DP2023-05

A Comparison of Professional andRecreational Tennis Players' Selfpresentation on Instagram
Vera Paola SHODA
Gian Philip R. ANTOLINAngela Chriselle Y. VAQUILAR

March 6, 2023

A comparison of professional and recreational tennis players’ selfpresentation on Instagram
Vera Paola Shoda
Centre for Computational Social Science, Kobe University, Kobe, Japanvshoda@rieb.kobe-u.ac.jp ; ORCiD: 0000-0002-6365-7099

Gian Philip R. Antolin
Cesar E.A. Virata School of Business, University of the Philippines, Quezon City, Philippines
grantolin@up.edu.ph; ORCiD: 0000-0002-5028-9063

Angela Chriselle Y. Vaquilar
College of Arts and Sciences, University of the Philippines, Manila, Philippines
ayvaquilar1@up.edu.ph; ORCiD: 0000-0002-8816-1890

A comparison of professional and recreational tennis players’ selfpresentation on Instagram
This study examines the self-presentation activities of female tennis players on the social media
outlet Instagram. Drawing on Goffman's (1959) theory of self-presentation, this study sought tounderstand whether differences existed between the adoption of Instagram by professional andrecreational tennis players. Instagram accounts of 63 professional and 50 recreational Japanese
female tennis players were examined using content analysis. The findings reveal significant
differences in self-presentation tactics of professional and retired tennis players on the focus of
the photo, brand or logo visibility, athlete's presence in their photos, the proximity of the shot,
facial expression, motion, and touch. Implications for self-branding and image management offemale athletes are discussed. Overall, these findings contribute to a better understanding of the
Instagrammable aesthetic and female athlete's online behavior in social media.
Keywords: social media; self-presentation; impression management; women; athletes

Introduction
Studies on computer-mediated communication reveal the tremendous impact that social
media technologies have had on sports (Sanderson 2011). It affected how sports are presented(Schultz and Sheffer 2010) and consumed (Clavio and Kian 2010; Kassing and Sanderson2010). Sports events are streamed live on social media platforms, allowing fans to immersethemselves in action and share their reactions in real time. Furthermore, many athletes haveembraced social media and are building their brands through their accounts. Found that
athletes and sports organizations use social media to promote brand awareness (Eagleman2013), retrieval and dissemination of information (Browning and Sanderson 2012; Sanderson2013), interaction with fans and stakeholders (Hambrick et al. 2010; Hambrick and Kang
2015) and communication with family and friends to cope during high-pressure environments(Hayes et al. 2019).
Studies also show that social media accounts give athletes a powerful tool to shape
their representation (Emmons and Mocarski 2014; Geurin-Eagleman and Burch 2016) unlike

