Relationship between the athlete's environment and sports performance with regard to psychological factors

Johanna Weber RELATIONSHIP BETWEEN THE ATHLETE’S ... 82 OPEN ACCESS Submitted: 01 June 2025 Accepted: 2 September 2025 ORCID Johanna Weber https://orcid.org/0000-0002-3735-4254 Cite this article as: Weber, J. (2025). Relationship between the athlete’s environment and sports performance with regard to psychological factors. Journal of Applied Sports Sciences, 9(2), pp. 82 - 101. DOI: 10.37393/JASS.2025.09.02.6 Th is work is licensed under a Attribution-Non Commercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) 82 Article Classifi cation: Research article RELATIONSHIP BETWEEN THE ATHLETE’S ENVIRONMENT AND SPORTS PERFORMANCE WITH REGARD TO PSYCHOLOGICAL FACTORS Johanna Weber MLU Halle, Halle, Germany ABSTRACT Sports performance is infl uenced by several factors. Psychological factors are a main contributor to sports performance, but are themselves infl uenced by the athlete’s environment, e.g., social environment, fi nancial situation, and so on. It is therefore necessary to assess the infl uence of environmental factors on competitive performance, which is likely to occur through psychological mechanisms. To this purpose, 592 athletes between 13 and 65 years of age (28.04 ± 9.15, 357 female, 204 male, 2 of unspecifi ed gender) at various performance levels were surveyed using a set of questionnaires (MIPS, CSAI, GSE, TDEQ5 and additional questions) regarding their psychological and competitive performance as well as their perceptions of their social situation and social environment. Diff erences in environmental and psychological factors were found between diff erent athlete groups, e.g. according to gender (e. g. self-effi cacy being higher in male participants, p ≤ .010, η = .114), handedness (e. g. left-handers perceiving their social surroundings as more perfectionistic), and sport involved (e. g. higher values for CSAI worry in team sports, p ≤ .038, η = .009). Connections between performance and several environmental factors (e.g., highest competitive level and family, p ≤ .017, r = -.102) and intercorrelations among psychological scales, such as MIPS and CSAI, were also found. Self-effi cacy correlated with highest competitive level in the current main sport (p ≤ .006, r = -.120) as well as CSAI worry (p ≤ .002, r = -.138), thus showing a probable eff ect of expected self-effi cacy and CSAI worry on sports performance, while self-effi cacy itself correlated with and was therefore most probably infl uenced by several environmental factors, for instance social environment (p ≤ .001, r = -.170), fi nances (p ≤ .001, r = -.206) and sleep quality (p ≤ .001, r = -.252), amongst others, and CSAI worry was infl uenced by perfectionism of the environment (e. g. MIPS coach, p ≤ .001, r = -.3420). Therefore, it can be said that sports performance is most likely infl uenced by a set of environmental factors (e.g., family, social surroundings, perfectionism of family, coach, and team) via psychological factors such as self-effi cacy and CSAI. Psychological performance itself is a factor that infl uences sports performance, but it is most likely also the link through which environmental factors infl uence sports performance. Keywords: social environment, sports performance, sports psychology, psychosocial factors INTRODUCTION Sports performance is limited by constitution, coordination, training condition, technique, tactical skill, psychological performance, and social factors (Weineck, 2010). Psychological factors include, e.g., motivation, volition, mental toughness, perfectionism, emotional control, anxiety, perception of self-effi cacy, ability to cope with injuries, as well as the affi nity to doping (Adams, Brassington, Steiner & Matheson, 2004; Johnson, 2007; Barkoukis, Lazuras, Tsorbatzoudis & Rodafi no, 2008; Crombie, Lombard & Noakes, 2009; Gonçalves, Rama & Figueiredo, 2012; Moesch, Hauge, Wikman & Elbe, 2013; Baron-Thiene & Alfermann, 2015). Performance-limiting factors vary across diff erent types of sports. How- JOURNAL of