Match Statistics Research Articles

On the single networked links between classifications of sci-tech information. (Part 2. «The converters» between classifications. Distribution of headings semantic relation types)

For today, the RNPLS&T’s single network of classifications comprises four classification systems, i. e. State Rubicator of Sci-tech Information (GRNTI, version 2021) as the network foundation and core of the State System of Sci-tech Information, UDC (UDC tables in Russian), Classification Codes of the Higher Attestation Commission, WoS categories and subject areas, and international OECD classification. The semantic matches between the GRNTI headings and codes of the other classifications are defined as logical matching relations, extension inclusion and overlapping including that of key terms and word combinations used for indexing the thematic subject. Three types of semantic relations are determined: equivalency, hierarchy, and association. The statistics of matches in hierarchical levels of GRNTI thematic sections are cited. Based on the statistical analysis, the principal type of semantic matches is identified, along with the largest ratios for every type of relations for each of the classification under examination. The functional options for the single networked headings as the system of interrelated classifications of science objects are demonstrated as exemplified by crossrubrication of subject areas within the Critical Technologies List.

Localized versus wide-ranging effects of the post-Soviet wars in the Caucasus on agricultural abandonment

Wars are frequent and can affect land use substantially, but the effects of wars can vary greatly depending on their characteristics, such as intensity or duration. Furthermore, the spatial scale of the effects can differ. The effects of wars may be localized and thus close to conflict locations if direct mechanisms matter most (e.g., abandonment because active fighting precludes farming), or wide-ranging, e.g., farther away from conflict locations, if indirect mechanisms predominate (e.g., no access to agricultural inputs). Our goal was to quantify how the very different wars in the Caucasus region during post-Soviet times most likely affected agricultural abandonment at different scales. We analyzed data on conflict locations plus Landsat-derived land-cover data from 1987 to 2015, and applied matching statistics, difference-in-differences estimators, and logistic panel regressions. We examined the localized versus wide-ranging effects of the different wars on permanent agricultural abandonment and inferred to direct and indirect mechanisms that may have resulted in agricultural abandonment. While permanent agricultural abandonment was overall surprisingly limited across the Caucasus, up to one third of abandonment was most likely related to the wars. Among the wars, the war in Chechnya was by far the most intense and longest, but its effect on abandonment was similar to the less intense and relatively short war in Abkhazia. 47 % and 45 % of agricultural abandonment was related to each war, respectively. The reason was that the effect of the war in Chechnya was more localized, and abandonment occurred near conflict locations, in contrast to Abkhazia, where the effect was wide-ranging and abandonment occurred farther away from conflict locations. In contrast, the war in South Ossetia showed no significant effect on abandonment, and the war in Nagorno-Karabakh had the surprising pattern that abandonment was higher where no war had occurred. For each of the wars, abandonment was predominately related to the nearest conflict locations, but in Abkhazia additional conflict locations within 10 km further increased the probability of abandonment. We infer that the direct mechanisms of the war such as bombing, and active fighting most likely resulted in a localized effect close to conflict locations in Chechnya and in Nagorno-Karabakh. However, in Nagorno-Karabakh subsidies for new settlers after the war, (i.e., a positive wide-ranging effect), potentially reduced the amount of abandonment there. In contrast, negative wide-ranging effects such as refugee movements and post-war restrictions on their return is related to broad-scale abandonment in Abkhazia. In summary, permanent agricultural abandonment was not necessarily higher in a war with a high overall intensity. Instead, the effect of a given war varied in scale, and was related to the relative importance of direct and localized versus indirect and wide-ranging mechanisms, including postwar events and policies, which is likely the case for other wars, too.

What does it take to win or lose a soccer game? A machine learning approach to understand the impact of game and team statistics

This study investigates what it takes to win a soccer game in three major soccer leagues, (i) English Premier League (EPL), (ii) Spain’s La Liga and (iii) Major League Soccer (MLS) at the USA. To do so, a set of supervised machine learning models were trained and used to predict the outcome of soccer games and investigate the importance of predictors (features) contributing to the outcome. A total of 91 game and team related features (for both home and away teams, and the differences between them) that cover match statistics, attempt types, card situations, pass types and squad information were taken into consideration. The best prediction performance was achieved by a random forest (RF) model with a classification accuracy of 86.31% of the combined dataset. Relative importance percentages of the game and/or team features for each league are investigated to identify the most crucial factors of the winning/losing teams. Furthermore, each league’s characteristics are discussed and compared through the lenses formed by the intuitive experiment results. For instance, the home field advantage in the MLS games has been shown to be two times more important than this advantage in the La Liga and EPL games.

