Matching Results Research Articles

Construction of 2022 Qatar World Cup match result prediction model and analysis of performance indicators

This research investigates the influence of performance metrics on match outcomes and constructs a predictive model using data from the Qatar World Cup. Employing magnitude-based decision and an array of machine learning algorithms, such as Decision Trees, Logistic Regression, Support Vector Machines, AdaBoost, Random Forests, and Artificial Neural Network, we examined data from 59 matches, excluding extra time. Fourteen performance indicators were integrated into the model, with two types of match outcomes—winning and non-winning—serving as the output variables. The ANN model exhibited the highest predictive performance, achieving an accuracy of 75.42%, an AUC of 76.96%, a precision of 72.73%, a recall of 65.31%, a specificity of 77.03%, and an F1 score of 68.82%. SHAP analysis revealed that “On Target”, “Shooting Opportunity”, and “Ball Progressions” were the most influential features. These findings underscore the critical role of shooting accuracy and the creation of scoring opportunities in determining match outcomes. Consequently, this study developed an accurate model for predicting match outcomes and meticulously analyzed the match performance. Coaches should prioritize the sensitive indicators identified in this study during training and structure training sessions accordingly.

BIOM-61. CSF CTDNA ANALYSIS OF PATIENTS WITH DIFFUSE GLIOMAS REVEALS DIAGNOSTIC MOLECULAR ALTERATIONS

Abstract There is a critical need for better strategies to diagnose and monitor patients with diffuse gliomas. Liquid biopsies are less invasive than tissue biopsies and are amenable to serial collection at multiple timepoints. However, analysis of circulating tumor DNA (ctDNA) in cerebrospinal fluid (CSF) in patients with diffuse gliomas remains underutilized in clinical practice due to technical challenges and a scarcity of studies demonstrating clinical utility. We evaluated ctDNA in 49 CSF samples from patients with various types of diffuse gliomas with a next generation sequencing (NGS) panel interrogating 613 cancer-related genes. Cell-free DNA concentration obtained from CSF (0.4-5mL) ranged from 0.009 to 60ng/µL. Input DNA concentration ranged from 0.12-30ng. Mutations were detected in 29/49 (59.2%) samples including 22/49 (44.9%) with single nucleotide changes, 14/49 (28.6%) with copy number changes, and 1 fusion (2.0%). The majority of samples without mutations detected (n=20) had suboptimal DNA input (<30ng): 17/20 (85%). We identified diagnostic alterations in CSF including mutations in IDH1 (3/5 IDH-mutant gliomas), TERTp and EGFR amplification (7/34 glioblastomas), H3-3A (4/6 pediatric high-grade gliomas). Some of these mutations, in combination with other clinical parameters, allowed us to identify specific CNS tumor types based on the 2021 WHO classification of CNS tumors, resulting in the reclassification of 2 patients. Matched tumor tissue sequencing results were available for 23/49 (46.9%) patients and the results showed good concordance with CSF ctDNA analysis. In summary, our results suggest that the NGS analysis of CSF-ctDNA can provide clinically relevant information in patients with diffuse gliomas.

T-KAER: Transparency-enhanced Knowledge-Augmented Entity Resolution Framework

Entity resolution (ER) is the process of determining whether two representations refer to the same real-world entity and plays a crucial role in data curation and data cleaning. Recent studies have introduced the KAER framework, aiming to improve pre-trained language models by augmenting external knowledge. However, identifying and documenting the external knowledge that is being augmented and understanding its contribution to the model's predictions have received little to no attention in the research community. This paper addresses this gap by introducing T-KAER, the Transparency-enhanced Knowledge-Augmented Entity Resolution framework. To enhance transparency, three Transparency-related Questions (T-Qs) have been proposed: T-Q(1): What is the experimental process for matching results based on data inputs? T-Q(2): Which semantic information does KAER augment in the raw data inputs? T-Q(3): Which semantic information of the augmented data inputs influences the predictions? To address the T-Qs, T-KAER is designed to improve transparency by documenting the entity resolution processes in log files. In experiments, a citation dataset is used to demonstrate the transparency components of T-KAER. This demonstration showcases how T-KAER facilitates error analysis from both quantitative and qualitative perspectives, providing evidence on `"what" semantic information is augmented and ``"why" the augmented knowledge influences predictions differently.

