research paper about baseball

137 Baseball Essay Topics & Examples

Want to write an essay on baseball? Described as a national religion of the US, this sport is definitely worth exploring!

⚾ Baseball Research Topics

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Developed from folk games in early Britain, baseball has become the most popular bat-and-ball game in the world. About half of Americans claim to be its fans. In your paper about baseball, you might want to focus on its history. Another interesting idea is to talk about cultural impact of baseball. Whether you have to write an argumentative, descriptive, or informative essay, our article will be helpful. It contains baseball topics to research and write about. You can use them for a paper, presentation, or any other assignment. Best baseball essay examples are added to inspire you even more.

• The evolution of baseball form older bat-and-ball games
• History of baseball in the US
• The Massachusetts game and modern baseball: compare & contrast
• Baseball at the age of steroids
• Baseball in the US culture
• British and Finnish baseball: compare and contrast
• Baseball in the world literature
• Women in baseball
• Comparison of baseball and cricket
• The role of individual players in baseball
• Which Is More Profitable, Baseball or Football? There are other sports which are more profitable than the two but the argument here boils to which sport between the two is more profitable. In regard to the ticket price, baseball becomes more profitable […]
• Kansas City Zephyrs Baseball Club, Inc. The main reason for the contentious issues is the profitability disbursement to between the club operations and players. The owners want to maximize their interest through reduction of taxes yet the players want to get […]
• Fences: On Stubbornness and Baseball Even the play’s title, Fences, is a reference to “swinging for the fences” in addition to the literal and metaphorical fences Troy builds that keep the other characters out or in.
• History of Baseball and Its Impact on American History It is possible to hypothesize that the regional roots of baseball emphasize the special place of the rural culture in the construction of the contemporary American identity and promote the traits that the rural population […]
• Koprince’s “Baseball as History and Myth in August Wilson’s Fences” Although the connection between baseball and the thematic development of the play might seem tangential at first, a closer analysis of the manner in which the game I mentioned in the novel will show that […]
• Toronto Blue Jays Baseball Team’s Sport Marketing The team competes in Major League Baseball and represents the American League East division, and it is the only club in MLB that is not from the United States.
• Social Injustice in Negro League Baseball The lack of equal pay for African American players in the Negro Leagues during the 1920s and 1930s was a significant social injustice that exposed and sought to improve the inequality within the baseball industry.
• Linear Regression Applied to Major League Baseball Applying regression techniques by drawing a scatter plot of real-world data of MLB payroll amounts and win totals copied to the Excel spreadsheet, it is practical to establish the nature of the relationship between the […]
• Jackie Robinson, an American Baseball Player Robinson reached significant heights in baseball, becoming the first recipient of the MLB Rookie of the Year Award, becoming the National League’s Most Valuable Player, and being inducted into the Baseball Hall of Fame.
• The Role of Ezol’s Journal in Miko Kings: An Indian Baseball Story Outwardly the journal features the history of Ezol’s life, Ada’s citizens, and the Twin Territories; however, in truth, it goes beyond that and has a much deeper symbolic meaning. Ezol’s journal serves as a portrayal […]
• Promotional Campaign Plan for Sault Ste. Marie Baseball It will be a moment to harness the youthful talents of Sault Ste. The youths of Sault Ste.
• Geometry Web Quest for Soccer, Baseball, Basketball, Bowling, Golf, Volleyball and Pool Field for golf is the biggest and made of grass, sand and water and is the biggest and it has no fixed shape. Soccer field is made of grass or synthetic material and is the […]
• Fraud Within the Tallahassee Beancounters Baseball Team An additional impetus for the audit of the company’s accounts was the granting of a mortgage to the company for the construction of a new training facility.
• Benefits of Baseball League However this research is perhaps better placed in capturing the impact of baseball league because it is not subject to the different errors that are said to be experienced in the assessment of economic development […]
• Mechanics of the Baseball Swing During the game, the ball is to be hit hard by the batting team and the “hitter” to stop at a base before proceeding to other bases.
• Baseball Game Rules and Age Limit In the game of baseball or any other form of the game, the play of a boy corresponds to the work of an over-aged player.
• Baseball Career Personal Experiences Though I was nowhere near the standards of the so called best players, my interest and willingness to give my best, pleased the coach and I was mostly in the starting team.
• The Use of Steroids in Baseball The use of steroids may be used to improve the performance of the baseball teams but this comes at a great cost to the individual’s health and the integrity of the game.
• “Life in Baseball’s Negro Leagues” by Donn Rogosin This is the particular phase of racism that has made the dominance so very concrete that the title in itself declares the actual picturesque about the foregrounding towards this dominance.
• Negro Baseball League and Professional Players The work clearly tells the reader the saga of the tribulations and humiliations that a black player has to undergo because of the color difference, and the author points out how the game of baseball […]
• Professional Baseball Operation Strategy in Taiwan But when it comes to the professional market, the low attendance rate shows the dilemma of the league operation. To review the development and history of the free agency system in MLB.
• William Ellsworth Hoy, a Deaf Baseball Player In the nineteenth and beginning of the twentieth century, the overall social environment and a widespread hostile public attitude toward disability provided many obstacles to a successful career for any person with a disability.
• The 1994 Major League Baseball Strike and Conflict Although the strike was sometimes claimed to be the one that had the most significant impact on Major League Baseball, the result of the negotiations was not satisfactory to both parties. The conflict between the […]
• Oakland Athletics: Successful Baseball Team It is necessary to understand that this measure is crucial, and it can be combined with a slugging average to determine the capabilities of a particular player.
• Baseball in Sociological Research and Its Features This is followed by a careful determination of the research design to use while conducting a research. It also makes sure that the sociologist is in line with ethical standards of conducting a research in […]
• Media and Negative Ethnicity in Baseball The stakeholders in the game of baseball have made concerted effort to promote integration of major league baseball in the United States.
• Baseball Players’ Salaries Analysis This meant that the salaries of LA Dodgers players were evenly distributed relative to average salary with above-average distribution in NY Yankees and a weak distribution in NY Mets.
• The Financial Problems of Major League Baseball Meanwhile, as the players faced the problem of losing their salary for the last weeks of the season, the owners encountered a big problem since the World Series were wiped out for the second period.
• Data Collection of Major League Baseball The fact that the total population of the players in the Major League Baseball is relatively large made the researcher choose the sampling method to determine the salary that a player should earn.
• Major League Baseball’s Data Set General overview: after choosing the topic, the research team decided to review the available information to ensure that the base of the problem was wide and comprehensive; at this stage, the researchers were concerned with […]
• Major League Baseball Players Association The association also has a role in the modern world of negotiating the salaries of its players. The major league baseball association is a union that is of great help to the baseball players.
• Steroids in Baseball The rejuvenated use can be traced back to the role of the media in promoting sports as a form of entertainment.
• Factors that influence Major League and Minor League Baseball This perhaps leads to the appreciation of the significance of considering the team’s quality in determining the attendance of major and minor Baseball league.
• Baseball and Urbanization For instance, at the very beginning of the nineteenth century, the urban population in the United States was 5% of the total population.
• Technologically Advanced Baseball Bats Research The purpose of this research study is to investigate the advantages of using technologically advanced, or high priced, baseball bats in the Little League Baseball.
• Unions and Compensation in Major League Baseball This paper will discuss concepts of the unionization of professional baseball, impacts of the unionization of the game to players, managers and the game in general.
• 1919 World Series: How It Changed Baseball Forever?
• 2011 Major League Baseball National League Most Valuable Player Individual or Team Award?
• Comparison Between the Games of Baseball and Fastpitch Softball
• Comparison of American Pastime in Baseball and Football
• How Baseball Helped Me Coup Up with the Struggles of My Life?
• African Americans in Baseball
• Analysis of David Brook’s Baseball or Soccer
• Analysis of the Official Website of Major League Baseball
• Analysis of Baseball: An Important Part of American Pop Culture
• Analysis of Baseball Stadiums
• Analysis of the Economic Structure of the Major League Baseball
• Analysis of the Minor League Baseball
• New York Yankees, the Most Successful Franchise in Baseball History
• Baseball Hats Boost Employee Motivation And Job Performance
• Compare And Contrast Baseball And Basketball
• Differences And Similarities Between Baseball And Softball
• How African Americans Helped Shape The Major League Baseball
• How Baseball Has Changed My Life?
• How Baseball Survived the Great Depression?
• How Did Baseball Affect Cuba In The Mid Twentieth Century?
• How Television Has Changed The Game Of Baseball?
• How The Civil War Helped Formed Baseball Into The Great Game?
• How to be a Healthy Baseball Player?
• How To Play Fantasy Baseball?
• Salaries In Major League Baseball
• Stopping on Nine: Evidence of Heuristic Managerial Decision‐Making in Major League Baseball Pitcher Substitutions
• What Is The Status Of Steroids In Baseball?
• Why Baseball Is The Most Amazing Sport?
• Who Integrated Major League Baseball Faster Winning Teams or Losing Teams?
• Why Is Baseball My Favorite Game to Watch?
• A Bad Day in My Baseball Career
• A Background of America’s Favorite Pastime Baseball
• Biography and Life Work of Jack Roosevelt Robinson, a Professional Baseball Player
• Biography and Life Work of Joseph Jefferson Jackson, an American Baseball Player
• Life and Work of Roberto Clemente Walker, a Puerto Rican Baseball Player
• Biography of Babe Ruth
• Achievements of Baseball Legend Ted Williams
• Advertising in Baseball Stadiums
• History of African Americans in Major League Baseball
• History of Baseball in the American Civil War
• History of Steroid Use in the Major League Baseball
• History of the All American Girls Professional Baseball League in America
• Anabolic Steroids are Ruining Major League Baseball
• Evaluation of Customer Satisfaction for Fans Attending Baseball Games at Yankee Stadium
• Baseball and the Civil War of the United States
• Attendance and the Uncertainty-of-Outcome Hypothesis in Baseball
• Baseball, Football, and Basketball: Models for Business
• Baseball Revenue Sharing
• Cheating in the Game of Baseball
• Impact of the Globalization of Baseball
• Myth in Baseball
• National Pastime to Dismal Science: Using Baseball to Illustrate Economic Principles
• Pay and Performance in Major League Baseball: The Case of the First Family of Free Agents
• Physics Of Baseball
• Professional Baseball Stadiums ‘Old’ New Construction Trends
• Risk Management for the Use of a City Baseball Stadium
• Economic Impact on the Dominican Republic of Baseball Player Exports to the USA
• Twenty First Century Baseball and Economics
• Women’s Baseball Leagues in Historical Context
• Work Incentives And Salary Distributions In Major League Baseball
• How Did Racism Impact the Game of Baseball?
• Are Baseball Players Paid Too Much?
• How Did Babe Ruth Change Baseball?
• Does the Baseball Labor Market Contradict the Human Capital Model of Investment?
• How Has Baseball Changed Their Rules?
• Did Abner Doubleday Invent the Game of Baseball?
• How Did Baseball Survive the Great Depression?
• Can Women Really Play Baseball?
• How Was Baseball Changed by Jackie Robinson?
• Does the Baseball Labor Market Properly Value Pitchers?
• How Did Baseball Affect Cuba in the Mid-Twentieth Century?
• Are Major League Baseball Players Overpaid?
• Why Has Baseball Benefited From the New York Yankees?
• How Did Baseball Influence America?
• Does Option Theory Hold for Major League Baseball Contracts?
• How Has the Game of Baseball Been Affected by the Increase in Technology Over the Past Decades?
• Should Baseball Ban the DH?
• How Did Steroids and HGH Destroy Baseball?
• Should Baseball Players Who Used Steroids Be Allowed in the Hall of Fame?
• How Did Television Has Change the Game of Baseball?
• Were Major League Baseball Doubleheaders a Mistake?
• Why Are Americans Addicted to Baseball?
• How Do Baseball Players’ Mental States Influence Their Career?
• Should Baseball Expand the Use of Instant Replay to Review Close Plays on the Bases?
• Does Baseball Lose to Soccer in Some Us States?
• Should Baseball Be Financed by Is Citizens’ Taxes?
• Can Baseball Alleviate Mental Illness Symptoms?
• Should the Pricing Policy for Baseball Tournaments Be Reviewed?
• What Countries Can Complete With the USA in Baseball Ratings?
• NFL Research Topics
• Career Ideas
• Academic Achievements Research Topics
• Success Ideas
• College Education Essay Ideas
• Goals Questions
• Hard Work Research Topics
• Personal Growth Research Ideas
• Chicago (A-D)
• Chicago (N-B)

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• v.15(4); 2022 Aug

Current State of Data and Analytics Research in Baseball

Joshua mizels.

1 Department of Orthopaedic Surgery, University of Utah, 590 Wakara Way, Salt Lake City, UT 84108 USA

Brandon Erickson

2 Rothman Orthopaedic Institute, New York, NY USA

Peter Chalmers

Purpose of review.

