Applications of a working framework for the measurement of representative learning design in Australian football.

Representative learning design proposes that a training task should represent informational constraints present within a competitive environment. To assess the level of representativeness of a training task, the frequency and interaction of constraints should be measured. This study compared constraint interactions and their frequencies in training (match simulations and small sided games) with competition environments in elite Australian football. The extent to which constraints influenced kick and handball effectiveness between competition matches, match simulations and small sided games was determined. The constraints of pressure and time in possession were assessed, alongside disposal effectiveness, through an association rule algorithm. These rules were then expanded to determine whether a disposal was influenced by the preceding disposal. Disposal type differed between training and competition environments, with match simulations yielding greater representativeness compared to small sided games. The subsequent disposal was generally more effective in small sided games compared to the match simulations and competition matches. These findings offer insight into the measurement of representative learning designs through the non-linear modelling of constraint interactions. The analytical techniques utilised may assist other practitioners with the design and monitoring of training tasks intended to facilitate skill transfer from preparation to competition.

Introduction

A predominant challenge facing sports practitioners is the design and implementation of training environments that represent competition. This approach to training design has been referred to as representative learning design (RLD) [1]. Theoretically, RLD advocates for training to consist of key (informational) constraints that are experienced within competition to maximise the transfer of skill from training to competition [1, 2]. Constraints are categorised into Individual (e.g., physical attributes and emotions), Task (e.g., rules and ground dimensions) and Environmental (e.g., weather and gravity) classes [3, 4]. To assist with the design of representative training tasks, practitioners typically record the constraints of a competitive environment to ensure such constraints are designed into training [5]. However, understanding how these constraints interact to influence a performer’s actions and behaviours is an ongoing challenge for practitioners given the non-linearity and dynamicity of sports performance [6].

An important feature of a constraints-led approach to training design is the understanding that constraints do not exist in isolation. Rather, they dynamically interact with one another, often in a continuous manner [4, 7]. However, the measurement of the dynamic interaction of constraints has been somewhat neglected within the literature [6]. Whilst constraints can be collected from training and competition environments, such approaches often overlook constraint interaction and are unable to capture then analyse the complexity of systems in full [8, 9]. Recently, the interaction among constraints was examined via machine learning techniques in Australian football (AF) [6, 10]. The application of a rules-based approach enables the complexity of RLD to be measured, through the identification of key constraint interactions based on both their frequency and their displayed influence on behaviours. An informed RLD is vital for practitioners, as how constraints are enacted in training implicates skill development and learning transfer [1, 11–13].

Within many team sports, including AF, small sided games (SSGs) are used as a frequent training modality due to their perceived representativeness of competition matches and ease of constraint manipulation [14, 15]. Specifically, SSGs can be used to simulate sub-phases of competition, whilst to some extent, preserve the complex interactions between an athlete and their environment [16-18]. Match simulations are another common training strategy within preparation for performance models in team sports, as they afford practitioners with a practice landscape that can simulate scenarios commonly encountered within competition. Match simulations and SSGs are different types of training modalities and thus, the frequency and interaction of constraints may differ. The intent of these training modalities are different, and as such, their use within the broader training schedule should be carefully considered by coaches [19].

The primary aim of this study was to compare constraint interactions and their frequencies, between match simulations, SSGs, and competition matches in AF. These comparisons were facilitated using a rule-based algorithm. Secondly, the study aimed to determine the extent to which they influenced disposal type and effectiveness. Thirdly, this study sought to understand the sequential nature of disposals by examining whether the efficiency of a disposal was influenced by the preceding disposal. By addressing these aims, this study sought to progress the methodology of measurement for RLD in sporting environments.

