Foul Tip Impacts to Catchers’ in the American League East:

A Study of Head Impacts to Catchers

by

Graham Connell Tebbit

A thesis submitted in conformity with the requirements

for the degree of Master of Science

Graduate Department of Exercise Sciences

University of Toronto

© Copyright by Graham Connell Tebbit 2020

Foul Tip Impacts to Catchers’ Masks American League East: A Study of Head Impacts to Catchers

Graham Connell Tebbit

Master of Science

Graduate Department of Exercise Sciences University of Toronto

2020 Abstract

Purpose: To observe and describe foul tip impacts (FTIs) experienced by MLB catchers and to examine if batting performance was affected by FTIs. Method: Retrospective data analysis was conducted to examine FTIs in games (n= 648) observed using MLB TV. Descriptive statistics, incidence of, and absolute risk for FTIs were calculated. Mixed effects models were used to explore the association between FTIs and catchers’ batting performance. Results: 172 FTIs ranged in speeds (76.7-100.3mph) with 74% reaching 90mph or greater. There were 17.52 FTIs per 10,000 pitches thrown and 17 games with >1 FTI. FTIs varied between catchers (range: 0-32

FTIs). Neither FTI frequency nor speed predicted catchers’ batting performance. Contribution:

Described the characteristics and descriptive statistics of FTIs across four MLB teams. Catchers experienced many FTIs at speeds which caused concussion cited in previous research. These findings highlight the need for future research to explore the conditions surrounding FTIs and sports medical management practices.

ii

Acknowledgments

I would like to express my sincere appreciation to my supervisor, Dr. Lynda Mainwaring, for the opportunity to pursue a Master of Science degree. Your kindness, patience and wisdom have been invaluable in my pursuit of a research topic about which I am passionate. Under your tutelage, I have learned that success can only be achieved through persistence and devotion to my craft – with some focused, creative thinking, of course.

To my lab, you showed me what it means to be hardworking and diligent researchers. Your mentorship helped me grow, not only as a student but as a person, as well. Thank you for the lessons you taught me and the good memories we shared. I would also like to thank Alex Stringer for all his help and guidance that strengthened the statistical analysis of my data.

I would also like to thank my committee for “fielding” all of my questions over the last few years. Dr. Michael Hutchison, you taught me how to critically analyze the literature in your Neurorehabilitation and Exercise class which dramatically shifted the way I interpreted information. Dr. Doug Richards, your breadth of knowledge is astonishing, and I have learned much through our discussions. All your insight and guidance set me on a path to create the thesis it is today.

To my parents, Dave Tebbit and Debra Powell, words cannot convey what your tireless support and unconditional love has meant to me throughout this journey. You have always believed in me, through the highs and lows, which is all a son truly needs to know while in pursuit of his goals and dreams.

iii

Table of Contents

Acknowledgments...... iii

Table of Contents ...... iv

List of Tables ...... viii

List of Figures ...... ix

List of Appendices ...... x

Chapter 1: Introduction ...... 1

1.1 Current Knowledge Gaps ...... 3

Chapter 2: Review of Literature ...... 6

2.1 Head Impacts ...... 6

2.1.1 Baseball ...... 8

2.1.2 Subconcussive Impacts ...... 9

2.1.3 Potential Consequences ...... 10

2.1.4 Experimental Modeling of Mask Impacts...... 12

2.2 Performance in Sport, Baseball and Head Impacts ...... 14

2.2.1 Sports ...... 14

2.2.2 Baseball Performance ...... 16

2.2.3 Visual Acuity ...... 17

2.2.4 Vestibulo-Ocular Functioning ...... 17

2.2.5 Attention ...... 18

2.2.6 Reaction Time ...... 19

Chapter 3: Method...... 23

3.1 Operational Definitions ...... 23

3.1.1 Athlete Exposure ...... 23

3.1.2 Incidence of Catcher FTIs ...... 23

3.1.3 Absolute Risk of >1 FTI in a Game ...... 24 iv

3.1.4 Pitch Speed of FTIs ...... 24

3.1.5 On-Field Medical Attention ...... 25

3.1.6 Plate appearance (PA) ...... 25

3.1.7 At-bat (AB) ...... 25

3.1.8 Hit ...... 25

3.1.9 Batting Performance ...... 26

3.1.10 Expected-Weighted On-Base Average ...... 26

3.1.11 Slugging Percentage...... 27

3.1.12 Strikeout Rate...... 27

3.2 Pilot Study ...... 27

3.2.1 Results ...... 28

3.3 Purpose ...... 29

3.4 Research Questions ...... 29

3.5 Objectives ...... 29

3.6 Hypotheses ...... 30

3.6.1 Hypothesis 1...... 30

3.6.2 Hypothesis 2...... 31

3.7 Sample...... 31

3.8 Inclusion Criteria ...... 32

3.9 Data Collection ...... 32

3.9.1 Procedure ...... 32

3.9.2 MLB TV...... 32

3.9.3 Observation ...... 33

3.9.4 Baseball Savant and Statcast ...... 34

3.9.5 Batting Performance Statistics ...... 35

3.9.6 Data Management ...... 35 v

3.10 Data Analysis ...... 36

3.10.1 Incidence of Catcher FTIs ...... 37

3.10.2 Absolute Risk of >1 FTI in a Game ...... 37

3.10.3 Common FTI Factors ...... 37

3.10.4 Batting Performance ...... 38

Chapter 4: Results ...... 40

4.1 Description of FTIs ...... 40

4.1.1 Incidence of Catcher FTIs and Absolute Risk of >1 FTI in a Game ...... 40

4.1.2 Impacts by Team ...... 40

4.1.3 Catchers...... 41

4.1.4 Summary of FTI Pitch Speeds ...... 41

4.1.5 Pitch Type ...... 42

4.1.6 FTIs by Month ...... 43

4.1.7 Inning ...... 44

4.1.8 Count in the Plate Appearance ...... 45

4.1.9 Pitch location ...... 45

4.2 FTIs and Batting Performance ...... 45

4.2.1 Correlation of Response Variables ...... 46

4.3 Batting Performance for the Season ...... 46

4.3.1 The Number FTIs in Previous Seven Days by Games Played ...... 47

4.3.2 Group Performance by Frequency of FTIs Over the Previous Seven Days ...... 48

4.3.3 Individual Performance by the Frequency of FTIs ...... 50

4.3.4 Group and Individual Catcher Performance by FTI Release Speed ...... 50

Chapter 5: Discussion ...... 52

5.1.1 Incidence of Catcher FTIs ...... 52

5.1.2 Absolute Risk of >1 FTI in a Game ...... 53 vi

5.1.3 Common Characteristics of FTIs ...... 54

5.1.4 Catchers’ Batting Performance ...... 55

5.2 Limitations ...... 56

5.3 Delimitations ...... 57

5.4 Implications...... 58

5.4.1 Head Impact Exposure ...... 58

5.4.2 Catcher Mask Evaluations ...... 59

5.4.3 FTIs and Medical Assessment Decisions ...... 61

5.5 Future Directions ...... 61

5.5.1 Catcher Concussions ...... 62

5.5.2 Batting Performance ...... 63

Chapter 6: Conclusion ...... 65

References ...... 67

Appendix A: Sample Characteristics ...... 81

Appendix B: Inter-Rater Reliability Output for Mask Impact Locations ...... 82

Appendix C: FTI Descriptive Statistics ...... 84

Appendix D: Batting Performance Analyses and Results ...... 85

vii

List of Tables

Chapter 3

Table 1. Formulas for athlete exposures, incidence of catcher FTIs, and absolute risk of >1 FTI in a game.

Chapter 4

Table 2. Summary of FTI speeds (miles per hour)

Table 3. Seasonal pitch totals and FTIs by pitch type category

Table 4. Summary of FTIs by month and games played

Table 5. Summary of FTIs by early, middle, and late stages of the game

Table 6. Sampled catchers’ batting performance measures: Correlation of potential response variables

Table 7. Summary of batting performance results by season for starting catchers

Table 8. Summary of main findings

viii

List of Figures

Figure 1. Pitch and event description obtained through Baseball Savant.

Figure 2. Batting performance .csv file obtained through Baseball Savant.

Figure 3. Histogram of FTI release speeds.

Figure 4. FTI by month of the season. This figure illustrates the expected number of FTIs based on the average number of games played (orange dots) compared to the number of observed FTIs (blue bars).

Figure 5. FTI by location in the strike zone. This figure illustrates the number of FTIs that occurred at each location from the catcher's perspective. The red outline represents the middle strike zone locations.

Figure 6. Games played by the number of FTIs in the previous seven days. This figure illustrates the number for games played with the frequency of FTIs that occurred in the previous seven days from that competition day.

Figure 7. All catchers' pooled xwOBA by the number of FTIs over the previous seven days relative to the performance that day.

Figure 8. All catchers' pooled slugging percentage by the number of FTIs experienced over the previous seven days relative to the game being played on that date.

Figure 9. Catchers' pooled strikeout rate by the number of FTIs experienced over the previous seven days relative to the game being played on that date

Figure 10. All catchers' xwOBA by the average release speeds of FTIs sustained in the past seven days relative to the game being played on that date.

Figure 11. All catchers' slugging percentage by the average release speeds of FTIs sustained in the past seven days relative to the game being played on that date.

Figure 12. All catchers' strikeout rate by the average release speeds of FTIs sustained in the past seven days relative to the game being played on that date.

ix

List of Appendices

Appendix A: Sample Characteristics

Appendix B: Inter-Rater Reliability Output for Mask Impact Locations

Appendix C: FTI Descriptive Statistics

Appendix D: Batting Performance Analyses and Results

x

Chapter 1: Introduction

The cause of injuries to baseball players generally consist of player-to-player and player-to- stadium collisions, a hit by pitch while batting, or musculoskeletal injuries while running or throwing a ball (Conte, Camp, & Dines, 2016; Camp et al., 2018a; Camp et al., 2018b). Over the past two decades, there has been a significant increase of diagnosed concussions in Major League Baseball (MLB) (Sabesan et al., 2018; Ramkumar et al., 2018). Importantly, epidemiological research demonstrates catchers are significantly overrepresented with diagnosed concussions relative to the other eight on-field positions in baseball (Green et al., 2015).

In addition, foul tip impacts (FTI) have been identified as the leading cause of concussions to catchers (Green et al., 2015; Green et al., 2018). An FTI occurs when a hitter’s bat makes enough contact with a pitched ball to redirect it over the catcher’s glove and into his mask. The redirection of the FTI happens suddenly and the proximity of the catcher to the hitter affords them no time to react. A foul tip impact is different than the MLB’s definition of a “foul tip” which occurs when the pitched ball is redirected and securely caught in the catcher’s glove, as opposed to their mask.

Despite the catchers’ use of a mask in baseball, foul tip impacts can result in long-term impairment and, in some instances, these head impacts can have severe consequences. An example of the extent to which head trauma, caused by FTIs, can affect catchers is evident in the cases of Gold Glove Award winning catcher Mike Matheny, and MLB umpire Cal Drummond. Matheny, an MLB catcher, received six ball impacts to the mask in a week, the last of which was released at 100mph on May 31, 2006 (“Concussion Symptoms Force Matheny to Retire”, 2007; Ringolsby, 2017). Matheny was forced to retire a year later after suffering from post-concussion symptoms. In 1969, Cal Drummond, a major league umpire, received a foul tip impact to the mask but was seemingly unfazed. Drummond collapsed after the game and was rushed into hospital to remove a blood clot from his brain. The following spring Cal Drummond died from a cerebral infarction while umpiring a minor league game (“Cal Drummond Dies”, 1970). Examples such as these are not isolated cases of foul tip impacts having a detrimental effect on catchers’ and umpires’ health.

1 2

In addition, it is possible that many FTIs can occur during a season, many of which do not lead to a concussion or catastrophic brain injury; although, subconcussive impact research suggests these impacts may have unseen consequences. A subconcussive impact (SCI) is defined as “a cranial impact that does not result in a known or clinically diagnosed concussion” (Bailes et al., 2013, p.1236). Research suggests SCI and repetitive head impacts (RHI) may be associated with neuropsychological impairment (Tresilian, 2005; Clark et al., 2012; Muraskin, Sherwin, & Sajda, 2013; Hwang et al., 2017), the manifestation of neurological abnormalities (Bazarian et al., 2014; Breedlove et al., 2014; Poole et al., 2014; Bernick et al., 2015; Huber, Stein, Alosco, & McKee, 2016; Gallant, Barry, & Good, 2018), and a future risk of head injury in athletes (Rutherford, Stephens, Fernie, & Potter, 2009).

Catchers could be at risk of experiencing multiple impacts – in the same game or over several days – that exacerbate the consequences of prior head impacts or concussion. For example, concussion symptoms do not always manifest immediately after the mechanism of injury, rather the symptoms may develop over several hours (McCrory et al., 2017). The time necessary for the injured athlete to heal can vary and recent research suggests that physiological time to recovery may outlast symptom resolution (McCrory et al., 2017; Churchill et al., 2017). Recently, Francisco Cervelli, a catcher for the Pittsburgh Pirates, received an FTI to his mask and was st th removed from a game on June 1 only to be later diagnosed with a concussion on June 6 , 2017. After being placed on MLB’s 7-day concussion injured list, Cervelli returned to play in five games only to be removed with symptoms of a concussion. It is not clear whether Cervelli’s concussion was the result of exposure to FTIs preceding his injury because there is no system in place to track FTIs to catchers’ masks that do not result in diagnosed injury.

Multiple studies have found that catchers are significantly overrepresented with diagnosed concussions as compared to other positions in baseball (Dick et al., 2007; Collins et al., 2008; Green et al., 2015; Wasserman et al., 2015; Siu, Okonek, & Schot, 2016). Specifically, Green and colleagues (2015) found that catchers comprised 40.8% and 47.6% of all diagnosed concussions in Minor League Baseball (MiLB) and the MLB, respectively. Kilcoyne and colleagues (2015) found 11 catchers were diagnosed with a concussion from 2001 to 2010 in the MLB; and Wasserman et al. (2015) found 26 catchers were diagnosed with a concussion from 2007 to 2013. This indicates an increase in diagnosed concussions after 2007. The increase of catcher concussions is emphasized by public media reports:

3

Teams have put players on the disabled list (DL) due to concussions or head injuries 18 times this year, five more than all of last season, and seven more than in 2011, when the seven-day concussion DL was implemented. In 10 of those 18 instances, the players were catchers including Boston Red Sox’s David Ross twice (Ortiz, 2013, para. 3).

One possible reason for the increase in diagnosed concussions could be that players are more willing to take precautions with the implementation of Major League Baseball’s 7-day injured list in 2011; implemented solely for players who are diagnosed with a concussion. The DL – now termed “injured list (IL)” – allows teams to bring in substitute players from their minor league teams without that player taking up an active roster spot on their limited 25-man roster. However, prior to 2011, if a player was diagnosed with a concussion, he would be put on the 15- day injured list preventing the injured athlete from playing for 15 days regardless of recovery. The increase of reported concussions may be due to the improved flexibility of the 7- and 10-day injured lists rather than the previous 15-day IL which did not allow a player to return to competition for more than two weeks. This may have increased athletes’ willingness to disclose symptoms because they would miss fewer games. Support for this idea is evident in two recent studies which found significantly more concussions were diagnosed in the MLB after the implementation of the 7-day IL (Sabesan et al., 2018; Ramkumar et al., 2018).

1.1 Current Knowledge Gaps

Very little is known about the head impacts catchers sustain because previous literature has only described the characteristics of FTIs that caused concussion (Beyer, Rowson, & Duma, 2012) or used self-report questionnaires to approximate FTI frequency (Green et al., 2018). However, no research has attempted to describe or quantify all FTIs athletes experience over the course of a season. This is an issue because only using FTIs that caused concussions or relying on self-report questionnaires may not provide an accurate depiction of the FTIs that catchers typically experience.

Studies examining foul tip impacts or “batted ball” injuries use data from players who reported, or were diagnosed with, a concussion by medical personnel. To the knowledge of this researcher, the MLB currently keeps track of more than 88 different statistics using MLB databases; however, foul tip impacts are not among these statistics. Although video footage of foul tip impacts can be found online, not all impacts are recorded. At the time of the video recorded

4 impact, on more than one occasion, broadcasters have openly discussed FTIs that occurred earlier in the game or earlier that week; yet, there is no video evidence of these impacts. It may be that impacts that are potentially exciting, such as with screws flying out of Yadier Molina’s helmet, a ball lodging itself into Jose Lobaton’s mask, or notable impacts that result in a concussion are saved for later viewing on various online sources. The lack of documented foul tip impacts further illustrates the need to quantify the frequency, speed, and quantity of FTIs between catchers in a full season. The underlying motivation to conduct this study was to examine factors related to foul tip impacts with the hope that the results may provide a more complete understanding of head impacts to catchers in Major League Baseball and provide direction for future research on this topic.

Quantifying the incidence of FTIs may not be sufficient for catchers to determine if the level of risk is acceptable. A specific area of interest was whether foul tip impacts to catchers negatively impact their performance. Demonstrating that FTIs are associated with poor performance may be more meaningful to the athlete (i.e., being a proficient hitter) than simply stating the potential consequences. This statement is echoed by Fuller and Drawer (2004), “merely quantifying the level of risk, however, does not define whether a risk is acceptable or unacceptable to participants” (p.351). Athletes determine if these intrinsic and extrinsic factors justify the risk and the severity of a potential injury in their respective sports. Providing athletes with number of FTIs in addition to its potential association with a decrease in subsequent batting performance may better equip the athlete and owner to determine if the level of risk is acceptable.

No studies have examined whether foul tip impacts impair catchers batting performance, however, head impacts that resulted in concussions have been shown to decrease MLB hitters’ performance two weeks following RTP (Schwindel et al., 2014; Wasserman et al., 2015). It may be possible that increased exposure to head impacts that lead to concussion may impair hitters’ batting performance. Wasserman and colleagues (2015) suggest there may be subtle neurological decrements in hitters’ cognitive-motor function that affect their ability to hit a baseball four to six weeks after return from SRC. Foul tip impacts to catchers may result in subtle neuropsychological impairment, similar to that seen at symptom resolution, which could cause their batting performance would decrease.

5

In addition, demonstrating an association between head impacts and performance may result in the development of equipment and rules that could reduce the risk of SRC for catchers. Specifically, Ramkumar and colleagues (2018) state, “Performance and financial changes have relevance to individual players as well as franchises in strategic decision-making regarding contract negotiations, trades, and acquisitions” (p. 2). Owners of these multimillion-dollar teams invest large sums of money to capitalize on players that may contribute to a more successful team; which, in turn may bring more fans. Understanding the association between FTIs and batting performance may encourage the development of strategies to facilitate better performance in athletes while decreasing the risk of sustaining future head injury (e.g., recovery time or safer protective equipment). At the very least, the results from this study may be sufficient to determine if head impacts in the MLB should be investigated further.

In summary, there is a lack of information about the incidence of FTI events, their characteristics, and their influence on catchers’ batting performance. Describing head impacts to catchers may be an important contribution because the literature lacks head impact exposure data among “low” contact sports. In other words, this study has the potential to refine our understanding of the head impacts experienced in a sport outside of high contact and collision sport classifications. Therefore, the purpose of this study was twofold: 1) to observe and describe selected parameters of FTIs (e.g., incidence, the absolute risk of more than one FTI occurring in a game, and common pitch factors) in the American League East division of the MLB and 2) to examine the association between catchers’ batting performance and the FTIs that occurred over 648 games. The next chapter reviews the literature regarding head impacts in sport and baseball, and their general relationship with the neuropsychological functions required for successful batting performance.

Chapter 2: Review of Literature

This section reviews the current state of the literature relevant to FTIs and batting performance in baseball. Two areas of the literature are reviewed: Head impacts, and Performance in Baseball and Head Impacts.