Baseball has become one of the largest data-driven sports. In this review, we highlight the historical context of how big data and sabermetrics began to transform baseball, the current methods for data collection and analysis in baseball, and a look to the future including emerging technologies.

Recent Findings

Machine learning (ML), artificial intelligence (AI), and modern motion-analysis techniques have shown promise in predicting player performance and preventing injury. With the advent of the Health Injury Tracking System (HITS), numerous studies have been published which highlight the epidemiology and performance implications for specific injuries. Wearable technologies allow for the prospective collection of kinematic data to improve pitching mechanics and prevent injury.

Data and analytics research has transcended baseball over time, and the future of this field remains bright.

Introduction

While deeply rooted in tradition and considered America’s National Pastime [ 1 ], the baseball landscape is everchanging in today’s age of big data, artificial intelligence, and machine learning [ 2 – 4 ]. At the professional level, Major League Baseball (MLB) has created new sources of data such that up to seven terabytes of data are gathered at each game [ 4 ]. In this review, we will provide a historical perspective on the evolution of baseball analytics, where we stand today, and emerging technologies that are on the horizon.

Sabermetrics: a Historical Perspective on Data and Analytics in Baseball

Since the 1920s, people have been trying to utilize baseball data to their advantage to predict outcomes and develop winning teams. Ferdinand Cole Lane, a biologist turned editor-in-chief of Baseball Magazine in 1912, developed one of the first run expectancy models in the 1920s [ 3 , 5 ]. Based on how many runners are on base and how many outs there are, this model provided the probabilities that the batting team would score [ 3 ]. Afterwards, there had been many notable attempts at strategically utilizing baseball data, including the Dodgers hiring of a statistician to their front office in the 1940s [ 3 ].

The utilization of baseball statistics as we know it today did not really develop until 1974 when Dick Cramer, Bill James, and Pete Palmer co-founded the Society of American Baseball Research’s (SABR) Statistical Analysis Committee [ 3 ]. The term sabermetrics, coined from the SABR acronym, refers to “the search for objective knowledge about baseball” [ 6 ]—how best to succeed in an in-game situation, determine a player’s value, etc. Initially, this movement was met with resistance as a disturbance to baseball tradition. In particular, many teams rely upon professional baseball scouts, who watch hundreds of players and write reports containing their opinions as to the potential of these players. These scouts have become deeply ingrained into the fabric of baseball culture and have long argued that their assessments must be considered in concert with objective data. As a result, sabermetrics did not truly take hold until the early 2000s when the Oakland “Moneyball” A’s made their fourth straight postseason appearance under the guidance of their general manager, Billy Bean [ 3 ]. Bean favored statistics like on-base percentage and slugging percentage to build a winning team with a limited budget.

Today, sabermetrics has led baseball to become one of the biggest data-driven sports worldwide. At the fan level, websites like FanGraphs, Baseballsavant, Baseball Prospectus, and Pybaseball [ 7 ] have become increasingly popular where writers analyze baseball using sabermetrics. In fact, many have been hired to work for various MLB organizations [ 3 ]. At the organizational level, teams continue to use sabermetrics to build efficient rosters and predict player performance based on a seemingly endless list of performance metrics [ 3 , 8 ]. Even in orthopedic surgery and sports medicine, we use many of these same performance metrics to predict injury, outcome, and return to play [ 8 ].

Performance Metrics

There are seemingly endless sabermetrics that attempt quantify player performance [ 3 , 9 – 12 ]. While no single metric reliably quantifies an individual player’s value, these are taken into context with each other and the team [ 3 , 12 ]. Many statistics are imperfect and limited by measurement error and sample size [ 11 ]. For example, defense performance has been historically measured by errors and fielding percentage [ 11 ]. An error, by definition, is failing to get an out on a routinely batted ball that is expected to result in an out [ 13 ]. However, this requires a scorer’s judgement and brings in human error, as well as it does not account for “bad defense” if a player got a slow jump on a ball and did not make the play—this is technically not an error [ 11 ].

In today’s age of analytics, new metrics have been created to try to solve this issue. For example, Revised Zone Rating (RZR) tries to quantify how often a fielder turn batted balls into outs—similar in theory to errors and fielding percentage [ 11 ]. This should better quantify player skill as it accounts for every play, not just ones where fielder encountered the ball [ 11 ]. However, it is limited by the difficulty or importance of the play.

Defensive Runs Saved (DRS) and ultimate zone rating (URS) build upon RZR and quantify how many runs a player prevents while playing defense [ 11 ]. This controls for difficulty of the play relative to how frequently a play is made by the entire league [ 11 ]. If a play is made 40% of the time, and the player makes the play, he gets credit for 0.6 times the run value of the play [ 11 ]. The run value of the batted ball reflects how many runs should be scored on a single play—a difficult ground ball, which may only be fielded 40% of time, may only result in the batter reaching first base without any runs scored [ 11 ]. There is limited consequence to this missed play. However, these statistics are limited by number of truly difficult batted balls hit to every fielder each year. These examples demonstrate the complexity in understanding defensive play, which has long been considered the most poorly captured with traditional sabermetrics.

Using advanced technologies, like Statcast (see discussion below), newer defensive sabermetrics have been developed to improve defensive metrics [ 3 , 14 ]. For example, catch probability attempts to quantify outfield defense by measuring the difficulty of a batted ball based on how far the fielder had to go, how much time he had to get there, the direction he needed to go, and whether the proximity to the wall was a factor [ 14 ].

On the other hand, pitching and hitting have often been considered more well categorized. Generally, pitching metrics can be categorized as traditional (i.e., ERA, wins), advanced (i.e., FIP, WHIP, SIERA, BABIP), or value (i.e., WAR) [ 9 ]. They can also be categorized as workload measures (i.e., IP, G) or performance measures (i.e., ERA, WHIP, FIP). Finally, they can also be further subdivided as defense-dependent (i.e., hits, ERA) or defense-independent (i.e., strikeouts, walks, homeruns, FIP) [ 9 ].

Like defensive metrics, traditional pitching metrics are often imperfect and may not reflect the true performance of a pitcher. While pitchers may be solely responsible for an allowed homerun, the quality of their defense certainly influences the number of hits (versus making a play) or their earned run average (ERA) [ 15 ]. Field Independent Pithing (FIP), and advanced sabermetric, is an ERA estimator based on pitcher-controlled/defense-independent factors—strikeouts, walks, hit by pitches (HBP), and homeruns allowed—and adjusted for league average defense performance to balls in play and interpreted on the same scale [ 15 ]. Interestingly, FIP is a better predictor of future ERA than current ERA [ 15 ].

With the advent of Statcast, pitching metrics have also improved. Expected ERA (xERA) accounts for both type of contact (strikeout, walks, hit by pitch) and the quality of the contact (exit velocity and launch angle) [ 16 ]. This then credits either the pitcher or the hitter for the contact, which eliminates the confounding effects of ballpark, weather, or defense [ 16 ].

Finally, offensive or batting sabermetrics can be classified like those of pitching metrics. There are standard or traditional metrics (i.e., RBI, HBP), advanced (i.e., OPS, wRC), value (i.e., WAR), and Stacast (i.e., wRC+) [ 10 , 17 ]. Interestingly, offensive metrics have changed with time, particularly regarding walks which have not always been valued the way they are today [ 18 , 19 ]. Historically, batting average (BA) was used to quantify offensive production and is still reported today. Yet, this metric is imperfect and only counts official at-bats which excludes walks, hit by pitches, sacrifices, or catcher’s interference. These can be valuable plate experiences for a team by avoiding an out and putting an additional runner on base. Sabermetrics like on base percentage (OBP), which factors in walks, and slugging percentage (SLG), which accounts for extra base hits, were developed to better quantify offensive performance than BA [ 20 ].

Prior to Statcast, weighted on-base average (wOBA) and weighted runs created plus (wRC+) were considered the best metrics to evaluate a player’s offensive results [ 20 ]. wOBA, a scaled OBP, considers the weight of each offensive outcome (home runs are weighted more highly than walks) [ 20 ]. wRC+ adjusts for different ballparks and eras [ 20 ]. Now with Statcast, sabermetrics like expected weighted on-base average (xwOBA) remove defensive factors from the wOBA metric and account for the exit velocity, launch angle, and sprint speed of the batter on batted balls [ 21 ].

Table ​ Table1 1 provides a list of some of the most common sabermetrics and their definitions.

Examples of advanced and Statcast performance metrics

Statcast and Ballpark Sensors

By the 2015 season, all MLB stadiums had integrated the Statcast system to track the ball and every player on the field [ 2 , 36 ]. For ball-tracking, the Statcast system utilizes Trackman phased-array Doppler radar technologies which sits behind home plate [ 36 ]. This system approximates the path, spin rate, and velocity of each pitch, as well as the initial speed, and vertical and horizontal launch angles of the batted ball [ 36 ]. While Doppler radar technology is well suited for ball-tracking, player’s slower speeds make Doppler shifts too difficult to interpret. As such, Statcast uses a system of stereoscopic optical video from two arrays [ 36 ]. They are spaced 15 meters apart along the third base line, each with three high-resolution cameras that utilize optical video sensors and stereo vision techniques to allow for the precise tracking of player location on the field [ 2 , 36 ]. These arrays are time synchronized with the Trackman radar data and allow for the quantification of performance metrics, such as defender reaction time, route efficiency, and speed [ 36 ].

Using sensor data, many classic sabermetric analyses of player value are becoming increasingly obsolete. For example, batting average solely considers whether a batter got a hit. With sensor data, however, one can control for other confounding variables which are outside the scope of a simply reported batting average—opponent defender skill, ballpark dimensions, weather, pitch velocity, and spin rate [ 36 ].

Statcast has both enhanced the fan experience, displaying real-time data on telecasts and various media outlets, as well as provided baseball with a new source of data to challenge classic sabermetric models and revolutionize baseball strategy. The wealth of data has forced many clubs to hire teams of statisticians to better understand the data and to recommend how to act upon it.

Health and Injury Tracking System and Return to Play

Thomas et al. (2020) recently published a systematic review which described the RTP rates and performance of pitchers after both primary and revision ulnar collateral ligament (UCL) reconstruction (UCLR) [ 37 ]. This review included 29 studies published from 1980 to 2019 [ 37 ]. After primary UCLR, MLB pitchers had RTP rates from 80 to 97% at 12 months [ 37 ]. Return to the same level of play (RTSP) rates, however, were only 67 to 87% at 15 months [ 37 ]. Following revision UCLR, RTP ranged from 77 to 85% and RTPS ranged from 55 to 78% [ 37 ].

While this is a robust study about a specific injury, there are few similar studies in the literature. As such, to better understand player injury, the MLB, with its minor league affiliates, players’ unions, and healthcare experts, established the MLB Health and Injury Tracking System (HITS) in 2010 [ 8 , 38 ]. Previously, the understanding of the epidemiology of baseball injury was limited by disabled list (DL) reports. The DL only reported injury to a specific body region (rather than a specific injury) and was used as a roster management tool in addition to an actual accounting of injuries [ 38 ]. Since the establishment of HITS, numerous studies have been published which report the epidemiology of specific baseball injuries, their return to play characteristics, and related performance outcomes [ 8 , 38 – 46 ].

Notably, Camp et al. in 2018 published a comprehensive report of the 50 most common injuries in the MLB and Minor League Baseball (MiLB) from 2011 to 2016, with specialized fact sheets for each injury outlining specific characteristics and return-to-play times [ 38 ]. They reported nearly 50,000 injuries over this six-year period, with about 8000–8500 injuries per year. Of these, roughly 5000 were season-ending injuries, and among the non-season-ending injuries, there was a mean 16 days missed per injury [ 38 ]. The upper extremity accounted for 39% of all injuries and the lower extremity 35%. Interestingly, pitchers were the most injured position, 3.6 times more frequently than catchers, 5.1 times more than outfielders, and 5.8 times more than infielders [ 38 ]. This incidence of upper extremity injury and pitcher injury correlates with other epidemiologic studies of baseball injury [ 47 , 48 ]. These types of studies have greatly aided trainers in better understanding the prognosis and expected time course for return to play. Studies using the HITS database have also provided numerous insights into risk factors for injury, diagnosis, and best treatment practices.

Motion Analysis Techniques

The pitching motion has been well described as involving six specific phases of muscle movement, and improper mechanics in each phase has been associated with an increased risk for injury [ 49 ]. These phases are as follows: (1) wind-up, (2) stride or early cocking, (3) late cocking, (4) acceleration, (5) deceleration, and (6) follow-through [ 49 ]. There are a number of modalities with which one can analyze a pitcher’s mechanics including two-dimensional video, three-dimensional motion analysis in a laboratory, and emerging wearable technologies (see discussion below) [ 49 ]. Historically, three-dimensional motion analysis is considered the gold standard and provides detailed kinetic and kinematic data about each phase of the previously mentioned pitching motion [ 49 ].

