Change in the Frequency, Type, and Presentation Form of Statistics Used in NFL Broadcasts

It’s a Numbers Game: Change in the Frequency, Type, and Presentation Form of Statistics Used in NFL Broadcasts

Dustin A. Hahn, Matthew S. VanDyke, and R. Glenn Cummins Texas Christian University, USA

Although scholars have examined numerous facets of broadcast sports, limited research has explored the use of statistics in these broadcasts. Reference to statistical summaries of athlete or team performance have long been a component of sport broadcasts, and for some viewers the rise of fantasy sport has led to even greater interest in quantitative measures of athlete or team performance. To examine the presence and nature of statistical references in sport broadcasts, this study examines National Football League telecasts over time to identify changes in the frequency, type, and presentation form of statistics. Findings revealed an emphasis on individual player statistics over team statistics, as well as an increase in on-screen graphics over time. The study also revealed a simultaneous decrease in statistical references relayed orally by broadcasters. These ﬁndings illustrate the importance of statistics as a storytelling tool, as well as reﬂecting technological innovations in sports broadcasting. In addition, they suggest a possible evolution in audience consumption habits and desires.

Keywords: National Football League, player, team

The sports world has increasingly embraced the science of using a growing array of quantitative metrics to measure success on the rink, pitch, diamond, track, court, course, or ﬁeld (e.g., Belson, 2013; Bernstein, 2006; Greenberg, 2013). Even while some still question the value of such metrics (Hughes, 2013; Tuggle, 2000), quantitative measures of athlete performance are of increasing interest among many viewers (Woltman, 2014). While Farquhar and Meeds (2007) suggested that certain highly motivated sport fans in fantasy leagues are likely very interested in quantitative information related to sports, Wohn, Freeman, and Quehl (2017) identiﬁed some of these complex decision-making processes. In addition, research

Hahn is with the Dept. of Film, Television and Digital Media, Bob Schieffer College of Communication, Texas Christian University, Fort Worth, TX. VanDyke is with the Dept. of Advertising and Public Relations, College of Communication and Information Sciences, University of Alabama, Tuscaloosa, AL. Cummins is with the Dept. of Journalism and Creative Media Industries, College of Media and Communication, Texas Tech University, Lubbock, TX. Hahn ([email protected]) is corresponding author.

482 Statistics in NFL Broadcasts 483 demonstrates that these motivations can vary by age (Brown, Billings, & Ruihley, 2012) and between traditional, hybrid, and daily fantasy sport users (FSUs; Billings, Ruihley, & Yang, 2017; Weiner & Dwyer, 2017). Perhaps given the capital in these leagues, the potential impact of these fantasy venues on television viewership (Nesbit & King, 2010), and media dependency more broadly (Armﬁeld & McGuire, 2014), organizations like ESPN have sought out these fantasy sport audiences for quite some time (Tedesco, 1997). Likewise, advances in technology employed in the production of sport broadcasts have altered the onscreen presentation of sports to include information about individual players, teams, scores, and more via on-screen graphics (Nachman & Bennett, 2011). Despite the long-standing interest in sport statistics, sport communication scholars have failed to explore them in great detail. Although the content, structure, and subjects of mediated sport have been examined in a variety of studies (Lavelle, 2010; Morris & Nydahl, 1983; Sullivan, 2006; Williams, 1977), basic studies exploring the type, presentation, and form of statistics in sport broadcasts are nascent. Examination of the use of such metrics in sport broadcasts can potentially illuminate how and why producers of mediated sport might integrate such information into content. Thus, this study empirically documents the use of statistics in televised broadcasts of a popular league, the National Football League (NFL), over a 4-decade span. The purpose of this study was to investigate this previously unidentiﬁed area of sport-media research through a longitudinal content analysis of a popular American sport in order to uncover the change in frequency, type, and presentation form of statistical references made during broadcasts.

Literature Review Information as Motive for Sport Viewing The underlying assumption for the inclusion of statistical references about players or teams in broadcasts is to satisfy some audience motive or need. Fortunately, scholars have long explored the variety of reasons why viewers watch or listen to broadcast sports, and research has revealed a variety of cognitive, affective, and social motivations (Raney, 2006). For example, research has explored general motives for viewing sports (Frandsen, 2008; Gantz, 1981), differentiating motives between men and women (Gantz & Wenner, 1991), the role of personality traits (Devlin & Brown-Devlin, 2017), and sports-specific viewing motives (e.g., mixed martial arts, Cheever, 2009) or platform-specific motives (Rubenking & Lewis, 2016). One theoretical framework for exploring sport-viewing motives is uses and gratifications, which assumes audiences as motivated and goal-oriented (Katz, Blumler, & Gurevitch, 1974) and recognizes a variety of motivations (e.g., entertainment, escape, socialization). Research using this framework has demonstrated information-seeking or surveillance goals specifically associated with sport viewing (McDaniel, 2002; Tang & Cooper, 2012, 2017). Such research has consistently revealed an informational motive for some consumers, who display unique pre-, during-, and postviewing information seeking as part of their fanship (Gantz & Wenner, 1995). A parallel tradition in the sport communication literature is differentiation of various profiles of sport viewers. For example, Earnheardt and Haridakis (2008)