2.1 Head Impacts

Head impacts can differ in type, magnitude and frequency between sports (O’Connor, Rowson, Duma, & Broglio, 2017). These impacts can occur from collisions with players (intentionally or unintentionally), stadiums, or the ground. Intentional collisions are integrated into the rules of the game for collision-based sports (e.g., tackling a ball carrier in football and rugby) whereas unintentional collisions can happen in any sport regardless of the rules of the game, such as: falls in cheerleading, and colliding with teammates or the stadium while attempting to catch a baseball. In addition, head impacts can occur from projectiles (e.g., a puck in hockey, a line drive in baseball or a soccer ball) and equipment (e.g., a hitters bat or lacrosse/hockey stick). Though, projectile and equipment-related impacts may be limited to certain sports and occur less frequently in collision-based sports. For example, a study of 11 NCAA collegiate sports found that baseball, softball, and women’s field hockey had the highest ball-contact injury rates whereas football and men’s lacrosse had the lowest (Fraser et al., 2017).

Furthermore, it may be that ball specifications (e.g., mass, shape, density, size) and the object of hitting projectiles (e.g., a slapshot in hockey or hitting in baseball) rather than passing them influence the frequency of injury. Baseballs, softballs and field hockey balls have a lower mass but are denser than footballs, basketballs, and volleyballs (Fraser et al., 2017). Projectiles with lower impact compliance (i.e., high force impacts in a short duration) may distribute the force over a smaller area and decelerate faster when contacting the body of the athlete (Karton & Hoshizaki, 2018). Although low-compliance systems may cause more serious injury from direct body contact or lead to material failure, there can be a risk for concussion when head protection is used to absorb energy over a longer duration (Karton & Hoshizaki, 2018). This is evident from the high brain strain values observed from reconstructed low magnitude head accelerations in hockey and football (Karton & Hoshizaki, 2018). Although, it is unclear how brain strain values may differ between low compliance projectile head impacts (e.g., foul tip impacts or an impact

6 7 from a hockey puck) and more diffuse, low compliance impacts; such as, forward facing, body- to-body tackles in football or checking a player against the boards in hockey.

The differences in the frequency and type of impacts in sport have led to contact-level classifications with more emphasis being placed on the type of contact than the frequency between collision and non-collision sports. For example, sports can be categorized by general contact level, such as: collision, high, and low/non-contact sports (Kerr et al., 2015). Though, the type of impact seems to be considered when sorting a sport into the collision or high contact group. A systematic review found the number of head impacts per game in women’s collegiate soccer to be comparable to those in collegiate football (O’Connor et al., 2017). The distinction between a collision and high contact sport – such as soccer – is the type of impacts in each sport (e.g., heading a soccer ball versus tackling a player) rather than exposures. Collision sports can include football, boxing, wrestling, and ice hockey, whereas high contact sports may consist of soccer, basketball, and lacrosse (Kerr et al., 2015). However, sports are not consistently assigned to the same contact level between studies (Kerr et al., 2015; Tsushima et al., 2016; Tsushima et al., 2018) and some studies have opted for a binary distinction: contact and non-contact sports (McAllister et al., 2012; McAllister et al., 2014; Churchill et al., 2016; Kellar et al., 2018). Intra- sport differences (e.g., level of play, coaching style, player position) may influence impact exposures or the magnitude of impacts which could be a possible explanation for this lack of consistency in contact classifications.

Head impacts in sport can be logged and measured through instrumented mouthguards, patches, and helmet- or head-mounted accelerometers and software (O’Connor et al., 2017). Such instruments are useful for identifying differences in impact factors. For example, these systems can track linear and angular head accelerations, impact location, frequency, duration, and severity of impacts during practices or games (Mainwaring, Ferdinand Pennock, Mylabathula, & Alavie, 2018). Although devices – such as the Head Impact Telemetry system – can be useful in tracking head impacts in a season, the accuracy can depend on the fit of the helmet, and the cost may limit use without adequate funding (O’Connor et al., 2017; Kerr et al., 2015). Additionally, some researchers suggest that direct observation and video analysis should be used to characterize the impacts that athletes’ experience rather than relying on equipment alone (Nauman & Talavage, 2018; Kuo et al., 2018).

8

Cumulative exposures and magnitudes may be similar between- or within-sport at lower levels, however, as athletes progress to higher levels of competition (e.g., collegiate and professional) greater differences in exposure and impact kinematics may be apparent (Karton & Hoshizaki, 2018). For a collision sport such as football, O’Connor and colleagues (2017) found collegiate athletes can experience 420-1177 impacts a season and 95th percentile impact magnitudes of 63g to 69g, though the 95th percentiles magnitudes in the NFL are often higher than collegiate football for some positions (Guskiewicz & Mihalik, 2010; Karton & Hoshizaki, 2018). In collegiate hockey, players experienced 170-347 impacts per season with 95th percentile impacts of 42g, whereas soccer-related head impacts averaged 38g of linear acceleration and collegiate female soccer players averaged 4.6 impacts per session (O’Connor et al., 2017). Although, there may be greater differences between hockey and soccer at the professional level because collision sport athletes may be required to gain mass in order to be competitive at higher levels. Similarly, in sports with projectile impacts (such as, baseball, lacrosse, and cricket), the speed and cumulative exposure to impacts may increase as athletes become stronger and the seasons become longer. For example, similar linear accelerations to that of collegiate hockey and soccer players have been reported during the experimental modeling of ball impacts to catchers’ masks at faster speeds (Beyer et al., 2012; Laudner et al., 2014; Eckersley et al., 2018), though no studies have tracked the number of head impacts catchers experience.

2.1.1 Baseball

In a sport such as baseball, a pitch that impacts the batter’s helmet or a line-drive that hits a player in the head are conspicuous events but do not occur frequently. In contrast, foul tip impacts to catchers’ masks are seemingly frequent events that draw less attention because of the protective mask catchers wear. In sports such as football, rugby, boxing, or soccer, the impacts to the body and head are abundantly obvious. Trainers, coaches, players, and fans are – at least in- part – aware of what is at stake when bodies collide, and fists or balls contact the head without protective equipment; the frequency of these events draw a critical eye to the possibility of brain injury and neurological dysfunction. Yet, the innocuousness of FTIs may not draw the same attention as collisions in sport.

There are systems in place in the MLB to keep track of events that result in diagnosed injury, such as musculoskeletal injuries and concussions. However, there is yet to be a database that

9 tracks the frequency and description of foul tip impacts to catchers that do not result in diagnosed injury. Moreover, there are no studies that have examined game-recorded foul tip impacts to catchers; so, there is very little that can be known about the number of exposures, frequency, or the magnitude of these events.

In a comprehensive study conducted by Green and colleagues (2015), researchers described the causes of all concussions in the MiLB and MLB by positions; including “contact with ball (batted)” representing a foul tip event – only for catchers – that resulted in an injury. They found that catchers accounted for 40.8% of concussions in the minors and 47.6% of concussion in the MLB as compared to the other 8 positions on the field; and, 31% and 60% of concussions were caused by a foul tip impact in the MiLB and MLB, respectively. Given the lack of information regarding head impact exposure, the large proportion of concussions caused by FTIs suggest these impacts may occur more often than other mechanisms of injury.

In game situations, catchers may be at risk of a head impact approximately six times per week over a six-month MLB season. Each major league team will play 162 games in approximately six months. The length of the seasons differs greatly between most levels of the minor leagues in baseball; short-season A-league will play 56-76 games in an 80-day season, whereas AAA- league will play 140-144 games in a 150-day season. With respect to frequency of games, professional baseball teams will play more than six games per week for the entirety of their season. Although some minor league levels may play far fewer games than the MLB teams (therefore experiencing fewer head impacts), the frequency of FTIs may not be different for most levels of professional baseball.

2.1.2 Subconcussive Impacts

A subconcussive impact can be defined as any cranial impact that does not result in a known or clinically diagnosed concussion (Bailes et al., 2013). There is a lack of consistent terminology used in the subconcussion literature (Mainwaring et al., 2018). Although some studies have used the term “subconcussive trauma” to encompass impacts to the body and head of the athlete, subconcussive head impacts may be the only relevant term to FTI exposure.

A subconcussive impact differs from an undiagnosed concussion because that term implies that the athlete was aware of the manifestation of clinical symptoms of concussion but failed to report

10 them or did not have the means to be assessed by a physician. The difference between the two terms is more evident in a study by Meehan, Mannix, O’Brien, and Collins (2013) that examined undiagnosed concussion in athletes and found 30% of the 486 athletes reported experiencing one or more symptoms of concussion but had not been diagnosed prior to their current concussion. Although these athletes were not diagnosed with a concussion, it is clear that they were aware of the clinically relevant symptoms. In contrast, subconcussive impacts may result in neurological changes without the presentation of clinically relevant symptoms.

Foul tip impacts can be classified as a subconcussive impact, as they do not always result in a known or diagnosed concussion. A multi-partite categorical scale for FTIs can be used to conceptualize the varying severities of head impacts to catchers: (1) clinically diagnosed concussion, (2) undiagnosed clinical concussion, (3) subconcussive (clinically) with neuropathology of concussion, and (4) subconcussive (clinically) without such neuropathology. Although it is possible for an FTI to cause a concussion, for the purpose of this study, a foul tip impact was defined as a cranial impact that does not result in clinical symptoms of a concussion but may have the potential to impair the neuropsychological function of catchers.

2.1.3 Potential Consequences

Subconcussive impacts, overtime, may be associated with changes which are subtle and transient or chronically debilitating. For example, a systematic review of the subconcussion literature found head impact exposure is associated with microstructural and functional changes in the brain in males (Mainwaring et al., 2018). By comparison, RHI have been found to be related to the number of head injuries sustained by university soccer players (Rutherford et al., 2009) and the presence of chronic traumatic encephalopathy (CTE) in athletes (Huber et al., 2016). In addition, Huber and colleagues (2016) state, “most subjects (soccer, ice hockey, baseball, rugby, military servicemembers) have a history of concussions, however, 16% of CTE subjects have no history of concussion suggesting that subconcussive hits and cumulative exposure to trauma are sufficient to lead to CTE” (p.11). In other words, considering the number of head impacts to athletes, not just the impacts that result in concussions, is important. Physical activity and exercise are generally good for the overall health of individuals, although the benefits of exercise may be outweighed by the long-term consequences that can occur from repeated head trauma in sports such as football (Lemez & Baker, 2015). Evidence from the

11 literature suggests that participating in elite sport may benefit athlete's lifespan longevity (Lemez & Baker, 2015). However, the authors of a systematic review of elite athletes’ mortality and longevity state that researchers should consider sport type, player position, and weight (among other things) because these factors may provide insight into varying lifespan longevity (Lemez & Baker, 2015). For example, in a large sample of football (n= 3419) and MLB (n= 2708) athletes, Nguyen et al. (2019) found that football players had higher all-cause, cardiovascular, and neurodegenerative mortality rates compared to MLB players. Using survival curves from hypothetical populations of 1000 NFL and 1000 MLB players, the authors found there would be 11 more deaths with underlying or contributing causes from neurodegenerative conditions for NFL athletes (Nguyen et al., 2019). The authors suggested that factors (such as head trauma) may underlie the difference in long-term health outcomes between sports. A wealth of research has documented football players’ head impact exposure over the course of the season and by game (Guskiewicz & Mihalik, 2010; Beckwith et al., 2013; Stemper et al., 2018; Rowson et al., 2018) but there are no studies that have investigated such a topic in baseball. Given that baseball is classified as a low contact sport, it could be the case that some positions experience more head trauma than others and, therefore, are at greater risk of developing a neurodegenerative disease – despite there being relatively fewer events compared to football. For example, outfielders are often involved in diving catches where their body and head can impact the ground with considerable force, whereas catchers are hit by hitters backswings and foul tip impacts. Conversely, it is rare for a pitcher to be hit in the head by a line drive or a designated hitter to be hit in the head by a pitch. Unlike baseball, most football athletes on a roster are subject to many head impacts due to the nature of the game; assuming only starters play, this would put 33 athletes on the field every game (i.e., 11 players for each defensive, offensive and special teams play). Nearly half of a baseball roster will consist of pitchers (typically 12) who may not experience any head trauma in a season. Provided that catchers or other positions experience significantly more head impacts than pitchers, the difference in neurodegenerative-related deaths between football and baseball (Nguyen et al., 2019) may not be representative of baseball positions which experience the majority of head trauma.

In addition to the health consequences, the head impacts catchers experience may have implications for batting performance in baseball. Depending on the requisite cognitive-motor

12 demands of the sport, some functional impairments may not be noticeable depending on the sport; such as changes to reaction time in tenths or hundredths of a second required to successfully hit a ball, object recognition and subsequent precise motor-movements (Tresilian, 2005; Clark et al., 2012; Muraskin et al., 2013). Studies examining subconcussive impacts indicated that some athletes may experience subtle neuropsychological deficits such as increases in reaction time (Tsushima, Geling, Arnold, & Oshiro, 2016), and dysregulation of the sympathetic nervous system after a season of head impacts in their respective sports (Gallant et al., 2018). It should be noted that it is unclear whether the structural and functional changes associated with subconcussive head impacts are indicative of injury (Mainwaring et al., 2018). In baseball, players, coaches, and sports announcers commonly refer to a decrease in performance (spanning multiple games) to the axiom, “hitter’s slump”, meaning there is no clear reason as to why hitters are not successful. Although there are other factors that may affect batting performance, it is possible that ball impacts to catchers’ masks may result in neurological impairment that leads to decreased performance without drawing the attention that an injurious event would if it had occurred.

Moreover, repetitive hits to the head may subtly affect athletes without the presence of symptoms. It is possible that catchers returning to play after the minimum number of days on the IL from a head injury may be at risk of repetitive head impacts while their brain is still recovering. This is supported by a recent study by Churchill and colleagues (2017) that found altered brain structures and functioning in athletes who were cleared to return to play indicating that although symptoms had resolved, the athletes’ brain may still be recovering. Aside from abstaining from sport, it may be the case that some forms of head impacts, such as FTIs, may be unavoidable. However, profiling a season of head impacts may be important because the lack of information may cause some coaches or players to view FTIs as severe (McCrea et al., 2004). At the very least, tracking FTIs may increase the awareness of relevant stakeholders to subsequent impacts while athletes are still recovering from SRC.

2.1.4 Experimental Modeling of Mask Impacts

Many studies have examined forces exerted on a catcher’s mask in laboratory settings from projectiles at various speeds (Shain et al., 2010; Beyer et al., 2012; Laudner, Lynall, Frangella, & Sharpe, 2014; Siu et al., 2016; Eckersley et al., 2018); however, most of the impact speeds seem

13 to be arbitrarily chosen. Studies examining ball impacts to catchers’ masks at plate speeds of 60mph, 80mph, 84mph, 90mph and 95mph considered the impact location to be a significant factor in the linear or rotational accelerative effects of the applied forces. In addition, projectiles at speeds of one 67mph and four 78mph tests did not prevent the ball from impacting the headform (Eckersley et al., 2018) and projectiles at 90mph and 95mph deformed catchers’ masks (Laudner et al., 2014). These results suggest that FTIs may decrease the structural integrity of a catcher’s mask or may not stop the ball from impacting their face. However, these projectile tests do not represent the upper limit of pitching speeds with 17,616 pitches averaging 97.0mph to 99.7mph by the top fifty hardest throwing pitchers in 2017. Fastball-type pitches are the fastest pitches in a pitcher’s repertoire and consist of the greatest percentage of pitches thrown in the MLB (MLB, 2019). Some pitchers will consistently throw fastballs in excess of 100mph, such as Aroldis Chapman and Jordan Hicks, and FTIs from these pitches may result in greater head accelerations and may compromise a catcher’s mask. Given there are more fastballs thrown in the MLB than any other pitch type, it’s likely that there is a greater number of FTIs that occur when a fastball is thrown. Importantly, most speeds tested in the experimental modeling of mask impacts are slower than the average release speed of major league fastballs (92.5mph) and testing slower speeds may not accurately the depict the forces commonly exerted on MLB catchers’ masks.

Only one study experimentally modeled impacts to catchers’ masks using real-world FTIs that resulted in concussion (Beyer et al., 2012). Beyer and colleagues (2012) attempted to link the probability of sustaining a concussion from impacts to various catchers’ mask locations using the resulting linear or angular accelerations. The risk of concussion using Pellman’s risk curve (Pellman et al., 2003), found that linear acceleration exerted on catchers’ masks from 84mph pitches predicted a 9% risk of concussion from linear acceleration, and a risk between 4% to 45% chance of concussion from rotational accelerations (Beyer, et al., 2012). Based on the data from several studies examining catcher mask impacts, there is a non-linear relationship of linear head acceleration as the speed of the projectile increased (Shain et al., 2010; Beyer et al., 2012; Siu et al., 2016). Furthermore, greater accelerations are associated with a greater risk of concussion and, as Beyer and colleagues (2012) explained, the “impacts to the center-eyebrow and chin locations were the most severe” (p.158).

14

Although, athlete’s cumulative exposure to head impacts may be a better predictor of SRC than the magnitude of impacts alone. Several studies examined the magnitude of head impacts in sports and found that only 7 (<0.38%) of 1858 head impacts that exceeded 80g linear acceleration caused a concussion (Mihalik, Bell, Marshall, & Guskiewicz, 2007) while impacts of 90g and greater did not impair athletes (McCaffrey et al., 2007). More recent research suggests that the cumulative exposure to head impacts may be related to the onset of concussion (Beckwith et al., 2013; Beckwith et al., 2013; Stemper et al., 2018). Specifically, 72% of injured athletes had the most or second most severe head impact exposure for the season or on the date of concussion, whereas the majority of athletes diagnosed with a concussion had impact magnitudes associated with a less than 1% probability of injury (Stemper et al., 2018).

In their study, Beyer and colleagues (2012) documented the speed of FTIs that resulted in diagnosed concussions over three seasons. Of the ten FTIs that resulted in a concussion, only two FTIs were thrown at speeds exceeding 96mph while four had release speeds slower than 90mph. It is possible that FTIs to catchers may occur at greater speeds and leave catchers more susceptible to concussion from slower impacts. Although a concussion may be a potential consequence of an FTI, there is no clear threshold for injury which makes it increasingly difficult to recognize when athletes should be removed from games to be medically assessed.

2.2 Performance in Sport, Baseball and Head Impacts

In this section, the neurocognitive functions associated with high performance in sport and baseball are discussed. In addition, the relationship between head impacts and cognitive outcomes, and the neurocognitive functions required for hitting a baseball are compared. This overview should provide some insight into the possible consequences of foul tip impacts on catchers’ batting performance.

2.2.1 Sports

Quick and efficient interactions between cognitive systems are integral to athletes’ success in professional sport. Memory and ocular networks allow athletes to recognize patterns and form probabilistic expectations of opponents’ future movements (Yarrow, Brown, & Krakauer, 2009). In addition, attention and complex decision-making allow athletes to adapt to different kinematic movements from opponents which improve their movement efficiency. In a sporting scenario,

15 individuals use multiple regions of their brain, at the same time, to make a decision; and, the region with the strongest action potential informs the decision that the athlete will make (Yarrow, Brown, & Krakauer, 2009). This is important because expert athletes’ attention allows them to shift from processes that are no longer relevant, such as utilizing previous experiences (memory) involved in pattern recognition and anticipation to reactionary when an unexpected event occurs (e.g., a sudden deflection of a ball towards an athlete’ head) (Voss et al., 2010). In this instance, the vestibulo-ocular network would shift from memory-dependent functions to reactionary in a rapid, dynamic process (Yarrow, Brown, & Krakauer, 2009; Nakata, Yoshie, Miura, & Kudo, 2010).

The speed and efficiency of these interactions are developed through years of rigorous practice and competition which also allow athletes to recognize environmental cues that inform their subsequent movements (Yarrow, Brown, & Krakauer, 2009). In other words, athletes may be able to draw from a large databank of similar experiences that improve their movement efficiency and quality of their decisions (Persky & Robinson, 2017).

