Despite the massive popularity of the Asian Handicap (AH) football (soccer) betting market, its efficiency has not been adequately studied by the relevant literature. This paper combines rating systems with Bayesian networks and presents the first published model specifically developed for prediction and assessment of the efficiency of the AH betting market. The results are based on 13 English Premier League seasons and are compared to the traditional market, where the bets are for win, lose or draw. Different betting situations have been examined including a) both average and maximum (best available) market odds, b) all possible betting decision thresholds between predicted and published odds, c) optimisations for both return-on-investment and profit, and d) simple stake adjustments to investigate how the variance of returns changes when targeting equivalent profit in both traditional and AH markets. While the AH market is found to share the inefficiencies of the traditional market, the findings reveal both interesting differences as well as similarities between the two.
{"title":"Investigating the efficiency of the Asian handicap football betting market with ratings and Bayesian networks","authors":"A. Constantinou","doi":"10.3233/JSA-200588","DOIUrl":"https://doi.org/10.3233/JSA-200588","url":null,"abstract":"Despite the massive popularity of the Asian Handicap (AH) football (soccer) betting market, its efficiency has not been adequately studied by the relevant literature. This paper combines rating systems with Bayesian networks and presents the first published model specifically developed for prediction and assessment of the efficiency of the AH betting market. The results are based on 13 English Premier League seasons and are compared to the traditional market, where the bets are for win, lose or draw. Different betting situations have been examined including a) both average and maximum (best available) market odds, b) all possible betting decision thresholds between predicted and published odds, c) optimisations for both return-on-investment and profit, and d) simple stake adjustments to investigate how the variance of returns changes when targeting equivalent profit in both traditional and AH markets. While the AH market is found to share the inefficiencies of the traditional market, the findings reveal both interesting differences as well as similarities between the two.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47307274","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 paper considers the use of observed and predicted match statistics as inputs to forecasts for the outcomes of football matches. It is shown that, were it possible to know the match statistics in advance, highly informative forecasts of the match outcome could be made. Whilst, in practice, match statistics are clearly never available prior to the match, this leads to a simple philosophy. If match statistics can be predicted pre-match, and if those predictions are accurate enough, it follows that informative match forecasts can be made. Two approaches to the prediction of match statistics are demonstrated: Generalised Attacking Performance (GAP) ratings and a set of ratings based on the Bivariate Poisson model which are named Bivariate Attacking (BA) ratings. It is shown that both approaches provide a suitable methodology for predicting match statistics in advance and that they are informative enough to provide information beyond that reflected in the odds. A long term and robust gambling profit is demonstrated when the forecasts are combined with two betting strategies.
{"title":"Forecasting football matches by predicting match statistics","authors":"E. Wheatcroft","doi":"10.3233/JSA-200462","DOIUrl":"https://doi.org/10.3233/JSA-200462","url":null,"abstract":"This paper considers the use of observed and predicted match statistics as inputs to forecasts for the outcomes of football matches. It is shown that, were it possible to know the match statistics in advance, highly informative forecasts of the match outcome could be made. Whilst, in practice, match statistics are clearly never available prior to the match, this leads to a simple philosophy. If match statistics can be predicted pre-match, and if those predictions are accurate enough, it follows that informative match forecasts can be made. Two approaches to the prediction of match statistics are demonstrated: Generalised Attacking Performance (GAP) ratings and a set of ratings based on the Bivariate Poisson model which are named Bivariate Attacking (BA) ratings. It is shown that both approaches provide a suitable methodology for predicting match statistics in advance and that they are informative enough to provide information beyond that reflected in the odds. A long term and robust gambling profit is demonstrated when the forecasts are combined with two betting strategies.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JSA-200462","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48234473","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}
C. Ekstrøm, Hans Van Eetvelde, Christophe Ley, Ulf Brefeld
We introduce the Tournament Rank Probability Score (TRPS) as a measure to evaluate and compare pre-tournament predictions, where predictions of the full tournament results are required to be available before the tournament begins. The TRPS handles partial ranking of teams, gives credit to predictions that are only slightly wrong, and can be modified with weights to stress the importance of particular features of the tournament prediction. Thus, the Tournament Rank Prediction Score is more flexible than the commonly preferred log loss score for such tasks. In addition, we show how predictions from historic tournaments can be optimally combined into ensemble predictions in order to maximize the TRPS for a new tournament.
