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The Deceleration Deficit: A Novel Field-Based Method to Quantify Deceleration During
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Change of Direction Performance
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Type: Original Investigation
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Preferred Running Head: The Deceleration Deficit
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Abstract word count: 239
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Manuscript word count: 3254
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2 figure
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3 tables
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ABSTRACT
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The study investigated the relationship between linear and change of direction (COD) speed
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performance components and the individual differences between deceleration deficit (DD) and
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COD deficit (CODD). Thirty-six subjects (mean ±SD: age = 20.3 ± 2.9 years; stature = 175.2
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± 7.7 cm; body mass = 78.0 ± 16.7 kg) completed three trials of a 505 test in both turning
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directions (dominant (D); non-dominant (ND)) and three 15m linear sprints. DD was calculated
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via the 15m approach in the 505 test, minus the athlete’s linear 15m sprint time. To compare
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individuals CODD and DD, z-scores were calculated, and moderate worthwhile changes
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(MWC) were identified between these deficit z-scores. Significant correlations were identified
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between linear sprints and 505 time (D: r = 0.71, 0.74; P < 0.01. ND: r = 0.76, 0.75; P < 0.01)
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for 10m and 15m sprint respectively, and between 505 performance and CODD (D: r = 0.74;
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P < 0.01. ND: r = 0.77; P < 0.01) and DD (D: r = 0.41, P < 0.05. ND: r = 0.44, P < 0.01). DD
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was significantly related to CODD (D: r = 0.59; P < 0.01. ND: r = 0.62; P < 0.01); however,
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78% of subjects demonstrated differences between these deficit measures greater than an
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MWC. In conclusion, linear speed has the strongest significant relationship with 505
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performance. DD could provide a more isolated construct than CODD which may be related
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to an athlete’s deceleration capabilities.
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Key Words: Agility; Deceleration; Velocity; Braking; Multi-Directional
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INTRODUCTION
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Change of direction (COD) speed is a key physical quality for success in a range of sports (25).
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Assessment of COD speed often includes the 505 test (1, 9, 27, 29) or various cutting
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manoeuvres (3, 4, 18, 19), with performance quantified via the total time taken to complete a
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pre-determined course. However, COD speed is comprised of many constituent parts, including
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linear speed, deceleration and re-acceleration (25). Specifically, the 505 test involves a 15m
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approach sprint, a 180° turn, and a 5m exit; or more simply, a maximal acceleration, a
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deceleration to a complete stop (16) and a re-acceleration into the new direction. Despite the
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test already being relatively short in duration (~2.3 seconds) (1, 9), it is reported that on average
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only approximately 31% of the time is spent changing direction (23).
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Total time in a COD speed test is still a useful measure as the transfer of performance may
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primarily relate to the time taken to get from one point to another. However, an athlete may
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have varying capabilities in the different components that make up the test (acceleration,
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deceleration etc.). Providing greater insights into how the performance task is executed will
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enhance our ability to identify the main limiting factor(s); linear speed, deceleration or re-
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acceleration (22). The COD deficit (CODD) was designed to assess COD ability whilst
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controlling for linear speed, as COD abilities can be over or underestimated by total time
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measures due to an athlete’s linear speed capabilities (5, 22). The CODD is a useful tool to
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help understand if an athlete should focus on their linear speed capabilities or ability to
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decelerate, change direction and re-accelerate during the training process. However, the CODD
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is still a single variable which represents a range of qualities, such as deceleration, acceleration
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and technique factors. Therefore, delineating the primary limitations in performance remains
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challenging.
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The 505 test provides an opportunity to assess an athlete’s ability to decelerate, as momentary
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zero velocity must be attained following a 15m approach, prior to the change of direction.
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Graham-Smith et al., (11) reported that after approximately an 8.5m maximal sprint, it takes
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athletes approximately 6.5m to decelerate and come to a complete stop. Therefore, the
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approach period of the 505 test may be viewed as an 8.5m initial sprint followed by a 6.5m
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deceleration period (entry into the turn). By recording, 1) the amount of time taken to
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maximally accelerate and come to a complete stop within the 505 test (in this case 15m); and
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2) the amount of time required to cover the same distance but with no forced deceleration.