Emerging Technologies

Wearable imus.

Until recently, the kinetic and kinematic analysis of the pitching motion relied heavily on high-speed cameras to capture motion in three dimensions [ 50 – 53 ]. However, there are limitations associated with this technology including cost and the need for access to a controlled laboratory which has these capabilities [ 50 , 53 ]. In particular, pitchers will not pitch with full velocity in the laboratory, raising questions as to whether this data really represents in-game kinetics and kinematics. This limits the widespread analysis for all pitchers across all levels of baseball [ 52 ].

However, there have been recent advancements in wearable technology that has made gathering of this kinetic and kinematic data easier and less cumbersome, more accessible by limiting the need for the laboratory, and even less expensive [ 50 – 54 ]. These technologies utilize inertial measurement units (IMUs) to allow for real-time motion analysis in three-dimensions. IMUs are lightweight and small systems with embedded three-dimensional accelerometers, gyroscopes, and magnometers which allow for the biomechanical assessment of motor functions by dynamically tracking anatomical segments [ 54 ]. IMUs have already demonstrated utility in gait analysis [ 53 – 55 ] and are currently being used to analyze pitching mechanics [ 50 , 52 , 53 , 56 – 61 ].

The motusBASEBALL (Motus Global) IMU system was first approved for use in the MLB 2016 [ 51 , 62 ], since there has been increasing interest in the sports medicine literature using this technology to study pitching biomechanics [ 52 , 53 , 56 – 61 ]. Table ​ Table2 2 lists all studies published to our knowledge using the motusBASEBALL technology and their conclusions.

Studies using motusBASEBALL sensor

Lizzio et al. (2020) published a protocol to optimize data collection and how to troubleshoot device malfunction [ 50 ].

While promising, the current evidence conflicts as to the accuracy of the system [ 63 ]. Four studies have assessed the validity of wearable technology to date [ 52 , 53 , 63 , 64 ]. Camp et al. (2017) first validated wearable IMU technology against the “gold standard” of motion capture techniques to capture pitching kinetic and kinematic variables [ 53 ]. Wearing a compression sleeve with the IMU overlying the medial elbow, they recorded arm slot, arm speed, arm rotation, and elbow varus torque in 35 pitchers throwing fastballs [ 53 ]. These are well-studied variables and known risk factors for the development of pitcher injury [ 50 – 53 ]. The IMU data were subsequently transmitted via Bluetooth technology to a smartphone containing proprietary biomechanical algorithms [ 53 ]. They reported correlation coefficients ( r values) between the IMU analysis and motion capture techniques of 0.93 (varus torque), 0.94 (arm rotation), 0.95 (arm slot), and 0.85 (arm speed) [ 53 ].

Makhni et al. (2018) used similar IMU technology (sensor placed over medial elbow) to measure elbow torque, arm speed, arm slot, and shoulder rotation across multiple pitch types (fastballs, curveballs, and change-ups) [ 52 ]. By measuring outlier rate, they reported precision rates for the IMU system of 96.9% for fastballs, 96.9% for curveballs, and 97.9% for change-ups [ 52 ]. Boddy et al. (2019) compared wearable IMU technology to motion capture technology [ 64 ]. They found statistically significant correlations ( r values) between the two when measuring arm slot (0.975), shoulder rotation (0.749), and stress (0.667) while elbow extension velocity failed to reach statistical significance [ 64 ].

However, Camp et al. (2021) again attempted to validate the motusBASEBALL IMU technology with a dedicated study [ 63 ]. Comparing measurements obtained simultaneously by motion capture and the IMU system, they found this technology to be reliable for arm speed and not reliable for arm slot, arm stress, or shoulder rotation [ 63 ]. Overall, these results suggest that while this technology can be used to measure number of pitches and may be useful to compare one pitcher within themselves, it may not be accurate to compare between pitchers.

Even with these limitations, the technology may be useful for injury prevention and post-injury rehabilitation [ 49 , 50 ]. Wearable technology allows for the prospective collection of biomechanical data which would allow for real-time pitch counts, which is more convenient and accurate than prior methods [ 50 ].

Machine Learning and AI to Predict Injury

Machine learning (ML) and artificial intelligence (AI) have already been transforming many facets of society, from self-driving vehicles, targeted advertisements, healthcare and orthopedic surgery, and sports [ 4 , 8 , 65 – 69 ]. While AI is a broad term which refers to the analysis of large data sets using algorithms to gain useful inference, ML is a subset of AI which uses this data specifically to predict an outcome [ 67 ]. In ML, the machine studies and “learns” from “training sets” of real-world data using pattern recognition to determine relationships. Then, these machines are provided “testing sets” to create predictions and compare them with known outcomes. This process continuously repeats, helping to improve the accuracy of the models based on feedback from these “testing sets.” In theory, ML mirrors the way in which humans learn with constant improvement in analyses as new data becomes available [ 67 ].

Recently, Makhni et al. published a review article (2021) regarding ML and AI in orthopedics, its current impact on the field, and future applications [ 67 ]. These technologies will continue to help improve outcomes, reduce costs and inefficiencies, and improve the overall value of the care we provide. Notably, there has been a tenfold increase in ML publications in over the past twenty years [ 69 ], which highlights the impact it has had on this field. Currently, ML and AI are being used to predict readmission risk in total hip and knee arthroplasty [ 70 ], to interpret preoperative radiographs in the setting of revision arthroplasty to determine the prior implant class and manufacturer [ 68 ], and predict post-operative outcomes after total joint arthroplasty [ 71 ], among many other applications.

In baseball, in conjunction with classic sabermetrics and performance data, modern ML algorithms are being used to try to predict injury risk and specific anatomic injury location. After compiling a database of 13,982 player-years, Karnuta et al. applied both logistical regression (LR) and ML techniques to develop an algorithm to predict MLB injuries before they occurred [ 8 ]. They based these models on age, performance data (sabermetrics for hitting, pitching, and overall), prior injury history, and DL data. While the AUC ranged from 0.71 to 0.80 for predicting position player injury which was fairly reliable, the AUC for pitchers ranged from 0.61 to 0.69 and was considered poorly reliable [ 8 ]. However, in almost all (13 of 14) cases, ML was superior to LR when predicting player injury and demonstrated improved accuracy with subsequent iterations of the injury-prediction model [ 8 ].

While these models have yet to demonstrate true clinical reliability, they certainly highlight the bright future of baseball analytics and the limitations of historical logistical regression. In this study, as the ML model continued to “learn,” it continued to improve its reliability which is fundamental to ML. As ML models continue to improve, we may reach a point in baseball where we can reliably predict injury and intervene before injury actually happens.

Data and analytics research has transformed baseball into becoming one the largest data-driven sports worldwide. From its origins in sabermetrics, ML and AI are seemingly commonplace in the current analysis of baseball from a performance standpoint to injury prevention and rehabilitation. The future seems bright with wearable technologies, though more research is needed in this area to better validate this technology before it can be universally relied upon.

Declarations

The authors declare no competing interests.

All reported studies/experiments with human or animal subjects performed by the authors have been previously published and complied with all applicable ethical standards (including the Helsinki declaration and its amendments, institutional/national research committee standards, and international/national/institutional guidelines).

This article is part of the Topical Collection on Injuries in Overhead Athletes

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Research Article

Recognition of the game situation in baseball

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Medical Data Intelligence, Research Center for Health-Medical Data Science, Graduate School of Medicine, Hirosaki University, Hirosaki, Aomori, Japan, Faculty of Sustainable System Sciences, Osaka Metropolitan University, Osaka City, Japan, Department of Health, Sport and Communication, Kobe University of Future Health Sciences, Fukusaki Town, Hyogo, Japan

Roles Investigation, Project administration, Supervision

Affiliation Department of Health, Sport and Communication, Kobe University of Future Health Sciences, Fukusaki Town, Hyogo, Japan

Roles Formal analysis, Methodology, Validation

Affiliation Advanced Teacher Professional Development Program, Hokkaido University of Education, Asahikawa City, Japan

Roles Project administration, Supervision, Writing – review & editing

Affiliation Faculty of Sustainable System Sciences, Osaka Metropolitan University, Osaka City, Japan

Roles Conceptualization, Methodology, Supervision, Visualization, Writing – original draft, Writing – review & editing

Affiliation Faculty of Engineering, Nara Women’s University, Nara City, Japan

• Yasuhiro Hashimoto, 
• Hiroshi Takahashi, 
• Hiromu Nagaura, 
• Shinji Yoshitake, 
• Hiroki Nakata

• Published: August 20, 2024
• https://doi.org/10.1371/journal.pone.0309328
• Peer Review
• Reader Comments

This study examines baseball players’ recognition framework of out, ball, and strike counts in baseball games and clarifies the differences in psychological perspectives between batters and pitchers. The participants were 396 players (294 batters and 102 pitchers) belonging to baseball clubs at eight universities. Participants answered 288 questions for all game situations by combining out, ball, and strike counts and runner position. The advantages for batters or pitchers were evaluated using a 7-point Likert scale (from very advantageous for batters to very advantageous for pitchers). Factor analysis indicated four significant factors (36 items): “Batter’s advantage count,” “Pitcher’s advantage count,” “2 out young count,” and “0 out young count.” In a direct comparison of these factors between batters and pitchers, batters were more aware of their advantage over pitchers in the factors “Batter’s advantage count” and “0 out young count” and disadvantage in the “Pitcher’s advantage count.” Significant differences in recognition of these factors were observed between batters and pitchers. Batters were more susceptible to game situations than were pitchers. Our findings suggest that baseball players recognize several types of game situations, although not an infinite number.

Citation: Hashimoto Y, Takahashi H, Nagaura H, Yoshitake S, Nakata H (2024) Recognition of the game situation in baseball. PLoS ONE 19(8): e0309328. https://doi.org/10.1371/journal.pone.0309328

Editor: Nick Fogt, The Ohio State University, UNITED STATES OF AMERICA

Received: May 4, 2024; Accepted: August 2, 2024; Published: August 20, 2024

Copyright: © 2024 Hashimoto et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The minimal data set underlying the findings of this study is publicly available and can be accessed via the EMBL-EBI BioStudies database (DOI: https://doi.org/10.6019/S-BSST1392 ).

Funding: This study was supported by the Japan Society for the Promotion of Science, KAKENHI Grant-in-Aid for Early-Career Scientists (19K24298, 20K19500, and 24K20555) awarded to Y.H. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Sports dynamics are created by multiple overlapping factors such as the score, remaining time, player abilities, and tactics, leading to enjoyment. By recognizing and interpreting the game situation, players comprehend the importance of each game scene. Judging the importance of a game situation is related to individual experience and objective indicators [ 1 ] and may be divided into important and unimportant moments within a single game. In particular, important moments likely impose significant pressure and stress on players. Numerous studies have examined the performance changes induced by sports pressure [ 2 – 4 ]. In many sports games, the players’ performance constantly changes according to game situations. Therefore, measuring and evaluating these fluctuating data during a game is difficult.

Research on competitive sports can be broadly divided into three categories. The first category compares the performance and results before and after the game (mainly wins and losses) using physiological and psychological indicators. The game situation is often enquired immediately after the game, and the relationship between performance and victory or defeat is often examined. For example, a study investigated the relationship between Boccia athletes’ game results and their pre-competition mental state in which identity, anxiety, and self-efficacy in sports and expectations for success explained 49% of the variance in results [ 5 ]. Changes in psychological state during games have attracted the interest of many researchers. For example, a study described flow experience as “carried away” [ 6 ]. Researchers also described this phenomenon as “When performing an activity that provides challenges and requisite skills, and when both challenges and skills are high and in balance, an individual is not only enjoying the moment but is also improving their capabilities with the likelihood of learning new skills and increasing self-esteem and personal complexity” [ 7 ]. Several studies have reported the flow mechanism in various situations, such as dancing in music-based exergames [ 8 ] and video games [ 9 – 11 ]. However, these studies considered a single variable (e.g., game results and flow scores) and did not evaluate changes during the game. Furthermore, while reviewing the details after the game, the game results may affect judgments of the importance of the game situation.

The second research design targets the game itself, where physiological indicators and results are often collected for game analysis and big-data research. Several studies have obtained data on players’ heart rates during soccer [ 12 , 13 ], rugby [ 14 ], motorcycle racing [ 15 – 17 ], and kart racing [ 18 ]. However, because heart rate is directly affected by actual exercise, distinguishing the change in heart rate between physical and psychological factors was difficult. Furthermore, only a few studies have examined individual game situations and the specific factors (situations) that affect players’ emotions.