traditional media, where they have limited control. Notably, female athletes gain profoundbenefits from social media, such as having increased exposure, control over how they wish to
be represented, and the opportunity to showcase their athletic prowess and femininity.
Various studies have revealed the biased coverage between male and female athletes in
traditional and online media, where males receive more media exposure while females are
represented with their sexual, aesthetic, and personal features (Lebel and Danylchuk 2012;
Toffoletti and Thorpe 2018b). Since there is less coverage of women's sports in traditional
media outlets, social media has become an important channel for female athletes (Turková,
Macková, and Němcová Tejkalová 2021) since it can address this gap in coverage while also
contesting and reworking mainstream gender and sexual identities in sport (Toffoletti andThorpe 2018a). Therefore, the way female athletes interact, use, and present themselves onsocial media has captured the attention of scholars, leading to numerous studies analyzing
their self-presentation on these platforms. Analysis of female athletes' self-presentation
behavior on social media usually draws on Goffman's (1959) theory of self-presentation,
where social media serves as the platform through which individuals create a publicpresentation of themselves (Marshall 2010). For instance, Geurin (2017) found that theperceived audience affects self-presentation activities. Furthermore, social media also helps
women to resist cultural and religious norms and stereotypes (Ahmad and Thorpe 2020;
Kavasoğlu and Koca 2022). Female athletes as “role models” (Pocock and Skey 2022) were
found to affect how other women behave on social media (Santarossa et al. 2019).
Instagram is a visually rich social media platform for creative self-expression andsocial interaction and has often been utilized to examine methods of self-expression
employed by female athletes (Geurin-Eagleman and Burch 2016). Instagram allows users toshare multimedia content and interact with others through likes, comments, and direct
messaging. Research on Instagram suggests that women are more engaged in using Instagramthan men and play a significant role in shaping the content and culture of the platform (B. Li
et al. 2021). Female athletes use Instagram for various reasons, such as to connect with theirfans, promote their athletic brand, and share their experiences (Geurin-Eagleman and Burch2016). Research has shown that overall, Instagram provides female athletes with a powerfultool to reach a large and engaged audience, build their brand, and connect with fans more
personally than traditional media channels (Turková, Macková, and Němcová Tejkalová
2021).
Although existing literature describes social media platforms such as Instagram asproviding benefits and opportunities for female athletes, most works focus on analyzing theself-presentation of famous and high-ranking professional athletes (Bodaghi and Oliveira2022; Geurin 2017; Lebel and Danylchuk 2012; McGannon, Graper, and McMahon 2022).
Since these professional athletes only represent a small portion of the female athlete
population, it is necessary to consider the analysis of the "ordinary" female athletes, such asrecreational athletes (athletes not participating in competitive events). While focusing on
professional athletes' self-branding strategies can help to identify the impact of social media,
this article recognizes that most women using social media are those we could consider"ordinary" users (i.e., recreational athletes) whose social media posts take place in noncompetitive or relatively ordinary everyday settings. Understanding the differences between
the self-presentation strategies of professional and recreational athletes can help better
understand what is considered photographable or Instagram-worthy among female athletes.

Literature Review

Goffman’s theory of self-presentationGoffman (1959) discussed that a person could portray multiple versions of himself in front ofdifferent people. Goffman further explains that a human being's identity is not constant;instead, it continuously evolves as he socializes with people. This work also introduced theidea of "front stage" and "backstage" performances. The former is the behavior that anindividual shows when nobody is observing him, and the latter refers to the behavior thatsomeone puts on when he is conscious that people are watching him. Thus, people follow
social norms and conventions that may please their audience. The self-presentation theory iscommonly used to analyze the different performances that people portray in various settings.
More recently, the theory has been used to analyze social media behaviors.
Lebel and Danylchuk (2012) used Goffman's theory to conduct a gendered analysis ofprofessional tennis players' self-presentation on Twitter. The purpose of their study was toanalyze and categorize the tweets of athletes. The researchers tried to showcase the athletes'
differences in social media behavior based on gender, as previous studies revealed that maleathletes are featured more by the media. This study also highlighted the different types ofbackstage and front-stage frames that athletes used in their performances through their
tweets. Smith and Sanderson (2015) also conducted a similar study that used Goffman's selfpresentation theory to analyze athletes' Instagram behavior. This study specifically looked at
the significant similarities and differences between male and female athletes when posting on
Instagram. Through content analysis, they found that most image posts of both genders are
consistent with established gender norms.
Social media provides a platform for people to express themselves freely and in amanner of their choosing. Goffman's theory provides a way of understanding people's
behavior and the motives behind their tweets, posts, and captions. Based on the performancesthat they put out, people can control what they want to show and hide from the online world.

