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The BALCO scandal: the socialstructure of a steroid distributionnetworkNicholas C. Athey a & Martin Bouchard aa School of Criminology, Simon Fraser University , Burnaby ,CanadaPublished online: 24 May 2013.

To cite this article: Nicholas C. Athey & Martin Bouchard (2013) The BALCO scandal: thesocial structure of a steroid distribution network, Global Crime, 14:2-3, 216-237, DOI:10.1080/17440572.2013.790312

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Global Crime, 2013Vol. 14, Nos. 2–3, 216–237, http://dx.doi.org/10.1080/17440572.2013.790312

The BALCO scandal: the social structure of a steroid distributionnetwork

Nicholas C. Athey* and Martin Bouchard

School of Criminology, Simon Fraser University, Burnaby, Canada

The current study revisits the notorious Bay Area Laboratory Cooperative (BALCO)scandal involving the production and distribution of an undetectable anabolic-andro-genic steroid to professional athletes from late 1990s until 2003. Multiple sources arereviewed to re-create the social structure of BALCO and examine whether it formed aclose-knit community or instead multiple communities defined around a specific sportor discipline. Results from a community fragmentation analysis suggest that six com-munities could be identified as distinct in the BALCO network of 97 individuals –three structured around athletic interests (baseball, football and boxing), one aroundBALCO’s chemist, another around the eventual whistle-blower, and a ‘broker’ commu-nity, labelled the network’s core. The network’s core functioned as the best intermediatebetween communities because of the diversity of actors involved and the presence ofBALCO’s founder (Conte), who was brokering ties all over the network, though such astructure ultimately resulted in complete demise of the BALCO network.

Keywords: Bay Area Laboratory Cooperative (BALCO); criminal networks; socialnetwork analysis; community structure; brokers; anabolic-androgenic steroids (AASs)

Introduction

In the summer of 2003, the US Anti-Doping Agency (USADA) received a tip and usedsyringe containing traces of an unknown substance from an anonymous source.1 Thesource told officials that a nutrition company located in Northern California was pro-viding high-profile professional and Olympic athletes with the substance. Subsequently,the USADA sent the used syringe to Dr. Don Catlin of the UCLA Olympic AnalyticalLab who identified its chemical composition and developed a method to test for its pres-ence in the human body. The analytic lab later reported that the liquid was an undetectedanabolic-androgenic steroid (AAS) known as Tetrahydrogestrinone (THG). The anony-mous tip and results from Dr. Catlin’s work subsequently sparked an investigation byfederal and California State law enforcement agencies, which prompted a raid of theBay Area Laboratory Cooperative (BALCO) facility on 3 September 2003.2 During theraid, government officials collected incriminating documents with the names and dopingschedules of several high-profile professional and Olympic athletes, which they used as thefoundation for the forthcoming investigation of the BALCO and performance-enhancingdrug (PED) use in sports more broadly.3

*Corresponding author. Email: nathey@sfu.ca

© 2013 Taylor & Francis

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While the government’s interest in the BALCO scandal initially seemed concernedwith discovering which athletes had cheated by using PEDs, on 12 February 2004 theDepartment of Justice (DOJ) formally announced that the US grand jury would seeka 42-count indictment consisting of charges related to illicit drug distribution andmoney laundering against four individuals.4 Those initially charged were Victor Conte Jr.(BALCO founder and CEO), James Valente (BALCO vice-president), Greg Anderson (per-sonal trainer) and Remi Korchemny (Olympic sprinting coach), although the creator ofTHG (Patrick Arnold) and the whistle-blower (Trevor Graham) were later indicted andsentenced for their role in the scandal as well.5

The events leading to the scandal’s culmination demonstrate the unique processa legitimate company went through in becoming an illicit drug trafficking enterprise.Because the scandal involved a number of high-profile professional and Olympic ath-letes, it ultimately received considerable media attention, which documented intricatedetails about the people involved and their relation to one another. These media sourceswere used in combination with publicly available government reports to inform thecurrent case study, which focuses on the social structure of the BALCO network.Because the actors situated in the scandal originated from dissimilar backgrounds(e.g. music, business, athletics: bodybuilding, football, baseball, track and boxing) andhad various reasons for joining the network (e.g. distributing or obtaining the drug),our working hypothesis for this study is that two attributes likely shaped the net-work’s structure: (1) the actor’s role (e.g. coach, trainer and chemist) and (2) theactor’s discipline/sport/interest. To understand social structure, the current study drawsfrom Girvan and Newman’s community structure detector, which has been shownto provide a valid tool to describe the structure of a variety of social networks.6

Gould and Fernandez’s brokerage analysis will further examine how the network holdsup together through the presence of brokers.7 This article also contributes to thesmall literature on AAS trafficking, a topic which received very little attention incriminology.

The late-modern steroid marketplace

To date, the only academic research looking into the social structure of steroid distribu-tion is Kraska, Bussard and Brent’s study of the late-modern steroid marketplace. Theauthors’ results depict a highly decentralised, transnational market that operated primarilythrough the Internet. For presentation purposes, the authors initially focus their analy-sis on a central informant who was positioned within a large AAS distribution network.As a nationally ranked bodybuilder, their central informant saw his use of AASs as ameans to ‘getting huge’, which, in his opinion, required the consistent use of AASs.8 Theunfortunate reality that he and many other people interested in this lifestyle faced wasthe high cost of AASs at the retail level. Therefore, in an effort to obtain the substanceat a reduced cost, he frequented online bodybuilding communities and AAS websites,which provided a breadth of useful information about AAS manufacturing and distribu-tion. It was through this process that he discovered foreign pharmaceutical companieswilling to sell AASs and their chemical compounds, as well as the knowledge and toolsnecessary to ‘home brew’ an assortment of PEDs from the comfort of his apartment.9

Ultimately, their central informant produced various AASs that he supplied to a net-work consisting of roughly 25 mid-level dealers and 250 customers over a 60 square milearea.

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In the second phase of research, Kraska et al. demonstrated the robustness of theirmicro-level findings by content analysing nearly 200 AAS websites. Their findings sug-gested that more than 90% of the analysed websites were operational and sold a varietyof PEDs, not simply AASs. Thus, the results from their ethnographic research indicateda larger trafficking scheme operating through the Internet. In summary, the authors effec-tively demonstrate that (1) the late-modern AAS marketplace is highly decentralised andoperating globally – utilising the Internet and the ease of travel/trade to conduct busi-ness – and (2) the demand/consumer side is heavily cultivated by a cultural desire toimprove body aesthetic, athletic abilities and general health using pharmaceuticals. Kraskaet al.’s research uncovered the mechanics of AAS production and trafficking at both themacro and micro levels; however, the major disconnect between their participants andthose associated with the BALCO scandal is the athletes’ need to overcome stringentdrug tests and negative media in the latter case. This need for anonymity is pertinentto understanding why the BALCO’s business dealings became so lucrative and why itwas necessary to transition from a legitimate nutrition company to a distributor of illicitPEDs.