Movement (motor control) in sport can be used to gain a strategic advantage when paired with goals (Yarrow, Brown, & Krakauer, 2009). For example, in basketball and hockey, an athlete moving away from the crease can draw defenders away and allow their teammate to shoot from an uncontested position. The effectiveness of these movements is often determined by the level of automaticity of cognitive processes achieved by an athlete for specific sporting scenarios (Yarrow, Brown, & Krakauer, 2009). Specifically, research suggests that controlled processes are conscious, attention demanding, and inefficient whereas automatic processes can be rapid, effortless, demand little attentional capacity (Yarrow, Brown, & Krakauer, 2009). Professional- level athletes will typically have complex, diverse, and more experiences that improve their ability to interpret, anticipate and react to sensory information more effectively. Such experiences allow athletes to form predictions of the required motor movement to achieve a goal, which causes the nervous system to prepare athlete’s bodies to react quickly in response to anticipated stimuli. Although, some research suggests that cognitive functions may differ between sports depending on the style of play, such as static sports (e.g. swimming and running), interceptive (e.g., boxing and baseball), and strategic sports (e.g., basketball, volleyball, and hockey) (Voss et al., 2010).

16

2.2.2 Baseball Performance

Baseball is a game that requires a great deal of focus, fast reaction times, and precision in all facets of the game. The difference between successfully and unsuccessfully hitting, pitching, throwing, or fielding a baseball are fractions of a second or a few millimeters in body movement that result in missing an incoming ball, or missing the target. Most aspects of baseball require anticipating the movement of an incoming projectile at high speeds. Specifically, hitting a baseball requires not only fast reaction time but also high levels of precision to hit a 3-inch diameter baseball with a 2.25-inch diameter baseball bat. Hitters have approximately 400 milliseconds to recognize pitch-type and trajectory from a 90mph pitch, decide whether to swing or not, and accelerate a >29oz bat on the correct intercept path to hit the pitched ball (Gray, 2009). Wasserman and colleagues (2015) further explain that hitters must be able to recognize different types of pitches that will have varying movements in direction and distance, adding to the additional difficulty of hitting a pitched ball. A recent study found that sensorimotor abilities in professional baseball, like visual acuity, attention, reaction time, and eye-hand coordination are associated with batting performance statistics of on-base percentage, walk rate, and strikeout rate (Burris et al., 2018). It is for this reason that any subtle cognitive deficits from subconcussive impacts may be magnified in athletes’ hitting performance which requires synergistic precision of their cognitive functions like visual acuity, attention, reaction time and vestibular function.

Evidence suggests MLB players’ batting performance may be influenced by head trauma; however, previous research has only investigated hitting performance following RTP from concussion (Schwindel, Moretti, Watson, & Hutchinson, 2014; Wasserman et al., 2015; Sabesan et al., 2018; Ramkumar et al., 2018). Given that a large proportion of catcher concussions in the MLB are caused by FTIs, discussion of the potential influence of head impacts on the cognitive- motor requirements for batting performance is warranted. The next section addresses the cognitive-motor demands of hitting a baseball and its relevance to the literature regarding the detrimental effects associated with head impacts. Four segments are discussed: Visual Acuity, Attention, Vestibulo-Ocular Function, and Reaction Time.

17

2.2.3 Visual Acuity

Visual acuity is the sharpness or clarity of vision which allows individuals to discern qualities of objects, letters, and numbers at a distance. In baseball, teams and organizations devote part of their resources to training their athletes visual acuity for greater performance while hitting (Clark et al., 2012). As previously mentioned, a hitter in elite levels of baseball will have less than 400 milliseconds to react to a pitched ball at 90mph; however, hitters only have 170 milliseconds to decide to swing after accounting for the time required to process and swing the bat (Clark et al., 2012). Clark and colleagues (2012) examined the effect of visual training on batting average with the baseball team at University of Cincinnati and found only six weeks of training improved their batting average from the previous season (.251 to .285) and increased the number of runs batted in by thirty-five. Research by Muraskin et al. (2013) explain that there is “a link between visual identification and the required rapid motor response” (p.1). This is further supported by Burris and colleagues (2018) who found near-far quickness, target capture, and perception span to be associated with professional hitters’ ability to avoid striking out. Improving visual acuity allows for faster recognition of ball trajectory and more time for the motor response to movement times (Tresilian, 2005).

Catchers may be at risk for many subconcussive FTIs over the course of a season. Bailes et al. (2013) suggest that subconcussive impacts have the greatest deleterious effects through repetitive occurrence (Bailes et al., 2013). A commonly cited cognitive function required to hit a baseball is a hitter’s ability to see and recognize a pitched ball. Kellar and colleagues (2018) suggested that greater cerebellar activity among football players completing oculomotor tasks could indicate that athletes’ brains are compensating for subtle deficits from subconcussive impacts. Alternatively, high performance athletes could have more cerebellar activation as a result of the oculomotor demands in their sport and years of training (Kellar et al., 2018). Due to the high speeds of pitched baseballs, visual impairment may be associated with decreased batting performance over time.

2.2.4 Vestibulo-Ocular Functioning

Hitting a baseball requires balanced weight transfer and rapid, coordinated movements during the linear and rotational phases of a batter’s swing (Welch, Banks, Cook, & Draovitch, 1995). Welch and colleagues (1995) explain that dynamic balance and controlled weight shifts are an

18 integral aspect of the hitting motion in professional baseball players. Excessive rotations and movements or shift in balance “may contribute to a reduction of muscular efficiency as well as produce a disruption in the sequencing of segments of the body” (p.199) during the swing phase (Welch et al., 1995). Furthermore, in the early phase of a hitter’s swing, the vestibular system sends signals to the ocular system to begin making compensating eye movements to track the pitched ball, suggesting a link between the ocular and vestibular networks (Bahill & LaRitz, 1984). The vestibulo-ocular system allows hitters to view the trajectory of a pitch longer; contributing to a greater ability to make a successful, rapid interception with a bat (Tresilian, 2005). Additionally, eye-hand coordination was found to be associated with professional hitters’ “increased ability to draw walks” (Kellar et al., 2018, p.7).

An average fastball in MLB, approximately 92mph, reaches the plate in less than 400 milliseconds affording hitters little time to react. Any disruption in the sequence of movements provides less time for the hitter to see and identify the trajectory of a pitch, react to it, and hit the ball. Subconcussive impacts have been shown to be associated with vestibular dysfunction in soccer players after heading ten soccer balls at 25mph (Hwang et al., 2017). Results from Hwang and colleagues (2017) study found that athletes had vestibular dysfunction immediately after heading 10 soccer balls – launched at 25mph from 40 feet away – in 10 minutes. The inability of catchers who have recently experienced an FTI, to properly orient themselves while hitting may lead to poorer swings or less efficient movements that affect their batting performance.

2.2.5 Attention

Attention and focus are important components of hitting, especially when a brief lapse in focus can result in recognizing or reacting too late to hit a baseball. Research suggests that elite athletes perform better in cognitive tests of sustained attention than lower level athletes (Heppe et al., 2016). An athlete’s ability to maintain attention may separate low level from high level athletes because they are able to focus on drills more efficiently or sustain attention at critical moments in a competitive match (Yarrow, Brown, & Krakauer, 2009). Alternatively, where hitters place their attention can affect their performance at the plate. Castaneda and Gray (2007) explain that if experienced hitters focus on external events or stimuli, such as watching the ball hit their bat, that elite hitters performed significantly better than when their attention is directed

19 toward internal events, such as how their body should move. The possible implication is that if a hitter’s ability to maintain their focus is diminished then they may be less successful at the plate.

Repetitive, subconcussive impacts may be associated with decreased cognitive abilities in athletes (Belanger, Vanderploeg, & McAllister, 2016). Although few studies have examined the effect of subconcussive impacts to baseball players, research suggests that repetitive head impacts in football athletes may cause subtle effects, such as difficulty with attentional resource allocation (Wilson, Harkrider, & King, 2015) and impaired divided-attention ability in soccer players (Webbe & Ochs, 2003). A hitter in baseball may only have a few opportunities to hit a baseball in a game because pitchers at the major league level are extremely refined and precise when locating their pitches. If the hitter is unable to sustain their attention during their plate appearances, they may miss their opportunity to swing at a hittable pitch, commonly referred to as “getting their pitch to hit”. Not only may hitters miss their opportunity to swing but a brief lapse in attention may provide insufficient time to recognize a pitch or make contact with the baseball.

2.2.6 Reaction Time

The ability for a hitter, in baseball, to recognize a hittable pitch and react quickly is invaluable to the success of that athlete. In fact, much research has been conducted to improve visual and attentional abilities of athletes to give them fractions of a second longer to react to rapid movements in sports (Tresilian, 2005; Clark et al., 2012; Muraskin et al., 2013). In a sport like baseball, it is imperative that a hitter have fast reaction times (Lin, Huang, & Nien, 2007). A hitter’s reaction time may be the result of their ability to efficiently link cognitive-motor functions efficiently, for example visual identification and the required rapid motor response (Muraskin et al., 2013). In addition, reaction times may be a product of several cognitive functions that are influenced by an athletes’ ability to sustain their attention or visually distinguish subtle ball movements on its trajectory to the plate. It may be the case that dysfunction in any of the required cognitive-motor processes may lead to slower reaction times in baseball.

Head impacts can affect various cognitive functions that are required to hit a baseball, like speed of processing (Bernick et al. 2015) which may affect reaction time (Tsushima et al., 2016). In fact, repetitive subconcussive impacts can lead to poorer performance in visual memory, visual

20 motor speed, impulse control, and poor reaction time (Tsushima et al., 2018). The contribution of these factors could lead to decreased performance while attempting to hit a baseball because of the minimal time afforded to react to a pitched baseball.

It should be noted that there is a plethora of batting metrics that could be used to determine performance but not all of these may be appropriate for detecting subtle neuropsychological dysfunction. For example, the most commonly used batting performance metric, batting average, only measures the outcome of an at-bat and may not be a sensitive measure of a hitter’s performance following a concussion or subconcussive head impact. A ball hit into play does not have to be hit hard for a player to reach base nor does it need to be hit weakly to be an “out”. For that reason, batting average is not necessarily a true indicator of an athletes’ cognitive-motor abilities, and its use may lead to inconsistent results. On-base percentage (OBP) measures outcome, as well; however, the number of times a batter reaches base via “a walk” is considered – in addition to the number of hits – which may be an indicator of the batter’s inhibitory control, ocular network functioning and recognition. Alternatively, the MLB provides advanced statistics such as expected-weighted on-base average (xwOBA) which incorporates the angle and the exit velocity of the ball off the bat. A hitter’s xwOBA improves when he hits the ball harder with higher hit-probability trajectories. Therefore, xwOBA should be, theoretically, a batting performance measure that is more representative of a hitter’s cognitive-motor abilities than batting average or on-base percentage.

As demonstrated by Wasserman et al. (2015), the batting performance of MLB hitters was significantly decreased two weeks after clinical recovery from concussion as compared to MLB hitters who had returned from bereavement or paternity leave. The researchers further stated that hitters in the concussion group had slightly lowered batting performance at four-to-six weeks after their return but not significantly so (Wasserman et al., 2015). The decrease in performance may be explained by neurocognitive impairment in inhibitory control (Pontifex et al., 2009), visuo-motor speed (Broglio et al., 2007), vestibular function (Hwang et al., 2017) and reaction times (Collie et al., 2006) after symptom resolution.

An interesting finding by Wasserman and colleagues (2015) was that although the hitting performance was significantly worse for the post-concussion athletes, they found that both groups were equally as likely to hit the ball into play. This may indicate that hitters, at return-to-

21 play, may have subtle deficits in the cognitive-motor abilities required to inhibit their swings or may have imprecise reaction time or balance. Wasserman et al. (2015) further state, that although hitters from the concussion group made contact with the ball, the resulting contact may not occur with enough force or at the right timing to get the ball past the defensive fielders. It is important to consider the implications of subconcussive impacts that may affect the hitting performance of an at-risk population for repetitive head impacts such as MLB catchers. Despite subconcussion not being labeled an injurious event, it may have a qualitatively similar impact on hitting performance as demonstrated by Wasserman et al. (2015).

This review demonstrates that FTIs can have an impact on catcher’s health and performance. However, the steps toward improving the safety and health of the athlete are currently limited to the FTI information that caused concussions rather than the impacts catchers are exposed to in a season. Currently, attempts to mediate the consequences of head injury have led to adjustments to rules, injury tracking, and innovations in catcher’s equipment. In the MLB, growing awareness of the medical implications of SRC encouraged the implementation of the 7-day injured list for concussive events, as well as the Health and Injury Tracking System (HITS), and the development of new catchers’ masks that claim to absorb the forces of FTIs. In addition, the implementation of rule changes has reduced the number of concussions from homeplate collisions with catchers. However, prior to the ban of homeplate catcher collisions, foul tip impacts still consisted of a large proportion of injuries to catchers (Dick et al., 2007). Foul tip impacts that do not result in a concussion have not been adequately examined. Identifying the head impact exposure and speeds commonly associated with FTIs may lead to further adjustments to rules, tracking head impact exposure and improving the standards of catcher’s equipment.

The purpose of this study was to investigate FTIs to catchers’ masks from four teams in the American League East (ALE) division of the MLB. Identifying the incidence of FTIs, absolute risk of more than one FTI in a game, common factors of related to FTIs and the effect of FTIs on catchers’ batting performance fills a gap in science and the literature and provides direction for future research. Furthermore, describing the frequencies of, and factors related to FTIs, such as the pitch speed of FTIs, type of pitch thrown, location, etc., may provide greater insight into the head impact exposure that catchers typically experience. In addition, the findings from this study should contribute a greater understanding of FTIs in a sample of catchers (undiagnosed with

22 concussion) which could serve as a benchmark for similar research to be compared. If catchers’ batting performance decreases following foul tip impacts to the mask, there may be reason to investigate whether FTIs lead to cognitive dysfunction and, potentially, to the possibility of injury. The following chapter describes the method for the study.

23

Chapter 3: Method

This chapter outlines the method for the study. Eleven components comprise this section: Operational Definitions, Purpose, Pilot Study, Research Questions, Objectives, Hypotheses, Sample, Inclusion Criteria, Procedure, Data Collection, and Data Analysis.

3.1 Operational Definitions 3.1.1 Athlete Exposure

An athlete exposure was defined as any pitch that was thrown while the observed catcher was playing defence. Although catchers may be exposed to other forms of impacts while batting (e.g., hit-by-pitch) or running on the base paths (e.g., player collisions), foul tip impacts can only occur while the catcher is on defence. Of the ways athlete exposure could have been defined, the per pitch basis allowed for the greatest representation of actual exposures and can be presented in a manner that is consistent with commonly used baseball metrics. Typically, athlete exposures are measured by the number of hours, games and practices that are played by an athlete. However, Hopkins (2010) explains, “there is no clear answer as to which description is best. Each description, however, has its own benefits and drawbacks” (p.1). Foul tip impacts can only occur when a pitch is thrown. In baseball, the number of pitches thrown per game, or per hour for that matter, will vary depending on the pace of the pitcher between throws and the ability of that pitcher to get hitters out. In addition, tied games will continue past the ninth inning until one team completes the inning with the lead; therefore, some games may be longer in duration, have more innings played, and have significantly more pitches thrown. It is for this reason that athlete exposures, as pitches thrown, presents a precise measurement of the number of times catchers could experience an FTI.

3.1.2 Incidence of Catcher FTIs

The incidence of catcher FTIs could have be defined in various ways; for instance, injuries per 1,000 athlete-hours, injuries per 1,000 athlete exposures, or per 10,000 athlete-minutes of participation in practices or games (Hopkins, 2010). Incidence for this study was defined as the total number of foul tip impacts to the catchers’ mask divided by the total number of pitches thrown to each catcher (athlete exposures), then multiplied by 10,000 (Table 1). This number provides a rate of how often FTIs occur to catchers. In addition, the average number of pitches

24 thrown per game, and the quantity of FTIs per catcher were collected for context; otherwise, a rate of 10,000 pitches may not be intuitive for someone who does not watch baseball regularly.

3.1.3 Absolute Risk of >1 FTI in a Game

The absolute risk was used to determine the probability of more than one impact occurring in a game relative to the number of games observed in this study. The formula used to calculate the absolute risk is presented in Table 1.

3.1.4 Pitch Speed of FTIs

“Velocity” is the common term used to reference the speed of a thrown pitch in baseball; yet, velocity is calculated using both speed and direction. The direction of the pitch was not discussed in this thesis therefore, using the term “velocity” would not have been accurate. The speed of an FTI was defined as the speed of the ball at the point of release from the pitcher’s hand. Statcast offers two other types of velocity data, however the data for “plate velocity” and “perceived velocity” were not available to this researcher. Plate velocity is the ball speed as it crosses the plate, and perceived velocity is a combination of the release velocity and the release point or extension of the pitcher’s arm towards the plate. The speed of the pitch has an estimated 6-11% decrease in speed from the point of the release to home plate (Beyer et al., 2012; Siu et al., 2016). For this study, release speed values were adjusted to the estimated plate speeds when making comparisons to previous research which used plate speeds rather than release speeds.

25

3.1.5 On-Field Medical Attention

Each team employs physicians who are members of the MLB Team Physicians Associations (MLBTPA) and athletic trainers who belong to the Professional Baseball Athletic Trainers Society (PBATS). After implementation of the 7-day injured list in 2011, each team is required to have a designated specialist who is trained to examine mild traumatic brain injuries. The MLB Players Association (MLBPA) collective bargaining agreement states, “if a player is involved in an incident during a game that is associated with a high risk of concussion the game will be stopped and the player will be evaluated on the field” (MLBPA, 2017, article 13c, subsection 1b). It is not clear what the MLB’s criteria is for a “high risk of concussion” or whether any assessments are made off-field. Once a player has been diagnosed with a concussion, the team specialist must send a detailed report and supplementary details, including video evidence, to MLB’s medical director before the athlete may be placed on the 7-day concussion injured list (MLBPA, 2017). For every FTI in this study, the researcher recorded whether there was on-field medical attention by either the training staff or team doctor.

3.1.6 Plate appearance (PA)

A plate appearance is defined as a batter’s turn at the plate, whereas an at-bat does not include any time a batter is walked, has a sacrifice hit (resulting in an out), or a catcher’s interference event. A hitter’s plate appearance is not recorded if a runner on base is thrown out while attempting to advance or steal a base and is the last out to end the inning.

3.1.7 At-bat (AB)

At-bats are recorded based on the number of plate appearances that result in a hit, an out, an error, or a fielder’s choice – when a position player opts to get another baserunner out. At-bats are used as the denominator when determining the slugging percentage for a hitter.

3.1.8 Hit

A successful hit occurs when a batter hits a pitched baseball into fair territory – a predetermined boundary for balls struck to count as a hit – and reaches first base without a defensive error or fielder’s choice. Singles, doubles, triples, and home runs are four types of hits that indicate what base a batter reaches; and, all four types of hits are counted equally for batting average. If a batter hits a ball into play and attempts to advance an extra base (e.g., first to second base) but is

26 thrown out after reaching first, the type of hit (single, double, or triple) will still be scored as whichever base was safely touched prior to the out.

3.1.9 Batting Performance

Catchers’ batting performance was comprised of three separate outcome measures: expected- weighted on-base average (xwOBA), slugging percentage (SLG), and strikeout rate. Batting performance statistics are calculated and stored using MLB’s Statcast which is available to the public through Baseball Savant – an online database owned and maintained by the Director of Baseball Research and Development for the MLB. All batting performance statistics will be extracted using Baseball Savant.

3.1.10 Expected-Weighted On-Base Average

On-base average simply refers to how frequently a batter reaches a base per plate appearance. Expected-weighted on-base average is an advanced statistic describes how often a player reaches base safely while accounting for other variables such as hit probability and an applied weight to each of the outcomes of a plate appearance (MLB, 2019). Introduced in the 2017 season, hit probability considers the exit velocity of the ball after contact with a hitter’s bat and its trajectory to calculate a probability of the ball being caught by a defensive fielder. Each batted ball is given a percentage based on how often comparable balls resulted in hits since the 2015 MLB season when Statcast was implemented (MLB, 2019). For example, if balls hit with an exit velocity of 90mph and trajectories of 30 degrees were caught 30 times of 100, then a batted ball with a similar exit velocity and trajectory would have a hit probability of 70 percent. The linear weighting for xwOBA changes based on the yearly league-wide averages. The linear weights for each possible outcome in 2017 were: (1) Out = 0.000, (2) Unintentional Walk = 0.693, (3) Hit- by-Pitch = 0.723, (4) Single = 0.877, (5) Double = 1.232, (6) Triple = 1.552, (7) Homerun = 1.980. Each player’s xwOBA was calculated for each game prior to being uploaded to Baseball Savant.

