.04) and low preseason TL combined with high early-season TL and injury in the early-season phase (OR, 95% CI = 6.5, 1.1-35.5; p = 0.03) were associated with greater midseason noncontact injury risk. In addition, low preseason TL combined with previous injury was associated with increased risk of noncontact injury risk in the late season (OR, 95% CI = 12.2, 0.9-15.6, p = 0.05). Our results suggest players are at a greater injury risk during the late-season phase, with low preseason cumulative loads combined with a history of previous injury associated with increased in-season injury risk. Strength and conditioning coaches should therefore monitor cumulative preseason TL alongside screening for previous injury history to identify athletes at greater risk of noncontact injury risk during the competitive season.

Genetic Variants within NOGGIN, COL1A1, COL5A1, and IGF2 are Associated with Musculoskeletal Injuries in Elite Male Australian Football League Players: A Preliminary Study

INTRODUCTION

Australian Football is a dynamic team sport that requires many athletic traits to succeed. Due to this combination of traits, as well as technical skill and physicality, there are many types of injuries that could occur. Injuries are not only a hindrance to the individual player, but to the team as a whole. Many strength and conditioning personnel strive to minimise injuries to players to accomplish team success.

PURPOSE

To investigate whether selected polymorphisms have an association with injury occurrence in elite male Australian Football players.

METHODS

Using DNA obtained from 46 elite male players, we investigated the associations of injury-related polymorphisms across multiple genes (ACTN3, CCL2, COL1A1, COL5A1, COL12A1, EMILIN1, IGF2, NOGGIN, SMAD6) with injury incidence, severity, type (contact and non-contact), and tissue (muscle, bone, tendon, ligament) over 7 years in one Australian Football League team.

RESULTS

A significant association was observed between the rs1372857 variant in NOGGIN (p = 0.023) and the number of total muscle injuries, with carriers of the GG genotype having a higher estimated number of injuries, and moderate, or combined moderate and high severity rated total muscle injuries. The COL5A1 rs12722TT genotype also had a significant association (p = 0.028) with the number of total muscle injuries. The COL5A1 variant also had a significant association with contact bone injuries (p = 0.030), with a significant association being found with moderate rated injuries. The IGF2 rs3213221-CC variant was significantly associated with a higher estimated number of contact tendon injuries per game (p = 0.028), while a higher estimated number of total ligament (p = 0.019) and non-contact ligament (p = 0.002) injuries per game were significantly associated with carriage of the COL1A1 rs1800012-TT genotype.

CONCLUSIONS

Our preliminary study is the first to examine associations between genetic variants and injury in Australian Football. NOGGIN rs1372857-GG, COL5A1 rs12722-TT, IGF2 rs3213221-CC, and COL1A1 rs1800012-TT genotypes held various associations with muscle-, bone-, tendon- and ligament-related injuries of differing severities. To further increase our understanding of these, and other, genetic variant associations with injury, competition-wide AFL studies that use more players and a larger array of gene candidates is essential.

The Association between Pre-season Running Loads and Injury during the Subsequent Season in Elite Gaelic Football

The aim of this study was to determine if the quantity of running load performed in pre-season affects the incidence of injury in elite Gaelic footballers. It was hypothesized that a greater quantity of running loads completed will reduce the incidence rate of injury. A cohort of elite male Gaelic football players (n = 25) was prospectively monitored throughout one full season. This longitudinal study involved the collection of GPS data and injury data. Participants were retrospectively divided into two groups and assigned to a high (HTL, completed >50% of pre-season sessions, n = 13) or low (LTL, completed <50% of pre-season sessions, n = 12) training load group based on the percentage of pre-season sessions completed. Data for total distance, PlayerLoad™, meters covered at running speeds (4.0−4.84 m/s), meters covered at high running speeds (4.85−6.39 m/s), meters covered at sprint speeds (>6.4 m/s) and high-intensity running meters (high-speed running meters and sprint meters combined) were collected during all sessions. A one-way analysis of variance (ANOVA) was completed to understand the variation of external training load data across the different phases of the season. A series of repeated measures of ANOVA were subsequently completed to understand the variation of external training load data across seasonal phases within the training groups. Although the LTL group had a higher incidence rate of non-contact injuries (large effect size) per 1000 h of exposure in each phase of the season, statistical analysis revealed that there was no significant difference (F = 4.32, p = 0.173, partial η2 = 0.684, large) between the HTL (14.9 ± 4.17/1000 h) and the LTL (24.5 ± 7.36/1000 h) groups. Additionally, the HTL group was able to sustain greater running loads in the competitive phases of the season compared to the LTL group, total distance (F = 8.10, p < 0.001, partial η2 = 0.299, large), high-speed running distance (F = 8.74, p < 0.001, partial η2 = 0.304, large) and high-intensity running distance (F = 7.63, p < 0.001, partial η2 = 0.276, large). Furthermore, players who complete a greater proportion of running loads in pre-season may alter their body composition levels to more optimal levels, which in turn may reduce the risk of injury while also helping increase or maintain performance-related fitness markers such as aerobic fitness.

