Redefining player roles in professional women’s basketball: From traditional positions to functional profiles

Sergio J. Ibáñez; Javier Courel-Ibáñez; José Miguel Contreras-García; María Isabel Piñar-López

doi:10.1371/journal.pone.0330726

Peer Review History

Original SubmissionMay 11, 2025
4 Jun 2025 Decision Letter - Gustavo Costa, Editor PONE-D-25-25456 Redefining Player Roles in Professional Women’s Basketball: From Traditional Positions to Functional Profiles PLOS ONE Dear Dr. Courel-Ibáñez, 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. ============================== ACADEMIC EDITOR: Dear authors, Thank you for sending me the most recent version of the manuscript. After the reviewers have read it, I ask that all requested changes be accepted. I believe that such changes will improve the quality of the manuscript. I await the submission of the new version. ============================== Please submit your revised manuscript by Jul 19 2025 11:59PM. 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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 ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 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: 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 ****** 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: The article have a good structure and fit with the main criteria of the journal. It can be accepted without modifications. Reviewer #2: This manuscript offers a timely and well-constructed contribution to the field of basketball performance analytics by proposing a data-driven redefinition of player roles in professional women’s basketball. The study is grounded in a large and valid dataset, applies appropriate statistical procedures, and produces findings that are both theoretically meaningful and practically applicable. The structure of the manuscript is coherent, the writing is clear, and the discussion offers thoughtful reflections on limitations and applications. Particularly valuable are the use of time-normalized performance metrics and the operational framing of functional profiles, which offer actionable insights for coaching, scouting, and roster management. The study makes a strong case for moving beyond traditional positional models and demonstrates methodological competence throughout. With several clarifications and revisions related to transparency, statistical reporting, and interpretation, this work will be well-positioned for publication. Methods The methods section is generally clear, with well-documented sampling procedures, data collection, and normalization processes. However, the clustering procedures require more technical detail. The number of iterations for k-means, the distance metric used, and the distributional assumptions behind the Gaussian mixture models (GMM) are not reported. Furthermore, while a silhouette index plot is shown in the Results section, its use is not mentioned in the Methods section, nor is the average silhouette score reported for the selected solution (k = 9). This omission reduces the methodological transparency of the clustering process. The Delphi process used to assign functional labels to clusters is a valuable methodological choice, but the selection criteria for the panelists are not described. There is no information regarding the experts’ coaching backgrounds, years of experience, or competitive level. This limits the credibility of the labeling process. A short subsection introducing these attributes (e.g., “Each expert had more than 10 years of coaching experience at the national or professional level”) would improve methodological rigor. Lastly, while the reliance on box-score data is discussed as a limitation in the Discussion, it should also be clearly stated in the Methods. This would help define the scope of the dataset, emphasizing that variables such as off-ball actions, spatial control, and advanced defensive behaviors are not captured. Results The results section is comprehensive and well-organized, but several revisions are needed to enhance clarity and interpretability. First, key statistical tables—including ANOVA outcomes, post hoc test results, and descriptive statistics by cluster—are presented only in the supplementary material. These tables are central to interpreting the findings and should be integrated into the main manuscript. The statistical reporting is incomplete, as only p-values are presented. F-values and effect size measures (e.g., η²) should be included to assess the magnitude and practical relevance of the observed differences. This is especially important given the large sample size. There is also a mismatch between the interpretation of the silhouette index and its graphical representation. The authors refer to the second inflection point occurring at k = 5; however, visual inspection of Figure 3 suggests that the most pronounced change occurs around k = 4. This discrepancy between the narrative and the figure should be addressed. Some of the cluster labels are vague. For instance, “Role Player” and “Developmental Player” lack tactical specificity and may not accurately describe role functions. In particular, Cluster 6 is labeled as “Developmental Player” but is primarily characterized by low playing time and a high number of committed fouls—traits that do not generally signify a developmental role. This label should be reexamined or more clearly justified. Table 1 is difficult to read due to dense formatting. The addition of visual separators—such as horizontal lines or alternating row shading—would improve readability. Furthermore, the implications of time normalization, while acknowledged in the Methods and Results, are not systematically analyzed. It would be helpful to explore how normalization influenced cluster membership, especially for players with lower average playing time. Discussion The discussion is thoughtful, well-structured, and demonstrates a clear understanding of both theoretical and practical implications. The study’s limitations are appropriately acknowledged, including the exclusive use of box-score data and the narrow size of the Delphi panel. These admissions enhance the study’s transparency. However, the strategic implications of time normalization deserve further elaboration. For example, how might normalized performance data influence player valuation, lineup rotations, or recruitment