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Performance in these separate tasks could be compared and quantified as the ‘deceleration
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deficit’ (DD). This method could represent an athlete’s deceleration ability relative to their
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linear sprinting speed. Isolating this time required to come to a stop via the DD measure could
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provide important information for coaches due to the high eccentric demand experienced
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during deceleration not found in acceleration (10, 13).
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Deceleration is a key factor in COD speed performance, with the importance of braking
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impulse during the penultimate foot contact on COD performance previously reported (7, 8).
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However, our understanding of deceleration during COD speed is yet to be expanded past the
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penultimate step. The use of the DD in the 505 provides an opportunity for coaches to isolate
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the deceleration component of the task, providing unique insights into an individual’s limiting
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factor(s) when performing the test. However, there are inherent deceleration components in
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both 505 total time and the CODD. Therefore, the relationship between these measures should
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also be investigated to identify if the DD provides different and meaningful information for
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coaches.
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The aims of the current study were to; a) determine the relationship between linear sprint speed,
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505 performance, CODD and DD, and b) investigate the individual differences between DD
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and CODD. It was hypothesized that linear speed would demonstrate a strong relationship with
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505 time but not the CODD and DD. In addition, it was hypothesized that these two deficit
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measures would not be significantly correlated.
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METHODS
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Experimental Approach to the Problem
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The study utilised a cross-sectional design where subjects completed a 505 COD test in both
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turning directions and a 15m linear sprint. All experimental data for subjects was collected in
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a single testing session. Pearson’s correlation were used to determine the relationship between
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COD, linear speed and deceleration performance. Individual differences between DD and
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CODD times were compared via standardized metrics (z-scores).
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Subjects
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Thirty-six (nineteen female and seventeen male) recreationally active subjects (20.3 ± 2.9
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years; stature = 175.2 ± 7.7 cm; body mass = 78.0 ± 16.7 kg) volunteered and provided
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informed consent. Subjects were required to be competing in an invasion sport (either netball,
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hockey, rugby or football) and taking part in coached strength training at least once per week
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with experience of COD speed testing protocols. All subjects were also required to be currently
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free from injury and illness. This study was approved by the University of Gloucestershire
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institutional review board and procedures were performed in accordance with the declaration
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of Helsinki.
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Procedures
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Subjects first attended a familiarisation session to practice the 505 test and to collect
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anthropometric data. This was followed by formalized data collection 48 hours later, with three
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trials completed of the 505 in each turning direction in a randomised order, and finally three
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15m linear sprints. A two-minute rest period was provided between each recorded attempt. All
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testing was conducted on a Pulastic indoor sports floor and all subjects were instructed to wear
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clean indoor sports trainers. The testing session was preceded by a standardised warm up
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consisting of 5-min of various pulse-raising activities, including linear and multi-directional
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movements which mimicked the 505 test, and 5-min of dynamic muscle activation exercises
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such as body weight lunges and squats and dynamic stretches. Subjects were asked to refrain
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from alcohol 24 hours prior to testing and avoid caffeine ingestion the morning of testing
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procedures.