In the third category, qualitative research design involves recalling the game, performing cognitive assessments of individual game scenes, and analyzing the data. For example, a study examined the perception of flow during a game with 28 elite athletes in seven sports [ 19 ]. Another study reported self-confidence, uninterrupted focus, and concentration in athletes when they performed well [ 20 ]. Researchers have also surveyed 16 athletes in various sports using semi-structured interviews [ 21 ]. In this research method, examining players’ feelings during the game and cognitively evaluating each scene was possible. Compared to the two methods mentioned above, analyzing data directly during the game was helpful. In addition, interview data were used to measure the importance of the game situation. These data included the number of utterances and people who made similar remarks. However, in this research design, the analytical data tended to be constant, making it impossible to take advantage of the nuances and deep values that are the strengths of qualitative research.

Based on this research background, the present study aimed to elucidate the recognition of continuously changing out, ball, and strike counts in baseball games. As these variables continuously change at regular intervals, examining their stepwise changes in the game situation is excellent for organizing the subjective index of recognition. In baseball games, there is a period of non-play between plays, and the plays are divided sequentially. However, in goal-type sports (e.g., soccer, basketball, and handball), the game moves continuously. The factors, including the remaining time, score difference, and number of players sent off, determine the game situation and are intricately linked to the chances of winning. For example, when the score in soccer is 1–0, the meaning of victory depends on how much time remains. In net-type games (e.g., tennis, table tennis, and badminton), the score difference and number of points left until the game ends are important.

The present study examined each count multifacetedly by comparing the pitcher’s and batter’s perspectives. This examination helped understand the game situation by comprehending a single situation comprehensively. In baseball, the opposing relationship between the pitcher and the batter affects winning or losing. We analyzed changes in recognition during baseball games by recalling game situations using subjective and objective factors. The difference in characteristics between our study and the third research design is the objective factor. The present study included the out, ball, and strike counts as quantitative data. Therefore, we compared the data of the pitchers and batters and generalized them using statistical methods. Additionally, questions were based on variables that objectively represent the game situation (specifically, the number of outs, balls, and strikes). In other words, this study changed the data from a conventional research design to quantitative data and provided a structured framework for the questions. We assumed that our method could objectively investigate the effect of changes in game situations on player recognition. We hypothesized that the evaluation of the game situation would differ between pitchers and batters because they are in opposing positions. This study clarified the differences in psychological perspectives between pitchers and batters, even when in the same situation.

Participants

This study surveyed 544 baseball players from eight university baseball clubs using an online questionnaire (Google Forms) from April to July 2021. Of these, 396 (72.79%) players provided valid responses. The average age of the players was 19.7 ± 4.7 years, and the average game history was 12.1 ± 2.3 years. Several of the surveyed universities have produced professional baseball players. This study complied with the principles of the Declaration of Helsinki for human experimentation and was approved by the University Ethics Committee (approval number: 2021001). All the participants provided informed consent to participate in the study.

The participants were asked about their date of birth (age), university, dominant (throwing) hand, and perspective of response (batter and pitcher viewpoint and whether batter or pitcher is advantageous in game situations). Game situations were classified into out count (three levels), ball count (four levels), strike count (three levels), and runner position (eight levels). The participants answered 296 questions by combining all the game situations. In total, 288 questions and 8 items served as the basis for the Likert scale. In extreme cases participants may have answered the questions without reading the text, therefore analyzing these data may have yielded inaccurate results. Therefore, establishing the Likert scale ensured the accuracy of the responses before conducting the analysis. Many previous questionnaires used extreme questions to create baseline questions for the Likert scale, for example, “I have never lost a game.” However, by adding an extreme question here, the participants may have realized that this question was on the Likert scale. Therefore, the Likert scale in this study asked multiple questions (nine times in total) about the situation of 0 outs, 0 balls, 0 strikes, and no runners (hereafter abbreviated as Likert scale items). This study aimed to repeat the counts by changing the out, ball, and strike counts. We checked the consistency of the responses to determine whether they were valid.

To recognize the game situation, participants were instructed to answer from seven choices (1. Very advantageous for batters, 2. Quite advantageous for batters, 3. Slightly advantageous for batters, 4. Neither, 5. Slightly advantageous for pitchers, 6. Quite advantageous for pitchers, 7. The pitcher has a great advantage). They were also asked to select the item that they thought was most applicable. Prior to the survey, the participants were informed that all game situations were in the top of the first inning, the game score was 0–0, and to judge whether the batter or pitcher was more advantageous, the batting average of 0.250 was used. The batting average was set to 0.250 because the data in Nippon Professional Baseball (NPB) was 0.250 in 2020 [ 22 ] and 0.251 in 2021 [ 23 ].

For the Likert scale items, the mean value and standard deviation (SD) of the responses were calculated for each participant. The exclusion criteria for the Likert scale were typically based on a specific score threshold, either above or below. However, in this study, the Likert scale score was around the midpoint of the scale (1–7 points), which is approximately 4 points. Thus, participants with an average Likert scale score of 6 points or more or 2 points or less were excluded from the analysis as extreme responses. The same question was asked repeatedly. We considered that the reliability of the data was low for participants whose responses varied significantly for the same question. Therefore, data from participants with a standard deviation (SD) of 0.5 or more were also excluded from the analysis. Additionally, if participants gave the same response to all items (e.g., answered “4” to all questions), we evaluated that they did not recognize the game situation. However, excluding such participants using the two methods described above was difficult. Therefore, data from participants whose SD for all questions, including the Likert scale, was 0.5 or less, were excluded from the analysis.

The data on all 36 count items (a combination of out, ball, and strike counts) were subjected to an analysis of variance (ANOVA) using the factor of position (pitcher vs. batter). Ceiling and floor effects were not confirmed because all counts were included in the model. Factor analysis by the maximum likelihood method was performed on 36 items that averaged the positions of runners, and the number of factors was determined. Factor analysis by the maximum likelihood method and promax rotation was performed again. In the factor analysis, items with a factor loading of .30 or less for all factors, and those with an absolute difference in the absolute value of factor less than 0.1, were judged to have multiple loads and excluded from the analysis, and the factor analysis was repeated. Subsequently, the average value of the cognitive evaluation score of the game situation was calculated for each factor. The average value between the factors was compared using ANOVA. Additionally, we performed a two-way ANOVA using count and position factors. The data on the out, ball, and strike counts were separately analyzed because interpreting these interactions was quite complex. The Bonferroni correction was used for multiple comparisons. Values were expressed as mean ± standard deviation, and the significance level was set at p < 0.05. SPSS Ver. 26 for Windows (IBM) was used for the statistical analysis.

The number of valid responses in this study was 396 (of 544 respondents, 72.79% responded with 294 batters and 102 pitchers were valid). The average Likert scale items for all respondents (n = 544) was 4.12 ± 1.21, and the average SD was 0.38 ± 0.51 points. The average SD for all respondents (n = 544) and all items were 1.51 ± 0.43 points.

Table 1 lists the average values of the ball and strike counts based on the out count. All items averaged 3.50 ± 1.56 points (average 3.47 ± 1.39 points for batters and 3.50 ± 1.23 points for pitchers). ANOVAs for the mean value showed no significant differences between the pitcher and batter ( F (1, 394) = 0.20, p > 0.05, η 2 = 0.05), while that for the SD showed a significant difference between them ( F (1, 394) = 15.27, p < 0.01, η 2 = 0.45).

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https://doi.org/10.1371/journal.pone.0309328.t001

ANOVAs for the mean value showed significant effects of out count ( F (2, 790) = 403.14, p < 0.01, η 2 = 0.51), ball count ( F (2, 790) = 339.23, p < 0.01, η 2 = 0.46), strike count ( F (2, 790) = 554.02, p < 0.01, η 2 = 0.58), and runner position ( F (2, 790) = 412.86, p < 0.01, η 2 = 0.51). These data indicate that the values for out and strike counts increased with increasing counts ( p < 0.01). However, the ball count decreased with increasing counts ( p < 0.01, all).

Owing to the factor analysis by ball, strike, and out counts, 36 items from the four factors were extracted. The decreasing eigenvalues in order were 32.10, 28.58, 8.80, and 4.43, and the four-factor structure was considered appropriate. Therefore, the factor number was fixed at four, and factor analysis was performed using the maximum likelihood promax rotation ( Table 2 ).

https://doi.org/10.1371/journal.pone.0309328.t002

The cumulative contribution rate of these four factors was 72.17%. The first factor (F1) was named the “Batter advantage count.” This factor included 1 out, 3 balls, and 1 strike. The second factor (F2) consisted of 1 out, 2 balls, 2 strikes, etc., and was named “Pitcher’s advantage count.” The third factor (F3) was named “2 out young count.” This factor included 2 outs, 0 balls, and 0 strikes. The fourth factor (F4) was named “0 out young count.” This factor consisted of 0 outs, 0 balls, 0 strikes, etc. The α coefficients for each factor were F1 (α = 0.949), F2 (α = 0.939), F3 (α = 0.863), and F4 (α = 0.860).

Table 3 shows the differences in cognitive evaluation scores between pitchers and batters. The game situation recognized by the players as the batter’s advantage was 0 outs, 3 balls, and 0 strikes. However, the game situations that were recognized as the most advantageous for the pitcher were 2 outs, 0 balls, and 2 strikes. Significant differences were observed for 17 of the 36 items. The ANOVAs for the mean values of the position and the cognitive evaluation scores of the four factors showed significant interaction ( F (3, 1182) = 14.38, p < 0.01, η 2 = 0.035). Post-hoc multiple comparisons demonstrated significant differences among all groups, except between pitchers F2 and F3 when examining by factor ( p < 0.01, all). Significant differences were observed between the pitchers and batters in F1 ( p < 0.01), F2 ( p < 0.01), and F4 ( p < 0.01) ( Fig 1 ). The factors were F2 “Pitcher advantage situation,” F3 “2 out young count,” F4 “0 out young count,” and F1 “Batter advantage count” in the order of pitcher advantage.

https://doi.org/10.1371/journal.pone.0309328.g001

https://doi.org/10.1371/journal.pone.0309328.t003

The average of all items was 3.50 ± 1.56 points, which was lower than the middle score (Neither) and indicated a value slightly closer to the batter’s advantage. The reason is that each average value of the out, ball, and strike counts used the data obtained by averaging the positions of the runners (eight situations). In baseball games, the most frequently appearing situation is without a runner. Among the eight situations for the runner’s position, one situation is without a runner. A runner’s presence affects the defensive position of defensive players and is more advantageous to the batter. In this study, since the eight situations were averaged without weighting, the values were closer to the batter’s advantage.

We used a Likert scale that repeated the same questions and confirmed their consistency. For recurring questions, we selected 0 outs, 0 balls, and 0 strikes. The average score on this question was 4.03 ± 0.55 points. This value indicates that the perception of the game situation is generally neutral, in which neither the pitcher’s nor the batter’s advantage is inclined. Additionally, the average SD was 0.38 ± 0.51 points. SD 0.51 indicates the occurrence of the floor effect. Therefore, we reliably considered that many participants stably answered “Neither” during 0 outs, 0 balls, and 0 strikes.

However, the average SD for all items was 1.51 ± 0.43 points, which was larger than the average SD of 0.38 ± 0.51 for 0 outs, 0 balls, and 0 strikes. This data indicates that a few survey participants gave the same responses to all items. Additionally, Cronbach’s α coefficient during factor analysis was 0.8 or more for all four factors. Therefore, the reliability of the Likert scale used in this study was ensured.

The cognitive scores of the game showed significant effects of out, ball, and strike counts and runner position, which means that these factors affect the judgment of the advantages/disadvantages among baseball players. The effect size was highest in the order of runner position, strike count, ball count, and out count. Of these, the third strike is a strikeout, and the fourth ball is a walk, which is closely related to the rules of baseball games. However, increasing or decreasing the out count does not directly affect the hit results. Therefore, the effect size of the out count was likely low.

A previous study used On-base plus slugging (OPS) to evaluate batting results comprehensively and indicated that the average value of all counts was 0.742 [ 24 ]. Similarly, in this study, the maximum value was 1.761 with 3 balls and 0 strikes, and the minimum value was 0.476 with 0 balls and 2 strikes. The value for 0 balls and 0 strikes was 0.934, which was 0.192 times higher than the average value. Therefore, in actual baseball games, 0 ball and 0 strike counts provide the batter with an advantage, which may differ from the player’s perception. We hypothesized that baseball players misunderstand the actual advantages and disadvantages, especially for 0 balls and 0 strikes.

A study has recorded heart rates during actual baseball games and practices and investigated which factors varied among ball and strike counts and the presence or absence of runners [ 25 ]. A significant difference in heart rates was observed only for ball counts, indicating that this variable significantly affects the heart rate. Additionally, the pitcher’s ball speed, rotation speed, and release point change according to the ball and strike counts [ 26 ]. Therefore, our results support previous findings and suggest that the changing environment, including ball and strike counts, is directly related to changing performance.