Community detection and intercommunity brokerage

Community is used to describe the setting in which social relationships are formed andmaintained through reciprocated attachment and shared values, which enhance solidarityand, over time, trust. Although the rise of multiculturalism blurs its boundaries, member-ship in one community can affirm a sense of identity not felt in alternative associations.10

Therefore, a factor that likely influences group (or community) formation and longevityis homophily – the notion that people will interact with others similar to themselves moreoften than with dissimilar others.11 For example, in residential areas, similarity to one’sneighbour can affect the sense of community experienced.12 Vaisey similarly found that agroup’s shared ideologies and the ability to transfer those values into action were importantattributes affecting the experience of community.13 As people continue to interact with sim-ilar others, their close contacts, who by this logic are also similar, face a higher probabilityof interacting with one another. The result of this socialisation process is the formationof homogenous and densely connected groups, which are closed off from the rest of thenetwork.14 In this sense, communities within the network may not include a random set ofactors but rather a more densely populated and cohesively bonded population. To measurethis process of bonding in a network, a number of hierarchical clustering techniques havebeen developed. These methods uncover organic divides in the network using a variety ofsimilarity and strength of tie measures and construct communities or subgroups of actorsthrough an agglomerative – adding – or divisive – subtracting – process.15 Although notprincipally a hierarchical clustering technique, Girvan and Newman’s algorithm for detect-ing community structure focuses on this concept of grouping similar actors by emphasisingthe power of being ‘between’ communities. In this regard, the most highly connected actorsare those responsible for connecting many others.16

Researchers have employed the Girvan and Newman algorithm to identify commu-nity structure in a variety of networks. Studies using this method have shown its utilityfor extracting information about organisational structure, publishing trends and group for-mation. In each of these cases, the algorithm identified logical and organically occurringdivides in the network. For example, Girvan and Newman first supported their method by

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correctly partitioning US college football teams into their respective divisions and collab-orating scientists by research topic and methodology used.17 They were also able to revisitZachary’s18 study of a karate club and show the network’s evolution after the club’s admin-istrator and instructor separated and divided the students. Guimerá, Danon, Díaz-Guilera,Giralt and Arenas validated the method’s utility by extracting information about a complexhierarchical organisation using e-mail log files. In their case, the authors unravelled andcorrectly partitioned members of a university community based on working teams, depart-ments, faculties and colleges and the university collectively.19 Gleiser and Danon foundthat a network of jazz artists formed communities based on the musician’s race and geo-graphical location.20 Finally, Rodriguez and Pepe used Girvan and Newman’s algorithm toidentify communities within a co-authorship network. Their results showed clear dividinglines around the authors’ area of expertise (department) and the institutions to which theybelonged (affiliations).21

Identifying (sub)communities (or groups) that compose a network is important for ourunderstanding of structure; however, because opinions and behaviours are more homo-geneous within than between communities, it is also important to consider how andwhy they are interconnected to one another.22 And especially by whom, the brokers.Brokers frequently garner more benefits because of their ability to link disconnectedothers.23 Previous research on illegal networks has shown that this bridging propertyproduces more opportunities to conduct business,24 enhance freedom and flexibility25 andincrease security from detection.26 Brokers can also be found at the community level.For example, Paik, Southworth and Heinz showed that a political network partitioned into‘business’, ‘religious’, ‘residual’ and ‘libertarian’ blocks was connected together througha fifth community, or the ‘network’s core’, which functioned as the efficient intermediatefor otherwise disparate communities.27 At the aggregated level, a broker’s (or brokers’)connection to other communities produces access to diverse information and other desir-able commodities not readily obtainable by members with only intracommunity ties.28

The broker therefore functions as an intermediary, connecting his/her community mem-bers to alternative communities with diverse resources. Gould argued that this ability tobridge divided communities places one in a position of power when the communities areoppositional or ‘rival factions’, because communities are frequently marked by distinctcultural, language, distance and (mis)trust barriers. Thus, when an actor is able to medi-ate between communities, she/he opens an avenue for exchange (e.g. communication) thatwould otherwise not be present and trustworthy.29

Current study

A salient feature of the BALCO network is that it includes several disparate groups ofactors who would have little to no affiliation if not for their shared interest in illicit PEDs.As such, an important question then becomes whether this shared interest creates an oppor-tunity for individual actors and groups to unify, or whether their connectivity is limited tointragroup ties only. The current study offers a response to this question by focusing ontwo main objectives.

Objective 1: Determine whether athletic interest creates cohesive (sub)communities usingGirvan and Newman’s community identification algorithm.

Objective 2: Determine the extent of intercommunity connectivity using Gould andFernandez’s brokerage typologies.

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Data and methods

Data sources

Bright, Hughes and Chalmers recently suggested that data sources used to construct crimi-nal networks in previous research fall into five categories: (1) offender databases, (2) tran-scripts of physical or electronic surveillance, (3) written summaries of police interrogation,(4) transcripts of court proceedings and (5) online and print media.30 In line with Brightet al.’s categorisation, our data sources comprise a combination of categories four and five;however, we also introduce an additional data source, archival interview data. While thedata described under the fifth categorisation are the most cost effective and easily accessi-ble, their validity is easily questioned because ‘information included in media reports is notsubject to external scrutiny’.31 Therefore, to counterbalance the possible issues of validity,we include a triangulation approach which includes four different data sources.

First, we draw from Fainaru-Wada and Williams’ tell-all book about the BALCOscandal.32 When writing the book, the authors utilised a number of confidential andpublicly available sources. These included leaked grand jury testimonies from the courtproceedings, memoranda from interviews between federal agents and BALCO actors, affi-davits, search warrants and other court documents, e-mails exchanged between Conte andhis clients, evidence obtained during the investigation, memos addressed to federal agents,a recorded interview, more than 200 interviews conducted by themselves and bibliogra-phies written by professional baseball players.33 Second, we extract network informationfrom 25 newspaper and magazine articles (2003–2009) that were obtained through onlinesearch engines (i.e. an online University Library and Google search engine). Like Fainaru-Wada and William’s book, the media sources were written using a variety of first- andsecond-hand sources. For example, authors used interviews conducted between news sta-tions and BALCO actors, announcements made by government officials and BALCOactors, government reports (e.g. indictments and sentencing memorandum), announce-ments and reports posted by governing bodies (e.g. by the USADA) and organisations (e.g.USA Track & Field, wire reports) and other media sources (e.g. ‘USA Today’). Third, anABC News 20/20 interview conducted in late 2007 with BALCO founder and CEO VictorConte is used to verify and provide additional data on the history and specific playersassociated with BALCO.34 Finally, 12 government-released documents (between 2004 and2010) (three legal documents and nine court cases) about the BALCO scandal are contentanalysed for network information. While we are not immune to missing data issues inher-ent in analyses of illegal networks, we feel that the triangulation method applied in thisstudy further resists the inherent concerns about validity.

The coding of actors is validated through the data collection and coding processes bychecking the sociomatrix against new information provided by sources. When discrepan-cies exist, we retain information collected from the most reliable source. For example, ifa newspaper or magazine article shows the presence of a positive tie between two actors,but a court document or the interview with Conte himself suggests otherwise, we do notrecord a tie. In short, the decision to record a tie between two actors is made when (1) allsources are in agreement with one another or (2) when the data source with the highestreliability contradicts the less reputable sources. The ranking of data sources by reliabilityincludes (1) court cases and other government documents, (2) the ABC 20/20 interviewwith Conte, (3) the tell-all book and (4) newspaper and magazine articles. Because courtcases and government reports undergo a more scrupulous review process, wherein mul-tiple pieces of evidence and testimony are presented and weighed against one another,we decided to emphasise them most heavily.35 The interview with Conte was considered

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the second most reliable because it was the only time we could verify that all the docu-mented information came from an original source. While the BALCO book provided themost extensive coverage of the scandal and included a number of different sources, it wasconsidered third in reliability since we could not verify exactly where and how all theinformation was collected.36 Finally, because media sources do not undergo as scrupulousa review process and are not subject to outside scrutiny, we weighed them least heavily interms of reliability.37

