Assessments of agility, essentially requires tests of change of direction and/ or manoeuvrability in response to stimuli, whereby the athlete’s ‘perceptual-cognitive’ abilities along with their ability to move quickly (perceptual-motor ability) are evaluated. Whilst calls to move away from assessing pre-planned COD tests and incorporating true agility tests have been made (Young et al., 2021), as highlighted in the previous BLOG, limited options are currently available to do this, and such options have practical limitations making it difficult for practitioners to implement in the field. This BLOG highlights the need to assess agility and critically outlines current options to evaluate this construct in the field to provide food for thought for practitioners looking to evaluate ‘agility’ with their athletes.
The Importance of the Need to Assess Agility – A Performance Perspective
‘Agility’ is defined as ‘as a rapid and accurate whole-body movement with change of velocity, direction, or movement pattern in response to a stimulus’ (Jones & Nimphius, 2018). Agility (FIGURE 1) is largely dependent on perceptual-cognitive factors that leads to the selection of an action (perception-action coupling). These actions could be instantaneous (i.e., change of direction) or have a timed duration and involve travel (i.e., sprinting, side-shuffling, etc.). Locomotion and instantaneous actions (FIGURE 1) are dependent on technical and physical factors that are specific to each action. To assess and subsequently develop ‘agility’ attention should be paid to evaluating perceptual-cognitive abilities (e.g., the thinking element). The ability to perform instantaneous and locomotion actions efficiently and effectively (e.g., speed of the required action) is evaluated during pre-planned sprint, change of direction or manoeuverability tests (see previous BLOGs). Such tests are commonly implemented to evaluate the technical and physical ability to perform these actions, which allows practitioners to devise and then evaluate individualised training programmes to enhance speed, change of direction ability and manoeuvrability. However, whilst ‘agility’ drills involving responding to a variety of stimuli are commonly used in field-based training or rehabilitation in an effort to develop the athlete’s ability to read the opponent/ situation and execute an effective action. We do this without knowing how well they perceive the environment, select, and then execute an appropriate action and then we don’t evaluate whether training has been effective in developing this. Therefore, how can we assess agility to enable practitioners to better inform training programmes and whether such training programmes have been effective?
The Importance of the Need to Assess Agility – A Biomechanics Perspective
It has been well established that unanticipated (regardless of stimulus type) side-step cutting results in greater ‘plant step’ frontal and transverse plane knee joint loads (Besier et al., 2001; Houck et al., 2006; Lee et al., 2013) and greater ACL loading determined through musculo-skeletal modelling (Weinhandl et al., 2014) compared to pre-planned side-step cutting. These higher loads may be a result of undesired ‘plant-step’ postures such as reduced hip flexion (Lee et al., 2013), greater hip abduction and lateral foot placement (Lee et al., 2013a; Lee et al., 2016; Byrne et al., 2022), greater and later lateral trunk flexion (Lee et al., 2013a; Lee et al., 2016; Bryne et al., 2022) and less pelvic obliquity (Staynor et al., 2018; Bryne et al., 2022) during unanticipated compared to pre-planned side-step cutting. Unanticipated side-step cutting has also been shown to involve a more generalised muscle activation strategy as opposed to a selective recruitment strategy to support higher knee joint loads (Besier et al., 2003; Lee et al., 2019). Furthermore, unanticipated side-step cutting involves more in-phase trunk-pelvis and hip-knee couplings and greater co-ordination variability compared to pre-planned/ anticipated side-step cutting, suggesting they are entirely different tasks (Weir et al., 2019) and thus, has implications for evaluation and development of side-step cutting or similar change of direction tasks.
The greater loading experienced during such manoeuvres is due to less available time for the performer to make technical adjustments to accommodate the change in direction. Greater braking and/or postural adjustments during the penultimate step depends on whether the athlete has enough time to plan the upcoming movement. When the task becomes unanticipated (i.e., respond to a light signal) greater braking tends to occur during final foot contact (Jones et al., 2014) and less preparatory movements/ postures during penultimate step are observed (i.e., turning head, medial foot placement across mid-line of body (e.g., crossover step), oblique pelvis orientation and lateral trunk flexion toward intended direction of travel during penultimate foot contact) (Wheeler et al., 2010; Mornieux et al., 2014; Lee et al., 2016; Bryne et al., 2022). Thus, improving an athlete’s ability to anticipate the situation leads to greater use of preparatory steps to make appropriate postural adjustments prior to the plant step, resulting in lowered re-positional demands during the final ‘plant’ step (e.g., less lateral trunk flexion away from the intended direction of travel and lower lateral foot placement) and subsequent loads placed on the knee.