Owing to factor analysis, four factors (30 items) were extracted ( Table 2 ). The characteristics of each factor are as follows: The first factor (F1) is “Batter advantage count.” In this factor, 3 or 2 balls, 0 or 1 strike, and the ball counts of all items exceeded the strike counts. The average score for the first factor was 2.40 ± 0.72 points. The second factor (F2) is “Pitcher’s advantage count.” For this factor, all 12 factors, except 1 out, 1 ball, and 1 strike, were 2 strikes, and all items had the same number as the ball and strike counts, or the strike count was higher than the ball count. The average score of the second factor is 4.48 ± 0.80 points, while that of the entire survey is 3.50 ± 1.56 points. Based on these data, these counts are advantageous for pitchers. The third factor (F3) was “2 out young count.” This factor was 2 outs for all items. The ball or strike count was 0 or 1 and does not indicate which count is more advantageous for the batter or pitcher because of the average of 4.00 ± 0.73 points. The fourth factor (F4) was “0 out young count.” This factor was 0 for all items, and the ball or strike count was 0 or 1. The fourth factor is the batter’s favorable count, with an average of 2.75 ± 0.71 points. Regarding the result of the factor analysis as a whole, the cumulative contribution rate is 72.17%, and the α coefficient is 0.8 or more for all four factors. The reliability of the factor analysis in this study is high.

We excluded 2 outs, 3 balls, and 2 strikes, and 2 outs, 2 balls, and 1 strike. The common item was 2 outs, and judging the advantages or disadvantages may be difficult because they have elements of the first and second factors, which are opposite. We also excluded 1 out, 1 ball, and 0 strike; 1 out, 0 ball, and 0 strike; 0 out, 1 ball, and 1 strike; and 1 out, 0 ball, and 1 strike that have 1 out as a common item. The other “1 out young counts” were categorized as batter- or pitcher-favorable.

In this study, the three variables of out, ball, and strike counts were used as the game situation factors. The factors were divided into four categories. In this classification, the batter’s advantages and disadvantages were first determined by the relationship between the ball and strike counts. The other young counts were classified using the out count. In other words, factors that could not be judged by ball and strike counts were evaluated. However, according to baseball rules, ball and strike counts strongly influence batting results, unlike the out count, which may have a limited effect on the batter’s outcome. Changes between 0 and 2 outs are related to the expected score, which is the average number of points scored from 0 outs, no runners on base, 2 outs, runners on first base, etc., until the end of the inning. A study analyzed data from 373 games and showed that the expected score was lower in 2 outs than for 0 outs [ 27 ]. Thus, a game situation of 2 outs may give pitchers the psychological leeway to believe that a hit is unlikely to result in a score.

Significant differences in the perception of the game situation between pitchers and batters were observed for 17 out of the 36 items (47.22%). When this result was examined by a factor, a significant difference was found between positions (pitcher vs. batter) in the first (F1), second (F2), and fourth factors (F4) ( Fig 1 ). The results showed that batters were more aware than pitchers that they had an advantage over pitchers in the first (F1) and fourth (F4) factors and a disadvantage over pitchers in the second factor (F2). The first (F1) and fourth (F4) factors were game situations in which the batter’s advantage could be obtained, whereas the second factor (F2) was the pitcher’s favorable game situation. We assume that the batter’s perception is more likely affected by the game situation than the pitcher’s. This notion is supported by the fact that the SD of the batter is greater than that of the pitcher. We hypothesized that this was related to the differences in perception between batters and pitchers, which were in turn based on the difference in the number of plays. Specifically, once a batter is at-bat, there is an interval of eight batters before the next at-bat. By contrast, a pitcher faces all batters continuously. The small amount of batting play in a baseball game may lead batters to be more sensitive.

We also considered that our data could not apply to and fit all cases in the game situation. For example, when the score is tied, the importance of performance increases in the ninth inning than in the first inning [ 1 ]. In other words, expectations for victory have changed, depending on inning. If the score is the same, that is. 1–0, the meaning of the situation can be very different. For instance, in the first inning, there are nine chances to turn the game around; by contrast, in the ninth inning, there is only one chance to turn the game around. It is conceivable that batters and pitchers consider the ninth inning to be more important than the first inning. For this reason, we expect that the perception of advantage or disadvantage of batters and pitchers based on outs, balls, and strike counts will fluctuate more in the ninth inning than in the first.

There were some limitations in the present study. The first was that the valid response rate was not high at 72.79%. This is due to the large number of questions in the study (296) and the repetition of similar questions. When conducting a questionnaire, it is desirable to set questions in such a way that the valid response rate is as close to 100% as possible. However, considering the questions and content, the burden on survey participants was high, and we believe that this level of valid responses is an acceptable proportion. Second, we did not take into account some game situations and factors, such as the inning (early, middle, or late), top or bottom of the inning, number of pitches per pitcher, score, or home or away games. For example, research showed that the weighting in the probability of winning differed between the first and ninth innings [ 1 ]. Additionally, other factors such as the batter’s good or bad performance may be related to the recognition of actual baseball games.

In conclusion, we repeatedly conducted cognitive evaluations of the game situation of baseball players. The baseball game situation was classified into four factors (Batter advantage count, Pitcher’s advantage count, 2 out young count, and 0 out young count), and significant differences in recognizing these factors were observed between batters and pitchers. Batters were more susceptible to game situations than were pitchers. These findings indicate that baseball players differentiate game situations, not only by ball and strike counts, but also by out counts. Our findings suggest that baseball players recognize several game situations rather than an infinite number.

Acknowledgments

We express our deep gratitude to the players, managers, and staff who participated in the survey.

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Performance prediction in major league baseball by long short-term memory networks

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• Volume 15 , pages 93–104, ( 2023 )

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• Hsuan-Cheng Sun   ORCID: orcid.org/0000-0002-6494-5834 1 ,
• Tse-Yu Lin 2 &
• Yen-Lung Tsai 3  

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Player performance prediction is a serious problem in every sport since it brings valuable future information for managers to make important decisions. In baseball industries, there already existed variable prediction systems and many types of researches that attempt to provide accurate predictions and help domain users. However, it is a lack of studies about the predicting method or systems based on deep learning. Deep learning models had proven to be the greatest solutions in different fields nowadays, so we believe they could be tried and applied to the prediction problem in baseball. Hence, the predicting abilities of deep learning models are set to be our research problem in this paper. As a beginning, we select numbers of home runs as the target because it is one of the most critical indexes to understand the power and the talent of baseball hitters. Moreover, we use the sequential model Long Short-Term Memory as our main method to solve the home run prediction problem in Major League Baseball. We compare models’ ability with several machine learning models and a widely used baseball projection system, sZymborski Projection System. Our results show that Long Short-Term Memory has better performance than others and has the ability to make more exact predictions. We conclude that Long Short-Term Memory is a feasible way for performance prediction problems in baseball and could bring valuable information to fit users’ needs.

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Al-Asadi, M.A.M.: Decision support system for a football team management by using machine learning techniques. Xinyang Teach. Coll. 10 (2), 1–15 (2018)

Google Scholar  

Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. 1409.0473 (2014)

Baumer, B., Zimbalist, A.: The sabermetric revolution: Assessing the growth of analytics in baseball. University of Pennsylvania Press, Philadelphia (2014)

Book   Google Scholar  

Breiman, L.: Random forests. Mach. Learn. 45 (1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324

Article   MATH   Google Scholar  

Brown, L.D.: In-season prediction of batting averages: A field test of empirical bayes and bayes methodologies. The Ann. Appl. Stat. 2 (1), 113–152 (2008)

Cao, L.: Domain-driven data mining: Challenges and prospects. IEEE Trans. Knowl. Data Eng. 22 (6), 755–769 (2010)

Article   Google Scholar  

Cao, L., Zhang, C., Yang, Q., Bell, D., Vlachos, M., Taneri, B., Keogh, E., Philip, S.Y., Zhong, N., Ashrafi, M.Z., et al.: Domain-driven, actionable knowledge discovery. IEEE Intell. Syst. 22 (4), 78–88 (2007)

Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:14061078 (2014)

Cross, J., Davidson, D., Rosenbloom, P.: Steamer projections. http://www.steamerprojections.com/ (2009), Accessed 30-May-2021

Goldschmied, N., Harris, M., Vira, D., Kowalczyk, J.: Drive theory and home run milestones in baseball: an historical analysis. Percept. Motor Skills 118 (1), 1–11 (2014)

Graves, A.: Supervised Sequence Labelling with Recurrent Neural Networks. Studies in Computational Intelligence, Springer, Berlin, (2012) https://doi.org/10.1007/978-3-642-24797-2 , https://cds.cern.ch/record/1503877

Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural Netw. 18 (5–6), 602–610 (2005)

Hamilton, M., Hoang, P., Layne, L., Murray, J., Padget, D., Stafford, C., Tran, H.: Applying machine learning techniques to baseball pitch prediction. In: ICPRAM, pp 520–527 (2014)

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/cvpr.2016.90 , (2016)

Hearst, M.A.: Support vector machines. IEEE Intell. Syst. 13 (4), 18–28 (1998). https://doi.org/10.1109/5254.708428

Herold, M., Goes, F., Nopp, S., Bauer, P., Thompson, C., Meyer, T.: Machine learning in men’s professional football: Current applications and future directions for improving attacking play. Int. J. Sports Sci. Coaching 14 (6), 798–817 (2019)

Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9 (8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735

Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. 1502.03167 (2015)

Jain, A.K., Mao, J., Mohiuddin, K.: Artificial neural networks: A tutorial. IEEE Comput. 29 , 31–44 (1996)

Jiang, W., Zhang, C.H.: Empirical bayes in-season prediction of baseball batting averages. In: Borrowing Strength: Theory Powering Applications-A Festschrift for Lawrence D, pp. 263–273. Brown, Institute of Mathematical Statistics (2010)

Karnuta, J.M., Luu, B.C., Haeberle, H.S., Saluan, P.M., Frangiamore, S.J., Stearns, K.L., Farrow, L.D., Nwachukwu, B.U., Verma, N.N., Makhni, E.C., et al.: Machine learning outperforms regression analysis to predict next-season major league baseball player injuries: epidemiology and validation of 13,982 player-years from performance and injury profile trends, 2000–2017. Orthopaedic J. Sports Med. 8 (11), 2325967120963046 (2020)

Kingma, DP., Ba, J.: Adam: A method for stochastic optimization. 1412.6980 (2014)

Koseler, K., Stephan, M.: Machine learning applications in baseball: A systematic literature review. Appl. Artifi. Intell. 31 (9–10), 745–763 (2017)

Kumari, M.: Data driven data mining to domain driven data mining. Global Journal of Computer Science and Technology (2012)

Li, C., Zhan, G., Li, Z.: News text classification based on improved bi-lstm-cnn. In: 2018 9th International Conference on Information Technology in Medicine and Education (ITME), IEEE, pp 890–893 (2018)

LLC, SR.: Baseball-reference.com - major league statistics and information. https://www.baseball-reference.com/ (2008), Accessed 30-May-2021

Lyle, A.: Baseball prediction using ensemble learning. PhD thesis, University of Georgia (2007)

Nair, V., Hinton, GE.: Rectified linear units improve restricted boltzmann machines. In: Fürnkranz J, Joachims T (eds) ICML, Omnipress, pp 807–814, http://dblp.uni-trier.de/db/conf/icml/icml2010.html#NairH10 (2010)

Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., Zettlemoyer, L.: Deep contextualized word representations. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) https://doi.org/10.18653/v1/n18-1202 (2018)

Pinheiro, P., Cavique, L.: A bi-objective procedure to deliver actionable knowledge in sport services. Expert Syst. 37 (6), e12617 (2020)

Qing, X., Niu, Y.: Hourly day-ahead solar irradiance prediction using weather forecasts by lstm. Energy 148 , 461–468 (2018)

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. OpenAI blog 1 (8), 9 (2019)

Raza, S., Ding, C.: (2021) News recommender system: a review of recent progress, challenges, and opportunities. Artificial Intelligence Review pp 1–52

Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/cvpr.2016.91 , (2016)

Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Representations by Back-propagating Errors. Nature 323 (6088), 533–536 (1986) https://doi.org/10.1038/323533a0 , http://www.nature.com/articles/323533a0

Sawicki, G.S., Hubbard, M., Stronge, W.J.: How to hit home runs: Optimum baseball bat swing parameters for maximum range trajectories. Am. J. Phys. 71 (11), 1152–1162 (2003)

Saymborski, D.: Zips. https://blogs.fangraphs.com/the-2021-zips-projections-an-introduction/ , Accessed 30-May-2021 (2004)

Schumaker, R.P., Solieman, O.K., Chen, H.: Sports data mining: The field, pp. 1–13. Springer, US, Boston, MA (2010). https://doi.org/10.1007/978-1-4419-6730-5_1

Schumaker, R.P., Solieman, O.K., Chen, H.: Sports knowledge management and data mining. Annu. Rev. Inf. Sci. Technol. 44 (1), 115–157 (2010)

Seber, G.A., Lee, A.J.: Linear regression analysis, vol. 329. John Wiley & Sons, Hoboken, New Jersey (2012)

MATH   Google Scholar  

Silver, N.: Introducing pecota. Baseball Prospect. 2003 , 507–514 (2003)

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. The J. Mach. Learn. Res. 15 (1), 1929–1958 (2014)

Sun, HC., Lin, TY., Tsai, YL.: Lstm-based approaches for the performance prediction in mlb. In: International Workshop on Domain-Driven Data Mining, https://datascience.utk.edu/content/dddm/ (2021)

Sutskever, I., Vinyals, O., Le, QV.: Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp 3104–3112 (2014)

Tango, T.: Marcel. http://tangotiger.net/marcel/ (2004), Accessed 30-May-2021

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, AN., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems, pp 5998–6008 (2017)

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Sun, HC., Lin, TY. & Tsai, YL. Performance prediction in major league baseball by long short-term memory networks. Int J Data Sci Anal 15 , 93–104 (2023). https://doi.org/10.1007/s41060-022-00313-4

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101 Baseball Essay Topic Ideas & Examples

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Baseball is a sport that has captivated the hearts and minds of millions of fans around the world. From the excitement of a home run to the strategy behind a well-executed double play, there are countless aspects of baseball that can be explored and analyzed. If you are tasked with writing an essay on baseball and are struggling to find a topic, fear not! We have compiled a list of 101 baseball essay topic ideas and examples to inspire your writing.

