Journal of Sports Analytics最新文献

{"title":"Community structure of the football transfer market network: the case of Italian Serie A","authors":"Lucio Palazzo, Roberto Rondinelli, Filipe Manuel Clemente, Riccardo Ievoli, Giancarlo Ragozini","doi":"10.3233/jsa-220661","DOIUrl":"https://doi.org/10.3233/jsa-220661","url":null,"abstract":"The men’s football transfer market represents a complex phenomenon requiring suitable methods for an in-depth study. Network Analysis may be employed to measure the key elements of the transfer market through network indicators, such as degree centrality, hub and authority scores, and betweenness centrality. Furthermore, community detection methods can be proposed to unveil unobservable patterns of the football market, even considering auxiliary variables such as the type of transfer, the age or the role of the player, and the agents involved in the transfer flow. These methodologies are applied to the flows of player transfers generated by the 20 teams of the Italian first division (Serie A). These flows include teams from all over the world. We consider the summer market session of 2019, at the beginning of the season 2019-2020. Results also help to better understand some peculiarities of the Italian football transfer market in terms of the different approaches of the elite teams. Network indices show the presence of different market strategies, highlighting the role of mid-level teams such as Atalanta, Genoa, and Sassuolo. The network reveals a core-periphery structure splitted into several communities. The Infomap algorithm identifies 14 single team-based communities and three communities formed by two teams. Two of the latter are composed of a top team and a mid-level team, suggesting the presence of collaboration and similar market behavior, while the third is guided by two teams promoted by the second division (Serie B).","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135191392","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}

{"title":"Determining the playing 11 based on opposition squad: An IPL illustration","authors":"G. Gokul, Malolan Sundararaman","doi":"10.3233/jsa-220638","DOIUrl":"https://doi.org/10.3233/jsa-220638","url":null,"abstract":"Indian Premier League (IPL) is the most popular T20 domestic sporting league globally. Player selection is crucial in winning the competitive IPL tournament. Thus, team management select 11 players for each match from a team’s squad of 15 to 25 players. Different player statistics are analysed to select the best playing 11 for each match. This study attempts an approach where the on-field player performance is used to determine the playing-11. A player’s on-field performance in a match is computed as a single metric considering a player’s attributes against every player present in the opposition squad. For this computation, past ball-by-ball data is cleaned and mined to generate data containing player-vs-player performance attributes. Next, the various performance attributes for a player-vs-player combination is converted into a player’s performance rating by computing a weighted score of the performance attributes. Finally, an optimisation model is proposed and developed to determine the best playing-11 using the computed performance ratings. The developed optimisation model suggests the playing-11 that maximises the possibility of winning against a given opponent. The proposed procedure to determine the playing-11 for an IPL match is demonstrated using past data from 2008-20. The demonstration indicates that for matches in the league stage, the suggested playing-11 by model and the actual playing-11 have a ∼7% similarity across all teams. The remaining ∼3% are different from those selected in the actual team. Nevertheless, this difference approximately yields a ∼ Indian Premier League (IPL) is the most popular T20 domestic sporting league globally. Player selection is crucial in winning the competitive IPL tournament. Thus, team management select 11 players for each match from a team’s squad of 15 to 25 players. Different player statistics are analysed to select the best playing 11 for each match. This study attempts an approach where the on-field player performance is used to determine the playing-11. A player’s on-field performance in a match is computed as a single metric considering a player’s attributes against every player present in the opposition squad. For this computation, past ball-by-ball data is cleaned and mined to generate data containing player-vs-player performance attributes. Next, the various performance attributes for a player-vs-player combination is converted into a player’s performance rating by computing a weighted score of the performance attributes. Finally, an optimisation model is proposed and developed to determine the best playing-11 using the computed performance ratings. The developed optimisation model suggests the playing-11 that maximises the possibility of winning against a given opponent. The proposed procedure to determine the playing-11 for an IPL match is demonstrated using past data from 2008-20. The demonstration indicates that for matches in the league stage, the suggested playing-11 by model and the a","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135191239","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}

