Abstract The availability of large databases of athletic performances offers the opportunity to understand age-related performance progression and to benchmark individual performance against the World’s best. We build a flexible Bayesian model of individual performance progression whilst allowing for confounders, such as atmospheric conditions, and can be fitted using Markov chain Monte Carlo. We show how the model can be used to understand performance progression and the age of peak performance in both individuals and the population. We apply the model to both women and men in 100 m sprinting and weightlifting. In both disciplines, we find that age-related performance is skewed, that the average population performance trajectories of women and men are quite different, and that age of peak performance is substantially different between women and men. We also find that there is substantial variability in individual performance trajectories and the age of peak performance.
{"title":"Bayesian modelling of elite sporting performance with large databases","authors":"J. Griffin, Laurentiu C. Hinoveanu, J. Hopker","doi":"10.1515/jqas-2021-0112","DOIUrl":"https://doi.org/10.1515/jqas-2021-0112","url":null,"abstract":"Abstract The availability of large databases of athletic performances offers the opportunity to understand age-related performance progression and to benchmark individual performance against the World’s best. We build a flexible Bayesian model of individual performance progression whilst allowing for confounders, such as atmospheric conditions, and can be fitted using Markov chain Monte Carlo. We show how the model can be used to understand performance progression and the age of peak performance in both individuals and the population. We apply the model to both women and men in 100 m sprinting and weightlifting. In both disciplines, we find that age-related performance is skewed, that the average population performance trajectories of women and men are quite different, and that age of peak performance is substantially different between women and men. We also find that there is substantial variability in individual performance trajectories and the age of peak performance.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":"1 1","pages":"253 - 268"},"PeriodicalIF":0.8,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72628696","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}
This article studies whether a recent victory impacts attendance at sports events. We apply a regression discontinuity design to estimate the local average treatment effect of a win on the attendance of subsequent games in professional basketball. Using National Basketball Association data from seasons 1980–81 to 2017–18, we find that home team fan bases react to recent outcomes, with an increase in attendance of approximately 425 attendants (a 3% boost) following a close win relative to a close loss. The increment is approximately one-eighth of a recent estimate of the superstar effect. We do not find an attendance effect when the visiting team has a recent victory, which provides evidence against the existence of externalities. The positive fan base response to narrow home wins relative to narrow losses suggests that recent luck is rewarded in sporting attendance. We discuss possible mechanisms and document a gradual decline in the attendance response that coincides with the rise of alternative means for viewing games and secondary markets for tickets.
{"title":"Jumping on the bandwagon? Attendance response to recent victories in the NBA","authors":"Ercio Munoz, Jiadi Chen, Milan Thomas","doi":"10.1515/jqas-2020-0092","DOIUrl":"https://doi.org/10.1515/jqas-2020-0092","url":null,"abstract":"This article studies whether a recent victory impacts attendance at sports events. We apply a regression discontinuity design to estimate the local average treatment effect of a win on the attendance of subsequent games in professional basketball. Using National Basketball Association data from seasons 1980–81 to 2017–18, we find that home team fan bases react to recent outcomes, with an increase in attendance of approximately 425 attendants (a 3% boost) following a close win relative to a close loss. The increment is approximately one-eighth of a recent estimate of the superstar effect. We do not find an attendance effect when the visiting team has a recent victory, which provides evidence against the existence of externalities. The positive fan base response to narrow home wins relative to narrow losses suggests that recent luck is rewarded in sporting attendance. We discuss possible mechanisms and document a gradual decline in the attendance response that coincides with the rise of alternative means for viewing games and secondary markets for tickets.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":"23 2","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518606","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}
Abstract As a baseball game progresses, batters appear to perform better the more times they face a particular pitcher. The apparent drop-off in pitcher performance from one time through the order to the next, known as the Time Through the Order Penalty (TTOP), is often attributed to within-game batter learning. Although the TTOP has largely been accepted within baseball and influences many managers’ in-game decision making, we argue that existing approaches of estimating the size of the TTOP cannot disentangle continuous evolution in pitcher performance over the course of the game from discontinuities between successive times through the order. Using a Bayesian multinomial regression model, we find that, after adjusting for confounders like batter and pitcher quality, handedness, and home field advantage, there is little evidence of strong discontinuity in pitcher performance between times through the order. Our analysis suggests that the start of the third time through the order should not be viewed as a special cutoff point in deciding whether to pull a starting pitcher.