• The evolution of baseball: From its origins to the modern game.
• The impact of Jackie Robinson on breaking the color barrier in baseball.
• Analyzing the influence of Babe Ruth on the game of baseball.
• The role of statistics in baseball: How sabermetrics changed the game.
• The rise of analytics in baseball: Its advantages and disadvantages.
• The impact of performance-enhancing drugs on the integrity of baseball.
• The importance of sportsmanship in baseball: Examining famous incidents.
• The role of umpires in the game: How they shape the outcome.
• The impact of technology on baseball: From instant replay to pitch tracking.
• The significance of baseball in American culture: Reflections on its symbolism.
• The role of the designated hitter in baseball: Should it be adopted universally?
• The importance of team chemistry in successful baseball teams.
• The influence of the media on the perception of baseball.
• The significance of baseball stadiums: A comparison of iconic venues.
• The impact of international players on Major League Baseball.
• The role of baseball in promoting social change and activism.
• The psychology of baseball: Examining the mental aspect of the game.
• The role of managers in baseball: Their strategies and decision-making.
• The rise of women's baseball: Analyzing its growth and challenges.
• The impact of baseball on the economy: From ticket sales to merchandise.
• The role of youth baseball in developing future talent.
• The evolution of baseball equipment: From wooden bats to advanced technology.
• The importance of scouting and player development in baseball.
• The role of superstitions and rituals in baseball.
• The impact of free agency on player movement and team dynamics.
• The significance of baseball records: Breaking down the most notable ones.
• The role of baseball in promoting physical fitness and health.
• The impact of baseball on local communities: Case studies of minor league teams.
• The role of baseball in promoting diversity and inclusivity.
• The importance of teamwork in baseball: Lessons learned from successful teams.
• The influence of baseball on literature and popular culture.
• The role of baseball in fostering national unity during challenging times.
• The impact of the designated hitter rule on offensive strategies.
• The significance of the World Series: Examining its history and legacy.
• The role of baseball in promoting tourism: A study of baseball-themed attractions.
• The influence of baseball on other sports: Comparing strategies and techniques.
• The impact of globalization on the popularity of baseball.
• The significance of baseball cards: Their historical and monetary value.
• The role of baseball in promoting education and academic success.
• The psychology of a successful pitcher: Examining their mindset and strategies.
• The impact of the steroid era on the perception of baseball's golden age.
• The significance of baseball in wartime: Analyzing its role during conflicts.
• The influence of the media on player endorsements and sponsorships.
• The importance of sportsmanship in youth baseball: Lessons for young players.
• The role of baseball in promoting civic engagement and community service.
• The impact of the designated hitter on the National League: Should it be adopted?
• The significance of baseball in overcoming societal prejudices: Case studies.
• The evolution of baseball strategies: From small ball to power hitting.
• The role of baseball in the integration of immigrants into American society.
• The impact of injuries on player careers and team performance.
• The significance of baseball rituals and traditions: A comparative analysis.
• The influence of baseball on American slang and idioms.
• The importance of sportsmanship in the rivalry between baseball teams.
• The role of baseball in promoting gender equality and women's empowerment.
• The impact of rule changes on the pace of the game: Analyzing their effectiveness.
• The significance of baseball in building character and life skills in young athletes.
• The influence of baseball movies on popular culture and fan perception.
• The importance of baseball in preserving local history and heritage.
• The role of baseball in promoting environmental sustainability: Green initiatives.
• The impact of baseball on the tourism industry: A study on baseball tourism.
• The significance of baseball in shaping national identity: Case studies.
• The influence of baseball statistics on player contracts and salaries.
• The importance of baseball in promoting resilience and perseverance.
• The role of baseball in promoting social integration and breaking down barriers.
• The impact of analytics on player development and scouting.
• The significance of baseball in promoting community cohesion: Case studies.
• The influence of baseball on the development of sports broadcasting.
• The importance of baseball in teaching life lessons to young players.
• The role of baseball in promoting cultural exchange and diplomacy.
• The impact of baseball on the physical and mental health of fans.
• The significance of baseball in promoting patriotism and national pride.
• The influence of baseball on fashion and popular trends.
• The importance of baseball in promoting discipline and self-control.
• The role of baseball in promoting volunteerism and community service.
• The impact of baseball on the entertainment industry: From movies to music.
• The significance of baseball in promoting intergenerational bonding: Case studies.
• The influence of baseball on the development of sports journalism.
• The importance of baseball in promoting fair play and respect for opponents.
• The role of baseball in fostering a sense of belonging and identity.
• The impact of baseball on the physical and mental well-being of players.
• The significance of baseball in promoting social justice: Case studies.
• The influence of baseball on the development of sports photography.
• The importance of baseball in promoting teamwork and cooperation.
• The role of baseball in promoting cultural diversity and inclusivity.
• The impact of baseball on the tourism industry: A study on baseball-themed vacations.
• The significance of baseball in promoting physical education in schools.
• The influence of baseball on the development of sports broadcasting technology.
• The importance of baseball in promoting leadership skills and responsibility.
• The role of baseball in fostering a sense of community and belonging.
• The impact of baseball on the physical and mental health of youth players.
• The significance of baseball in promoting national unity: Case studies.
• The influence of baseball on the development of sports journalism ethics.
• The importance of baseball in promoting perseverance and resilience.
• The role of baseball in promoting cultural exchange and understanding.
• The impact of baseball on the tourism industry: A study on baseball-themed tours.
• The significance of baseball in promoting physical fitness in schools.
• The influence of baseball on the development of sports broadcasting techniques.
• The importance of baseball in promoting teamwork and collaboration.
• The role of baseball in fostering a sense of cultural diversity and acceptance.
• The impact of baseball on the physical and mental well-being of amateur players.
• The significance of baseball in promoting social cohesion and harmony.

With these 101 baseball essay topic ideas and examples, you are sure to find the perfect topic to write about. Whether you are interested in the historical aspects of the game, statistical analysis, or the impact of baseball on society, there is something for everyone. So grab your pen and paper, or fire up your computer, and start exploring the fascinating world of baseball through your essay!

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Baseball is the oldest of the four major professional sports leagues in the United States and Canada. While it traces its history back to the 18th century, it wasn't until the 20th century that it really became America's national pastime. And as it grew, it became big business. Going from a handful of teams in the early years to 30 Major League teams—15 in the National League and 15 in the American League—in 2018. Add to that, there are over 250 minor league teams all of which have their own fan bases.

Like all professional sports, baseball has the potential to bring in millions of dollars in profits. But only if people watch and continue to watch. In the beginning that meant watching in person during the day, but time brought changes and that meant more fans, and more fans meant more revenue. Baseball was first broadcast on the radio in 1921, the first night game was played in 1935, the first games on television were broadcast in 1948, and the number of games played per season increased. Radio and then television and cable each brought in additional revenue, which has supplemented the constant source of revenue from in-person attendance. In addition, the League has made money though licensing and other avenues.

One early example was the collecting of baseball cards. Baseball advertising in the United States began with the images of baseball players on cards sold with tobacco products in the 1880s through World War II. But then the baseball card industry took on a life of its own with the increasing popularity of the cards as collectibles. There has also been an increase in income from the licensing of baseball equipment as well as the sale of team related memorabilia for both major and minor league teams including jerseys, hats, etc. Teams and the League also look to special events like the World Series, the All-Star game/All-Star weekend, and spring training and to increase viewership and interest and to increase revenue.

The business of baseball has been impacted by the relationship between owners and players, leagues and MLB players' unions (minor league players are not unionized), and team lawyers and players' lawyers. Those that research this industry claim that the Major Baseball League's monopoly, negotiations for increasingly large salaries for players, strikes by players, ticket prices, and the aging of the core fan base are all factors that have contributed to the slowed growth of this industry in recent years. But baseball is not the only professional sport; it competes with others, most particularly basketball and football and increasingly soccer as well.

One newer trend in baseball is "sabermetrics" - the statistical analysis of baseball using statistics to measure in-game activity - which has also impacted the business end of the sport even though it is focused more on the sport itself. It is based on the acronym SABR, which stands for the Society for American Baseball Research. While its current form was pioneered by Bill James, it traces its history back to Earnshaw Cook’s book Percentage Baseball . A prime example of this is seen in the book Moneyball: The Art of Winning an Unfair Game by Michael Lewis.

Books & Periodicals

These are just a few of the more business-themed resources related to baseball. Note that there may also be relevant information in the General Resources section of this guide.

The following materials link to fuller bibliographic information in the Library of Congress Online Catalog . Links to digital content are provided when available.

Internet Resources

We have included some resources that are not business specific in an effort to provide sources that can help researchers understand the sport itself and its structure.

• "Baseball is struggling to hook kids — and risks losing fans to other sports" External This article was published in the April 5, 2015 Washington Post looks at how kids not becoming interested in baseball in the face of the popularity of other sports and how that may impact players and fans of the future.
• "The Economic History of Major League Baseball"  External This article from the online EH.net Encyclopedia of Economic and Business History, provides an overview of the economic history of baseball, and includes tables of players' salaries, ticket costs and franchise values from 1920-2002. This essay also includes a comprehensive bibliography.
• "Revenue Sharing in Major League Baseball: The Moments That Meant so Much" External This article by Duate Rockerbie and Stephen Easton looks at revenue sharing in Major League Baseball. It ran in the International Journal of Financial Studies (vol. 6, no. 3, September 2018). Also available in the ABI-INFORM database.
• "Savvy or collusion? Why baseball’s free-agent market has turned ice-cold" External This article from the February 25, 2018 Economist looks that the changing relationship players have with free agency and the salary cap.
• "Why minor league baseball players haven’t unionized" External In this article Marc Normandin looks at the particular issues that may explain why minor league baseball players haven't unionized even though major league players have.
• Banch Rickey scouting reports The collection at the Library of Congress, includes approximately 1,750 baseball scouting reports from the 1950s and 1960s.
• Baseball Almanac External A comprehensive and freely available Web site which includes information on the economic aspects of baseball.
• Baseball Americana Baseball Americana features items from the Library of Congress collections and those of its lending partners to consider the game then and now—as it relates to players, teams, and the communities it creates.
• Baseball Resources at the Library of Congress The is a guide to baseball-related materials available on the Library's website and in its physical collections.
• Business of Baseball Committee (SABR) External Business of Baseball Committee was founded in 1994 to study all aspects of baseball administration and off-the-field activity including economic, organizational, labor and legal issues. The committee's old site offered a variety of research tools for studying the business aspects of professional baseball, including databases and spreadsheets, documentation by jurisdictions exploring relocation and/or expansion, current and historical documents, biographies of individuals related to the business of baseball, selected book reviews, articles, papers, and commentaries, as well as interviews with individuals that have had an impact on the business. The archived version External is still available online.
• Economic: Business of Baseball External This is series of lesson plans for teachers that they can use to teach students to basic economic concepts such as goods and services, supply and demand, and competition. Developed by the National Baseball Hall of Fame.
• Emergence of Advertising in America: Tobacco Advertisements External Images of baseball trading cards used by tobacco companies between 1850 and 1920 to advertise their products.
• Giamatti Research Center - Baseball Hall of Fame External Search the online catalog of the National Baseball Hall of Fame Library. Contains the most extensive collection of archival material devoted exclusively to baseball in the world.
• Sean Lahman Baseball Archive External Created to be a repository for statistics and historical information. Covers individual and team statistics that covers the game back to 1871.
• Society for American Baseball Research (SABR) External Established in Cooperstown, New York in August, 1971. Fosters the study of baseball, assists in developing and maintaining the history of the game, facilitates the dissemination of baseball research, stimulates interest in baseball, and safeguards the proprietary interests of its members' research efforts.
• Spotrac.com - MLB External The site was begun as a tool for fantasy players but not includes team payroll, player valuation, and is more of an overall research tool.