Methodology

Data were collected from official matches and training sessions from one Australian Football League (AFL) club across the 2018 and 2019 (pre)seasons. All 2018 regular season matches were included (n = 22, disposal instances = 3,478). Specific tasks from training sessions were included, consisting of match simulations (n = 13, disposal instances = 1,298) and SSGs (n = 24, disposal instances = 2,677). Seven versions of SSGs were included ranging from seven to 18 athletes. Ground dimensions ranged from approximately 20 x 20 m to 60 x 60 m. Number inequalities were included in some SSGs, with the largest discrepancy between team numbers being three additional attackers compared to defenders. Given the applied nature of this research, these design features were hard to control. Ethical approval was granted by the University Human Research Ethics Committee (application number: HRE18-022), and written consent was gained from the organisation to use de-identified data as collected per regulation training practices.

Match footage was provided by Champion Data (Melbourne, Australia, Pty. Ltd.), whilst training tasks were filmed by club staff from the same perspective as the competition match footage (behind the goals and side view). All footage was then subjected to notational analysis via SportsCode (version 11.2.3, Hudl). The lead author and a performance analysts coded all footage using a code window developed with a weighted kappa statistic of >.80, indicating very good reliability [20]. Constraints collected included: disposal type, pressure, time in possession and disposal effectiveness (Table 1). These constraint types were based upon similar literature [6, 10, 21]. The nature of the options for each constraint sampled limited bias in the rule-based approach, as all constraints had the same number of sub-categories (Table 1).

Table 1

Description of constraints sampled, their sub-category, and definition.

Constraint sampled	Sub-category	Definition
Disposal Type	Kick	Disposal of the football with any part of the leg below the knee
	Handball	Disposal of the football by hitting it with the clenched fist of one hand, while holding the football with the other
Pressure	Pressure	Opposition player defending the ball carrier from any direction
	No Pressure
Time in Possession	> 2 sec	Time with ball in possession from receiving the football to disposing of it
	< 2 sec
Disposal Effectiveness	Effective	An effective kick is of more than 40 m to a 50/50 contest or better for the team in possession, or a kick of less than 40 m that results in retained possession
	Ineffective

All analyses were undertaken in the R computing environment (version 3.6.1, Vienna, Austria) and included a three-stage process. All code for the following analyses are available on Github (www.github.com/PeterRBrowne). First, association rules were generated for all disposals for match simulations, SSGs and competitive matches. Association rules are a type of machine learning algorithm which can identify underlying and frequent non-linear patterns in a large dataset [22]. The ‘Arules’ package was used to apply the Apriori algorithm [23] and to measure the association between multiple constraints on disposal efficiency. Minimum support and confidence levels were set at 0.0002 to allow for all possible rules to be generated. The minimum number of variables was set at four to ensure that each coded constraint was included. The association rules were arbitrarily assigned an alphabetical identity (ID), being then compared by levels of support and confidence [24].

The frequency with which a rule occurred and was then followed by a subsequent rule was then calculated using the ‘tidyr’ and ‘dplyr’ packages [25, 26]. The difference between training and competition frequencies was then calculated. The observed frequency of a third disposal being effective was calculated. This was visualised using a lattice plot, with colour hues to differentiate the observed frequency of an effective disposal. The level of observed frequency of an effective disposal was calculated as the weighted average of the confidence of a Rule ID and the frequency with which three sequential rules occurred.

Results

The association rules with assigned alphabetical ID are presented in Table 2, and the differences in rule frequency (A) and confidence levels (B) are displayed in Fig 1. The lowest support across all three environments was Rule E (0.012), and the largest was Rule G (0.316), with both occurring in the competition environment (Fig 1A). The support levels for match simulation rules were generally more representative of a competitive match, relative to SSGs, based on the constraints measured. Rule G, a pressured handball performed within 2s, showed the largest difference between competition matches and the SSGs (Fig 1A). Levels of support also varied between environments, with Rule G being the most frequent in matches and match simulations, whilst Rule D was the most frequent in SSGs (Fig 1A). With the exception of Rule C, rules corresponding to ‘kicks’ yielded lower confidence in competition matches relative to SSGs, but higher confidence relative to match simulations (Fig 1B). For rules relating to ‘handballs’, the confidence was highest in competition matches relative to the training tasks (Fig 1B).

Table 2

Breakdown of each possible association rule and its associated alphabetical ID.