Applied Sports Sciences 9(2)/2025 83 ever, psychological factors have been shown to be relevant in team sports (Grobbelaar & Eloff, 2011, amongst others) as well as in individual sports (D’Arripe-Longueville, Hars, Debois & Calmels, 2009). There are differences regarding gender (Christoforidis, Kalivas, Matsouka, Bebetsos & Kambas, 2010; Marczinka, 2011; Taylor et al., 2020) and probably other athlete-specific factors, like, e.g., playing position or handedness (Weber, 2014; Weber & Wegner, 2016; Weber & van Maanen-Coppens, 2018; Weber & Wegner, 2018a). Furthermore, it is likely that psychological factors such as motivation, volition, and action disposition are related to characteristics such as handedness and performance (Weber, 2014; Weber, 2021b; Weber, 2022a). Also, it has to be kept in mind that some factors, which are per se intrinsic to the athlete, might become social factors depending on how the athlete is perceived from the outside, e.g., age, gender, or handedness. For instance, young handball players or left-handed players might be placed in a position that is open on a team, regardless of whether they are suited to that particular playing position (Weber & Chittibabu, 2017). Handedness may also be important in individual sports. When competing in the spiral discipline of wheel-gymnastics, the turning direction that is best for the gymnast might not be the one preferred by the coach, and the preferred turning direction might also be influenced by handedness (Weber, 2021a). It seems that psychological performance is much more multifaceted than previously thought, and that, e.g., team roles and individual coaching play a crucial role regarding team performance (Beckmann & Elbe, 2008). Psychological performance demands on handball goalkeepers have been found to be largely psychological (Weber et al., 2018). Also, psychological traits and abilities do relate to mental toughness, handedness, and other factors. It is therefore necessary to put sports psychology and (psychological) sports performance into a much broader context, as has been done in previous literature, and to add the social and environmental elements. The focus on sports performance needs to be enhanced in this respect to further support athletes’ well-being and performance. Similar models have already been implemented in vocational contexts (Brown, 2002). Psychological performance does influence competitive results in sports, whether it is the athletes’ performance or that of judges, coaches, or other participants in a sports event. Individuals at the training or competition site can be favourable or detrimental to athletes’ performance, for instance, when judges, coaches, or managers are biased. The judges’ psychological performance on the other hand can, as an external factor, influence that of athletes, coaches and teams, e.g. in wheel-gymnastics (Weber, 2021 a), as can the behaviour of other participants in training or competition such as volunteers, fans (own or opposite), spectators and so on (Beckmann & Elbe, 2008; Karl, 2024 a & b; Braga & Guillén, 2012; Engler, Pelzer, Kaczmarek & Schaefer, 2023). Volunteers might provide false directions on the track, and fans might create pressure during competition (Braga & Guillén, 2012). In Biathlon, Harb-Wu and Krumer (2019) found different audience effects for high-level and less experienced athletes. Regarding the coach, the coach’s psychological ability can influence not only the coach himself, but also the athlete (Shapcott & Carr, 2020) or team, and, also quite importantly, the judges. At the same time, the athlete’s psychological and physical performance is or might be influenced by their (social) environment and framework, such as family, team members, coaches, religion, partner, workplace, or Johanna Weber RELATIONSHIP BETWEEN THE ATHLETE’S ... 