Multicriteria Decision Model for Player Evaluation and Selection

Jogadores, Modelo de Decisão Multicritério para Avaliação e Seleção de

doi:10.22167/2675-6528-202602811

Article

September 30, 2026

Multicriteria Decision Model for Player Evaluation and Selection

Multicriteria Decision Model for Player Evaluation and Selection

David Antonio Teixeira dos Santos; Elias Soares de Figueiredo

DOI: 10.22167/2675-6528-202602811

Article derived from a Final Course Work (TCC), with content based on the student’s original work and adapted to the editorial format of the E&S Magazine with the support of the ResumeAI tool, an artificial intelligence solution developed by Instituto Pecege for textual synthesis and organization.

Summary

Considering the professional football transfer market, characterized by high financial volumes and decision-making complexity, an analytical model was developed to support player selection, using data analysis and machine learning techniques. Athlete performance data were collected through sports APIs. The K-means algorithm was applied to group players into clusters of similar tactical roles, allowing for homogeneous comparisons. Subsequently, a multicriteria evaluation system (score) was developed, based on multiple performance metrics, to rank players within their contexts. Different classification models were evaluated, including KNN, SVC, Random Forest, and XGBoost, with hyperparameter optimization. The SVC model achieved the best performance, with an accuracy of 0.6371, in estimating the probability of transfer success. The results indicated that the approach identifies players with comparable tactical profiles and estimates the probability of transfer success, considering technical, tactical, and utilization criteria. It was concluded that the integration of clustering, ranking, and predictive modeling offers a more structured, quantitative, and less subjective approach to recruitment in professional football.

Keywords: Football; K-means; Machine learning; SVC.

1. Introduction

The professional football transfer market is an environment of high complexity and with significant financial volumes. The Premier League alone, for example, registered spending of approximately 21 billion reais on signings in the summer window of the 2025/2026 season (O Globo, 2025). In this context, the choice and selection of players represent a crucial challenge for clubs, as they involve the consideration of multiple factors that go beyond the individual technical quality of the athlete.

Decision-making in player selection requires balancing technical, tactical, and financial aspects. Traditional evaluation, often subjective, may not capture the entirety of these dimensions, creating the need for more structured and quantitative approaches. This problem aligns with a multicriteria decision-making approach, which aims to support the choice between different alternatives by considering multiple criteria simultaneously (Greco et al., 2016; Ishizaka & Nemery, 2013).

In football, multicriteria decision aid is particularly useful, as a player can excel in different attributes, such as defensive or offensive performance, and financial factors can influence the viability of a signing. The increasing professionalization of the sport and competitiveness drive the use of data and quantitative analysis, with performance analysis departments playing a strategic role in identifying and evaluating talent.

To reduce subjectivity and enhance analysis, machine learning techniques are employed. Unsupervised learning methods, such as the K-means algorithm, allow for grouping players into clusters that represent similar tactical functions (Lucey et al., 2013; Rein & Memmert, 2016; Andrienko et al., 2017). This segmentation is crucial for enabling more coherent comparisons between athletes who perform similar roles on the field, avoiding inadequate analyses between distinct profiles (Akhanli & Hennig, 2022).

The basis for this analysis is a set of continuous player performance data, obtained through sports APIs, which provide detailed and up-to-date information (Pappalardo et al., 2019). The use of these continuous variables is fundamental to capturing real performance differences and preserving the multidimensionality of the decision-making problem. Additionally, similarity-based learning techniques are applied to identify players comparable to a reference athlete, considering multiple performance dimensions (Decroos & Davis, 2019).

Complementarily, supervised learning techniques are used to evaluate and predict the suitability of recommended players. The comparison between different classification algorithms, with hyperparameter optimization, allows for the selection of the most suitable model for the problem, increasing the robustness of the decision support system (Géron, 2021). The integration of these approaches — multicriteria decision-making, unsupervised and supervised learning — offers a more structured and comprehensive method for player analysis.

The application of quantitative methods in football represents a significant advance compared to traditional approaches, providing greater transparency, consistency, and analytical capacity in the player selection process. By incorporating multiple criteria, exploring the similarity between athletes, and integrating predictive models, it becomes possible to reduce risks associated with signings and increase the probability of sporting and financial success for clubs. The work aims to develop an analytical decision support model for identifying players compatible with the needs of a football team, using machine learning techniques based on statistical similarity. The proposed approach seeks to analyze individual athlete performance and team context, offering recommendations based on historical data.

2. Material and Methods

This applied and experimental study used secondary data obtained from API-Football, a platform of public and licensed data on professional football. Access to the API, permitted for academic purposes, was granted upon registration and followed terms of use. The API provided detailed match statistics, player metrics, and contextual team information, in JSON format. Data collection was executed through automated HTTP requests in Python, respecting API limits. The data is public and aggregated, with no private personal information.

