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Exploring the Relationship Between Hardiness and Performance Exploring the Relationship Between Hardiness and Performance in Collegiate Baseball Players Kevin R. Lou West Virginia University, krl0018@mix.wvu.edu Follow this and additional works at: https://researchrepository.wvu.edu/etd Part of the Personality and Social Contexts Commons, and the Sports Studies Commons Recommended Citation Lou, Kevin R., "Exploring the Relationship Between Hardiness and Performance in Collegiate Baseball Players" (2020). Graduate Theses, Dissertations, and Problem Reports. 7972. https://researchrepository.wvu.edu/etd/7972 This Thesis is protected by copyright and/or related rights. It has been brought to you by the The Research Repository @ WVU with permission from the rights-holder(s). You are free to use this Thesis in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you must obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Thesis has been accepted for inclusion in WVU Graduate Theses, Dissertations, and Problem Reports collection by an authorized administrator of The Research Repository @ WVU. For more information, please contact researchrepository@mail.wvu.edu. Exploring the Relationship Between Hardiness and Performance in Collegiate Baseball Players Kevin R. Lou, B.S., B.A. Thesis submitted to the College of Physical Activity and Sport Sciences at West Virginia University Department of Sport Sciences in partial fulfillment of the requirements for the degree of Master of Science in Sport, Exercise, and Performance Psychology Scott Barnicle, Ph.D., Chair Samuel Zizzi, Ed.D. Justin Barnes, Ph.D. Department of Sport Sciences Morgantown, West Virginia 2020 Keywords: Hardiness, Personality, Objective Performance, Collegiate Baseball, Quantitative Copyright 2020 Kevin Lou Abstract Exploring the Relationship Between Hardiness and Performance in College Baseball Players Kevin Lou The purpose of this study was to explore the influence of the individual personality characteristic of hardiness on trait anxiety and objective performance within NCAA Division I collegiate baseball players. An updated version of the PVS III-R was used to measure hardiness after a confirmatory factor analysis (CFA) was conducted. Of the total 389 players that participated, 171 met inclusion criteria requirements and were split into two groups – hitters (N=94) and pitchers (N=80) – to identify differences in skills and how sub-constructs of hardiness affected performance through a descriptive correlational prospective design. The results show significant moderating effects of commitment for pitchers that accounted for the majority of variance in the relationship between perception of trait anxiety intensity and left on base percentage (LOB%) and wild pitches (WP). For hitters, significant moderating effects of control accounted for less variance in the relationship between perception of trait anxiety intensity on batting average on balls in play (BABIP) and double plays grounded into (GDP). The findings indicate there may be situational significance of hardiness’ moderating effect on the relationship between trait anxiety and objective performance that may not be present until runners are on base. Practitioners could use these findings to target mental skills that could build up a pitcher’s commitment or hitter’s sense of control to moderate their performance within certain situations within collegiate baseball settings. Future studies could aim to replicate this study under normal NCAA collegiate baseball seasons when possible to corroborate situational findings and the utilization of updated PVS III-R scale. Keywords: Hardiness, personality, objective performance, baseball, quantitative EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE iii Table of Contents Exploring the Relationship Between Hardiness and Performance in College Baseball Players .... 1 Hardiness ..................................................................................................................................... 3 Method ............................................................................................................................................ 7 Participants .................................................................................................................................. 7 Inclusion Criteria ..................................................................................................................... 8 Recruitment ............................................................................................................................. 9 Design and Procedures ................................................................................................................ 9 Measures.................................................................................................................................... 10 Hardiness ............................................................................................................................... 10 Trait Anxiety.......................................................................................................................... 11 Demographics ........................................................................................................................ 11 Objective Performance Baseball Statistics ............................................................................ 12 Data Analyses ............................................................................................................................ 13 Results ........................................................................................................................................... 14 Primary Analyses ...................................................................................................................... 14 Secondary Analyses .................................................................................................................. 14 Pitching Group ....................................................................................................................... 14 Wild Pitches (WP). ............................................................................................................ 15 Left On-Base Percentage (LOB%). ................................................................................... 16 Fielding Independent Pitching (FIP). ................................................................................. 17 Hitting Group ......................................................................................................................... 18 Batting Average on Balls In Play (BABIP). ...................................................................... 18 Grounding into Double Plays (GDP). ................................................................................ 19 Weighted On-Base Average (wOBA). .............................................................................. 19 Discussion ..................................................................................................................................... 20 Pitching Group Interpretations .................................................................................................. 20 Hitting Group Interpretations .................................................................................................... 22 Practical Applications ............................................................................................................... 24 Limitations ................................................................................................................................ 27 Future Research ......................................................................................................................... 