Dani Chu, M. Tsai, Ryan Sheehan, Jack Davis, R. Doig
The pacing strategy adopted by athletes is a major determinants of success during timed competition. Various pacing profiles are reported in the literature and its importance depends on the mode of sport. However, in 2000 metre rowing, the definition of these pacing profiles has been limited by the minimal availability of data. Purpose: Our aim is to objectively identify pacing profiles used in World Championship 2000 metre rowing races using reproducible methods. Methods: We use the average speed for each 50 metre split for each available boat in every race of the Rowing World Championships from 2010-2017. This data was scraped from www.worldrowing.com. This data set is publicly available (https://github.com/danichusfu/rowing_pacing_profiles) to help the field of rowing research. Pacing profiles are determined by using k-shape clustering, a time series clustering method. A multinomial logistic regression is then fit to test whether variables such as boat size, gender, round, or rank are associated with pacing profiles. Results: Four pacing strategies (Even, Positive, Reverse J-Shaped, and U-Shaped) are identified from the clustering process. Boat size, round (Heat vs Finals), rank, gender, and weight class are all found to affect pacing profiles. Conclusion: We use an objective methodology with more granular data to identify four pacing strategies. We identify important associations between these pacing profiles and race factors. Finally, we make the full data set public to further rowing research and to replicate our results.
{"title":"Identifying Pacing Profiles in 2000 Metre World Championship Rowing","authors":"Dani Chu, M. Tsai, Ryan Sheehan, Jack Davis, R. Doig","doi":"10.3233/jsa-220497","DOIUrl":"https://doi.org/10.3233/jsa-220497","url":null,"abstract":"The pacing strategy adopted by athletes is a major determinants of success during timed competition. Various pacing profiles are reported in the literature and its importance depends on the mode of sport. However, in 2000 metre rowing, the definition of these pacing profiles has been limited by the minimal availability of data. Purpose: Our aim is to objectively identify pacing profiles used in World Championship 2000 metre rowing races using reproducible methods. Methods: We use the average speed for each 50 metre split for each available boat in every race of the Rowing World Championships from 2010-2017. This data was scraped from www.worldrowing.com. This data set is publicly available (https://github.com/danichusfu/rowing_pacing_profiles) to help the field of rowing research. Pacing profiles are determined by using k-shape clustering, a time series clustering method. A multinomial logistic regression is then fit to test whether variables such as boat size, gender, round, or rank are associated with pacing profiles. Results: Four pacing strategies (Even, Positive, Reverse J-Shaped, and U-Shaped) are identified from the clustering process. Boat size, round (Heat vs Finals), rank, gender, and weight class are all found to affect pacing profiles. Conclusion: We use an objective methodology with more granular data to identify four pacing strategies. We identify important associations between these pacing profiles and race factors. Finally, we make the full data set public to further rowing research and to replicate our results.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47209640","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}
Indian Premier League (IPL) is the most popular T20 domestic league in the world. An essential aspect of this league is the “Mega-Auction”, which is of focus in this study. The mega auction occurs once every three years, and it is found that the auction process is inefficient as the time taken is long (∼2 days). This is because players specify their base price. Thus, this study focuses on the efficiency of the auction process and addresses it by prescribing the base price for players. The base prices are prescribed such that they are as close to the actual auction price of a player. Accordingly, in the past, only two past mega auctions occurred in 2014 and 2018, and both are considered in this work. Here, a two-stage algorithm to determine the base prices of players is proposed. In the first stage, K-Means clustering is used to group players. The base price for players allocated to a cluster is proposed using a developed assignment logic in the second stage. An empirical demonstration of the proposed algorithm indicates that the auction process has been made efficient as the time taken decreases by ∼17.6%and ∼31.1%for Indian and foreign players, respectively.