The dataset covered match data from the six major European leagues (Premier League, La Liga, Serie A, Bundesliga, Ligue 1, and Primeira Liga) from the 2018 to 2024 seasons. A total of 14809 matches, 564593 player statistics, and 29410 team statistics were counted. Player information included age, nationality, position, minutes played, goals, assists, shots, passes, defensive metrics, and disciplinary indicators.

Data preparation utilized Python with libraries such as `requests`, `pyspark`, and `pandas`. Treatment, cleaning, and standardization were applied, removing inconsistent records, handling missing values, and standardizing numerical variables. Player data were divided by minutes played to evaluate effectiveness; team variables were calculated as averages. Players with less than 300 minutes on the field and goalkeepers were excluded to reduce the influence of observations with low representativeness and due to the unique characteristics of the position.

Data standardization was performed using StandardScaler, which ensured that all features had zero mean and unit variance, guaranteeing that variables with different scales did not influence distance-based models, such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVC), maintaining consistency in the modeling pipeline.

For data clustering, the Scikit-learn library was used to train and apply the K-means algorithm. Two to ten cluster numbers were tested, and the final number of groups was determined by visual evaluation of the Elbow method and the Silhouette coefficient. Player groups were calculated for each field sector, differentiating into defenders (D), midfielders (M), and forwards (F).

For the comparative evaluation of athletes, a ranking system (score) based on multiple performance metrics was developed. The data were segmented by position, season, cluster, and league. Specific metrics, such as passing efficiency, dribbling ability, and duel performance, were defined. Rankings were constructed using the dense rank function within each partition. Players were ranked by minutes played, overall evaluation (rating), offensive and defensive metrics. The aggregate score was defined by subtracting the positions obtained in the rankings from a constant value.

To predict the success of transfers, an approach based on the comparison of multiple supervised classification algorithms was developed. The success of a transfer was defined by the player’s participation in at least 50% of the matches in the season following the team change. The data were divided into training and testing sets (75% and 25%, respectively), with a fixed seed for reproducibility. The SMOTE oversampling technique was applied to the training set to generate synthetic examples of the minority class.

Algorithms such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest, Gradient Boosting, Extreme Gradient Boosting (XGBoost), Gaussian Naive Bayes, and a baseline model (Dummy Classifier) were evaluated. Hyperparameter optimization for each model was performed using Randomized Search with five-fold cross-validation. The final evaluation of the models was conducted based on accuracy on the test set.

3. Results and Discussion

The application of the developed analytical model revealed significant insights into the evaluation and selection of players in professional football, demonstrating the ability to integrate different machine learning techniques to support decision-making. The initial research results indicated that segmenting players into tactically homogeneous groups, multicriteria evaluation, and predictive modeling of transfer success offer a more robust and less subjective approach than traditional methods. This integration allowed for an in-depth analysis of individual athlete performance and team context, providing recommendations based on historical data.

Grouping

The application of the K-means algorithm for player clustering, with the definition of four groups (k=4) for each position, was performed based on metrics normalized per minute of play. The choice of the number of clusters was guided by the visual evaluation of the Elbow method, which seeks a balance between the lowest inertia variation and the number of groups, and by the Silhouette coefficient, which measures the degree of similarity of a sample with its own cluster compared to others. Although the silhouette coefficients obtained were relatively low (0.147 for defenders, 0.159 for midfielders, and 0.165 for forwards), these values still indicate a moderate separation between the clusters.

This moderate separation suggests that, despite distinct behavioral patterns among players, there is an overlap zone between the identified profiles, which is an expected behavior in the context of football, where the tactical roles of athletes frequently overlap. However, the segmentation proved useful for identifying trends and grouping players with similar characteristics, allowing for a more coherent comparison within the same on-field position context. The clusters, even with silhouette values below the ideal, provide an interpretable and applicable structure to the recruitment problem, highlighting relevant variations in the tactical behavior of athletes.

The cluster analysis revealed specific tactical profiles that transcend simple positional classification. For defenders, four profiles were identified: the “Balanced Defender”, with moderate participation in passes, duels, and defensive actions; the “Conservative Defender”, with low offensive participation and lower volume of actions; the “Aggressive Defender”, with high involvement in duels and dribbles, presenting a more combative profile; and the “Builder Defender”, with a high volume of passes and strong participation in game construction. This distinction demonstrates the model’s ability to capture tactical nuances that formal position would not reveal.

Among the forwards, four distinct profiles were also identified. The “Intense Forward” is characterized by a high volume of duels, dribbles, and fouls, with a physical and participatory profile. The “Balanced Forward” presents moderate participation in all metrics. The “Finishing Forward” focuses on goals, with a high number of shots per minute. Finally, the “Support Forward” exhibits lower direct offensive volume, with a more complementary performance to the team. These profiles allow clubs to seek athletes who fit specific tactical needs, rather than limiting themselves to a generic evaluation based solely on position.