Much of the research regarding unanticipated side-step cutting, uses a generic light or arrow stimulus to evoke unanticipated conditions. The use of a generic-signal based system has been shown to result in different postural adjustments and knee moments than video-based (involving 1 or 2 defenders) systems (Lee et al., 2013a, Lee et al., 2016). Using video-based approaches allows the participant to read to the situation earlier than a generic signal, and thus, allows the participant to begin to make some postural adjustment, thus, experience lower knee joint loads compared to a generic signal (Lee et al., 2013a). Collectively, previous biomechanical research makes another argument for the inclusion of agility testing with athletes. Whilst it is important to evaluate the technique of athletes performing change of direction manoeuvres to better inform training interventions, evaluating technique using only a pre-planned approach may only tell half the story. Based on research (Lee et al., 2013a; Lee et al., 2016), evaluating an athlete’s technique whilst responding the sports specific stimuli rather than generic stimuli, maybe more informative (e.g., how well does the athlete perceive the visual scene and adapt and execute the movement?).
Assessment of Agility
Assessments of agility, essentially requires tests of COD and/ or manoeuvrability in response to stimuli, whereby the athlete’s ‘perceptual-cognitive’ abilities along with their ability to move quickly (perceptual-motor ability) are evaluated. There are currently three options for evaluating agility: (1) light or arrow systems, (2) video stimulus and (3) human stimulus.
Light or Arrow Systems
For more than 2 decades light (or visual-arrow) systems have been used in biomechanical studies (Besier et al., 2001) to compare between pre-planned and unanticipated COD tasks. Furthermore, commercially available systems with light stimuli have been available for a number of years (e.g., Smart Speed by Fusion Sport). Oliver and Meyers (2009) found such systems to be reliable measures of so called ‘agility’; however, light systems do not allow assessment of the ‘anticipatory (perceptual-cognitive) skills’ such as visual scanning, pattern recognition and knowledge of situations (Sheppard & Young, 2006).
Expert soccer players have been shown to display superior anticipatory skills than less skilled players (Helsen & Pauwels, 1993; Williams et al., 1994; Williams & Davids, 1998), due to being superior in recognising and recalling typical patterns of play from memory (Williams et al., 1993) and possess more effective visual search strategies (Helsen & Pauwels, 1993; Williams et al., 1994; Williams & Davids, 1998). Expert performers possess a superior ability to identify useful anticipatory information from early in their opponent’s movement patterns, termed ‘advanced cue utilisation’ (Williams et al., 1994; Williams, 2000; Vaeyens et al., 2007a). Moreover, skilled performers use their superior knowledge to control eye movement patterns (i.e., number of fixations, duration of fixations) to find and retrieve important sources of information (Williams, 2000), which may be influenced by the task (e.g., offensive vs. defensive situations) (Helsen & Pauwels, 1993; Williams et al., 1998), field of view (e.g., 11 vs 11 compared to 1 vs. 1 situations) (Williams et al., 1994; Williams et al., 1998) and situations (i.e., 4 vs. 3 or 5 vs. 3 offensive scenarios) (Vaeyens et al., 2007ab). Visual-perceptual skill differences between athlete ability levels has been shown to alter postural and knee joint loads during side-step cutting (Lee et al., 2013a; Lee et al., 2016). For instance, during side-step cutting to avoid 2 defenders (projected 3-dimensionally), greater hip abduction angles and knee valgus moments were experienced by lesser skilled compared to high skilled male soccer players (Lee et al., 2013), whereas less unsuccessful trials across all conditions (pre-planned, 1 defender, 2 defender, generic unanticipated) and maintenance of the push-off foot closer to the base of support were evident again in the 2 defender scenario (Lee et al., 2016) for higher skilled versus lower skilled players, suggesting that higher skilled players who possess a greater level of visual-perceptual skill perform unanticipated side-step cuts more safely and make less errors in reading the visual scene.
