Journal of Quantitative Analysis in Sports Impact Factor & Information

Publisher: De Gruyter

Journal description

Current impact factor: 0.00

Impact Factor Rankings

Additional details

5-year impact 0.00
Cited half-life 0.00
Immediacy index 0.00
Eigenfactor 0.00
Article influence 0.00
Website Journal of Quantitative Analysis in Sports website
Other titles Journal of quantitative analysis in sports
ISSN 1559-0410
OCLC 62324796
Material type Document, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

De Gruyter

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
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  • Restrictions
    • 12 months embargo
  • Conditions
    • Pre-print and abstract on author's personal website only
    • Author's post-print on funder's repository or funder's designated repository at the funding agency's request or as a result of legal obligation.
    • Publisher's version/PDF may be used, on author's personal website, editor's personal website or institutional repository
    • Authors cannot deposit in subject repositories
    • Published source must be acknowledged
    • Must link to publisher version and article's DOI must be given
    • Set statement to accompany deposit (see policy)
  • Classification
    ​ yellow

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: Predicting the outcome of a single sporting event is difficult; predicting all of the outcomes for an entire tournament is a monumental challenge. Despite the difficulties, millions of people compete each year to forecast the outcome of the NCAA men’s basketball tournament, which spans 63 games over 3 weeks. Statistical prediction of game outcomes involves a multitude of possible covariates and information sources, large performance variations from game to game, and a scarcity of detailed historical data. In this paper, we present the results of a team of modelers working together to forecast the 2014 NCAA men’s basketball tournament. We present not only the methods and data used, but also several novel ideas for post-processing statistical forecasts and decontaminating data sources. In particular, we highlight the difficulties in using publicly available data and suggest techniques for improving their relevance.
    Journal of Quantitative Analysis in Sports 03/2015; 11(1). DOI:10.1515/jqas-2014-0056
  • [Show abstract] [Hide abstract]
    ABSTRACT: Volleyball coaches are frequently forced to address the question of athlete service errors as a part of their overall service strategy. This is usually done in an ad hoc fashion with an arbitrarily selected maximum allowable service error fraction or maximum allowable service error-to-ace ratio. In this article, an analysis of service outcomes leads to a mathematical expression for the point-scoring fraction in terms of service ace fraction, service error fraction, and opponent modified sideout fraction. These parameters are assumed to be monotonic functions of an athlete or team’s serving aggressiveness and a linear model for the service error-to-ace ratio is used to close the point-scoring optimization problem. The model provides estimates of the optimal service error fraction for individual athletes based on their service ace fraction and the opponent modified sideout fraction against the server overall and also when restricted to only serves that led to perfect passes. Case studies of the Bay to Bay 17 Black Boys’ USAV Juniors team and the Brigham Young University Men’s NCAA Division I team are used to demonstrate the application of the model and standard errors for the predicted optimal service error fractions are calculated with bootstrap resampling.
    Journal of Quantitative Analysis in Sports 01/2015; DOI:10.1515/jqas-2014-0087
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we describe a procedure for constructing bicycle routes of minimal perceived exertion over a multi-day tour for cyclists of different levels of expertise. Given a cyclist’s origin, destination, selected points of interest she/he wants to visit, and a level of cycling expertise, this procedure generates a multi-day bicycle tour as a collection of successive daily paths that begin and end at overnight accommodations. The objective is to minimize the total perceived exertion. We demonstrate the implementation of this procedure on an example multi-day tour route in California and present the results of a survey designed to evaluate the daily paths constructed. Repeated measures analysis indicated that 108 of the 120 perceived exertion ratings of the routes generated by our method fit the reported perceived exertion levels of 175 avid cyclists who participated in an evaluation survey.
    Journal of Quantitative Analysis in Sports 01/2015; DOI:10.1515/jqas-2014-0071
  • [Show abstract] [Hide abstract]
    ABSTRACT: We show that a classical model for soccer can also provide competitive results in predicting basketball outcomes. We modify the classical model in two ways in order to capture both the specific behavior of each National collegiate athletic association (NCAA) conference and different strategies of teams and conferences. Through simulated bets on six online betting houses, we show that this extension leads to better predictive performance in terms of profit we make. We compare our estimates with the probabilities predicted by the winner of the recent Kaggle competition on the 2014 NCAA tournament, and conclude that our model tends to provide results that differ more from the implicit probabilities of the betting houses and, therefore, has the potential to provide higher benefits.