84 even political events (Washif, Farooq, Krug, Pyne, Verhagen, Taylor et al., 2021; Romdhani, Ammar, Trabelsi, Chtourou, Vitale, Masmoudi et al., 2022; Vollmann, Ehnold & Schlesinger, 2023). For example, coaches at different license and competitive levels displayed different personality traits (Wunder, Priem, Wagner, Stoll, 2024). Psychological performance in sports can also be influenced by sleeping habits, sleep quality, and other sleep-related parameters. In contrast, sleep itself is altered by various circumstances surrounding the athlete (Romdhani, Fullagar, Vitale, Nédélec, Rae, Ammar et al., 2022), for example, global or political events, which can also have an effect on training and competition and thus the athletes’ psychological and/ or physical state (Washif et al., 2021). As psychological performance is directly related to sports performance (as outlined above), it is necessary to assess possible connections between psychological performance and the athlete’s (social) environment. Regarding religion, fasting practices such as Ramadan or the closing of training facilities around Christmas can disrupt normal training, as can demands regarding sports clothing, which might be considered inappropriate in certain religions and cultures. On the other hand, religion can enhance sports performance (SremSai et al., 2021; Romdhani et al., 2022). Although it has been shown that social factors trigger neurological pathways that enhance sports performance (Davis, Hettinga & Beedie, 2020), a literature review using Google Scholar did not identify a tested concept of how social factors, in combination with psychological factors, contribute to or limit sports performance. Potential social and/or environmental factors contributing to optimum performance in sports might be religion, social environment such as peers, coaches, partner, workplace and financial security, gender, therapy settings, family or even political events and catastrophes like e.g. the Covid-pandemic, amongst others (Dyer, 1976; Adams et al., 2004; Satow, 2008; Madigan et al., 2019; Truong, Mosewich, Holt, Le, Miciak & Whittaker, 2020; Srem-Sai, Frimpong, Abieraba, Sorkpor, Hagan Jr et al., 2021; Taylor, Fujak, Hannon & O’Connor, 2022). Borggrefe and Cachay (2014) even view dual careers in higher education and competitive sports as a problem similar to inclusion. Hickman & Metz (2015), for example, found that professional golfers underperform under monetary pressure. If the financial situation does not allow the athlete to focus solely on training, factors such as workplace, superiors, and travel to training and competition in an unfavourable financial situation might create difficulties for the athlete (Mumcu, 2019). Most research in this direction is conducted at sports schools (Beckmann & Elbe, 2008; Madigan, Curran, Stoeber, Hill, Smith et al., 2019) or for younger athletes (Zibung & Conzelmann, 2015), but less frequently at higher performance levels. The sports performance of children between seven and nine is influenced by their parents’ age, income, and education, as well as sports activities and competitions of the mother, older siblings, or other relatives, physical activity, living situation, playgrounds in the vicinity, and unqualified input from parents regarding sports technique (Krause, 2015). Lau & Walter (2018) claim that every athlete has an individual set of positively influencing factors and mention a supportive network focused on long-term development, a motivating peer group, communication with the trainer, challenging surroundings, acknowledgment of the athlete’s individuality, and a holistic approach as contributing factors. Self-efficacy could be a measurable expression of psychological performance in JOURNAL of Applied Sports Sciences 9(2)/2025 85 this context, as it is linked to sports performance (Bandura, 1977) and to environmental factors such as social and financial support (Cunningham, Bruening, Sartore, Sagas & Fink, 2005). At the same time, self-efficacy is influenced by positive learning experiences (Bandura, 1977), which are, in turn, connected to the social environment (House, 1987; Lent, Brown & Hackett, 2000). Also, competitive anxiety can give insights regarding an athlete’s ability to perform. In this study, psychological performance will therefore be measured using psychological questionnaires (GSE, CSAI). This