Given the differences that exist between expert and non-expert sports performers; higher-level players possessing superior ‘perceptual-cognitive’ abilities than lower-level players. An effective agility test (specifically the perception-response time within the test) should be able to discriminate between sport ability levels. To assess this, athletes need to respond to actions of an opponent or passages of play within an agility test (i.e., sport-specific) rather than general audio or visual stimuli. Based on the literature, there are two ways in which sports-specific stimuli can be delivered; a) video images (Farrow et al., 2005) or b) human stimulus (Sheppard et al., 2006). To substantiate this, Young et al. (2011) compared ‘reactive’ agility tests using video images (RATVIDEO) or arrows displayed (RATARROW) on a screen in front of an approaching athlete to indicate the direction change. The RATVIDEO, revealed significant differences between different ability levels of Australian rules football players, whereas the RATARROW revealed only trivial differences between ability levels; suggesting that to assess ‘perceptual-cognitive’ elements within an agility test and discriminate between ability levels, a stimulus involving actual movements of an opponent is required as the response stimulus. Additionally, Henry et al. (2011) compared RATVIDEO and light-based tests (RATLIGHT) with high- and low-level Australian Rules footballers and a group of non-footballers. The authors found a certain degree of commonality between RATLIGHT and RATVIDEO (r = 0.75), but the faster (shorter) decision times associated with the RATLIGHT suggested that a light stimulus does not allow enough ‘cognitive’ demand and thus, not a valid measure of agility. Thus, as expected, providing a visual stimulus involving actual movements of an opponent(s) is essential if we want to evaluate an athlete’s agility.
Video-based Systems
Video-based systems offer a solution to assess the ‘perceptual-cognitive’ element of agility. The earliest use of this approach was by Farrow et al. (2005). The authors developed a video-based system to evaluate agility of netball players, examining the performance of three groups of netball players (high (n = 12), moderate (n = 12) and low-skilled (n = 8) players) during a pre-planned and unanticipated tests. The test involved players side-shuffling through a start gate (gate 1) 4 m, then back 2 metres, before sprinting forward 1 m through a 2nd timing gate (gate 2). Gate 2 was linked to a laptop which triggered the playing of a netball specific video clip which was projected on a screen in front of the athlete (5 m away). The players had to respond to the visual stimulus and run through a third gate (4.1 m away) in either a left or right direction dependent on the visual cues from the video stimulus. Five measures were recorded: shuffle time (gate 1 to gate 2), sprint time (gate 2 to gate 3), total time (gate 1 to gate 3), perception-response time from 50 Hz video (time of display occlusion to 1st definitive foot contact initiating the direction change) and response accuracy. Pre-planned trials removed the video stimulus recording the same completion times. Significant differences in sprint time, total time and perception-response time were observed between high and low skilled players for unanticipated trials, with moderately skilled players significantly faster sprint times than low skilled. No differences were observed between groups in pre-planned trials, suggesting that the unanticipated protocol was able to discriminate between high- and low-skilled players, whereas COD ability (pre-planned trials) did not discriminate between player ability levels. Unanticipated and pre-planned sprint times shared 49% (R=0.7) common variance, suggesting that each test assessed independent qualities.
Similar approaches have been carried out in rugby league (Serpell et al. 2010), Australian Rules football (Young et al. 2011; Henry et al., 2011) and basketball (Spiteri et al., 2014), whilst video-based protocols have been used in several biomechanical studies (Cortes et al., 2011; Lee et al., 2013a; Lee et al., 2016; Lee et al., 2019). Serpell et al. (2010) used a similar approach to Farrow, but removed the initial side-shuffling component and involved a slightly longer sprint distance (10 m approx.). Fifteen NRL and 15 National youth rugby league players performed 8 trials with 8 videos randomly selected from 12 available. Pre-planned trials were performed without the video stimulus. No correlation was found between COD (pre-planned trials) and unanticipated trials (ρ = -0.08; P > 0.05). Furthermore, significant differences were observed between unanticipated sprint and response times between NRL and youth players, suggesting the video-based protocol could discriminate between ability levels and that unanticipated and pre-planned versions of the test assessed different qualities. Using a similar approach, Henry et al. (2011) found higher level Australian rules football players produced significantly faster agility and movement (time from response initiation to triggering the finish gate) times than non-players. On this occasion a moderate correlation (r = 0.68) was found between agility (video based) and pre-planned test performance.
