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

Publisher: Berkeley Electronic Press

Description

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  • 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

Berkeley Electronic Press

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • On non-commercial authors personal website, non-commercial authors open-access university and employers institutional repository and non-commercial authors course website
    • PubMed and UK PubMed after 12 months (automatic for several journals)
    • Publisher copyright and source must be acknowledged
    • Publisher's version/PDF may be used
  • Classification
    ​ green

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.
  • [Show abstract] [Hide abstract]
    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;
  • [Show abstract] [Hide abstract]
    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.
  • [Show abstract] [Hide abstract]
    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.
  • Journal of Quantitative Analysis in Sports 01/2013; 9(1):37-50.
  • [Show abstract] [Hide abstract]
    ABSTRACT: The importance of sports statistics to professional sports teams is clear and there are exciting new opportunities for statisticians to learn from the new types of data that are collected. The Journal of Quantitative Analysis of Sports has shown steady growth since its inception in 2005. The new editorial system and new directions and initiatives of the journal are described.
    Journal of Quantitative Analysis in Sports 01/2012; 8(1):1-1.
  • [Show abstract] [Hide abstract]
    ABSTRACT: This study analyzes how the efficiency of basketball players depends on experience, player’s nationality and team type. Using data for players in the Turkish Basketball League (TBL) from 1997 to 2010, we first show that efficiency of players initially increase with experience, but after a certain level decrease with it. Second, foreign players are on average more talented than native players. Third, the experience–efficiency link is not valid for foreign players indicating that more experience does not affect efficiency. Fourth, efficiency of foreign players in top-teams is no different than that of foreign players in regular (non-top) teams; whereas, native players in top-teams are more efficient than native players in other teams. Finally, in the presence of foreign player quota, top-teams hire the maximum number of foreign players allowed, while financially constrained regular teams can afford less foreign players. Consequently, under foreign player quota the quality of native players and the extent to which a team can pay for additional foreign players determine the team success.
    Journal of Quantitative Analysis in Sports 01/2012; 8(1):7-7.
  • [Show abstract] [Hide abstract]
    ABSTRACT: From an almost standing start at the beginning of the 1990s, the number of statues of U.S. baseball and English soccer heroes has risen inexorably. By 1<sup>st</sup> September 2011, 33 soccer players and 67 Major League Baseball (MLB) players were, or were soon to be, depicted by existing or commissioned subject specific statues inside or adjacent to the stadia they once performed in. Yet even amongst the very finest exponents of their sport, relatively few players are honored in this way. This paper investigates and compares the defining characteristics of stadium statue subjects in these two national sports. We first developed a shortlist of potential causal factors likely to influence subject selection by considering the motivations behind statue building. The MLB Hall of Fame and the English Football League “100 Legends†list were then used as samples of the best performers from each sport. Logistic regression models were built to test the effects of potential predictors for the selection of statue subjects; these included loyalty, locality, longevity, performance of the player and their team, national recognition, sympathy and the effect of nostalgia or memory (i.e., the era a player performed in). The optimal models for soccer and baseball correctly identified depiction or non-depiction for 87% and 90.6% of the respective samples, and their significant constituent effects indicated the importance of club loyalty and era. Players who played most or all of their careers at one club or franchise and those active in the 1950s and 1960s were most likely to be depicted. This latter finding in particular suggests that the role of a statue as a nostalgia/heritage marketing object impacts upon subject choice, which is thus dependent in part on the “chance†effect of birth era. Distinct characteristics of each sport, such as baseball franchise relocation and international soccer success, were also found to have a significant effect upon the probability of depiction. Predicted probabilities were calculated for players with statues who were not Football League “Legends†or MLB Hall of Famers; these confirm the viability of the model outside of the elite performers it was constructed upon.
    Journal of Quantitative Analysis in Sports 01/2012; 8(1):2-21.
  • [Show abstract] [Hide abstract]
