Individual Differences in Selective Attention to Information Graphics in Televised Sports

Abstract

Despite the ubiquitous presence of information graphics in sport telecasts, little research has explored how at-home spectators allocate attention to them or individual differences in selective attention. This study demonstrates two viewer characteristics that impact attention to information graphics in two excerpts from the 2012 World Series. Eye-tracking data reveal that although viewers universally attend to these graphics upon onset, those with greater interest in sports and sports statistics exhibited greater cognitive processing of these elements as indexed by gaze duration. Differences in selective attention provide a framework for studying attention in new sport media high in visual complexity.

Keywords

attention information graphics television eye tracking fanship

The use of on-screen graphics in televised sport has become a common means of enhancing telecasts and providing colorful insights into gameplay (Costa, 2013). Although the introduction of such elements on television broadcasts was initially decried as intrusive (Quindt, 2001), they are part and parcel of today’s sport telecasts. Moreover, such informational tools also typify subscription-based and new media sport portals, such as the National Football League’s RedZone channel or Major League Baseball (MLB)’s MLB at Bat app for tablet computers or smartphones (Flint, 2012; Snider, 2011). For example, the MLB at Bat app allows users to customize the interface to include a host of ancillary graphics or statistics related to the game, as well as monitor other events, all within a single screen. The visual complexity and viewer agency afforded by such new media interfaces introduces questions concerning how viewers allocate attention across these myriad streams of information. Although countless viewers may watch the same televised competition, they may exhibit marked differences in how they watch the telecast.

Despite the ubiquity of these graphics in both traditional televised sport and interactive portals, examination of individual characteristics that govern selective attention to such elements has escaped inquiry. However, an abundance of scholarship has demonstrated how viewer interest in sport impacts other aspects of spectator behavior such as pre- or postgame information seeking as well as viewing motives (Gantz & Wenner, 1995; Wenner & Gantz, 1989). Recent studies have also demonstrated differences in sport-viewing motives based on fantasy sport participation and knowledge of sport statistics (Billings & Ruihley, 2013; Brown, Billings, & Ruihley, 2012; Ruihley & Billings, 2013). Such findings suggest differences in how viewers might allocate attention to on-screen visual embellishments. The purpose of this study was to examine how these two characteristics impact selective attention to information graphics in one marquee sporting event—the 2012 World Series—and suggest a model for the study of selective processes in information-rich new sport media contexts.

Literature Review

As previously noted, contemporary sport telecasts feature an unprecedented number of on-screen graphics and visual embellishments to supplement game play (Sandomir, 2004). Surprisingly little research has examined viewer reception of or response to these embellishments, despite the considerable efforts that content producers invest in production elements (Krein & Martin, 2006). Much research has explored how the actual content of mediated sport (i.e., game play, athletic competition) interacts with viewer characteristics to impact viewer response (e.g., domain-specific interest in sport, team identification, dispositional affiliation with competitors; see Raney, 2006, 2009; Wann, 2006). However, such elements largely remain outside the control of content producers. In contrast, production elements governing how competition is depicted have received considerably less attention, save for a few exceptions (e.g., Greer, Hardin, & Homan, 2009; Hallmark & Armstrong, 1999).

Selective Attention Versus Selective Exposure

Since the earliest days of media research, preference for various types of content has been a focus of scholarship exploring selective exposure (e.g., Lazarsfeld, Berelson, & Gaudet, 1948). Mediated sport has had a place in this research due to its potential to excite understimulated viewers (Bryant & Zillmann, 1984), and research in this vein has shown selective exposure as a function of information utility (Valentino, Banks, Hutchings, & Davis, 2009), affect regulation (Christ & Medoff, 1984), and more. Despite the abundance of research supporting this phenomenon, its applicability in an information-rich media landscape begs an alternate conception of selectivity. As previously noted, sport telecasts are often visually complex and contain numerous elements that simultaneously compete for attention (i.e., actual competition, supplemental graphics). Moreover, some novel interfaces allow viewers to monitor multiple competitions simultaneously, further taxing viewers’ ability to attend to multiple information sources.

