Borda Count Research Articles

Predicting Team Advancement in Major League Baseball Postseason Using Borda Count

The prediction of sports competition outcomes has long been a topic of interest in academia and among sports enthusiasts. This study focuses on Major League Baseball (MLB) as its research subject, encompassing the years 2020, 2021, and 2022. By employing a set of established evaluation criteria, comprising five pitching and five hitting indicators from previous literature, the regular-season performance of the 30 MLB teams across both leagues (National League and American League) over the three-year period was compiled. Subsequently, a data normalization technique combined with the Borda count concept was proposed to develop a model for forecasting team advancement in the postseason. The predictive accuracy of the model presented in this study for determining MLB postseason qualifiers from 2020 to 2022 fell within the range of 55.6% to 66.7%, akin to models utilizing extensive datasets. Notably, the proposed model is more comprehensible and user-friendly, offering ease of understanding and application for sports enthusiasts and facilitating its potential utilization and dissemination in the sporting community.

Statistical and computational efficiency for smooth tensor estimation with unknown permutations

We consider the problem of structured tensor denoising in the presence of unknown permutations. Such data problems arise commonly in recommendation systems, neuroimaging, community detection, and multiway comparison applications. Here, we develop a general family of smooth tensor models up to arbitrary index permutations; the model incorporates the popular tensor block models and Lipschitz hypergraphon models as special cases. We show that a constrained least-squares estimator in the block-wise polynomial family achieves the minimax error bound. A phase transition phenomenon is revealed with respect to the smoothness threshold needed for optimal recovery. In particular, we find that a polynomial of degree up to ( m − 2 ) ( m + 1 ) / 2 is sufficient for accurate recovery of order-m tensors, whereas higher degrees exhibit no further benefits. This phenomenon reveals the intrinsic distinction for smooth tensor estimation problems with and without unknown permutations. Furthermore, we provide an efficient polynomial-time Borda count algorithm that provably achieves the optimal rate under monotonicity assumptions. The efficacy of our procedure is demonstrated through both simulations and Chicago crime data analysis.

Deep hybrid architecture with stacked ensemble learning for binary classification of retinal disease

PurposeEarly retinal disease identification is vital since symptoms are passive at initial stages but lead to irreversible vision loss at advanced stages. Globally, a substantial population is at risk of vision impairment, prompting researchers to investigate methods for efficient classification. MethodsThis work experimented one hundred and forty-four different hybrid architectures amalgamating each of the eight convolutional neural architectures (VGG, EfficientNet, Inception, ResNet, NasNet, DenseNet, InceptionResNet, Xception) with seven classifiers (Logistic regression, K-Nearest Neighbours, Support Vector Classifier, Decision Tree, Bagging classifier, Random Forest, Adaptive Boosting, Light Gradient Boost and Extra tree classifier). The top performing n (n=3,4,5) classifiers were ensembled with meta-learner using stacking strategy. The performance of the pipeline is evaluated with two distinct meta-learners, three different image sizes, and feature counts on the RFMiD dataset. ResultsThe architectures were assessed using (1) the performance metrics – accuracy, precision, recall, and F1-score, (2) statistical graphics to understand the prevalence of classifiers, Borda count voting method to identify the best CNN model, (3) Tukey's honestly significance difference test to identify best-performing architecture. Two architectures achieved a high accuracy of 92.34 percent and F1 scores of 95.11 and 95.19. ConclusionThis is the first work to experiment with 144 combinations to identify suitable deep architecture for binary retinal disease classification. The study recommends Xception for feature extraction ensembled with ExtraTreeClassifier, Light gradient boosting machine, Random Forest, AdaBoost classifiers, and meta-learner as Logistic Regression. Another best-performing architecture is similar with a variation in DenseNet121 for feature extraction and support vector as a meta-learner.