Female athletes’ self-presentation on social media
Online environments have turned into a platform for self-presentation (Ellison, Heino,
and Gibbs 2006). Various research studies reveal that most of the posts that athletes share onsocial media are personal. Findings of Geurin-Eagleman and Burch (2016) show that 66.8%
of photo posts on Instagram by athletes from different sports fields are about their personal
lives. According to Turková, Macková, and Němcová Tejkalová (2021) study on Czech
female athletes, content posted on Facebook during the athletes' on-season and off-season
varied. While the seasonal posts were jam-packed with sporting images and images of
podium victories, the off-season content frequently represented the players' personal lives,
such as glamour images of themselves as attractive ladies in fancy gowns or swimwear.
Some researchers focused solely on the self-presentation of athletes using image-centricsocial media platforms such as Instagram (Geurin-Eagleman and Burch 2016; Y. Li and Xie
2020; Smith and Sanderson 2015) due to the positive impact of image content on user
engagement, especially when it comes to high-quality, professionally taken photos, which
increase engagement on social media (Y. Li and Xie 2020) which athletes take advantage of
to boost their brand.
According to Krane et al. (2010), who researched how female college athletes desire
to be photographed, female athletes want to be seen in pictures as having strength and powerso that viewers will be impressed by their athleticism and physique. In their study, one of thefour primary higher-order themes they developed is 'being an athlete' which is similar to oneof the two themes developed by Devonport, Leflay, and Russell (2019), which is 'performing
an athletic identity' that has five sub-themes: 'featured in action,' 'showing good technique,''wearing of kit, and use of equipment associated with their sport,' 'displaying a sporting
physique' and 'demonstrating psychological assets' (p.727). The last sub-theme mentioned issimilar to the second major theme in the study of Krane et al. (2010), 'psychological strength,'which included being 'intense, focused, confident, calm and determined' (p.185). The third
theme of Krane et al. (2010) is 'social identities,' which shows the importance of having othersocial identities, including being female, besides their sports defining them (p.186-187), andthe last theme is 'Progressive interpretation of femininity' which features photos that were
purposefully provocative and gave athletes a chance to express alternate ideas about what itmeans to be a woman (p.188). By emphasizing these themes, the athletes welcomed theopportunity to act as role models and wished to inspire younger athletes. This is consistent
with the second of Devonport et al. (2019)'s two themes, 'intended messages .'As role models
for younger audiences, athletes wanted to emphasize the 'importance of hard work' andencourage them to 'give the sport a go' (p.733-734).
In the Instagram posts by the female athletes where they appear, most of them are in
non-sport settings (Geurin-Eagleman and Burch 2016), which is aligned to the fact that mostof their posts are about their personal lives. However, when dressed in athletic apparel,
Geurin-Eagleman & Burch's study (2016) shows that there are more photos where the female
athletes are just posing for a picture and not engaged athletically.
There are few Instagram pictures of female athletes dressed provocatively or whose
photos are exclusively focused on their sexual features (Geurin-Eagleman and Burch 2016;
Smith and Sanderson 2015). As a result, Smith and Sanderson (2015) questioned if it isnatural, instinctual, and anticipated for a woman to pose suggestively. In their study, thefemale athletes' photos in which this happened most frequently were those in which they
were dressed out for an occasion or gala and posed with other women. They may not haveintentionally chosen to pose a suggestive position; instead, their pose may reflect societal
standards (p.234).
Smith and Sanderson (2015) also investigated other forms of presentation used byfemale athletes in their Instagram posts compared to male athletes. Their study found that

female athletes are more likely to upload photos of themselves showing some form of touch,frequently with friends, such as hugging one another, or items like stuffed toys or trophieswon (p.353). Additionally, they discovered that most photographs featured the athlete from a
distance, presenting their entire body, which indicates an athlete's vanity. They also observedthat the least popular image athletes decided to post was a "selfie," a common photographic
phenomenon on Instagram. In addition, athletes appeared to utilize Instagram to express a
distinct aspect of their lives visually. A selfie tends to be restrictive because it concentrates
only on the picture's subject. While shooting a selfie, much detail is lost, which might
contradict what the athlete is attempting to convey (p.354). Finally, their research showedthat female athletes were significantly more likely to show signs of licensed withdrawal, suchas staring off into the distance, looking down instead of directly at the camera, lying back andappearing withdrawn, or being present in a group of people while not acting socially engaged(p.354–355).
Social media has also allowed athletes to communicate and promote a message andshare marketing content (Hambrick et al. 2010). Female athletes have sought new strategies
to market themselves on social media, where they convey self-love, self-disclosure, and selfempowerment (Toffoletti and Thorpe 2018a)

Purpose and Research Questions
This study aimed to analyze female tennis players' self-presentation on Instagram tounderstand how women use social media as a personal branding tool, discover what is
considered photographable or Instagrammable, and understand whether differences exist
between professional and recreational athletes.
Based on self-presentation theory and previous literature on athletes’ self-presentation
on social media outlets, two research questions were developed.



RQ1. How are female tennis players presenting themselves and their personal brands
on Instagram?



