Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: An application to rugby union.
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ABSTRACT:
SUBMITTER: Bunker R
PROVIDER: S-EPMC8460049 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Rugby union (RU) is a skill-collision team sport played at junior and senior levels worldwide. Within England, age-grade rugby governs the participation and talent development of youth players. The RU player development pathway has recently been questioned, regarding player performance and well-being, which sport science research can address. The purpose of this review was to summarise and critically appraise the literature in relation to the applied sport science of male age-grade RU players in England focussing upon (1) match-play characteristics, (2) training exposures, (3) physical qualities, (4) fatigue and recovery, (5) nutrition, (6) psychological challenges and development, and (7) injury. Current research evidence suggests that age, playing level and position influence the match-play characteristics of age-grade RU. Training exposures of players are described as 'organised chaos' due to the multiple environments and stakeholders involved in coordinating training schedules. Fatigue is apparent up to 72?h post match-play. Well-developed physical qualities are important for player development and injury risk reduction. The nutritional requirements are high due to the energetic costs of collisions. Concerns around the psychological characteristics have also been identified (e.g. perfectionism). Injury risk is an important consideration with prevention strategies available. This review highlights the important multi-disciplinary aspects of sport science for developing age-grade RU players for continued participation and player development. The review describes where some current practices may not be optimal, provides a framework to assist practitioners to effectively prepare age-grade players for the holistic demands of youth RU and considers areas for future research.
| S-EPMC7035403 | biostudies-literature
Project description:The metabolic perturbations caused by competitive rugby are not well characterized. Our aim is to utilize untargeted metabolomics to develop appropriate interventions, based on the metabolic fluctuations that occur in response to this collision-based team sport. Seven members of an English Premiership rugby squad consented to provide blood, urine, and saliva samples daily, over a competitive week including gameday (GD), with physical demands and dietary intake also recorded. Sample collection, processing and statistical analysis were performed in accordance with best practice set out by the metabolomics standards initiative employing 700 MHz NMR spectroscopy. Univariate and multivariate statistical analysis were employed to reveal the acute energy needs of this high intensity sport are met via glycolysis, the TCA cycle and gluconeogenesis. The recovery period after cessation of match play and prior to training recommencing sees a re-entry to gluconeogenesis, coupled with markers of oxidative stress, structural protein degradation, and reduced fatty acid metabolism. This novel insight leads us to propose that effective recovery from muscle damaging collisions is dependent upon the availability of glucose. An adjustment in the periodisation of carbohydrate to increase GD+1 provision may prevent the oxidation of amino acids which may also be crucial to allay markers of structural tissue degradation. Should we expand the 'Fuel for the work required' paradigm in collision-based team sports to include 'Fuel for the damage induced'?
| S-EPMC8400368 | biostudies-literature
Project description:Sequential pattern mining (SPM) has been applied in many fields. However, traditional SPM neglects the pattern repetition in sequence. To solve this problem, gap constraint SPM was proposed and can avoid finding too many useless patterns. Nonoverlapping SPM, as a branch of gap constraint SPM, means that any two occurrences cannot use the same sequence letter in the same position as the occurrences. Nonoverlapping SPM can make a balance between efficiency and completeness. The frequent patterns discovered by existing methods normally contain redundant patterns. To reduce redundant patterns and improve the mining performance, this paper adopts the closed pattern mining strategy and proposes a complete algorithm, named Nettree for Nonoverlapping Closed Sequential Pattern (NetNCSP) based on the Nettree structure. NetNCSP is equipped with two key steps, support calculation and closeness determination. A backtracking strategy is employed to calculate the nonoverlapping support of a pattern on the corresponding Nettree, which reduces the time complexity. This paper also proposes three kinds of pruning strategies, inheriting, predicting, and determining. These pruning strategies are able to find the redundant patterns effectively since the strategies can predict the frequency and closeness of the patterns before the generation of the candidate patterns. Experimental results show that NetNCSP is not only more efficient but can also discover more closed patterns with good compressibility. Furtherly, in biological experiments NetNCSP mines the closed patterns in SARS-CoV-2 and SARS viruses. The results show that the two viruses are of similar pattern composition with different combinations.