Strategic Choice And Responsible Innovation: Integrating Approaches To Foster Sustainable Business Development

The study focuses on addressing businesses' growing need to integrate sustainability into their decision-making processes, given the recognized worldwide environmental impacts derived from industrial and technological growth. The study proposes a decision-support artifact that aligns with the Sustainable Development Goals (SDGs). In light of the principles of Design Science Research (DSR), it combines the Strategic Choice Approach (SCA), which helps manage uncertainties, with Responsible Innovation (RI) principles to guide the socially and environmentally conscious innovation process. As another novel, this artifact adopted the Borda voting system to select the best sustainable development courses of action. Data from a Brazilian solar energy company were considered to demonstrate the artifact’s application. Our results deliver a decision-making support artifact whose results serve as inputs for strategic planning for the business. These artifacts highlight the importance of collaborations for decision-making within the scope of sustainable development. This paper proposes a novel and holistic approach that recognizes peculiar aspects of managerial commitment to advancing sustainability within a business context, contributing to the literature. Managers also can apply this decision artifact to improve sustainability performance.

Reliability-based ordinal consensus adjustment model for large scale group decision making

In large scale group decision-making (LSGDM), there are the substantial number of decision makers (DMs) with diverse knowledge, backgrounds, and interests related to the decision-making problem, and it is not possible to assure that all DMs are completely reliable. Thus, in order to enhance the quality of decision-making, it is necessary to analyze the reliabilities of DMs in LSGDM. This paper proposes the method to evaluate the reliabilities of DMs, sorts these DMs according to their degree of reliability, and investigates the consensus reaching process based on categories and an ordinal consensus measure. Considering the DMs' trust network, the uncertainty of a DM's evaluation information represented by a fuzzy preference relation (FPR), the deviation between a DM's FPR and those of the other DMs, and additive consistency of FPRs, the reliability of a DM is assessed using four criteria: PageRank centrality, professional competence, collaborative competence, and additive consistency. Following these reliability assessment criteria, ELECTRE-TRI is employed to sort DMs into three ordered categories according to DMs' different levels of reliability under the four assessment criteria. Furthermore, an improved ordinal consensus measure is designed to consider both the importance weights of positions and the deviation of Borda counts of the same alternative in two rankings. As for the consensus reaching process, due to the varied reliabilities of DMs in different categories, we propose a multiple strategies feedback mechanism for DMs in different categories. Finally, a numerical example is provided to illustrate the rationality and validity of the proposed model.

Developing an Early Warning System for Financial Networks: An Explainable Machine Learning Approach.

Identifying the influential variables that provide early warning of financial network instability is challenging, in part due to the complexity of the system, uncertainty of a failure, and nonlinear, time-varying relationships between network participants. In this study, we introduce a novel methodology to select variables that, from a data-driven and statistical modeling perspective, represent these relationships and may indicate that the financial network is trending toward instability. We introduce a novel variable selection methodology that leverages Shapley values and modified Borda counts, in combination with statistical and machine learning methods, to create an explainable linear model to predict relationship value weights between network participants. We validate this new approach with data collected from the March 2023 Silicon Valley Bank Failure. The models produced using this novel method successfully identified the instability trend using only 14 input variables out of a possible 3160. The use of parsimonious linear models developed by this method has the potential to identify key financial stability indicators while also increasing the transparency of this complex system.

A discovery and verification approach to pharmacovigilance using electronic healthcare data.

Pharmacovigilance is vital for drug safety. The process typically involves two key steps: initial signal generation from spontaneous reporting systems (SRSs) and subsequent expert review to assess the signals' (potential) causality and decide on the appropriate action. We propose a novel discovery and verification approach to pharmacovigilance based on electronic healthcare data. We enhance the signal detection phase by introducing an ensemble of methods which generated signals are combined using Borda count ranking; a method designed to emphasize consensus. Ensemble methods tend to perform better when data is noisy and leverage the strengths of individual classifiers, while trying to mitigate some of their limitations. Additionally, we offer the committee of medical experts with the option to perform an in-depth investigation of selected signals through tailored pharmacoepidemiological studies to evaluate their plausibility or spuriousness. To illustrate our approach, we utilize data from the German Pharmacoepidemiological Research Database, focusing on drug reactions to the direct oral anticoagulant rivaroxaban. In this example, the ensemble method is built upon the Bayesian confidence propagation neural network, longitudinal Gamma Poisson shrinker, penalized regression and random forests. We also conduct a pharmacoepidemiological verification study in the form of a nested active comparator case-control study, involving patients diagnosed with atrial fibrillation who initiated anticoagulant treatment between 2011 and 2017. The case study reveals our ability to detect known adverse drug reactions and discover new signals. Importantly, the ensemble method is computationally efficient. Hasty false conclusions can be avoided by a verification study, which is, however, time-consuming to carry out. We provide an online tool for easy application: https://borda.bips.eu.