How Does Lacking a Home Program Impact Otolaryngology Applicants?

To compare otolaryngology interview and match outcomes between applicants with and without home residency programs. Otolaryngology applicants from US allopathic medical schools during the 2019-2023 cycles who responded to the Texas Seeking Transparency in Application to Residency (STAR) survey were identified. Students were stratified based upon whether their medical school had an affiliated otolaryngology residency program. The primary outcomes were number of interviews and match rate. Wilcoxon-rank sum and χ2 testing was used to assess associations between home program status and interview and match outcomes. Of the 633 fourth-year medical students applying to otolaryngology during the 2019-2023 application cycles, 89 had no home program (NHP) and 544 had a home program (HP). Applicants with NHP completed significantly more away rotations than applicants with a HP (2.2 vs. 1.5; p < 0.01). There was no difference in mean number of applications submitted between applicants with NHP and applicants with a HP. However, applicants with a HP received a significantly greater number of interviews (14.7 vs. 11.8; p < 0.01), attended more interviews (12.4 vs. 11.3; p = 0.02), attended a lower percentage of their offered interviews (84.4% vs. 95.8%), and had a higher match rate (81.8% vs. 70.8%; p = 0.02) than applicants with NHP. Applicants with NHP interviewed at (1.9 vs. 1.3; p < 0.01) and matched at (33.7% vs. 23.9%; p = 0.048) significantly more away rotation institutions than applicants with a HP. Applicants with NHP received fewer interviews and had lower match rates. Away rotations may be especially important for applicants with NHP. NA Laryngoscope, 134:4494-4500, 2024.

Matching into Shoulder and Elbow Surgery Fellowships: Do USMLE Scores and AOA Status Still Matter?

IntroductionDespite the established importance of the United States Medical License Exam (USMLE) and Alpha Omega Alpha (AOA) status in orthopedic residency selection, their significance in the fellowship match remains unknown. This study evaluates the influence of USMLE scores and AOA status on interview invitation rates and match positions for shoulder and elbow surgery fellowship applicants. MethodsThis is a retrospective analysis of data from the San Francisco Match database from 2018 to 2023. The study included matched applicants for shoulder and elbow surgery fellowship, excluding unmatched applicants, osteopathic, and international medical graduates. USMLE scores were categorized into four tiers, and interview invitation rates were examined based on USMLE tier and AOA status. Additionally, the association between USMLE scores and AOA status were analyzed by applicant and fellowship rank positions. Statistical analysis included descriptive statistics, linear regression, and analysis of variance. ResultsData from 218 matched applicants were included. On average, applicants submitted 23.3± 9.7 applications, with an average interview rate of 76%. The mean Step 1 and 2 score was 247 ± 11 (n = 141) and 253 ± 12 (n = 171), respectively. USMLE scores showed a positive correlation with interview rates (Step 1: p < 0.0001, B = 0.768; Step 2: p < 0.0001, B = 0.727), indicating that higher scores were associated with increased interview opportunities. AOA members accounted for 38% of applicants (of 183 with available data), and they demonstrated a higher average percentage of interviews compared to non-AOA applicants (79% vs 75%, p < 0.001). While no significant difference in the average Step 1 (p = 0.17) or Step 2 score (p = 0.79) was observed across applicant rank positions, AOA membership was more prevalent among applicants in higher rank tiers (p < 0.001). Additionally, AOA applicants (p < 0.001) and those with higher Step 2 scores (p=0.04) were more likely to be ranked higher by their matched fellowship program. ConclusionUSMLE scores and AOA status correlate with interview invitation rates and applicant ranking for shoulder and elbow surgery fellowships. Future work should examine the influence of residency reputation and research productivity on shoulder and elbow surgery fellowship match outcomes.