RQ2. What differences exist between the self-presentation strategies used on
Instagram by female professional and recreational tennis players?

Methods
To address the purpose of the study, the Instagram accounts of female Japanese professionaland recreational tennis players were examined. Tennis players are categorized as professional
if they are currently active or are scheduled to participate in an official tennis tournament; if
not, they are categorized as recreational tennis players. A content analytic method was
employed to examine Instagram posts. A content analysis method was appropriate, as the aimof the study was to identify meanings from qualitative material (Patton 2010), and it is asystematic and replicable method for analyzing content, both written and visual (Riff 2014)

Data collection
Only public accounts with more than ten posts during data collection (September 1, 2022, toOctober 1, 2022) were included in the study. Instagram accounts of professional tennis
players were identified using a name search on Instagram. Names of Japanese female
professional athletes were based on the list of professional tennis players on
www.tennisexplorer.com. Upon name search and checking each account, we verified andcollected 63 accounts of professional female Japanese tennis players.
On the other hand, usable Instagram accounts of recreational tennis players wereidentified using a hashtag search. The hashtag “#テニス女子” (tennis girl) was used. We
visited each of the accounts of the Instagram posts with the target hashtag. We included theaccount in the final sample when the account description/biography explicitly stated that theaccount owner plays tennis (non-professional or retired professional). We continued to searchfor usable Instagram accounts using the hashtag and inclusion criteria until we were able toget a total of 50 accounts for recreational tennis players, of which five are retired professional
tennis players.
The final list of accounts has a total of 113, with 63 for professional and 50 forrecreational tennis players (including five retired professional players). Finally, the ten mostrecent post was pulled from each of the 113 accounts, totaling 1130 photos in the sample.
The procedure and sample size were consistent with previous content analytic research(Bodaghi and Oliveira 2022; Coche 2014; Geurin-Eagleman and Burch 2016; Lebel and
Danylchuk 2012).

Codebook and Intercoder Reliability
A codebook and coding protocol was developed based on previous studies of athletes' selfpresentations on social media (Bodaghi and Oliveira 2022; Emmons and Mocarski 2014;
Geurin-Eagleman and Burch 2016; Santarossa et al. 2019). The coding categories for thisstudy are summarized in Table 1. The following twelve variables were included: coder ID,
account name, number of followers, number of accounts following, number of posts, the date
the photo was taken, number of posts where the athlete's face is visible from the most recent
ten posts, number of pure self-presentation photos (athlete alone in the photo) from the mostrecent ten posts, photo type, prop, focus, and brand visibility. If the athlete appeared in thephoto, the following eight additional variables were coded: the number of people in thephoto, if the athlete is looking at the camera, clothing, proximity, facial expression, motion,
touch, and type of shot. Before coding the sample of photos, the primary researcher trained
two coders on the content analytic method and explanation of the coding categories. To
establish reliability (Riff 2014), each coder independently coded 25% of the sample (n=282).

Cohen's Kappa was calculated to determine intercoder reliability. As presented in Table 1, astrength of agreement ranged of Cohen's Kappa from "Moderate," "Substantial," and “Almost
Perfect” on all variables (i.e., 0.41-0.60 Moderate, 0.61-0.80 Substantial and 0.81-1.00
Almost Perfect; (Landis and Koch 1977). After validating the intercoder reliability, theremaining 848 photos from the sample were divided evenly and coded independently.

Findings

Followers and following count and self-presenting posts
The 113 Instagram accounts in the dataset had, on average, 28,660 followers, 455 followers,
and 344 posts. Professional athletes, on average, had the most followers (M=47,157,
SD=345,782) and accounted following (M=483, SD=290). Recreational athletes had the least
average number of followers (M=4265, SD=10,637) and following accounts (M=422,
SD=302).
Analysis of the most recent ten posts of each athlete in our dataset resulted in an
average number of self-presenting posts (posts where the athlete's face is visible) of 7.03
(SD=3.21) and the average number of pure self-presentation posts (where the athlete is alone
in the photo) of 5.19 (SD=3.03). Professional athletes had the highest number of selfpresenting posts (M=8.55, SD=1.27) and pure self-presentation posts (M=5.67, SD=2.56).
Recreational athletes had the lowest number of self-presenting posts (M=5.16, SD=3.82) andpure self-presenting posts (M=4.67, SD=3.44).