For midfielders, clustering also generated four profiles. The “Offensive Midfielder” shows high involvement in duels and dribbles, with relevant offensive contribution. The “Advanced Midfielder” has a higher frequency of shots and elevated offensive presence. The “Playmaking Midfielder” stands out for its high volume of passes and defensive actions, acting as a game organizer. The “Balanced Midfielder” presents an intermediate profile between attack and defense. This granularity in identifying tactical profiles corroborates the findings of Akhanli and Hennig (2022), who highlight the importance of segmenting players based on performance data to form more homogeneous groups, overcoming the limitations of simple classification by position on the field.

The results obtained reinforce the argument of Bialkowski et al. (2014), who point out that players can perform tactical roles different from their registered formal position. This makes analyses based exclusively on position insufficient to capture the complexity of the game. The use of clustering, therefore, allows for a more robust analysis of players, segmenting athletes into tactically homogeneous groups and enabling more coherent comparisons, which is fundamental for effective recruitment aligned with a club’s tactical needs.

Grade

The performance metric-based ranking system, called score, was developed to comparatively evaluate athletes within their respective contexts. The correlation analysis between the score and the performance variables in the 2022 season indicated that there is no strong association between the score and any single isolated metric. For example, the rating, an average evaluation of players, showed a low positive correlation of 0.18 with the score, suggesting that the score is not limited to reproducing this average evaluation but incorporates other dimensions.

It was observed that several metrics showed a negative correlation with the score, such as dribbles (-0.32), duels (-0.31), and fouls (-0.31). This indicates that higher values in these variables do not necessarily imply greater similarity with the profile analyzed by the score. This result reinforces the relative nature of the model, which assesses the proximity between players considering multiple dimensions simultaneously, rather than seeking the isolated maximization of specific indicators. The practically null correlation with the total number of games (0.01) suggests that the score is not directly influenced by the volume of matches, indicating that the model does not present a bias related to the athletes’ exposure time.

The application of the score to Real Madrid players in the 2022 season highlighted the model’s ability to identify athletes with high relative performance within their respective contexts. Players such as Karim Benzema and Federico Valverde showed high score and rating percentiles, indicating consistency between the traditional evaluation and the proposed metric. However, it was observed that some athletes presented a high rating but a lower score, demonstrating that the model captures multiple performance dimensions and not just the average evaluation.

These results demonstrate that the score represents a multidimensional measure, and cannot be explained by any isolated variable, which is consistent with the proposed multicriteria approach adopted in the study. In practice, this characteristic allows for the identification of players with balanced performance across different dimensions of the game, avoiding the overvaluation of athletes who excel only in specific metrics. Thus, the score proves to be an adequate tool to support decisions in multicriteria contexts, aligning with the approach of Decroos and Davis (2019), who use player vectors to represent playing styles considering multiple dimensions simultaneously.

Unlike other approaches that use spatial event data, this work employs performance metrics aggregated per match, which broadens its applicability to more accessible databases. The low correlation of the score with isolated individual metrics, such as dribbles and duels, reinforces the idea of Pappalardo et al. (2019) about the limitations of one-dimensional approaches, which tend to overvalue specific aspects of performance. Thus, the score contributes to a more complete and balanced evaluation of the player’s profile.

Model

To predict the success of player transfers, different classification algorithms were evaluated, including K-Nearest Neighbors (KNN), Support Vector Machine (SVC), Random Forest, Gradient Boosting, Extreme Gradient Boosting (XGBoost), Gaussian Naive Bayes, and a baseline model (Dummy Classifier). Hyperparameter optimization was performed using the Randomized Search technique with 5-fold cross-validation, and model evaluation was conducted based on accuracy on the test set.

The results indicated that the SVC model presented the best performance, with an accuracy of 0.6371. Next, KNN obtained 0.6225 and Random Forest reached 0.5907. The other models showed inferior performance, notably Gaussian Naive Bayes (0.5351) and Dummy Classifier (0.4020), the latter representing a minimum performance baseline. Although the accuracy of 0.6371 obtained by the SVC model is not high in absolute terms, it represents a good gain compared to the baseline model, indicating the presence of predictive signal in the data used.

This result is consistent with the multifactorial nature of the problem, as the success of transfers depends on multiple unobservable factors, such as the athlete’s adaptation to the new club, the occurrence of injuries, and psychological aspects. The moderate accuracy reflects the complexity inherent in predicting individual performance in team sports, as highlighted by Goes et al. (2021), who point out the challenges arising from the absence of relevant contextual variables, such as the team’s playing style and the athletes’ physical condition.