A Novel Group Decision Making Model to Compare Online Shopping Platforms

Over the years, E-commerce industry has been witnessing a phenomenal growth, thanks to rapid technological advancement in Industry 4.0. There has been a standout surge in the use of various online shopping platforms (OSP) for daily use. The recent pandemic has accelerated the growth trajectory and made a transformational change in the digital commerce landscape. As a result, there has been a proliferation of OSPs in the competitive domain. It is therefore pertinent to address the questions: How do the customers select their favorite OSP? To what extent the OSPs differ based on consumers’ preferences? The present work addresses these questions by proposing a novel group decision making framework. The ongoing study provides several innovative extensions of multi criteria decision making models like Borda count, criteria importance assessment (CIMAS), modified preference selection index (MPSI), and root assessment method (RAM). In this paper, the researchers provide a novel use of the Borda count method, integrated with CIMAS for determining criteria weights utilizing ranking of the criteria. Further, a novel extension of MPSI and RAM has been made with multiple normalizations. In this paper, the authors demonstrate a rare combination of vector and non-linear normalization using the Heron mean. The present paper derives the final criteria weights by combining Borda count, CIMAS and multi-normalization based MPSI (MNMPSI) using Bayesian logic. The criteria are selected based on Uses and Gratification theory (UGT). The findings reveal that interactive app interface and features (C16), user-friendly interface and search (C13), convenience in shopping (C14), product availability and variety (C12) and discounts and offers (C8) exert significant influence in selecting the OSP. Further, it is observed that Flipkart (A2) and Amazon (A1) are the top performers in the eyes of the users. The stability and reliability of the proposed methodology are examined by conducting a sensitivity analysis and comparing with several other models. The robustness of the proposed methodology and practical relevance of the findings of the present work shall provide notable impetus to the analysts and strategic decision-makers.

A novel parallel feature rank aggregation algorithm for gene selection applied to microarray data classification

Microarray data often comprises numerous genes, yet not all genes are relevant for predicting cancer. Feature selection becomes a crucial step to reduce the high dimensionality in these kinds of data. While no single feature selection method consistently outperforms others across diverse domains, the combination of multiple feature selectors or rankers tends to produce more effective results compared to relying on a single ranker alone. However, this approach can be computationally expensive, particularly when handling a large quantity of features. Hence, this paper presents a parallel feature rank aggregation that utilizes borda count as the rank aggregator. The concept of vertically partitioning the data along feature space was adapted to ease the parallel execution of the aggregation task. Features were selected based on the final aggregated rank list, and their classification performances were evaluated. The model’s execution time was also observed across multiple worker nodes of the cluster. The experiment was conducted on six benchmark microarray datasets. The results show the capability of the proposed distributed framework compared to the sequential version in all the cases. It also illustrated the improved accuracy performance of the proposed method and its ability to select a minimal number of genes.

Improving the aggregation and evaluation of NBA mock drafts

Abstract If professional teams can accurately predict the order of their league’s draft, they would have a competitive advantage when using or trading their draft picks. Many experts and enthusiasts publish forecasts of the order players are drafted into professional sports leagues, known as mock drafts. Using a novel dataset of mock drafts for the National Basketball Association (NBA), we explore mock drafts’ ability to forecast the actual draft. We analyze authors’ mock draft accuracy over time and ask how we can reasonably aggregate information from multiple authors. For both tasks, mock drafts are usually analyzed as ranked lists, and in this paper, we propose ways to improve on these methods. We propose that rank-biased distance is the appropriate error metric for measuring accuracy of mock drafts as ranked lists. To best combine information from multiple mock drafts into a single consensus mock draft, we also propose a combination method based on the ideas of ranked-choice voting. We show that this method provides improved forecasts over the standard Borda count combination method used for most similar analyses in sports, and that either combination method provides a more accurate forecast across seasons than any single author.