Constructing the networks

Using the aforementioned data sources, we construct a dichotomous and nondirec-tional sociomatrix for the analysis phase of research and a second, overlapping, valuedsociomatrix for the visualisation portion. In the first, a score (‘1’) is recorded between twoactors when the data indicate the presence of a positive tie (i.e. the two actors shared apositive relation) and a score is not recorded (‘0’) when the data suggest that either noaffiliation existed or the actor’s affiliation was negative.38 In the initial coding phase, any-one with ties to BALCO, its associates or its clients is added to the sociomatrix and all theirties to other members of the network are recorded. This produces a sample of 224 nodes.The second coding phase includes reviewing the names and affiliations of all actors inthe full sample, which shows that most were not part of the scandal. Instead, the initiallist of actors includes journalists who talked about the scandal, lawyers who either pros-ecuted or defended people implicated in the network, family members of network actorsand other people not directly associated to the scandal. Therefore, in the second phaseof coding, an actor is removed from the network unless he/she had a specific role in theBALCO’s expansion/success during the timeframe encompassing the scandal (late 1990sto September 2003). The new requirements for inclusion reduce the sample from the orig-inal 224 to its final size of 97 individuals. The final network includes 57 athletes (58.8%),11 BALCO associates and employees (11.3%), 22 athletic coaches and trainers (22.7%)and 7 other actors (7.3%), and shows high centralisation (degree centralisation = 55.3%)but low connectivity (density = 4.1%) (Table 1).39

To ensure consistency in coding, an actor is only retained in the second phase if a sourceindicates unequivocally that an actor participated (either directly or indirectly) in the scan-dal and that a positive relationship existed between two actors at one point. Undoubtedly,this approach is limited by its inability to account for changes in tie formation (e.g. Grahamand Conte initially had a positive relationship until their falling out), but after reviewingeach of the data sources it became apparent that this is an infrequent occurrence and, as theresults will demonstrate, it did not significantly affect the analysis.40

Table 1. Prevalence of actor roles and network measures.

Role N (= 97)

Athlete 57 (58.8%)BALCO associate 11 (11.3%)Coach/trainer 22 (22.7%)Other 7 (7.2%)

Structural network measure

Network density 4.1%Degree network centralisation 55.3% (avg. ties = 4.0)

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Methods

The analytic framework is separated into two sections which build on one another. First,the Girvan and Newman algorithm available in UCINET 6.041 is used to assess the level ofcommunity fragmentation. Second, Gould and Fernandez’s brokerage algorithm is used toexamine the level of intercommunity connectivity.

Community structure

The Girvan and Newman algorithm identifies cohesive subcommunities by generalisingFreeman’s42 notion of betweenness centrality to all edges in a network – what Girvanand Newman termed edge betweenness.43 Freeman’s betweenness centrality considers thenumber of times a node is positioned on the geodesic (i.e. shortest) path connecting anytwo vertices and therefore represents a measure of brokerage that the node has over thecontrol of information. In the community structure analysis, edge betweenness indicatesthe number of geodesic paths connecting nodes positioned on any one trajectory. Nodesare clustered when their edge betweenness score is low, because a low score implies thepresence of few (if any) brokerage opportunities. Therefore, when networks comprise cohe-sive subcommunities with a few intercommunity brokers, nodes with high betweennesscentrality scores are identified as being less connected to the larger community. In sum-mary, Girvan and Newman’s algorithm (1) calculates betweenness scores for all edges inthe network, (2) finds the edge with the highest score and removes it from the network,(3) recalculates betweenness for all remaining edges and (4) repeats from step 2. Whenthe algorithm finds the actor least similar to the rest of the community, it places it inan alternative vertex (i.e. divisive process) and repeats using an iterative process, whichis a quintessential element of the algorithm because, without it, the analysis would notaccount for subsequent changes in the network resulting from each actor’s removal.44 Afterremoving edges with the highest betweenness scores, the analysis effectively identifies thenetwork’s underlying community structure.

The community structure analysis is enriched by a goodness-of-fit measure describedas a modularity score (i.e. ‘Q’), which the authors developed to help researchers identify theappropriate number of communities within their network.45 Modularity measures the frac-tion of edges connecting similar nodes – thus forming communities – minus the expectedvalue that would occur if the same communities formed with random connections betweennodes. The modularity score ranges from Q = 0 to Q = 1 with higher scores indicatinga stronger community structure; when Q = 0, the occurrence of communities is no betterthan what would be expected by chance. The authors’ tests suggest that a reasonably highmodularity score is around a .40, but practical applications have identified scores rangingfrom .30 to .70.

Brokerage between communities

Gould and Fernandez’s five-item brokerage typology is used to determine whether com-munities identified by the Girvan and Newman analysis are interconnected through thepresence of brokers. However, because one type of broker is only concerned with intra-group ties (i.e. Coordinators), it was not included in the analysis. The remaining fourtypologies include Gatekeepers, Representatives, Cosmopolitans and Liaisons.46 Note,however, that because this network is nondirectional, the scores for representatives and

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gatekeepers are the same. This raises an important conceptual limitation of our data thatdeserves acknowledgement. Namely, the brokerage analysis assumes that the direction ofties are known (which would allow for a more qualitative discussion of exactly who is bro-kering for whom), something we lack here. Therefore, we (1) only report one score forgatekeepers/representatives and (2) interpret the brokerage results as the extent to whichan actor simply bridges two communities, rather than considering the direction of thatbridge.

Moreover, as part of their analysis, Gould and Fernandez developed a ‘total brokeragescore’,47 which is simply the number of times an actor performs each of the brokeragepositions summed together, and a weighed (i.e. ‘relative’) brokerage score that accountsfor the number of other actors who share any one brokerage position. Thus, one’s relativebrokerage score is higher when few others bridge the same two actors. This score sumsto one when all five typologies are included; however, because our analysis is only con-cerned with intercommunity brokerage, we do not report the total score. Instead, we usethe weighted scores and compare actors to one another; when an actor’s score is compara-tively higher for any given typology, we interpret their brokerage as being more importantto network connectivity.48 Therefore, to identify the most influential brokers, both in termsof frequency and relativity, we rely on both the raw and relative scores.

Results

Figure 1 presents the BALCO network. We started from the original network but wespecified (through a darker edge) on some ties if they involved a distribution path(with an arrow to indicate the direction of the distribution). The shapes represent vari-ous communities extracted from the Girvan–Newman analysis, and the black and whitecolours separate actors with only intracommunity ties (white) from those with at leastone intercommunity tie (black). Because such a large portion of actors were tied to Conte(N1), we excluded ties to N1 from consideration in the visualisation of intercommunityties. Before examining the social structure in more detail, we use the BALCO networkas a visual support and information from the data sources to understand its developmentfrom modest beginnings to a large-scale scandal involving many well-known athletes frommultiple disciplines.

The BALCO scandal

In the mid 1980s, Victor Conte Jr. (N1 – centre of network in Figure 1) was working asthe bass player for a popular American R&B band known as the Tower of Power, that is,until he concluded his music career and embarked on a more lucrative career path – sportsnutrition.49 In 1984, Conte founded BALCO and began providing chemical analysis (e.g.blood testing) and legal nutritional supplements to his growing clientele.50 Under his SNACtrademark, Conte created zinc monomethione aspartate (ZMA), which BALCO associatesclaim was being used as a PED by successful Olympic athletes.51 During the 1990s, Conteexpanded his business to include Vice-President James Valente (N11 – left centre of net-work) and professional athletes, such as Olympic sprinter Marion Jones (N2 – upper centre)and National Football League (NFL) player William Romanowski (N23 – bottom centre).52

Romanowski later introduced Conte to Olympic track and field coach, Remi Korchemny(N51 – bottom-right side).53 Through his brokerage position, Korchemny created a pathconnecting Conte to Olympic sprinters who were later charged with using banned PEDs.54

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Brokerage between networks Community structure

Intercommunity broker (not through N1)

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Figure 1. BALCO distribution network.