work aims to research the influence of environmental factors on psychological performance and on sports performance, as well as the influence of environmental factors (see Figure 1) and thus the athlete’s overall performance, and answer the following questions: Do environmental factors influence sports performance? Do environmental factors influence psychological performance? Does psychological performance influence sports performance? Are there differences in environmental factors across competitive levels? These questions will be concretized (e.g., regarding subgroups and interdependencies) in more detail in the methods section. It will be discussed how environmental factors influence psychological performance, which is most probably best measured via performance-limiting psychological factors and must be seen in the mentioned social and environmental context, and competitive success in sports. Differences between individual and team sports will be highlighted, along with special circumstances and requirements for different groups of athletes, e.g., semi-professionals, athletes of different genders, athletes participating in different sports, or athletes of different handedness. An attempt will be made to verify the influence of a set of social and psychological factors on each other and the competitive level of the athlete, using the German version of the General Self-efficacy Scale (GSE, Schwarzer & Jerusalem, 1995; Hinz, Schumacher, Albani, Schmid & Brähler, 2006), the Multidimensional Instrumentary of Perfectionism in Sports (MIPS, Stöber, Otto, Pescheck & Stoll, 2004; Madigan, 2006), the Competitive State Anxiety Inventory (CSAI, Martens, Vealey & Burton, 1990; Stöber & Pescheck, 2004) and the Talent-Development-Environmental Questionnaire (TDEQ5, Alfermann, Lobinger, Nesges, Martindale & Andronikos, 2023). Selected instruments include performance-related factors and psychosocial aspects. The aim is to determine which aspects the athletes deem to be most crucial for their competitive performance, for example, parents, peers, teammates, coaches, or spouses/partners, and test for possible effects of certain factors on each other and on sports performance, which will be measured via competitive level. Johanna Weber RELATIONSHIP BETWEEN THE ATHLETE’S ... 86 Figure 1. Possible contributors to sports performance. METHODOLOGY The questionnaire was online from October 1st, 2023, until February 29th, 2024. A total of 592 athletes provided informed consent and filled in the questionnaire, but only 563 participants (357 female, 204 male, 2 participants did not provide information regarding their gender) could be included due to missing or implausible answers. The age of participants ranged from 13 to 65 (28.04 ± 9.15), with a median of 25 and a mode of 23. Since the age distribution is skewed towards younger ages, the results might be more applicable to athletes aged 13-38 (see Figure 2). Figure 2. Age distribution JOURNAL of Applied Sports Sciences 9(2)/2025 87 The sample included various types of sports: American Football (3), Archery (4), Athletics (14), Badminton (11), Basketball (11), Bodybuilding (38), Bowling (2), Cheerleading (3), Chess (1), Climbing (15), Combat (21), Cricket (2), Crossfit (2), Cycling (22), Dancing (31), E-sports (2), Equestrian (8), Field Hockey (5), Fitness (56), Floorball (1), Football (66), Footy AUS Football (1), Golf (4), Gymnastics (9), Handball (15), Hiking (5), Icehockey (1), Juggling (1), Kayaking (3), Motor sports (1), Netball (1), Pilates (8), Poledance (4), Rowing (4), Rugby (2), Running (52), Scuba diving (1), Skiing (3), Snooker (1), Softball (1), Squash (2), Swimming (40), Table tennis (5), Tennis (12), Triathlon (3), Volleyball (21), Wheel-gymnastics (13), Yoga (20), and Unspecified (12). Athletes completed an online questionnaire that included the standardized inventories described above (GSE, CSAI, MIPS, and TDEQ5), along with additional items assessing demographic characteristics, sport-related information, environmental factors, and psychological variables. Demographic and sport-related variables