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This paper presents a landmark study of equity, diversity and inclusion (EDI) in the field of sports analytics. We developed a survey that examined personal and job-related demographics, as well as individual perceptions and experiences about EDI in the workplace. We sent the survey to individuals in the five major North American professional leagues, representatives from the Olympic and Paralympic Committees in Canada and the U.S., the NCAA Division I programs, companies in sports tech/analytics, and university research groups. Our findings indicate the presence of a clear dominant group in sports analytics identifying as: young (72.0 %), White (69.5 %), heterosexual (89.7 %) and male (82.0 %). Within professional sports, males in management positions earned roughly $30,000 (27 %) more on average compared to females. A smaller but equally alarming pay gap of $17,000 (14 %) was found between White and racialized management personnel. Of concern, females were nearly five times as likely to experience discrimination and twice as likely to have considered leaving their job due to isolation or feeling unwelcome. While they had similar levels of agreement regarding fair processes for rewards and compensation, females "strongly agreed" less often than males regarding equitable support, equitable workload, having a voice, and being taken seriously. Over one third (36.3 %) of females indicated that they "strongly agreed" that they must work harder than others to be valued equally, compared to 9.8 % of males. We conclude the paper with concrete recommendations that could be considered to create a more equitable, diverse and inclusive environment for individuals working within the sports analytics sector. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has led to a global shutdown of sporting activities. While professional sports competitions restarted in mid-2020, spectators were usually not allowed. This paper investigates the effect of absent fans and reduced social pressure on performance in professional darts – a setting where individual player performances can be well observed. Considering almost five years of tournament data, we use Bayesian multilevel models to investigate potential heterogeneity across players concerning reduced social pressure. For our analysis, we consider the two main performance measures in darts: the three-dart average and the checkout performance. Our results indicate that the effect of reduced social pressure on performance varies substantially across players. We further find experienced players to be less affected by social pressure compared to relatively inexperienced players. [ABSTRACT FROM AUTHOR]

Analytics and professional sports have become linked over the past several years, but little attention has been paid to the growing field of esports within the sports analytics community. We seek to apply an Adjusted Plus Minus (APM) model, an accepted analytic approach used in traditional sports like hockey and basketball, to one particular esports game: Defense of the Ancients 2 (Dota 2). As with traditional sports, we show how APM metrics developed with Bayesian hierarchical regression can be used to quantify individual player contributions to their teams and, ultimately, use this player-level information to predict game outcomes. In particular, we first provide evidence that gold can be used as a continuous proxy for wins to evaluate a team's performance, and then use a Bayesian APM model to estimate how players contribute to their team's gold differential. We demonstrate that this APM model outperforms models based on common team-level statistics (often referred to as "box score statistics"). Beyond the specifics of our modeling approach, this paper serves as an example of the potential utility of applying analytical methodologies from traditional sports analytics to esports. [ABSTRACT FROM AUTHOR]

Sports leagues consist of conferences subdivided into divisions. Teams play a number of games within their divisions and fewer games against teams in different divisions and conferences. Usually, a league structure remains stable from one season to the next. However, structures change when growth or contraction occurs, and realignment of the four major professional sports leagues in North America has occurred more than 25 times since 1967. In this paper, we describe a method for realigning sports leagues that is flexible, adaptive, and that enables construction of schedules that minimize travel while satisfying other criteria. We do not build schedules; we develop league structures which support the subsequent construction of efficient schedules. Our initial focus is the NHL, which had a need for realignment following the recent move of the Atlanta Thrashers to Winnipeg, but our methods can be adapted to virtually any situation. We examine a variety of scenarios for the NHL, and apply our methods to the NBA, MLB, and NFL. We find the biggest improvements for MLB and the NFL, where adopting the best solutions would reduce league travel by about 20%. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic forced almost all professional and amateur sports to be played without attending crowds. Thus, it induced a large-scale natural experiment on the impact of social pressure on decision making and behavior in sports fields. Using a data set of 1027 rugby union matches from 11 tournaments in 10 countries, we find that home teams have won less matches and their point difference decreased during the pandemic, shedding light on the impact of crowd attendance on the home advantage of sports teams. [ABSTRACT FROM AUTHOR]

Game theory is the study of tractable games which may be used to model more complex systems. Board games, video games and sports, however, are intractable by design, so "ludological" theories about these games as complex phenomena should be grounded in empiricism. A first "ludometric" concern is the empirical measurement of the amount of luck in various games. We argue against a narrow view of luck which includes only factors outside any player's control, and advocate for a holistic definition of luck as complementary to the variation in effective skill within a population of players. We introduce two metrics for luck in a game for a given population – one information theoretical, and one Bayesian, and discuss the estimation of these metrics using sparse, high-dimensional regression techniques. Finally, we apply these techniques to compare the amount of luck between various professional sports, between Chess and Go, and between two hobby board games: Race for the Galaxy and Seasons. [ABSTRACT FROM AUTHOR]