随着棒球比赛的进行，击球手面对特定投手的次数越多，表现就越好。投手表现从一次到下一次的明显下降，被称为“时间到顺序惩罚”(time through The order Penalty，简称TTOP)，通常归因于游戏中的击球手学习。尽管TTOP在很大程度上已被棒球界所接受，并影响了许多经理人在比赛中的决策，但我们认为，现有的估计TTOP大小的方法无法将投手在比赛过程中表现的连续演变与连续时间之间的不连续性区分开来。使用贝叶斯多项式回归模型，我们发现，在调整了诸如击球手和投手素质，手性和主场优势等混杂因素后，投手表现在不同时间之间通过顺序几乎没有很强的不连续性的证据。我们的分析表明，在决定是否拉先发投手时，第三次开始不应被视为一个特殊的截止点。
{"title":"A Bayesian analysis of the time through the order penalty in baseball","authors":"Ryan S. Brill, Sameer K. Deshpande, A. Wyner","doi":"10.1515/jqas-2022-0116","DOIUrl":"https://doi.org/10.1515/jqas-2022-0116","url":null,"abstract":"Abstract As a baseball game progresses, batters appear to perform better the more times they face a particular pitcher. The apparent drop-off in pitcher performance from one time through the order to the next, known as the Time Through the Order Penalty (TTOP), is often attributed to within-game batter learning. Although the TTOP has largely been accepted within baseball and influences many managers’ in-game decision making, we argue that existing approaches of estimating the size of the TTOP cannot disentangle continuous evolution in pitcher performance over the course of the game from discontinuities between successive times through the order. Using a Bayesian multinomial regression model, we find that, after adjusting for confounders like batter and pitcher quality, handedness, and home field advantage, there is little evidence of strong discontinuity in pitcher performance between times through the order. Our analysis suggests that the start of the third time through the order should not be viewed as a special cutoff point in deciding whether to pull a starting pitcher.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":"10 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81663623","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}
Abstract The FIFA Women’s World Cup tournament consists of a group stage and a knockout stage. We identify several issues that create competitive imbalance in the group stage. We use match data from all Women’s World Cup tournaments from 1991 through 2019 to empirically assess competitive imbalance across groups in each World Cup. Using least squares, we determine ratings for all teams. For each team, we average the ratings of the opponents in the group to calculate group opponents rating. We find that the range in group opponents rating varies between 2.5 and 4.5 goals indicating substantial competitive imbalance. We use logistic regression to quantify the impact of imbalance on the probability of success in the Women’s World Cup. Specifically, our estimates show that one goal less in group opponents rating can increase the probability of reaching the quarterfinal by 33%. We discuss several policy recommendations to reduce competitive imbalance at the Women’s World Cup.
{"title":"Quantifying the impact of imbalanced groups in FIFA Women’s World Cup tournaments 1991–2019","authors":"Michael A. Lapré, Elizabeth M. Palazzolo","doi":"10.1515/jqas-2021-0052","DOIUrl":"https://doi.org/10.1515/jqas-2021-0052","url":null,"abstract":"Abstract The FIFA Women’s World Cup tournament consists of a group stage and a knockout stage. We identify several issues that create competitive imbalance in the group stage. We use match data from all Women’s World Cup tournaments from 1991 through 2019 to empirically assess competitive imbalance across groups in each World Cup. Using least squares, we determine ratings for all teams. For each team, we average the ratings of the opponents in the group to calculate group opponents rating. We find that the range in group opponents rating varies between 2.5 and 4.5 goals indicating substantial competitive imbalance. We use logistic regression to quantify the impact of imbalance on the probability of success in the Women’s World Cup. Specifically, our estimates show that one goal less in group opponents rating can increase the probability of reaching the quarterfinal by 33%. We discuss several policy recommendations to reduce competitive imbalance at the Women’s World Cup.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":"19 1","pages":"187 - 199"},"PeriodicalIF":0.8,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88487746","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}
Abstract Competitive balance in a football league is extremely important from the perspective of economic growth of the industry. Many researchers have earlier proposed different measures of competitive balance, which are primarily adapted from standard economic theory. However, these measures fail to capture the finer nuances of the game. In this work, we discuss a new framework which is more suitable for a football league. First, we present a mathematical proof of a theoretically optimal situation where a football league becomes perfectly balanced. Next, a goal based index for competitive balance is developed. We present relevant theoretical results and show how the proposed index can be used to formally test for the presence of imbalance. The methods are implemented on the data from the top five European leagues, and it shows that the new approach can be helpful in explaining the changes in the seasonal competitive balance of the leagues. Further, using panel data models, we show that the proposed index is more suitable to analyze the variability in total revenues of the football leagues. We also discuss how the methods can be easily extended to develop other goal-based indices under different modeling assumptions.