{"title":"Evaluating one-shot tournament predictions","authors":"C. Ekstrøm, Hans Van Eetvelde, Christophe Ley, Ulf Brefeld","doi":"10.3233/jsa-200454","DOIUrl":"https://doi.org/10.3233/jsa-200454","url":null,"abstract":"We introduce the Tournament Rank Probability Score (TRPS) as a measure to evaluate and compare pre-tournament predictions, where predictions of the full tournament results are required to be available before the tournament begins. The TRPS handles partial ranking of teams, gives credit to predictions that are only slightly wrong, and can be modified with weights to stress the importance of particular features of the tournament prediction. Thus, the Tournament Rank Prediction Score is more flexible than the commonly preferred log loss score for such tasks. In addition, we show how predictions from historic tournaments can be optimally combined into ensemble predictions in order to maximize the TRPS for a new tournament.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/jsa-200454","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44336510","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}
M. V. Bommel, L. Bornn, Peter A. Chow-White, Chuancong Gao
Box score statistics are the baseline measures of performance for National Collegiate Athletic Association (NCAA) basketball. Between the 2011-2012 and 2015-2016 seasons, NCAA teams performed better at home compared to on the road in nearly all box score statistics across both genders and all three divisions. Using box score data from over 100,000 games spanning the three divisions for both women and men, we examine the factors underlying this discrepancy. The prevalence of neutral location games in the NCAA provides an additional angle through which to examine the gaps in box score statistic performance, which we believe has been underutilized in existing literature. We also estimate a regression model to quantify the home court advantages for box score statistics after controlling for other factors such as number of possessions, and team strength. Additionally, we examine the biases of scorekeepers and referees. We present evidence that scorekeepers tend to have greater home team biases when observing men compared to women, higher divisions compared to lower divisions, and stronger teams compared to weaker teams. Finally, we present statistically significant results indicating referee decisions are impacted by attendance, with larger crowds resulting in greater bias in favor of the home team.
{"title":"Home sweet home: Quantifying home court advantages for NCAA basketball statistics","authors":"M. V. Bommel, L. Bornn, Peter A. Chow-White, Chuancong Gao","doi":"10.3233/JSA-200450","DOIUrl":"https://doi.org/10.3233/JSA-200450","url":null,"abstract":"Box score statistics are the baseline measures of performance for National Collegiate Athletic Association (NCAA) basketball. Between the 2011-2012 and 2015-2016 seasons, NCAA teams performed better at home compared to on the road in nearly all box score statistics across both genders and all three divisions. Using box score data from over 100,000 games spanning the three divisions for both women and men, we examine the factors underlying this discrepancy. The prevalence of neutral location games in the NCAA provides an additional angle through which to examine the gaps in box score statistic performance, which we believe has been underutilized in existing literature. We also estimate a regression model to quantify the home court advantages for box score statistics after controlling for other factors such as number of possessions, and team strength. Additionally, we examine the biases of scorekeepers and referees. We present evidence that scorekeepers tend to have greater home team biases when observing men compared to women, higher divisions compared to lower divisions, and stronger teams compared to weaker teams. Finally, we present statistically significant results indicating referee decisions are impacted by attendance, with larger crowds resulting in greater bias in favor of the home team.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JSA-200450","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44784105","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}
As 3-point shooting in the NBA continues to increase, the importance of perimeter defense has never been greater. Perimeter defenders are often evaluated by their ability to tightly contest shots, but how exactly does contesting a jump shot cause a decrease in expected shooting percentage, and can we use this insight to better assess perimeter defender ability? In this paper we analyze over 50,000 shot trajectories from the NBA to explain why, in terms of impact on shot trajectories, shooters tend to miss more when tightly contested. We present a variety of results derived from this shot trajectory data. Additionally, pairing trajectory data with features such as defender height, distance, and contest angle, we are able to evaluate not just perimeter defenders, but also shooters’ resilience to defensive pressure. Utilizing shot trajectories and corresponding modeled shot-make probabilities, we are able to create perimeter defensive metrics that are more accurate and less variable than traditional metrics like opponent field goal percentage.
