Journal articles on the topic 'Sabermetrics'

Baseball is a rich mélange of tradition, spectatorship, evaluation, and fandom. Statistical fandom is presented as a cultural infrastructure, which influences how all fans perceive the game including what is valued in the game, how the game itself is played, and Major League Baseball as an industry. In building off of Halverson’s conception of a fantasy plane of baseball fandom, this research theorizes an additional statistical plane. Sabermetrics serve as a microcosm for a larger statistical turn in sports and reporting. The labor of saberfans builds a cultural algorithm through statistical analysis that shapes all fan engagement. Sabermetric inputs become an infrastructure of expertise through which the larger sporting public understands and evaluates baseball and culture.

This study investigates whether journalists have adopted sabermetrics, the use of advanced baseball statistics for making player projections and objectively measuring player performance, in their reporting of Major League Baseball stories since the print and theatrical release of Moneyball. Findings suggest that traditional baseball statistics still dominate baseball news reporting but the use of sabermetrics has significantly increased after Moneyball was published.

ABSTRACT Presidential scholars and baseball writers debate who were the greatest. While baseball analysis evolved from qualitative impressions of “experts” to rigorous, data-driven “sabermetrics,” analysis of presidential greatness continues to rely on “old-school” reputational rankings based on surveys of scholars’ qualitative assessments. Presidential-congressional relations and baseball are all about winning, but what fans (of sports and politics) find most intriguing is Wins Above Expectations (WAE)—did the team do better or worse than expected? This paper adapts the Pythagorean Expectations (PE) formula developed to analyze baseball to assess legislative success of presidents from Eisenhower to Obama. A parsimonious regression model and the PE formula predict annual success rates with 90% accuracy. The estimates of WAE from the two approaches, however, are uncorrelated. Regression analysis does not identify any president who systematically exceeded expectations, but sabermetric analysis indicates that Republican presidents outperform Democrats. Neither approach correlates with recent presidential greatness rankings.

La sabermetría es reconocida actualmente como una tendencia novedosa en el estudio del juego de béisbol. Con mucho auge y utilización en el análisis empírico, esta se basa en el estudio estadístico riguroso de la evidencia objetiva obtenida durante el juego. Teniendo en cuenta tanto sus aportes teóricos como prácticos, la sabermetría se fundamenta en una constante búsqueda por comprender cómo jugar mejor y más eficientemente al béisbol, lo cual se expresa y soporta mediante un tipo de análisis de actuación único entre todos los deportes colectivos. El presente trabajo aborda los aspectos esenciales de la sabermetría, fundamentando la necesidad de su surgimiento y utilización, como una forma de perfeccionar la manera en que tradicionalmente se ha llevado a cabo el análisis estadístico en el béisbol. Además, se brinda un resumen de los estadísticos sabermétricos más utilizados, tanto de bateo y picheo como otros de valor individual para el equipo, con el propósito de hacer más clara su comprensión, estudio y posterior utilización entre los seguidores de este deporte.Abstract. Sabermetrics is recognized as a new trend in the study of baseball game. This is based on the rigorous statistical study of the objective evidence obtained and has been used extensively in its empirical analysis. Considering both theoretical and practical contributions, sabermetrics involves the constant quest of understanding how to play baseball better and more efficiently, which is expressed and supported by an exceptional type of analysis performance unique among all team sports. This paper describes the essential aspects of sabermetrics, pointing in the necessity of its emergence and use, as a way to improve the traditional statistical analysis of baseball. Moreover, a summary of the sabermetrics statistics most widely used is given. Both batting and pitching, as well as others of individual value for the team are stated throughout this work in order to make sabermetrics understanding, study and further use clearer among followers of this sport.

Abstract Baseball is a statistically filled sport, and predicting the winner of a particular Major League Baseball (MLB) game is an interesting and challenging task. Up to now, there is no definitive formula for determining what factors will conduct a team to victory, but through the analysis of many years of historical records many trends could emerge. Recent studies concentrated on using and generating new statistics called sabermetrics in order to rank teams and players according to their perceived strengths and consequently applying these rankings to forecast specific games. In this paper, we employ sabermetrics statistics with the purpose of assessing the predictive capabilities of four data mining methods (classification and regression based) for predicting outcomes (win or loss) in MLB regular season games. Our model approach uses only past data when making a prediction, corresponding to ten years of publicly available data. We create a dataset with accumulative sabermetrics statistics for each MLB team during this period for which data contamination is not possible. The inherent difficulties of attempting this specific sports prediction are confirmed using two geometry or topology based measures of data complexity. Results reveal that the classification predictive scheme forecasts game outcomes better than regression scheme, and of the four data mining methods used, SVMs produce the best predictive results with a mean of nearly 60% prediction accuracy for each team. The evaluation of our model is performed using stratified 10-fold cross-validation.

