Effect of Relative Isometric Strength on Countermovement Jump Performance in Professional and Semi-Professional Soccer Players
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThe study proposed by the authors is interesting and is aimed at a much explored and attentive sector. The manuscript is well presented, although it could be improved in some parts and more detailed.
The introduction could be further expanded to better understand the theme and focus of the work by better describing some of the works cited. Also, the introduction is too segmented in my opinion, going from describing jumps in football to Bayesian analysis. The reading and understanding of the Work are not optimal in my opinion.
It would also be useful to provide some examples of how Bayesian analysis is more suitable for the world of sports science.
It would have been useful to have more characteristics to identify the athletic level of the group of players analyzed, such as VO2max and body composition for example. Also, were no other inclusion and/or exclusion criteria taken into account? Recent injuries have not been considered?
Given the brevity of the paragraphs, I would merge the paragraphs of the experimental setup and procedures for a more fluid reading.
Line 162: what does the wording "driver" mean?
Table 1 includes mRSI, what is it and how is it calculated? How are other parameters such as countermovement depth also calculated? Perhaps it would be better to provide a more detailed description of the parameters analyzed.
Line 172: better explain why the division between strong and weak occurs with this threshold. Are there any references?
Line 181-184: Are there any references for the classification of these thresholds?
In Figure 1 you can't read the letters A and B that distinguish the relative figures well.
In the figures shown in the manuscript, it is not very clear what the results are for the "Strong" and "Weak" subjects.
Line 264-270: in my opinion this is more of an introductory sentence than a discussion one.
In the discussions, the only parameter compared with other studies is the mRSI, and for all the other parameters has not a similar discussion been made to explain the result?
Line 284-287: also in this case, in my opinion they are more introductory judgments than discussions...
Linea 304-307: also in this case, in my opinion they are more introductory judgments than discussions...
Author Response
The study proposed by the authors is interesting and is aimed at a much explored and attentive sector. The manuscript is well presented, although it could be improved in some parts and more detailed.
The introduction could be further expanded to better understand the theme and focus of the work by better describing some of the works cited. Also, the introduction is too segmented in my opinion, going from describing jumps in football to Bayesian analysis. The reading and understanding of the Work are not optimal in my opinion.
It would also be useful to provide some examples of how Bayesian analysis is more suitable for the world of sports science.
We have attempted to improve the flow of the introduction to be less segmented, while also providing more insight into the Bayesian analysis within sport science. We hope the changes made are sufficient.
It would have been useful to have more characteristics to identify the athletic level of the group of players analyzed, such as VO2max and body composition for example.
No other characteristics are available to the authors, only what has been presented and the demographic data (i.e. they are all professional soccer players in the lower leagues of English football.
Also, were no other inclusion and/or exclusion criteria taken into account? Recent injuries have not been considered?
Inclusion exclusion criteria has now been included, all players had to be free from current injury and have no modifications to training based on previous injury (i.e. considered fit and ready to play for the clubs)
Given the brevity of the paragraphs, I would merge the paragraphs of the experimental setup and procedures for a more fluid reading.
We have attempted to improve the flow of the methods.
Line 162: what does the wording "driver" mean?
This has now been explained within the text.
Table 1 includes mRSI, what is it and how is it calculated? How are other parameters such as countermovement depth also calculated? Perhaps it would be better to provide a more detailed description of the parameters analyzed.
Further detail has been added but we believe full explanations of each metric are not necessary, we have supported the text with appropriate references which do explain these methods in full.
Line 172: better explain why the division between strong and weak occurs with this threshold. Are there any references?
The division was based on the average of the group (below average = weak, above average = strong), there a not current data sets for this population. Soriano et al. (2024) presents similar data in Spanish footballers, but they are stronger than the average of the population and different match demands. So, we have not used the same as presented by
Line 181-184: Are there any references for the classification of these thresholds?
A reference has now been included.
In Figure 1 you can't read the letters A and B that distinguish the relative figures well.
This has now been amended.
In the figures shown in the manuscript, it is not very clear what the results are for the "Strong" and "Weak" subjects.
They are Bayesian effect sizes figures as explained within the figure legends. We have now included some individual data plots.
Line 264-270: in my opinion this is more of an introductory sentence than a discussion one.
We believe this sentence reaffirms this concept within the discussion and specific paragraph, as it is identified within the introduction.
In the discussions, the only parameter compared with other studies is the mRSI, and for all the other parameters has not a similar discussion been made to explain the result?