Solving String Problems on Graphs Using the Labeled Direct Product

Suffix trees are an important data structure at the core of optimal solutions to many fundamental string problems, such as exact pattern matching, longest common substring, matching statistics, and longest repeated substring. Recent lines of research focused on extending some of these problems to vertex-labeled graphs, either by using efficient ad-hoc approaches which do not generalize to all input graphs, or by indexing difficult graphs and having worst-case exponential complexities. In the absence of an ubiquitous and polynomial tool like the suffix tree for labeled graphs, we introduce the labeled direct product of two graphs as a general tool for obtaining optimal algorithms in the worst case: we obtain conceptually simpler algorithms for the quadratic problems of string matching (SMLG) and longest common substring (LCSP) in labeled graphs. Our algorithms run in time linear in the size of the labeled product graph, which may be smaller than quadratic for some inputs, and their run-time is predictable, because the size of the labeled direct product graph can be precomputed efficiently. We also solve LCSP on graphs containing cycles, which was left as an open problem by Shimohira et al. in 2011. To show the power of the labeled product graph, we also apply it to solve the matching statistics (MSP) and the longest repeated string (LRSP) problems in labeled graphs. Moreover, we show that our (worst-case quadratic) algorithms are also optimal, conditioned on the Orthogonal Vectors Hypothesis. Finally, we complete the complexity picture around LRSP by studying it on undirected graphs.

Factors involved in applicant interview selection and ranking for chronic pain medicine fellowship

IntroductionApplicants to chronic pain medicine fellowship programs often express confusion regarding the importance of various selection criteria. This study sought to elucidate program directors’ considerations in applicant selection for fellowship...

On-Court Activity and Game-Related Statistics during Scoring Streaks in Basketball: Applied Use of Accelerometers.

The aim of this observational study was to determine if on-court activity and match statistics differed between periods of scoring streaks and regular play in basketball. Thirty-seven basketballers including professional women, semi-professional women and semi-professional men wore accelerometers during competitive matches throughout a season. Accelerometry-derived live-time individual on-court exercise intensity and team game-related statistics were compared between scoring streaks (periods of play where the teams participating in the study scored at least three times in a row), streaks against (periods of play where the opposition teams scored at least three times in a row) and regular play. Few differences existed in the average exercise intensity between streak types. During streaks against, there was a 5–15% lower proportion of 2-point attempts, 0.8–1.3 fewer defensive rebounds per minute and 0.3–1.6 fewer shot attempts per minute compared to regular play and scoring streaks, and there were 0.3 fewer offensive rebounds per minute compared to regular play. During scoring streaks, there were 0.5 more defensive rebounds per minute, 1.3 more shot attempts per minute, a 43% greater shooting percentage and a 10% lower proportion of 3-point attempts compared to regular play. To reduce the chances of streaks against, teams should focus on facilitating 2-point shot attempts and consider implementing a 3:1 ratio of 2-point to 3-point attempts to maximize scoring success, and they should focus on winning rebounds to facilitate more shot attempts.

Physical Demands during the Game and Compensatory Training Session (MD + 1) in Elite Football Players Using Global Positioning System Device.

The aims of this study were to analyze the differences of physical demands of non-starter players regarding the playing time during the competition and to evaluate the physical demands of the compensatory training (MD + 1C) for substitute players in elite football. The match statistics and MD + 1C of substitute players from a professional Spanish LaLiga football club were analyzed using a 10-Hz global positioning system (GPS) Apex GPS system device, which has been validated as a reliable and valid method to analyze performance in team sports, during all games of the 2016/2017, 2017/2018 and 2018/2019 seasons. The starting players showed both lower total distances covered and high-intensity actions compared to the substitutes. Regarding the minutes played by the substitutes, greater physical performance was found for the players with fewer minutes (5–15 min). Furthermore, no differences were found between first and second divisions regarding physical performance of substitutes (p > 0.05). This study highlights the importance of individualizing the workload of training sessions for substitutes and starters. Furthermore, the complementary session should be individualized according to the minutes played by the substitutes. These players are potentially under-loaded compared to starters, especially in terms of high-intensity actions, therefore additional session-specific training for each substitute would be useful to reach the optimal training load according to the minutes played during the game.