Within this context, a more useful framework to explore viewing behavior is selective attention. Although selective exposure often connotes choice among various messages or channels, selective attention refers to attention allocation to competing elements within one’s environment. Scholars have long asserted that individuals must employ selective attention as a coping mechanism to effectively operate in information-rich environments (Treisman, 1969). In the case of media interfaces or messages, selective attention can be contrasted with selective exposure by viewing it as intrastimulus selectivity versus interstimulus selectivity. Again, properties of new media interfaces that present competing streams of information simultaneously suggest the utility of this concept in examining how media users allocate attention to message elements (e.g., Josephson & Holmes, 2008; Kallenbach, Närhi, & Oittinen, 2007).

Analogous to the selective exposure literature, the study of individual and message factors that impact selective attention serves as a useful framework. Selective attention to visual elements of a stimulus can be the result of both automatic (i.e., bottom-up) and controlled (i.e., top-down) processes (Bucher & Schumacher, 2006; Pieters & Wedel, 2004; Van der Stigchel et al., 2009). Within communication research, scholars have characterized bottom-up elements as message elements that elicit or guide attention, often automatically (Bucher & Schumacher, 2006; Pieters & Wedel, 2004). For example, onset of a graphic element within a program can capture attention automatically due to novelty (Fox et al., 2004; Lang, Chung, Lee, Schwartz, & Shin, 2005).

In contrast, the top-down perspective focuses on individual characteristics that govern attention allocation in a more controlled fashion. Scholars have demonstrated selective attention processes as a number of individual characteristics: sensation seeking (Ball & Zuckerman, 1992); affect regulation (Isaacowitz, 2006); salience or relevance of an element within a stimulus (Parkhurst, Law, & Niebur, 2002; Sanders-Jackson et al., 2012); and age (Kirkorian, Anderson, & Keen, 2012). For example, Isaacowitz and his collaborators (e.g., Ersner-Hershfeld, Carvel, & Isaacowitz, 2009; Isaacowitz, Toner, Goren, & Wilson, 2008) have used eye tracking to demonstrate differences in attention as a function of age and mood, which illustrates how individual motivational forces can shape attention allocation (Isaacowitz, 2006).

However, to view attention exclusively as a property of the stimulus versus the viewer is a fallacy; instead, attention results from an interaction between the two (Bucher & Schumacher, 2006; Duchowski, 2007). Buswell (1935) argued that the human visual system neither randomly samples visual environment nor simply reacts to stimuli; instead, attention allocation is the result of an observer’s goals (i.e., top-down) interacting with the visual stimuli (i.e., bottom-up) to execute a behavior. Moreover, Van der Stigchel et al. (2009) posit that visual attention works in a sequential fashion where bottom-up processing precedes top-down or controlled processing. To apply this to information graphics within mediated sports, onset of a graphic element should automatically elicit attention; however, viewer identification with a team—a top-down characteristic—is what determines the motivational relevance of the information and results in controlled attention to the element (Cummins, Tirumala, & Lellis, 2011; Hillman et al., 2000; Potter & Keene, 2012).

Sports Spectators and Selective Attention

Given the myriad sources of information contained in sport telecasts, the applicability of selective attention is obvious as some viewers may choose to attend to information graphics while others elect to allocate less attention or ignore them altogether. Research demonstrating the importance of stimulus salience and motivational differences in attention informs how at-home spectators could exhibit differences in attention to information graphics (e.g., Isaacowitz, 2006; Sanders-Jackson et al., 2012). Within the context of mediated sport, three individual characteristics suggest similar motivational differences: general sport fanship, knowledge of sport statistics, and interest in fantasy sport.

Although simplification of sport fandom into a dichotomy (e.g., “die hard” vs. “fair weather” fans,” “fans” vs. “spectators,” etc.) is misleading (Gantz, 2011), this bifurcation provides a useful operational framework for differentiating those with greater interest in sport from those who watch for other motives (Gantz, Wilson, Lee, & Fingerhut, 2008). Compared to nonfans or fans of other genres, sport fans are more likely to seek out information about a competition prior to a game, are more affectively involved in viewing, and exhibit greater information seeking after a competition (Gantz & Wenner, 1995; Gantz, Wang, Paul, & Potter, 2006). Such differences should also be manifested via selective attention while watching, with those with the greatest interest in sport more closely scrutinizing ancillary graphics that contain granular information about game play.

