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DECISION SCIENCES INSTITUTE Exploring Leadership in Services: a Social Network Analysis of NFL Coaches

DECISION SCIENCES INSTITUTE Exploring Leadership in Services: a Social Network Analysis of NFL Coaches

Behara et al. Social Network Analysis of NFL Coaches

DECISION SCIENCES INSTITUTE Exploring Leadership in Services: A Social Network Analysis of NFL Coaches

(Full Paper Submission)

Ravi S. Behara Florida Atlantic University [email protected]

Preston J. Huang Spanish River Community High School [email protected]

C. Derrick Huang Florida Atlantic University [email protected]

ABSTRACT

This study examines coaching leadership in the NFL from a social network perspective. Specifically, the role of coaches is explored based on team coaching staff networks across all NFL teams. Utilizing coaching staff data from all NFL teams for the period of 2000-2013, we find the eigenvector centrality as a useful measure to identify influential coaches. In addition, contrary to the general belief of the importance of degree centrality, we find that this network measure is more of an indicator of “journeyman” than “winning” coach. We further explore the networks of successful coaches and identify different “schools” of coaching.

KEYWORDS: Social network analysis, Centrality measures, Leadership, , Coaching

INTRODUCTION

The National Football League (NFL) is the largest professional sports league in the U.S., generating annual revenues of more than $9 billion. The value of a NFL team ranges from just below $1 billion to over $2.3 billion (http://www.forbes.com/nfl-valuations/list/). Its total operating profits and franchise valuation of the NFL with 111 million fans are twice as much as those of the MLB, the next largest North American sports league (Quinn, 2012). Based on revenues, the NFL would make the Fortune 300 list: it is half the size of the pharmaceutical giant Bristol-Myers Squibb and about a tenth the size of Citi Group, Microsoft, or Boeing. Its profits are about the same as New York Life Insurance.

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Criteria Value Year/Season Source Estimated Size of the Entire Sports Industry, U.S. $ 470 B 2013 PRE NFL League Revenue $ 8.8 B 2011/12 Forbes Overall Operating Profit $ 1.3 B 2011/12 Forbes Number of NFL Teams 32 Teams 2011/12 NFL Average NFL Game Attendance (16 Game 67,579 2011/12 ESPN Season) Spectators Average NFL Team Value $1.11 B 2012 Forbes

Table 1: Overview of the National Football League (source: hwww.plunkettresearch.com)

In addition to players, coaches play a critical role in the performance of an NFL team. In this study, we analyze the social networks of NFL coaches, based on a data set containing all coaching staff of all NFL teams from 2000-2013, focusing on the following three aspects:  The characteristics of the coach network: Based on the overall network measures, can we identify the coach network with any of the theoretical networks?  Centrality of coaches: Does any of the centrality measures prove useful in predicting the “influential” coaches?  Comparison of long-tenured coaches: When we compared the networks of individual long-tenured coaches, can we identify characteristics that make them successful?

We begin by reviewing the pertinent literature of social network analysis and its applications. We then briefly discuss the data set used in this study and develop the characteristics of the NFL coach network. Then various centrality measures are calculated and their implications discussed, followed by the development and comparison of the networks of three long serving coaches. Finally, we conclude with the implications of this study and directions for future research.

LITERATURE REVIEW

Network analysis

A graphical representation of even a moderately sized social network can quickly become incomprehensible with many lines in the network diagram. Few, if any, useful conclusions can be drawn from scanning this type of dense network diagram showing relationships between coaching staff and NFL teams (as in this study). Such networks are better understood using a matrix and suitable network measures. The coach-team data can be represented by a rectangular i x j matrix, with i coaches and j teams. In this matrix a 1 represents an association of a specific coaching staff with a specific team, and a 0 represents no relationship. From this, we can derive a square i x i matrix of coaches, wherein a 1 represents a working relationship among coaches.

The most important characteristic of social network data is the existence of relations among the entities (Wasserman and Faust, 1994). This has implications for the unit of modeling and analysis. For example, the network modeling unit can be constructed as the individual entity or node (e.g., a coach), pair of nodes or dyads, or the entire network of nodes. Social network analysis measures can be used to identify “important” or “prominent” nodes in the network. The importance or prominence of nodes is usually defined as a measure of their location in the

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network. Measures used for this study include total degree (also referred to as degree), closeness, betweenness, and eigenvector centrality.