While relying primarily on the legal supplement (ZMA) to enhance his clients’ perfor-mance initially, Conte realised it would take more to elevate his athletes’ performanceabove the competition. In 2000, Conte contacted a chemist who gained recognition as theexpert on performance enhancers [Patrick Arnold (N35 – middle right side of the network)]and inquired about the availability of undetectable prohormones.55

Arnold helped BALCO by creating a designer steroid that would trump contemporarydrug tests.56 Beginning in 2000 and continuing until the investigation started in September2003, Conte and Arnold conspired to produce and distribute THG (i.e. ‘The Clear’) andother powerful PEDs with the intent that they be used to enhance athletic performance,while remaining untraceable by governing bodies.57 Because high-profile professional ath-letes are in a difficult position where winning and breaking records matter not only for theirpaycheck but for their fame as well, the latter point is just as (if not more) important thanthe former. As such, by introducing THG to the nutritional supplement market, BALCOsecured a reputation for being able to enhance athletic performance without sacrificinganonymity. Using this reputation, Conte quickly acquired contacts – generally personaltrainers and athletic coaches – that he used as brokers to obtain access to players from

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the NFL (N23, N87, N88 and N89), MLB (N38, N43, N44, N72, N78, N79 and N80),Olympic track and field teams (N2, N3, N5, N52, N53, N54, N59, N60, N61, N64, N75,N83, N84 and N97) and professional boxing (N94). However, as is the case in many illicitmarkets, the enterprise’s rapid growth was met with conflict – in this case, the competinginterest of an Olympic track and field coach.

Trevor Graham (N55 – upper middle) was a coach for Sprint Capitol USA from late1990s until 2006 when the US Olympic Committee banished him indefinitely for hav-ing a high number of athletes test positive for using illicit PEDs.58 After being expelled,Graham received a three-count indictment from the US federal government for makingfalse statements about his association to Angel Guillermo Heredia (N96) – the sourcethrough which he obtained illicit PEDs for his athletes.59 Notably, it was reported thatGraham also established a business relationship with Conte at one time and used his ser-vices to prepare two athletes (N2 and N5) for competition. However, in mid 2003, thetwo had an altercation and quickly grew to resent one another. The resentment influencedConte to write letters to the International Association of Athletics Foundation (IAAF) andUSADA describing Graham’s dealings with Heredia as well as his intentional and sys-tematic doping of athletes, though Conte choose to keep the documents in his possessionuntil they were confiscated as part of the raid in September of 2003. Adversely, Grahamacted on his resentment and sent the aforementioned syringe and anonymous tip to theUSADA, thus sparking the investigation of BALCO and doping in professional sports.60

Community structure and interconnectivity

The Girvan–Newman analysis is performed to determine the number of distinct commu-nities forming the social structure of the BALCO network. A review of the modularityscores (Table 2) indicates the optimal number of partitions is somewhere between six(Q = .495) and nine (Q = .498); an obvious divide occurs between five and six parti-tions (Q = .421 with five communities). However, upon further inspection the grouping ofactors seems most appropriate when we retained the more parsimonious six communities.In the first four, a majority of actors are athletes (54.6–63.0%) and athletic coaches or train-ers (17.5–36.0%). BALCO associates and employees, on the other hand, are absent in boththe second and third communities, evenly split between the fourth and fifth communities

Table 2. Community structure analysis.

Community

AttributeNetwork’s core

(n = 40)Antagonistic

(n = 11)Baseball(n = 25)

Football(n = 11)

Chemist(n = 6)

Boxing(n = 2)

LabelAthlete 60.0 54.6 60.0 63.0 33.3 50.0BALCO associate 15.0 – 4.0 18.2 33.3 –Coach/trainer 17.5 36.0 32.0 – 33.3 50.0Other 7.5 9.0 4.0 18.0 – –BALCO affiliate 85.0 54.6 52.0 36.4 50.0 –Distributed PED 3 3 1 1 2 –Density 9.50 36.4 11.5 34.6 40.0 100.0

Notes: ‘Distributed PEDs’ = the number of people in that community who distributed illicit PEDs. All figuresexcluding ‘Distributed PEDs’ are percentages.

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(two associates/employees in each), and most prevalent in the first (six associates/employees).

The first community is best described as the network’s core. As shown in Figure 1, mostactors positioned in this community are directly connected to N1 and have few additionalties, although there are some obvious exceptions (e.g. N11, N51, N85 and N75). Actorsare typically BALCO business associates/employees, track and field athletes, and otherathletes not affiliated with baseball or football, and just about all (85.0%) have direct ties toat least one BALCO associate/employee. In fact, of the four initially charged and sentencedfor their role in the scandal, three are in this group (N1, N11 and N51). The only one notincluded is N39 – a personal trainer for N38 – whose ties are more in line with the baseballsubgroup (community three). As Table 2 depicts, the first community is the largest (n =40) and most weakly connected (density = 9.5%). What members of community one havein common is the direct connection to Conte (N1) or other BALCO associates, and a smallnumber of intercommunity connections, a pattern that may explain (in addition to its largesize) that community one also displays the lowest density of all communities (9.5% vs. arange of 11.5% to 100.0% for others).

Communities two (top-middle of Figure 1) and four (bottom-middle-left of Figure 1)exhibit higher levels of cohesion, which seems fitting considering that the vast majorityof actors from both communities are athletes involved in similar disciplines/sports: trackand field athletes for community two (72.7%) and football for community four (63.6%).BALCO’s absence in community two but existence in community four suggests two impor-tant details about the network’s structure. First, the presence of BALCO associates in thelatter case demonstrates the enterprise’s continued existence outside the ‘network’s core’.This has direct implications for the company’s growth, because it ensures that BALCOassociates can continue distributing PEDs to the subgroup and offers more possibilitiesto recruit clients and broker new and pre-existing relationships. Adversely, the absence ofBALCO associates in the second community suggests that its reach is not ubiquitous andthat some track and field athletes inside the network do not feel the need to maintain closeties with Conte and other BALCO contacts. In fact, by reviewing Figure 1, it is evident thatcommunity two consists primarily of actors tied to Conte’s nemesis, Trevor Graham (N55).Here, the analysis appropriately identifies community two as the antagonistic communityto BALCO’s growing enterprise. As aforementioned, N55 was the coach of several trackand field athletes linked to BALCO who, at one time, had a business tie to Conte himself;however, after their quarrel in 2003, Graham exposed Conte and his business prospects tofederal authorities. The clear divide separating Graham and his affiliates from Conte andcommunity one seems logical.

Community three (left side of Figure 1) has the second largest subpopulation (n =25) and second lowest density (11.5%) of the six. Its actors include mostly athletes(60.0%) – among whom many are baseball players (86.7%) – and coaches/trainers(32.0%), and only one distributor of AASs – N39. Therefore, the community may bestbe described as the baseball community.

Community five stands out in the analysis because of the diversity of roles represented,especially given its small size (n = 6): two athletes, two BALCO associates/employees andtwo coaches/trainers. While the community does not have an overrepresentation of anyparticular role, it is best explained as the chemist community because it includes the manwho created THG (N35) and athletes as well as coaches/trainers he allegedly conductedbusiness with. Figure 1 clearly illustrates N35’s position in the network (right, middle sideof Figure 1), which is highly advantageous due to his association with Conte, and hints atthe possibility of an additional steroid distribution network occurring via N68.

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In community six, only two actors are present. It formed around Shane Mosley (N94) –a former professional middleweight boxer – who admitted to (unknowingly) using THGand other banned PEDs provided by BALCO in preparation for his championship fightagainst Oscar De La Hoya in 2003.61 Because the community is comprised of Mosley andhis trainer, it is best described as the boxing community. Their only contact in the BALCOnetwork was through Conte (N1).