29 EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE iv Conclusion ................................................................................................................................. 31 References ..................................................................................................................................... 33 Tables ............................................................................................................................................ 44 Figures........................................................................................................................................... 53 Appendix A: List of Hitting and Pitching Statistics and Formulas .............................................. 55 Appendix B: Hardiness Questionnaire – PVS III-R ..................................................................... 57 Appendix C: Confirmatory Factor Analyses Procedure for Updated PVS III-R Scale ................ 58 Appendix D: Competitive Trait Anxiety Questionnaire – CTAI-2 .............................................. 61 Appendix E: Demographic Questionnaire .................................................................................... 63 Appendix F: Extended Review of the Literature .......................................................................... 64 Individual Personality Characteristics Related to Competitiveness .......................................... 65 Hardiness ............................................................................................................................... 67 Hardiness Instruments. ....................................................................................................... 68 Hardiness Studies and Findings. ........................................................................................ 70 Mental Toughness.................................................................................................................. 78 Mental Toughness Instruments. ......................................................................................... 78 Mental Toughness Studies and Findings. .......................................................................... 79 Resilience............................................................................................................................... 82 Resilience Instruments. ...................................................................................................... 82 Resilience Studies and Findings. ....................................................................................... 84 Grit ......................................................................................................................................... 86 Grit Instruments. ................................................................................................................ 86 Grit Studies and Findings................................................................................................... 87 Summary of Competitiveness Characteristics........................................................................... 90 Individual Competitiveness Characteristics and Objective Performance in Sport Settings ...... 93 Future Research Directions ....................................................................................................... 98 Conclusion ............................................................................................................................... 101 EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 1 Exploring the Relationship Between Hardiness and Performance in College Baseball Players In the 1950s and 60s, sport personality was a widely popular field that captured researchers’ curiosities with the idea that specific personality profiles could predict successful athletic performance (Allen et al., 2013). Early researchers used inventories such as the Eysenck Personality Inventory (EPI; Eysenck & Eysenck, 1964) or the 16 Personality Factor (16PF) questionnaire developed by Cattell (1965) to attempt to predict personality profiles. Eventually, the field turned to the NEO Personality Inventory (NEO PI), developed by McCrae and Costa (1985) that combined elements of both the previous works of Cattell and Eysenck. The NEO PI measured five traits of personality, otherwise known by the acronym OCEAN, including openness, conscientiousness, extraversion, agreeableness, and neuroticism. Despite the NEO PI’s improvements upon the 16 PF and EPI, sport personality researchers still were not able to identify specific trait profiles that would determine future athletic performance. Steady research continued through the mid-1980s when researchers realized that despite the abundance of studies which had investigated sport personality, there were no clear patterns of trait profiles that existed to predict performance (Morris, 2011). Some reasons for these inconclusive patterns could be explained by the limitations of global personality measures, restrictions of personality traits across temporal settings, and/or a reliance on personality profiles as a predictor of sport performance (Allen et al., 2017). However, after a twenty-year hiatus, sport-personality researchers have recently resumed interest in the field with a different approach that focuses on individual personality characteristics and singular trait-type personality rather than a full profile of traits (Roberts & Woodman, 2017). One example of an individual personality characteristic that has gained support in the research is hardiness (Morris, 2011), while other characteristics such as trait anxiety (Spielberger, 1985) have increased in research interest as well. EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 2 The revival of research within the sport personality field emphasizing individual personality characteristics demonstrates the still tantalizing possibility of forecasting athletic performance. Although the efforts to define specific personality traits across a generalized population have been inconclusive so far (Morris, 2011), the continued research in this field could yield athletes, coaches, and consultants valuable information. Furthermore, certain individual personality characteristics such as hardiness have found to be dynamic and malleable over time (McAdams & Olson, 2010) and if practitioners could understand how to foster specific personality characteristics, then there may be significant advantages to be gained in performance (Roberts & Woodman, 2017). For example, similarly to hardiness, trait anxiety helps influence appraisal and coping mechanisms and previous researchers have found that positively interpreting anxiety as facilitative can help athletes perform under pressure (Hanton & Connaughton, 2002; Wadey & Hanton, 2008). Future sport-personality research could help practitioners understand and tailor individual interventions and practices to each player to encourage an athlete’s development (Allen & Laborde, 2014) and facilitate their athletic performance. In previous studies, conscientiousness had been linked to successful performance in collegiate athletes (Piedmont et al., 1999). Other researchers, such as Laborde et al. (2019), further examined this link in a recent mapping review by using a thematic analysis to map individual personality traits for sport performance onto its closest facet from the Big Five NEO. The 30 NEO-PI-R was used as a foundational framework because it captured fundamental components of human personality. One of the higher-order themes that the researchers identified was competitiveness. Within competitiveness, the thematic analysis used by the researchers identified grit, mental toughness, resilience, and hardiness as traits related to competitiveness. EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 3 These traits were all linked to the Big-Five trait of conscientiousness (Laborde et al., 2019). While conscientiousness has been previously