Official Sites

• World Baseball Softball Confederation (WBSC) External World Baseball Softball Confederation was established in 2013 by the historic merger of the International Softball Federation (ISF) and International Baseball Federation (IBAF), the former respective world governing bodies for baseball and softball. Website includes member directory, as well as rules, history, events, and other information about baseball and softball.
• Major League Baseball Players Association External History of this association reflects the labor history of baseball.
• Minor League Baseball (MiLB) External Includes, news, scores, states, history etc.
• Major League Baseball (MLB) External Includes, news, scores, states, history etc.
• CBS Sports - MLB External
• ESPN - Baseball External
• MLB Network External MLB Network launched on January 1, 2009 and features live regular season and Postseason game telecasts, original programming, highlights, and insights and analysis.
• Sports Illustrated - MLB External

Special Collections at the Library of Congress

If you are looking to search the catalog for more general titles see the Search the Library's Catalog page. Additional works on the baseball business in the Library of Congress may be identified by searching the Library of Congress Online Catalog under appropriate subject headings. Choose the topics you wish to search from the following list of subject headings to link directly to the Catalog and automatically execute a search that will allow you to browse related subject headings. For assistance in locating the many other subject headings which relate to baseball as a business, please consult a reference librarian .

• Baseball--Economic aspects.
• Baseball fields--United States--Finance.
• Baseball--Finance.
• Baseball--Management.
• Baseball--Law and legislation--United States.
• Baseball teams--History.
• Baseball teams--United States.
• Baseball team owners--United States.
• Baseball players--Labor unions.
• Baseball players--United States--Salaries.
• Collective bargaining--Baseball--United States.
• Major League Baseball (Organization).
• << Previous: General Resources
• Next: Basketball >>
• Last Updated: Jul 3, 2024 11:51 AM
• URL: https://guides.loc.gov/sports-industry

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Baseball Research Topics

• Baseball’s Development From Earlier Bat-And-Ball Games
• American Baseball’s Past
• A Comparison and Contrast of the Massachusetts Game and Modern Baseball
• Baseball in the Steroid Era
• Baseball in American Society
• A Comparison of British and Finnish Baseball
• Baseball in International Literature, Number Seven
• Baseball for Women
• Baseball and Cricket Comparison
• Baseball Players’ Roles

Interesting Baseball Topics to Write About

• How the 1919 World Series Changed Baseball for Good?
• Who Won the National League Most Valuable Player Individual or Team Award for Major League Baseball in 2011?
• Baseball and Fast Pitch Softball Games Are Comparable
• Baseball and Football as American Pastimes Are Contrasted
• How Baseball Aided Me in Overcoming Life’s Struggles?
• Black Baseball Players
• A Review of Baseball or Soccer? by David Brooks
• Evaluation of the Major League Baseball Website
• Baseball Analysis: A Vital Aspect of American Pop Culture
• Baseball Stadium Analysis
• Analysis of Major League Baseball’s Economic Structure
• Minor League Baseball Analysis
• The New York Yankees, Baseball’s Most Successful Franchise,
• Baseball Caps Increase Worker Motivation and Productivity
• Baseball and Basketball: A Comparison and Contrast
• Baseball and Softball: Differences and Similarities

 Baseball Research Paper Topics

• The Contribution of African Americans to Major League Baseball
• What Baseball Has Done for Me?
• How Baseball Made It Through the Great Depression
• What Impact Did Baseball Have on Cuba in the Mid-20th Century?
• What Impact Has Television Had on Baseball?
• How the Civil War Aided in Creating Baseball’s Great Game?
• How to Play Baseball While Staying Healthy?
• How Does Fantasy Baseball Work?
• Major League Baseball Wages
• Baseball Pitcher Substitutions in Major League Baseball: Evidence of Heuristic Managerial Decision-Making in Stopping on Nine
• What Is Baseball’s Steroid Situation?
• Why Is Baseball the Most Incredible Sport?
• Winning or Losing Teams: Who Emerged Into Major League Baseball Quicker?
• Why Do I Enjoy Watching Baseball So Much?

 Baseball Argumentative Essay Topics

• A Negative Episode in My Baseball Career
• Baseball’s History as One of the United States Favorite Sports
• The Life Story and Works of Professional Baseball Player Jack Roosevelt Robinson
• The Life Story and Works of American Baseball Player Joseph Jefferson Jackson
• Roberto Clemente Walker, a Baseball Player From Puerto Rico, and His Career
• An Account of Babe Ruth
• Sports Legend Ted Williams’ Career Highlights
• Baseball-Related Stadium Advertising
• Major League Baseball’s Past Involvement With African Americans
• The American Civil War’s Baseball History
• Major League Baseball Has a History of Steroid Use
• History of the American All-American Girls Professional Baseball League
• Anabolic Steroids Ruin Major League Baseball.
• Assessment of Baseball Game Attendees’ Customer Satisfaction at Yankee Stadium
• A Connection Between Baseball and the American Civil War
• Baseball Attendance and the Hypothesis of Uncertainty of Outcome
• Models for Business in Baseball, Football, and Basketball
• Divvying Up Baseball Revenue

Baseball Essay Titles

• The Practice of Fraud in Baseball
• Baseball’s Globalization and Its Effects
• The Baseball Myth
• Baseball Used as a National Pastime to Illustrate Economic Principles
• Major League Baseball Pay and Performance: The First Family of Free Agents Case
• Baseball Physics
• Stadiums for Professional Baseball Are “Old,” Yet New Building Trends
• Risk Control for Baseball Use in a City Stadium
• The Economic Effects of Baseball Players Exported to the USA on the Dominican Republic
• Baseball in the 21st Century and Economics
• Baseball Leagues for Women in History
• Major League Baseball Work Incentives and Salary Distributions

 Baseball Research Questions

• What Effect Did Racism Have On Baseball?
• How Much Do Baseball Players Get Paid?
• What Impact Did Babe Ruth Have On Baseball?
• Does the Labor Market for Baseball Conflict With the Human Capital Investment Model?
• How Have Baseball’s Rules Changed?
• Was Baseball Invented by Abner Doubleday?
• How Did Baseball Make It Through the Depression?
• Could Women Play Baseball?
• How Did Jackie Robinson Impact Baseball?
• Are Pitchers Valued Fairly in the Baseball Labor Market?
• What Impact Did Baseball Have on Cuba in the Middle of the 20th Century?
• Do MLB Baseball Players Get Paid Too Much?
• How Have the New York Yankees Helped Baseball?
• What Impact Has Baseball Had on America?
• Does Option Theory Apply to Contracts in Major League Baseball?
• How Has the Development of Technology Over the Past Decades Affected Baseball?
• Should the DH Be Banned in Baseball?
• How Did Baseball Get Ruined by Steroids and HGH?
• Should Steroid-Use Baseball Players Be Allowed in the Hall of Fame?
• How Has Baseball Been Affected by Television?
• Doubleheaders in Major League Baseball: Were They a Mistake?
• Winning or Losing Teams: Which Integrated Major League Baseball Faster?
• Why Is Baseball Such a Big Deal in America?
• What Does a Baseball Player’s Mental Health Affects Their Career?
• Should Baseball Use Instant Replay More Frequently to Review Close Plays at the Bases?
• Do Some Us States, Baseball Lose to Soccer?
• Should Tax Be Paid by Citizens Fund Baseball?
• Can Baseball Reduce the Symptoms of Mental Illness?
• Should the Baseball Tournament Pricing Policy Be Reviewed?
• Which Nations Can Rival the United States in Baseball Ratings?

Cosmetics Essay Topics

The little rock nine: everything you need ....

Matthew Lynch

Related articles more from author, cognitive therapy essay topics, questions about illegal immigration for research paper, civil rights essay topics, fascinating civil engineering essay topics, fascinating topics to write about children’s rights, good research topics about international studies.

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Any ideas for an MLB related research topic?

Hey guys, I'm writing a research paper for my Comp class and I'd really like to center it around baseball/MLB. Right now the plan is to write it about drug policy in MLB and which drugs should be tested, should their be a standardized penalty for first time abusers, should there be an eventual lifetime ban for repeat offenders, what drug use does the integrity of the game, etc..

I'm worried that I won't be able to write 10-15 pages on this, however. So, is there anything you guys have in mind? Anything you'd like to read or know more about? It's pretty much wide open as far as what the topic can be. The only rule he gave us was that it has to research a complex question, and not a simple yes/no deal.

Any help is GREATLY appreciated!

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Publication Guidelines

SABR’s publications are written by SABR members showcasing the latest baseball research and advancement of our knowledge of the sport. Cutting across a wide range of interests and scholarly disciplines, SABR publications encompass history, statistical analysis, sociology, psychology, physiology, physics, geography, architecture, botany, and much more, as befits the society’s mission to advance baseball knowledge.

We’re interested in hearing from any member who wants to share a piece of research on any baseball topic that would interest a portion of the membership. (Note: You must be a SABR member to be considered for inclusion in a SABR journal or publication. If you are not a member, click here to join .)

If you’re interested in writing a story for a SABR publication, here’s some information that should help:

Ongoing Writing Opportunities

SABR members have many opportunities to publish their research.

The Baseball Research Journal (BRJ) is SABR’s flagship publication, published twice a year (spring and fall). Established in 1972, BRJ was created so that members could publish and share their research with like-minded students of baseball. Today, BRJ provides a unique mix of cutting-edge baseball research and historical and biographical articles. Click here to learn more about submitting an article for the Baseball Research Journal .

The National Pastime is SABR’s convention-focused journal. Published annually, this journal provides in-depth articles on all aspects of baseball history related to the region in which that year’s convention is being held.

Articles may also be considered for online publication at SABR.org, especially if they are more timely in nature or they do not fit the theme of an upcoming journal as chosen by the editors. You can also check out our list of research committees , which publish regular newsletters that include member articles. Some, like the Baseball Cards Committee or the Baseball Landmarks Committee , also run their own websites/blogs and are always looking for articles on those subjects.

Contact Publications Editor Cecilia Tan at [email protected] or Director of Editorial Content Jacob Pomrenke if you have questions on which editor should receive your article.

• Submit an article to the Baseball Research Journal
• Submit an article for the SABR Baseball Biography Project
• Submit an article for the SABR Games Project
• Get involved: Click here to learn more about our upcoming book projects and how you can contribute as a writer, editor, or fact-checker
• New web or book projects: Click here to learn more about submitting a new web or book project proposal to the SABR Editorial Board

Submission Guidelines

1) Query first

If you’re unsure if your topic would be suitable for submission, please feel free to query first by emailing a short synopsis of the article and expected word length. Email queries to [email protected] . A good query includes not only a paragraph that is a brief but detailed encapsulation of the project, but also why you are interested in it or qualified to research it. Be specific about the sources you plan to use and what of your research is original. If you can estimate the length of the finished article, that is good, too. Typical BRJ articles are about the size of a midterm paper, not a masters thesis. Articles should typically be between 2000 and 7500 words in length. For pieces outside this size range, please query by email to [email protected] . Once you query, the editor will give you an idea of whether the idea sounds like a fit.

All articles must meet academic standards of quality and conform to the Chicago Manual of Style. For more details, see below.

2) Proper preparation of a manuscript

The document should have your name removed from the byline and be anonymized. Do not include a bio as part of the main document and be sure your digital signature has been wiped from the document “Properties.” Also do not include your name in the filename.

Academic standards of quality does not mean the paper should be written in “academic language.” It does mean the paper should be free of hyperbole, unsupported opinion, and plagiarism, and all facts and assertions should be include endnote citations. A personal touch is acceptable, but research should not rely heavily on personal reminiscence except in rare exceptions.

All submissions should conform to the Chicago Manual of Style standards of punctuation, grammar, and citation. We expect the paper to include endnote citations in Chicago style. More details below but let us stress up front that proper citation and endnotes are very important. Please note that “Ibid.” and “Op. Cit.” are no longer the standard for repeated citations AND WILL NOT BE ACCEPTED.

Before submission make sure you have used spell-check on the document and also that you have given it a basic fact-checking pass. At minimum that means double-checking the spelling of every proper noun, name, or place, and every date, and every number. (Please note that sometimes the date of reporting for a historical newspaper will be the same date as an occurrence, while other times the date of an article is the day after the occurrence.)