A possible limitation of the video-based approach is perhaps the time-consuming preparation required to develop and update a video library to carry out regular assessments of agility performance. Furthermore, the development of video clips requires careful consideration to develop true ‘match like’ scenarios to present to the athlete. Other factors to consider are the cost of additional hardware and software and expertise to develop the protocol. Moreover, whilst the stimulus in each study was displayed as a life-sized image on a screen, the 2D presentation of the image may limit the amount and specificity (i.e., lack of depth) of the cues for which the athlete must react to (Farrow et al., 2005) which influences the visual search strategy used to complete the task in comparison to a 3D stimulus (Lee et al., 2013b). Another potential limitation of video-based systems is that due to the large potential response variability associated with different scenarios presented to athletes often mixed reliability of the protocols is reported. For instance, Farrow et al. (2005) reported an ICC of 0.83 for completion time in their video-based protocol. Whereas Young et al. (2011) reported poor reliability for their video-based test (ICC = 0.33; CV =2.7%; TE = 0.07s) in 50 junior Australian Rules football players. The authors did however report that the reliability was better than the arrow-based test (ICC = 0.1; CV = 3.4%; TE = 0.09 s) used in that study. Spiteri et al. (2014) reported good reliability (ICC = 0.81; CV= 3.3%), despite using a more complex protocol involving two CODs in response to two different video stimuli. Serpell et al. (2010) reported good reliability for completion times (ICC = 0.82; SEM = 0.01), but poor reliability for perception-response time (ICC = 0.31; SEM = 0.01) perhaps due to the low sampling rate of video (50 Hz) (e.g., being 1 frame out in identifying the video occlusion and the definitive plant step of the athlete would lead to a 0.04 s error) used to evaluate perception-response times. Farrow et al (2005) also collected video at 50 Hz to measure perception-response time in their study, which may have hindered the ability to discriminate between moderate and low skilled netball players in that study. To improve precision in determining the time difference between video occlusion and the athlete’s definitive foot contact prior to initiating the direction change (perception-response time) high speed video (>100 Hz) is recommended.
Human Stimulus
An alternative approach to video-based systems involves using the movements of a tester to provide the stimulus for athletes to react to. The ‘Reactive Agility Test (RATHS)’ developed by Sheppard et al. (2006) and advocated by Young et al. (2021) as a way forward regarding agility testing. The test involves the tester beginning on a timing mat (although timing cells could be used, see FIGURE 2) whilst an athlete begins on a start line (5 m apart). Timing cells are placed 5 m either side of the athlete and tester 2 m in front of the athlete. Once the tester leaves the timing mat or crosses timing cells, and runs to one side, the athlete needs to respond to the direction of the tester and sprint through the timing cells to the side the tester turned to (i.e., mimicking a defensive situation) [FIGURE 2]. The athlete responds to one of four scenarios performed by the tester outlined in FIGURE 2. The authors found that elite athletes recorded faster times than sub-elite athletes (d = 1.23), which was not the case for the sprint and pre-planned COD tests. Furthermore, low association between sprint and RATHS (r = 0.333) and pre-planned COD and RATHS (r = 0.331), suggested the RATHS could discriminate between athletes of different performance levels and is an independent quality from a pre-planned COD test. The RATHS requires less preparation time and is easier to administer than video-based systems and because of these reasons is perhaps the ‘go-to’ at the moment, if practitioners are looking for a way to assess agility with their athletes.
RATHS protocols have been widely used in rugby league (Gabbett et al., 2008; Gabbett & Benton, 2009; Gabbett et al., 2011a; Gabbett et al., 2011b), basketball (Scanlan et al., 2014a; Scanlan et al., 2014b), Australian rules football (Veale et al., 2010) and soccer (Trajkovic et al. 2020). As with video-based tests, the RATHS and variations (completion time and / or perception-response time) has repeatedly shown to be able to discriminate between athletes of different performance standards (Gabbett et al., 2008; Gabbett & Benton, 2009; Veale et al, 2010; Trajkovic et al. 2020). Moreover, Gabbett et al (2011b) found that reactive agility was a factor significantly associated (although low; r = 0.29) with the number of line break assists (an offensive match statistic) in professional rugby league match play.