linked to successful performance, the relationship between athletic performance and these four traits is unclear and necessitates more research support to help forecast future performance. Among the traits within the higher-order theme of competitiveness, grit, mental toughness, resilience, and hardiness may seem very similar and difficult to differentiate between (Price, 2019). However, researchers have faced difficulties conceptualizing the definitional construct of mental toughness (Gucciardi, 2017), have been limited by the narrow construct of resilience (Martin et al., 2015; Reivich et al., 2011), and have not been certain about grit and possible misinterpretations of statistical significance during initial studies (Crede et al., 2017). Therefore, hardiness may be the most viable construct of the four competitiveness constructs within the higher-order theme to be linked to athletic performance. Hardiness The construct of hardiness has come into focus for researchers as it meets the criteria for a personality characteristic of having both a theoretical base and allowing for developmental research (Morris, 2011). A theoretical basis of hardiness was developed by Kobasa (1979) in a landmark study where researchers investigated hardiness as a factor of whether employees of a telephone business company facing high levels of stress would fall ill. Those who reported high levels of stress and low levels of illness also reported higher levels of hardiness and had a stronger commitment to themselves, an attitude of commitment toward the environment, and an internal locus of control (Kobasa, 1979). Hardiness was thus defined by the three key factors of challenge, control, and commitment. Kobasa (1979) defined commitment as the willingness to engage oneself fully in whatever one is doing, control as the ability to influence the events of EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 4 their experience, and challenge as the idea that change is exciting and essential to further development. Among studies conducted on hardiness since Kobasa’s work, extant research (Eschleman et al., 2010; Florian et al., 1995) has examined hardiness’ influence in a range of sport and non sport populations. The importance and potential of hardiness can be summed up in a meta analytic study that included 180 studies investigating hardiness’ antecedents and consequences across all domains. One finding from the study included that hardiness was positively correlated with job performance (r=.17, ρ=.26, k =5, N=676) and school performance (r = .21, ρ=.23, k=3, N=623). Eschleman et al. (2010) concluded that hardiness is one of the better predictors of well being in general populations compared to other health-oriented dispositions, such as self-esteem or locus of control. Specifically, in non-sport settings such as with military training, researchers have examined the influence of hardiness as a psychological resource in Israeli Defense Forces recruits (Florian et al., 1995). Researchers found that hardiness components helped individuals appraise combat training as less threatening, feel more capable of coping, and use more coping strategies. Commitment was positively associated with secondary appraisal (r=.33), inversely related to threat appraisal (r=-.31), and the use of distancing (r=-.16) and emotion-focused coping (r=-.30). Also, patterns of appraisal and coping related to higher levels of hardiness and led to better mental health. Researchers in non-sport settings have demonstrated hardiness’ importance related to overall well-being and coping with stressful situations in military settings. The ability to improve hardiness to deal with stress and anxiety in the military could be related to similar situations found in sporting contexts. One way that hardiness has been explored within sport is its influence on sport injury. Wadey et al. (2012) monitored 694 participants over the span of two years to observe injury EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 5 occurrences. Researchers found that hardiness inversely correlated with injury occurrence, specifically as a participant’s hardiness score increases, their risk of injury decreases (Wald test=32.922, p<.001). Athletes who reported higher levels of hardiness experienced demanding athletic situations similarly to athletes who reported low levels of hardiness but appraised the situational demands as less stressful. The researchers hypothesized that this appraisal decreased significance of the stress response and possible future risk of injury. Individuals who reported higher levels of hardiness also transformed major life events from negative experiences into growth opportunities through appraisals, coping, and social support. Other studies with samples of 121 (Ford et al., 2000), 20 (Salim et al., 2016), and 10 (Wadey et al., 2012b) participants have also examined the relationship between hardiness and sport-injury. Outside of sport-injury, hardiness has been researched across differing competition levels. Sheard and Golby (2010) found that athletes in both individual and team sports at higher competition levels scored higher in hardiness than those at subordinate levels specifically regarding the subcomponents of commitment and control. The researchers found a significant effect for commitment between competition levels (p< .001, 2p =.05) which indicated that international competitors scored higher on commitment than national or club performers. There was also a significant effect for control (p< .001, 2p=.04). This finding was also supported by later research by Thomas et al. (2013) (Cohen’s d = .6) in the individual sport of motorcycle racing and by Golby and Sheard (2004) in rugby. Hardiness has also been examined by type of sport, specifically in high-school female-athletes (Devin et al., 2015). Researchers found that individual sport female athletes (r=0.553, p<.05) were significantly better than team sport athletes (r=0.435, p<.001) when reporting psychological hardiness and the three subcomponents of challenge, control, and commitment. Finally, researchers have also explored hardiness from a EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 6 qualitative point of view as Thompson and Morris (2017) conducted an intervention to promote and develop hardiness within three elite rugby players. The researchers found that hardiness was an individual personality characteristic that could be developed even with elite level athletes and recommended similar future interventions. Despite the previous research on hardiness, there has been a limited amount of research to re-examine the potential links between hardiness and objective performance. Some previous researchers have looked at hardiness as a predictor of flow in performance (Vealey & Perritt, 2015) and included hardiness as a part of a psychological skills training program in swimmers (Sheard & Golby, 2006). But still these researchers did not look specifically at the correlations between objective performance metrics and hardiness subscales. Other studies have recommended future research examining hardiness and anxiety interpretation and its relation to specific sport performance as a logical next step (Hanton et al., 2013), but there is a dearth of research in this regard. One study that has investigated the interaction between objective performance and psychological constructs was a study conducted by Zizzi et al. (2003) where researchers examined the effect of emotional intelligence among college baseball players. The researchers used performance statistics from hitters and pitchers from NCAA Division I universities over a length of a season and found a modest link between performance and emotional intelligence in pitchers (r(21) = .484, p<.05). Although this study does not specifically relate to hardiness, the methodological designs used in the study would be valuable to replicate in future studies