Laumann et al v. National Hockey League et al

Filing 347

OPINION AND ORDER re: (277 in 1:12-cv-01817-SAS) JOINT MOTION To Exclude Opinions And Testimony Of Plaintiffs' Expert Dr. Roger G. Noll filed by Chicago Blackhawks Hockey Team Inc, Lincoln Hockey LLC, Hockey Western New York LLC, NHL Interactive Cyberenterprises LLC, Comcast-Spectacor L.P., NHL Enterprises L.P., New Jersey Devils LLC, San Jose Sharks LLC, Lemieux Group, L.P., National Hockey League, New York Islanders Hockey Club L.P., (354 in 1 :12-cv-03704-SAS) MOTION to Preclude the opinions and testimony of Plaintiffs' expert Dr. Roger Noll filed by Pittsburgh Baseball, Inc, MLB Advanced Media L.P., Chicago White Sox, Ltd., The Phillies, L.P., The Baseball Club of Seattle, L.P., San Francisco Baseball Associates, L.P., Major League Baseball Enterprises Inc., MLB Advanced Media, Inc., Colorado Rockies Baseball Club, Ltd., Athletics Investment Group, LLC, Officer of the Commissioner of Bas eball: For the reasons set forth above, defendants' motion to exclude the opinions and testimony of Dr. Roger Noll is GRANTED in part and DENIED in part. The Clerk of the Court is directed to close this motion, Dkt. No. 277 in 12 Civ. 1817, and Dkt. No. 354 in 12 Civ. 3704. (Signed by Judge Shira A. Scheindlin on 5/14/2015) (tn)

USOCSIJNY
DOCUMENT
UNITED STATES DISTRICT COURT
SOUTHERN DISTRICT OF NEW YORK

ELECTRONICALLY FILED

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--------------------------------------------------------------------.: DA11! l'1Ull>:
it
THOMAS LAUMANN, ROBERT SILVER,
GARRETT TRAUB, and DAVID DILLON,
representing themselves and all other similarly
situated,

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r,L'i.h)
~

OPINION AND
ORDER

Plaintiffs,
- against -

12-cv-1817 (SAS)

NATIONAL HOCKEY LEAGUE, et al.,
Defendants.

x
MARC LERNER, DEREK RASMUSSEN, and
GARRETT TRAUB, representing themselves and all
other similarly situated,
Plaintiffs,
- against -

12-cv-3704 (SAS)

OFFICE OF THE COMMISSIONER OF BASEBALL,
et al.,
Defendants.

--------------------------------------------------------------------- x
I.

INTRODUCTION
These cases challenge restraints in the market for baseball and hockey

broadcasting. The essence of plaintiffs' argument is that the leagues - Major

1

l:

League Baseball (“MLB”) and the National Hockey League (“NHL”) — have
conspired with regional sports networks (“RSNs”), who produce broadcasts for
individual teams, as well as multichannel video programming distributors
(“MVPDs”), who sell broadcasts to consumers, to maintain a system of “territorial
exclusivity” that limits viewing options and inflates prices.
The details of that system are described in detail in a companion
Opinion, also issued today, addressing the issue of class certification.1 This
Opinion addresses the admissibility of the damages model proffered by plaintiffs’
expert, Dr. Roger Noll (the “Daubert Opinion”). For the purpose of that task, the
important background is that RSNs are currently prohibited — by league-wide
agreement —from broadcasting their content to baseball and hockey fans who live
outside an RSN’s home team territory. Consequently, if a fan of an out-of-market
team wishes to watch that team’s games, she is forced to buy an out-of-market
package (“OMP”) that contains broadcasts of all games in the league.
Plaintiffs believe that this arrangement reflects an unlawful restraint
of trade, and that if the league-wide agreement preventing out-of-market RSN
distribution were eliminated, fans of out-of-market teams would be able to
subscribe to “a la carte channels,” which would carry broadcasts only of the

1

See Certification Opinion.
2

subscriber’s preferred team — at a lower price than the OMP. For example, a
Yankees fan living in Iowa now has to purchase an OMP if she wants to watch a
season’s worth of Yankees’ games — whereas in the but-for world envisioned by
plaintiffs (“BFW”), the same fan would have the option of purchasing an OMP or
getting an a la carte subscription from the Yankees’ RSN.
According to plaintiffs, the absence of a la carte options in the actual
world has insulated the OMPs from competition, allowing the leagues, the RSNs,
and the MVPDs to command super-competitive subscription fees — leading to
overcharge. The purpose of Dr. Noll’s model is to model the extent of that
overcharge, by comparing the price of OMPs in the actual world to the projected
price of OMPs in the BFW, once the territorial restraints are lifted, and the supply
chain is reconfigured accordingly.
Defendants have moved pursuant to Rule 702 of the Federal Rules of
Evidence (“FRE”) to exclude Dr. Noll’s expert opinions, alleging that his model
suffers from numerous methodological flaws that render his opinions unreliable as
a matter of law. For the foregoing reasons, defendants’ motion is GRANTED in
part and DENIED in part.
II.

LEGAL STANDARD
The proponent of expert evidence bears the initial burden of

3

establishing admissibility by a “preponderance of the evidence.”2 For expert
testimony to be admissible under FRE 702, the witness must be “qualified as an
expert by knowledge, skill, experience, training, or education[.]”3 The court must
then “compare the area in which the witness has superior knowledge, education,
experience or skill with the subject matter of the proffered testimony.”4
To be admissible, the proposed expert testimony must be based “on a
reliable foundation.”5 In assessing reliability, the trial judge should consider
whether:
(1) the testimony is based upon sufficient facts or data, (2) the
testimony is the product of reliable principles and methods, and
(3) the witness has reliably applied the principles and methods to
the facts of the case.6
Although the Supreme Court has instructed district courts to focus “on [the]
principles and methodology” employed by the expert and “not on the conclusions
that they generate,”7 “nothing in either Daubert v. Merrell Dow Pharmaceuticals
2

United States v. Williams, 506 F.3d 151, 160 (2d Cir. 2007).

3

Fed. R. Evid. 702.

4

United States v. Tin Yat Chin, 371 F.3d 31, 40 (2d Cir. 2004).

5

Daubert v. Merrell Dow Pharms., Inc., 509 U.S. 579, 597 (1993).
Accord Kumho Tire Co., Ltd. v. Carmichael, 526 U.S. 137, 147-49 (1999).
6

Fed. R. Evid. 702 (emphasis added).

7

Daubert, 509 U.S. at 595.
4

or the Federal Rules of Evidence requires a district court to admit opinion evidence
that is connected to existing data only by the ipse dixit of the expert.”8 Indeed, “[a]
court may conclude that there is simply too great an analytical gap between the
data and the opinion proffered.”9 For this reason, “even where an expert’s
methodology is reliable, if the analysis is not based upon relevant and reliable data,
the expert’s opinion will be inadmissible.”10
District courts are charged with acting as “‘gatekeeper[s] to exclude
invalid and unreliable expert testimony,’”11 and are given “broad discretion” to
make such determinations.12 However, trial courts must consider only the
admissibility of expert evidence rather than its weight or credibility. “As the
Supreme Court has explained, ‘[v]igorous cross-examination, presentation of
contrary evidence, and careful instruction on the burden of proof are the traditional

8

Kumho Tire, 526 U.S. at 157 (quotation marks and citations omitted).

9

General Elec. Co. v. Joiner, 522 U.S. 136, 146 (1997).

10

Johnson Elec. N. Am. Inc. v. Mabuchi Motor Am. Corp., 103 F. Supp.
2d 268, 283 (S.D.N.Y. 2000).
11

Baldwin v. EMI Feist Catalog, Inc., 989 F. Supp. 2d 344, 349
(S.D.N.Y. 2013) (quoting Bickerstaff v. Vassar Coll., 196 F.3d 435, 449 (2d Cir.
1999)).
12

Davis v. Carroll, 937 F. Supp. 2d 390, 413 (S.D.N.Y. 2013). Accord
Amorgianos v. National R.R. Passenger Corp., 303 F.3d 256, 265 (2d Cir. 2002).
5

and appropriate means of attacking shaky but admissible evidence.’”13
Finally, it is often the case that some, but not all, of an expert’s
opinions will meet the criteria of FRE 702. Indeed, it is routine for a party to retain
a single expert to opine on a variety of issues that, while related, can be analyzed
independently under the Daubert standard. In such cases, the court, as gatekeeper,
has discretion to decide which opinions are reliable and which are not, from which
it follows that a court may exclude portions of an expert report while admitting
other portions.14
III.

PLAINTIFFS’ EXPERT
Dr. Noll, a nationally-recognized sports economist, has submitted an

expert report explaining why the prices of baseball and hockey broadcasts would
decrease in the BFW.15 In support of this expert report, Dr. Noll designed an
economic structural model to simulate how consumers and RSNs would behave if

13

Amorgianos, 303 F.3d 256 at 267 (quoting Daubert, 509 U.S. at 596).

14

See, e.g., Louis Vuitton Malletier S.A. v. Sunny Merch. Corp., No. 13
Civ. 5242, 2015 WL 1499449, at *17 (S.D.N.Y. Mar. 31, 2015) (striking some, but
not all, of a single expert’s opinions); Federal Hous. Fin. Agency v. Nomura
Holding Am., Inc., No. 11 Civ. 6201, 2015 WL 640875, at *3 (S.D.N.Y. Feb. 16,
2015) (same).
15

Defendants do not challenge Dr. Noll’s academic or professional
qualifications.
6

territorial restrictions were lifted.16 Dr. Noll claims that his model is based on a
similar study conducted by Drs. Gregory Crawford and Ali Yurukoglu (the “C&Y
Model”), which measured how the hypothetical unbundling of cable television
packages would impact consumer welfare over the short-run.17 How closely Dr.
Noll’s model follows the approach of the C&Y Model is the subject of major
disagreement among the parties, but at least one difference between the two models
is undisputed: in Dr. Noll’s model, unbundling reduces prices for the consumer; in
the C&Y Model, it does not.
In broad strokes, both models are built on the interplay between
consumer demand and preferences for content (the “Demand Side”) and the supply
chains that distribute that content (the “Supply Side”). In attempting to predict
future outcomes in a hypothetical universe, the models depend both on existing
data in the actual world and assumptions about how consumers and distributors

16

See 2/23/15 Corrected Reply Declaration of Roger G. Noll (“Dr. Noll
Reply Decl.”); 9/19/14 Supplemental Declaration of Roger G. Noll (“Dr. Noll
Supp. Decl.”); 2/18/14 Declaration of Roger G. Noll (“Dr. Noll Decl.”). For
reasons explained below, Dr. Noll twice tweaked the model he offered in his first
declaration in response to criticism from defendants’ experts. Yet all of these
models are considered “interim” models — the Court did not require a final model
at this early class certification stage of the litigation.
17

See Gregory S. Crawford & Ali Yurukoglu, The Welfare Effects of
Bundling in Multichannel Television Markets, 102 Am. Econ. Rev. 643 (2012)
(“C&Y”).
7

will behave in the BFW.
Defendants attack Dr. Noll’s methodological approach to constructing
both the Demand Side and the Supply Side, arguing that design flaws in each
constitute independent bases for rendering his model unreliable as a matter of law
under FRE 702 and Daubert.18 In support of their motion, defendants retained
several experts to vet both sides of Dr. Noll’s model, all of whom — along with
Dr. Noll — were examined and cross-examined over the course of a three-day
Daubert hearing.19 On the Demand Side, defendants’ primary concern is that Dr.
Noll did not rely on sufficient data about fan preferences in estimating what their
demand would be for products in the BFW. On the Supply Side, defendants’
grievance centers on Dr. Noll’s conceptual assumptions; they argue that Dr. Noll
has not justified all the building blocks of his structural model. In this Opinion, I
separately discuss the Demand Side and the Supply Side of Dr. Noll’s model.
18

See generally Memorandum of Law in Support of Defendants’ Joint
Motion to Exclude Opinions and Testimony of Plaintiffs’ Expert, Dr. Roger Noll
(“Def. Mem.”); Reply Memorandum of Law in Support of Defendants’ Joint
Motion to Exclude Opinions and Testimony of Plaintiffs’ Expert, Dr. Roger Noll
(“Reply Mem.”).
19

See 3/17/15 Transcript of Proceedings, Dkt. 339 (“Day 1 Tr.”);
3/18/15 Transcript of Proceedings, Dkt. 341 (“Day 2 Tr.”); 3/19/15 Transcript of
Proceedings, Dkt. 343 (“Day 3 Tr.”). The hearing concluded with summations by
counsel for plaintiffs and defendants addressing both the reliability, or lack thereof,
of Dr. Noll’s model and how the Court’s decision on the Daubert challenge bears
on class certification. See Day 3 Tr.
8

IV.