Above all else, the community analysis shows that the network is in fact divided intomultiple, disparate communities bounded primarily by their athletic interest. The salienceof intercommunity ties is therefore evident because, without them, there is a high proba-bility that the various communities would splinter off into smaller networks – essentiallyreturning to their pre-BALCO structure. For this reason, the following presents results fromthe Gould and Fernandez brokerage analysis and illustrates how the relative lack of broker-age between communities keeps it heavily centralised around Conte (N1) and the network’score.

Table 3 shows a matrix of raw brokerage scores between communities. Communitiesidentified in each of the rows are brokering ties between their respective communities andthose identified in the columns. Numbers positioned within parentheses indicate the num-ber of people from that community (identified in the row) who broker at least one tie to theremaining five communities, while the figure outside the parentheses represents the num-ber of times any of the four abovementioned brokerage typologies (i.e. Cosmopolitans,Gatekeepers/Representatives and Liaisons) occur.

Results indicate that 11 actors from the network’s core, 6 from the antagonistic com-munity, 5 from the baseball community, 5 from the football community, 2 from thechemist community and 1 from the boxing community have at least one intercommunitytie – totalling 30 in all, thus suggesting some level of connectivity between communities.However, by focusing on the ties between actors specifically, we find that intercommunitybrokerage is only present between the network’s core and all others, except for oneoccurrence between the antagonistic community and the baseball community and onebetween the antagonistic community and the chemist community. In other words, almostall connections have to go through the core.

Table 4 ranks the 30 intercommunity brokers in order from the most to least active, interms of raw brokerage. Because the analysis did not include coordinators, the scores rep-resent the number of times an actor functions as a gatekeeper/representative, cosmopolitan

Table 3. Sociomatrix of brokerage between communities.

Network’s core(n = 40)

Antagonistic(n = 11)

Baseball(n = 25)

Football(n = 11)

Chemist(n = 6)

Boxing(n = 2)

Network’s core – 157 (7) 271 (4) 194 (4) 113 (2) 36 (1)Antagonistic 48 (6) – 6 (1) 4 (1)Baseball 101 (4) 2 (1) –Football 34 (6) –Chemist 3 (2) 2 (1) –Boxing 1 (1) –

Notes: Numbers presented in parentheses indicate the number of people in that community brokering betweencommunities, and numbers outside the parentheses indicate the number of times a brokerage occurs.The raw brokerage scores do not differentiate between gatekeepers and representatives, which are only meaningfulin directed networks. When discussing the brokers individually, this is accounted for, but it is important to notethat the gatekeeper/representative scores are counted twice here.

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Table 4. Intercommunity brokers (n = 30).

Gould and Fernandez intercommunity brokerage

Actor Actor role Community Gate/rep Cosmopolitan Liaison Total

N1 BALCOassociate

1 710 (1.38) 48 (.09) 296 (.26) 1054 (1.73)

N39 Coach/trainer 3 36 (.87) 2 (.05) – 38 (.92)N5 Track athlete 2 30 (2.21) 6 (.44) – 36 (2.65)N38 Baseball athlete 3 32 (1.03) – – 32 (1.03)N23 Football athlete 4 20 (1.37) 2 (.14) – 22 (1.50)N85 BALCO

associate1 15 (1.39) – 6 (.25) 21 (1.64)

N11 BALCOassociate

1 17 (1.52) 2 (.18) – 19 (1.70)

N51 Coach/trainer 1 12 (1.38) – 2 (.10) 14 (1.48)N2 Track athlete 2 11 (1.66) – – 11 (1.66)N55 Coach/trainer 2 7 (.80) – – 7 (.80)N4 Coach/trainer 2 6 (2.46) – – 6 (2.46)N18 Coach/trainer 1 5 (.90) – – 5 (.90)N87 Football athlete 4 4 (2.87) – – 4 (2.87)N30 BALCO

associate4 3 (1.08) – – 3 (1.08)

N31 BALCOassociate

4 3 (1.08) – – 3 (1.08)

N35 BALCOassociate

5 3 (2.15) – – 3 (2.15)

N57 Coach/trainer 2 3 (1.44) – – 3 (1.44)N60 Track athlete 1 3 (2.87) – – 3 (2.87)N6 BALCO

associate3 2 (2.87) – – 2 (2.87)

N44 Baseball athlete 3 2 (1.44) – – 2 (1.44)N52 Track athlete 1 2 (1.15) – – 2 (1.15)N53 Track athlete 1 2 (1.91) – – 2 (1.91)N54 Track athlete 1 2 (.82) – – 2 (.82)N61 Track athlete 1 2 (2.87) – – 2 (2.87)N67 Coach/trainer 5 2 (2.87) – – 2 (2.87)N3 Coach/trainer 2 1 (2.87) – – 1 (2.87)N7 Coach/trainer 3 1 (1.44) – – 1 (1.44)N59 Track athlete 1 1 (2.87) – – 1 (2.87)N89 Football athlete 4 1 (2.87) – – 1 (2.87)N94 Boxing athlete 6 1 (2.87) – – 1 (2.87)

Notes: Gate/Rep = gatekeeper/representative.Scores outside the parentheses are for raw brokerage and scores inside the parentheses are for relative brokerage.

and/or liaison, while the last column totals the number of times the four brokeragetypologies occur.62 The score reported outside the parentheses is the number of times eachactor operates as a broker, while the score inside the parentheses is the actor’s relativebrokerage score. In terms of raw brokerage, the most proficient intercommunity brokerwas Conte, who performed the four brokerage roles more than the remaining 29 com-bined (Conte = 1054 vs. 29 others = 249). Without Conte, the largest proportion ofintercommunity ties ceases to exist. At the same time, Conte’s weighted brokerage score isrelatively low at 1.73, because he shares many of his brokering ties with others. Thus, one’srelative brokerage score is higher when few others bridge the same two actors. To assess

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Conte’s influence as an intermediate, we reanalyse the network with Conte removed andfind that 25 of the 96 actors are no longer connected to the network (9 isolates and8 isolated pairs – 16 nodes). The chemist (N35), whose product reached every commu-nity through Conte, now has only one intercommunity tie (to the antagonistic community).Finally, whereas the network initially had 30 intercommunity brokers, Conte’s removalreduces the number of brokers to 20. Network density also drops (from 4.1% to 3.0%),but its decentralisation is far more noticeable (from 55.3% to 13.99%). Thus, while amajority of the network remains connected through at least one intercommunity broker,Conte’s position is essential to maintaining BALCO’s social structure of production anddistribution.

While Conte operates as the necessary connection between communities, the rankingof subsequent brokers illustrates the significance of considering an actor’s role as well. Forexample, the second most accomplished intercommunity broker, Greg Anderson (N39), isa personal trainer with three ties to the network’s core, while the following three brokersare athletes, from communities two through four, with three to five ties to the core. In eachcommunity, a minimum of one athlete functions as a broker to the network’s core at leastas effectively as the most proficient coach/trainer. Moreover, whereas BALCO associatesand coaches/trainers function as intercommunity brokers most frequently (they represent6 of the top 10 in terms of raw brokerage), athletes become more influential when weconsider the (lack of) overlap in bridging shared between all actors (athletes represent 7 ofthe top 10 relative brokers). In this sense, the relative brokerage score highlights the factthat athletes, albeit being infrequent brokers, are influential in bridging actors when few (ifany) others do.

The four broker typologies provide qualitative meaning to these figures. Notably, onlythree (10.0%) of the brokers function as liaisons and five (16.7%) as cosmopolitans, whileall the brokers have at least one gatekeeper/representative tie. Unsurprisingly, all threeliaisons are BALCO associates (2) and coaches/trainers (1) and all are present in the net-work’s core. This shows the importance of BALCO associates and coaches/trainers, aswell as the network’s core, for connecting different groups together; Conte functions asa liaison frequently (296 times) and N51 – one of the two brokers in the chemist com-munity – bridges the football and chemist community through his brokerage. While thisfinding illustrates the importance of considering varying forms of brokerage, the remain-ing three typologies illuminate the relative absence of connectivity between communitiesbecause very few actors function as intermediaries outside the gatekeeper/representativetypology.