strategies? A paragraph discussing these aspects would improve the applied relevance of the findings. Additionally, the nine functional clusters introduced in the Results section are not revisited in the Discussion. Including brief tactical reflections on selected clusters—such as “Ball-Dominant Scorer” or “Floor General”—would strengthen the practical utility of the classification framework. Lastly, the Conclusion section includes language that is overly definitive in tone. For example, the final paragraph states that the model “makes a novel and foundational contribution to the scientific understanding of performance in professional women’s basketball” and “offers a practical tool for guiding systematic training, roster construction, and technical decision-making.” While the study presents promising and well-substantiated findings, such statements should be moderated to reflect the study’s scope and limitations—namely, that the data derive from a single national league and rely exclusively on box-score metrics. Rephrasing these claims in a more cautious manner (e.g., “offers preliminary evidence” or “may provide a useful framework”) would be more appropriate and aligned with the study’s actual contribution. ****** 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: Yes: Burak Karaca ******** [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. 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Revision 1
22 Jul 2025 Author Response Dear Editor, Firstly, thank you for providing the peer-review reports of all reviewers. We have carefully considered all reviewers' considerations of the paper (PONE-S-25-33239). Please find enclosed our detailed answers to reviewers' queries. We hope the changes meet your expectations. Your contributions have significantly improved the quality of the article. To make your work easier, all corrections to the article are displayed in red. Reviewer #2_1: This manuscript offers a timely and well-constructed contribution to the field of basketball performance analytics by proposing a data-driven redefinition of player roles in professional women’s basketball. The study is grounded in a large and valid dataset, applies appropriate statistical procedures, and produces findings that are both theoretically meaningful and practically applicable. The structure of the manuscript is coherent, the writing is clear, and the discussion offers thoughtful reflections on limitations and applications. Particularly valuable are the use of time-normalized performance metrics and the operational framing of functional profiles, which offer actionable insights for coaching, scouting, and roster management. The study makes a strong case for moving beyond traditional positional models and demonstrates methodological competence throughout. With several clarifications and revisions related to transparency, statistical reporting, and interpretation, this work will be well-positioned for publication. Answer #2_1: We would like to express our sincere gratitude for your thorough and positive review of our manuscript. We are honored that you value the timeliness and robustness of our contribution to the analysis of performance in professional women's basketball, both in terms of the sample size of the database used and the appropriateness of the statistical procedures applied. Your recognition of the structural coherence, the clarity of the writing, and the depth of our reflections on limitations and applications reinforces our conviction that the approach adopted is relevant to practice and research. We especially appreciate your comments on the usefulness of standardized metrics and the definition of functional profiles of female basketball players. Your suggestions for improving methodological transparency, statistical reporting, and interpretation will be incorporated with the utmost rigor to further strengthen the work. We deeply appreciate the time and expertise dedicated to this review process, which will undoubtedly contribute to refining the quality and potential impact of our study. Methods Reviewer #2_2: The methods section is generally clear, with well-documented sampling procedures, data collection, and normalization processes. However, the clustering procedures require more technical detail. The number of iterations for k-means, the distance metric used, and the distributional assumptions behind the Gaussian mixture models (GMM) are not reported. Furthermore, while a silhouette index plot is shown in the Results section, its use is not mentioned in the Methods section, nor is the average silhouette score reported for the selected solution (k = 9). This omission reduces the methodological transparency of the clustering process. Answer #2_2: Thank you very much for your feedback, as it helps explain the method used. The k-means algorithm was run for 10 iterations and used Euclidean distance, automatic procedures used by default, following previous methodologies in similar sports contexts (Bianchi et al., 2017). Bianchi, F., Facchinetti, T., & Zuccolotto, P. (2017). Role revolution: towards a new meaning of positions in basketball. Electronic Journal of Applied Statistical Analysis, 10(3), 712-734. DOI: 10.1285/i20705948v10n3p712 For the application of Gaussian mixture models (GMM), it was assumed that the data of each cluster followed a multivariate normal distribution, which allows modeling performance patterns with greater flexibility than other methods such as k-means. In addition, the use of the silhouette index was explicitly incorporated as a complementary criterion for choosing the optimal number of clusters. The silhouette plot showed a maximum value at k = 2 (around 0.26), but this solution was discarded due to its low practical utility. For k = 9, the average value was approximately 0.12, acceptable considering the complexity of functional behavior in professional basketball. A similar number of clusters has already been used in similar studies on professional basketball players (Kalman & Bosch, 2020) Methods have been improved as follows: The k-means algorithm was run for 10 iterations and used Euclidean distance, automatic procedures used by default, following previous methodologies in similar sports contexts [8]. For the application of Gaussian mixture models (GMM), it was assumed that the data of each cluster followed a multivariate normal