{"title":"Base price determination for IPL mega auctions: A player performance-based approach","authors":"Vaseekaran Chittibabu, M. Sundararaman","doi":"10.3233/jsa-220633","DOIUrl":"https://doi.org/10.3233/jsa-220633","url":null,"abstract":"Indian Premier League (IPL) is the most popular T20 domestic league in the world. An essential aspect of this league is the “Mega-Auction”, which is of focus in this study. The mega auction occurs once every three years, and it is found that the auction process is inefficient as the time taken is long (∼2 days). This is because players specify their base price. Thus, this study focuses on the efficiency of the auction process and addresses it by prescribing the base price for players. The base prices are prescribed such that they are as close to the actual auction price of a player. Accordingly, in the past, only two past mega auctions occurred in 2014 and 2018, and both are considered in this work. Here, a two-stage algorithm to determine the base prices of players is proposed. In the first stage, K-Means clustering is used to group players. The base price for players allocated to a cluster is proposed using a developed assignment logic in the second stage. An empirical demonstration of the proposed algorithm indicates that the auction process has been made efficient as the time taken decreases by ∼17.6%and ∼31.1%for Indian and foreign players, respectively.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49335358","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}
Here we analyse competitive surfing, specifically the 2019 Men’s World Surf League, using formal statistical methods. We use generalized Bradley-Terry likelihoods to assess a number of hypotheses of interest to the surfing community. We quantify the dominance of the top competitors using likelihood techniques, and go on to study the “Brazilian storm” phenomenon using reified entities in two ways. Firstly we assess the supposed Brazilian preference for beach break and point break wave types; and secondly we consider results from the perspective of tournament theory and test for competitors modifying their strategy in the presence of compatriot rivals. We quantify the evidence for these commonly assumed features of contemporary competitive surfing and suggest further avenues of research.
{"title":"Analysis of competitive surfing tournaments with generalized Bradley-Terry likelihoods","authors":"T. Driver, R. Hankin","doi":"10.3233/jsa-220596","DOIUrl":"https://doi.org/10.3233/jsa-220596","url":null,"abstract":"Here we analyse competitive surfing, specifically the 2019 Men’s World Surf League, using formal statistical methods. We use generalized Bradley-Terry likelihoods to assess a number of hypotheses of interest to the surfing community. We quantify the dominance of the top competitors using likelihood techniques, and go on to study the “Brazilian storm” phenomenon using reified entities in two ways. Firstly we assess the supposed Brazilian preference for beach break and point break wave types; and secondly we consider results from the perspective of tournament theory and test for competitors modifying their strategy in the presence of compatriot rivals. We quantify the evidence for these commonly assumed features of contemporary competitive surfing and suggest further avenues of research.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48770569","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}
Choosing the right formation is one of the coach’s most important decisions in football. Teams change formation dynamically throughout matches to achieve their immediate objective: to retain possession, progress the ball up-field and create (or prevent) goal-scoring opportunities. In this work we identify the unique formations used by teams in distinct phases of play in a large sample of tracking data. This we achieve in two steps: first, we trained a convolutional neural network to decompose each game into non-overlapping segments and classify these segments into phases with an average F 1-score of 0.76. We then measure and contextualize unique formations used in each distinct phase of play. While conventional discussion tends to reduce team formations over an entire match to a single three-digit code (e.g. 4-4-2; 4 defender, 4 midfielder, 2 striker), we provide an objective representation of teams formations per phase of play. Using the most frequently occurring phases of play, mid-block, we identify and contextualize six unique formations. A long-term analysis in the German Bundesliga allows us to quantify the efficiency of each formation, and to present a helpful scouting tool to identify how well a coach’s preferred playing style is suited to a potential club.
{"title":"Putting team formations in association football into context","authors":"Pascal Bauer, Gabriel Anzer, Laurie Shaw","doi":"10.3233/jsa-220620","DOIUrl":"https://doi.org/10.3233/jsa-220620","url":null,"abstract":"Choosing the right formation is one of the coach’s most important decisions in football. Teams change formation dynamically throughout matches to achieve their immediate objective: to retain possession, progress the ball up-field and create (or prevent) goal-scoring opportunities. In this work we identify the unique formations used by teams in distinct phases of play in a large sample of tracking data. This we achieve in two steps: first, we trained a convolutional neural network to decompose each game into non-overlapping segments and classify these segments into phases with an average F 1-score of 0.76. We then measure and contextualize unique formations used in each distinct phase of play. While conventional discussion tends to reduce team formations over an entire match to a single three-digit code (e.g. 4-4-2; 4 defender, 4 midfielder, 2 striker), we provide an objective representation of teams formations per phase of play. Using the most frequently occurring phases of play, mid-block, we identify and contextualize six unique formations. A long-term analysis in the German Bundesliga allows us to quantify the efficiency of each formation, and to present a helpful scouting tool to identify how well a coach’s preferred playing style is suited to a potential club.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41969732","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 cricket fraternity described “An unsettled batting position of number four” as one of the major causes for India’s exit from International Cricket Council men’s world cup 2019. Consistent chopping and changing batsmen at the sensitive fourth batting position proved to be a disaster for team India then. Therefore, ranking of all the batsmen, in the then Indian cricket team, who were deemed to be eligible for this position remained a much-debated issue both before and after the world cup 2019. In the present paper, Kaplan-Meir curves are used to make multiple comparisons for respective batting performances among the batsmen who have ever played in the middle order position. In this paper, frailty of these batsmen is studied through Bayesian analysis at the start of their innings and during the time-interval of transition to their best playing ability by considering respective run scores. Posterior summaries of innate player ability are obtained by deploying a Markov Chain Monte Carlo algorithm which is then used to assess and compare the individual batting performances. Estimation of incomplete innings is handled via censoring strategies.
