Machine learning (ML) is an emerging topic in Sports Science. Some pioneering studies have applied machine learning to prevent injuries, to predict star players, and to analyze athletic performance. The limited number of studies in the literature focused on predicting athletic performance have adopted the cluster-then-predict classification approach. However, these studies have used the independent variable to represent athletic performance at both the clustering and classification stages. In this study we used only physiological parameters in the classification of bocce athletes. Their performance classes were predicted with high accuracy, thus contributing new findings to the literature. The support vector machines-radial basis function (SVM-RBF) kernel correctly predicted all athletes from the high-performance bocce player (HPBP) cluster and 75% of the athletes in the low-performance bocce player (LPBP) cluster. Using machine learning to predict athletic performance from balance data was found to be a time-saving approach for selecting high-potential bocce athletes.
{"title":"Predicting athletic performance from physiological parameters using machine learning: Example of bocce ball","authors":"Mehmet Şimşek, İnci Kesilmiş","doi":"10.3233/jsa-200617","DOIUrl":"https://doi.org/10.3233/jsa-200617","url":null,"abstract":"Machine learning (ML) is an emerging topic in Sports Science. Some pioneering studies have applied machine learning to prevent injuries, to predict star players, and to analyze athletic performance. The limited number of studies in the literature focused on predicting athletic performance have adopted the cluster-then-predict classification approach. However, these studies have used the independent variable to represent athletic performance at both the clustering and classification stages. In this study we used only physiological parameters in the classification of bocce athletes. Their performance classes were predicted with high accuracy, thus contributing new findings to the literature. The support vector machines-radial basis function (SVM-RBF) kernel correctly predicted all athletes from the high-performance bocce player (HPBP) cluster and 75% of the athletes in the low-performance bocce player (LPBP) cluster. Using machine learning to predict athletic performance from balance data was found to be a time-saving approach for selecting high-potential bocce athletes.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43777876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Biathlon is an Olympic sport combining cross-country skiing with rifle shooting, giving a penalty for each target miss. The biathletes ran different race formats, including the pursuit race. During this race, the biathletes chase the leader with a start time identical to the result of the sprint race previously achieved. So, pursuit involves different skills (such as tactics or management of emotional pressure) that are not present during races with an interval-start procedure like sprint. Nevertheless, final pursuit rankings are strongly correlated to sprint ones, which prevents a spectacular comeback after a disappointing sprint race. We present here an alternative pursuit ranking system that is nearly decorrelated to sprint rankings. This simple ranking system is based on comparisons with previous pursuit results. The current and the alternative rankings were then compared on different pursuit rankings, using a database of 148 results from men pursuit world cups. The alternative ranking was shown to strongly modify a single pursuit ranking but these modifications were smoothed on a whole world cup season. Advantages and limitations of the alternative ranking system are discussed, paving the way to a fairer modification of the current pursuit ranking to increase surprise and suspense in biathlon pursuit races.
{"title":"An alternative ranking system for biathlon pursuit races","authors":"Rémi Servien","doi":"10.3233/jsa-200598","DOIUrl":"https://doi.org/10.3233/jsa-200598","url":null,"abstract":"Biathlon is an Olympic sport combining cross-country skiing with rifle shooting, giving a penalty for each target miss. The biathletes ran different race formats, including the pursuit race. During this race, the biathletes chase the leader with a start time identical to the result of the sprint race previously achieved. So, pursuit involves different skills (such as tactics or management of emotional pressure) that are not present during races with an interval-start procedure like sprint. Nevertheless, final pursuit rankings are strongly correlated to sprint ones, which prevents a spectacular comeback after a disappointing sprint race. We present here an alternative pursuit ranking system that is nearly decorrelated to sprint rankings. This simple ranking system is based on comparisons with previous pursuit results. The current and the alternative rankings were then compared on different pursuit rankings, using a database of 148 results from men pursuit world cups. The alternative ranking was shown to strongly modify a single pursuit ranking but these modifications were smoothed on a whole world cup season. Advantages and limitations of the alternative ranking system are discussed, paving the way to a fairer modification of the current pursuit ranking to increase surprise and suspense in biathlon pursuit races.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44680390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An important aspect of facility management is the development of a comprehensive risk management plan. Player safety has only recently been a consideration when developing a risk management plan. Field conditions have not received much attention as it relates to player safety. Several injuries at Optus Stadium in Perth, Australia raised questions about the playing surface being the cause. The purpose of this study was to determine the ability of established athletic field agronomic measures to predict injuries from football fields and soccer pitches. Logistic regression was used to predict injury based upon soil compaction, soil moisture, surface firmness, and turfgrass quality. Results indicate that athletic fields that met good standards had the lowest probability of injury and injury probability is the highest when field conditions are considered poor. These results provide parameters facility and athletic field managers can use to determine whether an athletic field demonstrates a low risk of injury, needs to be improved, or a game should be canceled.