Submissions should be made as MS Word (.doc or .docx) or OpenOffice (.odt) documents attachments. Please use Times New Roman 12 point for the text of the document, and if you prefer, any sans serif or monospace font for tables and charts (Arial, Helvetica, Courier, etc.) PDF submissions are acceptable for peer review purposes but a Word or .odt version will be needed if the paper is approved for publication. If you are using LaTeX to produce your manuscript, you will need to convert it to MS Word if it is accepted.

Upon initial submission, graphics such as photos, pie charts, plots, and large mathematical equations should be embedded in the main document. If necessary, they may also be submitted separately as JPG, PNG, TIF, or PDF format. All graphics should be the creation of the author, be in the public domain, or bear permission to reprint from the rightsholder of the image. (Some exceptions may apply. Please inquire with the editor if you are unsure.) Captions and copyright credits should be included alongside the images within the main document.

Note : Do not submit anything that you do not hold the rights to reproduce (such as charts or graphs pulled off the Internet or photocopied out of books, photos downloaded from the Internet, or other graphics you did not create or which you cannot prove are in the public domain).

Pre-Submission Checklist:

• Is your manuscript document anonymized? (your name and identifying elements removed)
• Are your endnote citations numbered sequentially throughout the document?
• Have you used a short form of citation when sources are repeated (and NOT ibid or op.cit.)?
• Have you avoided using Wikipedia as a source? (Wikipedia is not accepted as a source.)
• Is your SABR membership up to date?

More details on the above checklist is in the FAQ below.

3) HOW TO SUBMIT

Submissions should be made by submitting to our online submission portal: sabrjournals.moksha.io

Any supplementary material (additional photos, large scans & graphics, supplementary spreadsheets, etc) should be submitted via email to [email protected] .

You’ll receive an acknowledgement email from the Moksha system denoting your place in the queue. In a separate email, you’ll also receive a copy of the permission form that SABR will need signed in order for us to have your permission to print the paper if it is accepted. Please sign and return the form according to the instructions you receive.

4) FREQUENTLY ASKED QUESTIONS

What is peer review?

All manuscripts that are formally submitted go through anonymous peer review. (The peer reviewers will not know the name of the author, nor vice versa.) The peer review process includes the recruiting of reviewers with expertise in relevant areas of knowledge, the soliciting of their specific feedback, and the collation of the feedback among two, or possibly three reviewers. This process may take anywhere from several weeks to 6-9 months, depending.

Each paper must have two peers recommend a paper for publication for it to pass review. If one recommends it and the other does not, it goes to a “tie-breaker” third reviewer.

Peer reviewers, even when they recommend a paper for publication, may have cogent suggestions for rewrite, clarification, or expansion of the original paper. They may also be able to provide leads or sources that the researcher lacked.

The majority of papers, even when enthusiastically endorsed for publication, will still require revision after review.

Others are rejected by the peer reviewers and sent back to the drawing board to start over completely.

How long does it take to get published in the BRJ?

The short answer is that it really depends. The average time to publication is 4-8 months, with 1 in 5 papers taking 8-12 months, and the occasional paper taking over one year to appear. (Note: in 2020 and 2021 submission volume ballooned and so did turnaround time, with several articles still in queue over one year later, but we hope to return to a normal schedule by 2023.)

Because the typical process for a paper takes longer than 6 months, there is typically no set deadline for an upcoming issue of the BRJ.

What happens if a paper is accepted? What’s the process?

If the peer reviewers agree a paper is a go, the author typically makes one last pass at revision based on their suggestions before the final version is submitted to editing. The rewritten draft should again be prepared in conformity with the same prep guidelines as before. (If the paper includes incorrectly used footnotes, failed to use proper citation form, etc… the paper will be returned to the author for revision again.)

The revised paper is then sent to the editor along with the high resolution versions of any graphics (300 dpi at 3.5 inches wide, at least), all captions and photo credits, and the author’s up to date bio (one for each contributor if there are multiple authors on a paper).

The paper will then go through one to two editing passes in-house at SABR. If that pass is light enough, it will also go through a fact-checking pass. After fact-checking and editing, the paper is then typically returned to the author to address any final queries.

Once a paper passes through copyediting and fact-checking, the next stage is layout and typesetting. A PDF version of the page proofs will be sent to the author for one more look as well as be professionally proofread before going to press. (We may come back to you for yet one more round of corrections if the proofreader finds problems.)

What should I write in my contributor bio?

Each paper when it is published is accompanied by a 50-100 word contributors note or bio that we may print with the piece. This note should be in the third person, i.e. “Barry Winters has been a SABR member since 1989.” and not in first person (i.e. do NOT write your note as “I have been in SABR since 1989.”) Typically a bio briefly mentions any previous publications, academic or institutional affiliations, and sometimes research interests, regional interest, and may also include contact information such as an email address if desired.

What is “Chicago Style”? (Can’t I use MLA or APA instead?)

Any publication has a style guide. At SABR we have our own, known as the SABR Style Guide, for all things relating to baseball terminology and statistical style, but in all other matters we conform the the Chicago Manual Of Style . This means no, you cannot use MLA, APA, or AP style instead, unless there is a really good reason why your discipline requires a different style. The SABR Style Guide is available online. The Chicago Manual of Style has many free online resources, including a citation guide .

What’s the difference between footnotes and endnotes?

We use endnotes and not footnotes, but the difference between them is that footnotes appear on every page, whereas endnotes all appear at the end of each article.

How do I do endnotes?

Use the built-in function of your word processor to insert notes, but use “end notes” instead of “footnotes.” (If you started with footnotes there is a setting in MS Word/Open Office to convert them to endnotes with the click of a button… just be consistent with whether you enter them as one or the other) Use the built-in function of MS Word (or OpenOffice) to automatically number the notes as they are inserted. This way of you move text around as you revise, the numbers will automatically re-number if you cut or rearrange the text in such a way as to require it.

You should be citing ALL your sources except when “common knowledge.” Tacking on a “Sources” list or “Bibliography” at the end is not sufficient in most cases. In fact, if your “Sources” list merely duplicates information that is contained in your end notes, do not include a Sources list. A Sources list should be included if it lists sources that would be vital to further research on your paper’s subject but which are NOT already cited in the notes.

A good online guide to when to put in a reference note is found at Johns Hopkins University: http://jhussi.org/308788

PLEASE NOTE: If you are adding your end notes by hand instead of using the built-in numbering function of your word processor: Each Numeral Can Only Be Used ONCE, and they must be used IN NUMERICAL ORDER. First note must be #1. Second must be #2. If the third citation is from the same source as #1 DO NOT “RE-USE” THE NUMERAL ONE. Third note must be #3 and either reiterate the entire citation or an abbreviated form on additional uses such as Last Name, Date, #.

ALSO NOTE: We do NOT use Ibid. or Op. Cit. anymore—please use a short version of a citation when a source is repeated.

What are the most important things to do to conform to Chicago Style?

Here’s a little checklist:

1) Endnotes should be numbered. Each number can be used only ONCE and they must be in numerical order in the text.

2) Be sure to italicize book titles, major newspapers, and journals/magazines in both the body of your manuscript and in your citations.

3) We use American date style. So Month Day, Year (i.e. April 17, 1967). No ordinals like “nd” or “th” on dates in either the article text nor in notes. Example: “Opening Day was on April 4 at the big ballpark.” not “April 4th”

4) End each note with a period.

5) Proper name format for citation notes is “First Last” not “Last, First.” (Bibliography style uses Last, First. These are end notes, though, and NOT a bibliography. Bibliography style is different.)

You might try using an online citation tool like Citation Machine which is free. To use it you visit the website http://www.citationmachine.net/ and select CHICAGO as the style, then search for the source you wish to cite. Copypaste the citation in and voila.

Examples of correct reference format:

David Halberstam, October 1964 (New York: Villard Books, 1994).

If referencing specific pages:

David Halberstam, October 1964 (New York: Villard Books, 1994), 84, 87, 101-102.

Newspaper articles

if bylined:

Mark Feinsand, “A-Rod to Skip HR Derby,” New York Daily News, June 30, 2008, D5.

if not bylined:

“Selig Announces Format Change,” Washington Post , May 30, 1996, 10.

If page number is unknown in a newspaper reference, simple leave it out. If it is from a newspaper website, style as above and include the access date and full URL of access:

Mark Feinsand, “A-Rod to Skip HR Derby,” New York Daily News, June 30, 2008. Accessed December 10, 2010: http://dailynews.com/sports/arod-to-skip.html.

Personal interviews

Joe Torre, telephone interview, May 8, 2007.

Articles from journals

Trent McCotter, “Hitting Streaks Don’t Obey Your Rules,” The Baseball Research Journal 37 (2008): 62-70.

When possible cite as if from a newspaper or magazine, but include the full URL to the article. Include the “Date accessed.”

Many more examples and details are available at the Chicago Manual of Style website, on their Notes and Bibliography style Quick reference page, which is FREE to access: http://www.chicagomanualofstyle.org/tools_citationguide.html

(make sure you’re looking at NOTES style and not bibliography)

What are the most common mistakes people make that break SABR style?

• Use the plural RBIs (not RBI, and not R.B.I.)
• Home run is two words.
• Center field, center fielder, two words.
• Spell out all months (October, not Oct.).
• No periods in most abbreviations (USA, MLB, RBI)
• Use full four-digit years when possible (1947) and do not abbreviate as ’47 unless necessary for style (i.e in a direct quote or brand name style etc).
• Spell out, i.e. “baseball in the fifties” and NOT “baseball in the ’50s.”
• Plural of a decade has no apostrophe (“baseball in the 1950s”).
• When the decade is used as an adjective, then a possessive s is needed (“a 1950’s style of uniform”).
• State names should be spelled out and be separated by two commas, i.e. “He was born in Wrentham, Massachusetts, and grew up in Boston.” and not “He was born in Wrentham, MA” nor “Wrentham, Mass.”

How do I prepare my graphics and charts? Are they good enough?

Electronic copies are preferred in JPEG or TIFF format at a minimum of 300 dpi with at least one side a minimum of 3.5 inches long for print publication. If you don’t know if your graphics are high resolution or low resolution, email a sample to [email protected] and we will let you know. We will have to reject low resolution images if they won’t reproduce well.

Interior graphics in our print journals are black and white. If your graphs have colored lines, they will be printing as gray. If you have both black and white and color versions of something, please send both as the color version can be used for the website. INCLUDE A CAPTION FOR EVERY GRAPHIC YOU SEND. Tables and charts should have titles.

Where can I find historical photos to illustrate my article?

If you have come across images in your research that would be apt illustrations, please note where they come from and research their availability. Photos published prior to 1926 are in the public domain. The Library of Congress has many public domain photos available for download and free use. Some other libraries have collections that can be used for a small fee (Brooklyn Public Library, University of Texas Library). SABR also controls The Rucker Archive of historical photos and we have partnerships with the Baseball Hall of Fame and some other providers.

If you are providing photos, be sure to provide whatever copyright and credit is necessary for all photos or artwork. If a photo comes from a library or book it needs to be credited and often permission to use it is necessary. Before you secure any permissions or pay any permissions fees, please check with SABR first.

Do I have to be a SABR member to submit?

It is longstanding SABR policy that only the work of SABR members is published in our journals. If you are not a member yet, you can wait until the results of your peer review are received before you register or renew. If you have multiple authors on your paper, only one needs to be a SABR member. You can register or renew your membership online at SABR.org/about/members-info (Also make sure we have your correct address in your member record, so you can receive your copy of the journal.)

I can’t get my tables to line up, nicely. What do I do?

If you have a table or a list that has more than two columns, the easiest way to make sure everything “lines up” properly is to do it in a spreadsheet like MS Excel. But within MS Word you can also do it by putting a SINGLE tab between each item in a row. No extra spaces or extra tabs, just one SINGLE tab between column items in a row, and we can typeset it smoothly. Do not try to make it all “line up” by hitting the space bar multiple times. That will mess everything up in layout. Word also has a “Table” function you can use. Once you have just a single tab between each item, Convert Text to Table, and Word (or OpenOffice) will align everything in cells like a spreadsheet.

Any other formatting instructions I should know?

Thanks for asking! Once your manuscript is all ready to go, make a final pass for these formatting issues:

a) only place ONE space after a period at the end of a sentence, not TWO.

b) do not put a tab or multiple smacks of the space bar at the beginning of each paragraph. (Instead use the ‘indent’ function of your word processor to create the indent for ease of readability.)

c) do not put spaces after an open parenthesis nor before a close parenthesis. (Like a period, the parenthesis should butt right against the words and other punctuation.)

d) when using an em dash or double-dash, do not put spaces before and after it.

e) do not place a blank line between each paragraph.

Copyright If we accept your article, we will send you a Contributor’s Release Form , which will make a few points clear. The two main ones are these: (1) you retain copyright, (2) you grant us First Serial Rights and the right to use the article on the SABR website and in any future SABR compendiums.

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