distribution. The silhouette index was explicitly incorporated as a complementary criterion for selecting the optimal number of clusters [27] Reviewer #2_3: The Delphi process used to assign functional labels to clusters is a valuable methodological choice, but the selection criteria for the panelists are not described. There is no information regarding the experts’ coaching backgrounds, years of experience, or competitive level. This limits the credibility of the labeling process. A short subsection introducing these attributes (e.g., “Each expert had more than 10 years of coaching experience at the national or professional level”) would improve methodological rigor. Answer #2_3: Thank you for this observation. We have now clarified the selection criteria for the expert panel involved in the Delphi process. Specifically, we have included a brief description of their academic and professional backgrounds to strengthen the methodological rigor of the labeling procedure. The following sentence has been added to the Methods section: “Functional group labels were established through a Delphi method involving four PhD-level experts with published research in performance analysis and over 10 years of coaching experience in both men’s and women’s basketball at the national or professional level.” Reviewer #2_4: Lastly, while the reliance on box-score data is discussed as a limitation in the Discussion, it should also be clearly stated in the Methods. This would help define the scope of the dataset, emphasizing that variables such as off-ball actions, spatial control, and advanced defensive behaviors are not captured. Answer #2_4: Thank you for highlighting this important point. We agree that the limitations of using box-score data should be acknowledged not only in the Discussion but also in the Methods section to clarify the scope of the dataset. We have now added a statement in the Methods to explicitly address this limitation. The following sentence has been added to the Variables section in the Methods: “Although box-score statistics are limited to capturing off-ball actions, spatial dynamics, or advanced defensive behaviors, these variables quantify each player’s contributions across various aspects of the game and were selected based on their tactical relevance and representation of individual and team performance.” Results Reviewer #2_5: The results section is comprehensive and well-organized, but several revisions are needed to enhance clarity and interpretability. First, key statistical tables—including ANOVA outcomes, post hoc test results, and descriptive statistics by cluster—are presented only in the supplementary material. These tables are central to interpreting the findings and should be integrated into the main manuscript. Answer #2_5: Thank you for this suggestion. All supplementary material is now included in the main manuscript to enhance the clarity and accessibility of the results section. Reviewer #2_6: The statistical reporting is incomplete, as only p-values are presented. F-values and effect size measures (e.g., η²) should be included to assess the magnitude and practical relevance of the observed differences. This is especially important given the large sample size. Answer #2_6: Thank you for this observation. We acknowledge the value of including full statistical details such as F-values and effect sizes. However, in the context of this study, the one-way ANOVA was used as a confirmatory analysis to verify that the identified clusters represent statistically distinct groups based on performance indicators. Given the large sample size and the primary focus on unsupervised clustering and functional interpretation, our emphasis was placed on establishing independence between clusters rather than quantifying the magnitude of each difference. Nonetheless, we agree that including effect size measures could enhance interpretability and are open to incorporating them in future analyses or as supplementary material upon request. Reviewer #2_7: There is also a mismatch between the interpretation of the silhouette index and its graphical representation. The authors refer to the second inflection point occurring at k = 5; however, visual inspection of Figure 3 suggests that the most pronounced change occurs around k = 4. This discrepancy between the narrative and the figure should be addressed. Answer #2_7: Thank you for pointing out this discrepancy. Upon re-evaluating the silhouette index curve in Figure 3, we acknowledge that the most pronounced inflection indeed occurs at k = 4 rather than k = 5. We have revised the text to accurately reflect this observation and ensure consistency between the figure and the narrative. Reviewer #2_8: Some of the cluster labels are vague. For instance, “Role Player” and “Developmental Player” lack tactical specificity and may not accurately describe role functions. In particular, Cluster 6 is labeled as “Developmental Player” but is primarily characterized by low playing time and a high number of committed fouls—traits that do not generally signify a developmental role. This label should be reexamined or more clearly justified. Answer #2_8: Thank you for this constructive observation. We agree that the original labels “Role Player” and “Developmental Player” lacked sufficient tactical specificity. In the revised classification, we have replaced these terms with more descriptive and behavior-based labels derived directly from the normalized performance indicators. Specifically, Cluster 6 is now labeled Low-Usage Support Player, reflecting its consistently low values across all box-score categories, rather than implying a developmental status. This new label better captures the actual statistical profile—minimal offensive or defensive involvement, low turnovers, and low foul generation—suggesting a situational or peripheral role rather than one tied to player potential or career stage. Similarly, Cluster 5 is now termed Efficient Support Guard, emphasizing its balanced contribution with low turnover and moderate assist values. These changes improve the clarity and tactical relevance of the functional labels across the nine-cluster framework. Reviewer #2_9: Table 1 is difficult to read due to dense formatting. The addition of visual separators—such as horizontal lines or alternating row shading—would improve readability. Furthermore, the implications