{"title":"Using agronomic data to minimize the impact of field conditions on player injuries and enhance the development of a risk management plan","authors":"E. Walker, Kristina S. Walker","doi":"10.3233/jsa-200538","DOIUrl":"https://doi.org/10.3233/jsa-200538","url":null,"abstract":"An important aspect of facility management is the development of a comprehensive risk management plan. Player safety has only recently been a consideration when developing a risk management plan. Field conditions have not received much attention as it relates to player safety. Several injuries at Optus Stadium in Perth, Australia raised questions about the playing surface being the cause. The purpose of this study was to determine the ability of established athletic field agronomic measures to predict injuries from football fields and soccer pitches. Logistic regression was used to predict injury based upon soil compaction, soil moisture, surface firmness, and turfgrass quality. Results indicate that athletic fields that met good standards had the lowest probability of injury and injury probability is the highest when field conditions are considered poor. These results provide parameters facility and athletic field managers can use to determine whether an athletic field demonstrates a low risk of injury, needs to be improved, or a game should be canceled.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70125535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pace-of-play is an important characteristic in soccer that can influence the style and outcome of a match. Using event data provided by Wyscout covering one season of regular-season games from five European soccer leagues, we develop four velocity-based pace metrics and examine how pace varies across the pitch, between different leagues, and between different teams. Our findings show that although pace varies considerably, it is generally highest in the offensive third of the pitch, relatively consistent across leagues, and increases with decreasing team quality. Using hierarchical logistic models, we also assess whether the pace metrics are useful in predicting the outcome of a match by constructing models with and without the metrics. We find that the pace variables are statistically significant but only slightly improve the predictive accuracy metrics.
{"title":"Analyzing pace-of-play in soccer using spatio-temporal event data","authors":"Ethan Shen, Shawn Santo, O. Akande","doi":"10.3233/jsa-200581","DOIUrl":"https://doi.org/10.3233/jsa-200581","url":null,"abstract":"Pace-of-play is an important characteristic in soccer that can influence the style and outcome of a match. Using event data provided by Wyscout covering one season of regular-season games from five European soccer leagues, we develop four velocity-based pace metrics and examine how pace varies across the pitch, between different leagues, and between different teams. Our findings show that although pace varies considerably, it is generally highest in the offensive third of the pitch, relatively consistent across leagues, and increases with decreasing team quality. Using hierarchical logistic models, we also assess whether the pace metrics are useful in predicting the outcome of a match by constructing models with and without the metrics. We find that the pace variables are statistically significant but only slightly improve the predictive accuracy metrics.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47077919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The evaluation of player performance typically involves a number of criteria representing various aspects of performance that are of interest. Pareto optimality and weighted aggregation are useful tools to simultaneously evaluate players with respect to the multiple criteria. In particular, the Pareto approach allows trade-offs among the criteria to be compared, does not require specifications of weighting schemes, and is not sensitive to the scaling of the criteria. The Pareto optimal players can be scored according to their ranks or according to their distance from the global optimum for informative comparisons of performance or for evaluating trade-offs among the criteria. These multi-criteria approaches are defined and illustrated for evaluating batting performance of Major League Baseball players.
{"title":"A multi-criteria approach for evaluating major league baseball batting performance","authors":"S. Wulff, Priyantha G. De Silva","doi":"10.3233/jsa-200298","DOIUrl":"https://doi.org/10.3233/jsa-200298","url":null,"abstract":"The evaluation of player performance typically involves a number of criteria representing various aspects of performance that are of interest. Pareto optimality and weighted aggregation are useful tools to simultaneously evaluate players with respect to the multiple criteria. In particular, the Pareto approach allows trade-offs among the criteria to be compared, does not require specifications of weighting schemes, and is not sensitive to the scaling of the criteria. The Pareto optimal players can be scored according to their ranks or according to their distance from the global optimum for informative comparisons of performance or for evaluating trade-offs among the criteria. These multi-criteria approaches are defined and illustrated for evaluating batting performance of Major League Baseball players.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44251361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, I propose and evaluate a novel extension of the analytics revolution in professional sports: probability-split trades. Under this plan, teams could trade probability shares in draft assets held. For example, a team like the New York Knicks could trade their first and second round picks for a 5% chance of winning the 1st overall pick. In the last two decades, the analytics revolution has transformed professional sports. General managers, coaches, and even players leverage the underlying math to gain any sort of competitive advantage, while major sports leagues view the analytics revolution with passive glee, as their potential viewer segments continue to expand. This paper is an extension of that revolution, outlining the details, feasibility, and potential benefits of a novel plan with the potential to increase exchange efficiency, boost revenue and sustain league growth in the NFL and NBA.