Enhancing Metabolic Syndrome Detection through Blood Tests Using Advanced Machine Learning

The increasing prevalence of metabolic syndrome (MetS), a serious condition associated with elevated risks of cardiovascular diseases, stroke, and type 2 diabetes, underscores the urgent need for effective diagnostic tools. This research carefully examines the effectiveness of 16 diverse machine learning (ML) models in predicting MetS, a multifaceted health condition linked to increased risks of heart disease and other serious health complications. Utilizing a comprehensive, unpublished dataset of imbalanced blood test results, spanning from 2017 to 2022, from the Laboratory Information System of the General Hospital of Amfissa, Greece, our study embarks on a novel approach to enhance MetS diagnosis. By harnessing the power of advanced ML techniques, we aim to predict MetS with greater accuracy using non-invasive blood test data, thereby reducing the reliance on more invasive diagnostic methods. Central to our methodology is the application of the Borda count method, an innovative technique employed to refine the dataset. This process prioritizes the most relevant variables, as determined by the performance of the leading ML models, ensuring a more focused and effective analysis. Our selection of models, encompassing a wide array of ML techniques, allows for a comprehensive comparison of their individual predictive capabilities in identifying MetS. This study not only illuminates the unique strengths of each ML model in predicting MetS but also reveals the expansive potential of these methods in the broader landscape of health diagnostics. The insights gleaned from our analysis are pivotal in shaping more efficient strategies for the management and prevention of metabolic syndrome, thereby addressing a significant concern in public health.

Multifaceted multi-criteria decision making framework to prioritise the electric two-wheelers based on standard and regional driving cycles

The primary objective of the carried-out research work is to utilise the multi-criteria decision-making (MCDM) techniques to rank the top-selling electric two-wheeler (E2W) models based on the different performance features stated by original equipment manufacturers and estimated through standard and regional driving cycle (DC) analysis. First and foremost, the vehicle structural, powertrain, performance features, and cost component-related data of eight top-selling E2W models (M1–M8) are gathered from the official websites of OEMs and local dealers. Then, performance features like energy consumption per km (EC/km), driving range (DR), well-to-wheel (WTW) CO2 emission, total cost of ownership (TCO), and total cost of ownership per km (TCO/km) are estimated under standard and regional DCs through the developed energy consumption and TCO models. Further, the ten different MCDM tools are utilised for prioritising the E2W models under each DC with the attributes’ weights estimated from the entropy methods. To enhance the trustworthiness of ranking results, sensitivity analysis is done using six distinct combinations of attribute weights. Moreover, the M7 E2W is identified as the best E2W by utilising Borda Count to integrate all the ranking results. Overall, this study provides an in-depth understanding for users about how to select E2W with minimal effort.

How Would Preferential Voting Have Changed Taiwan’s 2024 Presidential Election?

Would the results of Taiwan’s 2024 presidential election have differed under preferential voting rules? Observers assume that the split in the opposition virtually guaranteed Lai Ching-te’s victory, but no analysis has aimed to measure ranked preferences across the three presidential candidates. This analysis combines original pre-election survey data that includes preference ordering of candidates with simulation analysis of the actual election results under the alternative vote and Borda count systems. The original survey finds Ko Wen-je would have won under both preferential systems, while simulating preference ordering using the 2024 election results suggests Lai would have won under alternative vote but lost to Hou You-ih under Borda count.

How voting rules impact legitimacy

Collective action is essential for addressing the grand challenges of our time. However, for such action to be successful, decision-making processes must be perceived as legitimate. In this study, we investigate the legitimacy of different voting methods. Using a pre-registered human subject experiment, 120 participants cast their votes using four voting methods: majority voting, combined approval voting, range voting, and the modified Borda count. These methods represent a range of preference elicitation designs, from low to high complexity and flexibility. Furthermore, we developed a legitimacy scale upon which the participants rate the voting methods. The experiment was conducted in a non-political setting (voting on color preferences) and a political context (voting on COVID-19-related questions). Our findings suggest that the perceived legitimacy of a voting method is context-dependent. Specifically, preferential voting methods are seen as more legitimate than majority voting in a political decision-making situation, but only for individuals with well-defined preferences. Furthermore, preferential voting methods are more legitimate than majority voting in a highly polarized situation.