ID	Type	Pressure	Time in Possession (seconds)
A	Kick	No Pressure	<2
B	Kick	No Pressure	>2
C	Kick	Pressure	>2
D	Kick	Pressure	<2
E	Handball	No Pressure	>2
F	Handball	No Pressure	<2
G	Handball	Pressure	<2
H	Handball	Pressure	>2

Fig 1

Variation in levels of support (A) and confidence (B) of each Rule ID match simulations and Small Sided Games relative to competition matches. Where zero is equal to competition matches. Positive values reflect greater values for competition matches.

The differences between sequential Rule IDs were calculated between training and competition environments (Table 3). Positive values reflect a greater frequency of occurrence within competition matches, whereas negative values indicate greater frequency of occurrence in the training environment. Match simulations were more similar to competition matches, relative to SSGs in levels of support (Table 3A) and confidence (Table 3B). Disposal sequence differed more between competition matches and SSGs, with eight sequences having a greater than a ±20% difference between environments (Table 3B). Whilst for similar disposal sequences between training and competition environments, both match simulation and SSGs were similar with twelve and eleven sequences having less than ±1% difference respectively (Table 3).

Table 3

Difference between frequency of second pass following first pass for competition matches and match simulations (A), and competition matches and SSGs (B).

Values are expressed as percentage differences (%).

A		Second Pass
		A	B	C	D	E	F	G	H
First Pass	A	-2	9	3	2	1	-6	-6	-2
	B	4	-18	2	9	-3	-3	11	-2
	C	0	7	-9	5	0	-2	1	-2
	D	-1	5	-11	2	0	-4	12	-4
	E	3	-18	13	0	NA	8	0	-5
	F	-2	-7	0	6	0	3	3	-3
	G	-7	1	2	-1	-1	-4	11	-2
	H	-8	-3	-2	0	0	-2	23	NA
B		Second Pass
		A	B	C	D	E	F	G	H
First Pass	A	-18	4	-4	-7	3	6	16	1
	B	-4	-24	0	6	0	0	23	-1
	C	-3	19	-22	-26	0	3	28	2
	D	-1	13	-20	-26	0	4	28	1
	E	2	-8	-4	2	NA	8	2	-2
	F	-7	-2	-1	0	-1	-2	12	0
	G	-2	4	-2	3	0	-3	2	-3
	H	-3	-3	-3	-19	0	5	26	NA

Note: Greater negative values (the deeper the orange hue) indicate greater frequency of the rule sequence in the training environment. Larger positive values (the deeper the blue hue) indicate a greater frequency of the rule sequence in the competition environment. NA represents where the two rule IDs did not occur sequentially. Values closer to ‘0’ denote closer similarities between training and competition.

Fig 2 depicts the observed frequency of effectiveness of the third disposal following two sequential disposals across competition matches (A), match simulations (B) and SSGs (C). The variation between competition and training environments are visualised through colour hues, in addition to the observed frequency being overlayed. The third disposal in the sequence was more likely to be effective in SGGs, relative to competition matches and match simulation. Specifically, the observed frequency of the third disposal in the sequence being effective ranged from 54 to 89% for competition matches, 49 to 84% for match simulations, compared to 77 to 88% for SSGs (Fig 3). The majority of competition match third disposal effectiveness was above 70%, with only six disposal sequences less than 70% effectiveness. Comparatively, 28 disposal sequences during match simulations resulted in less than 70% effectiveness (Figs 2 and 3).

Fig 2

The observed frequency of effectiveness of the third disposal following two sequential disposals across competition matches A) competition match B) match simulation C) Small Sided Games. Values expressed as percentages (%). Note: The scale moves from orange to blue with the deeper the hue the greater observed frequency of an effective third disposal. Blank sections are those which did not have two sequential passes. Grey circles reflect those sequences of passes which did not continue to a third disposal.

Fig 3

Density plot of the observed frequency of effectiveness of a third disposal.

This was based upon the previous two disposals across competition matches (green), match simulations (red) and Small Sided Games (blue).