Empirical research shows network centrality as affecting the node’s influence (Fombrun, 1983; Brass, 1984; Ronchetto et al., 1989). Measures of total degree centrality and betweenness centrality are extensively used in assessing the prominence of nodes in networks (Freeman, 1979; Faust, 1997). Individuals or organizations with high betweenness centrality are potentially influential, positioned to broker connections between groups, bear the influence of one group on another, and/or serve as a gatekeeper between groups to control access and information flow. Career success and influence of individuals in organizations have been shown to tie to the structure of that individual’s social networks (Brass, 1995; Ibarra, 1993; Lin and Huang, 2005). Specifically, Burt (1992; 1997) finds that individuals who have social networks with more structural holes are promoted faster and obtain higher levels of pay. In a complete network, such as the one we have in the NFL coach network, Brass (1984) finds that network centrality is associated with power. Wade et al. (2011) suggest that central players in inter-organizational networks will be more likely to be promoted across organizations.

There have been a few attempts made to analyze the NFL coaching network. Fast and Jensen (2006) investigate the NFL coaching network by considering it as a complex adaptive system, and show mentoring as being impactful on the success of a coach. Wade et al. (2011) find that coaches with higher centrality are much less likely to leave the NFL and more likely to be promoted internally or externally. Vaz de Melo et al. (2012) have attempted to predict the success of teams based on team network dynamics, but apply it to the NBA and MLB. But overall, while these are some early efforts at investigating social networks in professional sports leagues, there are significant areas that have been left unaddressed. We address this gap by systematically investigating the NFL coach social network in this study, both at an entire network level as well as from the perspective of a specific node (coach). This is discussed in the remainder of this paper.

MODEL DEVELOPMENT

Data Collection and Analysis Methodology

The first step in data collection for network analysis is to define the boundary and sampling. Laumann et al. (1989) define two distinct approaches to boundary specification in social network studies: the realist approach and the nominalist approach. The realist approach is one in which the boundary and membership is as perceived by the network agents themselves, while the nominalist approach is based on the theoretical concerns of the researchers. We adopt the realist approach, since the NFL is a well-defined professional league, and the coaching staff of the 32 teams makes up the group of interest. However, we also partially adopt a nominalist approach in the sampling of the NFL seasons of interest. We selected 2000-2013 as the years of interest to include the new entrants to the league.

We collected the coaching data of all NFL teams from 2000 to 2013. During this period, one team—the —was added in the 2002 season. All key coaching positions—, assistant coach, , , etc.—are included according to teams’ reports; however, only head coach position is identified. The affiliation of the coaches with the corresponding teams cover the playing season; any coaching staff changes (hiring and firing) during the season are not reflected in the data. This is not

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considered a significant issue, as such changes are infrequent. The data are obtained via publically available sources such as teams’ websites and http://coachingroots.com/. After the data set is downloaded, we cleaned and organized the data using Excel to produce a .csv file of bi-nodal relationships of coach and team-year. The resulting sample size is 6,172. To analyze the data, we use Gephi, an open-source graph visualization tool, and ORA, a dynamic meta-network assessment and analysis tool from the Center for Computational Analysis of Social and Organizational Systems (CASOS) at Carnegie Mellon University, USA (Carley et al., 2013), to conduct network analysis. The results from Gephi and ORA are presented below.

Characteristics of NFL Coach Network

We first examine the overall network measures. The coach network from 2000 to 2013 contains 877 nodes, each representing an individual coach. An edge is formed when two coaches worked on the same team in the same year, and there are a total of 2,161 edges in the network. This node-edge combination gives the network density of 0.006 and average degree of 2.5. In addition to these simple statistics, other key measures help characterize a network. The average path length between any two nodes is 3.7, while the network diameter—the longest of all the calculated shortest paths in a network—is 6. The clustering coefficient, which measures the "all-my-friends-know-each-other" property, is 0.166. And the whole network is one weakly connected component, meaning that everyone is connected if the edges are all treated as undirected.

It is customary to use the Erdos-Renyi random graph as a benchmark for studying real-world networks. A Erdos-Renyi network, or G(N,p) model, is created by connecting N nodes randomly with other nodes in the network with a probability of p. To examine the similarities and differences between the coach network and an Erdos-Renyi random graph, we use Gephi to generate a random network with N = 877 (the number of nodes) and p = 0.006 (connecting probability same as the network density). The result is show in Table 2. The randomly generated Erdos-Renyi network is clearly much “sparser” than the coach network, with higher diameter and average path length, as well as five weakly connected components. Its clustering coefficient is orders of magnitude lower than that of the coach network, indicating that coach network has considerably more triads than the random network.