    ABSTRACT: We examine the question of whether or not momentum exists in an NFL football game. The concept of momentum is often cited by coaches, players, commentators and fans as a major factor in determining the outcome of the game and, consequently, in-game decision making. To examine the existence of momentum, we analyze particular game situations tied to what we consider to be “momentum-changing plays†(MCPs). These MCPs include fourth down conversions/stops, turnovers and scores allowed. We hypothesize that evidence of positive (negative) momentum would be characterized by increases (decreases) in yards gained, higher (lower) probability of converting a first down and greater (lesser) likelihood of scoring after a positive (negative) MCP. Our data set includes all plays from the 2002 to 2007 NFL seasons. We limit our analysis to game situations where the outcome of the game is still in doubt by removing plays that occur when a team is facing an insurmountable score differential. We use a pairwise matching comparison where we control for the game situations of home/away team, field position, time of game and score differential. We find little evidence for the existence of momentum in these events. Our results are in line with previous papers that find little empirical evidence of momentum in sports. While our findings cannot conclusively disprove the existence of momentum in the NFL, they further support the argument that momentum should not be a guiding factor for in-game decision making.
    Journal of Quantitative Analysis in Sports 01/2012; 8(1):8-8.
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    ABSTRACT: The San Francisco Giants were crowned champions of Major League Baseball in 2010 after defeating the Texas Rangers in the World Series. The World Series matchup may have come as a surprise to many baseball fanatics; the Rangers ended the regular season with the worst record of any of the eight playoff teams, and the Giants ended with the fourth worst. Did these two teams simply catch fire at the right time? Or were they better than their regular season records showed? To answer these questions, the regular season statistics of individual players on each team were used to simulate the postseason. These simulations determined the probability with which each playoff team could have been expected to win the 2010 World Series.
    Journal of Quantitative Analysis in Sports 01/2012; 8(1):10-10.
  • [Show abstract] [Hide abstract]
    ABSTRACT: The men’s NCAA basketball tournament is a popular sporting event often referred to as “March Madness.†Each year the NCAA committee not only selects but also seeds the tournament teams. Invariably there is much discussion about which teams were included and excluded as well as discussion about the seeding of the teams. In this paper, we propose an innovative heuristic measure of team success, and we investigate how well the NCAA committee seeding compares to the computer-based placements by Sagarin and the rating percentage index (RPI). For the 2011 tournament, the NCAA committee selection process performed better than those based solely on the computer methods in determining tournament success.
    Journal of Quantitative Analysis in Sports 01/2012; 8(1):4-4.
  • [Show abstract] [Hide abstract]
    ABSTRACT: Significant work has gone into the development of team and individual statistics in the NBA; for example, the team measures of the “Four Factors.†Less work has been conducted using multivariate analyses of these metrics, including identifying possible new statistical techniques to analyze these data. In particular, this research examines the feasibility of using structural equation modeling (SEM) for multivariate analyses of NBA Four Factors data. SEM consists of both confirmatory factor analysis (CFA) and path modeling. Before SEM is employed, this research first examines the effects of offensive and defensive Four Factors in a linear regression model, expanding previous research and providing a baseline for the SEM. In doing so, the data show the importance of effective field goal percentage. Next, structural equation modeling is employed. The CFA finds that offensive Four Factors are indicators of a single latent factor, labeled “offensive quality.†The “defensive quality†latent factor is estimable when replacing opposing teams’ free throw rate with steals per possession. The SEM is extended to regress winning percentage on latent offensive and defensive quality as well as salary. Salary is an important and often overlooked part of multivariate models examining team statistics, but it is easily incorporated in SEM. The explained variance for the regression in the SEM is similar to that of the linear regression model and indicates the importance of both offensive and defensive quality, with offensive quality having a larger effect. Team salaries are related to offensive quality, but not defensive quality or winning. As such, a second structural equation model is proposed where the effect of salary on winning is mediated by its relationship with offensive and defensive quality. Since salary is related to offensive quality but not defensive quality, and offensive quality is more important to winning percentage, this suggests that money spent is done so for offensive performance and affects winning through the performance paid for. These results suggest potential team strategies, as well as the applicability of SEM to sports analytics, not only to NBA data, but to other sports data as well.
    Journal of Quantitative Analysis in Sports 01/2012; 8(1):5-5.

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