Discussion

The primary aim of this study was to compare constraint interactions and their frequencies between match simulations, SSGs, and competition matches in AF using a rule-based algorithm. Secondly, it aimed to determine the extent to which they influenced disposal type and effectiveness. Thirdly, this study sought to understand the sequential nature of disposals and whether disposal sequences are dependent upon the preceding disposals. Accordingly, this study aimed to progress the methodology for the measurement of RLD beyond recording a single instance and to account for continuous nature of match events and the constraints impacting them. For instance, by extending the rule-based approach from exploring a single disposal as in Browne, Sweeting [10], this study sought to understand disposal sequences, and the extent to which disposals are dependent on preceding disposals. Results demonstrated that the frequency and confidence of different disposal types and constraint interactions varied between match and training environments. These differences varied depending on the training task, with match simulations yielding a greater level of representativeness to matches relative to SSGs. However, the level of representativeness and intent of each SSG may differ, the efficacy of each approach may vary depending on the given context.

With respect to the primary aim, this study demonstrates that an understanding of the differences between support and confidence levels of constraint interactions within training and competition environments is an important consideration for the design of representative training tasks. For example, match simulations generally showed greater similarity to competition matches, with respect to disposal type. However, competition matches incurred a greater frequency of pressured handballs performed within 2s (Rule G), relative to match simulations. These differences between training and competition environments could exist for a number of practical reasons. Notably, the design features of the SSGs could intentionally favour a specific disposal type (e.g. kick), whilst, in general, the training environment could incur less physical pressure relative to a competitive match, given differences in physical exertion and intensity [21]. Practitioners may therefore use this information to better understand the influence of constraints on performance, which could, improve the representativeness of such training tasks through informed manipulation, such as increasing (or decreasing) playing field dimensions to encourage differing levels of pressure on disposals [27, 28]. Moreover, this study provides a methodology to better understand the design of training tasks that may aid practitioner decision-making in implementing appropriate SSGs for their desired intent.

Due to the intent of the match simulations compared to SSGs, it is unsurprising to note that match simulations were more representative of competition compared to the SSGs. Thus, it is reasonable to suggest that not all training tasks will yield the same level of representativeness, potentially due to their explicit intent. For example, a practitioner may manipulate certain constraints of a SSG to facilitate greater disposal efficiency, reducing representativeness relative to competition, but still achieving the intended task goal. Conversely, a practitioner may want to challenge disposal efficiency within a SSG by manipulating temporal and spatial constraints so the training task is harder (with reference to time and space) than what is afforded within competition. Although it is likely that practitioners’ do not plan for every training task to express near perfect representativeness, this methodology provides a platform by which ‘target’ areas could be identified, informing practitioners as to how frequent non-representative actions are performed within practice. Such information could better guide the macro- and meso- structures of practice, ensuring less representative training tasks are coupled with more representative tasks. Further, this aligns with the principles of periodisation for skill acquisition [19], emphasising the importance of being able to measure the influence of constraint interaction within training tasks [10]. A training task classification systems may be able to aid practitioners in this process to ensure the appropriate tasks are conducted together based on its characteristics and intent [29]. However, the ideal balance of representative versus non-representative practice to gain the greatest performance benefit in competition, is currently unknown.

The third aim sought to explore concomitant disposal sequences. Differences between the training and competitive performance environments were found when exploring the observed frequency of a third sequential disposal being classified as ‘effective’. Understanding disposal sequences is a key feature of complexity. This is essential for RLD as it enables understanding of not just the current status, but which interactions occur after. For matches and match simulations, the observed frequency of an effective third sequential disposal was lower compared to the SSGs. This practice task yielded the highest range of observed frequency for an effective third disposal, likely due to the task design of the SSGs, which may encourage a more continuous, effective, chain of disposal. This could have been intentionally designed within the SSGs through the systematic manipulation of player numbers (task constraint) to favour the offensive team (for example, a SSG consisting of 6 vs. 4). Nonetheless, this analysis demonstrates how a chain of disposals could partially shape future disposal effectiveness, thus providing some evidence that the effectiveness of a disposal may not be independent from preceding events.