Participants reported their age and gender, as well as their handedness (right-handed, left-handed, or ambidextrous). They also provided information on their sport affiliation (e.g., student, semi-professional, professional), their main and secondary sport(s), the competitive level of their main sport, the highest competitive level achieved across all sports they had engaged in, and their type of sport organization (club, school, university, none, or other). Environmental factors Several variables assessed athletes’ perceived environment using single-item ratings from 1 (detrimental) to 10 (optimal). These included perceived support from the general social environment, family, partner, and education/work context, as well as sleep quality, the ability to find time to sleep when needed, distance to the training location, financial situation, and the extent to which religious demands facilitated or hindered athletic participation. Psychological variables Perceived perfectionism from coach, team, and parents was assessed using the MIPS (1 = never to 6 = always). Self-efficacy was measured using the General Self-Efficacy Scale (1 = not at all true to 4 = exactly true). Competitive anxiety and self-confidence were measured using the CSAI subscales for cognitive anxiety, somatic anxiety, and self-confidence (1 = not at all to 4 = very much so). The talent development environment was assessed using the TDEQ5 dimensions: long-term development, alignment of expectations, communication, holistic quality of preparation, and social network (1 = strongly agree to 6 = strongly disagree). Self-efficacy was used as a measure for psychological performance. Also, CSAI was used to determine psychological ability to perform under pressure. The study addressed four main research questions: (1) whether athletes at different competitive levels differ in psychological performance and/or environmental factors, and whether these differences vary across subgroups (e.g., gender, handedness, or type of sport); (2) whether environmental factors are associated with psychological performance (GSE, CSAI), and whether these associations differ between athlete subgroups (e. g. gender, handedness, type of sport, affiliation); (3) whether environmental factors are linked to sport performance, and whether these connections vary between subgroups; and (4) whether psychological performance predicts sport performance, and whether this relationship varies across subgroups. Johanna Weber RELATIONSHIP BETWEEN THE ATHLETE’S ... 88 Group differences in psychological performance and environmental factors between athletes of different competitive levels were examined using univariate ANOVAs with Scheffé post hoc tests. Correlations among all variables within athlete groups were calculated using Pearson’s correlation coefficient. Because the highest competitive level was coded with the lowest numerical value (1), correlations involving this variable were interpreted with this vector effect in mind. Statistical significance was set to p ≤ .05 and trends were considered at p ≤ .10. Correlation coefficients were interpreted as low (≥ .10), medium (≥ .30), or high (≥ .50), and effect sizes as small (≥ .10), medium (≥ .24), or large (≥ .37). All analyses were performed using SPSS 29. RESULTS Results will be presented for all four research questions in consecutive order. Differences with regard to the first research question were found for athletes of different genders or handedness and at different performance levels, and these differences vary between athlete subgroups. Values for male and female athletes differed regarding TDEQ social network, perceived situation regarding studies and job, and perceived self-efficacy (see Table 1). Moreover, athletes of different handedness differed in their perceptions of perfectionism from the coach, team, parents, and the TDEQ social network. Furthermore, athletes in team and individual sports showed differences in social environment, CSAI worry, and CSAI confidence (see Table 1). Table 1. Group Differences Across Sex, Handedness, and Sport Type. Comparison