Journal of Quantitative Analysis in Sports (J Quant Anal Sports )

Publisher: Berkeley Electronic Press


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    Journal of quantitative analysis in sports
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    Document, Periodical, Internet resource
  • Document type
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Berkeley Electronic Press

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    • Author can archive a post-print version
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    • On non-commercial authors personal website, non-commercial authors open-access university and employers institutional repository and non-commercial authors course website
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Publications in this journal

  • [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.
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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.
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    ABSTRACT: We consider the modeling of individual batting performance in one-day international (ODI) cricket by using a batsman-specific hidden Markov model (HMM). The batsman-specific number of hidden states allows us to account for the heterogeneous dynamics found in batting performance. Parallel sampling is used to choose the optimal number of hidden states. Using the batsman-specific HMM, we then introduce measures of performance to assess individual players via reliability analysis. By classifying states as either up or down, we compute the availability, reliability, failure rate and mean time to failure for each batsman. By choosing an appropriate classification of states, an overall prediction of batting performance of a batsman can be made. The classification of states can also be modified according to the type of game under consideration. One advantage of this batsman-specific HMM is that it does not require the consideration of unforeseen factors. This is important since cricket has gone through several rule changes in recent years that have further induced unforeseen dynamic factors to the game. We showcase the approach using data from 20 different batsmen having different underlying dynamics and representing different countries.
    Journal of Quantitative Analysis in Sports 01/2014;
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    ABSTRACT: The UEFA Champions League Round of 16 is characterized by restrictions that prevent teams from the same preliminary group and the same nations from matches against each other. Together with the draw procedure currently employed by UEFA, this leads to odd probabilities: in 2012/2013, there were more outcomes of the draw with German Schalke 04 facing Ukrainian Shakhtar Donetsk than there were results where they were matched with Galatasaray Istanbul. In contrast, the probability of Schalke being drawn against Galatasaray exceeded that of playing Shakhtar. We show that this strange effect is due to the group restriction and the mechanism used by UEFA for the draw. Additionally, we provide procedures with which UEFA could produce adequate probabilities for the draw.
    Journal of Quantitative Analysis in Sports 08/2013; 9(3):249-270.
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    ABSTRACT: The desire to promote healthier and more environmentally conscious methods of commuting has generated increased interest in professional and recreational bicycling in recent years. One of the most important factors cyclists consider when riding is the amount of exertion they will perceive on a given path. In this paper, we build and test a model of the perceived exertion of different categories of cyclists on a daily path within a long bicycle tour. We first propose an additive formula for calculating the perceived exertion of cyclists on component parts of a tour and then present the results of a survey designed to verify the accuracy of the model. Distance, elevation gain, average percent grade, maximum percent grade, and cyclists’ level of expertise are shown to be significant predictors of perceived exertion (p<0.005). Repeated measures analysis indicated that 109 of the 120 perceived exertion levels produced by our model fit the reported perceived exertion levels of the 242 avid cyclists who participated in the validation survey.
    Journal of Quantitative Analysis in Sports 05/2013; 9(2):203-216.
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    ABSTRACT: Nowadays many approaches that analyze and predict the results of soccer matches are based on bookmakers' ratings. It is commonly accepted that the mod-els used by the bookmakers contain a lot of expertise as the bookmakers' profits and losses depend on the performance of their models. One objective of this article is to analyze the explanatory power of bookmakers' odds together with many additional, potentially influental covariates with respect to a national team's success at European football championships. Therefore a pairwise Poisson model for the number of goals scored by national teams competing in European football championship matches is used. Moreover, the generalized linear mixed model (GLMM) approach, which is a widely used tool for modeling cluster data, allows to incorporate team-specific ran-dom effects. Two different approaches to the fitting of GLMMs incorporating variable selection are used, subset selection as well as a LASSO-type technique, including an L 1 -penalty term that enforces variable selection and shrinkage simultaneously. Based on the two preceeding European football championships a sparse model is obtained that is used to predict all matches of the current tournament resulting in a possible course of the European football championship (EURO) 2012.
    Journal of Quantitative Analysis in Sports 01/2013; 9(1).
  • Journal of Quantitative Analysis in Sports 01/2013; 9(1):37-50.
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    ABSTRACT: Recreational golf events di¤er from professional events in that the primary goal is not simply to identify the best golfer, but rather for all golfers to enjoy and participate meaningfully in the competition. For this reason, "handicapping" systems are often used in order to enable players of varying skill levels to compete on a level playing …eld in a golf competition. Many golf event organizers use the handicapping systems recommended by the United States Golf Association (USGA) in their events. We …nd that, for the common "scramble" golf format, tournaments that use the USGA system are markedly biased toward certain teams, and thus the purpose of handicapping is not achieved. In this paper we develop a new handicapping system that is intended to achieve fair scramble competition between teams of golfers with disparate skill levels, and we demonstrate that our system reduces the bias in outcomes signi…cantly relative to the USGA system. Furthermore, it involves only simple arithmetic, enabling its implementation in a spreadsheet or other user-friendly platform.
    Journal of Quantitative Analysis in Sports 10/2012;
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    ABSTRACT: We present a regularized logistic regression model for evaluating player contributions in hockey. The traditional metric for this purpose is the plus-minus statistic, which allocates a single unit of credit (for or against) to each player on the ice for a goal. However, plus-minus scores measure only the marginal effect of players, do not account for sample size, and provide a very noisy estimate of performance. We investigate a related regression problem: what does each player on the ice contribute, beyond aggregate team performance and other factors, to the odds that a given goal was scored by their team? Due to the large-p (number of players) and imbalanced design setting of hockey analysis, a major part of our contribution is a careful treatment of prior shrinkage in model estimation. We showcase two recently developed techniques -- for posterior maximization or simulation -- that make such analysis feasible. Each approach is accompanied with publicly available software and we include the simple commands used in our analysis. Our results show that most players do not stand out as measurably strong (positive or negative) contributors. This allows the stars to really shine, reveals diamonds in the rough overlooked by earlier analyses, and argues that some of the highest paid players in the league are not making contributions worth their expense.
    Journal of Quantitative Analysis in Sports 09/2012;
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    ABSTRACT: We use three-dimensional optical tracking data on the 25-frames-per-second positional data of 2,400 free throw shots by the twenty players with at least twelve tracked makes and twelve tracked misses over the course of the 2010-2011 NBA season, fit each trajectory to a comprehensive physics model to find the implied backspin, initial launch height, velocity, angle, and left-right deviation, and examine the differences of those five factors between makes and misses for each player with sufficient attempts in our sample. We find that usually one or two factors are most responsible for a given player's misses, but the particular factors at fault differ across players. Thus, the causes of successes and failures in free throw shooting are idiosyncratic. This framework may also be useful in analyzing jump shots taken during the game.
    Journal of Quantitative Analysis in Sports 07/2012; 8(3).
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    ABSTRACT: The NBA’s age eligibility rule is controversial. To examine the on-court efficacy of the NBA’s age eligibility rule, we test the effect of age of entry on NBA career performance. Our data set comprises the 332 players selected in the first round of the NBA draft from 1989 to 2000. Using censored normal regression models, we found that players drafted at a relatively younger age have more successful NBA careers across three different metrics. To explore a beneficial effect of one year in college, group selection bias tests were conducted by comparing differences in career success between “one and done” players and players who entered the NBA straight out of high school. The results were consistent with our main analyses – players who moved into the NBA directly from high school generally perform better than players with a single year of college experience. We find no systematic evidence in support of the on-court efficacy of the NBA’s age eligibility rule.
    Journal of Quantitative Analysis in Sports 06/2012; 8(2).