Considerations for the Medical Management of the Circus Performance Artist and Acrobat

Medical management of the circus performer encompasses a wide variety of multicultural, transdisciplinary and multifaceted decision-making considerations. There is a paucity of research evidence investigating both the unique diversity of skill sets and cultural considerations in addition to injury patterns of performers within the circus environment. Since a previously established framework for supporting the health and well-being of the circus performer across various aspects of medical management does not exist in the literature, most recommendations in this regard must come from practical experience working with this highly specialized performance athlete population. The purpose of this clinical commentary is to provide the reader with a greater understanding of the unique challenges associated with the medical management of performance artists and acrobats as well as recommendations for developing an integrated approach for mitigating injury risk within a highly specialized, diverse athlete population.

LEVEL OF EVIDENCE

5.

ACL injury prevention: Where have we come from and where are we going?

Anterior cruciate ligament (ACL) injuries are one of the most common and severe knee injuries across sports. As such, ACL injury prevention has been a focus of research and sports medicine practice for the past three-plus decades. Examining the current research and identifying both clinical strategies and research gaps, the aim of this review is to empower clinicians and researchers with knowledge of where the ACL injury prevention literature is currently and where it is going in the future. This paper examines the mechanism of ACL injury prevention, screening, implementation, compliance, adherence, coronavirus, and areas of future research. Clinical significance: The time lag between research and practical implementation in general healthcare settings can be as long as 17 years; however, athletes playing sports today are unable to wait that long. With effective programs already established, implementation and adherence to these programs is essential. Strategies such as coaching education, increasing awareness of free programs, identifying barriers, and overcoming implementation obstacles through creative collaboration are just a few ways that could help improve both ACL injury prevention implementation and adherence.

Lower-limb injury in elite Australian football: A narrative review of kinanthropometric and physical risk factors

OBJECTIVE

This review aims to provide a succinct and critical analysis of the current physical and mechanical demands of elite Australian football while examining lower-limb injury and the associated physical and kinanthropometric risk factors.

METHODS

MEDLINE, PubMed, Web of Science and SPORTSDiscus electronic databases were searched for studies that investigated the playing demands, injury trends, and physical and kinanthropometric injury risk factors of elite Australian football. Articles from similar team sports including soccer and rugby (union and league) were also included.

RESULTS

While the physical demands of elite AF have steadied over the past decade, injury rates continue to rise with more than two-thirds of all injuries affecting the lower-limbs. Body composition and musculoskeletal morphological assessments are regularly adopted in many sporting settings with current research suggesting high and low body mass are both associated with heightened injury risk. However, more extensive investigations are required to determine whether the proportions of muscle and fat are linked. Repeated assessment of musculoskeletal morphology may also provide further insight into stress fracture rates.

CONCLUSIONS

While kinanthropometric and physical attributes are highly valued within elite sporting environments, establishing a deeper connection with injury may provide practitioners with more insight into current injury trends.

Evolution of Physical Demands of Australian Football League Matches from 2005 to 2017: A Systematic Review and Meta-Regression

BACKGROUND

There is extensive research investigating the match demands of players in the Australian Football League (AFL).