DEMAND SIDE
A.

Summary of Dr. Noll’s Opinions20
The price of the league bundles and a la carte channels in the BFW is

driven by consumer demand.21 To predict those prices, Dr. Noll’s model follows a
two-step approach. First, he captures a mathematical curve of consumer demand
in the actual world, with territorial restrictions, based on observed consumer and
producer behavior and various corresponding assumptions, by designing
mathematical equations designed to replicate the observed behavior.22 Second, he
simulates markets in the BFW using consumer demand as determined by the
estimates in the first step.23 The general idea, according to Dr. Noll, is to “us[e] a
demand estimate [] derived from the status quo to analyze another counterfactual
world that has territorial restrictions removed.”24 Determining how Dr. Noll
arrives at these step-one estimates is critical to analyzing the reliability of the
20

As mentioned above, Dr. Noll ultimately made two adjustments to his
original model in designing the Demand Side in response to criticism from the
defendants’ experts. The most recent version of Dr. Noll’s model, outlined in his
February 23, 2015 declaration, was the primary focus of the Daubert hearing. It is
this third version I will analyze here, unless otherwise noted.
21

See, e.g., Day 1 Tr. at 64-65.

22

See id. at 66.

23

See id.

24

Id. at 67.
9

Demand Side.
At bottom, these estimates are predicated on the notion that consumers
derive welfare, or utility, from spending time watching the live telecasts of their
preferred teams, but only to a certain point — especially in light of price
considerations and the corresponding utility of doing things other than watching
televised sporting events.25 Therefore, the Demand Side “requires a large sample
of consumer viewing data across the channels in a bundle . . . to calculate the
means and standard deviations of time spent viewing each sports channel and
engaging in other activities.”26 The viewing data on which Dr. Noll relies is drawn
from subscriber information for the OMPs provided over the Internet and through
DirecTV for the 2012 MLB season and for the OMPs provided over the Internet
for the 2011-2012 NHL season.27 Dr. Noll then uses this data to estimate statistical
distributions of consumers’ preferences for each team in the bundles.
1.

The Underlying Data

Before delving into the technicalities of the statistical analysis

25

See Dr. Noll Decl. at 100.

26

Id.

27

See Dr. Noll Supp. Decl. at 5. Dr. Noll states that the data that the
MVPD providers for the NHL OMP produced were “too fragmentary to support
estimating the same model.” Id.
10

underpinning the Demand Side, it is important to review the underlying consumer
viewership statistics — the observed, real-world data buttressing the demand
component of the model — in greater detail, as well as some corresponding
assumptions Dr. Noll makes using that data. Because there is no observable data
about consumer behavior in the BFW, utilizing real-world information is
paramount in estimating otherwise unknown consumer preferences.28
For its MLB OMP (“MLB Extra Innings”), DirecTV records the time
and duration of a single session of viewing a channel for each subscriber.29 This
amounts to 1,178,100 viewing records for 3,236 subscribers over the course of the
season. The DirecTV data does not include the location of the subscriber or the
specific package that the subscriber purchased — there is some variation in price
among packages, in part due to discounts for purchasing them a certain amount of
time in advance of the start of the season.30 MLB’s Internet OMP (“MLB.tv”)
provides substantially more data points, totaling 64,562,268 viewing records of
521,352 unique subscribers, but those records do not record the total time spent

28

See Dr. Noll Decl. at 100.

29

See Dr. Noll Supp. Decl. at 25.

30

See id. at 26. For the purposes of modeling, Dr. Noll assumes that all
subscribers paid the price of the most popular package. See id.
11

viewing each game.31 Accordingly, Dr. Noll estimated the mean viewing time for
each team by multiplying the number of that team’s games viewed by the MLB.tv
subscriber by the average viewing duration for that team in the DirecTV data.32 As
with DirecTV, MLB.tv package prices vary slightly, so Dr. Noll used the price of
the most popular package for estimation purposes.33 For the NHL Internet OMP
(“NHL GameCenter Live”), Dr. Noll’s data set included 4,166,577 records for
99,966 subscribers for the 2011-2012 season.34 This data includes information
regarding viewing time and subscriber location for each game.35 Finally, for both
the MLB and NHL OMPs, Dr. Noll calculates relevant market shares as the
number of subscribers to the services divided by the number of U.S. households
that watched that sport’s championship series, assuming the latter figure to be an
upper bound of the potential market for the package.36
Dr. Noll uses this viewing data and various assumptions stemming
from it to build the foundation of the Demand Side. Notably, as defendants are
31

See id. at 26-27.

32

See id. at 27.

33

See id.

34

See id.

35

See id.

36

See id. at 27-28.
12

quick to point out, this foundation lacks any significant additional information
regarding consumer demand and preferences. For instance, in modeling demand,
Dr. Noll admits that he (1) never sought to obtain demographic data or other
personal characteristics about OMP subscribers or baseball and hockey fans more
generally, (2) never conducted or attempted to conduct any type of survey of
subscribers or non-subscribers regarding viewing preferences or tastes for new
products (to wit, a la carte offerings), (3) never sought to obtain specific
information regarding price sensitivities of consumers, and (4) that he ignores
information regarding package price variation.37 Similarly, based on the observed
data for baseball, only four percent of all World Series watchers — Dr. Noll’s
assumed upper bound of the market — actually subscribed to an OMP; lacking
additional preference data, it follows that all Dr. Noll knows concretely about
ninety-six percent of fans in the potential market is that they did not buy a
package.38
2.

Recovering the Demand Curve

So, how does Dr. Noll use the observed data and his assumptions to
estimate demand? This is where things get complicated. According to Dr. Noll,

37

See, e.g., Day 3 Tr. at 476-478, 512.

38

See Day 1 Tr. at 130-131.
13

“[t]he goal of the econometric estimation is to recover the distribution of consumer
preferences for live telecasts of the games of each team [] as well as consumer
preferences for viewership and price sensitivity.”39 To accomplish this task, he
builds a structural model which originates with sample mathematical equations that
are meant to replicate the actual data reflecting the behavior of participants in a
market on both the supply and demand sides. Included in those equations are
unknown mathematical variables, which can be estimated by attempting to match
them to existing viewership data.40 In economic terms, these variables are referred
to as “parameters.”41 The parameters the model seeks to estimate “measure the
responsiveness of the consumer to price, the value they place on having access to
the bundle, and the value they place on viewing time of each of the teams or RSNs
that they view.”42 Put differently, the parameters gauge the relative utility a
consumer derives for each out-of-market channel as compared to all other nonsports-watching activities, which can then be applied to predicting outcomes in the
BFW.

39

Dr. Noll Supp. Decl. at 31-32.

40

See Day 1 Tr. at 67-68.

41

See id. at 68.

42

Id.
14

These parameters are ultimately determined using the Generalized
Method of Moments (“GMM”), an estimation procedure commonly deployed by
economists to study demand in markets with differentiated products. Without
going into great mathematical detail — many pages of Dr. Noll’s declarations
consist almost entirely of complex equations beyond the comprehension of the
Court or the lawyers in this case — GMM works by using an iterative process in
which experimental values are assigned to these parameters with the goal of getting
the sample equations to produce results as close as possible to the actual, observed
data.43 In other words, and at the risk of oversimplifying it, the GMM component
of the model essentially runs through a series of sixty-six mathematical formulas
over and over again, each of which predicts “moments” (measures of the statistical
distribution) of data relating mainly to viewing time, until the moments predicted
by a given, experimental formula nearly replicate the actual moments of viewing
data collected from MLB Extra Innings, MLB.tv, and NHL GameCenter Live,
described above.44 When the GMM process concludes, and the predicted moments
43

See id. at 68-69.

44

See id. at 69. Because a perfect match between predicted moments
and observed moments is impossible, the GMM procedure stops once the match is
as close as possible. The moments themselves characterize the shape of the
statistical distribution of a particular variable. See id. at 70-71. They are
calculated by using the means and standard deviations of viewing data, bundle
market share, and price-cost margin.
15

closely match the observed ones, the demand parameters of the model are
established.
Dr. Noll’s predicted moments come close to matching the actual
moments drawn from the observed data in the existing world. Consequently, Dr.
Noll concludes that “the explanatory power of the model to replicate the data from
which it is estimated is high.”45 While there is some variation in match accuracy
across individual teams, Dr. Noll insists that “what is actually important is the
power of the model itself to explain all of the data,” not just the results for specific
teams.46
After recapturing the demand curve, Dr. Noll turns to the second step
of the model — predicting prices in the BFW. To do so, Dr. Noll uses data about
demand in the actual world to simulate demand in a market where out-of-market
RSNs are available a la carte.47
3.

Categorizing Fans and the Logit Error

One of the realities of the BFW, assuming bundles continue to exist, is
that fans will have the choice between purchasing one or more a la carte channels

45

Id. at 80.

46

Id.

47

See id. at 82. For a detailed discussion of the Supply Side, see infra

Part IV.
16

or a traditional OMP. To simulate this competition on the Demand Side, Dr.
Noll’s model sorts potential subscribers into one of three categories: (1) singleteam fans, (2) two-team fans, and (3) multi-team fans.48
Single-team fans subscribe to a bundle for access to only one team.
These are fans that “do relatively little viewing of any other team.”49 Two-team
fans are those who subscribe to OMPs because they have an interest in two teams
— for example, a husband and a wife who have different favorite teams.50 Multiteam fans are interested in watching anywhere between three teams and the
maximum number available to them.51
Dr. Noll relies on the mathematical estimation procedures driving the
GMM to help him sort fans into various categories.52 Significantly, these
48

See Day 1 Tr. at 91. His first two models did not distribute fans in
this manner. The adjustment in the third model responded to critiques of
defendants’ Demand Side expert, who demonstrated that Dr. Noll’s second model
was unresponsive to drastic changes in viewership patterns, producing the same
results regardless of those changes. See Dr. Noll Supp. Decl. at 12-13. Therefore,
the fan categories of the third model attempt to account for a wider range of
viewership patterns.
49

Day 1 Tr. at 80.