문자중계와 몬테카를로 시뮬레이션 기반 배구 경기 승패 예측에 관한 연구

With advancements in science and technology, a vast amount of data from various media is being generated in the field of sports. Applying statistical methods to extract meaningful information from this data has become essential. This study aimed to predict volleyball match outcomes by performing Monte Carlo simulations based on live text commentary data from the 2023-2024 women's professional volleyball season. By generating the necessary variables for the simulation from the text commentary data and conducting 10,000 iterations of Monte Carlo simulations, the study confirmed that the simulations accurately mirrored actual match outcomes. In particular, when one team demonstrated overwhelming performance, the accuracy of set score predictions was notably high, and the simulation results demonstrated the potential for developing customized strategies. However, limitations were observed in predicting outcomes in specific scenarios, such as when a team with a high total score loses 3-2. To address these limitations, future work should focus on more precisely modeling the complex interactions between various situational and variable factors by converting qualitative factors, which have been challenging to quantify, into objective indicators for integration into the model.

Quantifying Tennis Players’ Performance and Analyzing Momentum Fluctuations Based on Weighted Logistic Regression Model

This paper proposes a dual-model approach to analyze tennis players’ performance and predict match outcomes, which combines a weighted logistic regression model and a decision tree model. By examining key factors such as scoring rate and serving status, the models calculate the scoring probabilities at different time points, quantifying the performance of players throughout the match. Furthermore, the paper constructs a decision tree model to predict shifts in the match situation based on momentum fluctuations. The research findings underscore the importance of momentum in determining match outcomes and provide valuable insights for training and match strategies. This dual-model approach offers a fresh perspective on the impact of momentum in tennis matches, allowing players to better understand and adapt to the dynamics of momentum fluctuations throughout a match. Additionally, the study suggests targeted training, simulated match conditions, psychological training, real-time data tracking, and physical preparation as strategies to enhance performance and adapt to momentum changes. This comprehensive approach provides players with valuable insights to optimize their performance and strategies in tennis matches.

Quantification Analysis of Sports Momentum Based on a Multi-Index Approach and TOPSIS

This study quantifies the concept of momentum in sports, inspired by Novak Djokovic's unexpected loss in 2023, and evaluates its impact on game outcomes. Utilizing a structured multi-index approach, we categorized performance into fourteen primary and three secondary indexes. The TOPSIS model enhanced by entropy weighting, along with the Delphi model, was applied for weight assessments of these indexes. Our analysis, incorporating the Mann-Kendall trend test and Spearman correlation coefficient, revealed a significant correlation of 0.51 between momentum and match results, underscoring its pivotal role. The results contribute to a deeper understanding of momentum, enabling coaches to refine strategies and optimize performance based on dynamic changes in momentum. This study provides a comprehensive framework for quantifying momentum, with potential applications extending beyond tennis to other sports where strategic momentum management could influence outcomes.

Research on match fluctuation prediction and strategy based on OFS algorithm

The aim of this paper is to construct a model for predicting on-field fluctuations in matches by using OFS algorithm, combined with random forest and lattice point search algorithms, and to analyse the key factors in order to improve the coach's decision-making ability in matches. Cross-validation and hyper-parameter tuning techniques were used during the model building process to find the best hyper-parameter combinations through lattice point search to optimise the model performance and generalisation ability. The study provides a detailed description of the model implementation process, including data processing, feature selection, parameter setting, model building, training and evaluation. Meanwhile, a series of targeted recommendations are proposed to address the variability of match fluctuations, including the analysis of past matches, the focus on recent performance, the assessment of psychological quality, the study of opponent's weaknesses, the flexible response to changes in matches and the maintenance of focus, with a view to helping athletes and coaches to formulate a more effective match strategy and to achieve better match results.

Tennis Player Momentum Analysis and Match Flow Prediction Model Based on PCA

Momentum is a fluctuating phenomenon that occurs during the course of a game when the seemingly dominant side scores consecutive points or influences the course of the entire game. And as tennis is a popular sport all over the world, exploring the effects and mechanisms of athletes' momentum on match changes and outcomes in tennis matches can provide reasonable match advice to athletes. To this end, this paper systematically establishes a prediction model based on tennis players' momentum analysis and match direction. In this paper, this text first cleaned the data for the 2023 Wimbledon tennis tournament obtained from the American Collegiate Mathematical Modeling Contest website and supplemented the missing values using cubic Hermite interpolation. The processed data is then analyzed using principal component analysis, where a linear combination of each principal component is defined as a momentum value, thus quantifying the momentum and creating a mathematical model between the indicator and the momentum. Finally, a momentum plot of the player is created to visualize the relationship between the player's momentum and the outcome of the race.