Focus, photo type, use of props, and brand visibility
Pictures with the athletes as the focus were the most common, comprising 60.44% (n=683) of
the sample. Next were family and friends (n=199), others (n=158), and lastly, scenery (n=90).

In analyzing the photo type, the photos in the sample were most identified as sport-related at
47.52% (n=537), followed by personal at 43.8% (n=495), and sponsored or others at 8.67%(n=98). No usage of props was the most common at 58.85% (n=665). Finally, logos or brands
were found to be non-visible in most posts, comprising 57.52% (n=650) of the sample.

Athletes appearing in photos
Overall, athletes appeared in 907 of the photos coded, or 80.26% of the total sample. Of
those, professional players appeared more often than recreational players. Professional
athletes appeared in 93.01% of their photos, while recreational athletes appeared in 64.20%of theirs. Of those photos in which the athlete appeared, the most common type of shot was
the full body (n=592) followed by half body (n=226), selfie (n=69), and headshot (n=31).
The majority of the photos coded also showed touch (n=700). The most common motion was
passive non-sport (n=384). Further looking at the facial expression of the athlete, the mostcommon was happy with 57.43% (n=649), then others with 15.66% (n=142), and intensewith 14.33% (n=130).
With regards to proximity, photos coded had more wide shots with 35.31% (n=399)
then, medium with 35.72% (n=324), and tight with 20.95% (n=190). The athletes were foundto look at the camera most of the time, with 61.19% (n=555). Athletes were also found to bealone in their photos more at 65.27% (n=592), followed by pair (n=205) and group (n=126).
In terms of clothing, athletes were more commonly wearing their tennis uniform or tennis
wear (n=541).

Differences between professional and recreational athletes
We used Chi-square analysis to examine the differences between the self-presentation tactics
of professional female athletes and recreational female athletes. We used a 0.005 p-valuethreshold for statistical significance (Benjamin et al. 2017). The Chi-square results revealed asignificant difference between the player type and focus of the photo (χ2 = 111.13, df = 3, p
< .001), brand or logo visibility (χ2 = 27.64, df = 1, p < .001), the athlete appearing in thephoto (χ2 = 146.13, df = 1, p < .001), proximity (χ2 = 24.41, df = 2, p < .001), facial
expression (χ2 = 41.62, df = 2, p < .001), motion (χ2 = 55.15, df = 3, p < .001), and touch (χ2= 13.26, df = 1, p < .001). The findings are summarized in Table 2.
Welch's T-test results revealed a significant difference in the face visibility of professionaland recreational athletes. There was a significant effect of the athlete's playing status on theface visibility (p <0.001) on their Instagram photos at the .005 level. However, nosignificance was found on other variables such as the number of followers (p = 0.32), numberof accounts following (p = 0.30), number of posts (p = 0.11), and number of posts alone (p =0.11).

Discussion
This study examined how female tennis players engaged in self-presentation using Instagram
and the player status (i.e., professional vs. recreational) differences that emerged from theirself-presentation behaviors. Previous research has found differences in self-presentation
behaviors online concerning gender (Geurin-Eagleman and Burch 2016; Smith andSanderson 2015), age (Bodaghi and Oliveira 2022), and sport category (i.e., individual sport
and team sports) (Shreffler, Hancock, and Schmidt 2016). This research aimed to expand on
the literature regarding the self-presentation of female athletes, specifically focusing on avisual social platform and player status. The findings show noteworthy points that athletes,
researchers, and sports organizations can utilize.