attempting to identify possible associations between hardiness and objective performance measures that are widely available via baseball box scores. During the time since, baseball analytics has also become more nuanced and lends itself to more statistically available data that EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 7 would support individual objective performance. Including these new statistics would be a superlative way to add significance to studies examining associations to objective performance in baseball. Based on the need for continued research regarding the individual personality characteristic of hardiness, the present study aims to explore how hardiness may help athletes who experience anxiety perform, using objective performance metrics in NCAA Division I collegiate baseball players. This study’s research questions include: (1) how does hardiness affect objective performance for pitchers and hitters; (2) is there a moderating relationship of hardiness on trait anxiety and performance; (3) are there differences in moderation effects between pitchers and hitters? Participants Method Participants in this study included 389 male collegiate baseball players from 18 NCAA Division I baseball teams and 14 different conferences. The mean age of participants was 19.85 (SD = 1.24, range = 18-23) with 54.2% of participants reported having some experience previously with sport psychology. The mean athletic class standing was 2.25 (SD = 1.12, range = 1-6) with example codes representing freshman as 1, redshirt freshmen as 1.5, and graduate students as 6. In terms of race/ethnicity demographics, 76% (N = 296) identified as White or Caucasian, 8% (N = 30) identified as Black or African American, 7% (N = 27) identified as Hispanic or Latino, 2% (N = 7) identified as Asian or Asian-American, 3% (N = 12) identified as biracial, and 4% (N = 17) preferred not to provide information. Of the 389 participants, 32 participants did not provide identifying information and therefore their questionnaire data was not able to be matched to their performance metrics. Fifty EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 8 eight of the rest of the 357 players who provided identifying information did not participate in any games during the 2020 shortened season. The remaining 299 players included 138 pitchers, 148 hitters, and 13 players who both pitched and hit at some point during the shortened season. Participants were separated into two different groups – hitters and pitchers – for the purpose of separating the tasks required of different players on the baseball field. However, it was possible that players were included in both the hitting and pitching groups if they met the inclusion criteria for both. After excluding pitchers and hitters who did not meet the inclusion criteria during the shortened season, the final number of participants in the hitting and pitching groups were 94 and 80 respectively for a total of 171 participants (N=171) with three players qualifying for both groups. Inclusion Criteria In order to participate in this study, athletes had to: (1) be a listed member on the NCAA team roster; (2) provide provision of agreement to informed consent; and (3) be at least 18 years old. Inclusion criteria for hitters required at least two plate appearances per game and criteria for pitchers required at least two-thirds of an inning per outing. These inclusion criteria were modeled after a study that also measured objective performance in baseball (Zizzi et al., 2003). Due to the shortened season, teams played between 13 to 21 games before the COVID 19 global pandemic terminated the remainder of the NCAA Division I 2020 season. This is equivalent to approximately one-quarter to one-third of the total number of games that collegiate baseball seasons typically play. This range also includes more non-conference games played and fewer within-conference games than usual, as within-conference games are typically played during the middle or end of the collegiate baseball season. Using the lowest number of the games played, inclusion criteria were multiplied by 13 to reach the minimum number of at-bats or EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 9 innings that hitters and pitchers needed to fall within the inclusion criteria. Therefore, to be included in the study, hitters must have at least 26 official at-bats to be included and pitchers must have pitched at least 8 innings to be included. Recruitment Participants were recruited via a convenience snowball sampling method. Head coaches from 280 (94%) of the 299 total NCAA Division I baseball teams were contacted across all 32 conferences via email to ask if they were willing to let their athletes participate in the study during the offseason. Of the 280 teams that were contacted, 40 (14%) teams and coaches responded and of those, 25 (9%) teams agreed to participate. Additional information, including method of delivery of the questionnaires, the estimated time required of the athletes, and how to return the completed questionnaires to the researcher were provided to coaches who agreed to let their athletes participate. After agreeing to participate and sending out questionnaires, 18 (6%) of teams returned completed surveys comprising of the final 18 teams included in this study. Coaches and athletes who returned completed surveys were provided a follow-up report on personality and statistics from the shortened 2020 season with team-based findings to help understand the current team’s personality and performance. Design and Procedures A prospective descriptive correlational design was utilized in this study, in which participant questionnaire data was matched with publicly available objective performance statistics from the 2020 COVID-19 shortened NCAA Division I baseball season. This approach modeled the descriptive correlational design utilized in the study by Zizzi et al. (2003) to explore objective performance among baseball players. EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 10 After obtaining institutional review board (IRB) approval, head coaches of 280 NCAA Division I baseball teams were contacted via email. Upon agreement, informed consents forms and questionnaires were distributed either online, through a Qualtrics link, or via paper and pencil surveys sent in the mail, depending upon each team’s preference. In both scenarios, the researchers were not present during the administration or completion of the surveys, but a member of the team or a coach acted as a conduit and was given instructions to administer, collect, and send the questionnaires back. PVS III-R and CTAI-2 measures were counterbalanced before being distributed. Each completed questionnaire was given an anonymous code in order to de-identify the data after matching to each athlete’s performance data. Performance statistics were tracked through each respective baseball team’s website, which were made publicly accessible by the team after an official scorer tracked each game over the span of the season. Measures Hardiness Hardiness was originally measured using the Personal Views Survey III-R (PVS III-R), an 18-item scale with six items pertaining to each of the three sub-scales of challenge, commitment, and control (Maddi et al., 2006). PVS III-R utilized a four-point Likert scale ranging from 0 (not at all true) to 3 (very true). An example item was “Trying your best at what you do usually pays off in the end.” The PVS III-R had an internal consistency coefficient alpha of 0.80 (Maddi et al., 2006) and strong positive inter-correlations were reported between the three subcomponents of hardiness and the total hardiness scale (Maddi, 2012). For more details, please reference Appendix B. After reliability statistics on the collected responses revealed poor loadings onto the three sub-scales of hardiness, a confirmatory factor analysis (CFA) was conducted. This