{"title":"5% of Zion: Evaluating the potential for probability-split trades in professional sports","authors":"Dawson M. Brown","doi":"10.3233/jsa-200490","DOIUrl":"https://doi.org/10.3233/jsa-200490","url":null,"abstract":"In this paper, I propose and evaluate a novel extension of the analytics revolution in professional sports: probability-split trades. Under this plan, teams could trade probability shares in draft assets held. For example, a team like the New York Knicks could trade their first and second round picks for a 5% chance of winning the 1st overall pick. In the last two decades, the analytics revolution has transformed professional sports. General managers, coaches, and even players leverage the underlying math to gain any sort of competitive advantage, while major sports leagues view the analytics revolution with passive glee, as their potential viewer segments continue to expand. This paper is an extension of that revolution, outlining the details, feasibility, and potential benefits of a novel plan with the potential to increase exchange efficiency, boost revenue and sustain league growth in the NFL and NBA.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45266721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the past decade, many data mining researches have been conducted on the sports field. In particular, baseball has become an important subject of data mining due to the wide availability of massive data from games. Many researchers have conducted their studies to predict pitch types, i.e., fastball, cutter, sinker, slider, curveball, changeup, knuckleball, or part of them. In this research, we also develop a system that makes predictions related to pitches in baseball. The major difference between our research and the previous researches is that our system is to predict pitch types and pitch locations at the same time. Pitch location is the place where the pitched ball arrives among the imaginary grids drawn in front of the catcher. Another difference is the number of classes to predict. In the previous researches for predicting pitch types, the number of classes to predict was 2∼7. However, in our research, since we also predict pitch locations, the number of classes to predict is 34. We build our prediction system using ensemble model of deep neural networks. We describe in detail the process of building our prediction system while avoiding overfitting. In addition, the performances of our prediction system in various game situations, such as loss/draw/win, count and baserunners situation, are presented.
{"title":"Prediction of pitch type and location in baseball using ensemble model of deep neural networks","authors":"Jae Sik Lee","doi":"10.3233/jsa-200559","DOIUrl":"https://doi.org/10.3233/jsa-200559","url":null,"abstract":"In the past decade, many data mining researches have been conducted on the sports field. In particular, baseball has become an important subject of data mining due to the wide availability of massive data from games. Many researchers have conducted their studies to predict pitch types, i.e., fastball, cutter, sinker, slider, curveball, changeup, knuckleball, or part of them. In this research, we also develop a system that makes predictions related to pitches in baseball. The major difference between our research and the previous researches is that our system is to predict pitch types and pitch locations at the same time. Pitch location is the place where the pitched ball arrives among the imaginary grids drawn in front of the catcher. Another difference is the number of classes to predict. In the previous researches for predicting pitch types, the number of classes to predict was 2∼7. However, in our research, since we also predict pitch locations, the number of classes to predict is 34. We build our prediction system using ensemble model of deep neural networks. We describe in detail the process of building our prediction system while avoiding overfitting. In addition, the performances of our prediction system in various game situations, such as loss/draw/win, count and baserunners situation, are presented.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49643923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Javadpour, Jessica Blakeslee, Mehdi A. Khazaeli, Pete Schroeder
In a close game of basketball, victory or defeat can depend on a single shot. Being able to identify the best player and play scenario for a given opponent’s defense can increase the likelihood of victory. Progress in technology has resulted in an increase in the popularity of sports analytics over the last two decades, where data can be used by teams and individuals to their advantage. A popular data analytic technique in sports is deep learning. Deep learning is a branch of machine learning that finds patterns within big data and can predict future decisions. The process relies on a raw dataset for training purposes. It can be utilized in sports by using deep learning to read the data and provide a better understanding of where players can be the most successful. In this study the data used were on division I women’s basketball games of a private university in a conference featuring top 25 teams. Deep learning was applied to optimize the best offensive play in a game scenario for a given set of features. The system is used to predict the play that would lead to the highest probability of a made shot.