A limitation of this study was that it grouped all SSGs together, despite it being possible that some SSGs had differing task intentions and subsequent challenge points, diluting their representativeness. Accounting for intent in SSGs may allow for a more complete insight into their representativeness relative to competitive matches. Future research can look to apply the methodological advancements from this study to further understand the differences between various SSGs. Additionally, a limited number of constraints were used to model RLD, and thus the model presented here is a truncated view of RLD. The sampling of appropriate constraints is an evolving process, as better and new measures become available. The use of experiential coach knowledge could aid in the informed selection of constraints, however experiential knowledge is dependent on the individual, subject to biases and the environment in which it is applied. Further, this study focused solely on the ball carrier, with it being likely that other constraints, such as opposition and teammate location and the individual’s action capabilities, additionally influenced the disposal outcome. Models that consider these factors will likely further explain disposal effectiveness, but their performance must be considered against any decrease in interpretability that may arise from the utility of larger constraint sets. Additionally, future studies could look to examine the frequency of rule occurrence in a defined period of time [21]. For instance, a SSG played in a small area may have a higher frequency of disposals per minute, compared to a larger area.

Conclusion

Disposals are influenced by the interaction of constraints in training and competition environments in elite AF. Variation exists in the frequency whereby disposals occur under specific constraints across the competition matches, match simulations and SSGs. Although training and competition environments differed, this study found greater levels of representativeness existed between match simulations and competition matches compared to the grouped SSGs and competition matches. These insights can aid the comprehension of how constraints interact to shape the emergence of specific disposals and their effectiveness, affording practitioners with a platform for the development and measurement of representative training tasks. The analytical techniques applied in the present study are not limited to AF and may assist in designing representative training tasks across other sports via the consideration of constraint interaction. Importantly, this study provides a methodological advancement in the measurement of constraint influence, frequency and accounting for the continuous nature of sport.

(CSV)

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11 Sep 2020

PONE-D-20-14878

Applications of a working framework for the measurement of representative learning design in Australian football

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Reviewer #1: In general, the article presents a problem of great relevance, is well written and presents methodological advances related to the analysis of the degree of representativeness of training tasks. However, some corrections are required and all comments were included in the attached file.

Reviewer #2: The manuscript “Applications of a working framework for the measurement of representative learning design in Australian football” compared constraint interactions and their frequencies in training (match simulations and small sided games) with competition environments in elite Australian football. The paper brings new and important knowledge for practitioners and I recommend the manuscript publication. The comments provided below aimed to improve the quality of the manuscript.

• Methods

Reliability issues - According to the authors, “All footage was then subjected to notational analysis via SportsCode (version 11.2.3, Hudl).”

It is important to provide more information about the notational analysis to assure its reliability. Was the analysis performed by one or two authors? Do they have experience in codification? Is there any measure of reliability?

• Results

The authors performed a descriptive analysis of tables A and B. Is it possible to perform an inferential analysis to corroborate the descriptive findings?

• Discussion

I agree with the authors that it is unsurprising to note that match simulations were more representative of competition compared to the SSGs. Are there studies that show similar results in other sports? If so, which methods were used by the previous studies? This discussion may highlight the advantages of the currently proposal.

• Limitation

The SSGs were analyzed together, but it was already recognized by the authors.

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Reviewer #2: Yes: Júlia Barreira

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4 Oct 2020

Reviewer 1:

Abstract:

Line 31: What do the authors mean by "disposal type and effectiveness across environments"?

• Response: This sentence has been amended to improve its clarity – please refer to line 7-9

Line 32/33: What constraints? What do the authors mean by association rules?

• Response: Constraints are defined here as boundaries in which skills emerge and shape their outcome, such as opposition pressure. Association rules are a machine learning approach that analyses the underlying and interacting patterns in data, helping us to understand non-linear interactions in greater detail.

• We have since amended this sentence to better define both constraints and association rules (please see line 9-11). Note, however, word restrictions on the abstract do limit our capability to expand on these further.