Recent research has highlighted additional characteristics beyond mere fanship that differentiate spectators. Even among “die-hard fans,” individual differences may yield heterogeneity of motivations and behaviors. With the rapid growth of fantasy sport participation, scholars have examined motives and behaviors that characterize fantasy sport participants (Bowman, McCable, & Isaacson, 2012; McGuire, Armfield, & Boone, 2012; Nesbit & King, 2010). Raney (2006) observed that sport fans are often “walking encyclopedias” of sport information (p. 346). This sentiment may be particularly suited for describing the fantasy sport participant. Billings and Ruihley (2013) reported a constellation of characteristics that differentiate fantasy sport participants, including two variables related to sport knowledge, Schwabism and mavenism. Schwabism denotes how much a person considers himself or herself as having adequate knowledge when it comes to a particular activity. The related concept of mavenism posits that an individual not only actively seeks sport information but also does so to serve as a source of sport information. Both factors have been useful in distinguishing fantasy sport participants from nonparticipants (Billings & Ruihley, 2013) as well as younger versus older fantasy sport participants (Brown et al., 2012).

Given the information contained within graphics in sport telecasts (i.e., statistical summaries of play, player performance), such information should be of particular interest to those high in these distinguishing characteristics. Indeed, scholars have noted that game consumption, information seeking, and knowledge of sport statistics are characteristics of the fantasy sport participant (Bowman et al., 2012; Farquhar & Meeds, 2007; McGuire et al., 2012, Nesbit & King, 2010). Thus, one would expect to see that those highest in fantasy sport knowledge should allocate greater attention to information graphics. Although hardly the sole individual characteristics could impact selective attention, sport fanship along with interest in fantasy sport and sport statistics suggest potential for differentiating attention allocation among viewers.

Hypotheses and Research Question

In sum, research has demonstrated that although viewers consistently allocate attention to the onset of information graphics in television, selective attention processes can govern how much attention viewers allocate to these elements. As such, we predict that viewers will universally orient to information graphics upon onset (Hypothesis 1). However, more sustained attention allocation will be greater for more avid sport fans (Hypothesis 2) as well as for those with greater self-reported knowledge of sport statistics and fantasy sport (Hypothesis 3). Finally, to illuminate differences between general sport fans and those with a particular interest in sport statistics and fantasy sport, we ask which characteristic better predicts attention to information graphics in sport telecasts (Research question).

Method

To examine the research question and hypotheses, a quasi-experiment was conducted whereby participants were randomly assigned to watch one of two excerpts from the 2012 World Series that contained four types of information graphics, while eye-tracking apparatus recorded their gaze. After viewing, they completed posttest measures of sport fanship, mavenism, and Schwabism, along with demographic measures. The formal design of the study was a 2 (game segment) × 4 (infographic type) × 2 (individual characteristics: fanship, knowledge of sport statistics and fantasy sport) mixed measures design, where game segment and the individual characteristics were between-subject variables, and infographic type was a within-subject variable with four levels. Note that the individual characteristics were not fully crossed (see Predictor Variables).

Participants

A convenience sample of 62 participants was recruited from a major southwestern university in the United States to participate for extra course credit or to fulfill course research requirements. Seven participants’ gaze data were excluded due to data loss during viewing. Of the remaining 55 participants, their age ranged from 19 to 31 years (M = 21.38, standard deviation [SD] = 2.59). In all, 20 were male and 35 were female. Participants spent 5.21 hours watching actual sport events per week (SD = 6.21) and 5.85 hours watching sport-related programs per week (SD = 10.93).

Procedure

Participants were recruited to watch a baseball game without knowing the purpose of this study. Data were collected in individual sessions. Participants were seated approximately 24 inches from a computer monitor. The researcher first calibrated the participant’s gaze using a 9-point calibration image to account for individual differences in eye biology. Participants were then asked to watch an approximately 15-min baseball game segment as they normally would.