Analysis of Twitter Engagement in a Progressively More Competitive Urology Match: A Mixed Methods Study

ObjectiveTo analyze Twitter engagement in response to the urology match during the COVID-19 pandemic. MethodsTweets containing the hashtags “#uromatch” or “#AUAmatch” during the 2021 and 2022 Match Week were reviewed. Date, author type and number of followers, general content, and engagement with each Tweet was collected. Differences in engagement between author type and content were analyzed using the Kruskal-Wallis H test. Tweet characteristics were compared between the 2021 and 2022 Match Cycles using the Chi-Square test. ResultsThere were 656 Tweets in total, with 272 (43.5%) from 2021 and 353 (56.5%) from 2022. Medical students’ and residency programs’ posts received significantly more Tweet engagement than those by residents/fellows, attendings, or the AUA (P <.05). Tweets focusing on announcing a new residency class and personal announcements of match results received significantly more engagements than other content categories (P <.05). In 2022, there was a significantly higher percentage of Tweets about advice for unmatched applicants (2.2 vs 12.5; P <.001), match statistics (0.4 vs 2.9; P = .028) and focus on underrepresented groups in urology (0.7 vs 3.4; P = .029). ConclusionThe Twitter response to the urology match between 2021 and 2022 mirrored the increase in competitiveness, with greater participation and an increasing focus on the difficulty of matching. During Match Week, Twitter is a readily available source of information for programs, matched students, and unmatched students alike. As we continue to embrace virtual platforms, we believe that Twitter will remain a major source of match-related information and can be an instrumental tool for broader networking in our field.

Comparison of fellowship match opportunities and results across pediatric surgical subspecialities

There is a nationwide shortage of pediatric ophthalmologists, with trends in fellowship applicants foreshadowing a continued reduction in the number of active pediatric ophthalmologists in the years ahead. In this study, we investigated whether similar fellowship applicant shortages exist in other pediatric surgical subspecialties. We compared the match statistics of six pediatric surgical fellowships from 2015 to 2020. During the 6-year interval, a median of 12.2 pediatric ophthalmology fellowship positions (IQR, 12.1-12.4) were offered per 100 graduating residents, 8.7% of ophthalmology residents (IQR, 8.3%-9.3%) pursued pediatric subspecialty training, and 72% of available pediatric fellowship positions (IQR, 69%-74%) were filled. Pediatric general surgery had the highest percentage of available fellowship positions filled, 99% (IQR, 98%-100%), which was significantly higher than for pediatric ophthalmology (P = 0.024). None of the other subspecialties hada significant difference in available positions filled compared to pediatric ophthalmology (P > 0.05).

Allele frequencies and minor contributor match statistic convergence using simulated population replicates.

Probabilistic genotyping permits a comparison of forensic evidence given hypotheses regarding the origin of observed short tandem repeat alleles in a mixed DNA profile. Using the publicly available R package forensim, it has been proposed that mixtures with non-contributors from low genetic diversity populations are more likely to be mistakenly identified as contributors to a mixture than non-contributors from high genetic diversity populations. We hypothesized that these observations are attributed to the unique distribution of alleles in the reference population and may not generalize to other samplings of the same population. We used forensim to simulate 200 US populations (50 each of self-reported African-American, Asian-American, European-American, and Hispanic descent). We compared likelihood ratios for 2400 mixtures to those derived from published data and identified stark differences. A minimum of ten population replicates were required to reduce observed differences relative to published data. Deviations from Hardy-Weinberg equilibrium and allele frequency distributions suggest that simulated populations should be sufficiently evaluated for expectations of population genetic parameters prior to use in DNA mixture modeling experiments. Overall, our findings support the utility of forensim and further describe its suitability to model population genetic parameters but suggest that a single population replicate (directly ascertained or simulated) may be insufficient to make conclusions about a given DNA mixture.

Is There a Sex Difference in Technical Skills among Youth Soccer Players in Norway?

Female soccer has recently experienced an impressive increase in the number of players, and an impressive improvement in the quality of elite matches. Still, studies show sex differences in match statistics on passing accuracy and the ability to control the ball in international matches, which is explained by a lower skill of level in female soccer players as compared to male players. Therefore, the aim of this study was to evaluate if female youth soccer players had bridged the gap in technical skills to reach the level that boys have traditionally attained. Sixteen male and 17 female youth soccer players of the same age and experience level took part in technical skill tests of reception of the ball on the ground and long passes. The results show a significant difference between the sexes in reception performance in favour of the male players (p < 0.05, ES = 1.09), but no significant difference in the long pass test (p = 0.11, ES = 0.43). This leads to the conclusion that the lower score on ball reception is probably the result of experience in small-sided self-organised soccer games during childhood among the male players, which influences reception skills but not the ability to make accurate long passes.

Fast and compact matching statistics analytics.