Line 34: Difficult to follow

• Response: This sentence has been amended to improve its clarity – please refer to line 11-13.

Introduction:

Line 52: Something to reflect on: Is that really the biggest challenge? Does the coach want to reproduce the same constraints present in the competitive environment? Is that the intention? If so, there is nothing more representative than match simulations and the competition environment itself. Should we want to reproduce that same environment in training contexts? Or should we develop players' competencies and skills through tasks that maintain key sources of information that guide their actions and decisions? How to prepare youth players to deal with different types of constraints through training tasks with less difficulty and complexity than competitive environment and simulated matches? Here's the big challenge!

• Response: This is an interesting point raised by the reviewer. Indeed, the goal of training designs is not to replicate competition, but is to ensure that key sources of information experienced in competition are present in training activities so that the relevant information-movement couplings are preserved. As correctly alluded to by the reviewer, this may not always ‘look’ like a game. Further, it is why we refer to representative learning design being the process of maintaining the key information constraints within the first paragraph (Line 25 – 28) – the role of the coach is to therefore manipulate these informational constraints to make training easier (or harder) to promote athlete learning. Also, there is a lot that we still don’t know about how constraints influence performance in competition and training environments.

Line 76/77: Here we need to be very careful. Can both SSCG and Match simulations be considered training tasks? I say this, because in fact the match simulations will present a high level of representativeness, as well as larger SSCG configurations. But, because of that, will I always use match simulations during training sessions? Or do we need to think about development through different stages, preparing athletes to manage the constraints of simulated matches and, consequently, with the competition? Otherwise, I can lead readers to believe that it would be better to just train through simulated matches.

• Response: This is again an interesting point raised by the reviewer. Firstly, it is our perspective that both are training tasks, as both are performed in a practice environment with the primary intent of improving multiple aspects of performance in competition. Secondly, it was not our intent to suggest that training should only consist of these modalities, and indeed, their use should be matched to the broader practice schedule used by the coach or organisation. Please refer to lines 56-58 where we have clarified this.

Line 81/82: Difficult to follow

• Response: This has been amended accordingly – please refer to line 63-65.

Methods:

Line 89: We need more information about those SSCG used, regarding players number, pitch dimension, rules, etc. These constraints will affect players performance during SSCG.

• Response: Given the reviewers comment, we have included additional information regarding the general design features of the SSGs used. Please refer to lines 73-77 in our revised manuscript. We also acknowledge the reviewer’s consideration of our cited limitation of the SSGs stated in our original submission.

Line 101/Table 1: Here is my biggest doubt: to measure representativeness, can we use only ball carrier actions? Technical skills? What about actions without a ball?

• Response: This is a fair point raised, and clearly, this additional information in practice will be crucial in better understanding the representativeness of task designs. Given the proposal of a new methodological way of analysing data (not necessarily capturing it), we were unable to include a greater array of constraints impacting upon representativeness. Accordingly, we have included an additional section of text describing this as a study limitation – please refer to lines 254-256.

Discussion:

Line 192: Again, I agree that the simulated matches always present this highest level of representativeness. But, could these results have been influenced by the SSCG used in the study? We don't have any information about the types of games used and which key tasks constraints were manipulated, and for me that makes all the difference!

• Response: We now feel that this issue should be partly alleviated for the reviewer in amendments made in response to their earlier comments (please see lines 73-77 for additions on the SSG design features). Further, we absolutely agree with the reviewer that grouping the design features of the SSGs into one training modality could have somewhat diluted their representativeness, and is why we specifically asserted this as a limitation to our work in our original submission from line 247-256.

Line 205: Exact! For me, the biggest problem is in the design process of training tasks, and not in using SSCG or not. The big challenge is to know which SSCG to use, when to use it and for whom to use it.

• Response: We are glad the reviewer agrees with our sentiments here, and wish to further clarify them in our response. Specifically, we do not wish to propose that SSGs or match simulations are the only training modalities that should be used by coaches in high performance sport. As suggested by the reviewer a few times, the challenge for coaches is not to just use these modalities, but to understand when and why such modalities would be used in the broader training schedule.