The purpose of xwOBA is to put a greater emphasis on the quality of contact a hitter makes rather than the outcome. The variables included in the numerator of the equation is base hits (multipled by a hit probability factor), walks, hit-by-pitches, and intentional walks; however, fielder’s choice, errors, and outs count against the xwOBA. Although, on-base percentage would

27 be a relevant indicator of a batter’s ability to reach base or avoid making an out, xwOBA additionally considers the sensorimotor abilities of the hitter and may more accurately reflect the precision of each swing.

3.1.11 Slugging Percentage

Slugging percentage is a statistic in baseball that indicates the magnitude of the hit. It is calculated by adding the total number of bases a player reaches after a hit (e.g. a single would be a 1.000, double would be 2.000, triple would be 3.000, and a homerun would be 4.000). Batters must be exceptionally fast runners or hit a ball far enough away from defensive players to reach more bases; however, its main purpose is to quantify hitter’s power (Burris et al., 2018). Greater SLG can occur in a few ways: a player can have a greater number of hits than outs, or a player can have a greater number of extra base hits – in this instance, outs would not impact SLG as much.

3.1.12 Strikeout Rate

A strikeout is defined as the third strike of an at-bat that is caught by the catcher or swung at but not touched by the hitter (i.e., a swinging strike in the dirt). Strikeout rate was calculated by taking the total number of strikeouts and dividing it by the denominator, plate appearances. This batting metric is important because not all outs are equal – some outs have strategic purposes – and one of the worst outcomes is a strikeout. Strikeouts do not advance base runners, and there is no chance for the fielders to make an error or a ball to get passed the defense for a hit. High strikeout rates indicate that a hitter’s ability to recognize the type of pitch thrown and its trajectories is diminished.

3.2 Pilot Study

A pilot study of 10 randomly selected games was conducted to determine the feasibility of this thesis. To determine the feasibility, identifying the time requirements to view each game was necessary. Secondly, an estimation of how many FTIs the researcher could expect to see in a MLB season was useful for determining whether FTIs occurred frequently enough to warrant further investigation.

28

MLB TV was used to observe and record FTI information for this thesis. The researcher was able to refine the viewing process in the first four games – determining an efficient way reduce the time requirement for observation. Specifically, the researcher was able to skip irrelevant portions of the game (e.g., advertisements, umpire deliberations, etc.) by using the fast-forward and inning selection features provided by MLB TV. The fast-forward feature allows the user to skip the next 10 seconds of the game allowing the research to minimize observation time between pitches thrown. The researcher was careful to note the number of pitches thrown prior to fast-forwarding to avoid any missed events. The inning selection feature allowed the researcher to skip to the beginning of a new inning instead of manually attempting to skip through commercial breaks.

For the first four games, the researcher used a template of a game log and FTI checklist consisting of factors related to the FTI events. The factors listed in the log and FTI checklist were adjusted to maximize efficiency and reduce the time required to complete each – for example, coding most of the categorical factors numerically required less time to transfer data into a format suitable for analysis.

All logs and checklists for the observed games were completed regardless of an FTI event occurring. Details regarding the number of pitches thrown, date of the game, name of the catcher, and any notes were recorded in the game log. Upon the event of an FTI, the researcher paused the game, reviewed the impact to confirm the ball had contacted the mask, recorded the number of pitches thrown, and noted the time of the event in case it needed to be reviewed at a later time. The game logs and FTI checklists were printed on double-sided pages and organized in chronological order.

3.2.1 Results

After conducting a pilot study of 10 randomly selected MLB games, there were four games in which one or more FTIs occurred. Foul tip impacts occurred in 70% of games in 1,493 pitches thrown, and three games with more than one FTI. All catchers played the remainder of the game apart from one who was ejected from the game after arguing with the homeplate official. The number of FTIs observed in this pilot study was great enough to warrant further investigation.

29

3.3 Purpose

The purpose of this study was twofold: 1) to observe and describe selected parameters of FTIs (e.g., incidence, the absolute risk of more than one FTI occurring in a game, and common pitch factors) in the American League East division of the MLB and 2) to examine the association between catchers’ batting performance and the FTIs that occurred over 648 games.

3.4 Research Questions

The following four research questions were examined:

1. What is the incidence of catcher FTIs from four teams in the ALE over the course of the 2017 MLB season?

2. What is the absolute risk of more than one FTI occurring in a game in the 2017 American League East season?

3. Can FTIs be described by any common features of the thrown pitch?

4. Does an increase in the number or pitch speed of FTIs over the previous week predict a decrease in the subsequent batting performance of catchers in the 2017 ALE regular season?

a. Does an increased frequency or pitch speed of FTIs over the previous week predict a decrease in the xwOBA of catchers?

b. Does an increased frequency or pitch speed of FTIs over the previous week predict a decrease in the subsequent SLG of catchers?

c. Does an increased frequency or pitch speed of FTIs over the previous week predict an increase in the subsequent strikeout rate of catchers?

3.5 Objectives

The general objectives of this study were to identify catchers’ exposure to FTIs, describe factors related to FTI pitches, and examine the association between FTIs and catchers’ batting performance. The specific objectives were as follows:

30

1. To identify how often catchers experienced foul tip impacts by describing the incidence of FTIs provided a description of the impacts observed in this study

2. To identify catchers’ exposure to multiple impacts occurring in the same game using absolute risk.

3. To provide a description of FTIs by examining the factors related to the thrown-pitch (e.g., location, pitch type and speed), impact location, and the circumstances in which they occurred (e.g., inning of the game and month of the season).

4. To examine the association between the frequency and speed of FTIs and catchers’ expected-weighted on-base average, slugging percentage and strikeout rate.

3.6 Hypotheses

Hypotheses were not needed to investigate all the research questions in this study. Descriptive statistics were used to establish the incidence of catcher FTIs, the absolute risk of more than one FTI in a game, and to identify the common factors of FTIs. However, inferential statistics were needed to determine if there was a significant change in catchers’ batting performance measures as the pitch speed and frequency of FTIs increased over the previous seven days, relative to the game being played on that day. To address the fourth research question, the following alternative hypotheses were produced:

3.6.1 Hypothesis 1

An increase in the number of FTIs over the previous seven days, relative to the game on that day, will predict a decrease in batting performance for catchers in the ALE division of the MLB.

Hypothesis 1a) An increase in the frequency of FTIs over the previous seven days will predict a decrease in catchers’ xwOBA in the current game.

Hypothesis 1b) An increase in the frequency of FTIs over the previous seven days will predict a decrease in catchers’ SLG in the current game.

Hypothesis 1c) An increase in the frequency of FTIs over the previous seven days will predict an increase in catchers’ strikeout rate in the current game.

31

3.6.2 Hypothesis 2

An increase in the average maximum pitch speed of FTIs over the previous seven days (relative to each game played) will predict a decrease in catchers’ batting performance.

Hypothesis 2a) An increase in the average maximum pitch speed of FTIs over the previous seven days will predict a decrease in xwOBA in the current game.

Hypothesis 2b) An increase in the average maximum pitch speed of FTIs over the previous seven days will predict a decrease in SLG in the current game.

Hypothesis 2c) An increase in the average maximum pitch speed of FTIs over the previous seven days will predict an increase in strikeout rate in the current game.

3.7 Sample

The sample consisted of 15 MLB catchers from four teams in the American League East in the 2017 season; New York Yankees, Boston Red Sox, Baltimore Orioles, and Tampa Bay Rays. Each team completed all 162 games in their regular seasons accounting for a grand total of 648 games. Information regarding catchers’ age, season of MLB experience, number of games started, and game appearances were separated into two tables for all catchers, and a comparison of starting and backup catchers (Appendix A, Table 1D and 2D). Backup catchers were any catchers’ who had 10 or fewer game appearances. Statistics from the 2017 season listed a total of 15 catchers between the four teams, six of those catchers had 10 or fewer game appearances. Lastly, six-digit catcher identification numbers (assigned by the MLB) were used instead of their full names.

The sample differed based on the objective. All 15 catchers from the four teams in the American League East were included to determine the incidence of catcher FTIs and absolute risk of more than one FTI in a game in the 2017 season. To quantify the association – and its statistical significance – between catchers’ batting performance, and the number and pitch speed of FTIs, the sample consisted of the nine catchers who appeared in more than 10 games. Players whom appeared in 10 or fewer games represented a small sample of catchers and may have been callups from the minor leagues or mid-season transactions who participated in games that were not observed by the researcher.

32

3.8 Inclusion Criteria

A foul tip impact was included if the delivered pitch contacted the hitter’s bat before colliding with the catcher’s mask. The impact had to result in visible movement of the catcher’s mask or must have a distinguishable contact location on the mask. If it was unclear whether a pitched ball made contact with a hitter’s bat before impacting the catcher’s mask, the researcher assumed that there must have been slight contact with the bat and was included as a FTI. It is worth noting, that it is unlikely for a professional catcher to completely miss a pitched ball without interference from the hitter’s bat.

3.9 Data Collection 3.9.1 Procedure

The use of game footage for video analysis has been an effective means for data collection in previous research (Krosshaug et al., 2007; Hutchison, Comper, Meeuwisse, & Echemendia, 2015; Pollack et al., 2016). Major League Baseball provides access to high definition footage of all games for 30 teams in the two most recent seasons – a total of 9,720 games. In lower levels of baseball, like university or minor league baseball, the number of teams that provide quality footage of their games becomes infrequent and difficult to access. Furthermore, the collection of batting performance statistics becomes less reliable as players or volunteers are more likely to record game details and bias the results in their team’s favour. As the premier professional organization in baseball, the MLB provides a feasible way for anyone to observe and record FTIs to catchers’ masks while gathering objective and reliable statistics.

3.9.2 MLB TV

MLB TV provides video footage that operates at 60 frames per second (60fps) and can be viewed using Apple TV. HD quality video is available on any screen size providing sufficient bandwidth is available. The researcher viewed game footage using Apple TV on a 43” flat screen Samsung TV and on a 15” ASUS laptop screen with a 2.50GHz processor.

All footage was viewed from one angle, behind the pitcher’s mound, however, the angles differed slightly depending on the positioning of the home stadiums camera.

33

3.9.3 Observation

All data were compiled while observing publicly accessed footage from MLB TV occurring from April 2nd to October 1st in the 2017 season. Games in the MLB can range from two and a half to three and a half hours, or more. Initially, the time required for the observation of one game (both teams) was one hour if the researcher manually skipped the commercial breaks and other delays. The researcher implemented two strategies to further decrease the time required to watch each game. First, the researcher determined that he could skip irrelevant pitches (e.g., “ball”, “strikes”, “swinging strike”, etc.) provided the pitch descriptions obtained from Baseball Savant (Figure 1) appeared to have acceptable face validity. Secondly, if FTIs only occurred on foul-type pitches (e.g., “foul”, “foul bunt”, “foul tip”) the researcher could manually skip innings that did not have the possibility of an FTI (i.e., innings without foul-type pitch descriptions).

During the first 50 games, the sole researcher observed every thrown pitch (n= 7364) to ensure it correctly corresponded with the reported “description” from the Baseball Savant database (Figure 1). In addition, relevant FTI information was recorded in a separate column to note if replay footage was available, describe umpire and catcher reactions, if there were on-field medical assessments, as well as the time at which the FTI occurred (Figure 1). All “foul tip” labeled pitch descriptions (n=53) for the first 50 games were securely caught in the catcher’s

Figure 1. Pitch and event description obtained through Baseball Savant. glove, whereas a number of FTIs were labeled as “foul balls” (n=17). Following the first 50 games, catchers were observed during every defensive inning in which a “foul” or “foul bunt” event occurred, rather than observing all pitch descriptions (Figure 1) that were listed in the game (e.g., “ball”, “hit_into_play”, “strike”, “called strike”, etc.). To decrease the time further, the researcher began using the 10 second fast forward and inning selection feature only observe

34 foul balls in each game. The time required to view each team game was reduced from 25-30 minutes in the pilot study to 5-20 minutes per game which took 14 weeks, observing an average of 46 games per week.

3.9.4 Baseball Savant and Statcast

Details of every pitch thrown in a game and batting performance data were collected from the Baseball Savant database, which obtains its data from Statcast. Introduced to the MLB in 2015, Statcast is a highly advanced system designed to measure and track previously unquantifiable aspects of baseball, like the probability of an outfielder making a catch given their route efficiency (to the hit ball), running speed, and the speed and trajectory of the ball hit in the air (MLB, 2017). Statcast collects data from all thirty MLB ballparks using three, high-resolution optical Chyron Hego cameras positioned on each side of the foul line designed to track and quantify the movement of athletes on the field on every play. In combination with the high- resolution cameras, the Trackman Doppler Radar system can record detailed information about the movement of the ball on-field at 20,000 frames per second (e.g., movement of a thrown pitch and spin rate of the ball). All data were stored and accessible through www.BaseballSavant.com. Baseball Savant was developed and is maintained by Darren Willman whom is the Director of Baseball Research and Development for the MLB.

A quality check of 33% of FTI events (n=57) was conducted to ensure all FTI information collected matched the original data recorded during observation. In addition, agreement between three mask impact regions of the lateral to medial plane of the mask (i.e., left, center, or right side) and mask component impact location (i.e., chin, mouth/nose, eye/eyebrow, and forehead mask components) from 57 FTIs were assessed by two raters with baseball experience. Inter- rater reliability can be calculated with Cohen’s Kappa (Cohen, 1960), which can be categorized by strength of agreement from “no agreement” (휅= 0.0-0.2) to “almost perfect agreement” (휅=0.81-1.0). However, the assigned interpretations for Cohen’s levels of agreement have been criticized for the veracity of the implied meaning (McHugh, 2012). For instance, McHugh (2012) suggests any value of Kappa less than 0.60 should indicate inadequate agreement because a large percentage of the data are not reliable; whereas, Cohen’s Kappa interprets lower values as fair (휅= 0.21-0.40) and moderate (휅= 0.41-0.60) but these terms do not reflect the reliability of the data. The percentage of data that are reliable for a fair and moderate level of agreement for

35

Kappa range from 4%-15% and 15%-35%, respectively. For this study, McHugh’s interpretation of Cohen’s Kappa was used and described as follows: none (휅= 0-0.2), minimal (휅= 0.21-0.39), weak (휅= 0.40-0.59), moderate (휅= 0.60-0.79), strong (휅= 0.80-0.90), almost perfect (휅= >0.90). Results for the inter-rater level of agreement for the mask impact regions and mask component impact locations are provided in Appendix B, Tables 1B-4B.

3.9.5 Batting Performance Statistics

Catchers’ batting performance consisted of xwOBA, SLG, and strikeout rate. Batting performance statistics were gathered retrospectively following the observation of all 648 games. Batting performance statistics for each catcher were collected using Baseball Savant where the outcomes of each game are organized by date for the entirety of the season (Figure 2).

3.9.6 Data Management

Figure 2. Batting performance .csv file obtained through Baseball Savant.

All collected data were converted from comma separated value files (.csv) and stored in Microsoft Excel as Excel workbook files (.xlsx). Using Excel allowed the researcher to easily organize, edit, and extract data to use in other programs such as SPSS or R Studio. Complete season thrown pitch details obtained through Baseball Savant were stored separately by team, whereas pitch details specific to FTIs were stored together in the same Excel file. Two charts organized the FTI details. The first chart contained individual team and player information including: games played, games started, athlete exposures, number of FTIs, incidence of FTI and

36 the absolute risk of more than one FTI in a game. The player-organized section was useful in determining any individual or team differences in foul tip impacts or athlete exposures that may support investigation for future research.

The second chart was made up of all FTI descriptive game details obtained through Baseball Savant. In addition, data that was not provided from Baseball Savant was recorded in the “impact description” column, such as: time of the impact on MLB TV, mask impact location, whether there was medical attention, and any supplementary notes of the event. The researcher noted if the catcher was removed from play after being attended to by medical personnel on the field. Medical attention (MA) was logged as a dichotomous variable: ‘yes’ (1) the player received medical attention or ‘no’ (0) they did not receive medical attention on the field.

3.10 Data Analysis

The aim of this study was to establish the incidence of FTIs, the absolute risk of more than one FTI in a game, identify common factors related to FTIs, and to determine if the batting performance of catchers in the current game was predicted by the frequency or average pitch speed of FTIs over the previous seven days in American League East division of the MLB.

Summary statistics of FTIs were collected for analysis to identify common factors related to an impact occurring. These summary statistics were also useful in determining if there were any individual differences between the FTIs experienced by players and teams. All graphs were created using R Studio, specifically, histograms were used to display the results from FTI-related factors, and boxplots were used to explore the data related to FTIs and catchers’ batting performance. A visual inspection – using boxplots – of catchers’ batting performance by the number or pitch speed of FTIs was conducted prior to the use of linear mixed effects models. If there appeared to be a meaningful change in batting performance measures, the analysis would proceed to test hypotheses one and two, comprising six specific null hypotheses:

1. Null hypothesis 1a: The frequency of FTIs over the previous seven days does not predict a significant change in catchers’ xwOBA in the current game.

2. Null hypothesis 1b: The frequency of FTIs over the previous seven days does not predict a significant change in catchers’ SLG in the current game.

37

3. Null hypothesis 1c: The frequency of FTIs over the previous seven days does not predict a significant change in catchers’ strikeout rate in the current game.

4. Null hypothesis 2a: The average maximum pitch speed of FTIs over the previous seven days does not predict a significant change in catchers’ xwOBA in the current game.

5. Null hypothesis 2b: The average maximum pitch speed of FTIs over the previous seven days does not predict a significant change in catchers’ SLG in the current game.

6. Null hypothesis 2c: The average maximum pitch speed of FTIs over the previous seven days does not predict a significant change in catchers’ strikeout rate in the current game.

3.10.1 Incidence of Catcher FTIs

To address the first research question, the incidence of catcher FTIs was calculated by dividing the number of FTIs by the total number of pitches in a season, and then multiplied by 10,000. The incidence of catcher FTIs informs the reader of the approximate number of FTIs expected per 10,000 pitches or 66 games based on the average number of pitches thrown between the four teams; or, the number of FTIs could occur if all thirty teams were to play each day for two days.

3.10.2 Absolute Risk of >1 FTI in a Game

The absolute risk was calculated using the number of games with more than one FTI divided by the total number of games observed. Extra-inning games can occur for every team in the MLB and excluding these games from the analysis would not represent the true number of times multiple FTIs could happen in a game to catchers. Therefore, any game which extended into extra innings (>9 innings) was included as well.

3.10.3 Common FTI Factors

Summary statistics were used to examine FTIs that occurred for catchers, teams, and several situational factors. The FTI situational factors included the following: pitch speed, pitch category, count in the plate appearance, pitch location, and the number of FTIs by month of the season and inning. In addition, Poisson log-linear models were used to examine if there were significant differences within each factor (e.g., do the number FTIs differ significantly between months of the season?).

38

3.10.4 Batting Performance

To examine the six null hypotheses, Linear Mixed Effects models were used to quantify the association between starting catchers’ batting performance and the number and average pitch speed of FTIs sustained in the previous seven days, and its statistical significance. Catchers’ batting performance on the current day was defined by three response variables: (a) xwOBA, (b) SLG, and (c) strikeout rate. The covariates, structure of the model, and the interpretation of each parameter in the model are described. All batting performance analyses were completed using R Studio. However, there did not appear to be any meaningful change in catchers’ batting performance for the pitch speed nor the number of FTIs in the previous seven days upon visual inspection of the boxplots. Despite the lack of visual evidence of a significant difference, correlations of fixed effects and Linear Mixed Effects models were completed and included in Appendix D, Table 1D-6D.