Fast, lightweight methods for comparing the sequence of ever larger assembled genomes from ever growing databases are increasingly needed in the era of accurate long reads and pan-genome initiatives. Matching statistics is a popular method for computing whole-genome phylogenies and for detecting structural rearrangements between two genomes, since it is amenable to fast implementations that require a minimal setup of data structures. However, current implementations use a single core, take too much memory to represent the result, and do not provide efficient ways to analyze the output in order to explore local similarities between the sequences. We develop practical tools for computing matching statistics between large-scale strings, and for analyzing its values, faster and using less memory than the state-of-the-art. Specifically, we design a parallel algorithm for shared-memory machines that computes matching statistics 30 times faster with 48 cores in the cases that are most difficult to parallelize. We design a lossy compression scheme that shrinks the matching statistics array to a bitvector that takes from 0.8 to 0.2 bits per character, depending on the dataset and on the value of a threshold, and that achieves 0.04 bits per character in some variants. And we provide efficient implementations of range-maximum and range-sum queries that take a few tens of milliseconds while operating on our compact representations, and that allow computing key local statistics about the similarity between two strings. Our toolkit makes construction, storage and analysis of matching statistics arrays practical for multiple pairs of the largest genomes available today, possibly enabling new applications in comparative genomics. Our C/C++ code is available at https://github.com/odenas/indexed_ms under GPL-3.0. The data underlying this article are available in NCBI Genome at https://www.ncbi.nlm.nih.gov/genome and in the International Genome Sample Resource (IGSR) at https://www.internationalgenome.org. Supplementary data are available at Bioinformatics online.

Identification-Robust Inference With Simulation-Based Pseudo-Matching

We develop a general simulation-based inference procedure for partially specified models. Our procedure is based on matching auxiliary statistics to simulated counterparts where nuisance parameters are calibrated neither assuming identification of parameters of interest nor a one-to-one binding function. The conditions underlying the asymptotic validity of our (pseudo-)simulators in conjunction with appropriate bootstraps are characterized beyond the strict and exact calibration of the parameters of the simulator. Our procedure is illustrated through impulse-response (IR) matching in a simulation study of a stylized dynamic stochastic equilibrium model, and two empirical applications on the New Keynesian Phillips curve and on the Industrial Production index. In addition to usual Wald-type statistics that combine structural or reduced form IRs, we analyze local projections IRs through a factor-analytic measure of distance which eschews the need to define a weighting matrix.

Circuit Activity Fingerprinting Using Electromagnetic Side-Channel Sensing and Digital Circuit Simulations

This paper introduces a novel circuit identification method based on “fingerprints” of periodic circuit activity that does not rely on any circuit-specific reference measurements. We capture these “fingerprints”, consisting of fifty harmonics of the circuit activity, using digital circuit simulations and near-field measurements of the EM backscattering side-channel. Utilizing a novel technique and algorithm, we augment our measurements, removing sources of noise and other irregularities not present in the simulation, in order to relate an unknown circuit measurement with a known circuit simulation. A matching threshold of less than 1 dB difference between the simulated and measured fingerprints is set, and the matching performance is evaluated across multiple hardware instances exhibiting a strong resistance to false positives. Using various match statistics, decisions on the circuit identity can be made based on the simulated and measured fingerprint pair with the best matching performance. The results show that we can identify fingerprints of digital circuits with up to 95% accuracy using the proposed method.

Prediction of football match results with Machine Learning

Football is one of the most popular sports in the world, so the perception of the game and the prediction of results is of general interest to fans, coaches, media and gamblers. Although predicting football results is a very complex task, the football betting business has grown over time. The unpredictability of football results and the growing betting business justify the development of prediction models to support gamblers. In this article, we develop machine learning methods that take multiple statistics of previous matches and attributes of players from both teams as inputs to predict the outcome of football matches. Several prediction models were tested, with the experimental results showing encouraging performance in terms of the profit margin of football bets.

Exploring Item Bank Stability through Live and Simulated Datasets

LanguageCert manages the construction of its tests, exams and assessments using a sophisticated item banking system which contains large amounts of test material that is described, inter alia, in terms of content characteristics such as macroskills, grammatical and lexical features and measurement characteristics such as Rasch difficulty estimates and fit statistics. In order to produce content and difficulty equivalent test forms, it is vital that the items in any LanguageCert bank manifest stable measurement characteristics. The current paper is one of two linked studies exploring the stability of one of the item banks developed by LanguageCert [Note 1]. This particular bank has been used as an adaptive test bank and comprises 820 calibrated items. It has been administered to over 13,000 test takers, each of whom have taken approximately 60 items. The purpose of these two exploratory studies is to examine the stability of this adaptive test item bank from both statistical and operational perspectives. The study compares test taker performance in the live dataset with over 13,000 test takers (where each test taker takes approximately 60 items) with a simulated ‘full’ dataset generated using model-based imputation. Simulation regression lines showed a good match and Rasch fit statistics were also good: thus indicating that items comprising the adaptive item bank are of high quality both in terms of content and statistical stability. Potential future stability was confirmed by results obtained from a Bayesian ANOVA. As mentioned above, such item bank stability is important when item banks are used for multiple purposes, in this context for adaptive testing and the construction of linear tests. The current study therefore lays the ground work for a follow-up study where the utility of this adaptive test item bank is verified by the construction, administration and analysis of a number of linear tests.