{"title":"A bayesian perspective of middle-batting position in ODI cricket","authors":"Ranjita Pandey, H. Tolani","doi":"10.3233/jsa-220640","DOIUrl":"https://doi.org/10.3233/jsa-220640","url":null,"abstract":"The cricket fraternity described “An unsettled batting position of number four” as one of the major causes for India’s exit from International Cricket Council men’s world cup 2019. Consistent chopping and changing batsmen at the sensitive fourth batting position proved to be a disaster for team India then. Therefore, ranking of all the batsmen, in the then Indian cricket team, who were deemed to be eligible for this position remained a much-debated issue both before and after the world cup 2019. In the present paper, Kaplan-Meir curves are used to make multiple comparisons for respective batting performances among the batsmen who have ever played in the middle order position. In this paper, frailty of these batsmen is studied through Bayesian analysis at the start of their innings and during the time-interval of transition to their best playing ability by considering respective run scores. Posterior summaries of innate player ability are obtained by deploying a Markov Chain Monte Carlo algorithm which is then used to assess and compare the individual batting performances. Estimation of incomplete innings is handled via censoring strategies.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45315926","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}
Joey Gullikson, L. R. Gale, John Mayberry, John Kim, L. Killick
Previous research has adapted the use of economic production functions to estimate the scoring production of teams in professional sports. Most of these studies have focused on professional male team sports, most notably, US baseball, basketball, and association football. This study adds to the literature by utilizing a new and distinctive data set of shooting statistics from 88 men’s and 38 women’s NCAA water polo contests to estimate production functions for United States’ collegiate water polo games and identify the most important variables for predicting margin of victory in such competitions. The results show that shots on goal, average shot distance, number of counterattacks, quick shots, and efficiency in power play conversions are all significant predictors of goal differentials in men’s contests while shots on goal, average shot distance, counterattacks, and center shots are significant predictors in women’s matches. Previous season win percentage, rebounds, exclusions, and missed shots were not significant predictors in the models. These conclusions confirm and extend previous discriminatory studies of elite international water polo contests.
{"title":"Production functions of NCAA men and women water polo matches","authors":"Joey Gullikson, L. R. Gale, John Mayberry, John Kim, L. Killick","doi":"10.3233/jsa-220600","DOIUrl":"https://doi.org/10.3233/jsa-220600","url":null,"abstract":"Previous research has adapted the use of economic production functions to estimate the scoring production of teams in professional sports. Most of these studies have focused on professional male team sports, most notably, US baseball, basketball, and association football. This study adds to the literature by utilizing a new and distinctive data set of shooting statistics from 88 men’s and 38 women’s NCAA water polo contests to estimate production functions for United States’ collegiate water polo games and identify the most important variables for predicting margin of victory in such competitions. The results show that shots on goal, average shot distance, number of counterattacks, quick shots, and efficiency in power play conversions are all significant predictors of goal differentials in men’s contests while shots on goal, average shot distance, counterattacks, and center shots are significant predictors in women’s matches. Previous season win percentage, rebounds, exclusions, and missed shots were not significant predictors in the models. These conclusions confirm and extend previous discriminatory studies of elite international water polo contests.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47727293","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}
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":" ","pages":""},"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}
Evaluating players’ performance for decision-makers in the sports industry is crucial in order to make the right decisions to form and invest in a successful team. One way of assessing players’ performance is to group players into specific “types”, where each type represents a level of performance of its players within. In this paper, we develop a novel clustering approach in order to cluster types of players in the NBA. The proposed methodology is initialized by a k-Means clustering, then the prescribed clusters inform weights of a weighted network, in which players are the nodes and the arcs between them carry those weights that represent a numerical similarity between them. We then call upon a weighted network clustering approach, namely, the Louvain method for community detection. We demonstrate our methodology on six years of historical data, from seasons ranging from 2014–2015 to 2019–2020. Considering these seasons allows us to use a new type of data, called Tracking Data, instated into the league in 2014 which further differentiates our research from other player clustering approaches. We show that our approach can detect outliers and consistently clusters players into groups with identifying features, which give insights into league trends. We conclude that players can be categorized into eight general archetypes and show that these archetypes improve upon the traditional five positions and previous research in terms of explaining variation in Win Shares.
