Pub Date : 2022-01-01DOI: 10.1142/9789811250217_0006
L. MacLean, W. Ziemba
{"title":"The Game Box Score in Basketball: Linking Statistics to Game Outcomes","authors":"L. MacLean, W. Ziemba","doi":"10.1142/9789811250217_0006","DOIUrl":"https://doi.org/10.1142/9789811250217_0006","url":null,"abstract":"","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73000659","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_0012
L. MacLean, W. Ziemba
{"title":"Efficiency in NFL Betting Markets","authors":"L. MacLean, W. Ziemba","doi":"10.1142/9789811250217_0012","DOIUrl":"https://doi.org/10.1142/9789811250217_0012","url":null,"abstract":"","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":"293 3 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79661164","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_0025
W. Ziemba
{"title":"The Pegasus World Cup III: Accelerate vs. City of Light","authors":"W. Ziemba","doi":"10.1142/9789811250217_0025","DOIUrl":"https://doi.org/10.1142/9789811250217_0025","url":null,"abstract":"","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":"3 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82675914","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}
We present a new, simple knockout format for sports tournaments, that we call “Choose Your Opponent”, where the teams that have performed best during a preliminary group stage can choose their opponents during the subsequent knockout stage. The main benefit of this format is that it essentially solves a recently identified incentive compatibility problem when more than one teams from a group advance to the knockout stage, by effectively canceling the risk of tanking. This new design also makes the group stage more exciting, by giving teams a strong incentive to perform at their best level, and more fair, by limiting the risk of collusion and making sure that the best group winners are fairly rewarded in the knockout round. The choosing procedure would add a new, exciting strategic component to the competition. Advancing teams would choose their opponent during new, much anticipated TV shows which would attract a lot of media attention. We illustrate how this new format would work for the round of 16 of the UEFA Champions League, the most popular soccer club competition in the world.
{"title":"“Choose your opponent”: A new knockout design for hybrid tournaments †","authors":"Julien Guyon","doi":"10.3233/jsa-200527","DOIUrl":"https://doi.org/10.3233/jsa-200527","url":null,"abstract":"We present a new, simple knockout format for sports tournaments, that we call “Choose Your Opponent”, where the teams that have performed best during a preliminary group stage can choose their opponents during the subsequent knockout stage. The main benefit of this format is that it essentially solves a recently identified incentive compatibility problem when more than one teams from a group advance to the knockout stage, by effectively canceling the risk of tanking. This new design also makes the group stage more exciting, by giving teams a strong incentive to perform at their best level, and more fair, by limiting the risk of collusion and making sure that the best group winners are fairly rewarded in the knockout round. The choosing procedure would add a new, exciting strategic component to the competition. Advancing teams would choose their opponent during new, much anticipated TV shows which would attract a lot of media attention. We illustrate how this new format would work for the round of 16 of the UEFA Champions League, the most popular soccer club competition in the world.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48981048","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}
Many research papers on tennis match prediction use a hierarchical Markov Model. To predict match outcomes, this model requires input parameters for each player’s serving ability. While these parameters are often computed directly from each player’s historical percentages of points won on serve and return, doing so fails to address bias due to limited sample size and differences in strength of schedule. In this paper, we explore a handful of novel approaches to forecasting serve performance that specifically address these limitations. By applying an Efron-Morris estimator, we provide a means to robustly forecast outcomes when players have limited match data over the past year. Next, through tracking expected serve and return performance in past matches, we account for strength of schedule across all points in a player’s match history. Finally, we demonstrate a new way to synthesize historical serve data with the predictive power of Elo ratings. When forecasting serve performance across 7,622 ATP tour-level matches from 2014-2016, all three of these proposed methods outperformed Barnett and Clarke’s standard approach.