This paper uses K-means cluster analysis and multinomial mixture models to categorize professional golfer performance for the period 1980 to 2006. We collected, cleaned and analyzed final annual money list standings for PGA Tour players from www.pgatour.com. Correlation patterns between other measures suggested that career performance was well described by the proportion of years a player finished in specific meaningful money list categories such as Top 10 or outside the top 125. Using both clustering methods, we found that players divide into five natural and interpretable groupings, one being a small `Elite' group and four others which we refer to as `Distinguished,' `Established,' `Journeymen' and `Grinders.' We used analysis of variance to compare groups on the basis of other career performance measures including consistency, streakiness, longevity, and others as well as to investigate differences in the clusters produced by the two methods. A summary of these measures for the entire set gives a bleak outlook for any prospective professional golfer and singles out the greatness of the elite few, epitomized by Tiger Woods. This methodology extends to any sport or endeavor in which performance is measured in terms of end of year rankings or money lists. [ABSTRACT FROM AUTHOR]

The placing of NHL players into categories such as stars, average journeyman forwards, defensive forward, etc., is a popular pastime of fans and professional sports commentators. This is usually done in an ad hoc manner, sometimes contentiously, and without reference to any detailed analysis of empirical data. This paper structures the debate and provides a statistical basis for such categorization. This is achieved through an application of cluster analysis to formally allocate players to these categories. Using variables from the earnings determination literature, we are able to define the style of play of both forwards and defensemen. In addition, there appears to be a link to external outcome measures. [ABSTRACT FROM AUTHOR]

Professional sports leagues are motivated to promote competitive balance in order to maintain fan interest. The National Football League (NFL) has taken several steps to promote competitive balance, most notably free agency and the salary cap, which were instituted prior to the 1994 season. Previous research into competitive balance in sports focused on the variability of team strengths but ignored the year-to-year autocorrelation in team strengths. We present a Bayesian state-space model for paired comparisons that allows regression on the variance parameters. By modeling the variance parameters in a regression framework, we are able to simultaneously compare the variance and autocorrelation in team strengths over time. The autocorrelation of NFL team strengths has decreased over time while there has been little change in the variance in teams strengths since the 1970s. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Generalizability (G-) theory is increasingly being used by assessment professionals in a variety of assessment contexts, especially those involving performance assessments. This study expands the use of G-theory and its possible application to highly competitive sporting events that involve judging panels. Specifically, using G-theory, this study examined both the scoring variability and reliability of all four events of the 2004 World Figure Skating Championships. For each event, it examined the independent sources of score variation contributing to the variability of scores assigned by the raters (judges), and the score reliability (or generalizability coefficient). For the Ice Dancing event, an additional examination of the impact of treating "rater" and "rating" as different facets of G-theory studies on the score reliability was conducted. The results show that rater/rating was identified as a component of concern in all events. For the Singles events, in particular, the percentages of the variance for rater (judge) reached approximately 10 percent of the total variance, indicating that there were serious concerns about the scoring reliability. The generalizability coefficients for the events of Men's Singles and Ladies' Singles were both 0.93. However, the generalizability coefficients for Pairs and Ice Dancing were 0.99 and 0.98, respectively, indicating that the scores for these two events were extremely reliable. Using rater vs. rating in the Ice Dancing scores appeared to result in very little difference. [ABSTRACT FROM AUTHOR]

National Football League (NFL) teams spend substantial time and money trying to predict which college quarterbacks eligible to be drafted into the NFL will have successful professional careers. But despite this investment of resources, it is common for quarterbacks to perform much better or worse than anticipated. Prior work on this "quarterback prediction problem" has concluded that NFL teams are poor at determining which quarterbacks are likely to be successful based on information available prior to the draft. However, these analyses have generally focused only on quarterbacks who played in the NFL, ignoring those who were drafted but did not appear in a professional game. Using data on all quarterbacks drafted since 1997, we considered the problem of predicting NFL success as defined by two metrics (games played and Net Points), based on when a quarterback was drafted and his performances in college and at the NFL Combine. Our analyses suggest that college and combine statistics have little value for predicting whether a quarterback will be successful in the NFL. Contrary to previous work, we conclude that NFL teams aggregate pre-draft information--including qualitative observations--quite effectively, and their inability to consistently identify college quarterbacks who will excel in the professional ranks is a consequence of random variability in future performance due to factors which are unlikely to be observable. [ABSTRACT FROM AUTHOR]

At young ages, a few extra months of development can make a big difference in size, strength, and athletic ability. A child who turns 5 years old in January will be nearly 20% older by the time a child born in December has their 5th birthday. In many sports, including hockey, children born in the early months of the calendar year get noticed by their coaches because of the superiority they demonstrate due to their age advantage. As a result, boys born early in the year are more likely to reach the professional ranks of the National Hockey League (NHL). The phenomenon just described has been labeled the relative age effect (RAE). Previous work studying the RAE in the NHL has focused on individual NHL seasons, often encompassing many of the same players across multiple seasons. We investigate the RAE using complete data on every player who has ever played in the NHL. We focus the majority of our analysis on Canadian born players and examine the RAE across hockey position and hall-of-fame status. For the first time, we provide strong evidence of when the RAE began to manifest itself in Canada. Our change point analysis indicates that the RAE began for players born since 1951. Finally, we make a case for what initiated this change in the way young hockey players develop, particularly in Canada, which produced over 90% of NHL players at that time. [ABSTRACT FROM AUTHOR]