Discussion and conclusion

The methods of producing and trafficking illicit PEDs have remained largely unstudied inthe academic literature and until this time no studies have approached the phenomenonfrom a social network analysis perspective. As the results depict, the BALCO networkis highly centralised and loosely connected when the entire sample (n = 97) is consid-ered. In that sense, the BALCO network does not seem to differ much from other drugtrafficking networks. The network density (4.1%) and centralisation (55.3%) identified forBALCO are comparable to that found in previous network studies of illicit drug traffick-ing (density range = 3.4–14.9% and network centralisation = 50.4–71.1%).63 This furthersupports the utility and accuracy of our data sources, but it also implies many possible sim-ilarities between illicit PED distribution and other forms of illicit drug distribution. Whencompared to the only other study of AAS trafficking, however, the BALCO network’s

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high centrality seems conflicting. There are two likely explanations for this disparity incentrality.

The two possible factors affecting centrality in this case are the substance beingdistributed and the distributors themselves. Contrary to Kraska et al.’s central informant– who manufactured and distributed more generic PEDs, BALCO associates produced a‘designer steroid’ and distributed it to high-profile clients who depended on anonymity lesttheir reputation be contaminated by a failed drug test. Because THG produced remarkableenhancements in body aesthetic and athletic abilities without risking the user’s reputa-tion, it seems logical that these high-profile athletes would gravitate towards Conte andthe BALCO, rather than utilising the Internet or an acquaintance from the gym to procurePEDs that have already been detected by athletic governing bodies (e.g. the InternationalOlympic Committee). Additionally, the higher centrality identified here may be the result ofConte’s central role in the distribution process. This is especially likely when one considersthat Kraska et al.’s study included the macro-level steroid marketplace, which was under-standably decentralised because of its transnational scope. From the two primary analyses,we identify structural characteristics that suggest illicit PED distribution networks may beunique in other ways.

By expanding the prerequisite for network inclusion to the entire scandal, rather thanthose particularly involved in the production/trafficking process (e.g. our study includedthe clients who consumed the drug as well), we identify two important findings about theoverall structure of illicit PED distribution. First, from the community structure analysis,we find that the network comprises a core group of actors – which the network centres on –and an additional five peripheral communities that form around athletic interest (e.g. track,baseball or football), competition (the antagonist community) and unique specialisation(the chemist community). The analysis showed that, despite their shared interest in THG,the athletes and coaches/trainers remained largely confined to their respective communi-ties. They could do so because of the presence of a network core which was diversifiedenough in terms of roles and interest (all roles are represented in the core, see Table 2) tobroker their services to at least one individual from all other communities. Moreover, theanalysis identified the network’s core as being the largest and least connected of all sixcommunities. Reflecting on the importance of homogeneity for group formation, this find-ing appropriately explains the core’s lack of connectivity as the result of low homogeneity,which seems fitting since it is the only community in the network without a communalfoundation. Evidently, then, the higher density witnessed in the remaining five communitiesis the result of more homogeneity (e.g. shared interest in a particular sport). The networkcore, however, was not structured in a way that made it immune to detection. Quite thecontrary; the main protagonist, Conte, was himself connected to 56 of the 96 other actorsin the network. Any defection, small or large, would lead to him and the demise of theentire network – and this is exactly what happened.

Although the actors were primarily confined to their respective communities, theresults from Gould and Fernandez’s broker analysis demonstrate that each community isconnected to the network’s core by at least one broker and that the broker is typicallyan athlete. Acknowledging that the chemist community is also connected to the antag-onistic community is important for our understanding of competition and connectivitywithin the network. Notably, it suggests that one of the most influential actors – and theone with sole access to the illegal commodity being produced and disseminated initially –is not restricted to conducting business with only one actor, Conte. This directly affects thepresence of competition, which undoubtedly shapes the network’s structure. For example,without alternative sources to obtain the drug, the antagonistic community may have never

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formed – Trevor Graham and his athletes would have likely realised the vulnerability oftheir position and been more submissive to Conte in exchange for easy access to THG.64

Had this occurred, the difference may have been detected in the community analysis; theantagonistic community may have formed, for example, a track and field community orbecome an extension of the network’s core. Additionally, the brokerage analysis shows thatmost athletes are not dependent on their coaches/trainers to obtain THG; instead, becauseof their high brokerage to the network’s core, they can often find alternative routes to thedrug source.

The study suffers from a number of limitations that require acknowledgment. Althoughthe data sources were cost effective and easily obtainable, they may suffer from issues ofvalidity because media sources do not undergo a scrupulous review process prior to beingpublished. This shortcoming involves the issue of missing data inherent to network anal-ysis. While we retained all the actors unequivocally identified by our sources, we did notassume actors knew or distributed PEDs to one another. This assumption was the safest wayto avoid recording ties that did not belong, but some evidence suggests social relations andillicit drug exchange occur between professional athletes.65 Therefore, many links betweenindividuals that are part of the 97 are certainly missing, and multiple actors who were tiedto BALCO may not be included here. We have made attempts to mitigate this possibil-ity by using an extensive triangulation method consisting of multiple sources. Anotherlimitation particular to this study is the network bias towards Conte. Conte was the pioneerand most central actor in the scandal, but the vast majority of sources available use himas the main point of observation, to the detriment of others. This bias is also present forhigh-profile celebrities, such as Barry Bonds (N38) (professional baseball player), MarionJones (N2) (Olympic sprinter) and William Romanowski (N23) (professional Americanfootball player). Another possible shortcoming that is particular to network analysis regardsboundary specification. While we tried to control for this during the data coding phase, itis probable that the BALCO network is only one part of a larger PED distribution chainencompassing many more professional and world-level athletes – Conte alludes to this inhis interview with ABC.

The heightened anonymity that THG guaranteed attracted high-profile clients toBALCO who depended on secrecy for their career and reputation. In that situation, thesocial structure of the BALCO network could have taken one of two paths: (1) decentralisedistribution activities to a maximum possible, in order to protect BALCO associates fromthe growing customer based, or (2) BALCO associates maintaining a close monitoring ofall activities, from production to consumption. This study showed that the latter structurewas closest to reality. We uncovered a network centring primarily on Conte, who main-tained contacts with every type of actor possible along the distribution chain. The structurewas detrimental to Conte who took the fall, but also to all of his contacts who necessarilywent down with him.

Notes1. Kimball and Dure, “BALCO Investigation Timeline”; Schmidt, “Coach is Charged.”2. Nafziger, “Circumstantial Evidence of Doping.”3. Fainaru-Wada and Williams, Game of Shadows; Kimball and Dure, “BALCO Investigation

Timeline.” Victor Conte maintained a journal of his clients’ doping schedules by placing lettersthat corresponded to different PEDs on various days of the month.

4. Department of Justice, “Four Individuals Charged.”5. Pogash and Schmidt, “Graham Sentenced”; US vs. Arnold. 3:05-cr-00703-SI; Williams,

“Graham Given House Arrest.”

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6. Girvan and Newman, “Community Structure,” 7821–6.7. Gould and Fernandez, “Structures of Mediation,” 89–126.8. Kraska et al., “Trafficking in Bodily Perfection,” 159–85.9. Ibid., 170.

10. Delanty, Community.11. McPherson et al., “Birds of a Feather,” 415–44. It is important to reiterate that similarity among

group members is only one of many elements constituting community, and while addressingthese elements specifically would be ideal, our data are limited to analyses of social structureonly. Therefore, we use the element of homogeneity as a starting point to explore communitydevelopment.