Covariates. The primary covariates of interest were: (a) the number of impacts during games in the last seven days, not including the current day/game; and, (b) the average pitch speed of those impacts. Games in which catchers experienced four to six impacts (n=13 games) in the previous seven days were removed from the analysis due to a low number of observations at each level. Consequently, the number of FTIs during the previous seven games had four categorical levels: 0, 1, 2, or 3 FTIs. The average pitch speed was taken over games, the fastest pitch speed of an FTI was used when a catcher sustained multiple impacts in a single game. All impacts contributed towards the FTI count if there were more than one impacts in a game.

The covariates were defined as follows:

• xij0: log of number of plate appearances for player i in game j

• xij1: the number of head impacts for player i in the seven days preceding game j

• xij2: the average maximum velocity of those impacts (maximum FTI pitch speed for each game summed and divided by the number of games played)

• xij3: the number of games played by player i in the 7 days preceding game j

The random error terms of the model were defined as follows:

39

• (u1,…,uI) = u ∼ N(0,Σu) is the player random effect

2 • ϵij ∼ N(0,σ ) is the standard error term

Structure of the Model. The structure of the model contained the main effects of the covariates, including the interaction between the primary covariates of interest. A nested model comparison was used to obtain a p-value corresponding to the null hypothesis that the reduced model (i.e., excluding the number of FTIs in the preceding seven days) would fit the data equally as well as the full model. If the models differed by only one variable, it would be a hypothesis test of whether that variable is significant. The final model statement was:

log(mean(yij)) = β0xij0 + β1xij1 + β2xij2 + β12xij1xij2 + β3xij3 + ui + ϵij

Interpretation of Model Parameters. The “mean parameters” and several variance parameters were included in the model. The interaction between each parameter was defined and interpreted as follows:

• β0: interpreted as a “deviation from offset”; this is used in the model to stabilize the estimation and is not physically representative of any quantity of interest

• β1: the expected additive change in the log-average xwOBA, SLG, or strikeout rate as a result of a player sustaining one additional FTI in the previous seven days.

• β2: the expected additive change in the log-average xwOBA, SLG, or strikeout rate as a result of a catcher’s average maximum pitch speed of FTIs in the previous seven games increasing by one.

• β12: the expected amount by which the expected additive change in the log-average xwOBA, SLG, or strikeout rate is a result of a catcher sustaining one additional FTI in the previous seven days changes as a catcher’s average maximum pitch speed of these impacts increases by one mile per hour. This quantified the notion that a catcher who sustain faster FTIs will need to have less of them in order to decrease his performance. For example, does a catcher have similar levels of performance impairment if he experiences five FTIs at 80mph compared to one FTI at 95 mph? This was a quantity of primary interest in this study.

40

• β3: the expected additive change in the log-average xwoba, SLG, or strikeout rate as a result of a catcher playing one additional game in the previous seven days.

• σ2: the within-player variability, how much do we expect a catcher’s xwoba, SLG, or strikeout rate to deviate from their baseline in a game, on average?

2 • σ u: between-player variability, how much do catchers’ baseline xwoba, SLG, or strikeout rate to deviate from each other, on average?

Chapter 4: Results

This study evaluated the incidence of catcher FTIs and the absolute risk of more than one FTI occurring in a game. Factors related to foul tip impacts, and the catchers that experienced FTIs were collected and are described below. In addition, this study sought to determine whether catchers’ batting performance in each game was predicted by the number and average pitch speed of FTIs over the previous seven days. The results are separated into two sections: (1) the description of factors related to the FTI events, and (2) results for the selected batting performance response variables.

4.1 Description of FTIs 4.1.1 Incidence of Catcher FTIs and Absolute Risk of >1 FTI in a Game

There were 172 FTIs over the course of 648 regular season games for four teams in the American League East division of the MLB. All catchers were included for the incidence of catcher FTIs and the absolute risk of more than one FTI in a game. The incidence of catcher FTIs was 17.52 FTIs per 10,000 pitches. The combined average number of pitches per game was 151.52 for the four teams, which represents 17.52 FTIs per 66 games. The absolute risk of more than one FTI in a game, for all four teams, was low (0.026) and there were 17 games with two or more FTIs in a game occurring to seven catchers. A quality check of 33% of FTI events (n=57) revealed no errors or missing values in the pitch characteristics obtained from Baseball Savant.

4.1.2 Impacts by Team

For all four teams, there was a combined total of 98,184 pitches thrown in the 2017 regular season; and, of this total, there were 17,454 foul balls which resulted in 172 FTIs. A relatively

41 low number of foul balls (n= 6) were unobservable due to instant replays, obstructed camera angles, or coverage of fans in the stadium. The number of impacts varied by team; catchers for the Boston Red Sox accumulated a total of 53 FTIs, whereas the Orioles, Yankees, and Rays had 45, 43, and 31 FTIs, respectively. All teams played the same number of games (n= 162).

4.1.3 Catchers

This sample consisted of 15 catchers, 14 of which were wearing the traditional two-piece mask and one who wore a hockey style mask. Of the 15 catchers observed over the course of 648 games, only two were not hit by an FTI. However, catchers who were not hit by an FTI only appeared in 13 games and started a total of 2 games. Catchers who started more than 10 games experienced anywhere from eight to 32 FTIs, averaging 18.4 FTIs per catcher (SD= 8.95). Lastly, the number of athlete exposures for starting catchers ranged from 7553 to 14,353 pitches caught while on defense.

An inter-rater reliability check was conducted to determine if there was sufficient agreement of lateral and medial mask impact regions (i.e., left, center, right), and the mask-component impact location (i.e., chin, mouth, eye, forehead areas) for the 57 FTIs reviewed between two raters in the quality check (Appendix B). The inter-rater agreement using Cohen’s Kappa coefficient was moderate (휅= 0.66) for the lateral and medial impact locations, and there was minimal agreement (휅= 0.22) for mask-component impact location between raters (Appendix B, Table 1B and 2B). For lateral and medial mask impact location, the majority of FTIs occurred to the middle (n=25) and right side (n= 21) of the catcher’s mask. In addition, the majority of FTIs impacted the eye- eyebrow region (n= 27) while the other three locations had a similar distribution of impacts (Appendix B, Table 2B). The approximate significance of agreement measures is displayed in Appendix B, Table 3B and 4B.

4.1.4 Summary of FTI Pitch Speeds

The mean, median, and mode of pitch speeds are displayed in Table 2. The release speed of all FTIs ranged from 76.7-100.3mph (Figure 3) with 127 (74%) of the impacts reaching pitch speeds of 90mph or greater. The frequency of FTIs with the corresponding pitch release speed is shown in Figure 3.

42

Table 2. Summary of FTI Pitch Speeds (miles per hour) Number of Mean (SD) Median Mode Range (Min-Max) FTIs 172 91.7 (4.86) 92.9 93 76.7-100.3

Figure 3. Histogram of FTI pitch speeds.

4.1.5 Pitch Type

Fastballs made up approximately 79% of all FTIs (n= 135), whereas breaking balls and off-speed pitches consisted of approximately 20% (n= 34) and 2% (n= 3), respectively. Full season pitch category totals and the number of FTIs by pitch type are listed in Table 3, and foul tip impacts by each pitch type are displayed in Appendix C, Table 1C. To determine if the number of FTIs was influenced by pitch type, three pairwise Poisson tests were run to compare count values, and the significance level set to 0.017 after using a Bonferroni correction. Fastballs resulted in significantly more FTIs than breaking balls (p< 0.001) and off-speed pitch types (p< 0.001), and

43 there were significantly more FTIs from breaking balls than off-speed pitches (p< 0.001).

4.1.6 FTIs by Month

A Poisson log-linear model revealed the number of foul tip impacts varied significantly across months after controlling for the number of games played per month (p= 0.008) (Figure 4). Notably, there was a greater number of impacts in the first two months following each rest periods: (1) April and May, (2) August and September (Table 4). The frequency of FTIs per game and average pitch speed of FTIs are organized by month in Table 4.

Figure 4. FTIs by month of the season. This figure illustrates the expected number of FTIs based on the number of games played (orange dots) compared to the number of observed FTIs (blue bars).

44

4.1.7 Inning

For the late stage of the game, the total inning count was lower because a full 9th – the top and bottom of the inning – is not always required to be played (Table 5). Although there were 154 fewer innings played in the late stage of the game, there were 10 and 15 more FTIs than the early and middle stage of the game, respectively. Table 5 illustrates the number of FTIs that could be

Table 5. Summary of FTIs by Early, Middle, and Late Stage of the Game. Stage of the game # Innings Expected FTIs (Assuming FTIs (%) (innings) Played equal innings per stage) Early (1-3) 53 (30.8) 1944 53 Middle (4-6) 48 (27.9) 1944 48 Late (7-9) 63 (36.6) 1790 68.6 Extras (10-19) 8 (4.7) 159 97.8 Total 172 5837 267.4 expected if each stage of the game had the same number of innings played. The number of FTIs and innings played for the ‘late’ and ‘extras’ stages of the game were adjusted proportionally to represent the expected number of FTIs if there were 1944 innings played for each stage. The total number of extra innings played were compiled (n=159), including extra innings where FTIs did not occur – innings 13, 15-19 (n=30). There was a total of 129 extra innings played that accounted for eight FTIs.

45

4.1.8 Count in the Plate Appearance

FTIs occurred most often in a new count – no balls, no strikes (n= 31) – and least often in hitters counts where there are more balls and one or fewer strikes: 3-0 (n=1), 3-1 (n=8), 2-0 (n=10), 1-0 (n=9). Of the 12 possible counts, three counts – 2-2, 0-1, 0-0 – accounted for nearly half of all impacts: 12% (n= 21), 13% (n= 22), and 22% (n=31), respectively. The number of impacts by count and the average pitch speed of those FTIs are displayed in Appendix C, Figure 1C.

4.1.9 Pitch location

Approximately, 70% of all FTIs (n= 120) occurred from pitches thrown in the middle locations (i.e., 2, 4, 5, 6, 8), whereas 30% of FTIs (n= 52) were pitches thrown in all other locations (Figure 5). There may be several explanations for this however, we did not account for the number of pitches thrown in each location of the strike zone.

Figure 5. FTI by location in the strike zone. This figure illustrates the number of FTIs that occurred at each location from the catcher's perspective. The red outline represents the middle strike zone locations. 4.2 FTIs and Batting Performance

Catchers’ xwOBA, SLG, and strikeout rate were examined as a group, and explored individually to determine if there were individual differences to head impacts. The frequency of head impacts over the prior seven days, relative to the current day’s batting performance, was recorded by the

46 number of games played corresponding to the frequency of impacts (e.g. catcher 1 played 30 games in which he experienced two FTIs in the previous seven days). Catchers’ batting performance was also examined with speed of the pitch as a covariate: (1) No impact, (2) 0- 80mph, (3) 81-90mph, (3) 91-100mph, (4) >100 mph.

4.2.1 Correlation of Response Variables

After examining the correlation of the original response variables, batting average was removed because it was highly correlated with weighted on-base percentage (r= 0.938). Correlations between potential response variables are shown in Table 6. The final response variables – xwOBA, SLG, and strikeout rate – had a weak to strong correlation (Table v) with each other; whereas batting average and weighted on-base average had a weak to very strong (r= 0.8-1.0) correlation with the other response variables.

Table 6. Sampled Catchers’ Batting Performance Measures: Correlation of Potential Response Variables Variables 1 2 3 4 5 1. Batting Average – 2. wOBA .94 – 3. xwOBA .65 .73 – 4. SLG .82 .97 .73 – 5. Strikeout Rate -.38 -.35 -.49 -.30 –

4.3 Batting Performance for the Season

Table 7 displays mean seasonal batting performances for individual starting catchers included in the study. All measures of batting performance – xwOBA, SLG, and strikeout rate – were highly variable for all catchers on a game-to-game basis (Appendix D, Figures 1D-3D).

47

Table 7. Summary of Batting Performance Results by Season for Starting Catchers. Expected-Weighted On- Strikeout Rate Slugging Percentage Base Average Player Mean SD Mean SD Mean SD ID 456078 0.321 0.215 0.277 0.228 0.470 0.515 467092 0.304 0.245 0.173 0.219 0.399 0.523 491696 0.282 0.197 0.197 0.254 0.366 0.455 506702 0.229 0.181 0.253 0.277 0.346 0.467 519083 0.264 0.196 0.259 0.223 0.371 0.575 519222 0.270 0.184 0.225 0.234 0.286 0.375 543376 0.272 0.236 0.284 0.279 0.413 0.515 543877 0.268 0.201 0.193 0.221 0.386 0.473 596142 0.346 0.257 0.246 0.231 0.524 0.566 All 0.288 0.219 0.236 0.243 0.407 0.504 catchers

4.3.1 The Number FTIs in Previous Seven Days by Games Played

The number of FTIs over the previous seven days – for each day in the season – ranged from 0-6 (Figure 6).

Figure 6. Games played by the number of FTIs in the previous seven days for each catcher. Each catcher has a unique, six-digit player ID number provided by the MLB. This figure illustrates the number for games played with the frequency of FTIs that occurred in the previous seven days from that competition day.

48

However, there was a relatively low number of games played in which there were 3-6 impacts over the last seven days (n= 47, M= 4.27, SD= 4.56, range: 0-16) as compared to two impacts (n= 91) and one impact (n= 256). For catchers included in the batting performance analysis, there was an average of 28 games played per catcher in which one FTI had occurred (SD= 10.83, range: 13-46), and 11 games played where there were two FTIs (SD= 7.09, range= 2-22) in the last seven days. Each response variable for batting performance was examined at 0, 1, 2, or 3 number of impacts over the prior seven days, although only six of the nine catchers had observations for three FTIs in the previous seven days.

4.3.2 Group Performance by Frequency of FTIs Over the Previous Seven Days

Visual observation of the boxplots for xwOBA, SLG, and strikeout rate did not appear to indicate any differences by the number of impacts over the previous week. The results from the Mixed Effects models support the conclusion that the frequency of head impacts did not predict catchers’ performance (Appendix D, Table 2D, 4D, & 6D). Additionally, there appears to be no improvement or a slight improving trend (visually) in batting performance measures (Figure 7- 9), however, the lack of statistical significance implies that random variation of catchers’ day-to- day performance was a reasonable explanation for this. No further analyses were conducted.

49

50

4.3.3 Individual Performance by the Frequency of FTIs

There appeared to be some differences between players’ xwOBA, SLG, and strikeout rate when considering the number of FTIs over the previous seven days (Appendix D, Figure 4D-6D). However, within-player variability was too high to determine if there was a meaningful change from a sample of nine catchers. Slight effects that were observed may be explained by the players’ variability in performance, but we are unable to conclude that there was any meaningful relationship between FTI frequency and any of our performance measures for each catcher.

4.3.4 Group and Individual Catcher Performance by FTI Release Speed

Group and individual catcher batting performance did not reveal a meaningful change by the pitch release speed of the FTI. Similar to catchers’ batting performance by the frequency of FTIs, the trend in group batting performance by release speed produced results contrary to what was hypothesized (Figure 10-12); small changes or improvements in batting performance for some levels of FTI pitch speeds were noted. In addition, only one catcher experienced an FTI that was

Figure 10. All catchers' xwOBA by the average release speeds of FTIs sustained in the past seven days relative to the game being played on that date. 80mph or slower and an FTI at 100mph or faster; effectively reducing observations for all other

51 catchers’ performance to ‘no impact’, ’81-90mph’, and ‘91-100mph’ FTIs. Individual catchers batting performance by FTI release speed produced similar results with only a few measures for select catchers resulting in a decrease in batting performance (Appendix D, Figure 7D-9D).

Figure 11. All catchers' SLG by the average release speeds of FTIs sustained in the past seven days relative to the game being played on that date.

Figure 12. All catchers' xwOBA by the average release speeds of FTIs sustained in the past seven days relative to the game being played on that date.

52

Chapter 5: Discussion

The purpose of this study was to examine foul tip impacts to catchers’ masks in the American League East. Previously, the only information known about foul tip impacts came from self- report data of FTI frequency (Green et al., 2018) and the speeds of impacts that caused concussion (Beyer et al., 2012); yet, these findings may not precisely describe the impacts catchers sustain in a season. Further, there is no information to suggest if factors, such as the number of athlete exposures on defence, contribute to the quantity of FTIs that occur. To address these knowledge gaps, this study examined the incidence of catcher FTIs, the absolute risk of more than one FTI in a game, and described the common factors related to FTIs.

5.1.1 Incidence of Catcher FTIs

This study is the first to quantify FTI exposure to MLB catchers in a season. Thirteen of fifteen catchers were hit by FTIs. However, it is important to note that all catchers are at risk of an FTI while playing defence. The incidence of FTIs equates to approximately one FTI every four games based on the average number of pitches per game for all four teams, combined. Unsurprisingly, catchers who were not hit by FTIs started a total of two games, whereas other backup catchers sustained nearly two impacts on average and started three to five games. In addition, catchers who started more than 10 games experienced anywhere from eight to 32 FTIs. The data supports findings from retrospective, self-report surveys that demonstrate 75% of MiLB and MLB catchers reported one or fewer FTIs per game (Green et al., 2018), with starting catchers experiencing FTIs between 14% and 38% of the games they started.

Our results suggest that the number of FTIs catchers’ experience is partially influenced by the number of pitches they caught while playing defence. Starting catchers who were hit by 13 or fewer FTIs caught less than 8700 pitches, whereas those who were hit 20 or more times caught more than 10,000 pitches. Furthermore, starting catchers who played 11-27 more games had seven to 24 more FTIs than starting catchers who started between 51-58 games. These findings indicate that catching more pitches may disproportionately increase the number of FTIs that catchers experience.

Foul Tip Impacts by Month. Although catchers sustained an FTI once every four games on average, the frequency of these impacts differed significantly by month of the season when

53 accounting for the number of games played. Specifically, FTIs occurred in 38% and 34% of the games in the months of May and September, but only 19% and 15% of the games in June and July, respectively. These changes may occur due to hitters being just fatigued enough to slightly miss (causing an FTI) or great enough to consistently vary in precision, leading to fewer FTIs. Coincidentally, pre-season games and the All-Star break may provide adequate rest for athletic performance, and these breaks occur two months prior to May and September. Specifically, there seems to be an upward trend in the quantity of FTIs leading up to these months following potential rest periods. By comparison, the lowest number of FTIs were in June and July which were the farthest (temporally) from a rest period. Although there is a rest period in July for the All-Star break, there may have been fewer FTIs in the games played before and after the break because hitters were too fatigued or not fatigued enough to consistently cause FTIs. For example, research suggests detachment from sport was positively related to physical and cognitive recovery in Dutch elite athletes (Balk, de Jonge, Oerlemans, & Geurts, 2017). The obvious implication is that catchers may be exposed to a greater risk of head impacts as hitters’ fatigue over the course of April and August.

5.1.2 Absolute Risk of >1 FTI in a Game

The absolute risk of more than one impact in a game was 3%, occurring 17 times to catchers in the 2017 season. In those 17 games, seven of the 15 catchers experienced between two to three FTIs in a single game and this occurred as many as four times each for two catcher. The majority of games with more than one FTI were to starting catchers who caught more than 10,000 pitches in a season (82%). Therefore, it may be that catching more pitches not only leads to a greater number of head impacts but also a greater chance of experiencing more games with more than one FTI in a season. In addition, the relatively low risk of more than one impact occurring in a game may explain why a small proportion of MiLB and MLB catchers reported experiencing one or more FTIs per game in a study by Green et al. (2018). We speculate the risk of a multi-FTI game would be similar if replicated with different samples of MLB teams because it’s likely influenced by a combination of factors that are not specific to any team or division (e.g., quantity of foul balls in a game, month of the season, catcher set-up position, and pitch characteristics in a game).

54

The relationship between the number of exposures to the number of impacts and games with more than one FTI may have implications for the vulnerability of starting catchers to concussion. Although no prior studies have examined baseball, there is evidence that increased head impact exposure on the day of competition or the season may be a mediating factor for the on-set of concussion in football athletes’ (Stemper et al., 2018). These findings may warrant further investigation into the onset of concussions based on exposure data for MLB catchers (e.g., the number of pitches caught or FTIs in a season).