IJSC Vol. 11, No. 4, 2018 484 Hahn, VanDyke, and Cummins asserted a distinction between sport fans and mere spectators, arguing that fans are more involved. Vallerand et al. (2008)relatesportfanshiptoanobsessivepassion resulting“from a controlled internalization of the activity into one’s identity”(p. 1280). Although “mere observers” may still consume sport content, avid fans have more at stake in their consumption and understanding of game play, perhaps leading to greater interest in relevant sport statistics. For example, today’s sport consumer actively employs social media during sport viewing to acquire information about a competition and maintain a valued identity as an expert in the subject matter (Wang, 2013). Furthermore, the growth in participation and popularity of fantasy sport has also demonstrated how some sport spectators exhibit heightened interest in quantitative measures of athlete or team performance, where a team owner’s success depends on how well individual athletes perform in real-life competition. Thus, one hallmark of FSUs may be a distinct interest in quantitative summaries of player or team performance that service this surveillance or information motive (Billings & Ruihley, 2013; Brown et al., 2012; Farquhar & Meeds, 2007; Wohn et al., 2017). Likewise, the ability to apply this sport knowledge is a salient driver of participation in fantasy sport (Lee, Seo, & Green, 2013). It is interesting that Brown et al. (2012) noted that younger FSUs consume more sport media and have greater surveillance desires than their older (above the age of 35) FSU counterparts. Coupling these findings with evolving technological developments, it is not surprising that broadcasters might depend more on quantitative information in their storytelling (Putterman, 2017) and incorporate it into on-screen graphics (Nachman & Bennett, 2011). Billings and Ruihley (2013) recognize that some fantasy sport users, by contrast to traditional sport fans, have a greater desire to see positive individual player achievement as this aids fantasy-team success. More- over, recent research demonstrated how viewers with greatest interest in sports pay greater attention to information graphics displaying athlete performance in televised baseball (Cummins, Gong, & Kim, 2016). Additional cognitive motivations exist for many sport fans (Raney, 2006)as they seek to learn more about players and teams (Gantz, 1981; Gantz & Wenner, 1995; Wenner & Gantz, 1998), recognizing the social impacts such knowledge can have with peers (Melnick, 1993). Beyond fantasy sports, some sport viewers are motivated to consume information and learn about players and teams for economic reasons. In 2018, the Supreme Court ruled that states may legalize sports betting, and many states have begun that process (Sheetz, 2018). While estimates of gambling can be difficult, some have put the economic impact on the U.S. economy in the hundreds of billions of dollars (e.g., American Gaming Association). Indeed, many viewers are motivated to consume televised sport because of these financial investments (Gantz & Wenner, 1995; Wann, 1995; Wann, Schrader, & Wilson, 1999). Such motivations may lead to greater interest in relevant statistics related to athletes and teams in sport broadcasts. In sum, viewers have long sought information about athletes and sport teams as a means of enacting their fanship, informing their fantasy-sport participation, improving conversational fodder, and aiding their economic investments. Together, these suggest that a sizable proportion of the contemporary sport-media audience has a heightened interest in objective indicators of athlete or team performance. The question remains whether this interest in base-rate information is reflected in how sport broadcasters present competition.

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Statistics in Broadcast Sport Given the demonstrated surveillance or information-seeking motive among sport fans in general (Gantz, 1981), as well as viewers who play fantasy sport (Brown, et al., 2012; Farquhar & Meeds, 2007), it is reasonable to consider that producers and consumers would benefit from increasingly incorporating statistics into broadcasts. Broadcasters may strategically employ more frequent references to player or team performance statistics for multiple reasons. One might simply be because of the greater variety of ways to quantitatively summarize performance or the relative ease of cataloging and computing such summaries. From the fastest NFL players in a regular-season game (e.g., Leonard Fornette at 22.05 miles/hr in 2017; Next Gen Stats, n.d.) to FIP (Fielding Independent Pitching) and WRC+ (Weighted Runs Created Plus) in Major League Baseball, sport consumers have more statistics at their fingertips than ever before. Todd Kalas, a broadcaster for the Houston Astros, noted, “We’re not going to replace ERA with FIP or batting average with WRC+. We’re just offering a different way to look at things” (Putterman, 2017). Quantitative summaries of player or team performance may be of unique interest to the most fervent fans with informational viewing motives or FSUs who value quantitative summaries of performance in order to make informed judgments about their own teams. For example, a variety of platforms have sought to capitalize on this interest by providing interactive apps or widgets that provide real-time data on player performance (Seifert, 2015). Although summarization and compilation of these statistics in online repositories may be important for some viewers who wish to evaluate or acquire information about athletes between competitions, some might yearn for these statistics during competition. As such, broadcasters may work to integrate novel quantitative measures into actual telecasts. Technological innovation has created the infrastructure needed to store and locate new analytics and archival data at a moment’s notice. Along with the growth in broadcast quality from standard to high definition (HD) and now ultra-HD, improvements in quality and ease of implementation have been made in on-screen motion graphics. Scholarly research on production features such as information graphics in sport has examined them in the context of other areas of investigation (e.g., advertising with graphics in college athletics, McAllister, 2008; and differences in coverage of men’s vs. women’s sports, Hallmark, & Armstrong, 1999; Tuggle, 1997), and limited research has examined the use of graphics as the central aspect of investigation. For example, Mullen and Mazzocco (2000) examined Super Bowl broadcasts spanning 4 decades to document changes in the production elements and structural aspects of the broadcasts. One of their findings was an increase in the use of on-screen graphics over time, which they partially attributed to technological innovation in broadcast sport. Visual presentation of quantitative measures of athlete or team performance is not the only mode of transmitting such information, and references to quantitative summaries of athlete or team performance are also are integral part of how sport commentators describe competition. George Blum, color commentator for the Houston Astros, underscored the importance statistics play in contemporary sport broadcasting: “When I realized I might have a career [on TV], I started paying more