As with the video stimulus tests, it is essential to assess perception-response time and tester movement time, again using a high-speed camera to the rear of the athlete (e.g., the athlete’s response time between definitive footfalls prior to initiating the COD between the tester and athlete [Gabbett et al., 2008; Young & Wiley, 2010]). Using high speed video during the RATHS in semi-professional Australian rules footballers, Young and Wiley (2010) measured: tester movement time (first forward movement (trigger) of tester to their definitive foot plant to initiate COD), the athletes perception-response time (time from tester definitive foot plant to initiate COD to athlete definitive foot plant to initiate COD), and response-movement time (athlete definitive foot plant to initiate COD to crossing the gates (stop timer)). Large associations were observed between total time and perception-response time (r = 0.77) and total time and response-movement time (r = 0.59). Whilst tester time reported a low co-efficient of variation (5.1%), total time and tester time showed a significant low association (r= 0.37). In another version of the RATHS, Scanlan et al. (2014a) reported large associations between response (duration from movement initiation of tester until the participant crossed timing gates that were added in at the start) (r = 0.76) and perception-response time (referred to as decision time) (r = 0.58) with total RAT movement time. Furthermore, response time was identified as the sole predictor (r2 = 0.58) of RAT time. As with the video-based tests, the perception-response time is an important component of the RATHS and should be evaluated, whilst tester time needs to be controlled by evaluating to identify its influence.
RATHS method is not without limitations. Firstly, in the RATHS athletes respond to stimuli under low velocity of approach conditions (Sheppard et al., 2006; Gabbett et al., 2008; Gabbett & Benton, 2009). Using a slightly modified version (Veale et al., 2010) that involved an initial 2 m sprint (between 1st and 2nd timing gates), a 45°cut in response to unanticipated human stimuli (as with other RATHS), then sprinting 5 m to another timing gate (3) were a pre-planned 45° COD was performed before sprinting 5 m through a 4th timing gate. This test set-up allows a response to stimuli under slightly higher approach velocity conditions. The authors found that the modified version of the RATHS was able to discriminate between different ability levels of U18 Australian rules football players. Research has shown the RATHS to be reliable and perhaps more so than the video-based systems reported earlier (Sheppard et al., 2006; Gabbett et al., 2008; Scanlan et al., 2014b), despite the large potential response variability associated with different scenarios presented to athletes during the RATHS. The reliability of tester and athlete movements have been investigated (Spiteri et al., 2012), during a modified version of the RATHS whereby an athlete moves forward to a line 3 m away, where they respond, by moving 2 m to either the left or right to movements of a tester that was situated 6 metres in front of the athlete. Spiteri et al. (2012) reported good reliability for movements performed by the human stimulus (ICC = 0.71-0.99; CV = 1.11-4.77%) and athlete running times (offensive: ICC = 0.91; CV = 3.30%; TE = 0.06, defensive: ICC = 0.90; CV = 3.60%; TE = 0.05). These findings support the implementation of a human stimulus to evoke athlete responses for agility testing, providing the tester is experienced with such protocols, which may require adequate training and familiarisation prior to administration. A final limitation of the RATHS is that such approaches may not truly assess the athlete’s ability to respond in game like scenarios with only 1 vs. 1 scenarios with 4 distinct movement patterns of the tester considered. This may have limited application across several field and court-based sports and does not fully cater for the ‘chaos’ of match play. For instance, Raynor (2020) found that 50% of the time defenders were to the side or behind attackers in Australian rules football. Further research is required to develop the RATHS to provide more game-like scenarios which incorporate match implements. That said, the consistency of testers to replicate such scenarios to ensure reliable performance testing is a major barrier to this. A summary of the advantages and disadvantages of each method outlined are summarised in TABLE 1.
Final Thought
Currently, the assessment of agility remains the ‘holy grail’ of performance testing, on the one hand practitioners know that the quality needs to be assessed, but satisfying the need to standardise, be reliable and remain ecologically valid may remain elusive. Unfortunately, since the advent of the development of these agility testing protocols, little further progress has been made in addressing such limitations to allow agility testing to be a mainstay of team testing protocols. Given the increase in number of publications utilising performance analysis methods to evaluate agility demands in sports (Bloomfield et al., 2008; Robinson et al., 2014; Morgan et al., 2021; Talty et al., 2022; Martinez et al., 2022; Martinez et al., 2023), perhaps such approaches may provide a solution in the applied setting to evaluate and consult with athletes regarding in game agility performance (e.g., did the athlete anticipate early enough? Did the athlete make the correct decision? Did the athlete execute the action effectively to beat the opponent?). Nevertheless, hopefully this BLOG provides food for thought for practitioners and researchers a like to reflect how we may be able to evaluate an athlete’s agility in the future.
This blog is adapted from a book chapter in Jones, P.A., & Dos’Santos, T. (2023). Multidirectional speed in sport: Research to Application. Routledge.
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