12. Obst and Smith, “Sense of Community,” 119–33.13. Vaisey, “Structure, Culture, and Community,” 851–73.14. Granovetter, “Strength of Weak Ties,” 1360–80.15. Scott, Social Network Analysis.16. Girvan and Newman, “Community Structure.”17. Ibid.18. Zachary, “Information Flow Model,” 452–73.19. Guimerá et al., “Real Communication Network,” 653–67.20. Gleiser and Danon, “Community Structure in Jazz,” 565–73.21. Rodriguez and Pepe, “Relationship,” 195–201.22. Burt, Brokerage and Closure.23. Burt, Structural Holes; Gould and Fernandez, “Structures of Mediation.”24. Natarajan, “Heroin Distribution Network,” 171–92.25. Morselli, “Career Opportunities,” 383–418; Morselli and Roy, “Brokerage Qualifications,”

71–98.26. Morselli, “Assessing Vulnerability,” 382–92.27. Paik et al., “Lawyers of the Right,” 883–917.28. Burt, Brokerage and Closure.29. Gould, “Power and Social Structure,” 547.30. Bright et al., “Illuminating Dark Networks,” 151–76.31. Ibid., 158.32. Fainaru-Wada and Williams, Game of Shadows; Mrozek, “Defense Attorney.”33. Fainaru-Wada and Williams, Game of Shadows, 299–329.34. Conte, interview by Martin Bashir, “ABC 20/20.”35. Porter, “Archival Data and Multidimensional Scaling,” 33–44.36. This is particularly true for the interviews, confidential documents and other sources not

readily available to the public because the authors documented their sources for eachchapter in great detail (see Fainaru-Wada and Williams, Game of Shadows, 299–329 forspecifics).

37. Bright et al., “Illuminating Dark Networks.”38. The decision to dichotomise ‘no affiliation’ and ‘negative affiliation’ was made for two reasons.

First, the data sources did not always clearly differentiate between the presence of a ‘negativeaffiliation’ and ‘no affiliation’ between actors. Second, because the study is primarily concernedwith formation of ties within and between communities, the presence of positive ties – whereexchange networks are most likely to exist – is of paramount importance. Thus, the decision todichotomise the presence or absence of positive ties between actors can be attributed to (1) datalimitations and (2) the study’s focus.

39. Although the final network identified 97 actors who participate in the scandal, we could notfind information linking two actors to a specific BALCO associate. The final network thereforehas two isolates.

40. A positive tie was recorded between Conte (N1) and Graham (N55), Conte and Jones (N2),and Conte and Montgomery (N5) – all of whom he had a negative relationship to at the scan-dal’s culmination – and yet the Girvan and Newman analysis partitioned the actors into theirappropriate subgroups. This result demonstrates the robustness of the method and supports thereliability of our findings.

41. Borgatti et al., Ucinet for Windows.42. Freeman, “Centrality in Social Networks,” 215–39.43. Girvan and Newman, “Community Structure.”

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44. Ibid.45. Newman and Girvan, “Community Structure in Networks,” 02611346. Gould and Fernandez, “Structures of Mediation.” Gatekeepers control whether an actor posi-

tioned outside the community receives access to his/her community (i.e. A → B → B).Similarly, actors who function as representatives advocate for another actor in their communityand form an intercommunity link (i.e. A → A → B). The third type of intergroup brokerage isa cosmopolitan, which signifies the intermediary between two actors who belong to the samegroup (i.e. A → B → A). Finally, a liaison denotes mediators who connect two actors fromdisparate communities (i.e. A → B → C).

47. Gould and Fernandez, “Structures of Mediation,” 102.48. Because we incorporate both brokerage scores, we consider an actor’s relative broker-

age in comparison to their frequency of brokerage. This is an important considerationsince actors may score high on relative brokerage, but, in terms of frequency, be ratherinactive.

49. Conte, “ABC 20/20.”50. Ibid.; Kimball and Dure, “BALCO Investigation Timeline.”51. Kimball and Dure, “BALCO Investigation Timeline.” ZMA is a legal PED that is still sold

today (e.g., see http://www.snac.com/shop/category/products/).52. Fainaru-Wada and Williams, Game of Shadows; Kimball and Dure, “BALCO Investigation

Timeline.”53. Kimball and Dure, “BALCO Investigation Timeline.”54. Ibid.55. Dohrmann, “Is this Dr. Evil?” 62; US vs. Arnold.56. Llewellyn, Anabolics. Designer steroids are a recently introduced class of AASs, which are

manufactured for the purposes of cheating drug tests.57. US vs. Arnold.58. Associated Press, “USOC Bans Track Coach.”59. US vs. Graham. 555 F. Supp. 2d 1046, Northern District of California, 2008.60. Fainaru-Wada and Williams, Game of Shadows.61. Smith, Shane Mosley Admits to Using BALCO Steroids.62. Recall that the relative score (in parentheses) is not an actual count, but a weighted and,

therefore, comparative score.63. Bright et al., “Illuminating Dark Networks”; Calderoni, “Drug Trafficking Mafias,” 321–49;

Morselli et al., “Efficiency/Security Trade-off,” 143–53; Natarajan, “Heroin DistributionNetwork.”

64. Without access to more detailed information about the network’s evolution, this possibilityassumes that the solidarity unifying Conte and the chemist was not enough to override thechemist’s interest in forming additional business ties.

65. Conseco, Juiced; Fainaru-Wada and Williams, Game of Shadows.

Bibliography

Associated Press. “USOC Bans Track Coach Graham from Training Sites.” ESPN ,August 7, 2006, Olympic Sports. Accessed June 3, 2011. http://sports.espn.go.com/oly/trackandfield/news/story?id=2538450.

Borgatti, S. P., M. G. Everett, and L. C. Freeman. Ucinet for Windows: Software for SocialNetwork Analysis. Harvard, MA: Analytic Technologies, 2002.

Bright, D. A., C. E. Hughes, and J. Chalmers. “Illuminating Dark Networks: A SocialNetwork Analysis of an Australian Drug Trafficking Syndicate.” Crime, Law andSocial Change 57 (2012): 151–176.

Burt, R. S. Brokerage and Closure: An Introduction to Social Capital. New York: Oxford,2005.

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Appendix 1. Data sources

Newspaper and magazine articlesAssociated Press. “USOC Bans Track Coach Graham from Training Sites.” ESPN , August 7,

2006, Olympic Sports. Accessed June 3, 2011. http://sports.espn.go.com/oly/trackandfield/news/story?id=2538450.

Crumpacker, J. “Caught in a Tempest.” San Francisco Chronicle, November 4, 2003. AccessedJune 3, 2011. http://www.sfgate.com/bayarea/article/PROFILE-Remi-Korchemny-Caught-in-a-tempest-2550893.php.

Crumpacker, J., and M. Fainaru-Wada. “Sports and Drugs; Star-Studded Day for the GrandJury; Giambi Brothers, 2 Raiders Appear Before Court.” The San Francisco Chronicle, C1,December 12, 2003.

Dohrmann, G. “Is this Dr. Evil?” Sports Illustrated 105, no. 14 (October 9, 2006).Dorhrmann, G. “Game Over?” Sports Illustrated 110, no. 10 (March 9, 2009): 64.Dure, B. “BALCO Investigation: Key Players.” USAToday.com, April 27, 2006. Accessed August 8,

2011. http://www.usatoday.com/sports/balco-players.htm.Elias, P. “‘Clear’ Chemist Gets Three Months.” Telegraph-Journal (August 5, 2006): B.7.Fainaru-Wada, M. “Track Coach Gets Probation in BALCO Scandal.” San Francisco Chronicle,

February 24, 2006. Accessed August 8, 2011. http://www.sfgate.com/cgi-bin/article.cgi?file=/c/a/2006/02/24/MNG5CHECUB6.DTL.