Offensive Performance Indicators: A Comparative Study of Winning, Drawing, and Losing Teams in the 2023 Malaysia Super League

This study aimed to identify significant differences in offensive performance metrics among winning, drawing, and losing teams in the 2023 Malaysia Super League (MSL) season. Data were collected from 91 matches during the MSL football league using the InStat® system. The analysed variables included set pieces, corners, free kicks, penalties, and shooting. The results showed that winning teams had discriminative offensive performance indicators such as set pieces with shots (4.48±2.17), % set pieces with shots (17.10±8.58), corners (5.90±3.16), corners with shots (2.05±1.63), % corners with shots (36.56±27.49), free kicks with shots (0.76±0.89), % free kicks with shots (22.02±27.49), penalties (0.30±0.58), penalties converted (0.26±0.52), % penalties converted (21.11±40.35), shots (16.53±6.08), shots on target (6.97±3.35), % shots on target (42.18±13.58), shots from outside the penalty area (7.16±3.41), shots from outside the penalty area on target (2.20±1.65), and % shots from outside the penalty area on target (30.68±21.07). In contrast, drawing teams had higher mean values in set pieces (28.67±5.87) and free kicks (4.34±2.21). In conclusion, these findings have practical implications for coaches in planning and implementing offensive tactics for successful performance. Future research could explore the impact of different tactical approaches and formations on match outcomes and analyse the effectiveness of in-match tactical adjustments.

Predicting soccer matches with complex networks and machine learning

Abstract Soccer attracts the attention of many researchers and professionals in the sports industry. Therefore, the incorporation of science into the sport is constantly growing, with increasing investments in performance analysis and sports prediction industries. This study aims to (i) highlight the use of complex networks as an alternative tool for predicting soccer match outcomes and (ii) show how the combination of structural analysis of passing networks with match statistical data can provide deeper insights into the game patterns and strategies used by teams. In order to do so, complex network metrics and match statistics were used to build machine learning models that predict the wins and losses of soccer teams in different leagues. The results showed that models based on passing networks were as effective as ‘traditional’ models, which use general match statistics. Another finding was that by combining both approaches, more accurate models were obtained than when they were used separately, demonstrating that the fusion of such approaches can offer a deeper understanding of game patterns, allowing the comprehension of tactics employed by teams relationships between players, their positions and interactions during matches. It is worth mentioning that both network metrics and match statistics were important and impactful for the mixed model. Furthermore, the use of networks with a lower granularity of temporal evolution (such as creating a network for each half of the match) performed better than a single network for the entire game.

Perspectives of osteopathic medical students on preclinical urology exposure: a single institution cross-sectional survey.

There is an increasing number of medical school graduates opting for surgical specialties, and the osteopathic applicant match rate for urology is lower than that of allopathic applicants. Factors influencing this may include alack of interest, perceived challenges in matching into urology, insufficient urology mentorship, limited research opportunities, and inadequate osteopathic representation in urology. The objective of this survey is to assess osteopathic medical students' perspectives on pursuing urology and enhancing preclinical exposure to and knowledge of urology. A 20-question survey addressing experiences andthe factors influencing osteopathic medical students' specialty selection and their interest in and perception of urology was designed by the investigators on Research Electronic Data Capture (REDCap) software. This survey was distributed via email listserv to all current osteopathic medical students attending Nova Southeastern University Dr. Kiran C. Patel College of Osteopathic Medicine over 2months. Responses were collected and analyzed utilizing Fisher's exact test. Among 150 respondents, 91 % found mentors crucial in selecting a medical specialty, 95 % emphasized the importance of early exposure, and 68 % lacked familiarity with urology, with more M1 students unfamiliar with urology compared to M2 (70.4 % vs. 59 %). A larger proportion of combined M1 and M2 (preclinical) students are considering urology as a specialty compared to M3 and M4 (clinical) students who are actively on rotations (56.5 % vs. 28.6 %; p=0.0064). Also, a greater percentage of males are considering urology compared to females (64.2 % vs. 42.7 %; p=0.0164). Among those considering urology (n=75), 57.3 % lack awareness of urology's scope, and 84 % report no preclinical discussions with urologists. Those students who report that they are considering urology value early exposure significantly more than others (98.7 % vs. 78.7 %; p=0.0001). They also express greater interest in having acore urology course (73.3 % vs. 38.7 %; p<0.0001). More urology-considering students are interested in extracurricular urology-related workshops, seminars, or conferences (61.3 % vs. 17.3 %; p<0.0001). Students who are considering urology as a specialty show greater interest in having a mentorship program (85.3 % vs. 28 %; p<0.0001). Results suggested that increased urology exposure during the preclinical years is important. Urology elective offerings and urology mentorship are of high interest among those considering urology. However, additional investigation is needed to determine the impact ofpreclinical urology curricula implementation on urology match outcomes.