| S-EPMC7118609 | biostudies-literature
Project description:Nonoverlapping sequential pattern mining, as a kind of repetitive sequential pattern mining with gap constraints, can find more valuable patterns. Traditional algorithms focused on finding all frequent patterns and found lots of redundant short patterns. However, it not only reduces the mining efficiency, but also increases the difficulty in obtaining the demand information. To reduce the frequent patterns and retain its expression ability, this paper focuses on the Nonoverlapping Maximal Sequential Pattern (NMSP) mining which refers to finding frequent patterns whose super-patterns are infrequent. In this paper, we propose an effective mining algorithm, Nettree for NMSP mining (NetNMSP), which has three key steps: calculating the support, generating the candidate patterns, and determining NMSPs. To efficiently calculate the support, NetNMSP employs the backtracking strategy to obtain a nonoverlapping occurrence from the leftmost leaf to its root with the leftmost parent node method in a Nettree. To reduce the candidate patterns, NetNMSP generates candidate patterns by the pattern join strategy. Furthermore, to determine NMSPs, NetNMSP adopts the screening method. Experiments on biological sequence datasets verify that not only does NetNMSP outperform the state-of-the-arts algorithms, but also NMSP mining has better compression performance than closed pattern mining. On sales datasets, we validate that our algorithm guarantees the best scalability on large scale datasets. Moreover, we mine NMSPs and frequent patterns in SARS-CoV-1, SARS-CoV-2 and MERS-CoV. The results show that the three viruses are similar in the short patterns but different in the long patterns. More importantly, NMSP mining is easier to find the differences between the virus sequences.
| S-EPMC8743106 | biostudies-literature
Project description:Several genes are involved in sport performance, especially in injuries incidence. The aim of this study was to investigate the association of ACE, ACTN3, COL1A1, and MCT1 genotypes and injuries in rugby players in order to find a genotype/phenotype correlation and provide useful information improving athletic performance. One-hundred male professional and semiprofessional rugby players were selected. Analysis was performed genotyping the genes ACE, ACTN3, COL1A1, and MCT1 as candidate gene of interest involved in athletic performance. A control group of non-athletic Italian male participants was analyzed to compare the results. We found statistical significance of MCT1 rs1049434 AA for total injuries (χ2 = 0.115; p = 0.003) and bone injuries (χ2 = 0.603; p = 0.007) in the rugby athlete population. No statistical significance was found between injury incidence and ACE, ACTN3, COL1A1 genotypes. The MCT1 AA genotype is associated with the incidence of total and bone injuries in the rugby player population. Although environmental factors such as lifestyle, diet, training, and stress can influence athletic performance, our data demonstrated the importance of genetic study in sport aimed at developing personalized training and achieving the best possible athletic excellence.