A point has been added around the braking phase characteristics and improvements in strength.
Line 284-287: also in this case, in my opinion they are more introductory judgments than discussions...
We believe this sentence reaffirms this concept within the discussion and specific paragraph, as it is identified within the introduction.
Linea 304-307: also in this case, in my opinion they are more introductory judgments than discussions...
We believe this sentence reaffirms this concept within the discussion and specific paragraph, as it is identified within the introduction.
Reviewer 2 Report
Comments and Suggestions for AuthorsDear authors,
This work fulfilled your stated objective. We appreciate the opportunity to perform a different statistical analysis, providing a perspective distinct from the analyses performed in other studies. The proposed improvements are intended to apparently correct some errors and provide a complete overview of the work to readers.
Title
The title should change, because there are not only professional soccer players
Abstract
The introduction is very good. However the objetive is different with objetive in introduction
Introduction
Dear Authors:
In this section, we recommend adding more information about the countermovement jump metrics: driver, strategy, outcome, and their details.
These metrics are shown in the methods, but are not explained in detail, creating a confusing picture for the reader. More information in a paragraph enhances your work for readers. For example, why these names, why these variables, and what do these variables mean?
Materials and methods
The authors should add an explanatory image/graph about the metrics in a force-time curve, in different phases.
The authors presented the metrics. However, they need to specify the calculated metrics in more detail. for example "All metrics were calculated automatically by the force plate proprietary software"
The authors need to explain these names. Why do they stand for "person," "driver," "strategy"? In my opinion, this should be explained in the introduction and taken up again in the discussion. In this paragraph " ...selected metrics represent the person, outcome, driver, strategy..."
For statistical analisys . Why you used g´hedges if you group is not little?. In the table of results showed Cohen´d
Results
It's necessary review the table
Discussion
276 TO 282
Dear authors, in the line 276 to 282 It would be very interesting if you could provide some reference on this idea, as a reference.
Dear authors
It´s possible other limitations how:
- data processing, so a procedure with MATLAB or PYTHON could show other results, since the cut-off points could have been different (other thresholds, using standard deviations of the signal, some smoothing filter)
- Differents article showed more trial to CMJ
- Possibly the training load of the week was missing, perhaps that modified results
Regards
Comments for author File: 
Comments.pdf
Author Response
Dear authors,
This work fulfilled your stated objective. We appreciate the opportunity to perform a different statistical analysis, providing a perspective distinct from the analyses performed in other studies. The proposed improvements are intended to apparently correct some errors and provide a complete overview of the work to readers.
We thank the reviewer for their review and believe the manuscript is improved for it. Please see replies to each comment below.
Title
The title should change, because there are not only professional soccer players
We thank the reviewer for noting this, this has now been amended.
Abstract
The introduction is very good. However the objetive is different with objetive in introduction
We thank the reviewer for noting this, this has now been amended.
Introduction
Dear Authors:
In this section, we recommend adding more information about the countermovement jump metrics: driver, strategy, outcome, and their details.
These metrics are shown in the methods, but are not explained in detail, creating a confusing picture for the reader. More information in a paragraph enhances your work for readers. For example, why these names, why these variables, and what do these variables mean?
As with comments for other reviewers we have now improved the flow and content of the introduction and hope this is sufficient.
Materials and methods
The authors should add an explanatory image/graph about the metrics in a force-time curve, in different phases.
The authors don’t think this is necessary especially with the greater description of the force-time analysis performed. If the reviewer believes this is a crucial element, we are happy to add something to this effect in.
The authors presented the metrics. However, they need to specify the calculated metrics in more detail. for example "All metrics were calculated automatically by the force plate proprietary software"
Some further information has now been included, but full explanation seems unnecessary for the purpose of the present study. Relevant references explaining the metric selection have now been included.
The authors need to explain these names. Why do they stand for "person," "driver," "strategy"? In my opinion, this should be explained in the introduction and taken up again in the discussion. In this paragraph " ...selected metrics represent the person, outcome, driver, strategy..."
This detail has now been added.
For statistical analisys . Why you used g´hedges if you group is not little? In the table of results showed Cohen´d
We thank the review for noting this, this was an error and Cohen’s d effect sizes were used throughout the manuscript not Hedge’s g.
Results
It's necessary review the table
We are not sure what the reviewer is wanting here, if they could be more specific on any suggested changes this would beneficial.