An investigation that leads to one of the significant contributions that sabermetrics has made to our understanding of baseball. Along the way, we illustrate the application of several principles of data analysis (Moore 1995, 95) in a context that is familiar to, and motivating for, many of our students.

Sports teams constantly search for a competitive advantage (e.g. bidding for free agents or scouting nontraditional markets). As popularized by Moneyball, we focus on advanced analytics in baseball. These sabermetrics are employed to provide objective information to management and coaches to support player management and in-game strategy decisions. Though widely used at the professional level, analytics use in college baseball is limited. Air Force Academy Baseball has been one win short of qualifying for the Mountain West tournament three straight years, resulting in the loss of potential income from media payouts and exposure for future recruiting efforts. Using a K-means clustering method for defensive shifting, we calculate an overall catch probability increase of 7.4% with a shifted outfield in a one-game case study. Based on our analysis, we provide evidence that Air Force Baseball can benefit from an outfield defensive shifting scheme that drives a competitive advantage and additional wins.

Abstract Since the advent of high-resolution pitch tracking data (PITCHf/x), many in the sabermetrics community have attempted to quantify a Major League Baseball catcher’s ability to “frame” a pitch (i.e. increase the chance that a pitch is a called as a strike). Especially in the last 3 years, there has been an explosion of interest in the “art of pitch framing” in the popular press as well as signs that teams are considering framing when making roster decisions. We introduce a Bayesian hierarchical model to estimate each umpire’s probability of calling a strike, adjusting for the pitch participants, pitch location, and contextual information like the count. Using our model, we can estimate each catcher’s effect on an umpire’s chance of calling a strike. We are then able translate these estimated effects into average runs saved across a season. We also introduce a new metric, analogous to Jensen, Shirley, and Wyner’s Spatially Aggregate Fielding Evaluation metric, which provides a more honest assessment of the impact of framing.

Background: Sports-related concussions have garnered significant attention in recent years because of the negative effects they can have on a player’s cognitive health and performance. In response to this growing concern, Major League Baseball (MLB) introduced a standardized concussion protocol during the 2011-2012 season. Purpose/Hypothesis: The purpose of this study was to compare the reported incidence of concussions and the subsequent performance of MLB players before and after the introduction of the standardized concussion protocol. We hypothesized that the introduction of the standardized concussion protocol would not have an impact on player performance postconcussion. Study Design: Cohort study; Level of evidence, 3. Methods: Players who suffered a concussion between 2001 and 2018 were identified from the MLB transactions page. Incidence and player performance were compared before and after the introduction of the standardized concussion protocol. Player performance was evaluated using traditional data and sabermetric data, which are advanced statistics used in conjunction with standard statistics to better compare players and teams. Player averages were calculated and compared using paired t tests for 30 days before and after concussion, 1 year before and after concussion, and career before and after concussion. Averages were also compared before and after the institution of the standardized concussion protocol using independent-measures t tests. Results: There were a total of 114 players who suffered 142 concussions, with 77% of those occurring after the introduction of the concussion protocol (32 before, 110 after). The average time missed because of concussion significantly decreased from 33.7 days (range, 10-122 days) to 18.9 days (range, 6-111 days) after the concussion protocol ( P = .0005). There was no difference in player performance (including batting average, on-base percentage, and slugging for batters; earned run average, fielding-independent pitching, and walks plus hits per inning pitched for pitchers) after concussion at any time point (30 days, 1 year, or career) when comparing these statistics before versus after the MLB concussion protocol. However, batter performance was significantly poorer 1 year after concussion and over the remainder of the players' careers ( P < .05). Conclusion: The number of reported concussions increased after the introduction of the MLB concussion protocol. However, players spent significantly less time on the disabled list without any adverse effect on player performance. Despite these changes, long-term batting performance was significantly poorer after concussion.

