Using machine learning pipeline to predict entry into the attack zone in football

Leandro Stival; Allan Pinto; Felipe dos Santos Pinto de Andrade; Paulo Roberto Pereira Santiago; Henrik Biermann; Ricardo da Silva Torres; Ulisses Dias

doi:10.1371/journal.pone.0265372

Peer Review History

Original SubmissionFebruary 28, 2022
24 Jun 2022 Decision Letter - Nguyen Quoc Khanh Le, Editor PONE-D-22-05975Using machine learning pipeline to predict entry into the attack zone in footballPLOS ONE Dear Dr. Santiag, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Aug 08 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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If consent was waived for your study, please include this information in your statement as well. Additional Editor Comments: Please improve the study especially in terms of novelty, algorithm implementation, and case studies. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Partly Reviewer #2: Yes Reviewer #3: No ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: No ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: No Reviewer #3: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: No ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: In this paper authors propose a framework to predict the success of ball possession in football. In particular, for each ball possession they extract a set of features defined relying on complex networks analysis. Then, they exploit Deep Learning techniques to predict a binary outcome for every ball possession. The outcome refers to the event of bringing the ball close to the opponents' goal. I appreciate the effort of the authors, and I believe this paper would be ready for publication if some issues will be addressed: - case study: given the nature of the paper, a set of use cases with the relative Shap local explanation are required to provide the reader some results from the application of the proposed methodology. A bit of of analytics would also improve the paper: who are the players mostly contributing with/without the ball to succesful ball possession? What are the network features that defines such players? - experiment replicability: authors provide both data and code of their work, this is a huge strong point compared with many other papers on the same topic. However, data description is missing, both within the paper and in the data repository. A broader data description would improve this aspect. Reviewer #2: This paper presented to Use machine learning pipeline to predict entry into the attack zone in football. Overall, the structure of this paper is well organized, and the presentation is relatively clear. The idea is interesting and potential. However, there are still some crucial problems that need to be carefully addressed before a possible publication. More specifically, 1. The motivations or remaining challenges are not so clear or what kinds of issues or difficulties are this task that is facing. Please give more details and discussion about the key problems solved in this paper, which is largely different from existing works. 2. A deep literature review should be given, particularly advanced deep learning models in image processing. Therefore, the reviewer suggests discussing some related works by analyzing the following papers in the revised manuscript. 3. Please clarify the contributions to this field, for example, which are the existing ones and which are your own ones? 4. How about the computational complexity of the proposed method? 5. Some future directions should be pointed out in the conclusion. Reviewer #3: The Research Paper stands Rejected and is NOT RECOMMENDED for Publication because of the following strong reasons: 1. The Information highlighted and the conceptual methodology is already known and lots of advanced papers are already published. 2. No specific novelty is there. 3. No System model and Algorithm is highlighted in the proposed approach. 4. No Real Time case study based discussion. ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No Reviewer #3: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0265372.r001
Revision 1
16 Aug 2022 Author Response Thank you for the opportunity to review our manuscript for the PLOS ONE. We appreciate the Reviewer’s constructive comments and through reviews. The content of the manuscript has been revised to address all the Reviewers’ concerns. After addressing all the comments and revising the manuscript, we are submitting the revised version of our article for your consideration. All changes made in the manuscript are highlighted. We considered all comments and notes carefully, and we are sure they represent a significant contribution to the quality of our manuscript. We thank you for this opportunity. Please let us know if there are any further questions or concerns. Sincerely Authors Title: Using machine learning pipeline to predict entry into the attack zone in football *Please note that all changes made to the manuscript, legends and figures are highlighted in yellow. Reviewer 2 1. The motivations or remaining challenges are not so clear, or what kinds of issues or difficulties are this task that is facing. Please give more details and discussion about the key problems solved in this paper, which is largely different from existing works. R: The main motivation for this work relies on the lack of effective approaches to predict if a ball possession may lead to a chance of scoring. Therefore, in this paper we aimed to create an effective framework that could lead to insights of how the attacking team may approach the penalty box, i.e., may have more chances of scoring. The main novelty of our work relies on the use of a complex network formulation. Other papers analyzed teams using similar metrics to ours, but for different applications and objectives. The main contribution is to show that the use of complex network features to understand and predict the attack behavior is viable. Another contribution relies on the combination of time series representations with convolutional networks, pre-trained with images, to produce effective predictions. To the best of our knowledge, no study in the literature has addressed this problem using the methodological steps investigated in this paper. 