{"title":"Schedule inequity in the National Basketball Association","authors":"R. Alan Bowman, Oskar Harmon, Thomas Ashman","doi":"10.3233/jsa-220629","DOIUrl":"https://doi.org/10.3233/jsa-220629","url":null,"abstract":"Scheduling factors such as a visiting team playing a game back-to-back against a rested home team can affect the win probability of the teams for that game and potentially affect teams unevenly throughout the season. This study examines schedule inequity in the National Basketball Association (NBA) for the seasons 2000–01 through 2018–19. By schedule inequity, we mean the effect of a comprehensive set of schedule factors, other than opponents, on team success and how much these effects differ across teams. We use a logistic regression model and Monte Carlo simulations to identify schedule factor variables that influence the probability of the home team winning in each game (the teams playing are control variables) and construct schedule inequity measures. We evaluate these measures for each NBA season, trends in the measures over time, and the potential effectiveness of broad prescriptive approaches to reduce schedule inequity. We find that, although schedule equity has improved over time, schedule differences disproportionately affect team success measures. Moreover, we find that balancing the frequency of schedule variables across teams is a more effective method of mitigating schedule inequity than reducing the total frequency, although combining both methods is the most effective strategy.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136166051","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}

{"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}

{"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}

{"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}

{"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}

{"title":"Stochastic Programming and Optimization in Horserace Betting","authors":"W. Ziemba","doi":"10.1142/9789814407519_0009","DOIUrl":"https://doi.org/10.1142/9789814407519_0009","url":null,"abstract":"AbstractRacetrack betting is simply an application of portfolio theory. The racetrack offers many bets that involve the results of one to about ten horses. Each race is a special financial market with betting, then a race that takes one or a few minutes. Unlike the financial markets, one cannot stop the race when one is ahead, or have the market going almost 24/7. There is a well-defined end point. Like standard portfolio theory, the key issues are to get the means right. In this case, it is the probabilities of, say, two, three or more horses finishing first, second, third, etc., in a given order, and to bet well. For the latter, the Kelly capital growth criterion is widely used and that maximizes the expected logarithm of final wealth. Transaction and price pressure odds changes fit well into the stochastic programming models.Professional syndicates or teams have been successful as hedge funds with gains approaching one billion over several years for the most successful. In the modern era, there are two features used extensively. First, there are rebates for large bettors of the track take similar to discounts at Costco. So instead of facing a 13–30% transaction cost, it's more like 10%. So to win, the bettors must make back this 10% disadvantage before profits ensue. And this is not easy as the markets are quite efficient. Also over half the betting is not recorded in the pools until the race is being run. This is because monies are bet near the start of the race and come from many off track sites which are combined with the on-track bets into the track pool. All this takes time. So estimates of future prices are crucial. Secondly, betting exchanges such as Betfair in London allow short as well as standard long bets. This allows for more arbitrage and the ability to take advantage of known biases. I have been involved in this research since the late 1970s with six books and a number of articles.In this paper I relate the theory, computations and examples of real races and experiences for various bets such as win, place and show, exactas, triactors, superfectas, super hi five, place pick all, double, pick 3, 4, 5 and 6. In the Halifax presentation I showed two of the greatest races ever by two undefeated female horses, one that was still then running (Zenyatta) and one retired (Personal Ensign) in 1988. The previous US undefeated horse was Colin in 1907! These and other great races can be seen free on the website chef-de-race.com.","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":"77143172","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}

{"title":"Risk Arbitrage in the 2021 NBA Championship","authors":"W. Ziemba","doi":"10.1142/9789811250217_0007","DOIUrl":"https://doi.org/10.1142/9789811250217_0007","url":null,"abstract":"Mean reversion risk arbitrage is an ideal way to bet on and watch the NBA.","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":"88811982","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}

{"title":"NFL Analytics I","authors":"L. MacLean, W. Ziemba","doi":"10.1142/9789811250217_0009","DOIUrl":"https://doi.org/10.1142/9789811250217_0009","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":"83439219","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}