IJSC Vol. 11, No. 4, 2018 486 Hahn, VanDyke, and Cummins attention to the numbers,” (Putterman, 2017, para. 2). Thus, oral presentation of such information is also a valuable means of providing this type of information to the audience. As such, exploration of how statistical references are presented in sport broadcasts should acknowledge this distinction, because it can reflect the work of separate production personnel (e.g., on-air personalities vs. graphics operators). These performance statistics presented in sport broadcasts can focus on the individual athletes or teams. Research examining the nature of sport commentary and the narratives surrounding competition illuminate this subject. Scholars have noted that the production of televised sport often emphasizes individual athletes as a means of crafting narrative surrounding competition (Clarke & Clarke, 1982; Whannel, 1992). For example, Clarke and Clarke assert that one means of generating interest in mediated sport is through emphasis on the individual athlete. Framing individual athletes through differences in gender (Emmons & Mocarski, 2014), race (Schmidt & Coe, 2014), and disabilities (Tanner, Green, & Burns, 2011) in addition to unique measures of athlete performance is a means of achieving this emphasis. Empirical examination of broadcast sport reflects this tension between emphasis on the team versus athlete. For example, Williams (1977) noted themes through commentary for teams as a whole during NFL broadcasts in 1975, but he also noted themes developed around individual players. Furthermore, shot selec- tion tended to emphasize the individual players, as well. Thus, the study of quantitative measures of performance in sport broadcasts should likewise reflect this distinction between focus on the team as a whole and focus on individual players. Thus, while announcers are paying more attention to the numbers and often emphasize narratives for teams and individual athletes, little is known about the present or historical implementation of this base-rate information used to describe both in sport broadcasts.

Research Questions To explore the use and nature of statistical or quantitative measures of performance in sport broadcasts, this study takes a longitudinal approach to the use of statistics in broadcast games in the NFL (e.g., Mullen & Mazzocco, 2000). For the purposes of this study, statistics are conceptually deﬁned as being numerical references that would require at least some effort to calculate. A deeper understanding of the presence and direction of base-rate data in sports broadcasting is necessary given the many modern motivations for such information (Billings & Ruihley, 2013; Brown et al., 2012; Farquhar & Meeds, 2007; Gantz & Wenner, 1995; Lee et al., 2013; Melnick, 1993; Raney, 2006; Wann, 1995; Wann et al., 1999), their narrative function in modern sport productions (Putterman, 2017), and simultaneous changes in technology (Nachman & Bennett, 2011). It is important, then, to consider ﬁrst how often, to what extent, and in what way sport media use statistics in their broadcasts. RQ1: Is there a longitudinal change in frequency of use of statistical information in NFL broadcasts?

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In addition to more macroscopic change over decades of television broadcasts, changes in the more microscopic structure of sport broadcasts may also be evident. For example, Williams (1977) suggested in his content analysis of six NFL broadcasts that quarters differed from one another in certain structural aspects. In order to test this assumption empirically in this content analysis, the following research question was posed. RQ2: Are there changes in frequency of use of statistical information throughout a game in NFL broadcasts? In addition to changes in the use of statistical references, the different ways that sport statistics can be presented in a sport broadcast also merit examination. As previously noted, commentators have recognized the need to embrace novel sport statistics in their coverage of competition (e.g., Putterman, 2017). However, the study of overt visual structural elements of sport broadcasts has long been a useful way to examine sport media (e.g., Mullen & Mazzocco, 2000; Williams, 1977). Thus, comprehensive empirical analysis of sport statistics must examine the presentation of this information in both aural and visual form. RQ3: Is there a difference in the frequency of aural versus visual references to statistical information in NFL broadcasts? Williams (1977) found more signiﬁcant emphasis on individuals in the structure of sport broadcasts through many close-up shots in the game, sideline shots of players, replays, and on-screen graphics. Furthermore, given the greater attention to individual players in fantasy sport and the way fantasy sport (Nesbit & King, 2010) can affect televised sports, the following research question is offered. RQ4: In terms of statistical references used in NFL broadcasts, will there be an emphasis on individual athletes or teams? Finally, given advances in television technology, it is important for broadcasters and scholars to better understand the structure of these broadcasts. Given broadcasters’ dependence on this information for storytelling (Putterman, 2017) and audiences’ informational motivations (Farquhar & Meeds, 2007; Katz et al., 1974), understanding the evolution of information relayed to audiences is imperative as a foundation for future studies examining the impact of these various structural changes. RQ5a: Have aural references changed in frequency of use over time? RQ5b: Have visual references changed in frequency of use over time? RQ6: What are the relationships between variables of time (longitudinally), quarter of play, type of reference, and presentation form?