Variable N (groups) Mean ± SD pη² Men vs. Women TDEQ Social Network 179 vs. 303 3.65 ± 1.24 vs. 3.90 ± 1.26 .036 .095 Studies/Job Interference 199 vs. 356 6.50 ± 2.25 vs. 5.89 ± 2.15 .002 .134 GSE 190 vs. 335 30.37 ± 4.78 vs. 29.21 ± 5.04 .010 .114 Handedness (Right vs. Left vs. Ambidextrous) MIPS Coach 432 / 42 / 18 2.34 ± 1.16 / 2.85 ± 1.31 / 2.06 ± 1.14 .014 .130 MIPS Team 421 / 42 / 18 2.08 ± 1.08 / 2.51 ± 1.24 / 1.91 ± 1.12 .041 .114 MIPS Parents 446 / 42 / 17 1.83 ± 1.08 / 2.42 ± 1.47 / 1.77 ± 0.87 .004 .148 TDEQ Social Network 419 / 42 / 17 3.82 ± 1.24 / 3.48 ± 1.23 / 4.28 ± 1.50 .073 .105 Team vs. Individual Sports MIPS Coach 122 vs. 366 2.86 ± 1.18 vs. 2.20 ± 1.14 .001 .060 MIPS Team 123 vs. 354 2.62 ± 1.06 vs. 1.92 ± 1.05 .001 .079 MIPS Parents 118 vs. 380 2.01 ± 1.18 vs. 1.81 ± 1.08 .087 .006 CSAI Worry 117 vs. 366 2.51 ± 0.72 vs. 2.35 ± 0.75 .038 .009 CSAI Confidence 116 vs. 369 2.52 ± 0.67 vs. 2.37 ± 0.74 .061 .007 Note. * Scheffé: right vs. left p ≤ .027; left vs. ambidextrous p ≤ .058; ** right vs. left p ≤ .055; *** right vs. left p ≤ .004; **** left vs. ambidextrous p ≤ .087 Regarding research question one, there were also differences among athletes at different performance levels across several factors (see Table 2). The differences for some values did not occur between the highest and lowest, but between the lowest and intermediate levels (see values below Table 2). JOURNAL of Applied Sports Sciences 9(2)/2025 89 Table 2. Differences in Psychological Measures Between Competitive Levels. Measure Total Sample (N)Total (M±SD)Highest Level (n; M±SD) Lowest Level (n; M±SD)pη2 Social Environment 548 7.24 ±1.89 19; 8.42 ± 1.64 323; 7.21±1.90 .042 .155 Family 550 7.38 ± 1.95 19; 8.42 ± 1.98 323; 7.25 ± 1.99 .064 .148 MIPS Coach 490 2.38 ± 1.18 17; 2.33 ± 0.85 283; 2.09 ± 1.08 < .001 .326 MIPS Team 479 2.13 ± 1.10 17; 1.92 ± 0.95 274; 1.92 ± 1.08 < .001 .251 MIPS Parents 501 1.88 ± 1.12 18; 2.08 ± 1.17 294; 1.75 ± 1.07 .059 .155 CSAI Worry 486 2.39 ± 0.74 18; 2.34 ± 0.76 278; 2.26 ± 0.74 .001 .219 CSAI Confidence 488 2.42 ± 0.72 18; 2.46 ± 0.80 282; 2.37 ± 0.74 .012 .182 TDEQ Aligns Exp. 418 3.41 ± 1.19 13; 3.03 ± 1.00 237; 3.50 ± 1.29 .061 .170 TDEQ Communication 434 3.52 ± 1.40 15; 3.33 ± 0.86 245; 3.67 ± 1.46 .040 .173 Note. M = Mean; SD = Standard deviation; η2 = Partial eta squared (presumed). Sample sizes (n) for Highest and Lowest levels differ due to missing data. Although further exploration of distinct subgroups regarding gender for every type of sport would expand the paper in an unjustified manner, it is notable that when considering football players in general, differences between performance levels occurred for a distinct set of factors; when solely considering female football players, these factors differed from those for football players of both genders. Correlations regarding the second, third, and fourth research questions (connections bet ween psychological factors, environmental factors, and performance) were found: between performance level and perceived self-efficacy; between the different questionnaires; and between environmental factors and performance level and self-efficacy. Different connections were found within rightand left-handers, as well as within male and female athletes; also, correlations differed between non-professionals, all semi-professionals, and male/female semi-professionals, when viewed separately, as well as between different sports (see Tables 3 - 6). When considering all tested athletes, the highest level ever competed in all sports correlated with the current level of competition in the main sport, self-efficacy, and support from family, as well as results from the MIPS questionnaire and CSAI worry. Current level in main sports showed correlations with the highest level, MIPS, CSAI worry, TDEQ communication, and TDEQ social network. Support from the partner correlated with TDEQ’s social network, and TDEQ’s holistic quality of preparation correlated with sleep quality and the compatibility of sports and religion. The financial situation was negatively correlated with CSAI somatic but positively correlated with CSAI confidence. It has to be noted that self-efficacy correlated positively with all surrounding social factors except family, but did not intercorrelate with other psychological factors. MIPS, CSAI, and TDEQ were partially intercorrelated (see Table 3). Johanna Weber RELATIONSHIP BETWEEN THE ATHLETE’S ... 