We also compare the coach network with Barabasi-Albert scale-free network, which allows for preferential attachment in network growth; that is, each new node would more likely attach to existing nodes with higher degrees than those with lower degrees. We generated such a network, using Gephi with the Generator plug-in, from an initial network of 32 nodes—the total number of NFL teams—to the final 877 nodes based on preferential attachment. The result is also shown in Table 2. We can see that, compared with the Erdos-Renyi network, the network characteristics of the Barabasi-Albert Network are a better fit with those of the coach network, albeit slightly “denser.”

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Network NFL Coach Erdos-Renyi Barabasi-Albert Measure Network Network Network Node 877 877 877 Edge 2161 2166 2187 Density 0.006 0.006 0.006 Average Degree 2.5 2.5 2.5 Diameter 6 8 5 Average Path 3.7 4.4 3.1 Length Clustering 0.17 0.008 0.31 Coefficient Weakly Connected 1 5 1 Component

Table 2: Comparison of the Coach Network to Theoretical Networks

This comparison of coach network to two theoretical networks suggests that the formation of the coach network is far more preferential than random. That is, when a new coach is entering NFL, he is more likely to work with coaches already having better connections than to randomly pick teams. Such a preference may be because the new staff recognizes the fact that connecting with highly connected coaches is better for the career. The fact that the coach network is not as dense as the Barabasi-Albert Network indicates that factors other than selecting the best connected coaches are at play when new staff coming to NFL. These factors, which may include salary, location, prestige, and so on, work to mitigate the preferential attachment effect and thus “randomize” the formation of the coach network, resulting in one that is similar to but less dense than the Barabasi-Albert Network.

RESULTS AND DICUSSION

Centrality of NFL Coaches

In graph theory, centrality measures the relative importance of a node in a graph. In social network analysis, centrality measures are often used to predict and identify the level of importance and/or influence of a particular player in the network. There are four commonly used measures of centrality: degree, eigenvector, betweenness, and closeness. In this section, we examine the centrality of the nodes (coaches) in the NFL coach network and attempt to provide an interpretation for these measures in this context.

Total degree centrality

Individuals in a social network who are "in the know" are those who are linked to many others and so, by virtue of their position have access to the ideas, thoughts, and beliefs of many others. These individuals are identified by the total degree centrality in the relevant social network. The top 10 coaches based on the measure of total degree centrality are presented in Table 3; Mike Sullivan stands out with the highest value. This is based on the coach x coach network with shared teams (877 coaches, network density 0.0501684). These coaches have the most direct connections to other coaches in the league. This enables them to have access

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to the knowledge, strategies, and contacts of the many coaches with whom they have a direct working relationship. The extensive connections may also allow for these top coaches to potentially become effective mentors by channeling their access to expertise to their mentees.

Rank Agent Value 1 Mike Sullivan 1.730 2 Mike Smith 1.269 3 1.234 4 1.190 5 1.160 6 George Stewart 1.150 7 1.150 8 1.145 9 1.120 10 1.101

Table 3: Top 10 Coaches in Total Degree Centrality

Eigenvector centrality

Individuals with high eigenvector centrality are those who are connected to those who are themselves highly connected to others. In other words, individuals who are connected to many otherwise isolated individuals will have a lower eigenvector centrality than those that are connected to groups that have many connections themselves. The top ten highly connected coaches are shown in Table 4. It is worth noting that the top 8 are all connected through .

Rank Agent Value 1 16.093 2 16.093 3 16.093 4 Ted Williams 15.576 5 Juan Castillo 15.231 6 John Harbaugh 15.222 7 Sean McDermott 14.838 8 Pat Shurmur 14.755 9 Mike Sullivan 14.393 10 13.354

Table 4: Top 10 Coaches in Eigenvector Centrality

Closeness centrality

Loosely defined, closeness centrality is the inverse of the average distance in the network from the node to all other nodes. The coaches in Table 5 have the lowest average distance to all the other coaches in the NFL during the years covered by the data.

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Rank Agent Value 1 Andrew Dees 45.578 2 Steve Hoffman 45.436 3 45.389 4 Greg Olson 45.155 5 45.108 6 Jerry Sullivan 44.377 7 Kevin O'Dea 44.087 8 Fred Graves 44.087 9 44.064 10 44.042

Table 5: Top 10 Coaches in Closeness Centrality

Betweenness centrality

Individuals with high betweenness centrality are potentially influential as they are positioned to broker connections between groups. They bring to bear the influence of one group on another or serve as a gatekeeper between them. They are on the shortest path connecting other individuals in the network. As shown in Table 6, Andrew Dees stands out as the one with the top betweenness centrality among all coaches.