2. A deep literature review should be given, particularly advanced deep learning models in image processing. Therefore, the reviewer suggests discussing some related works by analyzing the following papers in the revised manuscript. R: The actual machine learning applications cover several areas in football data. Our literature review is basically focused on predictions, and some of these works are described in the Section Related Work in the paper. Lines 55-133 provide an overview of studies that investigate predictions in soccer context and Lines 294-302 describe how deep learning has been used to predict the tracking of players. 3. Please clarify the contributions to this field, for example, which are the existing ones, and which are your own ones? R: The contributions presented in our project have proven that it is possible to use complex network features to predict if the team with ball possession will reach the attack zone, i.e., will have chances of scoring. 4. How about the computational complexity of the proposed method? R: The complete deep learning network architecture has 6.7 million of trainable parameters, where most of them (approximately 4 millions) belong to EfficientNet (the main backbone used in our formulation). Recall that those parameters (weights) are only fine turned in our trained strategy. Training the used architecture takes one day on a GPU NVIDIA (GTX 1050 Ti with 4 GB of RAM memory) with CPU (AMD FX-8350-e) and 16 GB of RAM memory. Recall that this machine has a very simple configuration, which demonstrates the advantage of using pre-trained models and simplifying our framework. This training procedure is performed off-line. The other components of the devised pipeline (e.g., construction of graphs, extraction of metrics from complex network, and the build of visual rhythms) take negligible time when compared to the training time of the network architecture. 5. Some future directions should be pointed out in the conclusion. R: Thanks for pointing out this issue. In the current version of the manuscript, we extended the discussion upon possible research directions for future work. Possible next steps include the implementation of graph-convolutional networks to improve the results and make the pipeline smaller. Another possible direction related to incorporating more spatial features related to the attacking and defending team, such as covered area and centroid of the team as Frencken and Lemmink work (FRENCKEN et al., 2011), We believe that the combination of complex network and spatial features may lead to improved prediction results. Reviewer 3 6. The Information highlighted, and the conceptual methodology, is already known and lots of advanced papers are already published. R: The paper aims to validate a new approach using concepts already consolidated, like graphs to analyze the players and teams behavior, visual rhythm in time series representation and machine learning to predict the play flow. It is possible that there are works using state-of-the-art algorithms, but our focus was to create and test the possibility of combination of graphs, time series representations and machine learning, in a unique pipeline with few data and heterogeneous teams. To the best of our knowledge, no study in the literature has investigated this combination for similar problems. 7. No specific novelty is there. R: No algorithm, or metric was the focus of innovation during the development of this project, our attention was kept to create an innovation in the method of how to apply great tools available to predict the movement of the teams. In this paper, we aimed to create an effective framework that could lead to insights of how the attacking team may approach the penalty box, i.e., may have more chances of scoring. The main novelty of our work relies on the using of a complex network formulation perspective. Other papers analyzed teams using similar metrics to ours, but for different applications and objectives. The main contribution is to show that the use of complex network features to understand and predict the attack behavior is viable. Another contribution relies on the combination of time series representations with convolutional networks, pre-trained with images, to produce effective predictions. To the best of our knowledge, no study in the literature has addressed this problem using the methodological steps investigated in this paper. 8. No System model and Algorithm is highlighted in the proposed approach. R: During the implementation, a neural network was utilized to make the predictions, the focus was to show how that makes it possible to create a pipeline that unify graph, time series, images and deep learning. It describes how a convolutional network is used and the amount of layers between lines 294 and 302. 