OBJECTIVE

This systematic literature review and meta-regression sought to analyse the evolution of in-game demands in AFL matches from 2005 to 2017, focusing on the relationship between volume and intensity.

METHODS

A systematic search of Ovid MEDLINE, Embase, Emcare, Scopus, SPORTDiscus, and Cochrane Library databases was conducted. Included studies examined the physical demands of AFL matches utilising global positioning system (GPS) technology. Meta-regression analysed the shift in reported volume (total distance and total match time) and intensity (metres per minute [m.min^-1], sprint duration and acceleration) metrics for overall changes, across quarters and positional groups (forwards, nomadics and defenders) from 2005 to 2017 inclusive and for each year between 2005 and 2007, 2007 and 2010, 2010 and 2012, and 2012 and 2015/2017 breakpoints.

RESULTS

Distance (p = 0.094), m.min^-1 (p = 0.494), match time (p = 0.591), time over 18 km·h^-1 (p = 0.271), and number of accelerations greater than 4 km·h^-1 (p = 0.498) and 10 km·h^-1 (p = 0.335) in 1 s did not change from 2005 to 2017. From 2005 to 2007 volume decreased (- 6.10 min of match time; p = 0.010) and intensity increased (6.8 m.min^-1 increase; p = 0.023). Volume and intensity increased from 2007 to 2010, evidenced by increases in total distance (302 m; p = 0.039), time over 18 km·h^-1 (0.31 min; p = 0.005), and number of accelerations greater than 4 km·h^-1 (41.1; p = 0.004) and 10 km·h^-1 (3.6; p = 0.005) in 1 s. From 2010 to 2012, intensity decreased, evidenced by reductions in metres per minute (- 4.3; p = 0.022), time over 18 km·h^-1 (- 0.93 min; p < 0.001), and number of accelerations greater than 4 km·h^-1 (- 104.4; p < 0.001) and 10 km·h^-1 (- 8.3; p < 0.001) in 1 s, whilst volume stabilised with no changes in distance (p = 0.068) and match time (p = 0.443). From 2012 to 2015/2017 volume remained stable and intensity increased with time over 18 km·h^-1 (0.27 min; p = 0.008) and number of accelerations greater than 4 km·h^-1 (31.6; p = 0.016) in 1 s increasing.

CONCLUSIONS

Changes in volume and intensity of AFL match demands are defined by discrete periods from 2007 to 2010 and 2010 to 2012. The interaction of rule and interpretation changes and coaching strategies play a major role in these evolutionary changes. In turn, modified game styles impact player game demands, training, and selection priorities. Standardisation and uniformity of GPS data reporting is recommended due to inconsistencies in the literature.

The Training-Performance Puzzle: How Can the Past Inform Future Training Directions?

Over the past 20 years, research on the training-load-injury relationship has grown exponentially. With the benefit of more data, our understanding of the training-performance puzzle has improved. What were we thinking 20 years ago, and how has our thinking changed over time? Although early investigators attributed overuse injuries to excessive training loads, it has become clear that rapid spikes in training load, above what an athlete is accustomed, explain (at least in part) a large proportion of injuries. In this respect, it appears that overuse injuries may arise from athletes being underprepared for the load they are about to perform. However, a question of interest to both athletic trainers (ATs) and researchers is why some athletes sustain injury at low training loads, while others can tolerate much greater training loads? A higher chronic training load and well-developed aerobic fitness and lower body strength appear to moderate the training-injury relationship and provide a protective effect against spikes in load. The training-performance puzzle is complex and dynamic-at any given time, multiple inputs to injury and performance exist. The challenge facing researchers is obtaining large enough longitudinal data sets to capture the time-varying nature of physiological and musculoskeletal capacities and training-load data to adequately inform injury-prevention efforts. The training-performance puzzle can be solved, but it will take collaboration between researchers and clinicians as well as an understanding that efficacy (ie, how training load affects performance and injury in an idealized or controlled setting) does not equate to effectiveness (ie, how training load affects performance and injury in the real-world setting, where many variables cannot be controlled).