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Performance Measures
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COD Test
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The COD speed test contained the same running pattern as the 505 test commonly utilised in
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other studies (1, 9, 22). Time was recorded using a smart speed timing gate system with gates
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placed at 0 and 10m and a smart jump contact mat (Smartspeed, Fusion Sport, Sumner,
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Australia) positioned at the 15m turning point (which was temporarily fixed to the floor to
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avoid slipping). The contact mat was used to record commencement and duration of plant step
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ground contact (Figure 1). For a 505 trial to be considered successful, subjects needed to ensure
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that their final plant step occurred on the contact mat and over the marked turn line, any trials
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where subjects missed the contact or turning line were discounted and repeated. In order to
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independently analyse performance of each task component, timing splits were utilised to
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measure 0-10m, 10-15m (repeated for the returning 5m acceleration) and the GCT of the plant
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step (Table 2). Subjects began each sprint 50cm behind the first gate, in a staggered, three-
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point stance and were instructed to run as fast as possible to the contact mat, turn on the marked
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point with either their left or right leg and the return as fast as possible back through the 10m
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gate. The turning leg used for the first trial was randomly allocated and then alternated between
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trials. Any trials where the marked turning line was not met were repeated. All timing variables
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were reported to the nearest 0.01 seconds. The three trials turning left or right were averaged
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and used for analysis. The dominant direction (D) was identified as the turning direction with
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the fastest 505 performance and the opposite direction was classified as non-dominant (ND)
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(2).
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*** Insert Figure 1 here ***
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15m Linear Sprint Test
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The linear 15m sprint test was utilised in order to assess maximal acceleration capability with
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gates placed at 0, 10 and 15m. Subjects were instructed to start 50cm behind the first gate, in a
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staggered, three-point stance and sprint all the way through the 15m gate as fast as possible.
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Three trials were completed with at least a 2 minutes rest between each. Time for each distance
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and all variables were recorded to the nearest 0.01 seconds with the average of three trials being
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included for analysis.
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COD and Deceleration Deficit Calculations
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CODD was calculated to represent the individual’s ability to change direction while controlling
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for their linear speed capabilities using the equation proposed by Nimphius et al. (22). The DD
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was calculated in order to quantify the time an individual needed to come to a stop relative to
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their own sprinting speed. The full approach time was used in order to represent the time
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required to approach the turn line as fast as possible while decelerating into a position to
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facilitate exit speed and overall 505 performance. Commonly braking force is still being
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applied through the first half of plant step ground contact, with the remainder of the step used
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for the application of propulsive forces (12). A force plate would be the optimum criterion
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measure to determine the relative contribution of these braking forces but is not practically
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viable in field-based testing. Therefore, the full approach includeed the time taken over the first
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half of ground contact in an attempt to capture the complete braking phase of the approach
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(12). This time was compared against the time the athlete needs to cover the same distance in
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a linear sprint. Equations to calculate the deficit measures are shown in Table 1.
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*** Insert Table 1 here ***
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Statistical Analysis
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Statistical analysis was conducted using SPSS (PASW statistics, Version 19, IBM Corporation,
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New York, U.S.A) and Microsoft Excel (version 14.6.4, Microsoft, Redmond, DC, USA).
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Descriptive statistics (mean ± SD) were calculated for all variables. Normality was assessed
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and confirmed for all variables using a Kolmogorov–Smirnov test. Relationships between
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performance measures, were assessed using Pearson’s product-moment correlation (2-way).
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The correlations were interpreted as follows: < 0.1 trivial, 0.1-0.3 = small, 0.3 – 0.5 = Moderate,
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0.5 – 0.7 large, 0.7 – 0.9 = very large, 0.9-1.0 = nearly perfect, 1.0 = perfect (14). Statistical
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significance was set as p < 0.05. In addition, further analysis examined whether the DD was
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able to identify athletes whose deceleration ability limits their COD performance to a greater
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extent than CODD. These were calculated within individuals using z-scores though the
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formula:
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z-score = (subject’s test score – group mean score)/SD.
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Worthwhile differences in z-score between DD and CODD were determined and compared to
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identify those with a moderate worthwhile change (Cohens d) between the two deficit scores
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(between subject SD multiplied by 0.5). A moderate worthwhile change was used in order to
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consider the potentially lower sensitivity of the deficit measures in a population with less multi-
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direction movement expertise (28). Subsequently, subjects that had a moderate worthwhile
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positive z-score difference indicated that the use of CODD alone may result in the
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overestimation of deceleration ability and a negative moderate worthwhile difference indicated
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an underestimation of deceleration ability if CODD is used in isolation.