Theoretical basis Resource-based view (RBV) theory (Barney, 1991; Barney and Mackey, 2016; Nagano, 2020) states that a firm’s tangible and intangible resources can represent a sustainable competitive advantage (SCA), a long-term competitive advantage that is extremely difficult to duplicate by another firm, when it meets four criteria (i.e. not imitable, are rare, valuable and not substitutable). In the context of this case, we believe there are three sources of SCA to be discussed using RBV – the major league soccer (MLS) team player roster, the use of artificial intelligence (AI) technologies to exploit this roster and the league’s single-entity structure: • MLS players: it has been widely acknowledged that a firm’s human resource talent, which includes professional soccer players (Omondi-Ochieng, 2019), can be a source of SCA. For example, from an RBV perspective, a player on the Los Angeles Galaxy roster: > cannot play for any other team in any other league at the same time (not imitable and are rare), > would already be a competitive player, as he is acquired to play in the highest professional league in the country (valuable) and > it would be almost impossible to find a clone player matching his exact talent characteristic (not substitutable) anywhere else. Of course, the roster mix of players must be managed by a capable coach who is able to exploit these resources and win championships (Szymanski et al., 2019). Therefore, it is the strategic human resource or talent management strategies of the professional soccer team roster that will enable a team to have the potential for an SCA (Maqueira et al., 2019). • Technology: technology can also be considered a source of SCA. However, this has been a source of contention. The argument is that technology is accessible to any firm that can afford to purchase it. Logically, any MLS team (or for that matter any professional soccer team) can acquire or build an AI system. For many observers, the only obvious constraint is financial resources. As we discuss in other parts of the case study, there is a fan-based assumption that what transpired in major league baseball (MLB) may repeat in the MLS. The movie Moneyball promoted the use of sabermetrics in baseball when making talent selection (as opposed to relying exclusively on scouts), which has now evolved into the norm of using technology-centered sports analytics across all MLB teams. In short, where is the advantage when every team uses technology for talent management? However, if that is the case, why are the MLB teams continuing to use AI and now the National Basketball Association (NBA), National Football League (NFL) and National Hockey League are following suit? We believe RBV theorists have already provided early insights: > “the exploitation of physical technology in a firm often involves the use of socially complex firm resources. Several firms may all possess the same physical technology, but only one of these firms may possess the social relations, cultural traditions, etc., to fully exploit this technology to implementing strategies…. and obtain a sustained competitive advantage from exploiting their physical technology more completely than other firms” (Barney, 1991, p. 110). • MLS League Single-Entity Structure: In contrast to other professional soccer leagues, the MLS has one distinct in-built edge – its ownership structure as a single entity, that is as one legal organization. All of the MLS teams are owned by the MLS, but with franchise operators. The centralization of operations provides the MLS with formidable economies of scale such as when investing in AI technologies for teams. Additionally, this ownership structure accords it leverage in negotiations for its inputs such as for player contracts. The MLS is the single employer of all its players, fully paying all salaries except those of the three marquees “designated players.” Collectively, this edge offers the MLS unparalleled fluidity and speed as a league when implementing changes, securing stakeholder buy-ins and adjusting for tailwinds. The “socially complex firm resources” is the unique talent composition of the professional soccer team and most critically its single entity structure. While every team can theoretically purchase an AI technology talent management system, its application entails use across 30 teams with a very different, complex and unique set of player talents. The MLS single-entity structure though is the resource that supplies the stability required for this human-machine (technology) symbioses to be fully accepted by stakeholders such as players and implemented with precision and speed across the entire league. So, there exists the potential for each MLS team (and the MLS as a league) to acquire SCA even when using “generic” AI technology, as long as other complex firm factors come into play. Research methodology This case relied on information that was widely reported within media, press interviews by MLS officials, announcements by various organizations, journal articles and publicly available information on MLS. All of the names and positions, in this case, are actual persons. Case overview/synopsis MLS started as a story of dreaming large and of quixotic adventure. Back in 1990, the founders of the MLS “sold” the league in exchange for the biggest prize in world soccer – the rights to host the 1994 Fédération Internationale de Football Association World Cup before they even wrote up the business plan. Today, the MLS is the highest-level professional men’s soccer league competition in the USA. That is a major achievement in just over 25-years, as the US hosts a large professional sports market. However, MLS has been unable to attract higher broadcasting value for its matches and break into the highest tier of international professional soccer. The key reason is that MLS matches are not deemed high quality content by broadcasters. To achieve higher quality matches requires many inputs such as soccer specific stadiums, growing the fan base, attracting key investors, league integrity and strong governance, all of which MLS has successfully achieved since its inception. However, attracting high quality playing talent is a critical input the MLS does not have because the league has repeatedly cautioned that it cannot afford them yet to ensure long-term financial sustainability. In fact, to guarantee this trade-off, the MLS is one of the only professional soccer leagues with an annual salary cap. So, the question is: how does MLS increase the quality of its matches (content) using relatively low cost (low quality) talent and still be able to demand higher broadcast revenues? One strategy is for the MLS to use AI playing technology to extract higher quality playing performance from its existing talent like other sports leagues have demonstrated, such as the NFL and NBA. To implement such a radical technology-centric strategy with its players requires the MLS to navigate associated issues such as human-machine symbioses, risking fan acceptance and even altering brand valuation. Complexity academic level The case is written and designed for a graduate-level (MBA) class or an upper-level undergraduate class in areas such as contemporary issues in management, human resource management, talent management, strategic management, sports management and sports marketing. The case is suitable for courses that discuss strategy, talent management, human resource management and brand strategy.