After viewing the stimulus, the researcher led the participant to a different computer to finish an online questionnaire that measured demographics, sport consumption, sport fanship, fantasy sport participation, and fantasy sport involvement.

Predictor Variables

Two individual characteristics served as predictor variables. Sport fanship was conceptually defined as the degree to which one self-identifies as a sport fan, not as a fan of a particular sport or team. It was operationalized using Wann’s (2002) Sport Fandom Questionnaire that asks participants to agree with statements such as, “Being a sports fan is very important to me.” All items were paired with 7-point Likert-type response scales (1 = Strongly Disagree, 7 = Strongly Agree). Responses were highly consistent (α = .97) and were averaged to create an overall score. For some analyses, participants were assigned to low (M = 3.13, SD = 1.23) and high (M = 6.27, SD = .64) sport fanship groups via median split (Mdn = 5.00). Between-groups differentiation was supported with independent samples t-tests, t(43.09) = 12.01, p < .001 (Levine’s correction for degrees of freedom applied for violation of equality of variances).

To demonstrate concurrent validity, hours spent watching televised sport and hours spent watching sport-related programming were compared between the two groups. Participants provided open-ended responses to questions asking them to estimate the number of hours dedicated to viewing actual sports and watching sports-related programming (e.g., “Sports Center,” sports talk programs, etc.). For both measures, results revealed a significant difference, hours watching sport, t(35.09) = 4.17, p < .001 and hours watching sport-related programming, t(29.60) = 2.88, p = .006). Avid sport fans reported an average of 8.44 hr per week watching sport (SD = 7.05) and 10.06 hr watching sport-related programs (SD = 14.14), which is significantly more than those assigned to the low sport fanship group (televised sport, M = 2.33, SD = 3.39; sport-related programming, M = 2.09, SD = 4.53).

Knowledge of sport statistics and fantasy sport was operationalized via measures employed in past studies of fantasy sport participation, Schwabism and mavenism (Billings & Ruihley, 2013; Brown et al., 2012; Ruihley & Billings, 2013). Knowledge of sport statistics was operationalized by a Schwabism scale that consisted of 5 items such as, “When someone has a question about sport statistics, they ask me first.” Knowledge of fantasy sport was assessed using a measure of mavenism. As previously noted, mavenism refers not only to knowledge of a topic but also whether an individual “enjoys sharing the knowledge and information with others” (Billings & Ruihley, 2013, p. 15). For example, participants rated agreement with the statement, “I like helping people by providing them with information about fantasy sport.”

Although conceptually distinct, measures employed to gauge these constructs suggest some overlap. For example, items measuring Schwabism and mavenism interchangeably reference both fantasy sport and sport statistics (e.g., Billings & Ruihley, 2013). With respect to the present data, principal components analysis using varimax rotation indicated that the measures of Schwabism and mavenism loaded strongly onto a single factor (Eigenvalue = 1.85, 14.24% variance explained), with responses to the measure of sport fanship loading onto a separate factor (Eigenvalue = 9.02, 69.41% variance explained). Rotated item loadings for measures of Schabism and mavenism were all >.61, and no items cross-loaded onto the fanship factor. Responses to the Schwabism and mavenism scales were averaged to yield a single index of knowledge of fantasy sport and sport statistics (α = .97). For some analyses, participants were categorized as high (M = 4.12, SD = 1.62) or low (M = 1.31, SD = .36) based on a median split (Mdn = 2.00). Group differentiation was supported via independent samples t-test, t(25.93) = 8.49, p < .001 (Levine’s correction for degrees of freedom applied for violation of equality of variances).

Although principal components analysis suggested differentiation between sport fanship and knowledge of fantasy sport and sport statistics, the two variables were nonetheless highly correlated, r(55) = .67, p < .001. Moreover, a chi-square test of association found an unequal distribution of frequencies among those high and low in the two variables (χ² = 19.70, p < .001, Φ = .60) and cell sizes were largely imbalanced. As a result, these two variables were examined via separate analysis of variance (ANOVA) tests.