Hybrid use of Borda count and PROMETHEE method for maintenance strategy selection problem

Abstract For long-term success, organizations and manufacturing companies must exploit the potential strengths of collective decision making in maintenance management. The maintenance strategy selection issue has been studied in a single decision-maker framework for a long time. This research is one of the first attempts at dealing with the enhancement of maintenance management through the participation of stakeholders in the decision making process. In this context, the author introduces a participatory multi criteria decision model that combines Borda count and PROMETHEE methodology to select the most appropriate maintenance strategy; in accordance with the decision makers’ preferences on a set of strategies evaluated according to conflicting criteria. Therefore, the PROMETHEE II method is used to manage the individual decisions of each stakeholder, while the Borda count is in charge of collectively selecting the best maintenance strategy, taking as a starting point stakeholder’s preferences being established thanks to PROMETHEE II. In the same context, the proposed model was applied to a real scenario: a textile company, and can be easily replicated in other industries.

International Market Selection Based on Integrated MCDM Methods: A Case Study of Iron and Steel Sector

International Market Selection (IMS) is crucial for companies seeking growth and expansion beyond their domestic boundaries. IMS is a strategic decision that requires careful evaluation of several factors to minimize risks and achieve long-term success in international markets. Accordingly, this study aims to find out optimal market alternatives for exporter companies operated in iron and steel sector. A total of twenty-three market alternatives were evaluated based on ten criteria. The alternatives and criteria were determined by literature review and expert opinions. The weight of criteria was calculated by the FUCOM and LOPCOW methods. Once the weight of criteria was determined, the alternatives were ranked using the SPOTIS, RSMVC, CoCoSo and Borda Count methods. The results showed that balance of trade and GDP were the most and least important criteria, respectively. Overall results revealed that Canada, the UAE, Germany, Japan and Malaysia were the best market alternatives, while Venezuela, Mexico, Peru, Colombia and the UK were the worst market alternatives for iron and steel exporter companies. Additionally, the sensitivity analysis was carried out to observe the robustness of the results.

Identifying and Prioritizing Waste in OCTG Production Lines Through Value Stream Mapping and Borda Count Method

Identifying and Prioritizing Waste in OCTG Production Lines Through Value Stream Mapping and Borda Count Method

The Borda and Condorcet winners coincide for lexicographic preferences

We show that, for any lexicographic preference profile, the sets of Borda and Condorcet winners coincide. Moreover, the sets of Borda and Condorcet losers also coincide. However the binary relations induced by the Borda Count and the majority pairwise comparisons are not the same.

An integrated risk assessment method for urban areas due to chemical leakage accidents

Chemical parks which hold large inventories of hazardous materials have become one important type of urban critical infrastructure. The hazardous chemical leakage such as toxic gas leakage has emerged in recent years, resulting in not only serious environmental pollution but also catastrophic consequences to certain urban areas. It is critical to make scientific risk assessment for such events to reduce negative effects. To address this issue, an integrated urban risk assessment (IURA) method coupling consequences of chemical leakage accidents and the vulnerability of local population is proposed in this work. The threat zones and hazard maps of toxic gas dispersion are present by using the ALOHA software. The population vulnerability is decided by three indicators including exposure, sensitivity, and adaptive capacity of residents in affected areas, the Grey Correlation-TOPSIS (GC-TOPSIS) method is used to rank the vulnerability of different regions. The ArcGIS software is applied to generate an integrated risk map through superimposing the hazard map and the population vulnerability map. The original risk matrix evaluation results were subdivided using the Borda count method. A case about an industrial city in Hubei, China is analyzed to test the method. Result shows the new integrated risk assessment method is more accurate and effective, which is beneficial for the government to develop emergency plans and enhance the safety of people.

Predicting Team Advancement in Major League Baseball Postseason Using Borda Count

The prediction of sports competition outcomes has long been a topic of interest in academia and among sports enthusiasts. This study focuses on Major League Baseball (MLB) as its research subject, encompassing the years 2020, 2021, and 2022. By employing a set of established evaluation criteria, comprising five pitching and five hitting indicators from previous literature, the regular-season performance of the 30 MLB teams across both leagues (National League and American League) over the three-year period was compiled. Subsequently, a data normalization technique combined with the Borda count concept was proposed to develop a model for forecasting team advancement in the postseason. The predictive accuracy of the model presented in this study for determining MLB postseason qualifiers from 2020 to 2022 fell within the range of 55.6% to 66.7%, akin to models utilizing extensive datasets. Notably, the proposed model is more comprehensible and user-friendly, offering ease of understanding and application for sports enthusiasts and facilitating its potential utilization and dissemination in the sporting community.