CFA led to the use of an updated PVS III-R scale which included just nine of EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 11 the original 18 items with three items for each subscale. The composite reliability estimate for the revised nine-item PVS-III R was 0.76. For more details on the confirmatory factor analysis process, please reference Appendix C. Trait Anxiety Trait anxiety was measured using the Competitive Trait Anxiety Inventory-2 (CTAI-2; Parfitt et al., 1990). This scale was modified from the Competitive State Anxiety Inventory-2 (Martens et al., 1990) by editing instructions that originally asked individuals to indicate how they felt at the current moment to how they usually felt right before competition to create a trait measure. The 27-item scale consists of three subscales with nine questions for somatic anxiety, cognitive anxiety, and self-confidence and one question reverse-scored within the somatic anxiety subscale. The scale is separated into two sections measuring intensity and interpretation of trait anxiety. The first section measured the perceived intensity of pre-competition anxiety and was measured on a four-point Likert scale ranging from 1 (not at all) to 4 (very much so). The second section of the scale measured whether the athlete interpreted the anxiety as facilitative or debilitative and was measured on a seven-point Likert scale ranging from -3 (very negative or debilitative) to 3 (very positive or facilitative). An example item was “I am concerned I may not do as well in this competition as I could.” The CTAI-2 has a reported Cronbach’s alpha value of 0.83 (Perry & Williams, 1998). For more details, please reference Appendix D. Demographics Demographic information collected included each participant’s name, age, ethnicity/race, college/university, position(s) played, current jersey number, previous experience with sport psychology, if any, and other NCAA DI sports played, if any. Participant names, college/university attended, and current jersey number were used as identifying information to EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 12 link objective performance data to questionnaires completed by specific players. For more details on instructions and layout of demographic questionnaire, please reference Appendix E. Objective Performance Baseball Statistics In addition to objective baseball performance statistics examined in a previous study (Zizzi et al., 2003), this study included newer baseball objective performance data that are being commonly used to make analytical decisions in professional baseball organizations. This study tracked total hits, total doubles, total walks, total double plays grounded into, and total strikeouts for hitters as well as total earned runs, total walks, total hits allowed, total strikeouts, and total wild pitches for pitchers to corroborate and compare to previously conducted studies (Zizzi et al., 2003). In addition to those statistics, this study also calculated the following baseball statistics: On-Base Percentage Plus Slugging (OPS), Weighted On-Base Average (wOBA), Batting Average on Balls in Play (BABIP) for hitters and Walks Hits per Innings Pitched (WHIP), Fielding Independent Pitching (FIP), and Left On-Base Percentage (LOB%) for pitchers. Each of these statistics were averaged over the span of the 2020 shortened season. For more detailed information and formulas to calculate each statistic, please reference Appendix A. All information about what these statistics mean and how to calculate them was found on FanGraphs’ website (Slowinski, 2010). These statistics were chosen based on the feasibility of calculation using the statistics given by the game-performance results found on publicly accessible team websites. The inclusion of these statistics also helps add to the range of objective performance metrics measured in existing sport psychology literature. With the increased use of statistics such as FIP and BABIP in professional baseball organization’s decision making, inclusion of these statistics in research adds to the relevance of extant research. EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 13 Data Analyses Responses to questionnaires that were completed via paper and pencil were entered through a double-data entry system, once by the researcher and once by a research assistant, in order to minimize data entry errors. Responses to questionnaires that were completed through Qualtrics were downloaded and aggregated together with paper and pencil responses after completion of double-data entry system. Data collected from publicly available statistics on team websites were entered for players who had provided identifying information and consented to be included in the study. Questionnaire data were then linked to available performance data and questionnaire and demographic information were de-identified and separated based on player group. After, researchers used the previously-decided inclusion criteria of at least 26 at-bats for hitters and 8 innings for pitchers to identify which players met the inclusion criteria. Using the updated 9-item scale for the PVS III-based on CFA (Appendix C), data analyses were conducted using the updated PVS III-R scale, CTAI-2 Intensity scale, CTAI-2 Interpretation scale, and selected objective performance statistics collected from the 2020 shortened baseball season. Pearson correlations were conducted in SPSS and moderation analyses were conducted using the PROCESS add-on in SPSS (Hayes, 2012, 2013). Pearson correlation analyses were conducted for all variables within both the hardiness and trait anxiety subscales, on both pitchers and hitters’ objective performance statistics. Following the Pearson correlations, moderation analyses were then conducted to identify the moderating role of players’ hardiness on the relationship between their trait anxiety and measures of their performance. During moderation analyses, the Johnson-Neyman technique was used to probe significant interactions beyond initial conditional effects if initial significance was obtained. This technique EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 14 was used to supersede the arbitrary pick-a-point approach at the 16th, 50th, and 84th percentiles used in conditional effects commonly conducted in PROCESS (Hayes, 2013). Results Primary Analyses Descriptive statistics for all variables across all groups are presented in Table 1. Correlations among primary variables are presented in Tables 2 and 3 for pitchers and hitters, respectively. Overall, pitchers showed evidence of mostly weak to moderate correlations between personality characteristics and objective performance statistics. Hitters, similarly, showed evidence of mostly weak to moderate correlations between personality characteristics and objective performance statistics. Secondary Analyses Moderation analyses were conducted to examine the conditional effects of commitment in relation to pitchers and control in relation to hitters. These analyses did not include challenge as it had the weakest loading during the CFA and has previously been questioned as strong of a sub-construct as commitment and control (Sheard & Golby, 2006). Full moderation analyses data tables for pitchers and hitters are presented in Tables 4 and 6 respectively, and conditional effects using Johnson-Neyman probing technique for pitchers and hitters are presented on Tables 5 and 7 respectively. Analysis of objective performance metrics were further narrowed down to examine the moderating effect of commitment on newer objective performance statistics for the pitching group. Pitching Group For pitchers, examination of the moderating effect of commitment on the relations between trait anxiety intensity and objective performance statistics revealed that for both wild EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 15 pitches and left on-base percentage, all