| S-EPMC9223017 | biostudies-literature
Project description:The use of physical tests to profile physical capabilities, and provide training direction to athletes is common practice. Likewise, in professional team sports, notational analysis codes the key contributions of each player during competition. Limited studies have however investigated relationships between physical capabilities and key performance indicators (KPIs) of rugby union match-play. Elite professional players, categorised as forwards (n = 15) or backs (n = 14), from an international rugby union squad (n = 29) undertook assessments of isometric mid-thigh pull (IMTP), bilateral and unilateral countermovement jumps (CMJ) and drop jumps (DJ; from 40 and 20 cm, respectively), and assessment of acceleration (10 m), a 5 m weighted sled drive, and a Yo-Yo intermittent recovery test level 1 (Yo-Yo IRTL1). Game statistics of the same players from 92 matches (~23 matches per player) during the 2014-15 season were analysed for effort and performance-based metrics. For forwards, Yo-Yo IRTL1 correlated significantly with; number of tackles made (r = 0.717), first three players at a ruck in both attack (r = 0.568) and defence (r = 0.581), number of effective rucks (r = 0.630), total possessions (r = 0.522), passes made (r = 0.651), percentage of carries over the gainline (r = 0.610), effective ruck success (r = 0.600), tackle success (r = 0.540), and the number of turnovers made (r = 0.518). Drop jump performance in forwards was associated with; the number of clean breaks (r = 0.558), dominant collisions (r = 0.589), and offloads (r = 0.594). For backs, the sled-drive test correlated with; number of carries (r = -0.751), first three players at an attacking ruck (r = -0.613), effective attacking rucks (r = -0.584), number of dominant collisions (r = -0.792) and offloads (r = -0.814). Likewise, for backs, IMTP peak force was related to; the number of possessions (r = 0.793), passes made (r = 0.792), effective attacking ruck percentage (r = 0.628), and the number of offloads (r = 0.621) whilst relative peak force correlated with; the percentage of carries over the gainline (r = 0.533), percent tackle success (r = 0.603) and effective attacking ruck percentage (r = 0.584). Regression analyses highlighted that only a small number of variables (i.e., carries, tackles, attacking and defensive first three at ruck) returned practically achievable changes (<20%) in physical qualities. In spite of this, and while leaving scope identification of further physical and/or performance predictors, greater strength, power and intermittent running performance were positively related to match-derived KPIs during competition. This may provide a basis for better integrating the strategies used by physical and technical performance-focused coaching staff to improve key performance indicators, and thus match performance, of rugby union players.
| S-EPMC6135371 | biostudies-literature
Project description:BackgroundThere is limited research on associations between playing rugby union and player health post-retirement.ObjectiveThis study investigated differences in self-reported sport injury history and current self-reported health characteristics between former New Zealand rugby and non-contact sport players with a view to identifying issues to be further investigated with stronger epidemiological research designs.MethodsUsing a cross-sectional design, the NZ-RugbyHealth study surveyed 470 former rugby and non-contact sport players (43.8 ± 8.1 years; 127 elite rugby, 271 community rugby, 72 non-contact sport) recruited from October 2012 to April 2014. Demographic information, engagement in sport, sport injuries, medical conditions, mood, alcohol and substance use and ratings of current health status were obtained from a self-report 58-item general health e-questionnaire. We highlighted standardised differences in means of > 0.6 and differences in relative percentages of > 1.43 for variables between groups as representing at least moderate effect sizes, and of being worthy of follow-up studies.ResultsHigher percentages of the elite rugby player group had sustained injuries of a given body-site type (e.g. neck sprain/strain, thigh bruising, hamstring strain) combination than the non-contact sports players. Higher percentages of the rugby groups reported having sustained concussion (94% for elite, 82% for community, 26% for non-contact), injuries requiring hospitalisation (73%, 46%, 25%), injuries that stopped participation in sport permanently (28%, 28%, 11%) and sport-related surgery (72%, 46%, 32%) during their playing career. Both rugby groups had a higher prevalence of osteoarthritis (37%, 18%, 6%) than non-contact athletes and community rugby players had higher levels of hazardous alcohol consumption (38%, 40%, 25%) in retirement than non-contact athletes. There was little difference between rugby players and non-contact sports athletes in self-reported mood, substance use and current physical or psychological health ratings.ConclusionsFormer rugby player groups were at higher risk than the non-contact player group for most injuries during their playing careers, and in retirement had greater prevalence of osteoarthritis and hazardous alcohol consumption. The relative youth of the groups (43.8 years on average) means that health issues that typically do not emerge until later life may not have yet manifested.