5.1.3 Common Characteristics of FTIs

Pitch Type. Prior to this study, it was not known whether FTIs could be characterized by any common features. Our results indicate that the number of fastballs thrown may contribute to the likelihood of a foul tip impact occurring. In our study, 79% of FTIs were the result of a fastball being thrown, significantly more often than breaking balls or off-speed pitches. These pitches represent the fastest pitches in each MLB pitcher’s repertoire, by pitch category. It may be the case that FTIs from faster pitches happen more often than other pitch types because hitters have less time to react reducing their ability to precisely predict the correct intercept path of their bat with the ball, both temporally and spatially. For instance, fastball foul tip impacts averaged 94mph while off-speed and breaking ball FTIs were approximately 10mph slower and consisted of 21% of FTIs, combined.

In addition, the flatter trajectory of a pitch may also contribute to the likelihood of a foul tip impact occurring. Of the fastball FTIs, 84% of these impacts were four-seam fastballs which are typically characterized by their fast but flat trajectory. The trajectory is easier to predict and may allow for smaller margins of error when swinging at these pitches leading to the large proportion of impacts for this pitch type. Interestingly, sliders consisted of 91% of breaking ball FTIs and they are traditionally described as moving laterally, on a flatter (i.e., less downward movement) and faster trajectory towards the plate compared to other breaking balls; aligning with the idea that flatter and faster pitches result in more FTIs. We surmise other divisions or teams in the MLB, with a greater proportion of fastballs thrown, may have a greater incidence or a slightly higher absolute risk of experiencing more than one FTI in a game at faster speeds.

FTI Pitch Speeds. The pitch speed of foul tip impacts ranged from 77-100mph, similar to but slightly faster than those which caused catcher concussion in previous research (Beyer et al.,

55

2012). Surprisingly, the median release speed for the impacts that caused concussions in the previous study is the same median release speed of FTIs in this study. Although Beyer and colleagues (2012) only observed ten foul tip impacts, the similarities in pitch speed are nearly identical to this study. In addition, the findings from this study build upon the FTI pitch speeds previously described by Beyer et al. (2012). Despite the similarity in range and median FTI pitch speeds, this study demonstrates a left-skewed relationship for observed FTI pitch speeds meaning FTIs typically occurred at speeds toward the higher end range (≥90mph) rather than the lower end (<90mph). Specifically, 74% of FTIs reached speeds of 90mph or greater and one- quarter of impacts were 95-100mph.

5.1.4 Catchers’ Batting Performance

While multiple studies have examined MLB athletes batting following their return from concussion (Schwindel et al., 2014; Wasserman et al., 2015; Ramkumar et al., 2018; Sabesan et al., 2018), examining batting performance relative to head impact exposure had not been explored previously. We did not find catchers’ batting performance in each game was predicted by the frequency nor the pitch speed of foul tip impacts in the previous seven days. However, there are several factors that should be considered in the interpretation of these findings.

The first explanation of these results is that foul tip impacts do not impair athletes or do not occur frequently enough to decrease performance. In other words, ball impacts may not be a form of head impact that significantly predicts lower batting performance in catchers. However, our analysis lacked observations at various categorical levels for the frequency and pitch speed of impacts over the previous seven days. These findings should be interpreted with caution because it is also possible that our results may be a false negative (i.e., Type II error) as a result of a small sample size. For example, there were nearly as many observations for ‘no impact’ in the previous seven days as there were for the three levels of FTI frequency combined (e.g., 1, 2, 3). Each subsequent level of FTI frequency had a third of the observations for batting performance and only six catchers experienced three impacts in the previous seven days. In addition, we analyzed batting performance using the average pitch speed of impacts in the prior seven games in four categorical levels; though, only one catcher had observations in the 0- 80mph and greater than 100mph category. This effectively reduced the observations for all catchers to 81-90mph and 91-100mph. These issues suggest there may have been more error in

56 batting performance at various levels of our predictor variables and may not be representative of all nine catchers in the analysis.

Fewer observations at each level may not provide an accurate depiction of FTI influence on batting performance, regardless of significance. Catchers’ day-to-day batting performance was highly variable, in general, even without factoring in the pitch speed or number of impacts. It follows that fewer observations at each level of the predictor variables may be more representative of the natural variation in batting performance than the influence of foul tip impacts.

Secondly, our results seem to suggest that catchers responded differently to the number of FTIs or the average maximum pitch speed of those impacts. Minor, individual differences were noticeable in the batting performance metrics of some catchers but not for others. Although this is likely due to chance, it is possible that catchers had varying tolerances to the pitch speed or frequency of head impacts typically experienced in an MLB season. Individual tolerance to head impact exposure in other sports has been suggested previously (Nauman & Talavage, 2018; Rowson et al., 2018). However, similar to group batting performance, each categorical level for the frequency of impacts was too low to make any statistically valid conclusions about these differences.

5.2 Limitations

This study had several limitations that should preface the implications of our findings. Although several factors may have influenced our analyses, each factor provides direction for future research and sets the parameters for which these findings can be understood. Specifically, selection bias, sample size and the retrieval of concussion history will be discussed in greater depth.

The method for choosing our sample was not random and, therefore, selection bias may have influenced our findings. Our sample was selected based on the assumption that the occurrence of foul tip impacts was not influenced by factors that are unique to any team or division. However, there is always a chance that a selected sample may have unique factors that bias the number or characteristics of FTIs observed in this study. Regardless of whether any unknown bias was present, the findings of this study are important because it provides a description of FTIs that can

57 occur which was not previously known. More research is needed to determine if the characteristics of FTIs are representative of the head impacts catchers experience in the MLB.

Due to the time required to observe each game in this study, only nine catchers were included in the batting performance analysis. Six catchers were excluded because they started a low number of games. Ideally, only starting catchers would be used to obtain a larger sample for analyzing batting with more FTI data. However, it would not have been feasible for a single researcher to observe an adequate sample size of starting catchers for the batting performance analysis. The six backup catchers only started a total of 14 games, and it is doubtful that one additional starting catcher would have significantly influenced the analysis.

Lastly, we were unable to reliably retrieve catchers’ concussion history from the minor leagues which could have been useful in our batting performance analyses. There is evidence to suggest athletes’ brains respond differently to head trauma for those with and without history of concussion (Johnson et al., 2014). It is possible that catchers’ concussion history may explain the differences in individual hitters’ performance following FTIs and it should be used in future studies, if possible. However, the injured list in the minor leagues did not consistently record what injury an athlete had sustained. Although several concussions were noted, there were twice as many undisclosed injuries and nearly twenty times the number of disclosed musculoskeletal injuries. Concussion history was not included because it was unclear whether these undisclosed injuries represented musculoskeletal injuries or concussion. Furthermore, obtaining concussion history for catchers in this study was difficult even when corroborating injury reports from several different online sources (e.g., MLB.com, Baseball Prospectus, minor league media guides). Although the use of concussion history could have aided our analysis, only using the disclosed concussions may not have accounted for potential undisclosed concussions to other catchers.

5.3 Delimitations

Methodological boundaries were set so this study could be manageable within the time constraints of a master’s thesis. One delimitation of this study has relevance to our batting performance results and reported head impact exposure; specifically, examining only one type of subconcussive impact to catchers.

58

Some forms of impacts were not included in the analyses but may have influenced catchers’ batting performance on any day of the season, including weeks with no recorded FTIs. On several occasions, the follow-through of a hitter’s bat impacted the head of the catcher. However, other forms of impacts were noted during observation, as well. Catchers were hit by foul balls in body regions that could have negatively impacted their batting performance, such as the neck, forearm, and groin. Furthermore, there were nearly 17,500 foul balls that could have hit various regions of a catcher’s body in this study which averaged pitch speeds that caused the highest injury rate for hit-by-pitches to MLB players in 2015 (Camp et al., 2018b). Additionally, catchers in our study could have sustained subconcussive blows to the body or head without being directly observed; such as, hit-by-pitch while batting, colliding with teammates or the stadium, or backswings from a hitter’s bat. Although, these types of impacts were outside the scope of our study. The time required to observe every foul tip impact was a factor that limited our ability to obtain all forms of impacts which could have influenced catchers’ performance and provided a more accurate reflection of true head impact exposure.

5.4 Implications

This study provided data that addressed several gaps in the literature examining foul tip impacts to catchers’ masks in the MLB. These findings may have important implications for head impact exposure research and practical applications for the laboratory modeling of catcher mask impacts; but, should be understood within the confines of the limitations and delimitations. Considerations for future research with catchers, the development of standards of catchers’ masks in the MLB and tracking FTIs to inform on-field medical assessment decisions are discussed.

5.4.1 Head Impact Exposure

This is the first study to quantify the head impact exposure of MLB catchers. The findings have several implications that should be considered when including catchers in future research; such as, accounting for position and playing time. As mentioned previously, the number of FTI exposures seems to be influenced by the number of pitches that catchers were exposed to on defence. Starting catchers played in more games, caught more pitches and experienced more foul tip impacts in contrast to backup catchers. This is important because backup catchers represented 40% of the sample whom experienced anywhere from no FTIs to two FTIs and only started 2%

59 of games, combined. Given a large proportion of catchers may be subject to only a few FTIs or as many as 32 in a season, selecting catchers indiscriminately may yield varying results for epidemiological studies or affect assessment outcomes in research.

The differences in exposures for starting and backup catchers merit re-examining the way concussion incidence is calculated for catchers. Studies have calculated the incidence of catcher concussions using all active catchers in the MLB over the number of seasons that occurred during observation (Kilcoyne et al., 2015; Green et al., 2015; Green et al., 2018); however, this may undervalue the incidence of concussions for catchers with the most playing time and, therefore, the highest likelihood of experiencing a greater number of all types of head impacts. For example, foul tip impacts are the leading cause of concussion for catchers (Kilcoyne et al., 2015; Green et al., 2018); yet, catchers who play fewer games were less likely to be hit by foul tip impacts. Furthermore, backup catchers only started 2% of games which limits their risk of being injured by other means as well (e.g., HBP, backswings, player collisions). Although we did not track other forms of head impacts, it may be safe to assume backup catchers experience far fewer impacts than starters, in general, given that starters may play 98% of games in a season and sustain 97% of FTIs. In addition, there were 113 active MLB catchers in 2017, 48% of those catchers appeared in fewer games than starting catchers (in this study) and 21% had fewer game appearances than backup catchers. Future epidemiological research regarding catcher concussions should differentiate between starting and backup catchers, given a large proportion of those athletes may have very few exposures to head impacts in a season.

5.4.2 Catcher Mask Evaluations

The high pitch speeds of FTIs may be particularly relevant for the development of new catcher mask standards and mask impact research. The findings of this study contribute FTI pitch speeds that could be used to precisely measure the head impacts that catchers commonly experience in the MLB. Previously, ball speeds for experimental modeling of catcher mask impacts used the plate speed of impacts that caused concussions (Beyer et al., 2012) or were chosen arbitrarily (Shain et al., 2010; Laudner et al., 2014; Siu et al., 2016; Eckersley et al., 2018). Although some foul tip impacts had similar pitch speeds to the ball-mask impacts used in experimental modeling (Beyer et al., 2012; Laudner et al., 2014), the majority of studies used plate speeds that were 10mph-30mph slower than the common FTI pitch speeds in this study. Beyer and colleagues

60

(2012) proposed the speed of a pitch decreases 6-11% from release to homeplate. Even after factoring in the decrease in release speed to the plate, the majority of FTIs occurred at speeds which are faster than those tested previously (Shain et al., 2010; Schwizer et al., 2016; Siu et al., 2016; Eckersley et al., 2018). Testing ball-mask impacts at speeds that are slower than most FTIs observed in this study may not represent the forces that are typically exerted on an MLB catcher’s head or mask. Only two studies have tested comparable impact speeds (Beyer et al., 2011; Laudner et al., 2014) and more research should be conducted using pitch speeds of FTIs typically observed in this study.

In addition to the pitch speeds of impacts, catchers experienced many FTIs over the course of the regular season and, in multiple instances, were hit as many as three times in the same game. Future mask standards should consider the effect of multiple impacts on the structural integrity of the mask. For example, 27% of FTIs would have a maximum plate speed of 90mph or greater which have the potential to fracture catchers’ masks (Laudner et al., 2014). In six instances, catchers experienced two or more FTIs that exceeded 95mph in the same game. Although the initial deformation of the mask may result in greater absorption of the imparted force, it is unclear what consequences may arise from subsequent impacts to a broken mask.

Nevertheless, the findings of this study may have the potential to influence the standards or testing of catchers’ masks to account for the quantity and pitch speeds of FTIs in the MLB. The National Operating Committee for Standards of Athletic Equipment (NOCSAE) specifically state “interested parties” should review the standards and provide suggestions for improvement based on several critical test parameters; notably, number of impacts (NOCSAE, 2019). The equipment certification for catchers’ masks are provided by NOCSAE based on drop tests and projectile impacts. These standards only examine several baseball impacts at 60mph and softball impacts at 70mph to multiple mask samples (NOCSAE, 2019). Furthermore, if a mask fractures during testing, it will result in a failure for that style of catcher’s mask; however, the speeds used to currently test masks are not comparable to FTI pitch speeds observed in this study. Given the notable difference in FTI pitch speeds to those currently tested, NOCSAE should consider adjusting their standards for catchers’ masks that may be used at an MLB level.

61

5.4.3 FTIs and Medical Assessment Decisions

The objective of this study was not to critique athletic trainers’ assessments of head impacts; yet, the high pitch speeds, substantial increases in FTI frequency and the low number of on-field medical assessments observed warrants discussion. Specifically, the FTIs that resulted in an on- field assessment seem to suggest the decision to evaluate may not be based on any quantitative information.

Of the four on-field medical assessments, only two occurred when the catcher had sustained more than one foul tip impact in the previous seven days, and there were no assessments after any of the 17 instances when catchers sustained multiple impacts in a game. In several cases, catchers were hit more than once per game (over multiple days) with FTIs reaching speeds in excess of 95mph. By contrast, three of the FTIs that resulted in medical assessment were 94mph or slower suggesting the decision to assess head impacts may be based on the subjective reaction of the catcher rather than objective information about the impact or cumulative exposure. This is problematic for two reasons: First, this requires the catcher to perceive an impact as serious enough to report - while in competition - and risk removal from the game. Secondly, the athletic trainer’s decision to evaluate may be based off a single impact rather than the quantity or frequency of FTIs.

Athletic trainers are responsible for treating multiple athletes during a game and they may not see every FTI that catchers experience. There were as many as 53 FTIs impacts experienced by catchers on a single team and it would be difficult to recall details of impacts that may justify on- field medical assessment. Furthermore, without a system in place to track FTIs, it is not reasonable to assume athletic trainers are precisely aware of the time between impacts, the number or frequency of FTIs for each catcher, and if any prior impacts warrant concern for the catchers’ health. The stark contrast between the quantity of FTIs and on-field assessments in this study suggests tracking impacts may yield more informed decisions to medically assess catchers on-field.

5.5 Future Directions

The results of this thesis generated potential research questions and directions that are useful for future research. This thesis described the incidence, frequency, and pitch speed of FTIs; and,

62 these factors provide a benchmark for which future FTI research can compare or assess their findings. Specifically, these findings merit further investigation as it relates to catcher concussions and future considerations for improving the examination of batting performance following FTIs.

5.5.1 Catcher Concussions

Recent research suggests that individual differences in head impact exposure could be a moderating factor in the on-set of SRC in football players (Stemper et al., 2018), but we do not currently know if differences in head impact exposure lead to an increased risk of concussion for catchers. Although the findings provide information regarding FTIs that may typically occur to MLB catchers, the overwhelming question remains, “which FTI factors contribute to catcher concussions?” Specifically, the similarity between the pitch speeds of FTIs and those which caused concussion previously suggest impact speed is not the only factor contributing to the onset of SRC. Rather, it may be a combination of factors that lead to injury, such as individual tolerance to impacts, cumulative exposure and the magnitude of impacts.

Examining the head impacts that contribute to the onset of concussion in catchers may provide a unique opportunity that does not exist in contact sports. Unlike collision sports, catchers were limited to a single impact at a time, to the front of the mask and from an object with the same mass each time (i.e., the ball). Specifically, FTIs occurred to front, rather than the top, back, or sides of the mask. The impact locations are easier to identify and there is no second impact with the ground or another player, unlike football, hockey, or rugby. For example, Karton and Hoshizaki (2018) explain that athletes’ heads (in contact sport) can be hit in a multitude of ways that uniquely differ in the impact parameters; such as velocity, location, mass, vector/direction. A review of the biomechanics of head impacts has previously suggested that impacts to the top of the head – in football – are associated with greater linear acceleration (Guskiewicz & Mihalik, 2011); however, the magnitude of force imparted to the head may range greatly depending on the mass, speed and direction of the other striking athlete(s). As a result, it may be harder to identify a specific impact profile or series of events that create the right conditions which lead to an increased susceptibility of athletes to concussion. Moreover, the results suggest catchers are typically hit once every four games rather than multiple times per game; whereas contact sports may experience many impacts per game (Nauman & Talavage, 2018). The multiple impacts

63 sustained per game in contact sports can differ greatly, increasing the difficulty of identifying the specific factors which lead to SRC. By contrast, the impact parameters of FTIs may not differ greatly between impacts, relative to contact sports. The FTIs sustained by catchers may provide valuable information to the literature because there are fewer differences in impact profiles that could influence a head injury. In other words, it may be easier to identify what factors contribute to SRC in catchers because the athlete is hit by the same object, in a fixed position, and less likely to sustain impacts from multiple sources.

5.5.2 Batting Performance

More sophisticated analyses of FTIs may be necessary for future research to determine if there is an effect of foul tip impacts on catchers’ batting performance. Perhaps most importantly, the use of FTI pitch speed and head impact frequency (exclusively) to measure batting performance is difficult because it does not account for the magnitude or direction of forces imparted to the head. Each FTI a catcher experienced may have differed in the impact characteristics which may or may not impair catchers’ batting performance. For example, foul tip impacts occurred to a variety of mask locations, angles off the bat, and were subjectively different in severity (e.g., glancing compared to flush with the mask and head).

In addition, the high variability of batting performance metrics suggests future research consider several directions to improve rigor. First, a larger sample of catchers is warranted given the low number of observations for batting performance with FTIs in the previous seven days. Although more practical methods of observing all FTIs in a season may be available in the future, our method of observation reduced the time requirement from approximately three hours to 5-20 minutes per team game. It may be possible for several researchers to collect a large enough sample for adequate statistical power without spending a copious amount of time observing games. Alternatively, researchers could consider a framework for weighting each FTI to account for differences in impacts that may not impair catchers in favour of observing more catcher seasons for statistical power. Despite the wealth of data provided for catchers’ batting performance, the most valuable information for this analysis is not quantified by the MLB (e.g., angle of impact, impact location, impact magnitudes). As a result, calculating batting performance may be difficult without the aid of the requisite technology to account for such

64 factors. It may be beneficial for future research to consider a partnership with the MLB to gain access to their advanced tracking systems and high-resolution footage.

65

Chapter 6: Conclusion

The purpose of the research was to investigate foul tip impacts to catchers’ masks in the 2017 ALE season. The incidence of FTIs, absolute risk of more than one impact in a game, and the common characteristics of FTIs served to profile ball impacts that catchers’ experienced. Despite there being limited information regarding foul tip impacts prior to this study, the results of this thesis align with, and expand upon, several findings reported previously. Specifically, this study supports self-report data of FTI frequency (Green et al., 2018) but contributes intricate details that were not previously known; such as, substantial increases or decreases in FTI frequency over several games relative to the rate reported for the season. When considering athlete exposures, catchers who caught more pitches had substantially more FTIs and typically experienced more than one impact on multiple occasions. Furthermore, the median and range of FTI release speeds were nearly identical to the impacts which resulted in concussion in previous research (Beyer et al., 2012). In addition, the FTIs in this study were typically characterized by pitch speeds that are far greater than those used in experimental modeling (Shain et al. 2010, Laudner et al., 2014; Siu et al., 2016; Eckersley et al., 2018) or for evaluating whether masks met adequate standards for competition (NOCSAE, 2019). This thesis also examined catchers’ batting performance in a game relative to the number and pitch speed of FTIs in the previous seven days. Although there was not a significant decrease in catchers’ performance, the small sample size of catchers and high variability in performance metrics (generally) suggest a need for a larger sample and more sophisticated research designs. The main findings of the thesis and its relevance are summarized in Table 8.