    Journal of Quantitative Analysis in Sports 01/2015; DOI:10.1515/jqas-2014-0055
  • [Show abstract] [Hide abstract]
    ABSTRACT: The widespread proliferation of and interest in bracket pools that accompany the National Collegiate Athletic Association Division I Men’s Basketball Tournament have created a need to produce a set of predicted winners for each tournament game by people without expert knowledge of college basketball. Previous research has addressed bracket prediction to some degree, but not nearly on the level of the popular interest in the topic. This paper reviews relevant previous research, and then introduces a rating system for teams using game data from that season prior to the tournament. The ratings from this system are used within a novel, four-predictor probability model to produce sets of bracket predictions for each tournament from 2009 to 2014. This dual-proportion probability model is built around the constraint of two teams with a combined 100% probability of winning a given game. This paper also performs Monte Carlo simulation to investigate whether modifications are necessary from an expected value-based prediction system such as the one introduced in the paper, in order to have the maximum bracket score within a defined group. The findings are that selecting one high-probability “upset” team for one to three late rounds games is likely to outperform other strategies, including one with no modifications to the expected value, as long as the upset choice overlaps a large minority of competing brackets while leaving the bracket some distinguishing characteristics in late rounds.
    Journal of Quantitative Analysis in Sports 01/2015; 11(1). DOI:10.1515/jqas-2014-0047
  • [Show abstract] [Hide abstract]
    ABSTRACT: Recently, the surge of predictive analytics competitions has improved sports predictions by fostering data-driven inference and steering clear of human bias. This article details methods developed for Kaggle’s March Machine Learning Mania competition for the 2014 NCAA tournament. A submission to the competition consists of outcome probabilities for each potential matchup. Most predictive models are based entirely on measures of overall team strength, resulting in the unintended “transitive property.” These models are therefore unable to capture specific matchup tendencies. We introduce our novel nearest-neighbor matchup effects framework, which presents a flexible way to account for team characteristics above and beyond team strength that may influence game outcomes. In particular we develop a general framework that couples a model predicting a point spread with a clustering procedure that borrows strength from games similar to a current matchup. This results in a model capable of issuing predictions controlling for team strength and that capture specific matchup characteristics.
    Journal of Quantitative Analysis in Sports 01/2015; 11(1). DOI:10.1515/jqas-2014-0054
  • Journal of Quantitative Analysis in Sports 01/2015; 11(1):1-3. DOI:10.1515/jqas-2015-0013
  • [Show abstract] [Hide abstract]
    ABSTRACT: Sports ranking systems are often viewed as inadequate for judging the quality of the teams or players involved. Meanwhile, statistical models have been shown to produce more accurate ratings for those competitors, based on their ability to forecast future results. However, whilst predictive power is a desirable property of any official ranking system, these systems must also be fair, transparent and insensitive to bias. Additional requirements may also be required, such as promoting major tournaments and deciding seedings. By considering rankings for ATP tennis players, we propose that statistical models can be used to improve the existing ranking system, in such a way that the resulting rankings are fair and usable by the governing body. In many cases, there is a trade-off between predictive power and other desirable properties, and so compromise is required to produce a system that can be implemented successfully.
    Journal of Quantitative Analysis in Sports 06/2014; 10(2). DOI:10.1515/jqas-2013-0101
  • [Show abstract] [Hide abstract]
    ABSTRACT: Multiple models are discussed for ranking teams in a league and introduce a new model called the Oracle method. This is a Markovian method that can be customized to incorporate multiple team traits into its ranking. Using a foresight prediction of NFL game outcomes for the 2002–2013 seasons, it is shown that the Oracle method correctly picked 64.1% of the games under consideration, which is higher than any of the methods compared, including ESPN Power Rankings, Massey, Colley, and PageRank.
    Journal of Quantitative Analysis in Sports 06/2014; 10(2):183-196. DOI:10.1515/jqas-2013-0063
  • [Show abstract] [Hide abstract]