Athlete first, female second

The first significant finding was that contrary to previous research (Geurin-Eagleman andBurch 2016; Smith and Sanderson 2015), female tennis players were found to have more
sports photos exhibiting more frontstage behavior than backstage behavior. In their study,
Geurin-Eagleman & Burch (2016) suggested that female athletes post more business life andsports photos to increase fan interest, engagement, and athletic credibility. Both professionaland recreational tennis players had more posts categorized as sports photos than personal life
photos, thus exhibiting that female athletes have improved their self-presentation tactics
online. As Aria et al. (2014) suggested, showing sports-related posts is important in building
a strong athlete brand. Results show that female athletes are more in control of social media
and can show their prowess and strength. This finding also reinforces the idea that womenathletes, when in control, prefer athletic depiction to sexualization and objectification (Kane,
LaVoi, and Fink 2013; Santarossa et al. 2019; Smallwood, Brown, and Billings 2014).
The second noteworthy point was that wider shots and full body shots were mostcommon, which supports existing literature (Smith and Sanderson 2015) suggesting thatfemale athletes, when given a choice, prefer to display their athletic competence and power.
Although a popular type of shot on Instagram, selfies were found to be not a popular choicesince this type fails to capture athletic performance and competence.
Another interesting point supporting that female athletes depict themselves as an
athlete first and females second on social media is that female athletes were found to look at
the camera more, suggesting engagement with their audience. This behavior contradicts
stereotypes of women, who are expected to avoid direct camera gaze and be unfocused andnot in control (Goffman 1976). Furthermore, athletes were also found rarely to wear sexual
clothing and frequently appear wearing their uniforms (i.e., tennis wear). This behavior
reinforces their membership in the sport and as part of their athletic brand. However, propsthat are used to exhibit professional legitimacy or credibility (Emmons and Mocarski 2014)

were not found to be commonly used. This suggests that female athletes, rather than using
props, express their athletic credibility through other methods.
However, research findings also reveal that female athletes were found to have morepassive motion in their photos. This result is in line with previous research (Trujillo 1991)
showing that females are more commonly shown in passive poses. Likewise, most photos
also showed female athletes touching others or objects. Goffman (1979) describes this as thepoint of ritualistic touching and associates it with feminine behavior. Thus, although female
athletes emphasize their athletic image on social media, they maintain their femininity.

Professional athletes as athletic brands
Professional and recreational players differed significantly in their photos' focus. Althoughboth have more photos focusing on the athlete themselves, professional players have more
photos related to their personal lives (spending time with family and friends). In comparison,recreational players have more photos on other topics. In addition, professional athletes
appear more in their photos than recreational athletes. This suggests that professional athletes
use social media for self-presentation and image management purposes, while recreational
athletes may have other purposes which can be explored in future research.
Although athletes have fewer photos showing off their sponsors' brands or logos,
there is a significant difference between professional and recreational players. Professional
players have more photos where brand names or logos can be seen than recreational players.
This shows that professional athletes serve as brand ambassadors and promote their sponsors'
brands on their social media accounts without overemphasizing and getting in the way ofbuilding an athletic brand and competence. On the other hand, our results show that few
recreational players serve as "influencers" that promote tennis-related brands.

In addition, in terms of the proximity of the shot, professional players preferred shorts
with wider proximity (medium and wide). This supports the idea that professional players
highlight their athletic legitimacy more by using shots that capture prowess and strength(Smith and Sanderson 2015), rather than attractiveness. Regarding facial expression, findings
show that professional athletes tend to have more intense expressions than recreational
athletes in their photos. This exhibits their desired credibility and perception of authenticity(Emmons and Mocarski 2014) in their sport. Lastly, although photos mainly were found tohave passive motion, regarded as a feminine behavior (Goffman 1976), professional athletes
had more active shots than recreational athletes. This further supports that professional
athletes tend to break away more from feminine stereotypes than recreational athletes.

Conclusion
This research is an essential first step to understanding the self-presentation behavior of
professional and recreational athletes and the control and opportunities that social media
allows them to break away from stereotypical media representations. The results show that
although some stereotypical expectations remain, female athletes have improved their selfpresentation tactics online, showing them as more in control and empowered over how theywant to be seen, thus enabling them to build their athletic brands.

Data availability
The data underlying this article will be shared on reasonable request to the corresponding author.
Acknowledgments
This work was supported by the Yamaoka Memorial Foundation under Grant 00018.
Disclosure
The authors report there are no competing interests to declare.