9. No Real Time case study based discussion. R: The idea to apply the concepts of this work is to consider the historical data of a time, create a tool to enable the understanding of how a team usually attacks and create a strategy to stop them. Real time application looks a great way to use this pipeline, but it can be a possible continuation of the work. Recall that a real-time application of machine learning algorithms on positional data is uncommon, despite its potential. We believe that is in part due to the enormous computational resources that might be needed. Reviewer 1 10. Case study: given the nature of the paper, a set of use cases with the relative Shap local explanation are required to provide the reader some results from the application of the proposed methodology. A bit of analytics would also improve the paper: who are the players mostly contributing with/without the ball to successful ball possession? What are the network features that define such players? R: Thanks for raining such an issue, Shap-based analysis may provide us the values for each channel of image (where each channel represents a metric). It is possible, therefore, to identify and appoint the most important players. However, in the development of the pipeline, this idea was discarded, because the training was realized with data of different teams. We plan to perform individual-based analyses in future work. 11. Experiment replicability: authors provide both data and code of their work, this is a huge strong point compared with many other papers on the same topic. However, data description is missing, both within the paper and in the data repository. A broader data description would improve this aspect. R: Originally, the description of data was not made available. The data provided is now accompanied with metadata, including for example descriptions of labels and how information is presented in each column: https://figshare.com/articles/dataset/REDSCAT2/19222746 This organization leverages the replicability of our work and allows the validation of our results. REFERENCES: FRENCKEN, W.; LEMMINK, K.; DELLEMAN, N.; VISSCHER, C. Oscillations of centroid position and surface area of soccer teams in small-sided games. European Journal of Sport Science, Taylor & Francis, v. 11, n. 4, p. 215–223, 2011. Attachments Attachment Submitted filename: Answerletter_PONE-D-22-05975R1.pdf* https://doi.org/10.1371/journal.pone.0265372.r002
2 Oct 2022 Decision Letter - Nguyen Quoc Khanh Le, Editor PONE-D-22-05975R1Using machine learning pipeline to predict entry into the attack zone in footballPLOS ONE Dear Dr. Santiag, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Nov 16 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols. We look forward to receiving your revised manuscript. Kind regards, Nguyen Quoc Khanh Le Academic Editor PLOS ONE [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #2: (No Response) Reviewer #3: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: All my comments have been addressed. I appreciate code and data sharing, it's not common among the papers in the field of sports data science. Reviewer #2: This paper proposed to use machine learning methods to predict entry into the attack zone in football Overall, the structure of this paper is well organized, and the presentation is clear. However, there are still some crucial problems that need to be carefully addressed before a possible publication. More specifically, 1. The motivations or remaining challenges are not so clear or what kinds of issues or difficulties are this task that is facing. Please give more details and discussion about the key problems solved in this paper, which is largely different from existing works. 2. A deep literature reviews should be given, particularly advanced and SOTA deep learning or AI models in data processing and high-level analysis. Therefore, the reviewer suggests discussing some related works by analyzing the following papers in the revised manuscript. 3. What are the main differences between the proposed attention techniques and existing machine learning related methods? 4. The reviewer is wondering how about the computational complexity of the proposed method? 5. It is well-known that the signals or images usually tend to suffer from various degradation, noise effects, or variabilities in the process of imaging. Please give the discussion and analysis. The reviewer is wondering what will happen if the proposed method meets the various variabilities. 6. Some future directions should be pointed out in the conclusion. Reviewer #3: The Revised Manuscript has incorporated all the revisions and suggestions as mentioned in the last review. And now the paper stands Accepted with no further revisions. ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: Yes: Paolo Cintia Reviewer #2: No Reviewer #3: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0265372.r003
Revision 2
17 Nov 2022 Author Response Response to Reviewers Thank you for the opportunity to review our manuscript for the PLOS ONE. We appreciate the Reviewer’s constructive comments and through reviews. The content of the manuscript has been revised to address all the Reviewers’ concerns. After addressing all the comments and revising the manuscript, we are submitting the revised version of our article for your consideration. We considered all comments and notes carefully, and we are sure they represent a significant contribution to the quality of our manuscript. We thank you for this opportunity. Please let us know if there are any further questions or concerns. Sincerely Authors Title: Using machine learning pipeline to predict entry into the attack zone in football Please note that all changes made to the manuscript, legends and figures are highlighted in yellow. This document needs to be compiled in Tex Live Version 2021 of Overleaf. The document will not compile in the new version 2022. Reviewer 2 1. The motivations or remaining challenges are not so clear or what kinds of issues or difficulties are this task that is facing. Please give more details and discussion about the key problems solved in this paper, which is largely different from existing works. R: The key problem addressed in this paper refers to mapping features related to the dynamics of players and their actions to relevant events in soccer matches. This paper focuses on how to construct an effective representation of teams' dynamics using individual player information. In our formulation, we consider features associated with both attacking and defending teams at the same time. Our representation of soccer matches stands out for combining techniques never before applied together: complex networks as a consolidated way to observe the behavior of teams; visual rhythms responsible for translating the metrics into a format that makes temporal consistency more latent; and image processing of these features to enable the use of consolidated neural networks to aid in our prediction. Thus, we can confirm that our main contribution is a novel way to represent, evaluate, and predict scoring opportunities based on features associated with ball possession in soccer matches, using a new approach based on already consolidated concepts to optimize our results. 