The main contribution of this work lies in the integration of different stages into a single analytical pipeline. This approach combines tactical segmentation by clusters, the construction of a multicriteria score based on a reference player’s profile, and the application of a predictive model to assess the probability of transfer success. Unlike studies that treat these stages in isolation, the proposed approach offers a more complete decision support system, with potential for practical application in player recruitment, providing a holistic and structured view.

As limitations of the study include the definition of transfer success based on player participation in at least 50% of matches in the following season. Although this criterion is an objective proxy for utilization and adaptation, it does not fully capture the athlete’s sporting impact, potentially classifying players with high participation but low performance as successful, or disregarding athletes who were underutilized due to external factors, such as injuries. Additionally, the model did not incorporate relevant contextual variables, such as team playing style, tactical formation, coach characteristics, and opponent level, factors that can significantly influence player performance and transfer success.

Another important limitation refers to the absence of financial variables, such as salary, market value, and transfer cost, which are determinants in the actual decision-making process of clubs. The exclusion of players with low playing time, although necessary to reduce statistical variability, may have removed athletes with relevant potential, such as young players at the beginning of their careers or players returning from injury. Similarly, the exclusion of goalkeepers limits the model’s applicability to all on-field positions. Finally, the metrics used were aggregated and normalized per minute, which, although allowing comparability, does not fully capture the context of the matches, and the moderate accuracy of the classification models reflects the complexity of the problem, which involves unobservable factors in the available data.

In summary, the developed analytical model demonstrated the feasibility of an integrated approach for decision support in player selection. Clustering allowed the identification of homogeneous tactical profiles, the multicriteria score offered a multidimensional performance evaluation, and the SVC predictive model estimated the probability of success in transfers. This combination of machine learning and multicriteria decision techniques offers a more structured, quantitative, and less subjective tool for professional football recruitment, despite the limitations inherent in the complexity of the domain and data availability.

4. Conclusion

The present study sought to develop an analytical decision support model for identifying players compatible with the needs of a football team, employing machine learning techniques based on statistical similarity. It was found that the application of the K-means algorithm allowed grouping players into clusters of similar tactical roles, revealing specific profiles that transcend traditional classification by on-field position. This segmentation enabled more homogeneous comparisons between athletes. Additionally, a multicriteria ranking system, the score, was developed, which evaluated player performance multidimensionally, without being limited to isolated metrics or match volume. In the predictive modeling stage, the Support Vector Machine (SVC) algorithm achieved the best performance, with an accuracy of 0.6371, in estimating the probability of transfer success. The main contribution of this work lies in the integration of these stages into a single analytical pipeline, offering a more structured, quantitative, and less subjective approach to recruitment in professional football.

However, the study presented some limitations, such as the definition of transfer success based on participation in at least 50% of matches, which does not fully capture the sporting impact or external factors like injuries. The absence of relevant contextual variables, such as team playing style and coach characteristics, and financial variables, which are crucial in clubs’ decision-making, was also observed. The exclusion of players with low playing time and goalkeepers also restricted the scope of the analysis. For future studies, it is suggested to incorporate advanced metrics, such as expected goals (xG), include financial and contractual data, and explore more complex models, such as neural networks, to enhance the system’s predictive capability.

Bibliographic References

AKHANLI, S. E.; HENNIG, C. Clustering of football players based on performance data and aggregated clustering validity indexes. Statistics and Computing, v. 32, n. 1, p. 1–22, 2022.

Andrienko, G.; Andrienko, N.; Budziak, G.; von Landesberger, T.; Weber, H. (2017). Visual analysis of pressure in football. Heidelberg: Springer.

BIALKOWSKI, A. et al. (2014). Large-scale analysis of soccer matches using spatiotemporal tracking data. In: IEEE International Conference on Data Mining, Shenzhen. Proceedings… Shenzhen: IEEE, p. 725–730.

DECROOS, T.; DAVIS, J. Player vectors: characterizing soccer players’ playing style from match event streams. In: BREFELD, U. et al. (Ed.). Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science, v. 11908. Cham: Springer, 2020. p. 569–584

GOES, F. et al. (2021). Unlocking the potential of big data to support tactical performance analysis in professional soccer. European Journal of Sport Science, v. 21, n. 4, p. 481–496.

Greco, S., Ehrgott, M.; Figueira, J. R. (2016). Multiple criteria decision analysis:state of the art surveys. New York: Springer.

GÉRON, A. Mãos à Obra: Aprendizado de Máquina com Scikit-Learn, Keras & TensorFlow. 2. ed. Rio de Janeiro: Alta Books, 2021.

Ishizaka, A.; Nemery, P. (2013). Multi-criteria decision analysis: methods and software. Chichester: John Wiley & Sons.

Lucey, P., Bialkowski, A., Monfort, M., Carr, P.; Matthews, I. (2013).Assessing team strategy using spatiotemporal data. New York: ACM.