50

See id.

51

See id.

52

See id. at 137 (“[T]he proportions of people who are one, two, and
many are determined by the estimation procedure to maximize the explanatory
value of the three-way categorization.”).
17

categories comprise “simulated fans” whose preferences are not directly
corroborated by any actual, observable data — instead, they are driven by moments
of viewing time.53 To put this in perspective at the Daubert hearing, defendants’
counsel and Dr. Noll addressed the process of classifying a hypothetical fan who
watches one team ninety percent of the time but still derives significant value from
the ability to view other games from time to time.54 Dr. Noll would likely classify
that fan as a single-team fan.55 In essence, while a fan with a primary allegiance to
a single team may in fact have a strong preference to watch other teams as well,
Dr. Noll admits that those preferences are “zeroed out” in his model.56
Dr. Noll insists that this type of viewing utility is accounted for by a
mathematical component built into his model called the “logit error.” The term
logit error does not connote a mistake; rather, the error is a random component of
the model that seeks to capture factors bearing on the utility a fan derives from

53

See id. at 137-138.

54

See id. Imagine a die-hard Yankees fan who spends ninety percent of
his baseball-watching time viewing only Yankees games, but ten percent of the
time, he might want to check in on games featuring the Yankees’ division rivals –
perhaps even more than ten percent of the time if the division is particularly
competitive that season.
55

See id. at 138.

56

Id.
18

watching a specific team beyond simply the time spent viewing that team.57
According to Dr. Noll, the logit error is a “random component value” factored into
the Demand Side to account for the fact that there are forms of utility from the
bundle other than pure viewing time.58 So the logit error adds to the model a
random distribution of utility to provide, per Dr. Noll, “a shock that isn’t measured
by what is already in there.”59 But, as Dr. Noll admits, “[t]here is no additional
information about [fans’] preferences other than the logit error that measures the
departure of the utility from the expected value.”60
4.

Results of the Model

Dr. Noll’s model yields significantly reduced prices of bundles in the
BFW. Specifically, the average monthly price of the MLB.tv package would
decrease from $20.05 to $14.50.61 The average monthly price of NHL GameCenter
Live would decrease from $26.28 to 18.08.62 The average monthly price of MLB

57

See id. at 140.

58

Id. at 171.

59

Id. at 175.

60

Id. at 179.

61

See Dr. Noll Reply Decl.

62

See id.
19

Extra Innings would drop from $33.59 to $24.59.63
Central to evaluating the reliability of those findings are the specific
results Dr. Noll’s model yields for fans’ preferences between a la carte channels
and bundles in the BFW. For both the MLB and the NHL, the following pattern
holds true. Of the three fan types in Dr. Noll’s model, the fans classified as
“single-team fans” — the ones primarily interested in watching one and only one
team — are the most likely to purchase the league package, and the least likely to
purchase an a la carte channel.64 Further, the fans most likely to purchase an a la
carte channel are those that are interested in the greatest number of teams.65 These
multi-team fans almost universally reject the opportunity to purchase a league
bundle in the BFW.66 The relevant data demonstrating this pattern is reflected for
MLB.tv in the chart below.

63

See id.

64

See Defendants’ Demonstratives and Exhibits (“Def.
Demonstratives”), Tab 8, at 13.
65

See id. Of course, there is a limit to the number of a la carte channels
that a fan would purchase — once the number of a la carte channels a fan is
interested in purchasing exceeds the price of the bundle, or is about the same, the
fan would be better off buying the bundle.
66

See id.
20

Chart 1: Purchasing Decisions by Fan Type in the BFW - MLB.tv67
Fan Type

% Purchasing Standalone

% Purchasing Bundle

Single-Team Fan

68

32

Two-Team Fan

81

19

Multi-Team Fan

99

1

B.

Defendants’ Key Modeling Criticisms
Defendants insist that these results are absurd and counterintuitive,

signaling that something is amiss about Dr. Noll’s model. After all, common sense
would suggest that the opposite of Dr. Noll’s model should hold true — fans
interested primarily in one team would buy an a la carte channel, and fans
interested in several teams would buy a bundle. To that end, defendants rely on
their own expert, economist Dr. Daniel McFadden, to highlight methodological
flaws on the Demand Side.68 Dr. McFadden offers a number of criticisms of Dr.
Noll’s model, ultimately characterizing it as “junk science.”69 All of the

67

See id.

68

See Day 2 Tr. at 359-360. Dr. McFadden won the Nobel Prize for
developing methods to study discrete choice — situations where consumers have
to choose between one product or another. See id. According to Dr. McFadden,
Dr. Noll “is using discrete choice models and analysis at the core of his demand
analysis.” Id. at 360. Plaintiffs do not challenge Dr. McFadden’s credentials as an
expert.
69

Id. at 383.
21

independent, perceived flaws that inform Dr. McFadden’s conclusion fit one
common theme: the Demand Side relies too heavily on mathematical assumptions
and random error, and too little on actual data about consumers and their
preferences.70
To illustrate this problem, Dr. McFadden closely examines the process
by which Dr. Noll simulates the behavior of consumers. As noted above, only four
percent of all World Series watchers — Dr. Noll’s assumed upper bound of the
market — actually subscribed to an OMP.71 This means that Dr. Noll has no data
for ninety-six percent of consumers in the potential market, other than that they
chose not to buy the package in the actual world. To design simulated consumers
(or “avatars”), Dr. Noll starts with real demand data on fans that subscribed to
league packages in the actual world — but these fans constitute only a very small

70

At the beginning of his testimony at the Daubert hearing, Dr.
McFadden offered three guiding principles for evaluating the reliability of the
demand component of structural models. First, when modeling demand, “you
should be using data on consumer behavior rather than say, for example,
mathematical assumptions.” Id. at 362 (emphasis added). Second, the model’s
predictions should be “falsifiable,” such that the model should not yield certain
kinds of results in situations where different demand inputs are used to alter the
expected predictions. See id. Third, the “model should be consistent with
observed consumer behavior[,] particularly on dimensions that are important for
that application.” Id. (emphasis added).
71

The same method is applied to the NHL and viewers of its
championship series, the Stanley Cup.
22

subset of the total BFW population. To account for the overwhelming majority of
BFW consumers, about which he knows very little based on the actual world, he is
forced to, in Dr. McFadden’s words, “assign[] a mathematical DNA to these
avatars” using “assumptions to essentially fill out the DNA which will determine
how these people make choices and behave.”72 Ultimately, the behavioral
properties of these avatars are not based directly on “anything in the real data that
[Dr. Noll] has.”73
As a result, while Dr. Noll is able to match predicted viewing times in
the BFW to observed viewing times in the actual world with some precision
through the GMM procedure, his model’s estimates about viewer preferences are
inaccurate.74 To prove this point, Dr. McFadden compares the actual league
subscriber data to the predicted habits of Dr. Noll’s avatars. The results of that
comparison are reflected in the chart below.

72

Id. at 366.

73

Id. at 369.

74

See id. at 373.
23

Chart 2: Comparison of Subscriber Behavior - NHL75
Subscriber Behavior

Actual %

Dr. Noll’s Model %

Watches a single RSN

22.1

50.6

Watches two RSNs

12.1

19.7

Watches more than two RSNs

65.8

29.7

This simple fitting test reveals major differences between viewers’ tastes as
defined by the actual, observed data and those predicted by the GMM.
As another example, Dr. McFadden runs a different test to show that
Dr. Noll’s model underpredicts the popularity of individual RSNs within league
bundles across the board. The general pattern is that bundle subscribers “watch a
lot more teams,” and a “higher share of them watch every team [or] any team” than
Dr. Noll’s model predicts.76 In fact, for all but two teams in the NHL, Dr. Noll’s
model predicts that a lower percentage of subscribers watch a given team than what
was observed in the actual data for package subscribers.77 This discrepancy is
particularly noteworthy, according to Dr. McFadden, because it represents a
departure from the C&Y Model, which “imposed as part of [its] moments the

75

Def. Demonstratives, Tab 8, at 9. Testimony regarding similar MLB
data was stricken from the record because Dr. McFadden’s MLB charts were not
disclosed to plaintiffs in advance of the Daubert hearing. See Day 2 Tr. at 374.
76

Day 2 Tr. at 377; see also Def. Demonstratives, Tab 8, at 12.

77

See Day 2 Tr. at 377; Def. Demonstratives, Tab 8, at 12.
24

condition that these actual and predicted shares had to match.”78 This is not the
only instance of Dr. Noll’s model failing to replicate the C&Y Model on the
Demand Side. At a more general level, Dr. McFadden testified that an important
difference between Dr. Noll’s model and that of Crawford and Yurukoglu is that
the latter was built on substantially more data.79 Dr. Noll conceded as much during
the Daubert hearing.80
When asked how he would remedy this problem, Dr. McFadden
testified that
[t]he standard procedure would be to try to get data . . .
from the entire population . . . certainly first to go look
and see if someone else has already collected it. But if
you can’t find that, it would [be] common procedure [] to
collect your own data, do your own survey, find out who
is, for example, in this case who’s a fan and who is not,
and perhaps also find out more about what their tastes
are, whether they would consider buying or not at various
suggested prices.81

78

Day 2 Tr. at 377.

79

See id. at 367.

80

See Day 1 Tr. at 123-128. In their study, Crawford and Yurokoglu
based their model on a variety of sources of information, including surveys of
random samples of consumers about media usage, consumer behavior, and
demographics. See C&Y at 653. The information they gleaned factored into their
GMM estimates. See id. at 668-671.
81

Day 2 Tr. at 367-368.
25

He added that collecting surveys is “almost a standard in market research where
this problem of estimating demand for new product” arises — “something that
firms deal with all the time, and there is now a long tradition and a long history of
using survey techniques to understand what’s going on and [to] make
predictions.”82
According to Dr. McFadden, the dearth of data in Dr. Noll’s model
culminates in nonsensical results – namely those reported in Table 1 — which are
driven by the logit error. Specifically, Dr. McFadden contends that Dr. Noll’s use
of logit error in his model is “inappropriate” in this circumstance because of what
is known as the “red bus/blue bus problem” — a function of the logit error that
forces an overprediction of how many consumers will buy standalone RSNs
instead of the bundle in the BFW.83 The red bus/blue bus problem — a known
characteristic of logit error models — draws from a hypothetical decision
commuters face: whether to travel by car or by a red bus. In the first instance, one
assumes that a commuter chooses between these options with equal probabilities.
Then, add to the hypothetical a third option — a blue bus, in addition to a red one.
In theory, the additional color choice should not affect the probability of whether a

82

Id. at 368.