Discussion
276 TO 282
Dear authors, in the line 276 to 282 It would be very interesting if you could provide some reference on this idea, as a reference.
A reference has now been included.
Dear authors
It´s possible other limitations how data processing, so a procedure with MATLAB or PYTHON could show other results, since the cut-off points could have been different (other thresholds, using standard deviations of the signal, some smoothing filter)
Different article showed more trial to CMJ
Possibly the training load of the week was missing, perhaps that modified results
We agree with the reviewer that there are numerous limitations as the reviewer identifies, however the use of thresholds and cut offs could apply to any literature and the authors do not have experience with MATLAB or PYTHON and believe it would be inappropriate to try and discuss any elements of this. Trial variability is important which is why average and not best trials were taken to account for natural variation, while acceptable reliability which has now been reported within the text. As testing was completed within the first week of pre-season with no intense training performed for 72 hours before testing, it is unlikely training load would have an extensive effect.
Reviewer 3 Report
Comments and Suggestions for AuthorsOverall, this manuscript is very well written. However, given that the Bayesian approach is a major focus of the study, it requires a more detailed explanation.
The use of a Bayesian approach appears novel and innovative, particularly in the context of sports science, where it is not yet widely adopted. At the same time, the authors also present traditional measures such as Cohen’s d, and in some cases, the results from the two approaches appear inconsistent. For example, body mass supports the null hypothesis based on the Bayesian analysis, yet the 95% confidence interval of the effect size is entirely negative. Despite this, the conclusion seems to follow the Bayesian result. This discrepancy is potentially confusing and should be clarified.
Figures 1–3 are presented as examples of the Bayesian approach. Since these seem to represent three different types of outcomes, I recommend adding a brief explanation in the figure captions to describe what each figure exemplifies.
Regarding the number of trials for CMJ and IMTP, is the range of 3–5 trials (Line 114) accurate? Based on the description of the IMTP procedure (Line 148), it seems that in some cases, only two trials were performed. Please clarify.
Line 247–248: The sentence beginning with “Suggesting that...” is a sentence fragment and grammatically incomplete. It would be clearer to either connect it to the previous sentence or rewrite it as a complete sentence.
Author Response
We thank the reviewer for their review and believe the manuscript is improved for it. Please see replies to each comment below.
Overall, this manuscript is very well written. However, given that the Bayesian approach is a major focus of the study, it requires a more detailed explanation.
We have provided more context on the use of Bayesian statistics within sport science within the introduction and hope this is sufficient.
The use of a Bayesian approach appears novel and innovative, particularly in the context of sports science, where it is not yet widely adopted. At the same time, the authors also present traditional measures such as Cohen’s d, and in some cases, the results from the two approaches appear inconsistent. For example, body mass supports the null hypothesis based on the Bayesian analysis, yet the 95% confidence interval of the effect size is entirely negative. Despite this, the conclusion seems to follow the Bayesian result. This discrepancy is potentially confusing and should be clarified.
This is an interesting aspect which is a potentially a negative aspect of using Bayesian analysis. A Bayesian approach is determining the level of evidence to support or reject the hypothesis, as the body mass was lower for the stronger athletes which we did not hypothesise (we could alter this if needed) we need to support the null hypothesis which was to a strong level of evidence. However, effect sizes neither support or reject the null hypothesis, they explain the magnitude of the observed difference (i.e. a moderate effect size), this is potentially confusing and has now been clarified.
Figures 1–3 are presented as examples of the Bayesian approach. Since these seem to represent three different types of outcomes, I recommend adding a brief explanation in the figure captions to describe what each figure exemplifies.
This has now been included within the captions.
Regarding the number of trials for CMJ and IMTP, is the range of 3–5 trials (Line 114) accurate? Based on the description of the IMTP procedure (Line 148), it seems that in some cases, only two trials were performed. Please clarify.
This has now been clarified within the text, as the additional trials refer to additional to three performed as per recommendations from Comfort et al. (2019)
Line 247–248: The sentence beginning with “Suggesting that...” is a sentence fragment and grammatically incomplete. It would be clearer to either connect it to the previous sentence or rewrite it as a complete sentence.
This has now been rewritten.
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsI thank the authors for the changes made.
Reviewer 2 Report
Comments and Suggestions for AuthorsDear authors: For my part, I have no further questions for you regarding this article. The improvements explain all my observations well.
Good work.