O GLOBO. Premier League quebra recorde e ultrapassa a barreira dos R$ 21 bilhões em gastos com reforços na janela. 2 set. 2025. Disponível em: https://oglobo.globo.com/esportes/noticia/2025/09/02/premier-league-quebra-recorde-e-ultrapassa-a-barreira-dos-r-21-bilhoes-em-gastos-com-reforcos-na-janela.ghtml#:~:text=A%20Premier%20League%20quebrou%20recordes,as%20cinco%20grandes%20ligas%20europeias. Acesso em 01/02/2026

PAPPALARDO, L. et al. PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach. ACM Transactions on Intelligent Systems and Technology, v. 10, n. 5, p. 1–27, 2019.

Rein, R.; Memmert, D. (2016). Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. Cham: Springer.

Article originating from the Final Course Work of the Specialization in Data Science and Analytics of the MBA USP/Esalq

To learn more about the course, click here and access the MBX Academy platform

Most recent

Economy, Investments And Banking

October 23, 2026

Educação Financeira, Comportamentos e Vieses na Tomada de Decisão Financeira

October 22, 2026

Challenges and Possibilities of the First School Year of Children with ASD for Teaching Work

October 22, 2026

Consumer Journey in the Decision to Purchase Infoproducts in the Digital Environment

October 22, 2026

Psychosocial Risks and Mental Health at Work in a Banking Institution

October 22, 2026

Digital Observatory of the Amazon: Data Science Applied to the Analysis of Social, Economic, Environmental, and Health Indicators

October 22, 2026

Continuous Learning as a Factor of Organizational Growth in the Perception of Collaborators

October 22, 2026

Statistical Regression in Microgrids for Operational Performance Estimation: Campusgrid Case

October 22, 2026

Forecast of Aggregate Default in the Brazilian Financial System by Sarimax Model with Macroeconomic Variables

October 22, 2026

Stakeholder Engagement Factors in International Technology Transfer to a Public Hospital

A gestão financeira pessoal representa um desafio crescente, influenciada por vieses cognitivos que afetam a tomada de decisão, mesmo em indivíduos com alto conhecimento. O presente estudo analisou a relação entre o nível de educação financeira e a ocorrência de vieses comportamentais na tomada de decisão financeira de adultos residentes no Brasil. Realizou-se um levantamento de campo de natureza aplicada, quantitativa e descritivo-correlacional, por meio da aplicação de um questionário estruturado a 163 respondentes. O instrumento contemplou variáveis sociodemográficas, conhecimento e hábitos financeiros, e situações hipotéticas para identificar vieses cognitivos. Os resultados indicaram que níveis mais elevados de educação financeira associaram-se a melhores práticas de controle financeiro, poupança e investimento, com correlações significativas com renda e escolaridade. Contudo, a presença de vieses comportamentais (ancoragem, aversão à perda, escassez, otimismo/negatividade, viés do presente, status quo, excesso de confiança e contabilidade mental) persistiu mesmo entre indivíduos com maior escolaridade e renda, sem associação estatisticamente significativa com as variáveis sociodemográficas analisadas. Concluiu-se que a educação financeira, embora essencial para a organização e o controle financeiro, não foi suficiente para neutralizar a influência de vieses cognitivos. Sugere-se a incorporação de abordagens comportamentais aos programas de educação financeira.

Palavras-chave: Comportamento financeiro; Economia comportamental; Finanças pessoais; Heurísticas cognitivas; Vieses cognitivos.

October 22, 2026

Challenges and Possibilities of the First School Year of Children with ASD for Teaching Work

The inclusion of children with Autism Spectrum Disorder (ASD) in Early Childhood Education presents significant challenges, given the difficulty of adapting to new environments and the intensity of stimuli. The objective was to analyze the teaching performance in Early Childhood Education in the initial schooling process of children with ASD. A case study was conducted, using narrative interview as a data production technique. The results revealed that the teaching performance was characterized by mediation and affection practices, with the flexibility of routine and the use of playful resources as essential strategies for the child’s development and sensory organization. The importance of the school support professional for the success of the pedagogical work was also highlighted. The child’s integral development was promoted through intentional teaching actions that prioritized environmental adaptation, strengthening of bonds, and attention to the specific needs of ASD. As a contribution, a guide of pedagogical strategies aimed at welcoming children with ASD in public daycare centers was developed.

Keywords: Early Childhood Education; School Inclusion; Teacher Narratives; Inclusive Pedagogical Practices; Autism Spectrum Disorder.