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RESULTS
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Descriptive statistics for all variables are reported in Table 2. Relationships between
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performance and turning directions are displayed in Tables 3 and 4 respectively. Significant
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correlations were shown between linear sprint speeds, full approach and 505 time. No
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significant relationships were reported between linear sprint speeds and CODD or DD. 505
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performance was significantly related to full approach speed, CODD and DD. Full approach
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was significantly related to CODD and DD showed a significant relationship with CODD.
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The difference in z-scores between DD and COD deficit are presented in Figure 2. 78% of
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subjects show a divergence between the two deficit measures greater than a MWC in one of
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the two turning directions (MWC = D: > 0.45. ND: > 0.44) between the CODD and DD z-
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scores on their D and ND turning directions respectively. Analysis of the effects of turning
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direction identified that 12 subjects show a MWC between deficit scores in both directions
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with the MWC consistently positive or negative, 15 subjects have a MWC in just one of the
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two turning directions and one participant has a MWC on both turning directions with the
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difference on either side changing from positive or negative.
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*** Insert Table 2, 3 and 4 here ***
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*** Insert Figure 2 here ***
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DISCUSSION
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The aims of the current study were to examine the relationships between linear sprint, 505 total
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time, CODD and DD; and investigate whether DD provides additional information to these
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previously reported variables in competitive university level athletes. The primary findings
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indicate that linear speed was related to 505 time but not CODD or DD. 505 time was
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significantly correlated to both DD and CODD and these deficit variables were significantly
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related to one another but with large individual variance.
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The results of the present study support previous research showing moderate to large
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correlations between 505 performance and linear speed (17, 22). In the current study, linear
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speed showed no significant relationship with CODD or DD. Although no previous research
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has compared DD and linear speed, relationships between these variables appear equivocal.
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Lockie et al. (17) reported a significant negative relationship between a linear 10m sprint and
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CODD (r = -0.77-82). Conversely, Nimphius et al. (22) did not find a significant relationship.
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Reasoning to elucidate these differences remain unclear and requires further investigation,
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however it may be that differences in technical proficiency were a contributing factor as the
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technical ability to apply force has been shown to be a key requirement for performance in
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linear and multi-directional tasks (7, 20). Therefore, the present results indicate that the
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athlete’s linear speed capabilities did not clearly influence either of the deficit variables.
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However, these conclusions should be considered within the performance level and technical
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proficiency of the subjects used. The subjects in this study were faster over 10m than division
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I and II womens soccer athletes (17) and were similar to experienced male cricketers (22). 505
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times were slightly slower than experienced male cricketers (22) but similar to Div II female
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soccer athletes (17). Furthermore, a moderate to large significant relationship between full
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approach and DD and large significant relationship between CODD and 505 time have been
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reported. These indicate that the inter-group performance variation was more likely due to each
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athlete’s ability to execute the COD (CODD) or deceleration (DD) components of the test.
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Therefore, DD or CODD may provide an independent measure of deceleration or COD speed
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ability respectively. These results suggest the importance of investigating both an athlete’s
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propulsive (linear sprint speed) and braking (DD or CODD) capabilities independently. This
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supports the conclusions of previous jumping tasks where concentric and eccentric impulse
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were unrelated (10, 15). This should be further investigated by assessing the DD with the
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addition of ground reaction force kinetics to explore the influence of braking and propulsive
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forces. This may be further warranted in subjects with higher levels of linear speed and
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acceleration as they may be required to decelerate from greater velocities and potentially higher
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levels of momentum (13). Caution is therefore required when applying the reported
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correlations to different populations with differing performance levels and further research is
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warranted.
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Our results suggest that while linear speed still appears to be the primary factor influencing
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505 total time, moderate significant relationships were also observed between DD and 505
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performance. Therefore, deceleration ability could also be considered an important component
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of effective COD speed performance. The importance of the penultimate foot contact braking
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impulse on COD performance has been reported previously (7, 8). However, it is likely that
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the deceleration in the 505 test was distributed over multiple steps prior to the penultimate foot
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contact as it has been reported that athletes take an average of 6.61m when accelerating and
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coming to a stop within 15m (11). The results of the present study indicate an average 0.56 ±
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0.13 seconds of additional time was required to come to a stop compared to continuing to
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accelerate over the same distance. The moderate nature of the relationship between 505 and
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DD may be due to the DD measure being relative to an individual’s linear speed capabilities.