three sub-scales of trait anxiety intensity were significantly moderated by commitment while fielding independent pitching was not significantly moderated by commitment. Wild Pitches (WP). For wild pitches, cognitive anxiety intensity, β = 0.05, SE = 0.01, t = 3.53, p < 0.05, 95% CI [0.02, 0.07], somatic anxiety intensity, β = 0.04, SE = 0.01, t = 3.18, p < 0.05, 95% CI [0.02, 0.07], and self-confidence intensity, β = 0.03, SE = 0.01, t = 2.94, p < 0.05, 95% CI [0.01, 0.05] were all significantly moderated by commitment. A pitcher would like to limit the number of wild pitches thrown as wild pitches occur while runners are on-base and usually indicates the pitcher threw a ball that allowed the runner to advance to the next base. The Johnson-Neyman technique indicated that for pitchers with scores above 6.37 - or above the 54th percentile - on commitment, a significant positive relationship was found between pitcher’s cognitive anxiety intensity and number of WP thrown for pitchers scoring higher on commitment, β = 0.05, SE = 0.03, t = 1.99, p < 0.05, 95% CI [0.00, 0.10]. These findings indicate that pitchers with higher levels of commitment threw fewer wild pitches when perceiving lower intensities of their cognitive anxiety. Commitment explained more than half of the variance in wild pitches thrown (R2 = 0.24, ΔR2= 0.13). For somatic anxiety intensity, the Johnson-Neyman technique indicated that for pitchers with scores above 7.05 - or above the 73rd percentile - on commitment, a significant positive relationship was found between pitchers’ somatic anxiety intensity and number of wild pitches thrown for pitchers scoring high on commitment, β = 0.06, SE = 0.03, t = 1.99, p < 0.05, 95% CI [0.00, 0.12]. These findings indicate that pitchers with higher levels of commitment threw fewer wild pitches when perceiving lower intensities of their somatic anxiety. Commitment explained more than half of the variance in wild pitches thrown (R2 = 0.20, ΔR2= 0.11). EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 16 For self-confidence intensity, the Johnson-Neyman technique indicated that for pitchers with scores above 7.13 - or above the 73rd percentile - on commitment, a significant positive relationship was found between pitcher’s self-confidence intensity and number of wild pitches thrown for pitchers scoring high on commitment, β = 0.04, SE = 0.02, t = 1.99, p < 0.05, 95% CI [0.00, 0.09]. These findings indicate that pitchers with higher levels of commitment threw fewer wild pitches despite perceiving low intensities of their self-confidence. Commitment explained approximately half of the variance in wild pitches thrown (R2 = 0.18, ΔR2= 0.09). Left On-Base Percentage (LOB%). Similarly, for LOB%, cognitive anxiety intensity, β = -0.004, SE = 0.001, t = -2.95, p < 0.05, 95% CI [-0.006, -0.001], somatic anxiety intensity, β = -0.003, SE = 0.001, t = -2.72, p < 0.05, 95% CI [-0.006, -0.001], and self-confidence intensity, β = -0.002, SE = 0.001, t = -2.10, p < 0.05, 95% CI [-0.004, 0.000] were all significantly moderated by commitment. A higher LOB% identifies that a pitcher was able to prevent runners that were allowed on base from scoring. The Johnson-Neyman technique indicated that for pitchers with scores above 8.78 - or above the 88th percentile - on commitment, a significant inverse relationship was found between pitchers’ cognitive anxiety intensity and LOB% for pitchers scoring high on commitment, β = -0.008, SE = 0.004, t = -1.99, p < 0.05, 95% CI [-0.02, 0.00]. These findings indicate that pitchers with higher levels of commitment had higher LOB% when perceiving lower intensities of their cognitive anxiety. Conversely, the Johnson-Neyman technique also indicated that for pitchers with scores below 5.06 - or below the 27th percentile - on commitment, a significant positive relationship was found between pitcher’s cognitive anxiety intensity and LOB% for pitchers scoring low on commitment, β = 0.005, SE = 0.003, t = 1.99, p < 0.05, 95% CI [0.00, 0.01]. These findings indicate that pitchers with lower levels of commitment had lower LOB% when perceiving lower EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 17 intensities of their cognitive anxiety. Commitment explained the majority of the variance in LOB% (R2 = 0.12, ΔR2= 0.10). For somatic anxiety intensity, the Johnson-Neyman technique indicated that for pitchers with scores above 8.86 - or above the 88th percentile - on commitment, a significant inverse relationship was found between pitcher’s somatic anxiety intensity and LOB% for pitchers scoring high on commitment, β = -0.008, SE = 0.004, t = -1.99, p < 0.05, 95% CI [-0.02, 0.00]. These findings indicate that pitchers with higher levels of commitment had higher LOB% when perceiving lower intensities of their somatic anxiety. Commitment explained the majority of the variance in LOB% (R2 = 0.10, ΔR2= 0.09). For self-confidence intensity, the Johnson-Neyman technique indicated that for pitchers with scores below 4.01 - or below the 8th percentile - on commitment, a significant positive relationship was found between pitcher’s self-confidence intensity and LOB% for pitchers scoring low on commitment, β = 0.005, SE = 0.002, t = 1.99, p < 0.05, 95% CI [0.00, 0.01]. These findings indicate that pitchers with lower levels of commitment had lower LOB% when perceiving lower intensities of self-confidence, however this is limited to a small percentile and group of pitchers who scored below this low percentile. Commitment explained the majority of the variance in LOB% (R2 = 0.07, ΔR2= 0.06). Fielding Independent Pitching (FIP). For FIP, cognitive anxiety intensity, β = 0.007, SE = 0.02, t = 0.45, p > 0.05, 95% CI [-0.03, 0.04], somatic anxiety intensity, β = 0.005, SE = 0.02, t = 0.33, p > 0.05, 95% CI [-0.03, 0.04], and self-confidence intensity, β = -0.004, SE = 0.01, t = -0.33, p > 0.05, 95% CI [-0.03, 0.02] were all not significantly moderated by commitment. A pitcher’s FIP identifies a pitcher’s ability to prevent runs independent of their defense. Commitment explained minimal variance in the relationships for cognitive anxiety EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 18 intensity (R2 = 0.12, ΔR2= 0.002), somatic anxiety intensity (R2 = 0.12, ΔR2= 0.001), and self confidence intensity (R2 = 0.12, ΔR2= 0.001) and FIP. Hitting Group Objective performance statistical analyses for hitters were further narrowed down to examine the moderating effect of control on the relationship between trait anxiety intensity on newer objective performance statistics. Examination of the moderating effects of control revealed that control significantly moderated the relationship between somatic anxiety intensity and batting average on balls in play and the relationship between self-confidence intensity and grounding into double plays. Batting Average on Balls In Play (BABIP). For BABIP, cognitive anxiety intensity, β = 0.001, SE = 0.001, t = 1.03, p > 0.05, 95% CI [-0.001, 0.004] and self-confidence intensity, β = 0.001, SE = 0.001, t = 0.96, p > 0.05, 95% CI [-0.001, 0.002] were both not significantly moderated by control. However, somatic anxiety intensity, β = 0.003, SE = 0.001, t = 2.15, p < 0.05, 95% CI [0.00, 0.006] was significantly moderated by control on somatic anxiety intensity’s effect on a hitter’s BABIP. Similar to batting average, higher BABIPs would indicate better objective performance for hitters. The Johnson-Neyman technique indicated that for hitters with scores below 5.50 - or below the 44th percentile - on control, a significant inverse relationship was found between hitter’s somatic anxiety intensity and BABIP for hitters scoring lower on control, β = -0.004, SE = 0.002, t = -1.99, p < 0.05, 95% CI [-0.09, 0.00]. These findings indicate that hitters with lower levels of control had lower BABIPs when perceiving high intensities of their somatic anxiety. Control explained half of the