The Relationship Between Playing Formations, Team Ranking, and Physical Performance in the Serie A Soccer League

The influence of playing formations and team ranking on the physical performance of professional soccer players is an open question that needs to be explored. The present study aimed to investigate the impact of these factors on the physical exertion of Serie A soccer players. We analyzed match data from 375 players, categorizing teams based on their final ranking and comparing performance across different playing formations. The Kruskal–Wallis test and the Dunn test with Bonferroni adjustment revealed that high-ranking (HR) teams exhibited a higher percentage of high-intensity (HI) accelerations compared to mid-ranking teams, suggesting the critical role of HI efforts in achieving favorable match outcomes. Moreover, the 4-3-3 playing formation was associated with greater acceleration demands than other formations, particularly in HR teams. Our study also established benchmarks for various performance metrics, enabling coaches to assess player performance and identify potential signs of overtraining. These findings contribute to a deeper understanding of the physical demands in soccer and offer practical implications for coaches and players in optimizing training and performance strategies.

A Study on Pigment Composition of Buddhist Cave Paintings Based on Hyperspectral Technology

The value of the Buddhist cave lies not only in the Buddha statues but also in the surface painting. Hyperspectral imaging technology, as an emerging and effective method for component identification, offers a non-contact and non-destructive approach to the preservation and restoration of oil paintings. This study employed hyperspectral cameras to capture common pigments on the surfaces of Buddhist caves. Then, the results were processed and used as a database to identify the paintings. Additionally, a series of experiments were conducted to examine the impact of binder, substrate types, and pigment sizes on the reflectance spectrum of the paints. The Spectral Angle Matching (SAM) algorithm was then used to analyze the Yuanjue Cave and Qiqushan Stone Carvings of the Tang Dynasty in China. The findings revealed that the position of absorption peaks in the reflectance spectra is not significantly influenced by the substrate but is affected by the binder. Moreover, the absorption depth varies regularly with particle size. Furthermore, the spectral matching results demonstrate that components can be accurately identified even for similar colors. Based on the pigment distribution, the study also inferred specific details of ancient paintings, including the painting steps and hidden information in the manuscript layout. These findings hold significant implications for the restoration of representative surface paintings of the Tang Dynasty Buddhist cave, providing a reference for the selection of restoration materials and methods.

MODELOS PREDITIVOS BASEADOS EM MACHINE LEARNING PARA OTIMIZAÇÃO DE ESTRATÉGIAS EM APOSTAS ESPORTIVAS NO FUTEBOL

This study presents an investigation into the application of predictive models based on Machine Learning to optimize sports betting strategies in football. The research was conducted using a combination of quantitative and qualitative methods, including the analysis of historical football match data and the implementation of machine learning algorithms. The results indicate that predictive models can be effective in forecasting football match outcomes, which can be useful for optimizing sports betting strategies. The practical application of these models can provide bettors with a competitive advantage, allowing them to make more informed decisions and potentially improve their financial outcomes. It is concluded that while predictive models offer a valuable tool for enhancing betting strategies, a careful approach and ongoing evaluation of the models are essential to maximize their effectiveness. The research suggests that future investigations could explore the integration of additional data and the improvement of algorithms to increase prediction accuracy.