Dependent Variables

Eye tracking was used to gauge attention. The technique has become an increasingly popular tool for examining viewer attention to visually complex interfaces such as television or websites (Josephson & Holmes, 2008; Kallenbach et al. 2007), as it can provide granular insights into specific message elements that receive attention. Eye movement is characterized by rapid saccadic movements joined by brief pauses known as fixations. During these pauses, the eye is generally stationary around a central point, and the object of attention falls within the eye’s fovea where visual acuity is greatest. A major assumption of eye tracking is that these movements and pauses are manifest indicators of covert attention allocation (Hoffmann, 1998; Jacob, 1995).

Attention to information graphics was operationalized via three metrics: fixation frequency, gaze duration, and observation frequency. Fixation frequency is a ratio-level measure that indicates the number of times gaze is relatively stationary within a defined area of interest (AOI). Fixations were operationalized here as moments where the eye was stationary within a 62-pixel radius around a central point >.100 seconds (Jacob, 1995; Josephson & Holmes, 2008). Gaze duration is a ratio-level measure of the combined duration of these fixations within the AOI. Although clearly linked, fixations denote which specific element is receiving attention, and duration corresponds with processing of that element (Just & Carpenter, 1976). Josephson (2005) noted that gaze duration is linked to the assumption that “the fixation time of an item is directly proportional to the processing time” (p. 64). Finally, observation frequency refers to the number of times gaze enters into a defined AOI or how many times it is observed.

Stimuli

Two innings from game two of the 2012 World Series were recorded in high definition and employed as stimuli. Average length of the segments was 14.63 min. Commercials were removed from the stimuli. A majority of participants reported watching some of the World Series (n = 39, 70.9%), although most (n = 38, 69.1%) indicated that they did not watch this particular game.

Both segments contained numerous repetitions of four categories of information graphics: pitcher statistics, batter statistics, game scores, and on-deck information (Figure 1). Due to the unequal frequency of the types of graphics within the two innings employed as stimuli, repetitions of the AOIs were averaged for each type within each inning. As such, a mean value for each gaze metric was computed for each category of information graphic.

Figure 1.

Game play illustrating two sample areas of interest (AOI; i.e., game score and batter statistics). AOIs are drawn here for illustration and were not imposed during viewing.

Apparatus

Stimuli were presented on a 19-inch widescreen monitor with 1,280 × 1,024 pixel resolution. An Applied Science Laboratories EyeTrac 6 control unit with high-speed optics was used to record gaze throughout exposure. The system is a bright pupil eye tracker (Duchowski, 2007) and is noninvasive. A small housing located just below the participant’s monitor contains a camera along with light-emitting diodes that emit infrared light into the participant’s eye. The light is reflected from the cornea, and the system continuously monitors the θ between this reflection and the pupil center to identify point of gaze. The accuracy between the true eye position and computed measurement is better than 0.5° visual angle. Under the viewing condition described earlier, accuracy corresponds to a circle of .21 inch in radius on the monitor. Gaze was sampled at 120 Hz, and data were recorded by Gazetracker software (V9.0.8000.1000, Eyetellect), which presented the stimuli and synchronized it with gaze data.

Results

To test the hypotheses, a series of mixed-measures ANOVAs examined the differences in mean observation frequency, fixation frequency, and gaze duration as a function of AOI and individual differences. Participant assignment into low and high levels of two grouping variables (sport fanship, knowledge of fantasy sport and sport statistics) were included as between-subject predictor variables to address the hypotheses. AOI was employed as a within-subject variable with four levels (pitcher stats, better stats, game scores, and on deck).

Frequency of Observation of Information Graphics

To determine whether onset of graphics would universally elicit attention, the first hypothesis predicted no differences in observation frequency of the graphics (i.e., the mean number of times gaze entered into an AOI) as a function of individual differences. Mixed-measures ANOVAs failed to reveal differences for those high and low in the two individual characteristics, sport fanship, F(1, 53) = 1.92, p = .17, η² _p = .04; knowledge of fantasy and sport statistics, F(1, 53) = .95, p = .34, η² _p = .02. As predicted, all participants equally observed the graphics.