variance in the relationship between somatic anxiety intensity and BABIP (R2 = 0.09, ΔR2= 0.05), but minimally for cognitive anxiety intensity (R2 = 0.05, ΔR2= 0.01) and self-confidence intensity (R2 = 0.04, ΔR2= 0.01). EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 19 Grounding into Double Plays (GDP). For number of double plays grounded into, cognitive anxiety intensity, β = -0.004, SE = 0.01, t = -0.36, p > 0.05, 95% CI [-0.03, .02] and somatic anxiety intensity, β = -0.02, SE = 0.01, t = -1.07, p > 0.05, 95% CI [-0.04, 0.01] were both not significantly moderated by control. However, self-confidence intensity, β = -0.01, SE = 0.007, t = -2.16, p < 0.05, 95% CI [-0.03, -0.001] was significantly moderated by control on self confidence intensity’s effect on the number of double plays grounded into. Hitters ideally would aim to avoid hitting into double plays and grounding into less double plays would represent a better hitter’s performance. The Johnson-Neyman technique indicated that for hitters with scores above 5.93 - or above the 43rd percentile - on control, a significant inverse relationship was found between hitter’s self-confidence intensity and number of double plays grounded into for hitters scoring high on control, β = -0.03, SE = 0.01, t = -1.99, p < 0.05, 95% CI [-0.05, 0.00]. These findings indicate that hitters with higher levels of control grounded into less double plays when perceiving high intensities of their self-confidence. Control explained one-quarter of the variance in the relationship between self-confidence intensity and number of double plays grounded into (R2 = 0.16, ΔR2= 0.04), but minimally for cognitive anxiety intensity (R2 = 0.08, ΔR2= 0.001) and somatic anxiety intensity (R2 = 0.09, ΔR2= 0.01) and number of double plays grounded into. Weighted On-Base Average (wOBA). For wOBA, cognitive anxiety intensity, β = 0.000, SE = 0.001, t = 0.24, p > 0.05, 95% CI [-0.002, 0.003], somatic anxiety intensity, β = 0.002, SE = 0.001, t = 1.31, p > 0.05, 95% CI [-0.001, 0.005], and self-confidence intensity, β = 0.001, SE = 0.001, t = 1.66, p > 0.05, 95% CI [0.000, 0.003] were all not significantly moderated by commitment. While batting average weighs all hits the same, wOBA weights home runs higher than singles and higher wOBA indicate better hitter performance. Control explained approximately one-third of the variance in the relationship between somatic anxiety intensity (R2 EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 20 = 0.06, ΔR2= 0.02) and self-confidence intensity and wOBA (R2 = 0.08, ΔR2= 0.03), but minimally for cognitive anxiety intensity and wOBA (R2 = 0.04, ΔR2= 0.001). Discussion While there have been a several studies that have examined the effect of competitive anxiety on sport performance (Lagos, 2008) and baseball specifically (Chang & Torres, 2019; Chen et al., 2019; Han, 2014; Strack, 2003), very few studies have examined the influence of personality and specifically hardiness on a player’s ability to perform in the presence of competitive anxiety in baseball. The current study provides contributions to further understand the influence of hardiness on objective performance statistics in the presence of competitive anxiety intensity within a collegiate baseball setting. This study also found similar objective performance results to previous correlational investigations found in the Zizzi et al. (2003) study. Pitching Group Interpretations It is important to interpret the results of this study within the context that each objective performance baseball statistics indicates. Among the moderating effects for pitchers, both statistically significant effects suggest that there were situational or contextual effects of commitment which may help a pitcher’s performance when perceiving high intensities of trait anxiety. Both left on base percentage and wild pitches are statistics that require the presence of runners on base and typically are considered to be higher stress or anxiety-provoking situations during games (Chang & Torres, 2019). Pitchers even change their stance on the mound from a windup position to a stretch position that is quicker and combats the likelihood or ability of a runner to steal a base against that pitcher. Pitchers may perceive their anxiety to be more intense during these situations and the data suggested that commitment moderated the effect of a pitcher’s perception of the intensity of anxiety and led to less wild pitches and higher left on-base EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 21 percentages. These effects were not found to be the case for fielding independent pitching. These results may indicate a more situational or state-like effect of the moderating effect of commitment on a pitcher’s objective performance. As recommended by a researcher via interview from a previous study (Zizzi et al., 2003), a useful area of future focus was situational effects that allowed pitchers to reflect on their own internal state and how it affects their performance. The results of this study seem to corroborate the situational moderating effects of commitment on a pitcher’s ability to throw fewer wild pitches and leave runners on base compared to fielding independent pitching, which is not as situational. Practitioners could use these findings to focus their interventions to improve a pitcher’s commitment during specific situations within baseball that might lead to higher anxiety moments to help pitchers increase their performance. The coefficients of determination (R2) for this groups’ moderation analyses indicate the amount of variance accounted for in pitching performance by trait anxiety intensity. For both left on-base percentage and wild pitches, the amount of R2 change that was accounted for by the moderating variable of commitment was at least half and in the case of left on-base percentage, commitment accounted for the majority of the variance between the relationship between trait anxiety and left on-base percentage. Although the overall R2 may be considered small, at higher levels of competitive sport, physical abilities become more comparable and small increases in mental skills or personality could lead to larger influences in performance outcomes (Zizzi et al., 2003). The R2 change values for both situational statistics of wild pitches and left on base percentage suggest that a large amount of variance on a pitcher’s performance during on-base situations could be influenced by a pitcher’s higher level of commitment with runners on base in the presence of trait anxiety. Especially in regard to LOB%, the majority of variance accounted EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 22 for by commitment suggests that improving a pitcher’s level of commitment could account for the majority of improvement in a pitcher’s LOB% and could be a focus for practitioners. Hitting Group Interpretations In comparison to the pitcher group findings, the hitter group moderation analyses of the subscale of control were mostly non-significant with the exception of one situational statistic and one non-situational statistic. These findings are particularly interesting as one significant finding was related to self-confidence while the other was somatic anxiety. In a previous study conducted by Davis and Sime (2005) within baseball, the researchers recommended sport psychologists to focus on improving self-confidence rather than reducing anxiety to help performance. However, if practitioners wanted to help baseball players address their anxiety, the findings in this study seem to provide some evidence that this could be done through development of their personality and particular subscales of hardiness. The findings indicate that practitioners could both try to improve self-confidence and address anxiety through improvement of control while for pitchers reduction of anxiety could be achieved through improvement of commitment to