Visible light infrared matching algorithm based on improved SuperPoint and linear converter

In order to solve the problems of high difficulty and mismatch rate in heterogeneous image matching of visible light and infrared images, a deep learning matching algorithm based on improved SuperPoint and linear transformer is proposed. The algorithm first introduces the idea of feature pyramid to construct a feature description branch based on the SuperPoint network structure, and trains it based on the hinge loss function, so as to better learn the multi-scale deep features of visible light and infrared images and increase the similarity of image point pair descriptors with the same name; in the feature matching module, the linear transformer is used to improve the SuperGlue matching algorithm and aggregate features to improve matching performance. The proposed algorithm is experimentally verified on multiple data sets. The results show that compared with the existing algorithms, the algorithm achieves better matching results and improves the matching accuracy.

Online bilateral matching methodology for anti-epidemic resources based on spatial transmission risk

This paper focuses on the online anti-epidemic resource allocation between demanders and suppliers considering the epidemic spatial spread. The spatial crowdsourcing with sharing platform is an effective way for anti-epidemic resource allocation, and a reasonable online matching strategy can improve the efficiency of resource utilization. The paper proposes online matching heuristic strategy (HSTRF-LRLUF strategy) and designs an online batch bilateral matching algorithm for anti-epidemic resources, which considers the impact of grid spatial aggregation and diffusion risk of emerging infectious diseases. The population distribution within grids and the commuting patterns between grids can provide decision support for selecting online matching strategies of anti-epidemic resources. A larger matching time window focusing on the spatial transmission risk (TR) of the epidemic can obtain better matching results. However, with a smaller matching time window, the decision makers can focus on spatial agglomeration risk (OR) or spatial diffusion risk (IR). The paper effectively combines the spatial crowdsourcing model with the anti-epidemic resource allocation to achieve the allocation of emergency resources to individuals. A combined anti-epidemic resource online matching heuristic strategies is designed from the spatial agglomeration risk and the spatial diffusion risk. Decision makers can dynamically adjust the online matching strategies of anti-epidemic resources by evaluating the spatial agglomeration risk, the spatial diffusion risk, and the overall spatial transmission risk based on the real-time spread of the epidemic and the supply of anti-epidemic resources.

Prediction of tennis match results based on logistic regression

More and more people like tennis, and the demand for the accuracy of predicting the results of tennis matches in sports betting is getting higher and higher. In this paper, a model is built to predict the results of tennis matches. In this paper, we first use the random forest model to analyze the factors that will have an impact on the outcome of the game, and the authors have selected out five factors that may have an impact on the outcome, which are: break of serve, Serve, Score, Net points , And they were assigned proportionally and the distribution was Break of serve 34.10%, Serve 24.30%, Score 38.00%, Net points 3.60% and using these proportions the influencing factors were calculated numerically, in this paper, the calculated value is named “momentum”. Then Pearson's correlation coefficient was used to analyze the relationship between momentum and match results, and the analysis found that the correlation between momentum and match results was as high as 0.807. Finally, a logistic regression model was used to predict and test the results of tennis matches. The final test results found that the model established in this paper has a high accuracy rate, and the research results also provide a new idea for tennis betting industry.

Research on Quantifying and Evaluating Tennis Match Momentum Based on Comprehensive Analytical Methods

Momentum is crucial in tennis matches, influencing player performance and match outcomes. However, quantifying and evaluating momentum has long been a challenge in sports science research. This study aims to quantify momentum in tennis matches by establishing a hierarchical analysis-based model for momentum flow. The model analyzes factors affecting momentum through five criteria: Service Level, Catch Level, Competition Result, Finishing Ability, and Winning Streak, with respective weights of approximately (0.1, 0.1, 0.2, 0.4, 0.2). Subsequently, the study assigns scores to these criteria based on different actions within a match. By multiplying the weights and scores, the momentum of each player is quantified, and the ratio of the two players' momentum provides a measure of momentum flow during the match. To assess the impact of momentum on match outcomes, the study employs k-means++ clustering analysis, violin plot analysis, and point-biserial correlation analysis. The point-biserial correlation analysis reveals a positive correlation coefficient of 0.66 between a player's momentum and their victory. Similar results are derived from the violin plot and clustering analysis. Therefore, it can be concluded that there is a significant association between the direction of momentum flow and a player's success in a match.