83

Id. at 380.
26

commuter chooses to commute by car or by bus, generally. But the potential flaw
of the logit error is that introducing the third option, a different type of bus, spreads
the odds evenly between car, red bus, and blue bus. As a result, the odds of
choosing to commute by car drop when they really should remain the same. The
more colors of buses that are added, the more likely a commuter will ultimately
choose a bus.84 In this case, the logit error flaw becomes apparent by substituting
cars and buses for bundles and a la carte channels — the logit error decreases the
probability of choosing the former as more types of the latter are added.85 Dr. Noll
never tested whether this problem affected his model, despite the fact that,
according to Dr. McFadden, “[i]t’s a limitation of the model which people are
warned against” and for which “there are tests.”86
Above all else, Dr. McFadden notes that this problem is exacerbated
by Dr. Noll’s heavy reliance on mathematical assumptions and equations to derive
properties of avatars. Dr. McFadden concludes that, “from a scientific point of
view,” the red bus/blue bus problem shows that the Demand Side of Dr. Noll’s
model is “very badly specified.”87 Dr. Noll’s results “violate[] common sense”
84

See id.

85

See id. at 380-381.

86

Id. at 381.

87

Id. at 382.
27

because “he’s making a mathematical inference on how choice behavior is going to
look without going into any real data on it[,] and that is itself simply unreliable.”88
C.

Dr. Noll’s Response
Dr. Noll claims that his model does not suffer from data deficiencies,

and moreover, that the seemingly counterintuitive results of his model make
economic sense in that they reflect differing price sensitivities among categories of
fans. According to Dr. Noll, “the parameters [] estimate[d] produce the result that
the multi-team [fans] are the most price sensitive.”89 This is because the viewing
time moment in his model is a “mechanism that picks up the degree to which teams
are substitutes for each other, and when you finally get to the multi-team fan, you
have lots of teams that are perfect substitutes, and so you can distribute your
time.”90 Expanding on this explanation, Dr. Noll offered a hypothetical during his
rebuttal testimony:
Suppose you are a Yankees fan. You couldn’t care less
what the price of the Houston Astros channel is. Right?
You are going to buy the Yankees — you’re pretty price
sensitive to it. All right? Now instead suppose you’re
someone who just likes baseball[,] and you don’t care
whether it’s the Houston Astros or the New York Yankees.

88

Id. at 383.

89

Day 1 Tr. at 182.

90

Id. at 180.
28

You are more likely to look at the relative price of those
two to decide which channel to subscribe to.91
Therefore, Dr. Noll insists that his model produces the result that for multi-team
fans price sensitivity outweighs preference for diversity.
During Dr. Noll’s rebuttal testimony at the Daubert hearing I asked
whether, in simply enjoying watching the game of baseball or hockey, multi-team
fans would buy a package instead of one or two RSNs, which would heavily
restrict the number of teams they could watch on any given day. Dr. Noll asserted
that the “coefficient in the regression in the utility function” of his model — part of
the logit error — accounts for that possibility.92 In support of that contention, Dr.
Noll dismissed Dr. McFadden’s conclusion about the effects of the red bus/blue
bus problem on the model’s logit error as “inaccurate.”93 Defending his use of the
logit error, Dr. Noll stated:
The red bus/blue bus problem is not a problem of the
model. The problem of the model is, in fact, in certain
circumstances, the logit error is driving results or is
affecting — I shouldn’t say driving — it is one of the
factors producing results. The right way to say it is [the
logit] introduces heterogeneity in consumer behavior. And

91

Day 3 Tr. at 439.

92

Id. at 444. Dr. Noll does not actually know the value for this
parameter but assumes the lowest possible value to be conservative. See id.
93

Id. at 493.
29

. . . one of the ways to change the results that is going to
make this look better, if you cared about it is, to make
assumptions that reduce the effect of the logit error.94
Dr. Noll also attempts to rebut defendants’ more pointed criticisms
regarding the data on which he relied, or failed to rely, in designing the Demand
Side. Mainly, he insists that it would have been impractical for him to do a
survey.95 This holds true as well for a conjoining analysis — a smaller type of
survey that reduces the sample size.96 Conjoining analyses are frequently
performed by companies that seek to introduce new products into the market.97
But Dr. Noll concludes that in this case, performing such an analysis would require
too large of a sample size to be practical.98
Dr. Noll also attempts to show that Dr. Ariel Pakes, one of
defendants’ Supply Side experts, relied on similar quantities and types of data in
constructing avatars for a 2004 study estimating the demand for automobiles.99 In

94

Id. at 494.

95

See id. at 480.

96

See id.

97

See id.

98

See id.

99

See id. at 430; Steven Berry, James Levinsohn, and Ariel Pakes
(“BLP”), Automobile Prices in Market Equilibrium, 63 Econometrica 841 (1995).
30

that paper, Dr. Pakes constructed demand for automobiles with a potential market
size of over one hundred million consumers even though the average annual sales
of automobiles in his actual world sample were slightly above ten million.100 Thus,
Dr. Noll asserts that Dr. Pakes knew next to nothing about a huge percentage of the
potential market other than that they did not purchase an automobile.101 However,
as Dr. Noll acknowledged on cross-examination, Dr. Pakes’ model relied on
demographic data from the census as well as random surveys conducted of over
thirty-seven thousand actual purchasers regarding automobile preferences.102
Further, the ultimate results of Dr. Pakes’ model were that less than one percent of
people in the potential market for automobiles became purchasers, as opposed to
forty-three percent of the potential market in Dr. Noll’s study.103
Finally, in response to Dr. McFadden’s observation that Dr. Noll’s
model underpredicts the number of channels bundle subscribers view, Dr. Noll
insists that the time spent viewing a channel is more important than the number of
overall channels a subscriber watches. According to Dr. Noll, “the ability of the
model to predict should be evaluated on the basis of the ability to predict viewing
100

See Day 3 Tr. at 430.

101

See id. at 431.

102

See id. at 476.

103

See id. at 476-478.
31

time, not [on the basis of] whether a bunch of subscribers [] spent very little time
watching that channel.”104 Thus, Dr. Noll asserts that his model’s predictions for
viewing time by team do match up very closely to the observed data.105
D.

Dr. Noll’s Demand Side Opinion Must Be Excluded
Calculating damages on the basis of predictions about hypothetical,

counterfactual scenarios is not an easy task. Further, estimating damages in
antitrust cases is especially challenging because “causes and effects in the realm of
economics are not nearly as clear-cut as they are in other disciplines.”106 It is
against this backdrop that plaintiffs bring an unusually complex and sweeping class
action lawsuit, premised on the theory that access to out-of-market baseball and
hockey telecasts would be cheaper in a counterfactual world without territorial
restrictions. Unless plaintiffs can prove that there is a scientifically-reliable way to
predict with some precision the prices of those telecasts in the future, they cannot
recover damages for being overcharged in the past.
There is no question that this task is enormously challenging, even for
the most seasoned and distinguished of experts. But it is not impossible — it has

104

Id. at 437.

105

See id. at 437-438.

106

In re Se. Milk Antitrust Litig., No. 08 Civ. 1000, 2010 WL 5102974, at
*2 (E.D. Tenn. Dec. 8, 2010).
32

been done before in similar circumstances.107 More importantly, the law is clear:
expert opinions are inadmissible if they are not “based on sufficient facts or data,”
or on a reliable application of scientific methods to those facts or data.108 This is
true no matter how burdensome or difficult collecting relevant data or devising
methods to apply to that data may be.109
Dr. Noll’s modeling of demand in the BFW is unreliable because the
Demand Side is largely untethered from the actual facts of this case.110 Defendants
offer a number of independent criticisms of the Demand Side, accusing Dr. Noll of
committing methodological flaws ranging from making inaccurate assumptions
about estimating market size to inappropriately using logit error in determining the
value fans derive from league bundles. Some of defendants’ criticisms are very
technical, none of which would be independently sufficient to win a Daubert

107

See C&Y at 16-19 (explaining how demand for individual channels
was estimated, using a combination of existing viewership data and demographic
data).
108

Fed. R. Evid. 702.

109

See, e.g., Fishman Transducers, Inc. v. Paul, 684 F.3d 187, 195 (1st
Cir. 2012) (excluding expert who failed to undertake “difficult, time-consuming
and expensive efforts” to obtain “direct testimony from customers, [or] market
research surveys of [product] purchasers as to their reasons for purchases,” noting
that, without them, “[the expert’s] report was merely a basis for jury speculation”).
110

See Fed. R. Evid. 702.
33

challenge.111 The problem for plaintiffs is that, at bottom, all of the examples
defendants and Dr. McFadden point to, and all of the tests they run on Dr. Noll’s
model, expose the same underlying problem, which is quite fundamental and fatal:
Dr. Noll’s estimates do not rely on sufficient data about consumer tastes and
preferences. Instead, time and time again, Dr. Noll substitutes actual, readilyobtainable information for mathematical assumptions in determining how hockey
and baseball fans will behave in the BFW.
For these reasons, I conclude that Dr. Noll’s expert opinions on
forecasting demand in the BFW must be excluded. And because a structural model
is only as reliable as its component parts, Dr. Noll’s model cannot be admitted to
calculate damages on plaintiffs’ theory of overcharge. As explained below, the
actual data on which Dr. Noll relies to extrapolate consumer demand in the BFW is
simply too sparse to survive defendants’ challenge under FRE 702 and Daubert.
In the antitrust context, economists are frequently asked to confront problems of
111

For instance, defendants complain that Dr. Noll’s model relied on
average monthly prices of the most popular league bundle subscriptions instead of
inputting a variety of subscription prices into the model to account for consumers’
tolerance to price variation. See Dr. McFadden Decl. ¶ 38. In isolation, this
modeling shortcut does not bear too heavily on the reliability of the overall model.
The cause for concern is that without surveys of consumers’ preferences for
products at various suggested price points, or additional data reflecting such
preferences, it is virtually impossible to gauge reliably how price sensitivities
would affect demand in the BFW, or how important it would be to build the
differing package prices into the model.
34

extraordinary complexity. In such studies, it is standard operating procedure to rely
on more data than Dr. Noll did here in attempting to measure consumer demand in
a counterfactual world.112
1.

The Foundation of the Recaptured Demand Curve Lacks
Sufficient Data

It is easy to detect the symptoms of Dr. Noll’s over-reliance on
mathematical assumptions, and under-reliance on actual data, in the initial demand
curve he derives through the GMM procedure. Dr. Noll’s approach to recapturing
this demand curve is casual, at best. In fact, his benchmark for estimating demand
is essentially a hodgepodge of data sets — varying in their levels of completeness
and detail — from MLB Extra Innings, MLB.tv, and NHL GameCenter Live,
combined with an assumption about the size of the market for OMPs.113 That is it.
And, as noted above, the prices Dr. Noll assigns to these OMPs are actually
average monthly prices — no price variation data is taken into account, despite the
fact that prices vary significantly depending on when consumers purchased the
package.114

112

See, e.g., C&Y; BLP.

113

See Dr. Noll Decl. at 27-28.

114

See Dr. McFadden Decl. ¶ 38 (noting that 47 percent of NHL
GameCenter Live subscribers, 73 percent of MLB.tv subscribers, and 34 percent of
MLB Extra Innings subscribers pay a different price from the one assigned by Dr.
35

This perfunctory approach impugns the overall reliability of the GMM
estimation, on which his entire model is built. For instance, Dr. Noll offers no
principled reason, and points to no actual data regarding fan preferences, for his
important assumption that the total number of viewers of a sport’s championship
series should constitute the upper bound of the market for that sport’s OMP. He
may be right, and this one assumption on its own does not necessarily sink the
model, but that is besides the point. For demand to be reliably estimated, Dr. Noll
needs a data-driven basis for his underlying assumptions, including those
pertaining to the important issue of market shares. If a swath of baseball fans had
been surveyed in some form, Dr. Noll might have gained a helpful insight into
whether setting the upper bound of the market at the total number of World Series
viewers was an appropriate assumption. Such survey data could have corroborated
his approach, or it could have caused him to refine it. Either way, without
preference data, the reliability of an important assumption driving demand in the
BFW remains in question. This type of unsupported assumption is all the more
problematic when the actual data sets Dr. Noll relies on are not as robust as they
could be. Some of these data sets contain subscriber location; others do not. Some
contain information about exactly how much time a fan spent viewing each team;

Noll).
36

others do not. While Dr. Noll cannot be faulted for not being provided with certain
information, in constructing a reliable model, he must do his best to fill the gaps.115
2.