{"title":"Forecasting serve performance in professional tennis matches","authors":"Jacob Gollub","doi":"10.3233/jsa-200345","DOIUrl":"https://doi.org/10.3233/jsa-200345","url":null,"abstract":"Many research papers on tennis match prediction use a hierarchical Markov Model. To predict match outcomes, this model requires input parameters for each player’s serving ability. While these parameters are often computed directly from each player’s historical percentages of points won on serve and return, doing so fails to address bias due to limited sample size and differences in strength of schedule. In this paper, we explore a handful of novel approaches to forecasting serve performance that specifically address these limitations. By applying an Efron-Morris estimator, we provide a means to robustly forecast outcomes when players have limited match data over the past year. Next, through tracking expected serve and return performance in past matches, we account for strength of schedule across all points in a player’s match history. Finally, we demonstrate a new way to synthesize historical serve data with the predictive power of Elo ratings. When forecasting serve performance across 7,622 ATP tour-level matches from 2014-2016, all three of these proposed methods outperformed Barnett and Clarke’s standard approach.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/jsa-200345","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43869189","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 NBA (National Basketball Association) is going through a transition process in its way of practice, planning, and comprehension of the game. With the exponential growth of the data that has been collected, detailed statistical analyses have been conducted for each part of the game. This has been overwhelming exploited in a way never seen before, especially when dealing with the three-point shot. In this paper, we are interested in characterizing NBA’s gameplay over time to identify trends and success factors. In particular, this study aims: (i) to identify which factors were crucial for teams’ regular season success in the past and understand the factors that are more relevant to succeed in the present day; and (ii) to group seasons and regular season winning teams into clusters of common characteristics and gameplay behavior. Historical events and trends help us to understand how teams were successful in past regular seasons, how they played, and how their style of play has changed. Leading to a better comprehension of the game. The game-related statistics of the NBA’s regular seasons, from 1979-80 to 2018-19, were analyzed using principal component analysis, cluster analysis, and LASSO regression. It is possible to identify three main Eras that we define as the Classic Era of the NBA (1980–1994), the Transitional Era of the NBA (1995–2013), and the Modern Era of the NBA (since 2013). As the results of this study make a historic analysis of the NBA, indicating the three eras of NBA regular seasons since the introduction of the three-point line, their playing styles, and their respective factors for success, this present research may be the base study that will help researchers better investigate the NBA, its past, present, and future.
{"title":"The three Eras of the NBA regular seasons: Historical trend and success factors","authors":"João Pedro Ramos Pereira da Silva, P. Rodrigues","doi":"10.3233/jsa-200525","DOIUrl":"https://doi.org/10.3233/jsa-200525","url":null,"abstract":"The NBA (National Basketball Association) is going through a transition process in its way of practice, planning, and comprehension of the game. With the exponential growth of the data that has been collected, detailed statistical analyses have been conducted for each part of the game. This has been overwhelming exploited in a way never seen before, especially when dealing with the three-point shot. In this paper, we are interested in characterizing NBA’s gameplay over time to identify trends and success factors. In particular, this study aims: (i) to identify which factors were crucial for teams’ regular season success in the past and understand the factors that are more relevant to succeed in the present day; and (ii) to group seasons and regular season winning teams into clusters of common characteristics and gameplay behavior. Historical events and trends help us to understand how teams were successful in past regular seasons, how they played, and how their style of play has changed. Leading to a better comprehension of the game. The game-related statistics of the NBA’s regular seasons, from 1979-80 to 2018-19, were analyzed using principal component analysis, cluster analysis, and LASSO regression. It is possible to identify three main Eras that we define as the Classic Era of the NBA (1980–1994), the Transitional Era of the NBA (1995–2013), and the Modern Era of the NBA (since 2013). As the results of this study make a historic analysis of the NBA, indicating the three eras of NBA regular seasons since the introduction of the three-point line, their playing styles, and their respective factors for success, this present research may be the base study that will help researchers better investigate the NBA, its past, present, and future.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/jsa-200525","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42917177","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}
Predicting athlete injury risk has been a holy grail in sports medicine with little progress to date due to a variety of factors such as small sample sizes, significantly imbalanced data, and inadequate statistical approaches. Data modeling which does not account for multiple interactions across factors can be misleading. We address the small sample size by collecting longitudinal data of NBA player injuries using publicly available data sources and develop a state of the art deep learning model, METIC, to predict future injuries based on past injuries, game activity, and player statistics. We evaluate model performance using metrics appropriate for imbalanced data and find that METIC performs significantly better than other traditional machine learning approaches. METIC uses feature learning to create interactive features which become meaningful in combination with each other. METIC can be used by practitioners and front offices to improve athlete management and reduce injury incidence, potentially saving sports teams millions in revenue due to reduced athlete injuries.