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For example, if an athlete does not require excessive time to come to a stop, a positive factor
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for COD speed performance, they still require effective linear speed in order to complete the
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COD task quickly. Therefore, to understand the role of deceleration on COD speed in greater
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depth, future research should investigate the deceleration phase over multiple steps and DD
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should be interpreted in conjunction with linear speed.
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Only a moderate amount of shared variance was shown between the CODD and DD, which
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was likely due to deceleration time contributing to the CODD outcome (21). However, it is
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unclear from the CODD how much deceleration was contributing to the time required to
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complete the COD compared to the re-acceleration phase. The difference between CODD and
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DD z-scores shows that 78% of subjects showed a divergence between the two deficit measures
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greater than a MWC in one of the two turning directions. This suggests 78% of athletes would
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either over or underestimate their deceleration ability in one of the two turning directions if
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only the CODD was used. In 22% of the subjects, CODD provides a fair representation of their
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deceleration performance and the DD does not contribute to their performance profile in either
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turning direction.
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During COD performance assessment it is important to consider directional dominance, which
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can be accurately identified utilising the CODD (5). Addition of the DD from this study may
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provide greater insights into the contributing factors of these directional differences. A larger
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correlation was identified between DD and full approach in the ND turning direction, indicating
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that the braking phase during the full approach on the ND direction had a greater influence on
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DD outcome than on the D turning direction (D: r = 0.43; P < 0.01. ND: r = 0.54; P < 0.01).
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This may be due to the ND turning direction braking phase being slower for some athletes and
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subsequently having a greater contribution to DD. However, this was not identified from the
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group mean times and requires further investigation. Independent analysis for each turning
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direction revealed that 58% and 55% of subjects (D and ND performance respectively) had a
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divergence between the two deficit measures greater than a MWC. Further analysis identifies
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12 subjects, where the over, or under-estimation of deceleration from CODD was consistent
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on both turning directions. 15 subjects have an over, or under-estimation of deceleration from
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CODD in just one of the two turning directions. Finally, one subject had a MWC on both
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turning directions that changes from positive or negative, indicating that CODD underestimates
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deceleration ability on the D turning direction, but overestimates deceleration on the ND
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direction. CODD was reported to be a suitable representation of deceleration ability for both
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turning directions for 8 subjects. It is subsequently unclear from this analysis if there was a
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trend for the turning direction dominance to influence an over or underestimation of
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deceleration ability by CODD and further investigation is warranted.
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While the current results suggest that DD provides unique information for the majority of
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athletes which may help coaches individualise training programs, it is important that this metric
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is further investigated with validity and reliability analysis to enhance our understanding.
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Furthermore, the reported correlations between DD and the other measures variables were all
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less than r = 0.5, meaning there was only approximately a 20% contribution of these variables
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to DD. Therefore, the factors which contribute to DD are currently unknown and warrant
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exploration. The DD itself may also be contributed to by a self-paced approach speed prior to
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the deceleration phase (24) where the athlete is reducing the load exposed to their non-
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dominant limb (26) or from a lack of deceleration ability requiring the braking phase to be
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spread over a greater number of steps and more time (7, 8). At present it is not possible to
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distinguish between contributing factors such as these and future research should look to
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investigate this further utilising a multiple regression analysis and including a broad range of
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physical measures such as eccentric strength (10, 13).
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The results of the current study suggest that DD could provide a unique insight to deceleration
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capabilities which was not captured in CODD for the majority of athletes. In addition, both the
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CODD and DD were not influenced by an athlete’s linear speed capabilities supporting the
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need for independent analysis of propulsive (linear speed), braking (DD) and multi-direction
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application (CODD) qualities during COD speed testing. Further research should be conducted
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to improve our understanding of the DD and how an individual’s deceleration ability impacts
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COD speed performance, turning direction dominance, speed control and mechanics.
