Tennis uses essentially the same scoring system in doubles as in singles. In this paper it is shown that the scoring system commonly used in men’s doubles has the potential to be unfair. Aspects of this potential unfairness are identified and methods for reducing it are outlined. The statistical characteristics of the present scoring system are compared with those of a proposed new system.
{"title":"New scoring system to reduce unfairness in men’s doubles","authors":"G. Pollard, Ken Noble, G. Pollard","doi":"10.3233/jsa-220607","DOIUrl":"https://doi.org/10.3233/jsa-220607","url":null,"abstract":"Tennis uses essentially the same scoring system in doubles as in singles. In this paper it is shown that the scoring system commonly used in men’s doubles has the potential to be unfair. Aspects of this potential unfairness are identified and methods for reducing it are outlined. The statistical characteristics of the present scoring system are compared with those of a proposed new system.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46291010","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}
Lochana K. Palayangoda, H. K. Senevirathne, Ananda B. W. Manage
In limited overs cricket, the goal of a batsman is to score a maximum number of runs within a limited number of balls. Therefore, the number of runs scored and the number of balls faced are the two key statistics used to evaluate the performance of a batsman. In cricket, as the batsmen play as pairs, having longer partnerships is also key to building strong innings. Moreover, having a steady opening partnership is extremely important as a team aims to build such a stronger innings. In this study, we have shown a way to evaluate the performance of opening partnerships in Twenty20 (T20) cricket and the performance of individual batsmen in One Day International Cricket (ODI) by modeling the joint distribution of runs scored and balls faced using copula functions. The joint survival probabilities derived from this approach are then used to evaluate the batting performance of opening partnerships and individual batsmen for different stages of the innings. Results of the study have shown that cricket managers and team officials can use the proposed method in selecting appropriate partnership pairs and individual batsmen in an efficient manner for specific situations in the match.
{"title":"Modeling joint survival probabilities of runs scored and balls faced in limited overs cricket using copulas","authors":"Lochana K. Palayangoda, H. K. Senevirathne, Ananda B. W. Manage","doi":"10.3233/jsa-220606","DOIUrl":"https://doi.org/10.3233/jsa-220606","url":null,"abstract":"In limited overs cricket, the goal of a batsman is to score a maximum number of runs within a limited number of balls. Therefore, the number of runs scored and the number of balls faced are the two key statistics used to evaluate the performance of a batsman. In cricket, as the batsmen play as pairs, having longer partnerships is also key to building strong innings. Moreover, having a steady opening partnership is extremely important as a team aims to build such a stronger innings. In this study, we have shown a way to evaluate the performance of opening partnerships in Twenty20 (T20) cricket and the performance of individual batsmen in One Day International Cricket (ODI) by modeling the joint distribution of runs scored and balls faced using copula functions. The joint survival probabilities derived from this approach are then used to evaluate the batting performance of opening partnerships and individual batsmen for different stages of the innings. Results of the study have shown that cricket managers and team officials can use the proposed method in selecting appropriate partnership pairs and individual batsmen in an efficient manner for specific situations in the match.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48609493","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":null,"pages":null},"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":null,"pages":null},"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}
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":null,"pages":null},"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}
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":null,"pages":null},"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":null,"pages":null},"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}
Pub Date : 2022-01-01DOI: 10.1142/9789811250217_0017
W. Ziemba
{"title":"Primer on Dosage and the 2012 Triple Crown","authors":"W. Ziemba","doi":"10.1142/9789811250217_0017","DOIUrl":"https://doi.org/10.1142/9789811250217_0017","url":null,"abstract":"","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73994974","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}
Pub Date : 2022-01-01DOI: 10.1142/9789811250217_0008
L. MacLean, W. Ziemba
{"title":"Winning Hockey: Team and Player Impact in the NHL","authors":"L. MacLean, W. Ziemba","doi":"10.1142/9789811250217_0008","DOIUrl":"https://doi.org/10.1142/9789811250217_0008","url":null,"abstract":"","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89337516","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}
Pub Date : 2022-01-01DOI: 10.1142/9789811250217_0028
W. Ziemba
{"title":"Dr Z’s Place & Show Racetrack Betting System at the First Breeders’ Cup","authors":"W. Ziemba","doi":"10.1142/9789811250217_0028","DOIUrl":"https://doi.org/10.1142/9789811250217_0028","url":null,"abstract":"","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91315207","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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