{"title":"Optimizing the best play in basketball using deep learning","authors":"L. Javadpour, Jessica Blakeslee, Mehdi A. Khazaeli, Pete Schroeder","doi":"10.3233/jsa-200524","DOIUrl":"https://doi.org/10.3233/jsa-200524","url":null,"abstract":"In a close game of basketball, victory or defeat can depend on a single shot. Being able to identify the best player and play scenario for a given opponent’s defense can increase the likelihood of victory. Progress in technology has resulted in an increase in the popularity of sports analytics over the last two decades, where data can be used by teams and individuals to their advantage. A popular data analytic technique in sports is deep learning. Deep learning is a branch of machine learning that finds patterns within big data and can predict future decisions. The process relies on a raw dataset for training purposes. It can be utilized in sports by using deep learning to read the data and provide a better understanding of where players can be the most successful. In this study the data used were on division I women’s basketball games of a private university in a conference featuring top 25 teams. Deep learning was applied to optimize the best offensive play in a game scenario for a given set of features. The system is used to predict the play that would lead to the highest probability of a made shot.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42895370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There is growing on-going research into how footballer attributes, collected prior to, during and post-match, may address the demands of clubs, media pundits and gaming developers. Focusing upon individual player performance analysis and prediction, we examined the body of research which considers different player attributes. This resulted in the selection of 132 relevant papers published between 1999 and 2020. From these we have compiled a comprehensive list of player attributes, categorising them as static, such as age and height, or dynamic, such as pass completions and shots on target. To indicate their accuracy, we classified each attribute as objectively or subjectively derived, and finally by their implied accessibility and their likely personal and club sensitivity. We assigned these attributes to 25 logical groups such as passing, tackling and player demographics. We analysed the relative research focus on each group and noted the analytical methods deployed, identifying which statistical or machine learning techniques were used. We reviewed and considered the use of character trait attributes in the selected papers and discuss more formal approaches to their use. Based upon this we have made recommendations on how this work may be developed to support elite clubs in the consideration of transfer targets.
{"title":"The collection, analysis and exploitation of footballer attributes: A systematic review","authors":"Edward Wakelam, V. Steuber, James Wakelam","doi":"10.3233/jsa-200554","DOIUrl":"https://doi.org/10.3233/jsa-200554","url":null,"abstract":"There is growing on-going research into how footballer attributes, collected prior to, during and post-match, may address the demands of clubs, media pundits and gaming developers. Focusing upon individual player performance analysis and prediction, we examined the body of research which considers different player attributes. This resulted in the selection of 132 relevant papers published between 1999 and 2020. From these we have compiled a comprehensive list of player attributes, categorising them as static, such as age and height, or dynamic, such as pass completions and shots on target. To indicate their accuracy, we classified each attribute as objectively or subjectively derived, and finally by their implied accessibility and their likely personal and club sensitivity. We assigned these attributes to 25 logical groups such as passing, tackling and player demographics. We analysed the relative research focus on each group and noted the analytical methods deployed, identifying which statistical or machine learning techniques were used. We reviewed and considered the use of character trait attributes in the selected papers and discuss more formal approaches to their use. Based upon this we have made recommendations on how this work may be developed to support elite clubs in the consideration of transfer targets.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48814900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
During the National Basketball Association (NBA) playoffs, teams are required to frequently travel to different venues to play opponents in series of up to seven games. Despite playoff schedules allowing for some rest between games, it is still possible for teams to face circadian misalignment when playing. Thus, the current study serves as a replication and extension of previous research, which has indicated that there is an advantage for teams playing closer to their circadian peak and when they are traveling east. This study specifically investigates the effects of travel, as well as time of game on various performance indicators in professional basketball. We examined a series of box-score statistics (e.g., game outcomes, points scored, shooting percentages, rebounds, assists, steals, blocks, turnovers, and personal fouls) from a total of 499 postseason games played between the 2013–14 and 2018–19 NBA seasons. Findings from our study indicate that teams traveling eastward scored more points than teams traveling within the same time zone. We also observed that teams playing evening games had higher three-point shooting percentages than teams playing in the afternoon. Our study demonstrates an extended impact of travel and time of day on more specific performance indicators in the NBA. Future directions and implications for professional basketball and other sports are discussed.
{"title":"Additional on-court advantages gained during eastward travel in the National Basketball Association (NBA) playoffs","authors":"S. Pradhan, R. Chachad, D. Alton","doi":"10.3233/jsa-200577","DOIUrl":"https://doi.org/10.3233/jsa-200577","url":null,"abstract":"During the National Basketball Association (NBA) playoffs, teams are required to frequently travel to different venues to play opponents in series of up to seven games. Despite playoff schedules allowing for some rest between games, it is still possible for teams to face circadian misalignment when playing. Thus, the current study serves as a replication and extension of previous research, which has indicated that there is an advantage for teams playing closer to their circadian peak and when they are traveling east. This study specifically investigates the effects of travel, as well as time of game on various performance indicators in professional basketball. We examined a series of box-score statistics (e.g., game outcomes, points scored, shooting percentages, rebounds, assists, steals, blocks, turnovers, and personal fouls) from a total of 499 postseason games played between the 2013–14 and 2018–19 NBA seasons. Findings from our study indicate that teams traveling eastward scored more points than teams traveling within the same time zone. We also observed that teams playing evening games had higher three-point shooting percentages than teams playing in the afternoon. Our study demonstrates an extended impact of travel and time of day on more specific performance indicators in the NBA. Future directions and implications for professional basketball and other sports are discussed.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48542765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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