| S-EPMC9213390 | biostudies-literature
Project description:BackgroundCollisions in rugby union and sevens have a high injury incidence and burden, and are also associated with player and team performance. Understanding the frequency and intensity of these collisions is therefore important for coaches and practitioners to adequately prepare players for competition. The aim of this review is to synthesise the current literature to provide a summary of the collision frequencies and intensities for rugby union and rugby sevens based on video-based analysis and microtechnology.MethodsA systematic search using key words was done on four different databases from 1 January 1990 to 1 September 2021 (PubMed, Scopus, SPORTDiscus and Web of Science).ResultsSeventy-three studies were included in the final review, with fifty-eight studies focusing on rugby union, while fifteen studies explored rugby sevens. Of the included studies, four focused on training-three in rugby union and one in sevens, two focused on both training and match-play in rugby union and one in rugby sevens, while the remaining sixty-six studies explored collisions from match-play. The studies included, provincial, national, international, professional, experienced, novice and collegiate players. Most of the studies used video-based analysis (n = 37) to quantify collisions. In rugby union, on average a total of 22.0 (19.0-25.0) scrums, 116.2 (62.7-169.7) rucks, and 156.1 (121.2-191.0) tackles occur per match. In sevens, on average 1.8 (1.7-2.0) scrums, 4.8 (0-11.8) rucks and 14.1 (0-32.8) tackles occur per match.ConclusionsThis review showed more studies quantified collisions in matches compared to training. To ensure athletes are adequately prepared for match collision loads, training should be prescribed to meet the match demands. Per minute, rugby sevens players perform more tackles and ball carries into contact than rugby union players and forwards experienced more impacts and tackles than backs. Forwards also perform more very heavy impacts and severe impacts than backs in rugby union. To improve the relationship between matches and training, integrating both video-based analysis and microtechnology is recommended. The frequency and intensity of collisions in training and matches may lead to adaptations for a "collision-fit" player and lend itself to general training principles such as periodisation for optimum collision adaptation. Trial Registration PROSPERO registration number: CRD42020191112.
| S-EPMC8776953 | biostudies-literature
Project description:The metabolic perturbations caused by competitive rugby are not well characterized. Our aim is to utilize untargeted metabolomics to develop appropriate interventions, based on the metabolic fluctuations that occur in response to this collision-based team sport. Seven members of an English Premiership rugby squad consented to provide blood, urine, and saliva samples daily, over a competitive week including gameday (GD), with physical demands and dietary intake also recorded. Sample collection, processing and statistical analysis were performed in accordance with best practice set out by the metabolomics standards initiative employing 700 MHz NMR spectroscopy. Univariate and multivariate statistical analysis were employed to reveal the acute energy needs of this high intensity sport are met via glycolysis, the TCA cycle and gluconeogenesis. The recovery period after cessation of match play and prior to training recommencing sees a re-entry to gluconeogenesis, coupled with markers of oxidative stress, structural protein degradation, and reduced fatty acid metabolism. This novel insight leads us to propose that effective recovery from muscle damaging collisions is dependent upon the availability of glucose. An adjustment in the periodisation of carbohydrate to increase GD+1 provision may prevent the oxidation of amino acids which may also be crucial to allay markers of structural tissue degradation. Should we expand the 'Fuel for the work required' paradigm in collision-based team sports to include 'Fuel for the damage induced'?
2021-09-06 | MTBLS2967 | MetaboLights
Project description:BACKGROUND: Identifying a regulatory module (RM), a bi-set of co-regulated genes and co-regulating conditions (or samples), has been an important challenge in functional genomics and bioinformatics. Given a microarray gene-expression matrix, biclustering has been the most common method for extracting RMs. Among biclustering methods, order-preserving biclustering by a sequential pattern mining technique has native advantage over the conventional biclustering approaches since it preserves the order of genes (or conditions) according to the magnitude of the expression value. However, previous sequential pattern mining-based biclustering has several weak points in that they can easily be computationally intractable in the real-size of microarray data and sensitive to inherent noise in the expression value. RESULTS: In this paper, we propose a novel sequential pattern mining algorithm that is scalable in the size of microarray data and robust with respect to noise. When applied to the microarray data of yeast, the proposed algorithm successfully found long order-preserving patterns, which are biologically significant but cannot be found in randomly shuffled data. The resulting patterns are well enriched to known annotations and are consistent with known biological knowledge. Furthermore, RMs as well as inter-module relations were inferred from the biologically significant patterns. CONCLUSIONS: Our approach for identifying RMs could be valuable for systematically revealing the mechanism of gene regulation at a genome-wide level.
| S-EPMC3333188 | biostudies-literature