Global Positioning System-Derived Workload Metrics and Injury Risk in Team-Based Field Sports: A Systematic Review

OBJECTIVE

To evaluate the current literature regarding the utility of global positioning system (GPS)-derived workload metrics in determining musculoskeletal injury risk in team-based field-sport athletes.

DATA SOURCES

PubMed entries from January 2009 through May 2019 were searched using terms related to GPS, player workload, injury risk, and team-based field sports.

STUDY SELECTION

Only studies that used GPS metrics and had injury as the main outcome variable were included.

DATA EXTRACTION

Total distance, high-speed running, and acute : chronic workload ratios were the most common GPS metrics analyzed, with the most frequent sports being soccer, rugby, and Australian rules football.

DATA SYNTHESIS

Many distinct workload metrics were associated with increased injury risk in individual studies performed in particular sport circumstances; however, the body of evidence was inconclusive as to whether any specific metrics could consistently predict injury risk across multiple team-based field sports.

CONCLUSIONS

Our results were inconclusive in determining if any GPS-derived workload metrics were associated with an increased injury risk. This conclusion is due to a myriad of factors, including differences in injury definitions, workload metrics, and statistical analyses across individual studies.

Association of Genetic Variances in ADRB1 and PPARGC1a with Two-Kilometre Running Time-Trial Performance in Australian Football League Players: A Preliminary Study

Genetic variants in the angiotensin-converting enzyme (ACE) (rs4343), alpha-actinin-3 (ACTN3) (rs1815739), adrenoceptor-beta-1 (ADRB1) (rs1801253), and peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PPARGC1A) (rs8192678) genes have previously been associated with elite athletic performance. This study assessed the influence of polymorphisms in these candidate genes towards endurance test performance in 46 players from a single Australian Football League (AFL) team. Each player provided saliva buccal swab samples for DNA analysis and genotyping and were required to perform two independent two-kilometre running time-trials, six weeks apart. Linear mixed models were created to account for repeated measures over time and to determine whether player genotypes are associated with overall performance in the two-kilometre time-trial. The results showed that the ADRB1 Arg389Gly CC (p = 0.034) and PPARGC1A Gly482Ser GG (p = 0.031) genotypes were significantly associated with a faster two-kilometre time-trial. This is the first study to link genetic polymorphism to an assessment of endurance performance in Australian Football and provides justification for further exploratory or confirmatory studies.

Workload a-WEAR-ness: Monitoring Workload in Team Sports With Wearable Technology. A Scoping Review

OBJECTIVES

To (1) identify the wearable devices and associated metrics used to monitor workload and assess injury risk, (2) describe the situations in which workload was monitored using wearable technology (including sports, purpose of the analysis, location and duration of monitoring, and athlete characteristics), and (3) evaluate the quality of evidence that workload monitoring can inform injury prevention.

DESIGN

Scoping review.

LITERATURE SEARCH

We searched the CINAHL, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Embase, HealthSTAR, MEDLINE, PsycINFO, SPORTDiscus, and Web of Science databases.

STUDY SELECTION CRITERIA

We included all studies that used wearable devices (eg, heart rate monitor, inertial measurement units, global positioning system) to monitor athlete workload in a team sport setting.

DATA SYNTHESIS

We provided visualizations that represented the workload metrics reported, sensors used, sports investigated, athlete characteristics, and the duration of monitoring.

RESULTS

The 407 included studies focused on team ball sports (67% soccer, rugby, or Australian football), male athletes (81% of studies), elite or professional level of competition (74% of studies), and young adults (69% of studies included athletes aged between 20 and 28 years). Thirty-six studies of 7 sports investigated the association between workload measured with wearable devices and injury.

CONCLUSION

Distance-based metrics derived from global positioning system units were common for monitoring workload and are frequently used to assess injury risk. Workload monitoring studies have focused on specific populations (eg, elite male soccer players in Europe and elite male rugby and Australian football players in Oceania). Different injury definitions and reported workload metrics and poor study quality impeded conclusions regarding the relationship between workload and injury. J Orthop Sports Phys Ther 2020;50(10):549-563. doi:10.2519/jospt.2020.9753.