• To help clarify these contentions, please refer to lines 204-213 where we indicate that our results could help coach decision-making in implementing SSGs for their desired intent.

Conclusion:

Line 265-267: I suggest that the authors be careful with the way they put that statement. In fact, the authors found this, but we need to remember the limitations mentioned above, mainly of not having considered the different constraints manipulated in SSCGs, which will have impacted the results found for the SSCG.

• Response: We thank the reviewer for highlighting this, it is a fair point, and we have since softened our wording in light of the reviewer’s suggestion (lines 277-279). Specifically, we have encouraged future research to extend upon our findings by applying the analytical approaches we used to compare differing SSGs (as opposed to grouping like was done in our study) and their level of representativeness to competition matches (please see lines 254 - 256).

Line 272: “Importantly, this study provides a methodological advancement in the measurement of constraint influence, frequency and accounting for the continuous nature of sport” In my opinion, this should be the focus of this study. The authors seem to value comparisons between contexts (competitive and training) more than in methodological advances to assess representativeness.

• Response: We feel that the paper does indeed centralise the progression of how representative design is measured in high performance sport – it is stated in the title of our paper. Furthermore, we cloak our aims within a statement that actually asserts the main objective of this study is to progress the measurement of representative learning design on line 66 and we have specifically referred to this advancement in the first paragraph of our discussion from line 183 – 186. Of course, to show the use of our methodology, we had to compare training modalities, and it would have been remiss to not appropriately discuss these modalities and subsequent results throughout the paper.

Reviewer 2:

Methods: Reliability issues - According to the authors, “All footage was then subjected to notational analysis via SportsCode (version 11.2.3, Hudl).”

It is important to provide more information about the notational analysis to assure its reliability. Was the analysis performed by one or two authors? Do they have experience in codification? Is there any measure of reliability?

• Response: The data was collected by the lead author and a professional performance analyst. Both had multiple year experience in manual coding of team sport data. Furthermore, the methods and code windows used have previously been tested for reliability and were found to have a weighted kappa statistic of >0.80 (Back, 2015). Accordingly, we have included additional information in our revised manuscript that answers the questions raised here by the reviewer. Please refer to lines 82-84.

Results: The authors performed a descriptive analysis of tables A and B. Is it possible to perform an inferential analysis to corroborate the descriptive findings?

• Response: We thank the reviewer for this comment. The intent was to use a rule-based approach to compare training and competition environments, and as such, including an inferential analysis here, which does not specifically answer an aim of the study, may unnecessarily confuse readers. Therefore, in this instance, we have decided against making an amendment to our manuscript.

Discussion: I agree with the authors that it is unsurprising to note that match simulations were more representative of competition compared to the SSGs. Are there studies that show similar results in other sports? If so, which methods were used by the previous studies? This discussion may highlight the advantages of the currently proposal.

• Response: Unfortunately, to the best of our knowledge, there are no studies which have compared match simulations and SSGs with competition matches, with respect to technical actions and the constraints captured. Indeed, studies have compared training activities with competition environments (Ireland et al., 2019) and others have looked at how manipulating SSGs influences physiological and tactical outputs (for example, Klusemann et al, 2012, Goncalves et al., 2017). However, these studies explore tactical components of performance and/or use discrete frequency counts of events, which is where our work has looked to advance, by using a rule-based approach. Please refer to lines 183-189 where we have highlighted this intent more directly.

Limitation: The SSGs were analysed together, but it was already recognized by the authors.

• Response: We acknowledge the reviewer’s consideration of our cited limitation of the SSGs stated in our original submission.

Submitted filename: Response to reviewers.docx

Click here for additional data file.

2 Nov 2020

Applications of a working framework for the measurement of representative learning design in Australian football

PONE-D-20-14878R1

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13 Nov 2020

PONE-D-20-14878R1

Applications of a working framework for the measurement of representative learning design in Australian football

Dear Dr. Browne:

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