Method To address these research questions, a content analysis examining NFL broadcasts sampled from more than 4 decades was conducted. Coders reviewed more than

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50 hours of archival game broadcasts to tabulate the frequencies of quantitative references, as well as noting the nature of these references by type (i.e., individual player, team, or other), by presentation form (i.e., aural, visual, or both), and by identifying when the reference occurred in the game and in what time epoch in NFL history.

Unitizing and Levels of Analysis Data were examined primarily at the level of individual statistical reference. Thus, recognition of what constitutes a statistical reference (i.e., unitizing) was a crucial step in establishing the validity of study findings, as inclusion of each and every reference to some quantitative phenomenon (e.g., player numbers, quarter of play) would provide an inflated sense of the frequency with which statistical indicators are employed. Instead, a more conservative definition was adopted. Statistical references were conceptually defined as references to quantitative measures of game events or player or team performance that required at least a minimal threshold of effort in counting or averaging. Operationally, statistical references were defined as any reference to a quantitative metric summarizing the past or present frequency of a phenomenon (e.g., number of injured players in a given season, total yards gained that season, etc.); any reference to a quantitative metric describing averages for teams, players, or others (e.g., average number of points per game, average yards completed per game that season, etc.); or any reference to a quantitative metric describing proportions or percentages (e.g., percent of touchdown conversions “in the red zone” that season, percent of catches per attempt, etc.). Finally, references were coded if they involved any quantitative metric describing ranks or relative comparisons (e.g., number three in the league in total rushing yards, having played in the league 4 years, or sixth- longest kick of all time). To further ensure a conservative record of statistical references, only one reference was unitized when numerous quantitative metrics were offered for a single subject (i.e., team or athlete) at once. Thus, an emphasis was placed on the number of subjects of statistical references, and codes only changed when subject of broadcaster commentary or on-screen graphics changed. For example, an announcer could mention a quarterback’s “overall success” and provide numerous metrics as evidence (e.g., touchdown-to-interception ratio, pass-completion percentage, and total yards thrown that season), all of which composed a single statistical reference in this analysis. For each unit of analysis, a single subject of one or more statistical references, coders identified the reference number, quarter of play, time within the broadcast, subject type, and presentation form. Coders then included relevant descriptions to aid in resolving disagreements during coder training. Once this was completed, the data collected from these 1,661 references were imported into SPSS for analysis.

Coding Scheme For each statistical reference, multiple variables were coded. Nominal-level reference information was collected including ID number and time in the video ﬁle to aid in intercoder reliability testing and quarter of play, time epoch, type, and

IJSC Vol. 11, No. 4, 2018 Statistics in NFL Broadcasts 489 presentation form of each statistical reference in order to address the research questions. Type of reference was a nominal-level variable to record the subject matter of the reference, individual player, team, or other. References to individual athletes were deﬁned as quantitative summaries of player performance, either in the current competition or over a longer time (e.g., season, career, etc.). Team references provided summaries of quantitative information relative to the entire team or a particular unit within the team (e.g., offense, defense, etc.). “Other” references were presentations of quantitative metrics that might relate to more global entities such as conferences, multiple games, or groups of players not on a single team. Presentation form was also a nominal-level measure designed to capture how the reference occurred within the broadcast, aurally, visually, or both. Oral references were those where commentators made a statement containing some quantitative information related to game play or player performance without an accompanying visual aid. Visual references were those where some on-screen graphic was presented containing quantitative information related to game play, team, or individual athlete performance but where commentators made no explicit mention of the visual element. References coded as both, then, were instances where on-screen presentation of quantitative information was accompanied by commentator review or explanation of the graphic, as long as at least one (aural or visual reference) included statistical information.

Population and Sampling The population of interest in this study is all televised broadcasts of the NFL since the merger of the AFL and the NFL in 1970. Given the explicit interest in changes in references over time, the sample must necessarily include broadcasts spanning the over-4-decade history of the league. Given the explicit challenges of obtaining such complete game-broadcast footage through random sample of the population, as no complete population sample was available, the convenience sample was obtained from two sources. To analyze older broadcasts in the sample, recordings were obtained from DVDs featuring the top 10 games. One set of original broadcasts featured the Dallas Cowboys, and the other set featured the Philadelphia Eagles. Together, the 20 games in this collection spanned from 1972 to 2008 and contained the complete, unedited television broadcasts. To capture more recent games in the sample, broadcasts were obtained through NFL’s Game Pass, which archives games from the past 5 years. To ensure a balance in the longitudinal nature of the sample, games were stratified into four time epochs. This allowed for greatest range in epochs used for analysis. Beginning with the first sample set, the two DVD collections, the 20 games were organized chronologically. The study included three time epochs separated as much as possible from one another. Thus there remained a sample of four games ranging from the 1972 season to the 1978 season grouped into the first epoch, the middle four games from the 1992 season to January 1994 grouped into the second epoch, and the last four games ranging from the 2003 season to January of 2008 grouped into the third epoch. The fourth epoch was collected via a random sampling of games from each of the 2013–2016 seasons. Thus the sample would

IJSC Vol. 11, No. 4, 2018 490 Hahn, VanDyke, and Cummins include four games from four epochs of study and span from 1972 to 2016 (see Table 1).