Rank Agent Value 1 Andrew Dees 3.171 2 Bob Ligashesky 2.286 3 Bill Davis 2.196 4 Steve Hoffman 1.985 5 1.960 6 Joe Barry 1.937 7 Tony Sparano 1.883 8 John Bonamego 1.753 9 Kevin O'Dea 1.717 10 1.633

Table 6: Top 10 Coaches in Betweeness Centrality

Clique membership count

A clique is defined as a group of three or more actors that have many connections to each other and relatively fewer connections to those in other groups. Table 7 shows the top ten coaches who have the most number of distinct cliques, and Mike Sullivan stands out again.

Rank Agent Value 1 Mike Sullivan 221.000 2 Tony Sparano 179.000 3 Greg Olson 166.000 4 Greg Knapp 151.000 5 Ron Milus 150.000 6 Joe Barry 149.000

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7 141.000 8 Chris Beake 141.000 9 Steve Hoffman 140.000 10 Jerry Sullivan 138.000

Table 7: Top 10 Coaches in Number of Distinct Cliques

Comparison of centrality measures

A quick examination of the top ten nodes in each case shows that the four centrality measures (plus clique membership count) produce overall different but in some cases similar results. Mike Sullivan ranks number one for degree centrality and clique membership count; Andrew Dees takes the first in closeness and betweenness centrality; and Andy Reed has the top eigenvector centrality. A close examination reveals the difference among the three. Mike Sullivan worked for five teams ( Browns, , San Diego Chargers, , and ) in 13 years. With the exception of New York Giants in 2007, none of the teams that he worked for can be considered successful in terms of win-loss ratios and playoff records. Andrew Dees only worked in NFL as a coach for 7 years but has been with as many as six teams, each having a losing record of that year. Both coaches can be regarded as “journeymen” that got bounced around in unsuccessful teams; it is their association with multiple teams in a short period of time that puts them on top of those centrality measures. The rest of the people on the top ten lists of the four measures (degree centrality, betweenness centrality, closeness centrality, and clique membership count) largely share similar characteristics.

Andy Reed, on the other hand, is among the longest-serving—13 years at Philadelphia Eagles—and the most respected coaches in the league. Many on the top ten list of eigenvector centrality were with Andy Reed at the Eagles; some such as John Harbaugh has moved on to become successful head coaches of their own. Because eigenvector centrality measures the connectedness with other importance nodes in a network, it helps identify important and successful “schools” centered on key coaches, in this case the “Andy Reid school,” one of the most successful of all times. (The concept of coaching schools will be further discussed in the next section.)

The above discussion draws out the application and usefulness of various centrality measures in analyzing NFL coaches using social networks. When a coach has high degree centrality, betweenness centrality, or closeness centrality, it is likely because of his journeys among teams, not his importance or influence. However, the eigenvector centrality tends to indicate how well trained a coach is through his “pedigree” and can be useful for predicting his success for the future.

Coaching Schools—Analysis of Long-Tenured Coach Networks

In this section, we compare the networks of three long-tenured head coaches—Andy Reid of Philadelphia Eagles, of Cincinnati Bangles, and of —in the data sample time frame. Their affiliations and records are listed in Table 8.

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Andy Reid Bill Belichick Marvin Lewis

Year Team W-L Team W-L Team W-L 2000 PHI 11--5 NE 5--11 BAL* 12--4 2001 PHI 11--5 NE 11--5 BAL* 10--6 2002 PHI 12--4 NE 9--7 WAS* 7--9 2003 PHI 12--4 NE 14--2 CIN 8--8 2004 PHI 13--3 NE 14--2 CIN 8--8 2005 PHI 6--10 NE 10--6 CIN 11--5 2006 PHI 10--6 NE 12--4 CIN 8--8 2007 PHI 8--8 NE 16--0 CIN 7--9 2008 PHI 9--6 NE 11--5 CIN 4--11 2009 PHI 11--5 NE 10--6 CIN 10--6 2010 PHI 10--6 NE 14--2 CIN 4--12 2011 PHI 8--8 NE 13--3 CIN 9--7 2012 PHI 4--12 NE 12--4 CIN 10--6 2013 KC 11--5 NE 12--4 CIN 11--5 * Marvin Lewis was a defensive coordinator at Baltimore and Washington. He first became a head coach at Cincinnati in the 2003 season.