increase performance. Different positions require different skills from players and the findings allow practitioners to potentially target these differences in multiple ways. Similar to wild pitches for pitchers, grounding into double plays for hitters require runners on base and is also considered a negative performance statistic. Typically, grounding into double plays is not seen to be in the control of a hitter as various factors by the opposing team or umpire could affect whether the hitter actually hits into a double play and require that there are fewer than two outs as well (Slowinksi, 2010). GDP situations may elicit higher feelings of self-confidence as there is a runner on base that makes it easier for the hitter to score the runner on base to help the team rather than having to a hit a home run on their own (George, EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 23 1994). As a situational statistic, the results from this study indicate that hitters who believe these situations with runners on base are more in their control and perceive high levels of self confidence will ground into fewer double plays and therefore allow their team to continue to hit. Unfortunately, this statistic does not give any insight to what type of other results the hitter may have hit into such as a home run or fly-out to right field. It does suggest that high intensities of self-confidence paired with high levels of control could lead to less double plays grounded into which would allow the team more opportunities to continue hitting and score runs. The non-situational statistic that was found to be significant was a hitters’ batting average on balls in play (BABIP) which has been discussed to be more in control of a hitter than a pitcher (Slowinski, 2010). It is understood that a hitter has control over how often they decide to put the ball in play and how hard they hit it, but not if it actually ends up being a hit due to the defense or luck. When considering the results in this study, it was demonstrated that when hitters had lower feelings of control, they had lower BABIPs and higher perceptions of their somatic anxiety. Practitioners and consultants in applied sport and exercise psychology may explore ways to increase a hitter’s perception of the situation being within their control through cognitive re-appraisal (De Castella et al., 2013). This may help increase hitter’s BABIP when they perceive their somatic anxiety to be high which corroborates results found in a previous study exploring competitive anxiety in baseball players (Strack, 2003). There were no significant differences found for weighted on base average perhaps indicating that hitters do not have much control over their weighted on-base percentage which includes contributions out of their control like being hit by pitches or walks. The coefficients of determination (R2) for this groups’ moderation analyses indicate the amount of variance accounted for in hitting performance by trait anxiety intensity. For the EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 24 majority of moderation relationships, the amount of R2 that was accounted for by the moderating effect of control was very small. However, the relationship between somatic anxiety intensity and BABIP was accounted for by the moderating variable of control was over half. In the relationship between self-confidence intensity and grounding into double plays, control accounted for just one-quarter of the variance despite the largest R2 value among hitter group moderating effects. Although the R2 change values for hitters do not explain for as much or as many of the trait anxiety variables as pitchers, a situational statistic with runners on base was still significant moderated by control as well as a non-situational statistic which was not found in pitchers and corroborates hunches by baseball analysts (Slowinski, 2010). Practical Applications A few practical contributions can be taken from this study for consultants or practitioners when working with baseball players. First, with the findings that pitchers are largely moderated by their level of commitment during pressure or anxious situations with runners on base, practitioners could aim to increase a pitcher’s ability to reframe the intensity of the anxiety that is perceived by focusing on their commitment towards the next pitch. Reminding pitchers that during situations with runners on base, the data shows that having a higher level of commitment in the pitches that they are throwing could lead to higher LOB% and less wild pitches even if they feel a high intensity of cognitive or somatic anxiety or a low feeling of self-confidence. Using the sub-construct definition of commitment, one’s “ability to persist in whatever one is doing, even when stress rises to precarious levels” (Kobasa, 1979), practitioners could help pitchers re-focus their commitment through imagery or breathing when they feel they are in those situations. As found in a previous study that identified that breathing helped decrease heart rate variability and improved performance on the golf course (Lagos, 2008), similar focus on the EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 25 breath within a batter’s routine could help refocus the pitcher’s sense of commitment. These situational statistics that were found to be significant in this study could be specific situations that practitioners focus on with pitches and focus on reframing pitchers to focus on their commitment instead of the high intensity of their anxiety during those situations. Also, the results show that regardless of the type of anxiety, commitment explains the majority of moderating effects which indicates that commitment is the driving force of the improvement in these situational statistics and provides a practical avenue to address with mental skills. Hitters do not seem to have as large of a moderating effect in as many situational settings, but control seems to moderate some overall performance but not to the extent found in pitchers. This finding corroborates previous conclusions that hitters had less control over their presenting situation than pitchers did (Zizzi et al., 2003). Also, in a previous study conducted with professional baseball players in the Korean Baseball Organization, Han et al., (2014) found that skills such as imagery could provide hitters with a flexible coping method for anxiety and help with attention shifting and performance enhancement. Parallel to the findings of this study, practitioners could help hitters reappraise the somatic anxiety intensity that they perceive into their control and redirect their attention to the task of hitting through imagery could potentially improve their BABIP. Practitioners could emphasize elements that are within a hitter’s control, such as choosing certain pitches or locations to swing at when in double play situations. Additionally, imagery was found to be useful to improve self-confidence within baseball players and helped improve their performance in a study conducted by Davis and Sime (2005). With the finding that higher levels of self-confidence help hitters ground into less double plays, practitioners could work on the cognitive reappraisal (De Castella et al., 2013) of their self EXPLORING RELATIONSHIP BETWEEN HARDINESS AND PERFORMANCE 26 confidence and help hitters practice brief imagery or mental rehearsals to improve their self confidence. Practitioners could use the findings of this study to target certain mental skills to help pitchers improve their commitment and hitters their sense of control. Hardiness and the sub constructs of challenge, control, commitment seem to lend themselves to mental skills and overlap with similar constructs taught to help address situational anxiety. Mental skills such as mindfulness awareness to help identify things that are within one’s control (Chen et al., 2019) could be one way to hel