Coder Training and Reliability Assessment Procedures for coder training and reliability assessment were adapted from guidelines for content analysis (Lombard, Snyder-Duch, & Bracken, 2002). Games were independently coded by the first two authors. Coders refined the coding scheme through three rounds of training and reliability checks using a nonsampled game with a high rate of incidence of the basic phenomenon of interest (Neuendorf, 2001). Coding decisions were subsequently reviewed and disagreements resolved through discussion and clarification of operational definitions of terms used in the coding scheme. Two additional rounds of training and pilot coding were conducted before formal coding of the study sample. The study’s lead author coded the entire study sample, and the second author independently coded eight quarters, or 10% of the study sample, for calculation of final intercoder reliability (Lombard et al., 2002; Wimmer & Dominick, 2003). Eight quarters were randomly selected from the sample. At least one quarter fell into each time epoch. Across the commonly coded materials, 226 units or statistical references were commonly coded. Krippendorff’s alpha was used as the preferred reliability statistic, as it corrects for chance agreement and can accommodate both ratio- and nominal- level data (Hayes & Krippendorff, 2007). The tests yielded high intercoder reliability for both type of reference (95.6% agreement, α = .89) and presentation

Table 1 National Football League Broadcasts Analyzed Game title Teams Date of game Super Bowl VI Cowboys vs. Dolphins January 16, 1972 The Hail Mary Game Cowboys vs. Vikings December 28, 1975 Super Bowl XII Cowboys vs. Broncos January 15, 1978 Miracle at the Meadowlands Eagles vs. Giants November 19, 1978 1992 NFC Championship Game Cowboys vs. 49ers January 17, 1993 Super Bowl XXVII Cowboys vs. Bills January 31, 1993 1993 NFC Championship Game Cowboys vs. 49ers January 23, 1994 Super Bowl XXVIII Cowboys vs. Bills January 30, 1994 4th and 26 Eagles vs. Packers January 11, 2004 2004 NFC Championship Eagles vs. Vikings January 16, 2005 TO’s First Game Back Eagles vs. Cowboys October 8, 2006 Perfect Storm Eagles vs. Cowboys December 28, 2008 2013 season, Week 16 Titans vs. Jaguars December 22, 2013 2014 season, Week 11 Lions vs. Cardinals November 16, 2014 2015 season, Week 9 Browns vs. Bengals November 5, 2015 2016 season, Week 10 Rams vs. Jets November 13, 2016

IJSC Vol. 11, No. 4, 2018 Statistics in NFL Broadcasts 491 form (92.5% agreement, α = .81). After the objectivity of the coding scheme was established, subsequent analysis used the lead author’s coded data to answer the proposed research questions.

Results A total of 1,661 units were coded across the entire study sample (see Table 2). With respect to type of reference, the majority of references were to individual athletes (56.5%), and the largest proportion were aural references (40.9%). In terms of simple frequency, the use of statistical references in sport broadcasts increased from the ﬁrst time epoch to the second and third and from the second and third to the fourth epoch (43.6%). The distribution of references was generally balanced across the four quarters of game play, although more in the second quarter (28.8%) than the ﬁrst, third, and fourth quarters.

Table 2 Frequency of Statistical References by Epoch, Quarter, Type, and Presentation Type: Presentation: player/team/other aural/visual/both Total Epoch 1 Quarter 1 38/19/1 39/4/15 Quarter 2 42/17/4 46/8/9 Quarter 3 27/26/0 38/6/9 Quarter 4 21/19/0 28/6/6 Epoch 2 Quarter 1 50/28/6 30/27/27 Quarter 2 62/39/2 42/22/39 Quarter 3 60/21/1 27/16/49 Quarter 4 41/27/3 35/11/25 Epoch 3 Quarter 1 69/39/7 26/42/47 Quarter 2 48/55/1 27/45/32 Quarter 3 36/21/1 14/20/24 Quarter 4 45/34/6 29/23/33 Epoch 4 Quarter 1 86/61/11 61/48/49 Quarter 2 97/108/4 80/79/50 Quarter 3 114/70/3 88/49/50 Quarter 4 83/83/5 69/46/56 Total 939/667/55 679/458/530 1,661

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Frequency of Statistical References Over Time RQ1 asked, “Is there a longitudinal change in frequency of use of statistical information in NFL broadcasts?” A chi-square goodness-of-fit test was conducted to compare the frequency of time periods in which the references fell. The chi- square goodness-of-fit test examines whether the observed frequency distribution for a variable differs from a hypothesized frequency distribution (Field, 2017). For this test, the null hypothesis predicted an even or equal distribution of the frequency of references across time epochs. The null hypothesis was rejected, indicating that the observed frequencies were not evenly distributed. Instead, statistical references were greatest in the most current time epoch. Thus the test revealed a significant difference in the frequency of references across epochs, χ2(3, N = 1,661) = 342.77, p < .001. Examination of frequencies revealed that 12.9% (n =214)ofreferenceswereinthefirst epoch. However, the number of references increased in the second (n = 360; 21.7%) and third epochs (n = 362; 21.8%), and again in the fourth epoch (n = 725; 43.6%; see Figure 1). Thus, the data indicate an increase from the first to second time period while the frequency of observations remained unchanged from the second to third time period. Yet the frequency of references doubled from the second and third time periods to the fourth. RQ2 asked, “Are there changes in frequency of use of statistical information throughout a game in NFL broadcasts?” A chi-square goodness-of-fit test was again conducted, this time examining the frequency of references across the four

Figure 1 — Number of references by time epoch.