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PRACTICAL APPLICATIONS
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The current study indicated that the use of the DD helps identify athletes whose COD speed
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performance may be limited by deceleration ability, assisting coaches in individualising
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training interventions. The protocols used provided an opportunity to measure propulsive
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(linear speed), braking (DD) and multi-direction application (CODD) qualities during COD
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while also obtaining a general COD speed performance measure (505 time) from just two easy
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to administer field-based tests, maximising efficiency in testing.
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ACKNOWLEDGEMENTS
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We would like to acknowledge our subjects for their contribution to the study.
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1839-1948, 2014.
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Figure 1: A Visual representation of the 505 COD test
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Figure 2: The difference between z-scores for COD Deficit and Deceleration Deficit with a
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moderate worthwhile change threshold.
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Table 1: A description of performance measures collected during the study
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Table 2: Descriptive statistics for linear speed and best trial COD performance measures. Mean
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± SD
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Table 3: Pearson’s correlation coefficient between performance measures in the dominant
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turning direction
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Table 4: Pearson’s correlation coefficient between performance measures in the non-dominant
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turning direction
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Figure 1: A Visual representation of the 505 COD test
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504
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0m Gate
10m Gate
15m Contact
Mat and Line
50cm
Start Line
Figure 2: The difference between z-scores for COD Deficit and Deceleration Deficit with a moderate worthwhile change threshold.
-3.00
-2.50
-2.00
-1.50
-1.00
-0.50
0.00
0.50
1.00
1.50
2.00
Z-score difference between DD and CODD
Participants
DOM z-score difference
Non-DOM z-score difference
CODD
underestimates
deceleration ability
CODD
overestimates
deceleration ability
Table 1: A description of performance measures collected during the study
Performance Measures
Description/Equation
Linear 10m Sprint (s)
Time from 0m to 10m when sprinting in a straight line, taken as a
split time from a 15m sprint.
Linear 15m Sprint (s)
Time from 0m to 15m when sprinting in a straight line.
505 Time (s)
Total time taken to complete the 505 test as calculated in previous
research (1).
Full Approach (s)
The time from the 0m gate to 50% of GCT of the plant step during
the 505 test.
DD (s)
Full approach during the 505 test – 15m time during linear sprint.
CODD (s)
505 time – 10m time taken during 15m linear sprint (22).
Table 2: Descriptive statistics for linear speed and best trial COD performance measures. Mean
± SD
10m Sprint
(s)
15m Sprint
(s)
505
(s)
Full
Approach (s)
CODD
(s)
DD
(s)
DOM
1.85 ± 0.14
2.59 ± 0.21
2.54 ± 0.22
3.15 ± 0.26
0.69 ± 0.16
0.56 ± 0.12
Non-DOM
2.63 ± 0.23
3.15 ± 0.28
0.78 ± 0.15
0.56 ± 0.15
Table 3: Pearson’s correlation coefficient between performance measures in the dominant
turning direction
10m
Sprint
15m
Sprint
505
Full
Approach
CODD
DD
10m Sprint
1
0.98**
0.71**
0.87**
0.06
-0.02
15m Sprint
1
0.74**
0.88**
0.12
-0.06
505
1
0.86**
0.74**
0.41*
Full Approach
1
0.40*
0.43**
CODD
1
0.59**
DD
1
* P<0.05; **P<0.01
Table 4: Pearson’s correlation coefficient between performance measures in the non-dominant
turning direction
10m
Sprint
15m
Sprint
505
Full
Approach
CODD
DD
10m Sprint
1
0.98**
0.76**
0.85**
0.16
0.05
15m Sprint
1
0.75**
0.85**
0.17
0.01
505
1
0.87**
0.77**
0.44**
Full Approach
1
0.48**
0.54**
CODD
1
0.62**
DD
1
* P<0.05; **P<0.01