The findings of this research have several practical implications. The pitch speeds and quantity of FTIs in this study warrant investigating multiple projectile impacts at pitch speeds that FTIs commonly occur at in the MLB. The results of this study provide valuable information that contributes a detailed description of the FTIs catchers can experience; however, the lack of information in a concussed population of catchers highlights the need for more research to be conducted in this area. Though the FTIs described may not be influenced by factors specific to any team or division, future research should determine if these findings can be generalized beyond the study’s parameters. Lastly, the findings suggest that FTIs from high speed pitches need to be explored further in research and by sports medical management practices.

66

Table 8.

Summary of Main Findings Finding Relevance • Extrapolated: the number of FTIs that could be Incidence of Catcher FTIs expected every two days if all 30 MLB teams were • 17.5 FTIs per 10,000 pitches to play each day. Absolute Risk of >1 FTI in a Game • Catchers experienced 0-4 games with >1 FTI • 3% risk of >1 FTI in a game • Range of FTIs in one game: 2-3 FTIs Head Impact Exposure (Starters) • Disproportionate increase in FTIs for starting • Catchers with >10,000 AE had catchers exposed to >10,000 pitches compared to ≥20 FTIs those with >8700 pitches • Catchers with <8700 AE had ≤13 • 84% of games with >1 FTI were to catchers with FTIs >10,000 pitches caught on defence

Head Impact Exposure (Backups • There is a substantial difference in FTI exposures & Starters) between starters and backups • Per catcher ranged: 0-32 FTIs • A considerable proportion of catchers are backups • Starters played 98% of the games who may be less likely to experience all forms of and sustained 97% of FTIs head impacts FTIs by Pitch Speed • FTIs can be categorized by its high pitch speeds • 127 FTIs were ≥90mph • Faster than most speeds used in experimental • 46 FTIs were ≥95mph modeling FTIs by Pitch Type • FTIs seem to be influenced by the fastest types of • 135 FTIs fastball pitch types pitch thrown by pitchers • 114 FTI fastballs were four- • Teams with more fastballs thrown or faster average seams fastballs may have a greater number of FTIs

References

Athiviraham, A., Bartsch, A., Mageswaran, P., Benzel, E. C., Perse, B., Jones, M. H., & Schickendantz, M. (2012). Analysis of baseball-to-helmet impacts in Major League Baseball. The American Journal of Sports Medicine, 40(12), 2808–2814. https://doi.org/10.1177/0363546512461754

Bahill, A. T., & LaRitz, T. (1984). Why can’t batters keep their eyes on the ball. American Scientist, 72(3), 249-253.

Bailes, J. E., Petraglia, A. L., Omalu, B. I., Nauman, E., & Talavage, T. (2013). Role of subconcussion in repetitive mild traumatic brain injury. Journal of Neurosurgery, 119(5), 1235–1245. http://doi.org/10.3171/2013.7.JNS121822

Balk, Y. A., de Jonge, J., Oerlemans, W. G. M., & Geurts, S. A. E. (2017). Testing the triple- match principle among Dutch elite athletes: a day-level study on sport demands, detachment and recovery. Psychology of Sport and Exercise, 33, 7–17. http://doi.org/10.1016/j.psychsport.2017.07.006

Barkhoudarian, G., Hovda, D. A., & Giza, C. C. (2011). The molecular pathophysiology of concussive brain injury. Clinics in Sports Medicine, 30(1), 33–48. http://doi.org/10.1016/j.csm.2010.09.001

Barth, J. T., Freeman, J. R., Broshek, D. K., & Varney, R. N. (2001). Acceleration-deceleration sport-related concussion: The gravity of it all. Journal of Athletic Training, 36(3), 253– 256.

Baseball Savant. (2018). [Results from pitch and batting performance statistics search, 2017]. Major League Baseball Advanced Media (MLBAM) Data Access from Statcast. Retrieved from https://baseballsavant.mlb.com/statcast_search

Baugh, C. M., Kroshus, E., Bourlas, A. P., & Perry, K. I. (2014). Requiring athletes to acknowledge receipt of concussion-related information and responsibility to report symptoms: A study of the prevalence, variation, and possible improvements. Journal of Law, Medicine and Ethics, 42(3), 297–313. http://doi.org/10.1111/jlme.12147

67 68

Baugh, C. M., Kiernan, P. T., Kroshus, E., Daneshvar, D. H., Montenigro, P. H., McKee, A. C., & Stern, R. A. (2015). Frequency of head-impact–related outcomes by position in NCAA division I collegiate football players. Journal of Neurotrauma, 32(5), 314–326. http://doi.org/10.1089/neu.2014.3582

Bazarian, J. J., Zhu, T., Zhong, J., Janigro, D., Rozen, E., Roberts, A., … Blackman, E. G. (2014). Persistent, long-term cerebral white matter changes after sports-related repetitive head impacts. PLoS ONE, 9(4), e94734. http://doi.org/10.1371/journal.pone.0094734

Beckwith, J. G., Greenwald, R. M., Chu, J. J., Crisco, J. J., Rowson, S., Duma, S. M., … Collins, M. W. (2013). Head impact exposure sustained by football players on days of diagnosed concussion. Med Sci Sports Exerc., 45(4), 737–746. https://doi.org/10.1249/MSS.0b013e3182792ed7

Beckwith, J. G., Greenwald, R. M., Chu, J. J., Crisco, J. J., Rowson, S., Duma, S. M., … Collins, M. W. (2013). Timing of concussion diagnosis is related to head impact exposure prior to injury. Med Sci Sports Exerc., 45(4), 747–754. https://doi.org/10.1249/MSS.0b013e3182793067

Belanger, H. G., Vanderploeg, R. D., & McAllister, T. (2016). Subconcussive blows to the head: a formative review of short-term clinical outcomes. Journal of Head Trauma Rehabilitation, 31(3), 159–166. http://doi.org/10.1097/HTR.0000000000000138

Bernick, C., Banks, S. J., Shin, W., Obuchowski, N., Butler, S., Noback, M., … Modic, M. (2015). Repeated head trauma is associated with smaller thalamic volumes and slower processing speed: the professional fighters’ brain health study. British Journal of Sports Medicine, 1–6. http://doi.org/10.1136/bjsports-2014-093877

Beyer, J. A., Rowson, S., & Duma, S. M. (2012). Concussions experienced by major league baseball catchers and umpires: Field data and experimental baseball impacts. Annals of Biomedical Engineering, 40(1), 150–159. http://doi.org/10.1007/s10439-011-0412-4

Bray, S. R., Graham, J. D., Martin Ginis, K. A., & Hicks, A. L. (2012). Cognitive task performance causes impaired maximum force production in human hand flexor muscles. Biological Psychology, 89(1), 195–200. http://doi.org/10.1016/j.biopsycho.2011.10.008

69

Breedlove, K. M., Breedlove, E. L., Robinson, M., Poole, V. N., King, J. R., Rosenberger, P., . . . Nauman, E. A. (2014). Detecting neurocognitive and neurophysiological changes as a result of subconcussive blows among high school football athletes. Athletic Training & Sports Health Care, 6(3), 119-127. http://dx.doi.org/10.3928/19425864-20140507-02

Broglio, S. P., Macciocchi, S. N., & Ferrara, M. S. (2007). Neurocognitive performance of concussed athletes when symptom free. Journal of Athletic Training, 42(4), 504–508. http://doi.org/10.1016/S0162-0908(09)79463-2

Burris, K., Vittetoe, K., Ramger, B., Suresh, S., Tokdar, S. T., Reiter, J. P., & Appelbaum, L. G. (2018). Sensorimotor abilities predict on-field performance in professional baseball. Scientific Reports, 8(1), 1–9. http://doi.org/10.1038/s41598-017-18565-7

Cal Drummond Dies. (1970, May 4). Sarasota Journal, pp. 10. Retrieved from https://news.google.com/newspapers?nid=1798&dat=19700504&id=t_YeAAAAIBAJ&s jid=Bo0EAAAAIBAJ&pg=1881,412369

Camp, C. L., Dines, J. S., van der List, J. P., Conte, S., Conway, J., Altchek, D. W., … Pearle, A. D. (2018a). Summative report on time out of play for Major and Minor League Baseball. The American Journal of Sports Medicine, 46(7), 1727–1732. https://doi.org/10.1177/0363546518765158

Camp, C. L., Wang, D., Sinatro, A. S., Angelo, J. D., Coleman, S. H., Dines, J. S., … Conte, S. (2018b). Getting hit by pitch in professional baseball: Analysis of injury patterns, risk factors, concussions, and days missed for batters. The American Journal of Sports Medicine, 46(8), 1997–2003. https://doi.org/10.1177/0363546518773048

Cassidy, J. D., Carroll, L. J., Peloso, P. M., Holst, H. Von, Holm, L., Kraus, J., & Coronado, V. G. (2004). Incidence, risk factors and prevention of mild traumatic brain injury: results of the WHO collaborating centre task force on mild traumatic brain injury. J Rehabil Med, 43, 28–60. http://doi.org/10.1080/16501960410023732

Castaneda, B., & Gray, R. (2007). Effects of focus of attention on baseball batting performance in players of differing skill levels. Journal of Sport & Exercise Psychology, 29(1), 60–77. http://doi.org/10.1123/jsep.29.1.60

70

Centers for Disease Control and Prevention (2017). Percent distributions of TBI-related emergency department visits by age group and injury mechanism—United States, 2006– 2010. National Center for Injury Prevention and Control. Retrieved from http://www.cdc. gov/traumaticbraininjury/data/dist_ed.html. Accessed 24 May 2017

Centers for Disease Control and Prevention. (2015). Report to Congress on traumatic brain injury in the United States: Epidemiology and rehabilitation. National Center for Injury Prevention and Control. Retrieved from http://www.cdc.gov

Churchill, N. W., Hutchison, M. G., Richards, D., Leung, G., Graham, S. J., & Schweizer, T. A. (2017). Neuroimaging of sport concussion: persistent alterations in brain structure and function at medical clearance. Scientific Reports, 7(8297), 1–9. http://doi.org/10.1038/s41598-017-07742-3

Clark, J. F., Ellis, J. K., Bench, J., Khoury, J., & Graman, P. (2012). High-performance vision training improves batting statistics for university of cincinnati baseball players. PLoS ONE, 7(1), 1–6. http://doi.org/10.1371/journal.pone.0029109

Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37-46. http://dx.doi.org/10.1177/001316446002000104

Collins, C. L., & Comstock, R. D. (2008). Epidemiological features of high school baseball. Pediatrics, 121(6), 1181–1187. http://doi.org/10.1542/peds.2007-2572

Conte, S., Camp, C. L., & Dines, J. S. (2016). Injury trends in Major League Baseball over 18 seasons: 1998-2015. The American Journal of Orthopedics, 45(3), 116–123.

Cusimano, M.D., Casey, J., Jing, R., Mishra, A., Solarski, M., Techar, K., Zhang, S. (2017) assessment of head collision events during the 2014 FIFA World Cup tournament. JAMA, 317(24):2548–2549. doi:10.1001/jama.2017.6204

Dick, R., Sauers, E., Agel, J., Keuter, E., Marshall, S. W., McCarty, K., & McFarland, E. (2007). descriptive epidemiology of collegiate men’s baseball injuries: National Collegiate Athletic Association Injury Surveillance System, 1988–1989 through 2003 – 2004.

71

Journal of Athletic Training, 42(2), 183–193. http://doi.org/10.1016/S0276- 1092(08)79204-6

Duncan, M. J., Fowler, N., George, O., Joyce, S., & Hankey, J. (2015). Mental fatigue negatively influences manual dexterity and anticipation timing but not repeated high-intensity exercise performance in trained adults. Research in Sports Medicine, 23(1), 1–13. http://doi.org/10.1080/15438627.2014.975811

Eckersley, C. P., White, T. R., Cutcliffe, H. C., Shridharani, J. K., Wood, G. W., & Bass, C. R. (2018). Foul tip impact attenuation of baseball catcher masks using head impact metrics. PLoS ONE, 13(6), 1–12. https://doi.org/10.7924/G81N7Z29

Fleisig, G. S., Barrentine, S. W., Zheng, N., Escamilla, R. F., & Andrews, J. R. (1999). Kinematic and kinetic comparison of baseball pitching among various levels of development. Journal of Biomechanics, 32, 1371–1375.

Fleisig, G. S., Kingsley, D. S., Loftice, J. W., Dinnen, K. P., Ranganathan, R., Dun, S., … Andrews, J. R. (2006). Kinetic comparison Among the fastball, curveball, change-up, and slider in collegiate baseball pitchers. The American Journal of Sports Medicine, 34(3), 423–430. http://doi.org/10.1177/0363546505280431

Fleisig, G. S., Laughlin, W. A., Aune, K. T., Cain, E. L., Dugas, J. R., & Andrews, J. R. (2016). Differences among fastball, curveball, and change-up pitching biomechanics across various levels of baseball. Sports Biomechanics, 15(2), 128–138. http://doi.org/10.1080/14763141.2016.1159319

Fraser, M. A., Grooms, D. R., Guskiewicz, K. M., & Kerr, Z. Y. (2017). Ball-contact injuries in 11 National Collegiate Athletic Association sports: The Injury Surveillance Program, 2009–2010 through 2014–2015. Journal of Athletic Training, 52(3). https://doi.org/10.4085/1062-6050-52.3.10

Fuller, C., & Drawer, S. (2004). The application of risk management in sport. Sports Medicine, 34(6), 349–356. http://doi.org/10.2165/00007256-200434060-00001

72

Gallant, C., Barry, N., & Good, D. (2018). Physiological arousal in athletes following repeated subconcussive impact exposure. Current Psychology, 1–8. http://doi.org/10.1007/s12144- 018-9780-3

Gray, R. (2009). A model of motor inhibition for a complex skill: Baseball batting. Journal of Experimental Psychology: Applied, 15(2), 91–105. http://doi.org/10.1037/a0015591

Green, G. A., Pollack, K. M., D’Angelo, J., Schickendantz, M. S., Caplinger, R., Weber, K., … Curriero, F. C. (2015). Mild traumatic brain injury in major and minor league baseball players. The American Journal of Sports Medicine, 43(5), 1118–1126. http://doi.org/10.1177/0363546514568089

Green, G. A., Porter, K. P., Conte, S., Valadka, A. B., Soloff, L., & Curriero, F. C. (2018). Preventing concussions from foul tips and backswings in professional baseball: catchers’ perceptions of and experiences with conventional and hockey-style masks. Clinical Journal of Sport Medicine, 1–7.

Guskiewicz, K. M., & Mihalik, J. P. (2010). Biomechanics of sport concussion: Quest for the elusive injury threshold. Exercise and Sport Sciences Reviews, 39(1), 4–11. http://doi.org/10.1097/JES.0b013e318201f53e

Harmon, K. G., Drezner, J. A., Gammons, M., Guskiewicz, M., Halstead, M., Herring, S. A., … Roberts, W. O. (2013). American Medical Society for Sports Medicine position statement: Concussion in sport. British Journal of Sports Medicine, 47, 15–26. http://doi.org/10.1136/bjsports-2012-091941

Heppe, H., Kohler, A., Fleddermann, M. T., & Zentgraf, K. (2016). The relationship between expertise in sports, visuospatial, and basic cognitive skills. Frontiers in psychology, 7, 904. http://doi.org/10.3389/fpsyg.2016.00904

Hopkins, W. G. (2010). Statistics used in observational studies in sports injury research (pp. 1– 23). Oxford University Press. 10.1093/acprof:oso/9780199561629.001.0001

73

Huber, B. R., Stein, T. D., Alosco, M. L., & McKee, A. C. (2016). Potential long-term consequences of concussive and subconcussive injury. Phys Med Rehabil Clin N Am., 27(2), 503–511. http://doi.org/10.1016/j.pmr.2015.12.007.Potential

Hutchison, M. G., Comper, P., Meeuwisse, W. H., & Echemendia, R. J. (2015). A systematic video analysis of National Hockey League (NHL) concussions, part I: who, when, where and what? Br J Sports Med, 49(8), 547-551.

Hwang, S., Ma, L., Kawata, K., Tierney, R., & Jeka, J. J. (2017). Vestibular dysfunction after subconcussive head impact. Journal of Neurotrauma, 34(1), 8–15. http://doi.org/10.1089/neu.2015.4238

Johnson, B., Neuberger, T., Gay, M., Hallett, M., & Slobounov, S. (2014). Effects of subconcussive head trauma on the Default Mode Network of the brain. Journal of Neurotrauma, 31, 1907–1913. https://doi.org/10.1089/neu.2014.3415

Karton, C., & Hoshizaki, T. B. (2018). Concussive and subconcussive brain trauma: The complexity of impact biomechanics and injury risk in contact sport. Sports Neurology (3rd ed., Vol. 158). Elsevier B.V. https://doi.org/10.1016/B978-0-444-63954-7.00005-7

Kawata, K., Rubin, L. H., Lee, J. H., Sim, T., Takahagi, M., Szwanki, V., … Langford, D. (2016). Association of football subconcussive head impacts with ocular near point of convergence. JAMA Ophthalmology, 134(7), 763–769. http://doi.org/10.1001/jamaophthalmol.2016.1085

Kellar, D., Newman, S., Pestilli, F., Cheng, H., & Port, N. L. (2018). Comparing fMRI activation during smooth pursuit eye movements among contact sport athletes, non-contact sport athletes, and non-athletes. NeuroImage: Clinical, 18, 413–424. http://doi.org/10.1016/j.nicl.2018.01.025

Kerr, Z. Y., Littleton, A. C., Cox, L. M., DeFreese, J. D., Varangis, E., …, Guskiewicz, K. M. (2015). Estimating contact exposure in football using the head impact exposure estimate. Journal of Neurotrauma, 32, 1083-1089. https://doi.org/10.1089/neu.2014.3666

74

Kilcoyne, K. G., Ebel, B. G., Bancells, R. L., Wilckens, J. H., & McFarland, E. G. (2015). Epidemiology of Injuries in Major League Baseball catchers. American Journal of Sports Medicine, 43(10), 2496–2500. http://doi.org/http://dx.doi.org/10.1177/0363546515597684

Kroshus, E., Daneshvar, D. H., Baugh, C. M., Nowinski, C. J., & Cantu, R. C. (2014). NCAA concussion education in ice hockey: an ineffective mandate. British Journal of Sports Medicine, 48(2), 135–140. http://doi.org/10.1136/bjsports-2013-092498

Krosshaug, T., Nakamae, A., Boden, B. P., Engebretsen, L., Smith, G., Slauterbeck, J. R., … Bahr, R. (2007). Mechanisms of anterior cruciate ligament injury in basketball video analysis of 39 cases. The American Journal of Sports Medicine, 35(3), 359–367. http://doi.org/10.1177/0363546506293899

Kuo, C., Wu, L., Loza, J., Senif, D., Anderson, S.C., Camarillo, D. B. (2018) Comparison of video-based and sensor-based head impact exposure. PLoS ONE 13(6): e0199238. https://doi.org/ 10.1371/journal.pone.0199238

Laudner, K. G., Lynall, R. C., Frangella, N., & Sharpe, J. (2014). Comparison of impact characteristics of traditional style headgear and hockey style headgear for baseball catchers. Journal of Athletic Enhancement, 3(1), 1–4. https://doi.org/10.4172/2324- 9080.1000135

Lemez, S., & Baker, J. (2015). Do elite athletes live longer? A systematic review of mortality and longevity in elite athletes. Sports Medicine - Open, 1(16), 1–14. https://doi.org/10.1186/s40798-015-0024-x

Lin, K.-H., Huang, Y.-M., & Nien, Y.-H. (2007). Investigation of reaction abilities of college- aged elite and sub-elite baseball hitters. Journal of Biomechanics, 40, S776.