The Preferences of Dr. Noll’s Avatars Are Heavily Impacted
by the Logit Error, Not Actual Data

Instead, Dr. Noll’s lack of reliance on actual data compounds the
potentially harmful impact of his unsupported assumptions. Consider Dr. Noll’s
sorting of consumers into single-team, two-team, and multi-team fans. Dr. Noll
categorizes fans through a mathematical estimation procedure tied to viewing time.
Acknowledging that fans may not distribute perfectly across these categories on
the basis of viewing time alone, Dr. Noll relies on the logit error to provide “a
shock that isn’t measured by what is already in there.”116 By Dr. Noll’s own
admission, “[t]here is no additional information about [fans’] preferences other
than logit error that measures the departure of the utility from the expected
value.”117 This is quite problematic, especially considering that Dr. Noll has no
real world data for ninety-six percent of the consumers in the potential market for
OMPs.
Worse still, the logit error he relies on to compensate for his lack of

115

See Fishman Transducers, Inc., 684 F.3d at 195.

116

Day 1 Tr. at 175.

117

Id. at 179.
37

preference data is susceptible to reliability issues because of the red bus/blue bus
problem. While the Court is not nearly proficient enough in econometrics to
evaluate the extent to which the red bus/blue bus problem might throw off Dr.
Noll’s predictions, this much is clear: if Dr. Noll had leaned more heavily on
actual preference data, he could have reduced his reliance on logit error and
enhanced the reliability of his model. And, at minimum, he could have tested his
model more thoroughly to ensure that the logit error was not muddying his
results.118 Indeed, Dr. Noll admits that the logit error has an important impact on
his model, stating during the Daubert hearing that “[t]he problem of the model is,
in fact, in certain circumstances, the logit error is driving results or is affecting — I
shouldn’t say driving — it is one of the factors producing results.”119
3.

The Results of Dr. Noll’s Model Demonstrate Its
Unreliability

Actual preference data would have enabled Dr. Noll to distribute fans
into his three categories, and to evaluate the importance of viewing time as
compared to other measures of potential bundle utility, in a much more reliable
fashion. To prove that point, the Court need look no further than the questionable,
hotly-debated results of his fan sorting experiment. For ease of reference, Chart 1,

118

See Day 2 Tr. at 379-381.

119

Day 3 Tr. at 494.
38

which illustrates those results, is reprinted below.
Chart 1: Purchasing Decisions by Fan Type in the BFW - MLB.tv
Fan Type

% Purchasing Standalone

% Purchasing Bundle

Single-Team Fan

68

32

Two-Team Fan

81

19

Multi-Team Fan

99

1

The parties thoroughly disagree over the meaning of these results, and the role the
logit error plays in driving them. To a layperson — even one who does not watch
sports — this distribution of results makes no sense: the more teams a fan is
interested in watching, the more likely he would be to buy a package of the
telecasts of all teams instead of the telecasts of only one team.
Dr. Noll has an explanation for this. Assuming his model’s
assumptions about viewing preferences by category are correct, then the model’s
results are economically sensible in that they are informed by the respective price
sensitivities of the categories of fans.
But, as with so many other opinions Dr. Noll offers on the Demand
Side, the real world data to support his price-sensitivity conclusion is nowhere to
be found in the model. As Dr. McFadden points out, if an expert modeler lacked
such information, “it would [be] common procedure [] to collect your own data, do
your own survey, find out . . . who’s a fan and who is not, and perhaps also find
39

out more about what their tastes are, whether they would consider buying or not at
various suggested prices.”120 Only then could consumer demand start to come into
focus. That is why Crawford and Yurukoglu relied on substantial real world
preference information and survey data, including demographic data, in their
study.121 And, what’s more, the emphasis on collecting real world data and
integrating it into the C&Y Model was hardly that model’s innovative feature, just
as Dr. Pakes’ use of surveys in his study was perfectly ordinary. Indeed, Dr.
McFadden stated that economists now follow “a long tradition and a long history
of using survey techniques to understand what’s going on and [to] make
predictions.”122
By contrast, Dr. Noll’s Demand Side model is so far removed from
actual viewer preferences and tastes that a finder of fact could only speculate as to

120

Day 2 Tr. at 367-368.

121

See C&Y at 653.

122

Day 2 Tr. at 368. Dr. Noll’s excuses for not conducting surveys or
attempting to incorporate additional information are unconvincing, especially
considering that his failure to do so seems to be a stark departure from the industry
norm. Additionally, throughout the course of expert discovery and the various
iterations of his model, Dr. Noll and plaintiffs were on notice of defendants’
concern that the Demand Side was not sufficiently tied to viewer preferences, but
they stood by the sparse data in Dr. Noll’s model anyway. Dr. Noll’s declarations
speak to the infeasibility of separating fans into 435 categories, but not to doing a
survey of a relatively small group of people using a conjoining analysis. See Day 3
Tr. at 482.
40

the reasons for the model’s seemingly nonsensical results. Dr. Noll claims that
multi-team fans are more price-sensitive because the “model of their demand
behavior . . . kicks out that result.”123 But the very model that produces that result
is unreliable because Dr. Noll never conducted any surveys or collected and
incorporated any additional data regarding viewers’ tastes. Doing so could have
enabled his model to predict more reliably the price sensitivities of various
categories of fans.
For instance, with some extra legwork, Dr. Noll might have uncovered
data that multi-team fans’ supposed price sensitivity would actually tend to drive
them out of the market altogether — after all, cable subscribers can watch a local
baseball game almost every night during the season without paying extra for an
out-of-market option.124 Or maybe he would have uncovered and incorporated
preference data into his model that reflected the opposite trend — that multi-team
fans genuinely value diversity to a greater degree than the logit error provides, and

123

Day 3 Tr. at 485.

124

Indeed, one can envision a number of ways in which a price-sensitive
fan — even a non-cable-subscriber — could get his baseball or hockey fix without
paying for a standalone RSN or OMP. Fans can read about every play of every
game online in real time and watch extensive highlights of every game on
MLB.com or NHL.com. They can also watch games at a bar or at the residence of
a friend who subscribes to cable. Many baseball games each season are broadcast
locally over-the-air such that a cable subscription is not even necessary. All of
these options may be preferable to paying for an a la carte channel.
41

strongly desire the option to watch any team on any given night, not just one
team.125 It is also possible that real-world data supports his price-sensitivity claim.
4.

Under FRE 702, Dr. Noll’s Testimony About Consumer
Demand Must Be Excluded

Whether any of these possibilities are accurate is irrelevant – what
matters is that Dr. Noll’s failure to obtain information about consumer tastes and
preferences and failure to study baseball and hockey viewing patterns more
thoroughly create “too great an analytical gap between the data and the opinion

125

It may be that a non-negligible percentage of “multi-team fans” are
fans not of many teams, but of many players across different teams. To this end, it
might have been useful to seek data regarding the impact of the rise of fantasy
sports on OMP subscriptions, including the growing trend towards daily fantasy
sports. In this context, fans are interested in observing the performance of a
collection of players across a range of different teams each night, as opposed to the
performance of only one or two teams. Participants in daily fantasy sports, who
pay to play, consume forty percent more sports content — across all media,
including television — once they begin playing. See Brent Schrotenboer, Leagues
See Real Benefits in Daily Fantasy Sports, USA Today (Jan. 1, 2015),
http://www.usatoday.com/story/sports/2015/01/01/daily-fantasy-sports-gambling-f
anduel-draftkings-nba-nfl-mlb-nhl/21165279/ (noting that “daily fantasy sports
consumption will have a steroid effect on television revenue, because nobody
watches live sports on television quite as intensely as fans with money at stake”).
But the teams they support — and are interested in watching — change every
night; they might only view a given game for a very short window of time, just to
check in on the at-bat of a single player. Fantasy sports aside, a “multi-team fan”
may wish to view games of different teams each night based on intriguing pitching
match-ups or other player-specific interests. Dr. Noll’s model does not incorporate
any real data regarding consumer tastes to account for any of these possibilities,
which may or may not have a significant impact on estimating demand.
42

proffered.”126 FRE 702 requires expert testimony to be based on “sufficient facts
or data.” Dr. Noll’s testimony about consumer demand is based on insufficient
facts and data. His Demand Side opinions are even less reliable in that they are not
the product of any significant, independent research or study, but have instead been
developed for the sole purpose of bolstering plaintiffs’ position in this litigation.127
For all of these reasons, the model must be excluded. Without a reliable way to
estimate demand in the BFW, plaintiffs cannot demonstrate with any precision the
potential monetary damages class members incurred as a result of defendants’
alleged overcharging for OMPs.
V.

SUPPLY SIDE
A.

Summary of Dr. Noll’s Supply Side Analysis
Because territorial restraints would no longer exist in the BFW, RSNs

126

Joiner, 522 U.S. at 146. Because the underlying demand data is the
same for the third model as it is for Dr. Noll’s first two, it is unnecessary to
examine the first two models more closely, to the extent that plaintiffs believe they
are still viable. All versions of Dr. Noll’s model suffer from the same fatal data
sufficiency flaw on the Demand Side.
127

See Daubert v. Merrell Dow Pharms., Inc. (Daubert II), 43 F.3d 1311,
1317 (9th Cir. 1995) (noting that “in determining whether proposed expert
testimony amounts to good science, we may not ignore the fact that a scientist’s
normal workplace is the lab or the field, not the courtroom or the lawyer’s office”);
see also Awad v. Merck & Co., 99 F. Supp. 2d 301, 304 (S.D.N.Y. 1999), aff’d sub
nom. Washburn v. Merck & Co., 213 F.3d 627 (2d Cir. 2000) (noting that in
determining reliability under Daubert, “a significant consideration is whether
research was conducted independently or for the sole purpose of litigation”).
43

would be able to sell their content — in a la carte form — directly to out-of-market
consumers. According to Noll, this change would spark a reconfiguration of the
overall market for sports broadcasting, leading to greater consumer welfare.
1.