Sport Fanship and Attention to Information Graphics

Hyothesis 2 predicted that viewers with greater interest in sport would allocate more attention to information graphics. To test the hypothesis, a pair of mixed-measures ANOVAs were conducted that included AOI and the two levels of fanship as fixed factors. Gaze duration and fixation frequency served as dependent variables. Regarding gaze duration, results showed a difference between high and low sport fans, F(1, 53) = 5.55, p = .02, η² _p = .10. See Table 1 for descriptive statistics for all gaze measures. The pattern of results can be seen in Figure 2, which presents time spent looking at each AOI as a function of sport fanship. Those with greater interest in sport uniformly spent more time looking at the graphics compared to those with less interest. Moreover, the analysis failed to yield a significant Fanship × AOI interaction, suggesting that the effect was robust across the various graphics, F(1, 53) = 1.47, p = .22.

Table 1.

Descriptive Statistics for Gaze Data.

		Sport Fanship		Knowledge of Fantasy Sport and Sport Statistics
		Low	High	Low	High
Pitcher stats	Duration	3.54 (SD = 2.19)	5.03 (SD = 3.14)	3.62 (SD = 2.13)	5.00 (SD = 3.24)
	Fixation frequency	10.29 (SD = .52)	12.83 (SD = .87)	9.93 (SD = 6.18)	13.36 (SD = 8.06)
Batter stats	Duration	1.98 (SD = .80)	2.20 (SD = .91)	2.00 (SD = .70)	2.19 (SD = 1.02)
	Fixation frequency	5.75 (SD = 2.20)	6.07 (SD = 2.37)	5.78 (SD = 1.91)	6.04 (SD = 2.67)
Game score	Duration	2.54 (SD = 1.66)	3.46 (SD = 3.38)	2.50 (SD = 1.67)	3.55 (SD = 3.40)
	Fixation frequency	6.95 (SD = 4.22)	8.00 (SD = 6.84)	6.64 (SD = 4.28)	8.41 (SD = 6.80)
On deck	Duration	.61 (SD = .44)	.91 (SD = .53)	.65 (SD = .49)	.88 (SD = .49)
	Fixation frequency	1.86 (SD = 1.36)	2.51 (SD = 1.39)	1.91 (SD = 1.48)	2.48 (SD = 1.26)

Note. SD = standard deviation.

Figure 2.

Time spent viewing information statistics as a function of participants’ general interest in sport (i.e., sport fanship).

However, fixation frequency data failed to reveal a difference between the groups, F(1, 53) = 2.01, p = .16, η² _p = .04. Thus, results partially support the hypothesis, as those with the greatest interest in sport spent significantly longer time examining information graphics in this sport telecast, although the number of fixations upon the graphics did not differ.

Knowledge of Sport Statistics and Attention to Information Graphics

The final hypothesis (H3) proposed that viewers with greatest knowledge of fantasy sport and sport statistics would allocate more attention to information graphics. To test this hypothesis, participant assignment to the two levels of knowledge served as the grouping variable. Again, gaze duration and fixation frequency served as dependent variables. The analysis found a difference between the groups, F(1, 53) = 5.28, p = .02, η² _p = .10. Those with the greatest knowledge of fantasy sport and sport statistics spent significantly longer time looking at on-screen graphics compared to those with less interest. This effect was not dependent upon a specific AOI, as the test found no interaction between the grouping variable and the AOI, F(1, 53) = 1.50, p = .22. Although this difference was not manifested when examining the fixation frequency data, the test approached statistical significance, F(1, 53) = 3.62, p = .06, η² = .06. Thus, the hypothesis was partially supported via the measure of gaze duration.

Predicting Attention to Information Graphics

The sole research question examined which individual characteristic—sport fanship or knowledge of fantasy sport and sport statistics—better predicted attention to the information graphics. As the previous ANOVA tests failed to yield between-group differences in attention as indexed by fixation frequency, gaze duration served as the dependent variable. Furthermore, because graphic type was a repeated measure, a series of regressions were employed to examine attention to each.