{"title":"A deep learning approach to injury forecasting in NBA basketball","authors":"Alexander Cohan, J. Schuster, José Fernández","doi":"10.3233/jsa-200529","DOIUrl":"https://doi.org/10.3233/jsa-200529","url":null,"abstract":"Predicting athlete injury risk has been a holy grail in sports medicine with little progress to date due to a variety of factors such as small sample sizes, significantly imbalanced data, and inadequate statistical approaches. Data modeling which does not account for multiple interactions across factors can be misleading. We address the small sample size by collecting longitudinal data of NBA player injuries using publicly available data sources and develop a state of the art deep learning model, METIC, to predict future injuries based on past injuries, game activity, and player statistics. We evaluate model performance using metrics appropriate for imbalanced data and find that METIC performs significantly better than other traditional machine learning approaches. METIC uses feature learning to create interactive features which become meaningful in combination with each other. METIC can be used by practitioners and front offices to improve athlete management and reduce injury incidence, potentially saving sports teams millions in revenue due to reduced athlete injuries.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/jsa-200529","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44781231","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 purpose of the current investigation was to develop and evaluate an analytics approach to identifying the disciplines that female modern pentathletes should focus on to most improve their total points score. The study comprises of three analyses as well as the description and evaluation of an analytics approach to identify the event that a modern pentathlete should focus on to most improve their overall points. Analysis I revealed that the proportion of total points score derived from the laser run was significantly greater under the currently used scoring system than under the scoring system used prior to 2014 (p < 0.001). Analysis II considered year to year change in points scored for a set of 243 athletes who had completed performances in successive calendar years. The variability of year to year change in points was significantly influenced by discipline (p < 0.001) with the highest variability being in the laser run followed by fencing, riding and swimming. Linear and inverse regression models of year to year change were created during Analysis III and used in a simulation package that allowed year to year change to be predicted synthesising increased emphasis being made on different disciplines. The simulation approach suggests that female athletes can expect to make the greatest gains by emphasising the laser run and fencing within training. An evaluation study using six cases largely agreed with this but there was one of the athletes whose highest actual points improvement was in riding.
{"title":"Women’s modern pentathlon scoring systems and predictive modelling for decision support","authors":"S. Im, P. O'Donoghue","doi":"10.3233/jsa-200593","DOIUrl":"https://doi.org/10.3233/jsa-200593","url":null,"abstract":"The purpose of the current investigation was to develop and evaluate an analytics approach to identifying the disciplines that female modern pentathletes should focus on to most improve their total points score. The study comprises of three analyses as well as the description and evaluation of an analytics approach to identify the event that a modern pentathlete should focus on to most improve their overall points. Analysis I revealed that the proportion of total points score derived from the laser run was significantly greater under the currently used scoring system than under the scoring system used prior to 2014 (p < 0.001). Analysis II considered year to year change in points scored for a set of 243 athletes who had completed performances in successive calendar years. The variability of year to year change in points was significantly influenced by discipline (p < 0.001) with the highest variability being in the laser run followed by fencing, riding and swimming. Linear and inverse regression models of year to year change were created during Analysis III and used in a simulation package that allowed year to year change to be predicted synthesising increased emphasis being made on different disciplines. The simulation approach suggests that female athletes can expect to make the greatest gains by emphasising the laser run and fencing within training. An evaluation study using six cases largely agreed with this but there was one of the athletes whose highest actual points improvement was in riding.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49096257","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 introduction of DRS and rapidly-degrading tires in 2011 boosted on-track overtaking levels in Formula 1 to unprecedented highs. Since then, overtaking has steadily decreased again, culminating in a 60-percent reduction in 2017. In this paper, using a Poisson model on individual-level overtaking data from 2011 to 2018, it was found that about half the decrease can be attributed to the cars, 20 to 30 percent to the reduction in field size and about 20 percent to more uniform race strategies.
{"title":"Overtaking in Formula 1 during the Pirelli era: A driver-level analysis","authors":"J. D. Groote","doi":"10.3233/JSA-200466","DOIUrl":"https://doi.org/10.3233/JSA-200466","url":null,"abstract":"The introduction of DRS and rapidly-degrading tires in 2011 boosted on-track overtaking levels in Formula 1 to unprecedented highs. Since then, overtaking has steadily decreased again, culminating in a 60-percent reduction in 2017. In this paper, using a Poisson model on individual-level overtaking data from 2011 to 2018, it was found that about half the decrease can be attributed to the cars, 20 to 30 percent to the reduction in field size and about 20 percent to more uniform race strategies.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JSA-200466","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48621223","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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