IJSC Vol. 11, No. 4, 2018 Statistics in NFL Broadcasts 493 quarters of a game. Analysis revealed a signiﬁcant difference in the number of references by quarter, χ2(3, N = 1,661) = 14.85, p = .002. As Table 3 indicates, more statistical references were observed in the second quarter (n = 479; 28.8%) than the ﬁrst (n = 415; 25.0%), third (n = 390; 23.5%), and fourth quarters (n = 377; 22.7%).

Presentation Format of Statistical References Given the relative simplicity of aural references to quantitative information when compared to on-screen graphics, RQ3 explored differences in the frequency of aural versus visual references to statistical information in NFL broadcasts. To answer this question, a chi-square goodness of fit test was conducted. Analysis revealed a significant difference in the form of presentation of statistical information, χ2(2, N = 1661) = 48.05, p < .001. Aural references (n = 679; 40.9%) were most frequent, whereas visual references (n = 452; 27.2%) were least frequent. Aural and visual references presented together (n = 530) accounted for 31.9% of the references. RQ4 asked, “Will there be an emphasis on individual athletes or teams?” To address this question, a chi-square goodness-of-fit test was conducted on the frequency of references by type (individual, team, or other). Analysis revealed a significant difference in the type of presentation of statistical information, χ2(2, N = 1,661) = 1178.08, p < .001. Indeed, individual player statistics (n = 1,160; 69.8%) far outnumbered team statistics (n = 475; 28.6%), with other references only accounting for 1.57% (n = 26) of statistical references. Next, RQ5 questioned whether the frequency of aural or visual statistical references changed over time. To determine the presence of such changes, a chi- square test of association was conducted to examine the relationship between presentation form and epoch. Analysis revealed a significant relationship, χ2(6, N = 1,661) = 133.71, p < .001, ϕ = 0.28. Examination of the frequencies revealed that aural references (as a percentage of the total number of references) decreased over the first three time periods and then rose slightly in Period 4. In contrast, the frequency of visual references (as a percentage of the total number of references) increased across the first three time periods and then remained constant (see Table 4). Finally, RQ6 queried the remaining relationships between variables of time, quarter, type of reference, and presentation form. A chi-square test of association

Table 3 Frequency of Statistical References in National Football League Broadcasts by Quarter Number of Percentage in quarter Quarter references of broadcast 1 415 25.98% 2 479 28.84% 3 390 23.48% 4 377 22.70%

IJSC Vol. 11, No. 4, 2018 494 Hahn, VanDyke, and Cummins identified a significant relationship between epoch and type of reference, χ2(6, N = 1,661) = 18.18, p = .006, ϕ = 0.11, such that there has been a slight decline in individual player statistics (Epoch 1, n = 128, 59.8%; Epoch 2, n = 235, 64.7%; Epoch 3, n = 198, 54.7%; Epoch 4, n = 380, 52.4%) and a slight rise in team statistics (Epoch 1, n = 81, 37.9%; Epoch 2, n = 115, 31.9%; Epoch 3, n = 149, 41.2%; Epoch 4, n = 322, 44.4%) since the early 1990s (see Table 5). Still, individual player statistics remain the most common type of statistical reference in NFL broadcasts. There was a significant relationship between type of reference and presentation form, χ2(4, N = 1,661) = 18.18, p < .001, ϕ = 0.18, such that individual player statistics are somewhat more likely to accompany on-screen references with aural comments (both forms; 37.4%) than presentations of team statistics are likely to accompany on-screen references with aural comments (both forms; 24.1%). No other significant relationships worthy of noting were identified.

Discussion Scholarly research has long been interested in the ways that information is presented in the media. Given the clear salience of statistics in NFL broadcasts

Table 4 Frequency of Presentation Form as a Function of Time Epoch Time Period Form of First Second Third Fourth reference epoch epoch epoch epoch Aural 151a 134b 96c 298b Visual 24a 76b 130c 222c Both 39a 150b 136b 205c Note. Each superscript denotes a subset of time-period categories whose column proportions do not differ signiﬁcantly from each other at the .05 level.

Table 5 Frequency of Type of Reference as a Function of Time Epoch Time Period Type of First Second Third Fourth reference epoch epoch epoch epoch Individual 128a,b 233b 198a 380a Team 81a,b 115b 149a,b 322a Other 5a 12a 15a 23a Note. Each superscript denotes a subset of time period whose column proportions do not differ signiﬁcantly from each other at the .05 level.