2. A deep literature review should be given, particularly advanced and SOTA deep learning or AI models in data processing and high-level analysis. Therefore, the reviewer suggests discussing some related works by analyzing the following papers in the revised manuscript. R: We included some recent studies regarding the mentioned topics in our related works section. In the current version of the manuscript, we describe and discuss how SOTA machine learning models have been applied to sports science problems, particularly soccer-related ones. These descriptions are included between the lines 150-184. 3. What are the main differences between the proposed attention techniques and existing machine learning related methods? R: In our process, we used a pre-trained network called EfficientNet B0, where we fine-tuned its predictions to suit our needs. Thus, our major contribution is not aimed at a new model or architecture, but rather the creation of a pipeline where multichannel images were processed by this network originally pre-trained for three-channel images. As achieved results suggest, the use of the proposed pipeline leads to very effective results in the considered prediction problem. Recall that the proposed pipeline is flexible and can easily accommodate the use of other architectures. The use of more effective pre-trained architectures would lead to even better results. 4. The reviewer is wondering how about the computational complexity of the proposed method? R: It took 1.5 Gb of vram to train the 44 million parameters, including fine-tuning the EfficientNet B0. Also, to obtain the graph metrics, we used the methods provided by the python library called networkx, and it required approximately 4.2 seconds to generate the metrics for each BPI. More details of the configuration used for training (such as time and hardware) are presented in the manuscript on the topic Computational Resources described in lines 362 -369. 5. It is well-known that the signals or images usually tend to suffer from various degradation, noise effects, or variabilities in the process of imaging. Please give the discussion and analysis. The reviewer is wondering what will happen if the proposed method meets the various variabilities. R: In our approach, we create our images from the Visual Rhythms obtained from the translation of the values computed from each metric into a pixel for the corresponding image. We understand that once there are several channels for each pixel (the frames in a sequence) the noise to the pixel value is not significant enough to affect the quality of the image. 6. Some future directions should be pointed out in the conclusion. R: As open lines left by our research, we can mention the investigation of features related to the team formation, such as, for example, the centroid or area of a team. Future work would then be concerned with the assessment of the use of this information for predicting relevant soccer events. Another research direction refers to directly extracting features from the graphs instead of computing image representations, such as visual rhythms. One suitable alternative would be to employ methods based on convolutional graph networks. Second awnsers 1. Please note that funding information should not appear in the Acknowledgments section/Funding section or Any other areas of your Manuscript. We will only publish funding information present in the Funding Statement section of the online submission form. Please remove any funding-related text from the manuscript. R: We have adjusted the Acknowledgements and removed all the funding information there. 2. Please provide additional details regarding participant consent. In the Methods section, please ensure that you have specified (1) whether consent was informed and (2) what type you obtained (for instance, written or verbal). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information. R: Our project was approved by the Research Ethics Committee of the State University of Campinas, protocol CAAE 56582616.8.0000.5404. And since all those present are of legal age, the need for consent was waived by the ethics committee, due to the video recording being done with proper cameras installed in the stadium in professional games Attachments Attachment Submitted filename: PONE-D-22-05975R2_ResponseReview2.pdf https://doi.org/10.1371/journal.pone.0265372.r004
1 Dec 2022 Decision Letter - Nguyen Quoc Khanh Le, Editor Using machine learning pipeline to predict entry into the attack zone in football PONE-D-22-05975R2 Dear Dr. Santiag, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Nguyen Quoc Khanh Le Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #2: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #2: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #2: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #2: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #2: The authors have well addressed the reviewer's comments. No more comments. It is ready to accept the current manuscirpt. ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #2: No ******** https://doi.org/10.1371/journal.pone.0265372.r005
Formally Accepted
27 Dec 2022 Acceptance Letter - Nguyen Quoc Khanh Le, Editor PONE-D-22-05975R2 Using machine learning pipeline to predict entry into the attack zone in football Dear Dr. Dias: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Nguyen Quoc Khanh Le Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0265372.r006

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