October 22, 2026

Consumer Journey in the Decision to Purchase Infoproducts in the Digital Environment

The expansion of e-commerce and technological transformation have driven the need to understand consumer behavior regarding infoproducts, whose perceived experience has become crucial for professional development. The study aimed to analyze the factors that influenced the purchase decision of infoproducts in the digital environment, considering the stages of the consumer journey. An applied, quantitative, and descriptive research was conducted through a field survey with 100 consumers from Serra Gaúcha. Data collection occurred via a structured questionnaire, using non-probabilistic sampling. The results revealed a qualified audience with strong “mobile-first” behavior. It was found that the availability of detailed information and social approval were the main attraction triggers in the pre-purchase phase. In the transactional and subsequent stages, the minimization of operational friction, the agility of immediate access, and system security proved crucial for conversion and satisfaction, although gaps in human support efficiency were identified. Retention was based on the trust generated by positive past experiences, overcoming the price factor. It was concluded that the infoproduct consumer journey operates as an integrated system, requiring alignment between technological infrastructure, content curation, and support humanization to ensure engagement.

Keywords: E-commerce; Consumer behavior; Consumer experience; Loyalty; Virtual business.

October 22, 2026

Psychosocial Risks and Mental Health at Work in a Banking Institution

Mental health in the workplace is a topic of growing relevance, impacting professional development and employee well-being. A study was conducted to identify aspects and situations that can influence absenteeism levels in a banking institution. The research involved 108 participants and used a structured questionnaire, based on the short version of COPSOQ II by Gonçalves (2021), which contained 45 closed Likert scale questions and two open-ended questions. Data were collected via an online platform and analyzed with a descriptive and quantitative approach. The results revealed a scenario that indicated the need for adjustments in the distribution of activities and in managers’ perception of behaviors and signs of disorders. Important evidence was identified in the participants’ open-ended responses. It was concluded that the study contributed to promoting the perception of a safe environment for mental health in the organization.

Keywords: Absences due to disorders; Professional development; Operational leadership; Medical leave; Mental health.

October 22, 2026

Digital Observatory of the Amazon: Data Science Applied to the Analysis of Social, Economic, Environmental, and Health Indicators

The state of Amazonas faced historical challenges in consolidating the Unified Health System (SUS), stemming from geographical barriers, socioeconomic inequalities, and structural limitations. This study described the development and implementation of a health information geoportal, designed to support monitoring, evaluation, and planning in the Amazonian context. A descriptive case study approach was adopted, based on documentary analysis and secondary data, covering the mapping of functionalities, technical infrastructure, and validation steps of the tool. The Portal was developed with open technologies and integrated multiple national databases, offering interactive dashboards, thematic maps, and a digital document repository. Among the results, highlighted were the georeferenced visualization of key indicators, the possibility of temporal comparative analyses, and the use of the platform in reviewing health plans and redirecting strategic actions. The portal can represent an advance in digital governance and evidence-based management, with potential for replication in other highly complex territories, provided it is accompanied by investments in technological infrastructure, professional training, and inter-institutional coordination.

Keywords: Amazon; Health Evaluation; Digital Health; Health Information Systems.

October 22, 2026

Continuous Learning as a Factor of Organizational Growth in the Perception of Collaborators

The impact of continuous learning and knowledge management on organizational growth was studied in a context of globalization and constant changes. The study’s objective was to analyze employees’ perception of learning culture and its importance for personal and professional development. Data were obtained through an anonymous survey, with a quantitative approach, using a form with “survey” type questions applied to 87 professionals from the technology, health, education, commerce, and services sectors. The results indicated that employees had a positive perception of the contribution of continuous learning to personal and organizational growth, recognizing its benefits for the development of new skills, innovation, and competitiveness. However, it was observed that the incentive for internal knowledge exchange and the offering of practical training still presented moderate levels of agreement. It was concluded that continuous learning significantly contributes to individual and organizational development, suggesting that companies should develop strategic policies for learning focused on new skills, aiming for greater competitive advantage and innovation.

Keywords: Continuous learning; Self-development; Organizational growth; Knowledge Management; Innovation.

October 22, 2026

Statistical Regression in Microgrids for Operational Performance Estimation: Campusgrid Case

A multiple statistical regression model was proposed to estimate the operational performance of UNICAMP’s CampusGrid microgrid, based on internal electrical quantities. The microgrid integrates photovoltaic generation, batteries, and a gas generator. Energy management system data were collected at five-minute intervals over one month. The Operational Reliability Index, defined as the ratio between supplied and demanded energy, was established as the dependent variable. The model was adjusted for the islanded operation mode, evaluating the impact of demand, state of charge, solar generation, and supplied energy. The results revealed high explanatory power, indicating that operational reliability depends on the interaction between supply and demand. It was observed that the nighttime period concentrated the highest rate of partial supply, attributed to the reduction in solar generation. It was concluded that multiple regression offers an interpretable and useful framework for microgrid monitoring and control strategy support.

Keywords: Battery storage; Photovoltaic generation; Reliability Index; Islanded operation; Energy Management System.