The COVID-19 Lockdown and Players’ Match Statistics in Serie A

Purpose: In March 2020, most sports competitions around the world were suspended, ended, or canceled. As a result of the COVID-19 lockdown, citizens and athletes from many countries had to stay at home. It was a very unusual break for athletes and it caused a lot of discussion how it could affect the performance of professional players. This study aims to check how the lockdown due to COVID-19 affected the players’ match statistics in the premier Italian football league – Serie A. The Italian league was chosen deliberately because this country was particularly hard hit by the onset of the COVID-19 pandemic.Design/methodology/approach: Five-fixture rounds played after the restart (since 20th June 2020) of the 2019/2020 season were analyzed and compared with the five-fixture rounds immediately preceding the lockdown (played in February and March). In this way, it was possible to check whether the break in the games and training had significant impact in the initial period after return to play. Findings: The results indicate that the players, in general, managed to stay in good overall shape during the lockdown, but when competition resumed, they attempted much fewer dribbles and tackles.Research and practical limitations/implications: The research focuses on the Italian league. The influence of Covid-19 on sport and society varied from country to country. As a result, lockdown in particular regions could have had different impacts.Originality/value: The value of the study is a description of the significance of a not-expected break in competition and training for the statistics of football players and thus the quality of football games.

Modelling Sport Events with Supervised Machine Learning

It has been very important to understand the change of multivariable systems to make predictions accordingly. The goal of supervised machine learning is to build a model of changing classes of observations depending on various variables and to make predictions about the coming situations. Due to the fact that sports are followed by the whole world modelling sports events and studies about predicting the results of future matches have gained importance. In this study, match statistics of the teams in the Turkey Super League were used, and it was examined how successfully the outcome of the match was predicted using a decision tree, random forest, k-nearest neighbor, naive Bayes, support vector machine. According to the tests done in Turkey Super League, the support vector machine performs the best.

Association between offensive and defensive playing style variables and ranking position in a national football league

ABSTRACT The aim of this study was to determine the association of playing style and efficacy variables with football success in a professional football league. Match statistics were obtained from 23 football teams competing in the 2017–2018 and 2018–2019 seasons of the Spanish national league(LaLiga). Offensive and defensive playing style and efficacy variables were calculated. Pearson’s correlation coefficient tests and principal component analysis (PCA) were applied to establish the influence of these variables on the number of points obtained at the end of the season and on the ranking position. In isolation, the efficacy of completion showed the highest association with ranking points and position. A two-dimension PCA explained 77.8% of the variance in the ranking position. In dimension-1 (58.5%), game initiative and attack building, and in dimension-2 (19.3%), efficacy of defensive containment and a lower rate of long passes were within the variables that explained more variance in the ranking position. Success in football, measured by ranking position at the end of the Spanish national league, was associated with several playing style and efficacy variables. Overall, a dominant game style with high efficacy to finish attacking plays, and an offensive game initiative, are most associated with successful football.

A COMPARATIVE STUDY OF THE DIFFERENT CLASSIFICATION ALGORITHMS ON FOOTBALL ANALYTICS

Sports analytics is on the rise, with many teams looking to use data science and machine learning algorithms to augment their teams research and boost team performance. This is especially true in the case of Football Clubs. In this work, we have taken the statistics of matches for each team from five major football leagues. These include the English Premier League, La Liga, Serie A, Bundesliga, and Ligue 1. We use this data for two kinds of classification to predict a teams win, loss, or draw. First, we implement Multiclass Classification using Naive Bayes classification, Decision Tree classification, and K-Nearest Neighbours classification. We use f1-score, recall, and precision to evaluate the model. Next, we use Binary Classification to predict if a team wins or does not win, i.e., a loss or a draw. We achieve this by using Support Vector Machines, Logistics Regression, K-Nearest Neighbours classification, Decision Tree classification, and Naive Bayes classification. We evaluate the results using the evaluation metrics mentioned above. Now, we compare the accuracy and efficacy of these algorithms based on the evaluation metrics. This will help standardize the means of classification in sports and football analytics in the future.