Mainwaring, L., Ferdinand Pennock, K. M., Mylabathula, S., & Alavie, B. Z. (2018). Subconcussive head impacts in sport: A systematic review of the evidence. International Journal of Psychophysiology, 1–16. http://doi.org/10.1016/j.ijpsycho.2018.01.007

75

Marar, M., Mcilvain, N. M., Fields, S. K., & Comstock, R. D. (2012). Epidemiology of Concussions among United States high school athletes in 20 sports. American Journal of Sports Medicine, 40(4), 747–755. http://doi.org/10.1177/0363546511435626

McAllister, T. W., Flashman, L. A., Maerlender, A., Greenwald, R. M., Beckwith, J. G., Tosteson, T. D., … Turco, J. H. (2012). Cognitive effects of one season of head impacts in a cohort of collegiate contact sport athletes. Neurology, 78, 1777–1784.

McAllister, T. W., Ford, J. C., Flashman, L. A., Maerlender, A., Greenwald, R. M., Beckwith, J. G., … Jain, S. (2014). Effect of head impacts on diffusivity measures in a cohort of collegiate contact sport athletes. American Academy of Neurology, 82, 63–69. https://doi.org/10.1212/01.wnl.0000438220.16190.42

McCaffrey, M. A., Mihalik, J. P., Crowell, D. H., Shields, E. W., & Guskiewicz, K. M. (2007). Measurement of head impacts in collegiate football players: clinical measures of concussion after high- and low-magnitude impacts. Neurosurgery, 61(6), 1236–1243. http://doi.org/10.1227/01.NEU.0000280153.11614.69

McCrea, M., Hammeke, T., Olsen, G., Leo, P., & Guskiewicz, K. M. (2004). Unreported concussion in high school football players: Implications for prevention. Clinical Journal of Sport Medicine, 14(1), 13–17. https://doi.org/10.1097/00042752-200401000-00003

McCrory, P., Meeuwisse, W., Dvorak, J., Aubry, M., Bailes, J., Broglio, S., … Vos, P. E. (2017). Consensus statement on concussion in sport — the 5th international conference on concussion in sport held in Berlin, October 2016. British Journal of Sports Medicine, pp. 1– 10. http://doi.org/10.1136/bjsports-2017-097699

McHugh, M. L. (2012). Interrater reliability: The kappa statistic. Biochemia Medica, 22(3), 276– 282. https://doi.org/10.11613/BM.2012.031

Meehan, W. P., Mannix, R. C., O’Brien, M. J., & Collins, M. W. (2013). The prevalence of undiagnosed concussions in athletes. Clin J Sport Med, 23(5), 339–342. http://doi.org/10.1097/JSM.0b013e318291d3b3

76

Mihalik, J. P., Bell, D. R., Marshall, S. W., & Guskiewicz, K. M. (2007). Measurement of head impacts in collegiate football players: an investigation of positional and event-type differences. Neurosurgery, 61(6), 1229–1235. http://doi.org/10.1227/01.NEU.0000280147.37163.30

Major League Baseball (2019). Glossary. MLB Advanced Media. Retrieved from www.mlb.com/glossary.

Major League Baseball Players Association. (2017). Collective Bargaining Agreement. (Article XIII, section C, p.258-271).

Munce, T. A., Dorman, J. C., Thompson, P. A., Valentine, V. D., & Bergeron, M. F. (2015). Head impact exposure and neurologic function of youth football players. Medicine and Science in Sports and Exercise, (14), 1567–1576. http://doi.org/10.1249/MSS.0000000000000591

Muraskin, J., Sherwin, J., & Sajda, P. (2013). A system for measuring the neural correlates of baseball pitch recognition and its potential use in scouting and player development. In 7th Annual MIT Sloan Sports Analytics Conference.

Nakata, H., Yoshie, M., Miura, A., & Kudo, K. (2010). Characteristics of the athletes’ brain: Evidence from neurophysiology and neuroimaging. Brain Research Reviews, 62(2), 197– 211. https://doi.org/10.1016/j.brainresrev.2009.11.006

Nauman, E. A., & Talavage, T. M. (2018). Subconcussive trauma. In B. Hainline & R. A. Stern, (Eds.), Handbook of Clinical Neurology (3rd ed., Vol. 158, pp. 245-255). https://doi.org/10.1016/B978-0-444-63954-7.00024-0

Nguyen, V. T., Zafonte, R. D., Chen, J. T., Kponee-Shovein, K. Z., Paganoni, S., Pascual-Leone, A., … Weisskopf, M. G. (2019). Mortality among professional American-style football players and professional American baseball players. JAMA Network Open, 2(5), 1–13. https://doi.org/10.1001/jamanetworkopen.2019.4223

NOCSAE (2019). Standard test method and equipment used in evaluating the performance characteristics of headgear/equipment DOC 001.

77

NOCSAE (2019). Standard performance specification for newly manufactured baseball/softball catcher’s helmets with faceguard DOC 024.

Ortiz, J. L. (2007). Baseball Taking Note of Concussion. USA Today. Retrieved from https://www.pressreader.com/

Ortiz, J. L. (2013). For Catchers, Concussion Dangers All Too Real. USA Today. Retrieved from https://www.usatoday.com/story/sports/mlb/2013/09/12/head-injuries-concussion-on-the- rise-in-mlb/2807979/

O’Connor, K. L., Rowson, S., Duma, S. M., & Broglio, S. P. (2017). Head-impact–measurement devices: A systematic review. Journal of Athletic Training, 52(3), 206–227. https://doi.org/10.4085/1062-6050.52.2.05

Pellman, E. J., Viano, D. C., Tucker, A. M., Casson, I. R., Waeckerle, J. F., Maroon, J. C., … Levy, M. L. (2003). Concussion in professional football: Reconstruction of game impacts and injuries. Neurosurgery, 53(4), 799–814. http://doi.org/10.1227/01.NEU.0000083559.68424.3F

Persky, A. M., & Robinson, J. D. (2017). Moving from novice to expertise and its implications for instruction. American Journal of Pharmaceutical Education, 81(9), 72–80. https://doi.org/10.5688/ajpe6065

Pollack, K. M., D’Angelo, J., Green, G., Conte, S., Fealy, S., Marinak, C., … Curriero, F. C. (2016). Developing and implementing major league baseball’s health and injury tracking system. American Journal of Epidemiology, 183(5), 490–496. http://doi.org/10.1093/aje/kwv348

Pontifex, M. B., O’Connor, P. M., Broglio, S. P., & Hillman, C. H. (2009). The association between mild traumatic brain injury history and cognitive control. Neuropsychologia, 47(14), 3210–3216. http://doi.org/10.1016/j.neuropsychologia.2009.07.021

Poole, V. N., Abbas, K., Shenk, T. E., Breedlove, E. L., Breedlove, K. M., Robinson, M. E., … Dydak, U. (2014). MR spectroscopic evidence of brain injury in the non-diagnosed

78

collision sport athlete. Developmental Neuropsychology, 39(6), 459–473. http://doi.org/10.1080/87565641.2014.940619

Post, A., & Blaine Hoshizaki, T. (2015). Rotational acceleration, brain tissue strain, and the relationship to concussion. Journal of Biomechanical Engineering, 137(3), 1–8. http://doi.org/10.1115/1.4028983

Ramkumar, P. N., Navarro, S. M., Haeberle, H. S., Pettit, R. W., Miles, T. J., Frangiamore, S. J., … Schickendantz, M. S. (2018). Short-term outcomes of concussions in Major League Baseball a historical cohort study of return to play, performance, longevity, and financial impact. The Orthopaedic Journal of Sports Medicine, 6(12), 1–7. https://doi.org/10.1177/2325967118814238

Rowson, S., & Duma, S. M. (2013). Brain injury prediction: Assessing the combined probability of concussion using linear and rotational head acceleration. Annals of Biomedical Engineering, 41(5), 873–882. http://doi.org/10.1007/s10439-012-0731-0

Rowson, S., Duma, S. M., Stemper, B. D., Shah, A., Mihalik, J. P., Harezlak, J., … McCrea, M. (2018). Correlation of concussion symptom profile with head impacts biomechanics: A case for individual-specific injury tolerance. Journal of Neurotrauma, 35, 681–690. https://doi.org/10.1089/neu.2017.5169

Ringolsby, T. (2017). Q&A: Matheny talks about concussions. MLB.com. Retrieved from http://m.mlb.com/news/article/232741470/cardinals-manager-mike-matheny-on- concussions/

Rutherford, A., Stephens, R., Fernie, G., & Potter, D. (2009). Do UK university football club players suffer neuropsychological impairment as a consequence of their football (soccer) play? Journal of Clinical & Experimental Neuropsychology, 31(6), 664-681.

Sabesan, V. J., Prey, B., Smith, R., Lombardo, D. J., Borrotto, W. J., & Whaley, J. D. (2018). Concussion rates and effects on player performance in Major League Baseball players. Journal of Sports Medicine, 9, 253–260. http://dx.doi.org/10.2147/OAJSM.S157433

79

Sarris, E. (2019). MLB moving from Trackman to Hawk-Eye tracking system. Retrieved May 15, 2019, from https://theathletic.com

Schwindel, L. E., Moretti, V. M., Watson, J. N., & Hutchinson, M. R. (2014). Epidemiology and outcomes of concussions in Major League Baseball. Annals of Orthopedics & Rheumatology, 2(3), 1022.

Schwizer, P., Demierre, M., & Smith, L. V. (2016). Evaluation of catcher mask impacts. Procedia Engineering, 147, 228–233. http://doi.org/10.1016/j.proeng.2016.06.218

Shain, K. S., Madigan, M. L., Rowson, S., Bisplinghoff, J., & Duma, S. M. (2010). Analysis of the ability of catcher’s masks to attenuate head accelerations on impact with a baseball. Clin J Sport Med, 20(6), 422–427. http://doi.org/10.1097/JSM.0b013e3181f7db25\r00042752-201011000-00007

Siu, J., Okonek, A., & Schot, P. (2016). Influence of baseball catcher mask design, impact location and ball trajectory on head acceleration. International Journal of Exercise Science, 9(5), 567–575. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=sph&AN=119293788&site=ehos t-live

Stemper, B. D., Shah, A. S., Arezlak, J. H., Rowson, S., Mihalik, J. P., Duma, S. M., … The CARE Consortium Investigators. (2018). Comparison of head impact exposure between concussed football athletes and matched controls: Evidence for a possible second mechanism of sport-related concussion. Annals of Biomedical Engineering, pp.1-16. https://doi.org/10.1007/s10439-018-02136-6

Tresilian, J. R. (2005). Hitting a moving target: Perception and action in the timing of rapid interceptions. Perception & Psychophysics, 67(1), 129–149. http://doi.org/10.3758/BF03195017

Tsushima, W., Geling, O., & Arnold, M. (2016). Are there subconcussive neuropsychological effects in youth sports? An exploratory study of high-and low-contact sports. Applied Neuropsychology: Child, 1–7. http://doi.org/10.1080/21622965.2015.1052813

80

Tsushima, W. T., Ahn, H. J., Siu, A. M., Yoshinaga, K., Choi, S. Y., & Murata, N. M. (2018). Effects of repetitive subconcussive head trauma on the neuropsychological test performance of high school athletes: a comparison of high, moderate, and low contact sports. Applied Neuropsychology: Child, 1-8.

Umpire Succumbs. (1970, May 5). The Spokesman Review, pp.8. Retrieved from https://news.google.com/newspapers?nid=1314&dat=19700505&id=wNhYAAAAIBAJ &sjid=WesDAAAAIBAJ&pg=1389,1942643

Voss, M. W., Kramer, A. F., Basak, C., Shaurya Prakash, R., & Roberts, B. (2010). Are expert athletes “expert” in the cognitive laboratory? A meta-analytic review of cognition and sport expertise. Applied Cognitive Psychology, 24, 812–826. https://doi.org/10.1002/acp.1588

Welch, C. M., Banks, S. a, Cook, F. F., & Draovitch, P. (1995). Hitting a baseball: A biomechanical description. The Journal of Orthopaedic and Sports Physical Therapy, 22(5), 193–201. http://doi.org/10.2519/jospt.1995.22.5.193

Webbe, F. M., & Ochs, S. R. (2003). Recency and frequency of soccer heading interact to decrease neurocognitive performance. Applied Neuropsychology, 10(1), 31–41. http://doi.org/10.1207/S15324826AN1001

Wilson, M. J., Harkrider, A. W., & King, K. A. (2015). Effect of repetitive, subconcussive impacts on electrophysiological measures of attention. Southern Med. Journal, 108(9), 559-566.

Yarrow, K., Brown, P., & Krakauer, J. W. (2009). Inside the brain of an elite athlete: the neural processes that support high achievement in sports. Nature Reviews Neuroscience, 10(9), 692–692. http://doi.org/10.1038/nrn2700

Zerpa, C., Carlson, S., Sanzo, P., Przysucha, E., Hoshizaki, T., Kivi, D., & Bay, T. (2017). The effect of angle of impact, neck stiffness, and impact location on measures of shear forces during helmet testing. 35th Conf. of the Int. Society of Biomech. in Sports, 35(1), 1020– 1023.

Appendix A: Sample Characteristics

Table 1A. American League East Catcher Characteristics (n= 15)

Mean (SD) Min Max

Age 27.93 (3.54) 22 37

MLB Experience 5.64 (2.37) 2 9 (seasons)

Games Started 43.2 (36.93) 0 99

Appearances 52.87 (41.93) 4 122

Table 2A. Comparison of Starting Catchers and Replacement Catchers

Catchers (>10 Appearances) Catchers (=/<10 Appearances)

Mean (SD) Min Max Mean (SD) Min Max

Age 28.11 (2.09) 24 31 27.6 (5.64) 22 37

MLB Experience 6.33 (1.87) 4 9 4.4 (2.88) 2 9 (seasons)

Games Started 70.44 (17.21) 51 99 2.33 (2.07) 0 5

Appearances 83.33 (21.49) 53 122 7.17 (2.48) 4 10

81 82

Appendix B: Inter-Rater Reliability Output for Mask Impact Locations

Table 1B. Inter-Rater Agreement Pivot Table for Lateral and Medial Mask Impact Region (n= 57) Lateral and Medial Mask Rater 2 Impact Region 1 2 3 Total 1 6 5 0 11 Rater 1 2 0 22 3 25 3 0 4 17 21 Total 6 31 20 57 Note. Lateral and medial mask impact regions: 1 = Left, 2 = Center, 3 = Right

Table 2B. Inter-Rater Agreement Pivot Table for Vertical Mask Impact Location (n= 57) Rater 2 Mask Impact Location 1 2 3 4 Total 1 3 4 2 0 9 2 2 1 7 1 11 Rater 1 3 2 2 16 7 27 4 1 0 1 6 8 5 1 1 0 0 2 Total 9 8 26 14 57 Note. Vertical Mask Locations: 1 = Chin, 2 = Mouth/nose, 3 = Eyes/Eyebrow, 4 = Forehead, 5 = Neck/Under the Mask

83

Table 3B. Symmetric Measures for Lateral and Medial Mask Impact Region (n= 57) Approximate Value Asymptotic Errora Approximate Tb Significance

Measure of .656 .087 6.687 p> 0.001 Agreement (Kappa) Valid Cases 57 a. Not assuming the null hypothesis b. Using the asymptotic standard error assuming the null hypothesis

Table 4B. Symmetric Measures for Vertical Mask Impact Location (n= 57) Approximate Value Asymptotic Errora Approximate Tb Significance Measure of .220 .087 2.872 p= 0.004 Agreement (Kappa) Valid Cases 57 a. Not assuming the null hypothesis b. Using the asymptotic standard error assuming the null hypothesis

84

Appendix C: FTI Descriptive Statistics

Table 1C. FTI Count and Mean Release Speed by Pitch Type

Pitch Category Pitch Type (Abbreviation) FTI Mean Speed (mph) SD (mph)

Four-Seam (FF) 114 94.1 2.5 Two-Seam (FT) 11 92.0 3.6 Fastball Sinker (SI) 5 92.3 3.1 Cutter (FC) 5 90.5 1.5 Slider (SL) 31 84.5 3.3 Breaking Ball Curveball (CU) 2 82.9 5.6 Knuckleball (KN) 1 76.6 NA Changeup (CH) 1 85.3 NA Offspeed Split-Finger (FS) 2 84.7 0.4 Note. All pitch type categories are used in accordance with those defined by Baseball Savant and Statcast.

85

Appendix D: Batting Performance Analyses and Results

Table 1D. Correlation of Fixed Effects for Catcher xwOBA # FTIs Avg. Max FTI Intercept Log(Plate Appearances) Prev. Pitch Speed Prev. Log(Plate -0.73 – Appearances) Number of FTIs 0.096 -0.004 – Previous 7 Days Average Max FTI -0.13 -0.025 -0.81 – Pitch Speed Number of Games -0.52 -0.030 -0.18 0.015 Previous 7 Days

Table 2D. Nested Models Comparison for Catchers’ Expected-Weighted On-Base Average Relative to the Frequency and Pitch Speed of FTIs in the Previous Seven Days df AIC BIC LogLikelihood Deviance ChiSq Chi df p-value xwOBA 6 566.4 593.5 -277.2 554.4 no FTI xwOBA 7 566.8 598.5 -276.41 552.8 1.54 1 0.215 with FTI

86

Table 3D. Correlation of Fixed Effects for Catcher SLG # FTIs Avg. Max FTI Intercept Log(Plate Appearances) Prev. Pitch Speed Prev. Log(Plate -0.76 – Appearances) Number of FTIs 0.13 -0.014 – Previous 7 Days Average Max FTI -0.14 -0.026 -0.81 – Pitch Speed Number of Games -0.46 -0.10 -0.21 0.029 Previous 7 Days

Table 4D.

Nest Models Comparison for Catchers’ Slugging Percentage Relative to the Frequency and Pitch Speed of FTIs in the Previous Seven Days Chi df AIC BIC LogLikelihood Deviance ChiSq p-value df

SLG 6 1357.8 1385.0 -672.89 1345.8 no FTI

SLG 7 1359.7 1391.4 -672.84 1345.7 0.103 1 0.749 with FTI

87

Table 5D. Correlation of Fixed Effects for Catcher Strikeout Rate # FTIs Avg. Max FTI Intercept Log(Plate Appearances) Prev. Pitch Speed Prev. Log(Plate -0.74 – Appearances) Number of FTIs 0.099 -0.005 – Previous 7 Days Average Max FTI -0.13 -0.025 -0.81 – Pitch Speed Number of Games -0.52 -0.036 -0.18 0.017 Previous 7 Days

Table 6D. Nested Models Comparison for Catchers’ Strikeout Percentage Relative to the Frequency and Pitch Speed of FTIs in the Previous Seven Days Chi df AIC BIC LogLikelihood Deviance ChiSq p-value df Strikeout 6 814.9 842.1 -401.44 802.9 no FTI Strikeout 7 816.6 848.4 -401.32 802.64 0.245 1 0.621 with FTI

88

Figure 1D. Full season catcher xwOBA by game date.

Figure 2D. Full season catcher SLG by game date.

89

Figure 3D. Full season catcher strikeout rate by game date.

Figure 4D. Individual catchers’ xwOBA by the number of FTIs over the previous seven days relative to the performance that day.

90

Figure 5D. Individual catchers’ SLG by the number of FTIs over the previous seven days relative to the performance that day.

Figure 6D. Individual catchers’ strikeout rate by the number of FTIs over the previous seven days relative to the performance that day.

91

Figure 7D. Individual catchers’ xwOBA by the average release speeds of FTIs sustained in the past seven days.

Figure 8D. Individual catchers’ SLG by the average release speeds of FTIs sustained in the past seven days.

92

Figure 9D. Individual catchers’ strikeout rate by the average release speeds of FTIs sustained in the past seven days.