It's a Numbers Game: Change in the Frequency, Type, and Presentation Form of Statistics Used in NFL Broadcasts
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International Journal of Sport Communication, 2018, 11, 482–502
https://doi.org/10.1123/ijsc.2018-0107
© 2018 Human Kinetics, Inc. ORIGINAL RESEARCH
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 (dustin.hahn@tcu.edu) is corresponding
author.
482Statistics 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 presen-
tation 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, 2018484 Hahn, VanDyke, and Cummins
asserted a distinction between sport fans and mere spectators, arguing that fans are
more involved. Vallerand et al. (2008) relate sport fanship to an obsessive passion
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 quanti-
tative 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 tele-
vised 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.
IJSC Vol. 11, No. 4, 2018Statistics in NFL Broadcasts 485
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 differ-
ences 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, 2018486 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 perfor-
mance 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 broad-
casting 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?
IJSC Vol. 11, No. 4, 2018Statistics in NFL Broadcasts 487
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 broad-
casters 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 impera-
tive 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
IJSC Vol. 11, No. 4, 2018488 Hahn, VanDyke, and Cummins
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 per-
centage, 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, 2018Statistics 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, 2018490 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, 2018Statistics 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
IJSC Vol. 11, No. 4, 2018492 Hahn, VanDyke, and Cummins
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) of references were in the first 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, 2018Statistics 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 informa-
tion, 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, 2018494 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 presenta-
tion 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, 2018Statistics 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 broad-
cast today.
The subject of these many statistical references remains steadfastly fixated on
the individual player despite, in this study, occurring during a team-sport broad-
cast. 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.
IJSC Vol. 11, No. 4, 2018496 Hahn, VanDyke, and Cummins
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 exem-
plification, 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, 2018Statistics 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 conve-
nience 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, 2018498 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 benefit of
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 perfor-
mance 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.
IJSC Vol. 11, No. 4, 2018Statistics in NFL Broadcasts 499
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