{"title":"Using in-game shot trajectories to better understand defensive impact in the NBA","authors":"L. Bornn, D. Daly-Grafstein","doi":"10.3233/jsa-200400","DOIUrl":"https://doi.org/10.3233/jsa-200400","url":null,"abstract":"As 3-point shooting in the NBA continues to increase, the importance of perimeter defense has never been greater. Perimeter defenders are often evaluated by their ability to tightly contest shots, but how exactly does contesting a jump shot cause a decrease in expected shooting percentage, and can we use this insight to better assess perimeter defender ability? In this paper we analyze over 50,000 shot trajectories from the NBA to explain why, in terms of impact on shot trajectories, shooters tend to miss more when tightly contested. We present a variety of results derived from this shot trajectory data. Additionally, pairing trajectory data with features such as defender height, distance, and contest angle, we are able to evaluate not just perimeter defenders, but also shooters’ resilience to defensive pressure. Utilizing shot trajectories and corresponding modeled shot-make probabilities, we are able to create perimeter defensive metrics that are more accurate and less variable than traditional metrics like opponent field goal percentage.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/jsa-200400","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41900238","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 the only model to date that predicts the discrete probability distribution of a golfer’s score for each hole of a tournament on a shot-by-shot basis. We first generalized Broadie’s technique of score-based skill estimation to allow a golfer’s skill (e.g. scoring average, driving spray, iron play, putting) to vary continuously by time-weighting data with exponential decay. Training a single-layer 50-node neural network to predict probabilities of scoring by hole resulted in an out-of-sample cross-entropy error of 0.974. We then added features of each hole (e.g. par, green size, sand area) onto the model, representing golfers and holes in an N-by-M dimensional space and achieved an error of 0.953. Adding in course features provided by ShotLink (e.g. fairway height, firmness, wind speed) dropped error to 0.9374. Finally, generalizing the model to update probabilities per shot further reduced error to 0.891. This work helps players understand which skill sets they should improve on, manage courses better (better to miss fairway right or left on hole 13 of Bethpage Black?) and select the best tournament to enter. It also revolutionizes the viewing experience of the PGA by live updating odds to win per shot (similar to WSOP) and helps sports books offer more accurate betting lines.
{"title":"Predicting golf scores at the shot level","authors":"Christian Drappi, Lance Co Ting Keh","doi":"10.3233/JSA-170273","DOIUrl":"https://doi.org/10.3233/JSA-170273","url":null,"abstract":". We present the only model to date that predicts the discrete probability distribution of a golfer’s score for each hole of a tournament on a shot-by-shot basis. We first generalized Broadie’s technique of score-based skill estimation to allow a golfer’s skill (e.g. scoring average, driving spray, iron play, putting) to vary continuously by time-weighting data with exponential decay. Training a single-layer 50-node neural network to predict probabilities of scoring by hole resulted in an out-of-sample cross-entropy error of 0.974. We then added features of each hole (e.g. par, green size, sand area) onto the model, representing golfers and holes in an N-by-M dimensional space and achieved an error of 0.953. Adding in course features provided by ShotLink (e.g. fairway height, firmness, wind speed) dropped error to 0.9374. Finally, generalizing the model to update probabilities per shot further reduced error to 0.891. This work helps players understand which skill sets they should improve on, manage courses better (better to miss fairway right or left on hole 13 of Bethpage Black?) and select the best tournament to enter. It also revolutionizes the viewing experience of the PGA by live updating odds to win per shot (similar to WSOP) and helps sports books offer more accurate betting lines.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JSA-170273","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70124335","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}
Estimating probability is the very core of forecasting. Increasing computing power has enabled researchers to design highly intractable probability models, such that model results are identified through the Monte Carlo method of repeated stochastic simulation. However, confidence in the Monte Carlo identification of the model can be mistaken for accuracy in the underlying model itself. This paper describes simulations in a problem space of topical interest: basketball season forecasting. Monte Carlo simulations are widely used in sports forecasting, since the multitude of possibilities makes direct calculation of playoff probabilities infeasible. Error correlation across games requires due care, as demonstrated with a realistic multilevel basketball model, similar to some in use today. The model is built separately for each of 20 NBA seasons, modeling team strength as a composition of player strength and player allocation of minutes, while also incorporating team persistent effects. Each season is evaluated out-of-time, collectively demonstrating systematic and substantial overconfidence in playoff probabilities, which can be eliminated by incorporating error correlation. This paper focuses on clarifying the use of Monte Carlo simulations for probability calculations in sports.
{"title":"Misadventures in Monte Carlo","authors":"Richard Demsyn-Jones","doi":"10.3233/JSA-170220","DOIUrl":"https://doi.org/10.3233/JSA-170220","url":null,"abstract":"Estimating probability is the very core of forecasting. Increasing computing power has enabled researchers to design highly intractable probability models, such that model results are identified through the Monte Carlo method of repeated stochastic simulation. However, confidence in the Monte Carlo identification of the model can be mistaken for accuracy in the underlying model itself. This paper describes simulations in a problem space of topical interest: basketball season forecasting. Monte Carlo simulations are widely used in sports forecasting, since the multitude of possibilities makes direct calculation of playoff probabilities infeasible. Error correlation across games requires due care, as demonstrated with a realistic multilevel basketball model, similar to some in use today. The model is built separately for each of 20 NBA seasons, modeling team strength as a composition of player strength and player allocation of minutes, while also incorporating team persistent effects. Each season is evaluated out-of-time, collectively demonstrating systematic and substantial overconfidence in playoff probabilities, which can be eliminated by incorporating error correlation. This paper focuses on clarifying the use of Monte Carlo simulations for probability calculations in sports.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JSA-170220","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70124098","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 paper proposes a simple method of forming two-player and four-player golf teams for the purposes of net best-ball tournaments in stroke play format. The proposal is based on the recognition that variability is an important consideration in team composition; highly variable players contribute greatly in a best-ball setting. A theoretical rationale is provided for the proposed team formation. In addition, simulation studies are carried out which compare the proposal against other common methods of team formation. In these studies, the proposed team composition leads to competitions that are more fair.