A multiple regression was initially conducted to simultaneously examine the relative contribution of both individual characteristics on attention to pitcher statistics. Although that test revealed that the overall regression equation was statistically significant, F(2, 52) = 3.73, p = .03, R² = .13, neither individual characteristic served as a significant predictor of attention, suggesting a concern with multicollinearity (Berry & Feldman, 1985). As such, a series of separate regressions were conducted to independently examine each characteristic. Results of these regressions are summarized in Table 2. Regression coefficients reveal discrepancies in which characteristic—sport fanship versus knowledge of sport statistics and fantasy sports—best predicted attention to the various information graphics. For example, sport fanship better predicted time spent examining pitcher statistics (B = .51, β = .35, p = .01) and the on-deck graphic (B = .09, β = .32, p = .02). In contrast, knowledge of sport statistics and fantasy sport was a significant predictor of attention to the batter statistics (B = .13, β = .28, p = .04) while sport fanship was not (B = .05, β = .10, p = .73).

Table 2.

Summary of Regressions Predicting Attention to Information Graphics.

	Pitcher Statistics		Batter Statistics		Game Score		On-Deck Graphic
	B	β	B	β	B	β	B	β
Sport fanship	.51	.35**	.05	.10	.24	.17	.09	.32*
Knowledge of fantasy sport & sport statistics	.44	.29*	.13	.28*	.36	.24	.06	.20

** p ≤ .01. *p < .05.

Discussion

Despite the ubiquitous use of information graphics in mediated sport, examination of audience response to these graphics has escaped empirical inquiry. Perennial interest in sport statistics (Schwarz, 2004) suggests that such features of sport telecasts should be of particular relevance to spectators who report greatest interest in sport as well as knowledge of sport statistics and interest in fantasy sport. As such, these two individual differences represent a useful starting point for exploring selective attention to visual elements of mediated sport. Results show that sport fans spent significantly more time examining information graphics while viewing portions of the 2012 World Series. Although these findings are admittedly intuitive, this finding provides a seminal framework for studying differences in spectator behavior in future sport media portals.

Differences in Attention Allocation

As predicted, viewers who reported higher levels of interest in sport and sport knowledge spent significantly more time examining information graphics that were employed in the sample sport telecast. As seen in Figure 2, this difference is most apparent when examining attention to graphics containing information about pitchers in the game. For example, those reporting greater interest in sport spent roughly 2 sec longer examining these graphics. Notably, these differences were primarily manifested when considering the amount of time viewers allocated to these graphics. The other two measures of attention, the number of times the graphics were merely observed or the number of fixations concentrated on these areas, failed to yield consistent differences between classes of viewers. Regarding the number of times graphics were merely observed, the failure to find a difference was consistent with the literature suggesting that humans generally allocate attention to novel elements within the visual field (Yantis & Jonides, 1984).

The somewhat discrepant findings between the two measures of attention (i.e., fixation frequency and gaze duration) resonate somewhat with the argument that the two index slightly different phenomena. Recall that one key assumption of eye tracking is that although allocation of fixations indicates what is being observed, the length of these fixations indexes processing of these elements (Josephson, 2005). As such, the findings suggest that viewers differ more in controlled processing of these elements than mere attention to on-screen graphics. Different patterns of results regarding these measures suggest two possibilities that are not mutually exclusive. One intuitive explanation is that the difference in time spent processing information graphics results from greater interest in the information contained therein. Thus, motivational differences among viewers yielded greater processing of information with the graphic elements (e.g., Hillman et al., 2000; Potter & Keene, 2012). A second possibility is that viewers with greater interest and knowledge of sport possessed the knowledge needed to understand the information graphics and therefore spent greater time reviewing the information. However, we should note that findings related to fixation frequency could also suggest the possibility of type II error as those tests approached marginal significance. More robust effects could be exhibited in response to other stimuli or experimental contexts.