{"title":"A weighted network clustering approach in the NBA","authors":"Megan Muniz, Tulay Flamand","doi":"10.3233/jsa-220584","DOIUrl":"https://doi.org/10.3233/jsa-220584","url":null,"abstract":"Evaluating players’ performance for decision-makers in the sports industry is crucial in order to make the right decisions to form and invest in a successful team. One way of assessing players’ performance is to group players into specific “types”, where each type represents a level of performance of its players within. In this paper, we develop a novel clustering approach in order to cluster types of players in the NBA. The proposed methodology is initialized by a k-Means clustering, then the prescribed clusters inform weights of a weighted network, in which players are the nodes and the arcs between them carry those weights that represent a numerical similarity between them. We then call upon a weighted network clustering approach, namely, the Louvain method for community detection. We demonstrate our methodology on six years of historical data, from seasons ranging from 2014–2015 to 2019–2020. Considering these seasons allows us to use a new type of data, called Tracking Data, instated into the league in 2014 which further differentiates our research from other player clustering approaches. We show that our approach can detect outliers and consistently clusters players into groups with identifying features, which give insights into league trends. We conclude that players can be categorized into eight general archetypes and show that these archetypes improve upon the traditional five positions and previous research in terms of explaining variation in Win Shares.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45669114","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":" ","pages":""},"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}
Elia Morgulev, R. Kenett, M. Arnon, R. Lidor, D. Ben-sira, Izy Tchinio
Describing, understanding, predicting, and controlling the improvement of athletic performance are pivotal aspects of sport sciences. Longitudinal trends of the achievements of elite performers, mainly in endurance (e.g., cycling, running, skiing, and swimming) and explosive-power (e.g., jumping, throwing, and weightlifting) sports, were examined in a series of studies. One of the observations in these studies was the significant improvement in performance in the above-mentioned sports over the 1960s, 1970s, and 1980s. In addition, several factors that can account for the observed improvements were outlined and discussed in the previous literature. The current study contributes to this line of research by examining the rate of improvement in free-throw (FT) shooting of National Basketball Association (NBA) players over a five-decade period –1969–2019. As opposed to many power and endurance sporting events, FT shooting is a fine-motor task performed in a stable and predicted environment. Based on an analysis of more than 2.6 million FT shots, we found that from 1969 to 2019 the FT shooting accuracy fluctuated at around 75%, but did not show any steady trend of improvement. We discuss this finding from a skill-acquisition perspective.
{"title":"Longitudinal trends in human accuracy: A five-decade analysis (1969–2019) of free-throw shooting in the NBA","authors":"Elia Morgulev, R. Kenett, M. Arnon, R. Lidor, D. Ben-sira, Izy Tchinio","doi":"10.3233/jsa-200597","DOIUrl":"https://doi.org/10.3233/jsa-200597","url":null,"abstract":"Describing, understanding, predicting, and controlling the improvement of athletic performance are pivotal aspects of sport sciences. Longitudinal trends of the achievements of elite performers, mainly in endurance (e.g., cycling, running, skiing, and swimming) and explosive-power (e.g., jumping, throwing, and weightlifting) sports, were examined in a series of studies. One of the observations in these studies was the significant improvement in performance in the above-mentioned sports over the 1960s, 1970s, and 1980s. In addition, several factors that can account for the observed improvements were outlined and discussed in the previous literature. The current study contributes to this line of research by examining the rate of improvement in free-throw (FT) shooting of National Basketball Association (NBA) players over a five-decade period –1969–2019. As opposed to many power and endurance sporting events, FT shooting is a fine-motor task performed in a stable and predicted environment. Based on an analysis of more than 2.6 million FT shots, we found that from 1969 to 2019 the FT shooting accuracy fluctuated at around 75%, but did not show any steady trend of improvement. We discuss this finding from a skill-acquisition perspective.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42825288","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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