The Supply Side in the C&Y Model

The C&Y Model, as described earlier, is a framework for assessing
the result of “unbundling” television distribution — i.e., of moving from (1) a
distribution chain in which consumers are required to purchase bundled packages
(of television channels) from MVPDs to (2) a distribution chain in which
consumers may either purchase bundled packages or purchase a la carte channels.
The C&Y Model examined the effects of unbundling on all broadcasting, not just
sports broadcasting. They were interested in determining whether consumers
would be better off in a world where they could pick and choose among individual
networks — The History Channel, and Arts and Entertainment Network, and so on
— instead of being forced to purchase a bundled cable package.
The C&Y Model documented two effects of unbundling. First,
greater consumer choice, resulting from the existence of a la carte options, spurred
competition, and tended to push prices down in the BFW. Second, the Supply Side
bargaining that would transpire in response to unbundling introduced new costs

44

into the supply chain, which tended to push prices up in the BFW.128 The reason
for this second finding — as Dr. Pakes explained — is that unbundling resulted in
a “[smaller] amount of money [] go[ing] back to each [network],” which meant that
to avoid “go[ing] out of business” networks were forced to negotiate higher fees
from MVPDs, which in turn meant that MVPDs would charge consumers higher
prices for each a la carte network.129 The end result was that in the BFW,
consumers ended up “slightly worse off” than they were in the actual world of
exclusively bundled options.130
In short, the C&Y Model concluded that the unbundling of television
channels had two different effects, pulling in opposite directions, on the market for
television distribution. The synthesis of these two effects is to restore consumers
to essentially the same position as they were in before unbundling.131
128

See C&Y at 4 (“There are two countervailing forces that largely
determine our results. First, for fixed input costs, unbundling unlocks consumer
surplus. . . . Allowing renegotiation, however, increases costs [and] [p]rices follow
suit, making the average consumer indifferent [to unbundling].”).
129

Day 2 Tr. at 298.

130

Id.

131

See id. at 315 (where Dr. Pakes explains that the C&Y Model did not
result in a “statistically significant” increase in prices). This is true, at least, with
respect to prices. It is still possible (indeed, it seems quite likely) that some
consumers would be better off in the BFW, even taking for granted the C&Y
Model’s assumptions about bargaining, depending on their specific preferences.
For example, a consumer that only wanted The History Channel — a rough
45

2.

Dr. Noll’s Basic Deviation from the C&Y Model

The crude way to summarize Dr. Noll’s analysis — and the essence of
defendants’ criticism — is that he adopted the first half of the C&Y Model while
ignoring the second. Following the C&Y Model, Dr. Noll posits that the
“unbundling” of sports broadcasts — i.e., the availability of a la carte RSNs —
would have a downward effect on prices. But from there, Dr. Noll parts ways with
the C&Y Model. Dr. Noll’s analysis does not include a Supply Side bargaining
dynamic that results in MVPDs imposing mark-ups when distributing either a la
carte channels or OMPs to consumers. Absent this bargaining dynamic, the second
effect documented in the C&Y Model — the increased price of each particular
RSN’s content — does not occur in Dr. Noll’s analysis. Accordingly, unlike in the
C&Y Model, whose end result was neutral for consumers, Dr. Noll’s analysis
shows an obvious benefit to consumers — choices multiply, and prices drop.
Dr. Noll has a rationale for this deviation. According to Dr. Noll,
there is no need to model bargaining between RSNs and MVPDs in the BFW,
because “internet delivery [of RSN feeds] is a competitive substitute for delivery

analogue to the single-team fan — would no doubt prefer the unbundled world,
even if the price per channel was substantially higher than it was in the bundled
world.
46

over an MVPD.”132 Assuming this premise is correct, MVPDs would have no
power to mark up prices above a profit-maximizing equilibrium. If they did raise
prices, consumers would migrate to Internet products. Therefore, Dr. Noll
concludes that it is unnecessary to model “the agreements between [] buyer[s] and
[] seller[s]” at an “intermediate” stage in the supply chain — i.e., the agreements
between RSNs and MVPDs — because there is no way that such agreements will
“affect [the] final price.”133
With respect to the bargaining issue, there is a notable mismatch
between what Dr. Noll has done and what defendants have accused him of doing.
Defendants’ experts repeatedly argue that Dr. Noll’s analysis ignores bargaining in
the BFW.134 This is misleading. Whether or not Dr. Noll’s deviation from the
C&Y Model is ultimately justified, it is important to understand the nature of his
deviation. By assuming that it is unnecessary to model bargaining between RSNs
and MVPDs in the BFW, Dr. Noll is not suggesting that no bargaining between the
RSNs and MVPDs would occur. He is suggesting that to capture the results of

132

Noll Decl. at 102.

133

Day 3 Tr. at 447.

134

For example, Dr. Pakes testified that “[t]he MVPDs don’t enter Dr.
Noll’s model at all. They’re just not there,” and likewise, that Dr. Noll “doesn’t
assume anything” about the MVPDs. Day 2 Tr. at 299. Accord Reply Mem. at 69.
47

bargaining between RSNs and MVPDs in the BFW, it is unnecessary to model the
process of bargaining. It is appropriate to assume that any bargaining will result in
profit-maximization for the RSNs — because the RSNs hold the power to control
the distribution of their content.
3.

Other Assumptions Built into Dr. Noll’s Analysis

In addition to the deviation from the C&Y Model, Dr. Noll’s analysis
also rests on three methodological assumptions — all contested by defendants —
that propel the conclusion that consumers will be better off in the BFW. First, Dr.
Noll assumes that RSN distribution in the BFW will not be subject to (or subject
only to de minimus) “double marginalization,” and therefore, that it is unnecessary
to account for double marginalization in the projection of BFW prices. Second, Dr.
Noll assumes that individual RSNs will pledge their content to the OMP in
exchange for one-thirtieth of the overall profit from OMP subscriptions (in
baseball) — i.e., Dr. Noll assumes that the RSNs will share in OMP profits
equally, such that no individual RSN, regardless of its market power, will be able
to negotiate for a higher share of the OMP profits vis-à-vis other RSNs. Third, Dr.
Noll assumes that the prices of a la carte channels will be set independently from
the price of the OMP, and vice versa. In other words, he assumes that the league
and the teams will price their broadcasts competitively — at arm’s length — not as

48

a joint venture.
B.

Dr. Noll’s Supply Side Analysis Meets the Daubert Test
1.

The Lack of Bargaining Between RSNs and MVPDs Is
Justified

Defendants attack the exclusion of a bargaining dynamic from Dr.
Noll’s analysis on three grounds. First, defendants argue that bargaining was the
“central innovation” of the C&Y Model,135 which means that when Dr. Noll
decided to eschew bargaining, he “deviate[d]” from the only “peer-reviewed or
otherwise reliable methodology” that his analysis conceivably relied on.136 Second,
defendants argue that Dr. Noll’s rationale for why it is unnecessary to model
bargaining between RSNs and MVPDs — that “[i]nternet delivery is a competitive
substitute for delivery over an MVPD”137 — is implausible. Third, defendants
argue that Dr. Noll has not justified the assumption running through his entire
analysis — that RSNs would continue to exist in the BFW.
a.

Deviation from the C&Y Model Is Not Ipso Facto
Problematic

Defendants’ first argument is, in essence, an appeal to authority —

135

C&Y at 2. Accord Def. Mem. at 15 (arguing that Dr. Noll
“eschewed” the core of the C&Y Model).
136

Def. Mem. at 13.

137

Noll Decl. at 102.
49

because C&Y included a bargaining model, Dr. Noll should have as well. But
plaintiffs have pointed to numerous reputable papers that use structural models but
do not include a bargaining dynamic.138 This makes it hard to sustain the claim
that by jettisoning bargaining, Dr. Noll’s analysis lost its foundation in “peerreviewed or otherwise reliable methodolog[ies].”139 Ultimately, the implicit
premise of defendants’ position is that bargaining is always essential to the
integrity of a structural model. Plaintiffs have offered more than sufficient
evidence to question that premise. It would be more accurate to say that
bargaining is often — but not always — essential to the integrity of a structural
model, depending on the specific features of an industry.
Here, the parties disagree about whether sports broadcasting is such an
industry. The details of that disagreement are not material to defendants’ Daubert
challenge. Plaintiffs argue that “modeling bargaining is not called for where

138

See Memorandum of Law in Opposition to Defendants’ Joint Motion
to Exclude Opinions and Testimony of Plaintiffs’ Expert Dr. Roger G. Noll (“Opp.
Mem.”), at 11 n.7 (noting that “since C&Y was published,” there has been “a
significant number of papers by well-known economists in top-tier journals that
employ structural models without bargaining” — and listing examples). It is also
worth noting that Dr. Crawford and Dr. Yurukoglu, the creators of the C&Y
Model, worked with Dr. Noll in crafting his analysis. See 10/22/14 Letter from
Plaintiffs to the Court, at 2 (Dkt. No. 273).
139

Def. Mem. at 13.
50

products are relatively similar.”140 And as a conceptual matter, defendants agree.
As Dr. Pakes explained during the hearing,
if there’s only one good that the producer is producing and there’s
only one good that the [distributor] is marketing, then it makes
sense . . . to devise a contract where we maximize the joint profits
from the endeavor and split it somehow between the two.141
When products are similar, it can be assumed that actors at different levels of a
vertical supply-chain (such as RSNs and MVPDs) will negotiate fee-splitting
arrangements that replicate acting in concert, which means from the perspective of
consumers — and for the purpose of assessing consumer prices — no bargaining
model is required. The real issue, then, is not whether it is sometimes permissible
to jettison bargaining from a structural model, but rather, whether plaintiffs were
right to do so in this case. And that is a question that deserves “[v]igorous crossexamination” at trial.142
b.

Dr. Noll’s Assumption About Internet-TV
Substitution Is Plausible

Defendants’ second argument — while more promising — goes to the
weight, not the reliability, of Dr. Noll’s analysis of the Supply Side. For analytic

140

Opp. Mem. at 12.

141

Day 2 Tr. at 304.

142

Amorgianos, 303 F.3d at 267 (internal citations omitted).
51

purposes, Dr. Noll’s rationale for jettisoning bargaining can be expressed in
conditional form — if (1) the Internet distribution of baseball and hockey
broadcasting is a competitive substitute for the television distribution of such
broadcasting, then (2) MVPDs will lack bargaining power in the BFW.
Defendants dispute both steps of this logic. First, they argue that Dr.
Noll has not proven his factual hypothesis — he has not shown that Internet
distribution serves as a competitive substitute for television distribution.
Furthermore, even if Dr. Noll has proven his hypothesis prospectively — that
plaintiffs are correct that Internet distribution increasingly will serve as a
competitive substitute for television distribution — it did not do so during much of
the class period.143 Defendants are correct — plaintiffs have not convincingly
shown that Dr. Noll’s assumptions regarding Internet distribution were true at all
points during the class period. But this observation is only relevant for the purpose
of damages, not for the purpose of injunctive and declaratory relief.144 With
respect to the latter, Dr. Noll’s assumption carries its burden. In light of the way
content distribution — across industries — has evolved in recent years and
continues to evolve, it is plausible that Internet distribution will increasingly serve

143

See Def. Mem. at 17.