October 22, 2026

Forecast of Aggregate Default in the Brazilian Financial System by Sarimax Model with Macroeconomic Variables

Aggregate non-payment in the Brazilian financial system is a crucial indicator of credit risk, the prediction of which is regulated by CMN Resolutions n.° 4.557/2017 and 4.966/2021 for the prospective measurement of expected losses. Aggregate non-payment was modeled and predicted using the SARIMAX model, projecting its trajectory until December 2026 under optimistic, base, and pessimistic scenarios. 164 monthly observations (July/2012 to February/2026) and macroeconomic variables such as unemployment, Selic, IPCA, IBC-Br, and household debt were used. The specification with contemporaneous variables was selected based on information criteria and coefficient stability, and residual diagnostics confirmed statistical adequacy, with a MAPE of 3.12%. Historical non-payment ranged between 4.33% and 7.64%. The estimation identified unemployment (β = 0.2534; p < 0.001) and Selic (β = 0.2128; p < 0.001) as the main positive determinants, with strong temporal persistence (AR(1) = 0.8965). Selic showed increasing correlation with non-payment (r = 0.62 at lag 12), while unemployment demonstrated a contemporaneous effect (r = 0.33). Projections indicated non-payment between 6.30% and 7.98% in December 2026, with a range of 1.68 percentage points, highlighting direct implications for provisioning in financial institutions.

Keywords: Unemployment; Predictive modeling; Monetary policy; Loss provisioning; Time series.

October 22, 2026

Stakeholder Engagement Factors in International Technology Transfer to a Public Hospital

This study described the critical factors perceived for stakeholder engagement in international technology transfer in a public hospital, with a focus on planning. The objective was to describe these conditioning factors, according to the perception of professionals. A single case study was conducted, applying a questionnaire to 25 professionals and using content analysis, complemented by descriptive tabulations. The main obstacles identified were resistance to change (72%), funding (64%), lack of communication (60%), and bureaucracy (40%). Only 28% of respondents recognized the existence of adequate tools for stakeholder management, while 48% denied it and 24% did not know. Exploratory cross-tabulations indicated variations in the assessment of influence according to time of practice, but did not establish causal relationships. Lack of engagement was associated with delays, low adherence, waste of resources, communication failures, compromised knowledge absorption, and risks to patient safety. The conclusions were limited to the perceptions of the researched group and supported preliminary recommendations for intersectoral communication and coordination.

Keywords: Case study; Project Management; Health innovation; Stakeholders; Public sector.

Economy

October 19, 2026

End of the 6×1 scale: what do the numbers allow us to conclude?

Following the promulgation of the end of the 6×1 shift system, the debate on the shorter workday moves to implementation and requires distinguishing social benefits from economic costs. The end of the 6×1 shift system transitioned from legislative discussion to a constitutional change in Brazil. On October 16, 2026, the National Congress promulgated Constitutional Amendment No. 141/2026, originating from Proposal […]

Artificial Intelligence

October 16, 2026

Why study if AI exists?

When answers become easy, learning comes to mean more than just arriving at them. Why do I need to learn this if I can ask an artificial intelligence? This type of question has accompanied education for decades. Why learn an operation if a calculator exists? Why memorize information if it can be found […]

Tax Management

October 15, 2026

Pink tax and the gender of price

How different marketing treatments for men and women are incorporated by consumption taxation Imagine a shelf on which two razors of the same brand and intended for the same purpose are displayed side by side: one has dark packaging and promises strength, precision, and efficiency; the other comes in shades of pink or […]

Health

October 13, 2026

The missing link in health transformation

Why is innovation not enough to drive changes in organizations and health systems Digital transformation has become a strategic priority for health systems worldwide. Electronic health records, artificial intelligence, telemedicine, remote monitoring, and data analysis are now part of the strategic agenda of governments and health organizations. Paradoxically, […]

Marketing

October 09, 2026

Bitcoin or pets: what Artificial Intelligence would not see

A real-life consumer intelligence case shows what gets left out of the brief A few years ago, an American insurance company hired a specialized consumer intelligence firm to answer three questions: The insurer was also considering launching paid insurance in bitcoin. At the time, the consultancy’s workflow was as follows: the analyst created a query […]

School Management

October 06, 2026

The school is not a casino: when trading cards become currency in the playground

Why, for certain students, does school manage to offer so few experiences of achievement? The World Cup is over. Brazil left the competition, the school holidays came, the final was played and the football calendar moved on. It would be reasonable to imagine that, with the end of the tournament and the return to classes, […] would also begin

Energy Transition

September 30, 2026

Renewable energies: the Brazilian advantage

Brazil needs to invest in the capacity to transform natural resources into innovation, productivity, and development. Talking about energy is talking about development. Behind industry, transportation, services, cities, and increasingly artificial intelligence, there is an energy infrastructure that sustains the economy. Understanding a country’s energy matrix is understanding […]

Data Analysis

September 30, 2026

When the data changes, has reality changed?