    ABSTRACT: Using NHL data for the 1997-1998 through 2011-2012 seasons, we examine the effect of age on scoring performance and plus-minus for NHL skaters (non-goalies) and on save percentage for goaltenders. We emphasize fixed-effects regression methods that estimate a representative age-performance trajectory. We also use a method based on the best performances over time, a method based on the age distribution of NHL players, and a "naïve" specification that does not correct for selection bias. In addition we estimate individual age-performance relationships to obtain a distribution of peak ages. All methods provide similar results (with small but understandable differences) except the naïve specification, which yields implausible results, indicating that correcting for selection bias is very important. Our best estimate of the scoring peak age is between 27 and 28 for forwards and between 28 and 29 for defencemen. Both forwards and defencemen exhibit near-peak performance over a wide range, going from about 24 to 32 and 24 to 34, respectively. Goaltenders display little systematic performance variation over most of the age range from the early 20s to late 30s.
    Journal of Quantitative Analysis in Sports 06/2014; 10(2). DOI:10.1515/jqas-2013-0085
  • [Show abstract] [Hide abstract]
    ABSTRACT: In 2012, 3 out of 10 singles players in the top 100 on the Association of Tennis Professionals (ATP) World Tour were 30 years old or older-nearly a four-fold increase over 20 years ago, suggesting that the "old at 30" view in men's tennis may be an old reality. In this paper, I investigate aging patterns among top ATP singles players between 1991 and 2012 and consider how surface effects, career length, and age at peak performance have influenced aging trends. Following a decade and a half of little change, the average age of top singles players has increased at a pace of 0.34 years per season since the mid-2000s, reaching an all-time high of 27.9 years in 2012. Underlying this age shift was a coincident rise in the proportion of 30-and-overs (29% in 2012) and the virtual elimination of teenagers from the top 100 (0% in 2012). Because the typical age players begin competing professionally has varied little from 18 years in the past two decades, career length has increased in step with player age. Demographics among top players on each of today's major surfaces indicate that parallel aging trends have occurred on clay, grass, and hard court from the late 2000s forward. As a result of the changing age demographic over the past decade, the age of tennis's highest-ranked singles players is now comparable to the age of elite long-distance runners. This evolution likely reflects changes in tennis play that have made endurance and fitness increasingly essential for winning success.
    Journal of Quantitative Analysis in Sports 06/2014; 10(2). DOI:10.1515/jqas-2013-0091
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper examines the performance of goalkeepers in the English Premier League. A commonly used metric to assess goalkeeper ability is the saves-to-shots ratio. It is shown that goalkeepers playing in weak teams have to defend against on-target shots that have a relatively high probability of scoring as compared to their counterparts in higher-performing teams. This tends to produce a downwards bias in the save-to-shots ratio of goalkeepers in weak teams, an effect which may lead to their ability being underestimated. A logistic regression model is used to adjust goalkeepers' saves-to-shots ratios for differences in save difficulty. The adjusted ratio is more stable across seasons, suggesting that it is a more reliable estimate of true goalkeeper ability than the unadjusted ratio.
    Journal of Quantitative Analysis in Sports 06/2014; 10(2). DOI:10.1515/jqas-2014-0004
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    ABSTRACT: This paper proposes a multiple-membership generalized linear mixed model for ranking college football teams using only their win/loss records. The model results in an intractable, high-dimensional integral due to the random effects structure and nonlinear link function. We use recent data sets to explore the effect of the choice of integral approximation and other modeling assumptions on the rankings. Varying the modeling assumptions sometimes leads to changes in the team rankings that could affect bowl assignments.
    Journal of Quantitative Analysis in Sports 03/2014; 8(3). DOI:10.1515/1559-0410.1471
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    ABSTRACT: The standard metric for American football field goal kickers is simply the percentage of attempts successfully converted. Due to variance in distance of attempts and other conditions (weather, altitude, defense, etc.), we argue that field goal percentage is an insufficient measure of kicker performance. Using three seasons of NFL data, we construct a multivariate logistic regression model for the success probability of a given attempt. This leads naturally to metrics in which a kicker’s performance is compared to model expectations, if a replacement-level player was attempting the same kicks. Player salaries correlate only weakly with our measures of field goal kicking success. We find that those kickers selected to the Pro Bowl and All-Pro teams were rather mediocre by our metrics, over the seasons studied. The relative difficulty of kicking in various stadiums is also considered. Finally, we discuss the degree to which field goal kicking is a skill that can be maintained over multiple seasons.
    Journal of Quantitative Analysis in Sports 02/2014; 10(1):49-66. DOI:10.1515/jqas-2013-0039