144

See Certification Opinion (certifying an injunctive class under Rule
23(b)(2), but not a damages class under Rule 23(b)(3)).
52

as a competitive substitute for television distribution. This is not to say that Dr.
Noll’s premise is ultimately correct. But it is sturdy enough to survive a Daubert
challenge.
Second, defendants also take issue with the conclusion of Dr. Noll’s
argument regarding Internet distribution. Assuming, arguendo, that Internet
distribution and television distribution would serve as competitive substitutes from
the perspective of consumers, it does not follow, in defendants’ view, that MVPDs
would lack bargaining power. For support, defendants point to the fact that in the
actual world, “the RSNs’ [] business model is premised upon negotiating for
carriage [of the content they produce] on MVPDs.”145 In other words, RSNs
receive most of their revenue today from “carriage fees” paid by MVPDs in
exchange for the right to distribute baseball and hockey broadcasts. According to
defendants, it therefore strains credulity to conclude that RSNs’ negotiation for
carriage on MVPDs, a key source of revenue today, would disappear entirely from
the BFW.
This argument is a red herring. The observation that RSNs currently
derive much of their business from carriage on MVPDs, though true, sheds no light
on the contours of the BFW. In the actual world, RSNs are effectively forced to

145

Def. Mem. at 17.
53

derive business from carriage on MVPDs. The whole point of plaintiffs’ legal
theory is that in the BFW, RSNs would have another way of making profits — by
selling directly to consumers via the Internet.
If RSNs made a greater share of their profits by selling directly to
consumers, it follows logically that they would make a lesser share of their profits
from carriage fees. But this observation, on its own, does not help defendants’
position. They argue that “MVPDs will not pay [the same] carriage fees” they do
in the actual world, if, in the BFW, “consumers can bypass the MVPDs and obtain
the same programming [online].”146 While true, all this implies is that in the BFW,
RSN profits would have a different composition — that a greater ratio of profit
would come from Internet distribution — as a result of a new equilibrium in the
market for baseball and hockey broadcasting. From that, however, it does not
follow that MVPDs will have bargaining power in the BFW. On the contrary, the
upshot of Dr. Noll’s analysis is that whatever equilibrium emerges in the BFW, it
will be the product of Supply Side renegotiations in which RSNs have a much
stronger bargaining position.
c.

The Existence of RSNs in the BFW

Finally, defendants argue that Dr. Noll has not justified his threshold

146

Id.
54

assumption that RSNs would exist in the BFW. According to defendants, this is
hardly a foregone conclusion, for “it is not clear what role, if any, [RSNs] would
have in the BFW given that RSNs are by definition ‘regional’ and fundamentally a
byproduct of [territorial restraints].”147 Indeed, “in a world with no territorial
limitations” — i.e., in the BFW — “popular clubs seeking national distribution
could exercise leverage by threatening to negotiate deals directly with national
networks or [] MVPDs.”148 If so, then RSNs, far from having more bargaining
power in the BFW, would have virtually none.
The problem with this argument is that the issue of who produces
baseball and hockey broadcasts has no bearing on how those broadcasts are priced.
When Dr. Noll describes the BFW in narrative form, defendants are correct that he
identifies RSNs as the producers of baseball and hockey broadcasts. But Dr.
Noll’s invocation of RSNs is simply a holdover from the actual world, not an
essential feature of his analysis. The point is that whoever produces the broadcasts,
Dr. Noll’s prediction is that the broadcasts will end up being distributed to
consumers at lower prices than in the actual world — i.e., that the supply chain in
the BFW, however it is precisely configured, will settle to a competitive, profit-

147

Id. at 18.

148

Id.
55

maximizing equilibrium. That prediction may end up being wrong. But if so, it
will be wrong for reasons that have nothing to do with what entity — RSNs, other
production outfits, or the clubs themselves, producing broadcasts in-house — is
responsible for creating content in the BFW. Not surprisingly, defendants have
made no effort to connect their observation that clubs could bypass RSNs in the
BFW to a claim about prices in the BFW. No such connection exists.
2.

Dr. Noll’s Other Assumptions Go to the Weight, Not the
Reliability, of His Supply Side Analysis
a.

No Double Marginalization

First, defendants fault Dr. Noll for failing to account for the
phenomenon of “double marginalization,” which, according to defendants, stems
from “the fact that independent businesses in vertical supply relationships” — here,
RSNs and MVPDs — “each set a price to earn a profit.”149 This complaint mirrors
defendants’ complaint about the lack of bargaining, in that defendants are
essentially taking the position that all supply-chains with tiered mark-ups result in
double marginalization — just as they take the position that all structural models
should incorporate bargaining — while plaintiffs maintain that only some supplychains with tiered mark-ups result in double marginalization.
Plaintiffs have the better of this argument. Double marginalization
149

Reply Mem. at 9.
56

refers to the adverse economic consequences that flow from a supply chain in
which two monopolists command super-competitive prices in vertical sequence.
When this occurs, the result is that prices rise so high, and output diminishes so
much, that (1) consumers lose out, but also (2) both monopolists are worse off than
they would be if they acted in concert. In this sense, double marginalization is bad
for all parties involved — producers as well as consumers — because prices are
marked up to super-competitive levels twice over, which causes demand to
plummet, curbing overall profit. Accordingly, producers always have an incentive
to avoid double marginalization whenever possible.
This observation alone disposes of defendants’ argument. Given that
producers have a natural incentive to avoid double marginalization whenever
possible, the question is whether it is possible, in this particular market, for
producers to avoid double marginalization. That is an issue of fact, not one of
methodological integrity. As such, it does not support a Daubert challenge.
Plaintiffs argue that in the BFW, RSNs and MVPDs would not tolerate any
significant amount of double marginalization, and that they would use profitsharing mechanisms — mechanisms already established in the industry — to
circumvent double marginalization.150 To this, defendants respond that certain
150

See Opp. Mem. at 14 nn.15-16 and accompanying text (explaining
how, in the actual world, contracts between clubs, RSNs, and MVPDs are designed
57

features of the sports broadcasting industry are likely to frustrate the circumvention
effort.151
There is room for reasonable disagreement about which side has the
more compelling view of the sports broadcasting industry.152 At this stage, suffice
it to say that Dr. Noll’s decision to assume the existence of an outcome that
generally works to the benefit of all interested parties does not warrant the
exclusion of his testimony under Daubert.
b.

Feeds to the OMP

Second, defendants believe Dr. Noll has made the implausible
assumption that RSNs would pledge their broadcasts to the OMPs in exchange for
one-thirtieth of the OMPs’ overall profits (in baseball). According to defendants,

to avoid double marginalization). Accord Day 1 Tr. 152-154 (Dr. Noll discussed
the mechanisms currently in use, and that would continue to be used, to avoid
double marginalization in sports broadcasting — e.g., setting mandatory retail
prices).
151

See Reply Mem. at 9-10. Accord Day 2 Tr. at 303-305 (explaining
why, when down-stream distributors — here, MVPDs — sell different products,
double marginalization can occur as a consequence of every up-stream supplier —
here, every RSN — maximizing its profits).
152

In passing, it bears note that even the authors of the C&Y Model —
Crawford and Yurukoglu — appreciated this feature of the television broadcasting
industry. See C&Y at 14 n.43 (acknowledg[ing] that their pricing assumptions are
“often considered unrealistic” due to the availability of means to circumvent
double-marginalization).
58

more popular clubs — e.g., the Yankees — would have an incentive to demand
more than one-thirtieth of the overall share, because their broadcasts are
comparatively more valuable than other clubs’ broadcasts. In short, why would
clubs like the Yankees not simply withdraw from the bundle and sell their content
exclusively a la carte?
To this, plaintiffs offer two responses. First, they point out that in the
actual world, teams are prohibited — by league rules — from withdrawing from
the bundle. In this sense, Dr. Noll is simply making the modest assumption that
current league rules would stay intact. Second, plaintiffs argue that even granting
defendants’ premise — that the league rules could change in the BFW — there is
no economic reason to think they would change.
Because plaintiffs’ first response disposes of the Daubert question, it
is unnecessary to address the second. Regardless of whether Dr. Noll was
ultimately right to assume that league rules would stay constant in the BFW, the
assumption is not an economic one. Rather, it is a factual assumption about the
leagues as institutions. That this assumption has economic implications —
potentially quite significant ones — does not change its nature. Assuming that
current league rules will stay intact in the BFW is akin to assuming that in the
BFW, the baseball season will continue to be one hundred and sixty-two games, or

59

that baseball playoffs will continue to consist of three rounds (rather than — as is
the case in hockey — four). In principle, there is nothing stopping the league from
modifying the length of the season, or changing the format of playoffs; just as, in
principle, there is nothing stopping the league from reforming its rules regarding
RSN feeds. To hypothesize that the status quo will persist, however, is not an
unreasonable factual assumption, even if it is a factual assumption that ends up
being wrong.
The thrust of defendants’ argument to the contrary is that
economically, there are reasons to think that current league rules will not stay
intact in the BFW — because it would be in the interest of many clubs to dissolve
the rules and permit deviation from the bundle. Plaintiffs disagree.153 But
153

Defendants argue that if the Yankees were to withdraw from the OMP
in the BFW (assuming the parameters of the BFW set by Dr. Noll’s analysis), all
teams would be better off — making the result economically rational. See Reply
Mem. 10-11. In response, plaintiffs suggest that this result, though accurate, is
misleading. Every lucrative club would have an incentive to withdraw, to the point
that the OMP, having lost its most valuable content, would cease to exist.
According to plaintiffs, it is well-established that, on balance, the OMP is lucrative
— i.e., the clubs would prefer that the OMP exist. Given this, and given the fact
that allowing individual clubs to withdraw from the OMP would result in the
OMP’s demise, plaintiffs reason that the league would decide to preclude
individual clubs from peeling off — just as it does today. As Dr. Noll put it, this
“problem [] is always true of collaborations [] in a world in which there is revenue
sharing,” because “it’s always the case that the most valuable member of the
collaboration doesn’t have a private incentive to participate” but “[will] still agree
to [collaborate] because it’s in their collective interests to do so.” Day 3 Tr. at 464465. In plaintiffs’ view, in other words, the league-mandated inclusion of feeds for
60

regardless of which side is ultimately correct, the important point at this stage is
that defendants’ argument is not only an economic claim; it is also a claim about
how the leagues operate. Specifically, defendants’ argument rests on the premise
that individual clubs’ economic interests determine the content of league rules.
This is not necessarily true. Leagues self-govern in different ways, with any
number of motivations. The economic impact of league rules on individual clubs
is one motivation — but hardly the only one. At trial, defendants are free to argue
that the league rules would change in the BFW. But that argument will bear on the
weight of Dr. Noll’s testimony, not its admissibility.
In a sense, defendants’ argument about league rules falls prey to the
same problem as their argument about double marginalization. In each setting, Dr.
Noll has made an assumption that, even if it proves unconvincing on the facts, is
facially plausible — indeed, a good deal more plausible than the contrary
assumption. First, Dr. Noll has assumed that RSNs and MVPDs will take steps to
circumvent an outcome — double marginalization — that typically undermines the
interests of all actors within the market. Second, Dr. Noll has assumed that current
league rules will stay intact. For the reasons just discussed, this position may turn
out to be wrong. But it strains credulity to suggest that this assumption is so

the OMP solves a collective action problem. And it would continue to do so in the
BFW.
61

unreliable as to merit discarding Dr. Noll’s Supply Side analysis.
c.

Joint Venture Pricing

Third, and finally, defendants argue that Dr. Noll erred in assuming
that in the BFW (1) the price of the OMPs and (2) the prices of a la carte channels
would be set competitively — as though the league and its clubs operate at arm’s
length. According to defendants, the more accurate model of the leagues and its
clubs would be that of a joint venture. If so, the proper framework for predicting
prices would be a “multi-product pricing” model — a framework whose economic
viability is “uncontested,” and whose application “would result in higher prices for
the two products” at issue here, the OMPs and the a la carte channels.154
Plaintiffs’ response is simple. If the league and the clubs were to set
prices as a joint venture, according to a “multi-product pricing” model, that itself
would be collusive. Put simply, the reason Dr. Noll assumed that prices would be
set competitively in the BFW is that to assume otherwise would be, in effect, to
allow “the leagues [to] replace the current anticompetitive prices (and inflated
prices) with other anticompetitive prices in the BFW.”155 This is a legal argument,
not an economic argument — but it is a legal argument that, in Dr. Noll’s view,

154

Reply Mem. at 12.