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Table 1. Coding categories
Category

Analysis options

Cohen’s
Kappa

Photo type

Personal / Sports / Sponsored, others

0.72

Focus

Athlete/ Family or friend(s)/ Scenery /0.52

othersProp

Yes / No

0.71

Is the brand or logo

Yes / No

0.63

Yes / No

0.89

Number of people

Solo/ Pair / Group

0.95

Is the athlete looking at

Yes / No

0.83

Clothing

Uniform / Casual, others

0.84

Proximity

Tight / Medium / Wide

0.73

Type of shot

Headshot/ Half-body / Full-body/ Selfie

0.81

Facial expression

Happy/ Intense/ Others

0.83

MotionActive sport / Active non-sport/ Passive

0.76

visible?
Is the athlete in thephoto?

the camera?

sport / Passive non-sport
Touch

Yes/ No

0.74

Table 2. Differences in photo types based on player status
Coding

Total

Total

Professional

Professional

Recreational

Recreational (%)

Chi-squarecategories

(n)

(%)

(n)

(%)

(n)

Sports537

47.52%

303

48.10%

234

46.80%

χ2=8.51, df=2,

Personal

495

43.81%

260

41.27%

235

47.00%

p=0.014Sponsored, Others

98

8.67%

67

10.63%

31

6.20%

No significanceAthlete683

60.44%

406

64.44%

277

55.40%

χ2=111.13, df=3,

Family / Friends

199

17.61%

150

23.81%

49

9.80%

p<0.001

Other

158

13.98%

36

5.71%

122

24.40%

Significant

Scenery

90

7.96%

38

6.03%

52

10.40%

significancePhoto type

Focus

Prop

χ2=4.16, df=1,

No

665

58.85%

354

56.19%

311

62.20%

p=0.04Yes

242

41.15%

311

43.81%

189

37.80%

No significanceBrand or logo visible?
No650

57.52%

319

50.63%

331

66.20%

χ2=27.64, df=1,

Yes

480

41.15%

311

49.37%

169

33.80%

p<0.001
Significant

Athlete in photo?
Yes

907

80.27%

586

93.02%

321

64.20%

χ2=146.13, df=1,

No

223

19.73%

44

6.98%

179

35.80%

p<0.001
Significant

Photos in which the athlete appeared
Number of people
Solo

576

63.51%

353

60.24%

223

69.47%

χ2=8.86, df=2,

Pair

205

22.60%

149

25.43%

56

17.45%

p=0.011

Group

126

13.89%

84

14.33%

42

13.08%

No significance60.64%

353

60.24%

199

61.99%

Athlete looking at camera?
Yes

550

No

357

39.36%

233

39.76%

122

38.01%

χ2=0.27, df=1,
p=0.60No significance

Clothing

χ2=3.63, df=1,

Uniform

541

59.65%

363

61.95%

178

55.45%

p=0.05

Casual, Others366

40.35%

223

38.05%

143

44.55%

No significance

Wide

393

43.33%

266

45.39%

127

39.56%

χ2=24.41, df=2,

Medium324

35.72%

226

38.57%

98

30.53%

p<0.001

Tight

190

20.95%

94

16.04%

96

29.91%

Significant

Full body581

64.06%

388

66.21%

193

60.12%

χ2=8.64, df=3,

Half-body226

24.92%

137

23.38%

89

27.73%

p=0.03

Selfie

69

7.61%

37

6.31%

32

9.97%

No significanceHeadshot

31

3.42%

24

4.10%

7

2.18%

Proximity

Type of shot

Facial expression
Happy

635

70.01%

409

69.80%

226

70.40%

χ2=41.62, df=2,

Others142

15.66%

67

11.43%

75

23.36%

p<0.001

Intense130

14.33%

110

18.77%

20

6.23%

Significant

Passive non-sport365

40.24%

258

44.03%

107

33.33%

χ2=55.15, df=3,

Passive sport

325

35.83%

160

27.30%

165

51.40%

p<0.001

Active sport

193

21.28%

150

25.60%

43

13.40%

Significant

Active non-sport

24

2.65%

18

3.07%

6

1.87%

Yes

693

76.41%

470

80.20%

223

69.47%

χ2=13.26, df=1,

No

214

23.59%

116

19.80%

98

30.53%

p<0.001

MotionTouch

Significant