{"title":"Net best-ball team composition in golf","authors":"Yifan Wu, Peter Chow-Whiteand, T. Swartz","doi":"10.3233/JSA-190311","DOIUrl":"https://doi.org/10.3233/JSA-190311","url":null,"abstract":"This paper proposes a simple method of forming two-player and four-player golf teams for the purposes of net best-ball tournaments in stroke play format. The proposal is based on the recognition that variability is an important consideration in team composition; highly variable players contribute greatly in a best-ball setting. A theoretical rationale is provided for the proposed team formation. In addition, simulation studies are carried out which compare the proposal against other common methods of team formation. In these studies, the proposed team composition leads to competitions that are more fair.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JSA-190311","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70124845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we study the relationship between sports analytics and success in regular season and postseason in Major League Baseball via the empirical data of 2014-2017. The categories of analytics belief, the number of analytics staff, and the total number of research staff employed by MLB teams are examined. Conditional probabilities, correlations, and various regression models are used to analyze the data. It is shown that the use of sports analytics might have some positive impact on the success of teams in the regular season, but not in the postseason. After taking into account the team payroll, we apply partial correlations and partial F tests to analyze the data again. It is found that the use of sports analytics, with team payroll already in the regression model, might still be a good indicator of success in the regular season, but not in the postseason. Moreover, it is shown that both the team payroll and the use of sports analytics are not good indicators of success in the postseason. The predictive modeling of decision trees is also developed, under different kinds of input and target variables, to classify MLB teams into no playoffs or playoffs. It is interesting to note that 87 wins (or 0.537 winning percentage) in a regular season may well be the threshold of advancing into the postseason.
{"title":"Empirical study on relationship between sports analytics and success in regular season and postseason in Major League Baseball","authors":"D. Chu, C. W. Wang","doi":"10.3233/JSA-190269","DOIUrl":"https://doi.org/10.3233/JSA-190269","url":null,"abstract":"In this paper, we study the relationship between sports analytics and success in regular season and postseason in Major League Baseball via the empirical data of 2014-2017. The categories of analytics belief, the number of analytics staff, and the total number of research staff employed by MLB teams are examined. Conditional probabilities, correlations, and various regression models are used to analyze the data. It is shown that the use of sports analytics might have some positive impact on the success of teams in the regular season, but not in the postseason. After taking into account the team payroll, we apply partial correlations and partial F tests to analyze the data again. It is found that the use of sports analytics, with team payroll already in the regression model, might still be a good indicator of success in the regular season, but not in the postseason. Moreover, it is shown that both the team payroll and the use of sports analytics are not good indicators of success in the postseason. The predictive modeling of decision trees is also developed, under different kinds of input and target variables, to classify MLB teams into no playoffs or playoffs. It is interesting to note that 87 wins (or 0.537 winning percentage) in a regular season may well be the threshold of advancing into the postseason.","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JSA-190269","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70124667","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 study explores regional bias in Heisman voting from 1990–2016 using a negative binomial regression model with player-year fixed effects. Analysis confirms finalists receive higher vote tallies in home regions, on average. Additionally, results show regional vote tallies are decreasing in the fraction of other finalists in-region. Furthermore, evidence reveals finalists receive higher vote tallies for each game played against in-region teams and lower vote tallies for each game played by other finalists against in-region teams. Analysis is augmented by showing the recent increase in national television coverage of college football has been accompanied by a decline in regional bias. 5
{"title":"The nature of regional bias in Heisman voting","authors":"Nolan Kopkin","doi":"10.3233/JSA-180284","DOIUrl":"https://doi.org/10.3233/JSA-180284","url":null,"abstract":"This study explores regional bias in Heisman voting from 1990–2016 using a negative binomial regression model with player-year fixed effects. Analysis confirms finalists receive higher vote tallies in home regions, on average. Additionally, results show regional vote tallies are decreasing in the fraction of other finalists in-region. Furthermore, evidence reveals finalists receive higher vote tallies for each game played against in-region teams and lower vote tallies for each game played by other finalists against in-region teams. Analysis is augmented by showing the recent increase in national television coverage of college football has been accompanied by a decline in regional bias. 5","PeriodicalId":53203,"journal":{"name":"Journal of Sports Analytics","volume":"21 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JSA-180284","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70124795","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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