How Has Workload Been Defined and How Many Workload-Related Exposures to Injury Are Included in Published Sports Injury Articles? A Scoping Review

OBJECTIVE

To describe how workload-related exposure variables have been defined in sports injury articles, and to identify the number of workload-related exposure variables included in comparative analyses.

DESIGN

Scoping review.

LITERATURE SEARCH

PubMed, SPORTDiscus, and Scopus were systematically searched on March 13, 2020. Two reviewers independently screened the retrieved literature and selected articles for inclusion.

STUDY SELECTION CRITERIA

Prospective cohort studies using workload-related variables as the primary exposure to sports injury were eligible for inclusion.

DATA SYNTHESIS

The type (eg, distance, balls bowled) and construct of workload-related exposure variables (eg, acute-chronic workload ratio) were extracted and summarized in frequency tables.

RESULTS

A total of 648 articles were identified, and 45 were eligible for inclusion. Workload definition differed greatly, as sports- and workload-related exposure variables could be, but were not limited to, distance, balls bowled, session rating of perceived exertion, accelerations, soreness, and sleep. Within and across articles, authors used different constructs for workload-related exposure variables. For example, distance was represented as total distance, distance per week, distance per 2 weeks, and acute-chronic workload ratio. The number of workload-related exposure variables included in comparative analyses ranged from 1 to 336.

CONCLUSION

Studies used different definitions of workload-related exposure variables. The number of workload-related exposure variables in a single study ranged from 1 to 336. J Orthop Sports Phys Ther 2020;50(10):538-548. doi:10.2519/jospt.2020.9766.

Relationship Between Mechanical Effectiveness in Sprint Running and Force-Velocity Characteristics of a Countermovement Jump in Australian Rules Football Athletes

Morris, CG, Weber, JA, and Netto, KJ. Relationship between mechanical effectiveness in sprint running and force-velocity characteristics of a countermovement jump in Australian rules football athletes. J Strength Cond Res XX(X): 000-000, 2020-This study evaluated the mechanical determinants of 40-m sprint performance in elite Australian Rules Football (ARF) athletes and identified variables of countermovement jumps (CMJs) that related to the sprint. Fourteen elite male ARF athletes (age = 22.7 ± 3.6 years; height = 1.88 ± 0.08 m; mass = 88.2 ± 9.38 kg) completed two 40-m sprints and 3 CMJs. Sprint mechanics were calculated using inverse dynamic methods from sprint times, anthropometric and spatiotemporal data, whereas CMJ variables were obtained from in-ground force plates. Associations between sprint mechanics, sprint performance, and CMJ variables were identified using Pearson's correlation coefficient. A p-value of <0.036 was considered statistically significant for all analyses after performing Bonferroni correction adjustment. Relative peak running power was significantly correlated (p < 0.036, r = -0.781 to -0.983) with sprint split times across all distances (5-40 m). Relative maximum horizontal force significantly correlated with acceleration performance (0-20 m, p < 0.036, r = -0.887 to -0.989). Maximum running velocity was significantly correlated (p < 0.036, r = -0.714 to -0.970) with sprint times across 20-40 m. Relative peak force in the CMJ was significantly associated (p < 0.036, r = -0.589 to -0.630) with sprint kinetics (power and horizontal force) and 5-20-m sprint times. Jump height and concentric time in the CMJ were significantly (p < 0.036) correlated with sprint time at 20 m (r = -0.550 and r = 0.546), respectively. These results indicate emphasis should be placed on training protocols that improve relative peak power, particularly in time-constrained environments such as team sports, focusing on maximal force production or maximal running velocity ability. Furthermore, associations between CMJ variables and sprint performance provide practitioners with an approach to assess sprint performance in-season, monitor training adaptations and further individualize training interventions, without requiring maximal sprint testing.

Are Elite Soccer Teams' Preseason Training Sessions Associated With Fewer In-Season Injuries? A 15-Year Analysis From the Union of European Football Associations (UEFA) Elite Club Injury Study

BACKGROUND

Preseason training develops players' physical capacities and prepares them for the demands of the competitive season. In rugby, Australian football, and American football, preseason training may protect elite players against in-season injury. However, no study has evaluated this relationship at the team level in elite soccer.