{"title":"A goal based index to analyze the competitive balance of a football league","authors":"S. Deb","doi":"10.1515/jqas-2021-0015","DOIUrl":"https://doi.org/10.1515/jqas-2021-0015","url":null,"abstract":"Abstract Competitive balance in a football league is extremely important from the perspective of economic growth of the industry. Many researchers have earlier proposed different measures of competitive balance, which are primarily adapted from standard economic theory. However, these measures fail to capture the finer nuances of the game. In this work, we discuss a new framework which is more suitable for a football league. First, we present a mathematical proof of a theoretically optimal situation where a football league becomes perfectly balanced. Next, a goal based index for competitive balance is developed. We present relevant theoretical results and show how the proposed index can be used to formally test for the presence of imbalance. The methods are implemented on the data from the top five European leagues, and it shows that the new approach can be helpful in explaining the changes in the seasonal competitive balance of the leagues. Further, using panel data models, we show that the proposed index is more suitable to analyze the variability in total revenues of the football leagues. We also discuss how the methods can be easily extended to develop other goal-based indices under different modeling assumptions.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":"60 7 1","pages":"171 - 186"},"PeriodicalIF":0.8,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86799945","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}
Abstract Recent measurement technologies enable us to analyze baseball at higher levels of complexity. There are, however, still many unclear points around pitching strategy. There are two elements that make it difficult to measure the effect of a pitching strategy. First, most public datasets do not include location data where the catcher demands a ball, which is essential information to obtain the battery’s intent. Second, there are many confounders associated with pitching/batting results when evaluating pitching strategy. We here clarify the effect of pitching attempts to a specific location, e.g., inside or outside. We employ a causal inference framework called stratified analysis using a propensity score to evaluate the effects while removing the effect of confounding factors. We use a pitch-by-pitch dataset of Japanese professional baseball games held in 2014–2019, which includes location data where the catcher demands a ball. The results reveal that an outside pitching attempt is more effective than an inside one to minimize allowed run average. In addition, the stratified analysis shows that the outside pitching attempt is effective regardless of the magnitude of the estimated batter’s ability, and the proportion of pitched inside for pitcher/batter. Our analysis provides practical insights into selecting a pitching strategy to minimize allowed runs.
{"title":"Pitching strategy evaluation via stratified analysis using propensity score","authors":"Hiroshi Nakahara, K. Takeda, Keisuke Fujii","doi":"10.1515/jqas-2021-0060","DOIUrl":"https://doi.org/10.1515/jqas-2021-0060","url":null,"abstract":"Abstract Recent measurement technologies enable us to analyze baseball at higher levels of complexity. There are, however, still many unclear points around pitching strategy. There are two elements that make it difficult to measure the effect of a pitching strategy. First, most public datasets do not include location data where the catcher demands a ball, which is essential information to obtain the battery’s intent. Second, there are many confounders associated with pitching/batting results when evaluating pitching strategy. We here clarify the effect of pitching attempts to a specific location, e.g., inside or outside. We employ a causal inference framework called stratified analysis using a propensity score to evaluate the effects while removing the effect of confounding factors. We use a pitch-by-pitch dataset of Japanese professional baseball games held in 2014–2019, which includes location data where the catcher demands a ball. The results reveal that an outside pitching attempt is more effective than an inside one to minimize allowed run average. In addition, the stratified analysis shows that the outside pitching attempt is effective regardless of the magnitude of the estimated batter’s ability, and the proportion of pitched inside for pitcher/batter. Our analysis provides practical insights into selecting a pitching strategy to minimize allowed runs.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":"17 1","pages":"91 - 102"},"PeriodicalIF":0.8,"publicationDate":"2022-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83392430","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}
Abstract Former NBA all-star forward Rasheed Wallace popularized the catchphrase “Ball Don’t Lie.” Rasheed would often shout this after an opponent missed a free throw. It was used by Rasheed to illustrate the mental impact on a free throw shooter from knowing the foul was questionable and its impact on likelihood of converting the ensuing free throw. The tendency to miss free throws associated with questionable foul calls—or the propensity for the ball to miss—would be followed by Rasheed’s “Ball Don’t Lie!” exclamation. This paper aims to test whether the ball was less likely to go through the hoop during free throws following questionable foul calls. We use a proxy to identify the questionableness of a foul call, one that Rasheed Wallace was very familiar with—whenever the original shooting foul was immediately followed by a technical foul. This proxy is meant to capture player and coach reactions to a shooting foul call. If the call was bad, or questionable, we expect more outrage from the team the foul was called on, which tends to draw technical fouls. Our findings do not support Rasheed’s prediction; the propensity to make a shooting foul free throw does not appear to change after a technical. In fact, using a subset of our data period under which the NBA changed technical foul rules to target complaining about foul calls, we find a small increase in free throw percentage after a technical foul call.