An analysis of almost 20 million teaching positions-year between 2020 and 2025 shows how changes in population composition and record production can alter the interpretation of historical series Between 2020 and 2025, the number of formal teaching employment positions identified in RAIS (Annual Relation of Social Information), from the Ministry of Labor, […]

Artificial Intelligence

September 28, 2026

Artificial intelligence and the trends

Why do so many people join in on the fun with ChatGPT? I was never the trend person. I still am not. Some pass through my timeline, I see them, I find them interesting but not to the point of trying them out. This was the case, inclusive, with some recent waves of images made by artificial intelligence, like the 80s trend, the one that […]

Tax Management

September 01, 2026

Tax reform and estate planning in the countryside

Efficiency in managing taxes facilitates governance and allows for the organization of family succession. Rural wealth organization has become a strategic measure in light of the tax reform in Brazil, which came into effect this year. The planning of activities and rural wealth management, through tools such as the transition from an Individual to […]

Renata de Gaspari Valdejão Almeida

Artificial Intelligence

August 05, 2026

Inside the company and off the radar: the risks of Shadow AI

According to Microsoft, 75% of professionals already use artificial intelligence at work without company approval. The use of generative artificial intelligence is already part of the corporate routine. Tools like ChatGPT, Copilot, and other platforms help professionals produce texts, analyze data, create presentations, and accelerate tasks in practically all sectors. But this advance […]

Renata de Gaspari Valdejão Almeida

Strategy

July 02, 2026

The future as a competence

Academic interest reinforces the projection of scenarios as an essential tool for decision-making. While some companies are still trying to get the next step right, others are already working with multiple possible futures — and preparing for all of them. But after all, what does it mean to “think about the future”? Is it a trend, a strategy, or a new competence that […]

Renata de Gaspari Valdejão Almeida

Innovation

Technology

May 21, 2026

Agro SP Corridor: innovation, institutional articulation and future

Pecege commands the executive operation of the 400 km ecosystem activities that became known as the Brazilian agro Silicon Valley In 2025, in addition to taking over the management of the Piracicaba Technology Park, the Pecege Institute was also designated to command the executive operation of the State Agribusiness Innovation Corridor activities, the so-called […]

Education

April 27, 2026

How to turn a TCC into a business

New management of the Piracicaba Technology Park offers consulting to MBA graduates The new project director of the Piracicaba Technology Park (PTP), Alexandre Barreto, often says that one of the greatest rewards he has ever had in life was seeing his MBA USP/Esalq students structure companies from their final projects […]

Technology

March 20, 2026

The human repertoire in the age of AI

Critical thinking and creativity are irreplaceable in the work of a marketing professional Artificial Intelligence (AI) is capable of incredible feats, but it does not yet possess the judgment, empathy, intuition, and strategic creativity necessary to do the job of a good marketing professional. In this area, it is important to know how to use the tool to automate […]

Renata de Gaspari Valdejão Almeida

Agribusiness

Innovation

February 23, 2026

ESG as a growth strategy in a cooperative in RS

Santa Clara balances social and environmental responsibility with innovation Founded in 1911 by Italian immigrants in Carlos Barbosa, in the Serra Gaúcha region, Cooperativa Santa Clara is an example that it is possible to balance economic growth with social and environmental responsibility. From a small cheese production company, the organization has evolved into a […]

Renata de Gaspari Valdejão Almeida

Agribusiness

Innovation

January 22, 2026

Brazil free of Foot-and-Mouth Disease: what changes for Brazilian beef?

With a herd of over 200 million head of cattle, the country is currently the leader in exports. Brazil recently received, for the first time, the certification of country free of foot -and-mouth disease without vaccination, conferred by the World Organisation for Animal Health (WOAH). The seal strengthens the country’s position as a reliable supplier of meat for the […]

Renata de Gaspari Valdejão Almeida

Innovation

December 09, 2025

Innovation: the new cycle of the Piracicaba Technology Park

Instituto Pecege assumes management, betting on the creation of new businesses Starting in 2026, Instituto Pecege will assume the management of the Piracicaba Technology Park (PTP). Among its responsibilities will be the administration, operation, maintenance, and expansion of the park, in addition to fostering activities in innovation, entrepreneurship, and science and technology. The choice was made […]

Renata de Gaspari Valdejão Almeida

July 06, 2026

Large-Scale Architecture: DSR-SCRUM Change Management

Mônica Alessandra Guerios; Maria do Carmo Assis Todorov Abstract generated by ResumeAI, an artificial intelligence solution developed by Instituto Pecege focused on synthesis and writing. Architecture and urbanism are configured as essential bases in an increasingly complex and challenging urban space, where the intrinsic connection between social, environmental, and technological issues […]