of time normalization, while acknowledged in the Methods and Results, are not systematically analyzed. It would be helpful to explore how normalization influenced cluster membership, especially for players with lower average playing time. Answer #2_9: Thank you for this helpful feedback. To improve readability, we have reformatted Table 1 by adding horizontal lines, as suggested. Regarding time normalization, we showed that normalization allows us to identify performance patterns that would otherwise be obscured by unequal playing time. As illustrated in Figures 6, 7, and 8, time normalization revealed notable improvements in performance indicators for players within lower-participation profiles, particularly Clusters 5 and 6. Despite limited court time, these players demonstrated substantial in-game impact when their contributions were adjusted to a 40-minute standard. In contrast, players from high-participation clusters—such as Clusters 1, 3, and 4—showed more balanced distributions across performance indicators, reflecting their sustained presence and consistent involvement throughout matches. This highlights the added value of normalization in identifying efficient contributors and refining functional classification. Discussion Reviewer #2_10: The discussion is thoughtful, well-structured, and demonstrates a clear understanding of both theoretical and practical implications. The study’s limitations are appropriately acknowledged, including the exclusive use of box-score data and the narrow size of the Delphi panel. These admissions enhance the study’s transparency. Answer #2_10: We deeply appreciate your kind comments on the discussion of our manuscript. We are pleased to know that you found it thoughtful, well-structured, and capable of solidly integrating the theoretical and practical implications of the study. Your words encourage us to continue to maintain this balance between analytical depth and methodological honesty, and to continue refining the manuscript to maximize its relevance and reliability. Reviewer #2_11: However, the strategic implications of time normalization deserve further elaboration. For example, how might normalized performance data influence player valuation, lineup rotations, or recruitment strategies? A paragraph discussing these aspects would improve the applied relevance of the findings. Answer #2_11: Thank you for this insightful comment. We have now added a dedicated paragraph in the Discussion section addressing the strategic implications of time normalization. “Normalizing performance metrics per minute or per possession allows for more equitable comparisons among players with differing amounts of court time. This adjustment enhances the identification of high-efficiency contributors who may have limited minutes but provide substantial impact relative to their playing time [36]. From an analytical standpoint, per-possession statistics support more robust modelling of lineup combinations based on net efficiency—defined as points scored and conceded per 100 possessions—facilitating strategic decisions that maintain team performance while managing player workload and fatigue. Incorporating tactical indicators such as ball reversal, which has been shown to promote spacing, decision-making, and offensive success in professional women’s basketball, may add further value to lineup modelling and team efficiency assessments [37]. Moreover, normalized metrics help control for variations in game pace, which is particularly important when comparing players across different leagues or competitive contexts [36]. This scalability is especially useful in recruitment and scouting, where per-minute and per-possession data provide a fairer assessment of talent across diverse playing environments. Additionally, long-term analyses of elite basketball players have shown that normalized statistics (e.g., points, assists, and rebounds per minute) are effective for tracking career development and identifying key performance trends, such as increased assist rates and free-throw percentages with experience [14]. Nonetheless, not only performance but also psychosocial factors such as group cohesion can influence team sustainability. Evidence shows that stronger task cohesion among players positively predicts their intention to persist in the same team across seasons, which may contribute to long-term lineup stability and enhance the value of efficient player combinations [38]” Reviewer #2_12: Additionally, the nine functional clusters introduced in the Results section are not revisited in the Discussion. Including brief tactical reflections on selected clusters—such as “Ball-Dominant Scorer” or “Floor General”—would strengthen the practical utility of the classification framework. Answer #2_12: Thank you for this suggestion. We have added the following paragraph to the Discussion section to reflect on selected clusters and highlight their Attachments Attachment Submitted filename: R1. Rebuttal letter.docx https://doi.org/10.1371/journal.pone.0330726.r002
6 Aug 2025 Decision Letter - Gustavo Costa, Editor Redefining Player Roles in Professional Women’s Basketball: From Traditional Positions to Functional Profiles PONE-D-25-25456R1 Dear Dr. Courel-Ibáñez, 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 will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact billing support. 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, Gustavo De Conti Teixeira Costa, Ph.D Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0330726.r003
Formally Accepted
Acceptance Letter - Gustavo Costa, Editor PONE-D-25-25456R1 PLOS ONE Dear Dr. Courel-Ibáñez, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps. Lastly, 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. You will receive an invoice from PLOS for your publication fee after your manuscript has reached the completed accept phase. If you receive an email requesting payment before acceptance or for any other service, this may be a phishing scheme. Learn how to identify phishing emails and protect your accounts at https://explore.plos.org/phishing. If we can help with anything else, please email us at customercare@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. Gustavo De Conti Teixeira Costa Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0330726.r004

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