{"title":"Does the ball lie? Testing the Rasheed Wallace hypothesis","authors":"B. Meehan, Javier E. Portillo, Corey Jenkins","doi":"10.1515/jqas-2020-0020","DOIUrl":"https://doi.org/10.1515/jqas-2020-0020","url":null,"abstract":"Abstract Former NBA all-star forward Rasheed Wallace popularized the catchphrase “Ball Don’t Lie.” Rasheed would often shout this after an opponent missed a free throw. It was used by Rasheed to illustrate the mental impact on a free throw shooter from knowing the foul was questionable and its impact on likelihood of converting the ensuing free throw. The tendency to miss free throws associated with questionable foul calls—or the propensity for the ball to miss—would be followed by Rasheed’s “Ball Don’t Lie!” exclamation. This paper aims to test whether the ball was less likely to go through the hoop during free throws following questionable foul calls. We use a proxy to identify the questionableness of a foul call, one that Rasheed Wallace was very familiar with—whenever the original shooting foul was immediately followed by a technical foul. This proxy is meant to capture player and coach reactions to a shooting foul call. If the call was bad, or questionable, we expect more outrage from the team the foul was called on, which tends to draw technical fouls. Our findings do not support Rasheed’s prediction; the propensity to make a shooting foul free throw does not appear to change after a technical. In fact, using a subset of our data period under which the NBA changed technical foul rules to target complaining about foul calls, we find a small increase in free throw percentage after a technical foul call.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":"77 1","pages":"87 - 95"},"PeriodicalIF":0.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90925597","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}
N. Carter, A. Harrison, Amar Iyengar, M. Lanham, Scott T. Nestler, Dave Schrader, Amir Zadeh
Abstract In NCAA Division III Wrestling, the question arose how to assign schools to regions in a way that optimizes fairness for individual wrestlers aspiring to the national tournament. The problem fell within cluster analysis but no known clustering algorithms supported its complex and interrelated set of needs. We created several bespoke clustering algorithms based on various heuristics (balanced optimization, weighted spatial clustering, and weighted optimization rectangles) for finding an optimal assignment, and tested each against the generic technique of genetic algorithms. While each of our algorithms had different strengths, the genetic algorithm achieved the highest value on our objective function, including when comparing it to the region assignments that preceded our work. This paper therefore demonstrates a technique that can be used to solve a broad category of clustering problems that arise in athletics, particularly any sport in which athletes compete individually but are assigned to regions as a team.