Fainaru-Wada, M., and L. Williams. “Agent of Change.” Sports Illustrated, 104, no. 25 (June 19,2006), 18–19. Accessed May 31, 2011. http://sportsillustrated.cnn.com/vault/article/magazine/MAG1111722/index.htm.

Fish, M. “‘Cream’ of the Flop.” ESPN.com, December 2, 2005. Accessed May 31, 2011. http://sports.espn.go.com/espn/news/story?id=2242819.

Hersh, P. “Graham Indicted in BALCO Scandal.” The Los Angeles Times, November 3, 2006.Accessed June 3, 2011. http://articles.latimes.com/2006/nov/03/sports/sp-balco3.

Kimball, B., and B. Dure. “BALCO Investigation Timeline.” USA Today.com, November 27, 2007.Accessed May 31, 2011. http://www.usatoday.com/sports/balco-timeline.htm.

Kluger, J., L. T. Cullen, and M. Frank. “The Steroid Detective.” Time Magazine US, March 1, 2004.Accessed June 3, 2011. http://www.time.com/time/magazine/article/0,9171,993474-1,00.html.

Kravets, D. “Alleged BALCO Supplier Indicted on Steroid Charges.” USA Today, November 4, 2005.Accessed June 3, 2011. http://www.usatoday.com/sports/2005-11-04-balco-chemist-indicted_x.htm.

Michaelis, V. “Indictments put Conte in Center of Scandal.” USA Today, February 12, 2004. AccessedMay 31, 2011. http://www.usatoday.com/sports/2004-02-12-balco-conte_x.htm.

Norton, J. M. “BALCO Supplier of ‘the clear’ Pleads Guilty.” USA Today, April 30, 2006. AccessedMay 31, 2011. http://www.usatoday.com/sports/2006-04-27-balco-scientist_x.htm.

Pogash, C., and M. S. Schmidt. “Graham Sentenced to Year’s House Arrest in Balco Case.” TheNew York Times, B17, October 21, 2008. Accessed May 31, 2011. http://www.nytimes.com/2008/10/22/sports/othersports/22graham.html?_r=1.

Schmidt, M. S. “Coach is Charged in BALCO Case.” The New York Times, November 3, 2006.Accessed May 31, 2011. http://www.nytimes.com/2006/11/03/sports/othersports/03balco.html.

Shipley, A. “Illinois Chemist Indicted in BALCO Scandal.” The Washington Post, November 4, 2005.Accessed August 8, 2011. http://www.washingtonpost.com/wp-dyn/content/article/2005/11/03/AR2005110302400.html.

Shipley, A. “Marion Jones Admits to Steroid Use.” The Washington Post, October 5, 2007. AccessedJune 3, 2011. http://mspriorhealthpe.cmswiki.wikispaces.net/file/view/Marion+Jones+Admits+to+Steroid+Use.pdf.

Shipley, A. “Jones Finally Comes Clean; Track Star Admits to Steroid Use; Pleads Guilty to TwoCounts of Lying to Investigators; Retires.” The Ottawa Citizen, (October 6, 2007): C1.

Smith, T. “Shane Mosley Admits to using BALCO Steroids.” NYDailyNews.com, September 29,2007. Accessed June 3, 2011. http://www.nydailynews.com/sports/more-sports/shane-mosley-admits-balco-steroids-article-1.247541.

Vinton, N. “Track Coach Trevor Graham Finishes Home Detention for Role in BALCOSteroid Ring.” NYDailyNews.com, October 24, 2009. Accessed May 31, 2011. http://articles.nydailynews.com/2009-10-24/sports/17934852_1_angel-memo-heredia-trevor-graham-balco.

Williams, L. “Graham given House Arrest.” San Francisco Chronicle, October 22, 2008. AccessedJune 3, 2011. http://www.sfgate.com/default/article/Graham-given-house-arrest-3264807.php.

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BooksFainaru-Wada, M., and L. Williams. Game of Shadows: Barry Bomds, BALCO, and the Steroids

Scandal that Rocked Professional Sports. New York: Gotham Books, 2007.

InterviewsConte, Victor, interview by Martin Bashir, “ABC 20/20: The Man Who Claims He Doped Marion

Jones,” ABC News, December 3, 2004. Accessed September 12, 2011. http://abcnews.go.com/US/video?id=3696132.

Government documents and court casesUS v. Barry Lamar Bonds, 580 F. Supp. 2d 925, Dist. Court, ND California 2008, No.

C 07-00732 SI. Accessed June 1, 2011. http://scholar.google.com/scholar_case?case=12657338659447006980&q=US+v.+%22Barry+Lamar+Bonds%22&hl=en&as_sdt=2,5.

US v. Barry Lamar Bonds. 608 F.3d 495, Court of Appeals, 9th Circuit 2010, No. 09-10079. AccessedJune 1, 2011. http://scholar.google.com/scholar_case?case=10269105231900148442&q=US+v.+%22Barry+Lamar+Bonds%22&hl=en&as_sdt=2,5.

US v. Tammy Thomas. 612 F.3d 1107, Court of Appeals, 9th Circuit 2010, No. 08-10450.Accessed June 1, 2011. http://scholar.google.ca/scholar_case?case=5841757738607321040&q=%22patrick+arnold%22+%22BALCO%22&hl=en&as_sdt=2,5.

US v. Tammy Thomas. 545 F.Supp.2d 1018, Dist. Court, ND California 2008, No. C06-00803 SI. Accessed June 1, 2011. http://scholar.google.ca/scholar_case?case=16127547601442287252&q=%22patrick+arnold%22+%22BALCO%22&hl=en&as_sdt=2,5.

US v. Patrick Arnold. Case number CR-05-00703-01 SI, Document 36, 2006. Accessed June 1, 2011.http://c.plainsite.org/ilcd/42394/1.pdf.

US v. Graham, 555 F. Supp. 2d 1046, 2008 U.S. Dist. LEXIS 61650 (N.D. Cal., 2008)US v. Graham, 2008 U.S. Dist. LEXIS 55630 (N.D. Cal., July 21, 2008).US v. Graham, 2010 U.S. Dist. LEXIS 133322 (N.D. Cal., December 6, 2010).US v. Victor Conte, Jr., James Valente, Greg Anderson, and Remi Korchemny. 118321,

Dist. Court, ND California 2004, No. CR 04-0044 SI. Accessed June 1, 2011.http://news.findlaw.com/hdocs/docs/sports/usconte21104ind.pdf.

Department of Justice. “Four Individuals Charged in Bay Area with Money Laundering andDistribution of Illegal Steroids: Grand Jury Returns 42-count Indictment Charging IndividualsAssociated with Bay Area Lab Cooperative (BALCO).” February 12, 2004. AccessedSeptember 12, 2011. http://www.justice.gov/opa/pr/2004/February/04_ag_083.htm.

Department of Justice. “Olympic Gold Medalist Marion Jones-Thompson Pleads Guilty to MakingFalse Statements in Two Separate Federal Criminal Investigations.” October 5, 2007. AccessedJune 1, 2011. http://www.justice.gov/usao/nys/pressreleases/October07/jonesthompsonpleapr.pdf.

Department of Justice. “Former Olympic Champion Marion Jones-Thompson Sentencedto 6 Months in Prison for Making False Statements in Two Federal CriminalInvestigations.” January 11, 2008. http://www.justice.gov/usao/nys/pressreleases/January08/jonesthompsonsentencingpr.pdf.

Mrozek, T. “Defense Attorney in BALCO Case Pleads Guilty to Charges Related to Leak of GrandJury Testimony in Steroid Case.” Department of Justice, release number 07-023, February 15,2007. Accessed September 12, 2011. http://www.justice.gov/usao/cac/Pressroom/pr2007/023.html.

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