155

Opp. Mem. at 16.
62

sets the parameters of “legitimate” economic modeling.156 Indeed, when defense
counsel pressed Dr. Noll during the hearing about his decision to model prices
competitively, the following exchange ensued:
Dr. Noll: [Your experts] think it’s perfectly fine for a standalone
joint venture to act in a way that attempts to maximize the
horizontal competitors’ joint profits. That’s fine. I don’t think
that’s a legitimate way to model it; your experts do.
Defense Counsel: Are you saying it’s unlawful?
Dr. Noll: I don’t know whether it’s unlawful. I’m simply saying
I believe that it’s illegitimate as [an] economist to have
cooperative price-setting among horizontal competitors as the way
you try to figure out damages in an antitrust case. I think that’s
not [correct].157
In response, defendants argue that Dr. Noll has overlooked the fact
that in the BFW, each club would “have a unilateral incentive to take into account
the effect on the related party” — i.e., the league — “when setting price[s].”158 In
other words, multi-product pricing would occur as a natural byproduct of the fact
that the clubs and the league have intertwined interests; no top-down coordination
would be necessary. It is one thing to hypothesize that the clubs would “take into
account the effect” of their prices on the league, and on the OMP. It is quite

156

Day 3 Tr. at 505.

157

Id.

158

Reply Mem. at 12 (emphasis in original).
63

another to model prices in the BFW the same way that — to borrow an example
from defendants’ expert, Dr. Pakes — a car company (“GM”) sets the prices of two
different brands that it owns (“Chevy” and “Pontiac”). If Chevy and Pontiac were
owned by different companies, the prices of both cars would naturally settle at a
competitive equilibrium — just as Dr. Noll argues that prices of the OMP and the a
la carte channels would. But “what happens if Chevy and Pontiac are [both owned
by] GM?” According to Dr. Pakes:
[N]ow GM is setting the price for both. They own both products.
They get the profits from both products. So [GM would] increase
the price of the Pontiac by one dollar. It gets a dollar from
everybody who stays, and some people leave, but [unlike in the
scenario where Chevy is owned by another company] they don’t
lose the mark-up on everybody who leaves. Why? Because some
of the people who leave go to the Chevy because it’s also a
family-sized car. So they’ll keep increasing the price more until
that equilibrium is established again. So that’s what’s going on in
multiproduct pricing. [And] [y]ou can . . . show [mathematically
that] it has to increase pricing.159
Dr. Noll decided that analyzing BFW prices this way would violate
“legitimate” principles of economic modeling — in essence, because he thought it
would reflect collusion. Defendants respond that Dr. Noll is wrong on the law;
that in fact, multi-product pricing would occur in the BFW “without []

159

Day 2 Tr. at 306.
64

collusion.”160 But this does not dispose of the legal question — it begs the legal
question. The Supreme Court has made it quite clear that joint ventures are not
immune from the antitrust laws. In this setting, they are subject to Rule of Reason
analysis.161 Whether or not the particular type of multi-product pricing
hypothesized by defendants would survive Rule of Reason scrutiny is unclear. It
presents a complicated legal question. What is clear is that Dr. Noll can hardly be
faulted, at this stage, for failing to incorporate into his analysis “a collusive
practice that he [] believes is illegal.”162 For now, the assumption about
competitive pricing stands.
3.

160

Dr. Noll’s Testimony About the Supply Side, Extracted
from the Damages Model, Is Admissible

Reply Mem. at 12.

161

See American Needle, Inc. v. National Football League, 560 U.S. 183,
200-02 (2010) (explaining that joint ventures, insofar as they give would-be
competitors cover for collusive action, trigger antitrust scrutiny). See also Starr v.
Sony BMG Music Entm’t, 592 F.3d 314, 327 (2d Cir. 2010) (noting that the
activities of joint ventures are subject to the Rule of Reason). For further
background on the Rule of Reason itself, see Leegin Creative Leather Prods., Inc.
v. PSKS, Inc., 551 U.S. 877, 885 (2007) (“The rule of reason is the accepted
standard for testing whether a practice restrains trade in violation of § 1 [of the
Sherman Act]. . . . ‘Under this rule, the factfinder weighs all of the circumstances
of a case in deciding whether a restrictive practice should be prohibited as
imposing an unreasonable restraint on competition.’”) (citing Continental T. V.,
Inc. v. GTE Sylvania, 433 U.S. 36, 49 (1977)).
162

Day 3 Tr. at 524.
65

The final question is whether the Supply Side analysis can be
analytically severed from Dr. Noll’s damages model. The answer is yes. Because
the damages model lacks a solid foundation in existing data, it does not reliably
demonstrate whether, and how much, class members were overcharged for OMPs.
But nothing about that defect spills over to Dr. Noll’s Supply Side analysis. The
shortcoming of Dr. Noll’s Demand Side analysis — and the unreliability of his
damage calculations — holds true whether or not the Supply Side is properly
configured. The admissibility of Dr. Noll’s Supply Side analysis stands (or falls)
on its own.
For the reasons set forth above, I conclude that Dr. Noll’s Supply Side
analysis, extracted from the damages model, survives scrutiny under Rule 702.
Some or all of Dr. Noll’s assumptions about the Supply Side may end up being
unconvincing — which would weaken plaintiffs’ case on the merits. But that issue
must be resolved by a fact-finder. It would be inappropriate for the Court to
exclude Dr. Noll’s Supply Side analysis at this stage.
VI.

CONCLUSION
For the reasons set forth above, defendants’ motion to exclude the

opinions and testimony of Dr. Roger Noll is GRANTED in part and DENIED in
part. The Clerk of the Court is directed to close this motion, Dkt. No. 277 in 12

66

Civ. 1817, and Dkt. No. 354 in 12 Civ. 3704.

SO ORDERED:

Dated:

May 14, 2015
New York, New York

67

- Appearances For Plaintiffs:
Edward A. Diver, Esq.
Howard I. Langer, Esq.
Peter E. Leckman, Esq.
Langer Grogan & Diver, P.C.
Three Logan Square, Suite 4130
1717 Arch Street
Philadelphia, Pennsylvania 19103
[phone]
Kevin M. Costello, Esq.
Gary E. Klein, Esq.
Klein Kavanagh Costello, LLP
85 Merrimac St., 4th Floor
Boston, Massachusetts 02114
[phone]
Michael Morris Buchman, Esq.
John A. Ioannou, Esq.
Motley Rice, LLC
600 Third Avenue
New York, New York 10016
[phone]
Marc I. Gross, Esq.
Adam G. Kurtz, Esq.
Pomerantz, LLP
600 Third Avenue
New York, New York 10016
[phone]
Robert LaRocca, Esq.
Kohn, Swift & Graf, P.C.
One South Broad Street
Suite 2100
68

Philadelphia, Pennsylvania 19107
[phone]
J. Douglas Richards, Esq.
Jeffrey Dubner, Esq.
Cohen, Milstein, Sellers & Toll, PLLC
88 Pine Street
New York, New York 10005
[phone]
Michael J. Boni, Esq.
Joshua D. Snyder, Esq.
Boni & Zack, LLC
15 St. Asaphs Road
Bala Cynwyd, Pennsylvania 19004
[phone]
For Defendants Office of the Commissioner of Baseball, Major League
Baseball Enterprises Inc., MLB Advanced Media L.P., MLB Advanced
Media, Inc., Athletics Investment Group, LLC, The Baseball Club of
Seattle, L.L.P., Chicago White Sox, Ltd., Colorado Rockies Baseball Club,
Ltd., The Phillies, Pittsburgh Baseball, Inc., and San Francisco Baseball
Associates, L.P. :
Beth A. Wilkinson, Esq.
Samantha P. Bateman, Esq.
Paul, Weiss, Rifkind Wharton & Garrison LLP
2001 K St. NW
Washington, D.C. 20006
[phone]
Bradley I. Ruskin, Esq.
Helene Debra Jaffe, Esq.
Jennifer R. Scullion, Esq.
Colin Kass, Esq.
Proskauer Rose LLP
11 Times Square
New York, New York 10036
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[phone]
Thomas J. Ostertag, Esq.
Senior Vice President and General Counsel
Office of the Commissioner of Baseball
245 Park Avenue
New York, New York 10167
[phone]
For Defendants National Hockey League, NHL Enterprises, L.P., NHL
Interactive Cyberenterprises, LLC, Chicago Blackhawk Hockey Team, Inc.,
Comcast-Spectator, L.P., Hockey Western New York LLC, Lemieux Group,
L.P., Lincoln Hockey LLC, New Jersey Devils LLC, New York Islanders
Hockey Club, L.P., and San Jose Sharks, LLC:
Shepard Goldfein, Esq.
James A. Keyte, Esq.
Paul M. Eckles, Esq.
Matthew M. Martino, Esq.
Skadden, Arps, Slate, Meagher & Flom LLP
Four Times Square
New York, New York 10036
[phone]
For Defendants Comcast Corporation, Comcast SportsNet Philadelphia, L.P.,
Comcast SportsNet Mid-Atlantic L.P., Comcast SportsNet
California, LLC, and Comcast SportsNet Chicago, LLC:
Arthur J. Burke, Esq.
James W. Haldin, Esq.
Davis Polk & Wardwell
450 Lexington Avenue
New York, New York 10017
[phone]
For Defendants DIRECTV, LLC, DIRECTV Sports Networks, LLC,
DIRECTV Sports Net Pittsburgh, LLC a/k/a Root Sports Pittsburgh,
DIRECTV Sports Net Rocky Mountain, LLC a/ka/a Root Sports Rocky
70

Mountain, and DIRECTV Sports Net Northwest, LLC a/ka/a Root Sports
Northwest:
Louis A. Karasik, Esq.
Andrew E. Paris, Esq.
Stephanie A. Jones, Esq.
Alston & Bird LLP
333 South Hope Street, 16th Floor
Los Angeles, California 90071
[phone]
For Defendant New York Yankees Partnership:
Jonathan Schiller, Esq.
Alan Vickery, Esq.
Christopher Duffy, Esq.
Boies, Schiller & Flexner LLP
575 Lexington Avenue
New York, New York 10022
[phone]
For Defendants The Madison Square Garden Company and New York
Rangers Hockey Club:
Stephen R. Neuwirth, Esq.
Deborah Brown, Esq.
Richard I. Werder, Jr., Esq.
Quinn Emanuel Urquhart & Sullivan, LLP
51 Madison Avenue, 22nd Floor
New York, New York 10010
[phone]
For Defendant Yankees Entertainment Sports Network, LLC:
John E. Schmidtlein, Esq.
Kenneth Charles Smurzynski, Esq.
James Harris Weingarten, Esq.
William Jefferson Vigen, Esq.
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Williams & Connolly LLP
725 Twelfth Street, N.W.
Washington, D.C. 20005
[phone]

72