90 Table 3. Correlations between social factors, psychological factors, and competitive level in the whole population. All athletes Highest levell all sports Current level main sport MIPS coach MIPS team MIPS parents Self-efficacy CSAI worry CSAI somatic CSAI confidence Long term development Align expectations Com mu - nication Holistic quality Social network Highest level all sports Pearson 1 -.209 -.122 -.130 -.120 -.138 -.044 -.050 .042 .017 .050 .045 .066 p .000* .000* .007* .004* .006* .002* .333 .269 .377 .732 .300 .350 .149 N 553 490 479 501 520 486 477 488 442 418 434 425 476 Current level main sport Pearson .595 -.334 -.258 -.125 -.059 -.187 -.072 -.083 .099 .042 .137 .008 .135 p.000* .000* .000* .005* .177 .000* .115 .068 .038 .394 .004* .865 .003* N546 490 479 500 519 485 476 487 441 418 433 425 475 Social environment Pearson -.026 .081 -.061 .024 -.014 .170 .065 .008 .008 .025 .053 .085 .017 .075 p.543 .060 .173 .602 .760 .000* .154 .867 .863 .598 .281 .076 .732 .101 N548 547 494 482 504 522 489 480 491 444 421 437 428 479 Family Pearson -.102 -.024 .015 .069 -.004 .082 .054 -.020 .035 .030 .042 .019 .004 .068 p.017* .570 .739 .127 .934 .060 .232 .661 .440 .531 .387 .695 .936 .136 N550 549 495 484 506 524 490 481 492 445 422 438 429 480 Partner Pearson -.055 -.006 -.002 .082 -.033 .144 .083 -.001 -.038 .074 .089 .097 .048 .103 p.218 .897 .967 .083 .474 .002* .078 .985 .424 .135 .078 .051 .340 .030* N507 507 456 447 466 483 453 444 455 411 390 405 398 445 Studies and job Pearson -.065 -.023 -.030 .056 -.017 .115 -.024 -.063 .041 .048 .004 .053 .021 -.007 p.131 .595 .503 .217 .711 .009* .595 .170 .366 .317 .935 .268 .658 .877 N548 547 493 483 504 522 488 479 491 444 421 437 428 478 Sleep quality Pearson -.047 .039 -.018 -.043 -.064 .252 -.056 -.017 .063 .052 .008 .022 .111 .052 p.268 .358 .695 .345 .151 .000* .218 .707 .165 .272 .875 .643 .021* .256 N552 551 497 486 508 527 492 483 494 447 424 440 431 482 Time for sleep Pearson -.014 .032 -.092 -.083 -.057 .191 -.052 -.064 -.004 .047 .033 .041 .076 .032 p.738 .447 .041 .067 .196 .000* .244 .161 .930 .316 .498 .392 .113 .489 N554 553 499 488 510 527 494 485 496 449 426 442 433 484 Distance to training Pearson .068 .017 -.071 .014 -.019 .114 -.033 -.036 .063 .040 .058 .065 .015 .032 p.109 .687 .112 .762 .670 .009* .465 .435 .162 .403 .234 .174 .757 .485 N552 551 497 486 508 526 492 483 494 447 424 440 431 482 Finances Pearson .024 .074 -.023 .024 .003 .206 -.093 -.119 .110 .045 .039 .058 .026 .083 p.579 .082 .612 .602 .939 .000* .039 .009* .014* .343 .427 .224 .589 .068 N550 550 495 484 506 524 490 481 492 445 422 438 429 480 Religion Pearson -.036 -.033 -.009 -.033 -.020 .141 -.044 -.066 .000 .025 -.029 -.001 .125 .060 p.397 .438 .834 .474 .650 .001* .335 .152 .993 .599 .551 .981 .010* .193 N547 546 494 483 505 522 489 478 489 444 421 437 428 478 MIPS coach Pearson -.209 -.334 1.000 .723 .525 .011 .342 .225 .142 -.057 -.170 -.225 -.087 -.175 p.000* .000* .000* .000* .806 .000* .000* .002* .235 .000* .000* .073 .000* N490 490 499 480 485 471 467 458 467 443 421 436 427 461 MIPS team Pearson -.122 -.258 .723 1.000 .504 -.011 .269 .200 .181 .020 -.081 -.178 -.127 -.170 p.007* .000* .000* .000* .808 .000* .000* .000* .675 .101 .000* .009* .000* N479 479 480 488 474 458 457 450 457 433 413 426 419 451 MIPS parents Pearson -.130 -.125 .525 .504 1.000 -.051 .340 .266 .020 .066 -.041 -.035 -.095 -.040 p.004* .005* .000* .000* .263 .000* .000* .663 .170 .399 .473 .051 .388 N501 500 485 474 510 481 472 464 474 434 415 429 420 465 Self-efficacy Pearson -.120 -.059 .011 -.011 -.051 1.000 .005 -.010 .029 .004 .035 .015 -.046 .000 p.006* .177 .806 .808 .263 .923 .837 .537 .931 .482 .767 .352 .994 N520 519 471 458 481 527 464 454 464 423 403 419 410 458 CSAI worry Pearson -.138 -.187 .342 .269 .340 .005 1.000 .653 -.320 .011 -.013 .023 -.055 .081 p.002* .000* .000* .000* .000* .923 .000* .000* .822 .788 .640 .262 .081 N486 485 467 457 472 464 494 474 485 438 418 431 425 462 CSAI somatic Pearson -.044 -.072 .225 .200 .266 -.010 .653 1.000 -.316 -.038 -.056 .016 -.066 -.003 p.333 .115 .000* .000* .000* .837 .000* .000* .428 .263 .743 .183 .953 N477 476 458 450 464 454 474 485 477 428 408 423 414 453 CSAI confidence Pearson -.050 -.083 .142 .181 .020 .029 -.320 -.316 1.000 -.006 .009 -.040 .192 -.064 p.269 .068 .002* .000* .663 .537 .000* .000* .906 .856 .404 .000* .167 N488 487 467 457 474 464 485 477 496 436 416 430 421 462 Note. Significant correlations*; Tendency JOURNAL of Applied Sports Sciences 9(2)/2025 97 Results are in part in accordance with previous research, for example, the impact of financial situation on CSAI results and self-efficacy. Vollmann et al. (2023) found that soldiers of the German Armed Forces were more likely to pursue a dual career in sports. Regarding financial factors, the current study aligns with the findings of Hickman & Metz (2015) and Mumcu (2019), which indicate that financial difficulty has been creating or is expected to create pressure on athletes. Self-efficacy, too, has been found to be linked to financial support and social factors in previous studies (Cunningham, 2005). Previous literature has stated that sports performance is influenced by psychological factors (Weineck, 2010). This is also true of the athletes in this study, since several psychological factors correlated with competitive level. Gender-related differences described by Christoforidis et al. (2010), Marczinka (2011), Taylor et al. (2020), and Weber (2022b) have also been found in this study. Handedness also led to differences in psychological factors, as previously described by Weber et al. (2018a and 2022a). Differences in coaches’ communication across performance levels (Wunder et al., 2024) align with this study’s findings, in which coach communication correlated with competitive level. Results might be helpful for individual athlete coaching and future coaching strategies, as previously suggested in the literature (Beckmann & Elbe, 2008). In the light of the current findings, it is very likely that psychological and therefore sports performance might be influenced by the athlete’s (social) environment and framework such as family, team members, coaches, religion, partner, workplace, or even political events, as previous literature suggests (Bandura, 1977; House, 1987; Lent, Brown & Hackett, 2000; Washif et al., 2021; Romdhani et al., 2022; Vollmann et al., 2023; Wunder et al., 2024). In this study, psychological performance in sports and sports performance were associated with sleep quality and sleep duration, consistent with the findings of Romdhani et al. (2022). A connection between sports performance and religion was found, as previously suggested by Srem-Sai et al. (2021) and Romdhani et al. (2022). A connection between social factors and neurological pathways that enhance sports performance (Davis et al., 2020) is likely, given the current findings on left-handers, and should be further evaluated. CONCLUSIONS Athletes can be influenced by, or inhibited by, their environmental and social factors, either directly or through the influence of social factors on their psychological performance. This has to be considered when training athletes, especially in inhomogeneous groups. Surrounding factors have to be taken into account, and training may need to be tailored to them, e.g., sleep quality and financial security in female football players. When analysing competitive outcomes, these factors also need to be evaluated, and measures may need to be taken based on the athlete’s needs, which may vary across different kinds of athletes, e.g., males/females, athletes of different handedness, or athletes with different occupations. In summary, there is likely an individual set of influencing factors, as Lau & Walter (2018) already claimed regarding the TDEQ. 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