IJSC Vol. 11, No. 4, 2018 Statistics in NFL Broadcasts 495 and reporter awareness of the importance of statistics in sport broadcasts (e.g., Putterman, 2017), an initial study examining their presence was overdue. This content analysis is the first step toward a better understanding of this popular form of communicating information in sport media. Even operating with a conservative definition of what “statistics” would be included in this study, a total of 1,661 references were empirically identified through this content analysis of 16 NFL broadcast games covering a span of 44 football seasons, an average of just over 100 statistical references per game. By any measure, this is a lot of statistics in a broadcast that, on average, lasts just under 2 hours. In addition to being a conservative definition of a statistical reference, some references that were coded included references with many statistics. For example, a single team statistical reference could include many statistics about that team. Not only are there a great many statistical references in NFL broadcasts, but there has also been a significant rise in these references from 53.5 references per game in the early 1970s to 181.25 in recent years. Indeed, the number of statistics presented in an NFL broadcast has doubled since the early 2000s. Sport statistics can be expected to appear roughly 1.5 times per minute during an NFL broadcast today. The subject of these many statistical references remains steadfastly fixated on the individual player despite, in this study, occurring during a team-sport broadcast. Finally, these longitudinal changes brought to light that while aural statistics with their relative simplicity are still most prevalent, on-screen visual references are increasing today. These results beg three questions. First, why is there such an emphasis on quantitative information in a sport broadcast? Second, why is there an emphasis on individual players? And third, why are on-screen statistics increasing while aural references have mostly decreased? These trends may be the combined result of fulfilling audience preferences in conjunction with technological evolution used in the presentation of broadcast sports. Regarding the first question about the emphasis on quantitative information in sport broadcasts, the answer is quite simple. Both broadcasters and the source they represent have recognized benefits of quantitative information in broadcasts. Broadcasters see it as another storytelling tool (Putterman, 2017) that provides one more way of portraying a televised event, and research has suggested the credibility positively associated with quantitative information. Furthermore, there may be an increasing emphasis in statistics in sport broadcasts because of sports viewers’ desire to consume this type of information. Gambling, fantasy sports, and even social motivations (Melnick, 1993) are likely contributing to growing desires for base-rate information in sport broadcasts today. Studies demonstrate that many viewers are driven by these economic interests (Gantz & Wenner, 1995; Wann, 1995; Wann et al., 1999), and Farquhar and Meeds (2007) suggest that sport fans, especially FSUs, are more likely to be interested in these sport statistics. Given the symbiotic relationship between fantasy-sport use and broadcast viewership (Nesbit & King, 2010; Randle & Nyland, 2008), it is reasonable that the increased emphasis on statistics in sport broadcasting, especially in recent years, is due to the continued rise of fantasy sports and gambling.

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Rising fantasy-sport use is perhaps the simplest answer to the second question regarding the rise in both aural and visual statistical references to individual players. Given the dependence on individual athlete performance, it stands to reason that these participants would be more inclined to seek such pertinent base- rate information. One last question remains regarding the increase of on-screen statistics in these broadcasts. Mullen and Mazzocco (2000) offer an explanation for the increase of sport statistics in broadcasts through their assertion that technological innovations have led to increased presentation of on-screen graphics. The easier these graphics are to create, the more likely, it would seem, that they are to appear in these broadcasts, especially considering their potential beneﬁt to credibility (Koetsenruijter, 2011) and as an alternative storytelling tool (Putterman, 2017). The utility of these statistics for fantasy-sport use and general sport fanship can also be seen through fans’ general information-seeking behaviors (Gantz & Wenner, 1995).

Implications Findings from this study have important implications for sport producers today. Lueng (2017) notes that broadcasters are exploring novel measures of athlete performance to cater to spectators’ interests. As the NFL and sport-broadcast producers continue to gauge fan interests, “learning what fans want” (para. 12) and working to “tighten up that game presentation” (para. 13), it is conceivable that use of on-screen statistics will continue to increase. While this content analysis has demonstrated past and present trends, producers should use these trends to further their inquiries into fan audience desires. This study thoroughly demonstrates the ubiquity of individual and, to a lesser extent, team statistics, but investigation into fan enjoyment and utility of such information is lacking. Although the focus of this study is on statistical representations of athlete performance in sport broadcasts, scholars outside the field of sport communication have begun to examine novel quantitative measures of athlete performance but from different perspectives (e.g., legality, monetization, and ownership of real-time data gleaned from athletes; Gale, 2016a, 2016b; Rodenberg, Holden, & Brown, 2015). Thus, this examination of precisely how those statistics are presented may be of wider interest beyond the field of sport communication. While past research has recognized the effects of exemplars, particularly misrepresentative ones, in media (Brosius & Bathelt, 1994; Zillmann, 1999; Zillmann & Brosius, 2000) and has been used to test fanship in sport broadcasting (Hahn & Cummins, 2018), extant research identifying the presence and portrayal of base-rate information in media is largely lacking. Thus, although this content analysis does not alter understanding of exemplification theory, it demonstrates an increased emphasis in sport broadcasting on an understudied property of exemplification, namely, base-rate information. Precious little is known about desires for and interests in base-rate information given its oft-considered “pallid” appeal (Aust & Zillmann, 1996; Callison, Gibson, & Zillmann, 2012; Zillmann, Gibson, Sundar, & Perkins, 1996), yet this study introduces a counterintuitive finding in that presentation of base-rate information is increasing, serving as the foundation for future research investigating the

IJSC Vol. 11, No. 4, 2018 Statistics in NFL Broadcasts 497 phenomenon. Thus, while prior research suggests that sport fans, FSUs, and gamblers may be more interested in relevant statistics, research is lacking to empirically identify individual differences in consumption habits of such base-rate information by these audience groups. Findings that such forms of communication are increasing in sport broadcasting should lead researchers to investigate the phenomenon further.