Table 8: Win-Loss Records for Reid, Belichick and Lewis

Although all three have long, distinguished careers, their paths are somewhat different. During Andy Reid’s tenure, Philadelphia Eagles was very successful in the early years, but the team performance dropped off considerably and became inconsistent starting 2006, leading to his firing after the 2012 season. Marvin Lewis slowly built up Cincinnati since 2003 and reached playoff in 4 of 5 years since 2009. Since 2001, Bill Belichick’s Patriots have reached playoff in every season other than 2009 and captured three Superbowl titles in 5 appearances. No other team in NFL during the data time frame has had a more consistent and dominating record than the Patriots. In the following, we examine the individual networks of these three coaches to see if network characteristics can provide clues to their coaching records.

Andy Reid’s Network

The ego network, consisting of a focal node ("ego") and the nodes to whom ego is directly connected to plus the ties, can be expressed as an absolute value and as a percentage of the nodes in the original input network. The Reid ego network has 47 nodes, which represents 5.36% of all coaches in the study. This large number of nodes indicates that Reid has had significant staff turnover during his twelve years in Philadelphia. Additionally, many coaches in recent years have left to become head coaches of other teams, some very successfully (most notably John Harbaugh).

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Figure 1: Immediate network of Andy Reid

Bill Belichick’s network

The Belichick ego network has 30 nodes which represents 3.42% of all coaches in the study. It is significantly smaller and more compact than Andy’s Reid’s network, characterized by core group of assistant coaches that have stayed with him in New England (most notably Dante Scarnecchia). Some coaches (such as ) have moved on to become head coaches for other teams, but they are mostly on the fringe of the network.

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Figure 2: Immediate network of Bill Belichick

Marvin Lewis’ network

The Lewis ego network has 43 nodes which represents 4.90% of all coaches in the study. Size- wise, it is in between Reid’s network and Belichick’s network. But an examination of Figure 3 shows that his network has a significantly different structure than the other two: instead of one giant component, Lewis’ network is composed of three weakly connected groups, each roughly corresponding to a team that he coached at.

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Figure 3: Immediate network of Marvin Lewis

The three networks provide clues to the difference in performance records of the three coaches. Since he became head coach for New England in 2000, Bill Belichick quickly assembled a team of assistance coaches, many of whom stayed with him for a long time. Some did move on to other teams, but they were mostly on the fringe of the network (i.e., not working very long with Belichick as those core people did). This stable and tightly knitted core coaching staff likely contributes to the steady and consistent performance of the team throughout the 12 years. Andy Reid, on the other hand, has a network more than 50% larger than Belichick’s, likely as a result of attrition particularly in the later part of his tenure. It is hard to keep the team together with large number of coaching staff churning, and this may contribute to the dropping of team performance towards the end of Reid’s tenure in Philadelphia. On the contrary, Marvin Lewis went through three teams in this time frame. He first became the head coach at Cincinnati in 2003. As the network shows, his connections at Washington and Baltimore are quite different from the coaching group in Cincinnati. It is likely that he took the time to build the coaching staff as the head coach, because he did not (or would not) take advantage of the previous connections. This helps explains his slow start and later successes with the Bangles. This analysis shows that the difference in the structure of the ego network can shed light on the reason behind a head coach’s record.

CONCLUSIONS

In this study, we examine coaching leadership in the National Football League in the from a social network perspective. We show that, compared to theoretical network formation, the coaching network strongly exhibits the property of a scale-free network, implying

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that the network is likely to have formed in a preferential-attachment fashion. We evaluate the different node centrality measures and find that eigenvector centrality is likely to be the most important measure for identifying an influential coach managing a successful team. Lastly, we compare the networks of three long-tenured head coaches and show that the structure of the network can help explain the history and performance record of a particular coach. This is the first study of its kind, and it adds to the literature of social networks by extending and comparing the applicability of network analyses and measures in the setting of an important real-world network.

Beyond the theory of network analysis, our study also contributes to the identification of the different approaches to leadership by examining the leaders’ social network structure. We find that for the NFL coaching leadership, “whom you know” (as represented by eigenvector centrality) is more important than how many connections one has (represented by degree centrality) or how closely one is tied with others (represented by closeness and betweenness centrality). Moreover, having a compact and tightly knitted network is likely to contribute more to a NFL coaching leader’s long-term success than having large number of loosely connected staff, however capable they might be. We believe that these findings not only apply to the NFL coaching leadership, but also provide important implications in general to organizations in selecting their key managers and executives as well as to individuals aspired to become business leaders.

With such a rich data set, many analyses can be performed to generate insights into how coaching and staffing work in the NFL. Future study can include, for instance, the evolution of coaching network over time, career mobility of NFL coaches, and knowledge transfer among teams via the coach network. We believe that this is a potentially productive area for ongoing research.

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