PURPOSE/HYPOTHESIS

The aim of this study was to investigate whether the number of preseason training sessions completed by elite soccer teams was associated with team injury rates and player availability during the competitive season. It was hypothesized that elite soccer teams who participate in more preseason training will sustain fewer injuries during the competitive season.

STUDY DESIGN

Descriptive epidemiology study.

METHODS

We used the Union of European Football Associations (UEFA) injury dataset to analyze 44 teams for up to 15 seasons (total, 244 team-seasons). Separate linear regression models examined the association between the number of team preseason training sessions and 5 in-season injury measures. Injury-related problems per team were quantified by totals of the following: (1) injury burden, (2) severe injury incidence, (3) training attendance, (4) match availability, and (5) injury incidence.

RESULTS

Teams averaged 30 preseason training sessions (range, 10-51). A greater number of preseason training sessions was associated with less injury load during the competitive season in 4 out of 5 injury-related measures. Our linear regression models revealed that for every 10 additional preseason training sessions that the team performed, the in-season injury burden was 22 layoff days lower per 1000 hours (P = .002), the severe injury incidence was 0.18 severe injuries lower per 1000 hours (P = .015), the training attendance was 1.4 percentage points greater (P = .014), and the match availability was 1.0 percentage points greater (P = .042). As model fits were relatively low (adjusted R² = 1.3%-3.2%), several factors that contribute to in-season injury outcomes were unaccounted for.

CONCLUSION

Teams that performed a greater number of preseason training sessions had "healthier" in-season periods. Many other factors also contribute to in-season injury rates. Understanding the benefit of preseason training on in-season injury patterns may inform sport teams' planning and preparation.

Subsequent Injury Risk Is Elevated Above Baseline After Return to Play: A 5-Year Prospective Study in Elite Australian Football

BACKGROUND

The risk of sustaining a subsequent injury is elevated in the weeks after return to play (RTP) from an index injury. However, little is known about the magnitude, duration, and nature by which subsequent injury risk is increased.

PURPOSE

To quantify and describe the risk of injury in a 12-week period after RTP from an index injury in Australian football players.

STUDY DESIGN

Cohort study; Level of evidence, 2.

METHODS

Injury data were collected from 79 players over 5 years at 1 Australian Football League club. Injuries were classified with the Orchard Sports Injury Classification System and by side of the body. Furthermore, injury severity was classified as time loss (resulting in ≥1 matches being missed) or non-time loss (no matches missed). Subsequent injury was categorized with the SIC-2.0 model and applied to the data set via an automated script. The probability of a time loss subsequent injury was calculated for in-season index injuries for each week of a 12-week period after RTP via a mixed effect logistic regression model.

RESULTS

Subsequent injury risk was found to be highest in the week of RTP for both time loss injuries (9.4%) and non-time loss injuries (6.9%). Risk decreased with each week survived after RTP; however, it did not return to baseline risk of participation (3.6%).

CONCLUSION

These findings demonstrate that athletes returning to play are at an increased risk of injury for a number of weeks, thus indicating the requirement for tertiary prevention strategies to ensure that they survive this period.

Association Between Preseason Training and Performance in Elite Australian Football

PURPOSE

To examine the association between preseason training variables and subsequent in-season performance in an elite Australian football team.

METHODS

Data from 41 elite male Australian footballers (mean [SD] age = 23.4 [3.1] y, height =188.4 [7.1] cm, and mass = 86.7 [7.9] kg) were collected from 1 Australian Football League (AFL) club. Preseason training data (external load, internal load, fitness testing, and session participation) were collected across the 17-wk preseason phase (6 and 11 wk post-Christmas). Champion Data© Player Rank (CDPR), coaches' ratings, and round 1 selection were used as in-season performance measures. CDPR and coaches' ratings were examined over the entire season, first half of the season, and the first 4 games. Both Pearson and partial (controlling for AFL age) correlations were calculated to assess if any associations existed between preseason training variables and in-season performance measures. A median split was also employed to differentiate between higher- and lower-performing players for each performance measure.