Theoretical Explanations Uses-and-gratifications theory (Katz et al., 1974) has provided a useful framework for understanding why audiences are motivated to consume these statistics in broadcasts. Given its interactive elements, FSUs fulfill the active audience assumption. With the introduction and growth of fantasy sports, both individual player (e.g., quarterback, wide receiver, running back) and team (e.g., a team’s defense) statistics have become more important to the modern sport-television viewer. It seems rational then that producers would benefit from increasing the use of statistics in their broadcasts, as other studies have demonstrated that certain audience members are interested in these data (Farquhar & Meeds, 2007). Although no studies have specifically examined the frequency, type, and presentation form of statistics in television presentation of the NFL, others have indeed identified structural differences in sport broadcasting (e.g., Mullen & Mazzocco, 2000; Williams, 1977). Whereas Williams noted stylistic differences across networks, this piece adds to literature to demonstrate changes within an individual broadcast. Thus, this formative study on the use of quantitative information has identified the ample use of and change in the frequencies and types of statistical references over the lifetime of the NFL. This research has been the first to empirically identify the presence and change in statistics used, as a completely new area to be examined in the field of structural analysis of sport media, and it should draw attention to and awareness of the inclusion and change in statistical references.

Limitations and Future Studies Despite the breadth of coverage examined in this content analysis, the sample of NFL broadcasts employed here does present limitations. Specifically, the convenience sample used for the first three time epochs included many postseason games, maintained minimal variation in teams (one team in the first three time epochs was either the Dallas Cowboys or Philadelphia Eagles), and did not control for network. However, the latest epoch consisted of a random sample of recent broadcasts and yielded similar patterns in proportions of type and presentation forms, which suggests that the nature of the sample for older games is not a threat to the validity of findings in regard to statistical references in postseason play or as a function of specific teams or network broadcast. Although the explicit purpose of this paper was to measure the frequency and nature of statistical references in sport broadcasts, other methods could also provide valuable insight on how audiences interpret this information. For example, other methods such as in-depth interviews with program producers or detailed case studies would also yield insights that could further enhance findings from this

IJSC Vol. 11, No. 4, 2018 498 Hahn, VanDyke, and Cummins study (e.g., Gruneau, 1989). The current study also operationally defined a statistic conservatively (certain base-rate information like time on the clock, player numbers, etc. was not included) and counted observations based on subject rather than individual statistic. As mentioned previously, many references included a number of statistics about an individual player or team but were sometimes too convoluted or simply too complex to effectively break into relevant pieces for meaningful analysis. Nonetheless, future studies could learn from such decisions in continued analysis and eventual experimentation with the presence and impact of statistics in sport broadcasting. Now that this study has laid the foundation for the use of statistics in the NFL, it would be highly beneficial to both academics and sport-media practitioners to understand the impacts of this type of content in game broadcasts. One benefitof the strategic integration of quantitative measures of team or athlete performance may be enhanced perceptions of the credibility of sport broadcasters. Use of quantitative information in news reporting and coverage has been linked to increases in perceptions of credibility in other contexts (Koetsenruijter, 2011). Past research has demonstrated how personal experience as an athlete leads to enhanced perceptions of sportscaster credibility (Keene & Cummins, 2009). Likewise, strategic integration of references to quantitative measures of performance by sport broadcasters could enhance their status as a trusted source of sport information. This is clearly a topic for empirical examination. Next, the sport-media landscape is vast one, and the NFL, while clearly an influential one in the United States, is far from the only sport league worth investigating. Major League Baseball (MLB), the National Basketball Association (NBA), and the Fédération Internationale de Football Association (FIFA) could all offer valuable information and help provide a more complete picture of the use and impacts of including statistical references in these sports’ broadcasts. Additional study of other sports might reveal nuances in the presentation of quantitative measures of athlete performance. Finally, with billions of dollars spent on gambling each year (e.g., American Gaming Association) in conjunction with recent legislation allowing states auton- omy to permit gambling (Sheetz, 2018), it is worth considering how the future of sport broadcasts will continue to evolve in terms of their presentation of base-rate information. Given the speed of current technological innovations, the investment in sports by sport fans worldwide, and the introduction and growth of fantasy sports, there is reason to believe that the statistical information included in these broadcasts is not likely to go away or diminish. Perhaps as sport fans become increasingly comfortable in deciphering and retaining these statistics, the inclusion of these references will increase. For now, we have empirically identified the salience of statistics in sport broadcasts and gained an understanding as to why this might be the case. Furthermore, we understand the continuous emphasis on the individual athlete through statistical references in these broadcasts and that shifts have occurred in the presentation form of these references. Thus, while this initial study yielded many significant findings, it is imperative that future research continue to identify the characteristics of these references in sport broadcasts, as well as seek to explain this phenomenon through interviews or other qualitative approaches with sport-media content producers.

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