Results also demonstrated differences in which individual characteristic—sport fanship versus knowledge of fantasy sport and sport statistics—impacts attention to various types of graphics. The analysis painted a curiously inconsistent picture when examining how these two predictors impacted attention to the four types of graphics contained within the stimuli. In short, the findings illustrate that “sport fans” are not a homogenous group. Case in point, sport fanship failed to predict attention to batter graphics, whereas knowledge of fantasy sport and sport statistics did. Moreover, the opposite relationship was observed with respect to the appearance of “on-deck” graphics. In sum, even when watching the same event, fans may exhibit differences in how they watch a competition based on more granular distinctions within this population. These demonstrated differences in watching a sport telecast in a traditional fashion (i.e., television broadcast) suggest a useful framework for studying sport spectators in new media contexts.

Conclusion

The increased visual complexity of contemporary sports media along with the increased agency they permit invite questions regarding how viewers will watch content via interactive technologies. Viewer selectivity—both in terms of selective exposure and selective attention—is likely if not compulsory. Content providers are creating new ways for sport fans to watch via interactive portals that represent a new way to monetize portions of the audience who value the insights that detailed statistical information present (Manly, 2006). To revisit the earlier example, MLB’s MLB at Bat app can present game play surrounded by a bevy of additional graphic elements such as player statistics, pitch count, or information about other games (Snider, 2011). This increased viewer agency in crafting the viewing experience is characteristic of the 21st-century entertainment technology (Bryant & Love, 1996) and substantiates predictions of a truly active audience (Ruggiero, 2000).

As Raney (2013) noted, scholars must now work to test extant theory in such new technological environments, including those where choice is greatly multiplied. As previously noted, the two characteristics examined here are hardly the only variables that govern viewer selectivity in new media sports portals. For example, the disposition theory of sport fanship (Raney, 2006) has long provided a useful theoretical framework for predicting spectator response as a function of individual disposition toward competitors, with maximum euphoria resulting from seeing a loved team or athlete defeating a heated foe (Zillmann, Bryant, & Sapolsky, 1989). Moreover, the arousal and suspense generated by the closeness of competition as well as production techniques amplifies such affective responses via the theoretical mechanisms within excitation transfer (Bryant & Raney, 2000; Cummins, Wise, & Nutting, 2012). Likewise, Wann (2006) has amassed considerable body of research demonstrating how an individual’s identification with a team or athlete governs a host of responses to a competition.

However, properties of new sports media portals may limit or inhibit theoretical processes from unfolding. To again turn to time-tested tenets of affective disposition and team identification, research would suggest selective exposure to competition where a liked team is featured in hopes of seeing a victory, potentially yielding enjoyment. However, new media portals also present the opportunity to simultaneously view disliked teams in action, ostensibly in hopes of seeing them defeated, which should also yield enjoyment. Given such a scenario, it is plausible that events within the two contests could jointly act to elicit and amplify arousal, which is an empirical question. However, it is equally plausible that if a liked team was suffering defeat, viewers could choose to selectively avert gaze (i.e., selective attention) or even manipulate the viewing portal (i.e., selective exposure) to avoid witnessing the defeat as a coping mechanism in order to regulate affect. Alternately, fantasy sport participants in this scenario could alter their mode of appreciation (as well as literally alter the viewing interface) to focus on statistical performance of athletes. Even if such behavior was not manifested, the competing streams of information and visual complexity could inhibit enjoyment (e.g., Cummins, Youngblood, & Milford, 2011).

In conclusion, these scenarios illustrate and demonstrate the need for subsequent research to examine selective elements of viewer behavior in new sports media contexts, as they can circumvent well-established theories of mediated sport spectatorship. In conclusion, future research could take several forms. The idiosyncratic nature of the viewing interfaces made possible by new technology make controlled or quasi-experimental designs challenging. Thus, rich descriptive insights could be gleaned from mere observation of spectator selectivity in new media portals. Moreover, survey-based research could likewise shed insights into these phenomena by testing relationships between viewer characteristics and self-reported tendencies to consume information-rich sports content as well as interactive sports media portals. Regardless, the findings presented here provide a theoretical and empirical foundation for examining factors that differentiate sports viewers and impact selective attention.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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