{"title":"Clustering algorithms to increase fairness in collegiate wrestling","authors":"N. Carter, A. Harrison, Amar Iyengar, M. Lanham, Scott T. Nestler, Dave Schrader, Amir Zadeh","doi":"10.1515/jqas-2020-0101","DOIUrl":"https://doi.org/10.1515/jqas-2020-0101","url":null,"abstract":"Abstract In NCAA Division III Wrestling, the question arose how to assign schools to regions in a way that optimizes fairness for individual wrestlers aspiring to the national tournament. The problem fell within cluster analysis but no known clustering algorithms supported its complex and interrelated set of needs. We created several bespoke clustering algorithms based on various heuristics (balanced optimization, weighted spatial clustering, and weighted optimization rectangles) for finding an optimal assignment, and tested each against the generic technique of genetic algorithms. While each of our algorithms had different strengths, the genetic algorithm achieved the highest value on our objective function, including when comparing it to the region assignments that preceded our work. This paper therefore demonstrates a technique that can be used to solve a broad category of clustering problems that arise in athletics, particularly any sport in which athletes compete individually but are assigned to regions as a team.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":"57 1","pages":"113 - 125"},"PeriodicalIF":0.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74351693","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}
Abstract Choosing the right defensive corner-strategy is a crucial task for each coach in professional football (soccer). Although corners are repeatable and static situations, due to their low conversion rates, several studies in literature failed to find useable insights about the efficiency of various corner strategies. Our work aims to fill this gap. We hand-label the role of each defensive player from 213 corners in 33 matches, where we then employ an augmentation strategy to increase the number of data points. By combining a convolutional neural network with a long short-term memory neural network, we are able to detect the defensive strategy of each player based on positional data. We identify which of seven well-established roles a defensive player conducted (player-marking, zonal-marking, placed for counterattack, back-space, short defender, near-post, and far-post). The model achieves an overall weighted accuracy of 89.3%, and in the case of player-marking, we are able to accurately detect which offensive player the defender is marking 80.8% of the time. The performance of the model is evaluated against a rule-based baseline model, as well as by an inter-labeller accuracy. We demonstrate that rules can also be used to support the labelling process and serve as a baseline for weak supervision approaches. We show three concrete use-cases on how this approach can support a more informed and fact-based decision making process.
{"title":"Individual role classification for players defending corners in football (soccer)","authors":"Pascal Bauer, Gabriel Anzer, J. Smith","doi":"10.1515/jqas-2022-0003","DOIUrl":"https://doi.org/10.1515/jqas-2022-0003","url":null,"abstract":"Abstract Choosing the right defensive corner-strategy is a crucial task for each coach in professional football (soccer). Although corners are repeatable and static situations, due to their low conversion rates, several studies in literature failed to find useable insights about the efficiency of various corner strategies. Our work aims to fill this gap. We hand-label the role of each defensive player from 213 corners in 33 matches, where we then employ an augmentation strategy to increase the number of data points. By combining a convolutional neural network with a long short-term memory neural network, we are able to detect the defensive strategy of each player based on positional data. We identify which of seven well-established roles a defensive player conducted (player-marking, zonal-marking, placed for counterattack, back-space, short defender, near-post, and far-post). The model achieves an overall weighted accuracy of 89.3%, and in the case of player-marking, we are able to accurately detect which offensive player the defender is marking 80.8% of the time. The performance of the model is evaluated against a rule-based baseline model, as well as by an inter-labeller accuracy. We demonstrate that rules can also be used to support the labelling process and serve as a baseline for weak supervision approaches. We show three concrete use-cases on how this approach can support a more informed and fact-based decision making process.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":"14 1","pages":"147 - 160"},"PeriodicalIF":0.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82466879","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}
Abstract Two new Bayesian methods for estimating and predicting in-game home team win probabilities in Division I NCAA men’s college basketball are proposed. The first method has a prior that adjusts as a function of lead differential and time elapsed. The second is an adjusted version of the first, where the adjustment is a linear combination of the Bayesian estimator with a time-weighted pregame win probability. The proposed methods are compared to existing methods, showing the new methods are competitive with or outperform existing methods for both estimation and prediction. The utility is illustrated via an application to the 2012/2013 through the 2019/2020 NCAA Division I Men’s Basketball seasons.
{"title":"Bayesian estimation of in-game home team win probability for college basketball","authors":"Jason Maddox, Ryan Sides, Jane L. Harvill","doi":"10.1515/jqas-2021-0086","DOIUrl":"https://doi.org/10.1515/jqas-2021-0086","url":null,"abstract":"Abstract Two new Bayesian methods for estimating and predicting in-game home team win probabilities in Division I NCAA men’s college basketball are proposed. The first method has a prior that adjusts as a function of lead differential and time elapsed. The second is an adjusted version of the first, where the adjustment is a linear combination of the Bayesian estimator with a time-weighted pregame win probability. The proposed methods are compared to existing methods, showing the new methods are competitive with or outperform existing methods for both estimation and prediction. The utility is illustrated via an application to the 2012/2013 through the 2019/2020 NCAA Division I Men’s Basketball seasons.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":"2 1","pages":"201 - 213"},"PeriodicalIF":0.8,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79075522","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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