RESULTS

Preseason training activities appeared to have almost no association with performance measured across the entire season and the first half of the season. However, many preseason training variables were significantly linked with performance measured across the first 4 games. Preseason training variables that were measured post-Christmas were the most strongly associated with in-season performance measures. Specifically, total on-field session rating of perceived exertion post-Christmas, a measurement of internal load, displayed the greatest association with performance.

CONCLUSION

Late preseason training (especially on-field match-specific training) is associated with better performance in the early season.

Improvement of Prediction of Noncontact Injury in Elite Australian Footballers With Repeated Exposure to Established High-Risk Workload Scenarios

OBJECTIVES

To assess the effect of multiple high-risk-scenario (HRS) exposures on noncontact injury prediction in elite Australian footballers.

DESIGN

Retrospective cohort study.

METHODS

Sessional workload data (session rating of perceived exertion, global positioning system-derived distance, sprint distance, and maximum velocity) from 1 club (N = 60 players) over 3 seasons were collated; several established HRSs were also defined. Accumulated HRS sessional exposures were calculated retrospectively (previous 1-8 wk). Noncontact injury data were documented. Univariate and multivariate Poisson regression models determined injury incidence rate ratios (IRRs) while accounting for moderating effects (preseason workload volume and playing experience). Model performance was evaluated using receiver operating characteristics (area under curve).

RESULTS

Very low (0-8 sessions: IRR = 5.76; 95% confidence interval [CI], 1.69-19.66) and very high (>15 sessions: IRR = 4.70; 95% CI, 1.49-14.87) exposures to >85% of an individual's maximal velocity over the previous 8 wk were associated with greater injury risk compared with moderate exposures (11-12 sessions) and displayed the best model performance (area under curve = 0.64). A single session corresponding to a very low chronic load condition over the previous week for all workload variables was associated with increased injury risk, with sprint distance (IRR = 3.25; 95% CI, 1.95-5.40) providing the most accurate prediction model (area under curve = 0.63).

CONCLUSIONS

Minimal exposure to high-velocity efforts (maximum speed exposure and sprint volume) was associated with the greatest injury risk. Being underloaded may be a mediator for noncontact injury in elite Australian football. Preseason workload and playing experience were not moderators of this effect.

The Individual and Combined Effects of Multiple Factors on the Risk of Soft Tissue Non-contact Injuries in Elite Team Sport Athletes

Aim: Relationships between athlete monitoring-derived variables and injury risk have been investigated predominantly in isolation. The aim of this study was to evaluate the individual and combined effects of multiple factors on the risk of soft-tissue non-contact injuries in elite team sport athletes. Methods: Fifty-five elite Australian footballers were prospectively monitored over two consecutive seasons. Internal and external training load was quantified using the session rating of perceived exertion and GPS/accelerometry, respectively. Cumulative load and acute-to-chronic workload ratios were derived using rolling averages and exponentially weighted moving averages. History of injuries in the current and previous seasons was recorded along with professional experience, weekly musculoskeletal screening, and subjective wellness scores for individual athletes. Individual and combined effects of these variables on injury risk were evaluated with generalized linear mixed models. Results: High cumulative loads and acute-to-chronic workload ratios were associated with increased risk of injuries. The effects for measures derived using exponentially weighted moving averages were greater than those for rolling averages. History of a recent injury, long-term experience at professional level, and substantial reductions in a selection of musculoskeletal screening and subjective wellness scores were associated with increased risk. The effects of high cumulative loads were underestimated by ~20% before adjusting for previous injuries, whereas the effects of high acute-to-chronic workload ratios were overestimated by 10-15%. Injury-prone players, identified via player identity in the mixed model, were at > 5 times higher risk of injuries compared to robust players (hazard ratio 5.4, 90% confidence limits 3.6-12) despite adjusting for training load and previous injuries. Combinations of multiple risk factors were associated with extremely large increases in risk; for example, a hazard ratio of 22 (9.7-52) was observed for the combination of high acute load, recent history of a leg injury, and a substantial reduction in the adductor squeeze test score. Conclusion: On the basis of our findings with an elite team of Australian footballers, the information from athlete monitoring practices in team sports should be interpreted collectively and used as a part of the injury prevention decision-making process along with consideration of individual differences in risk.