Realignment in the National Football League: Did They Do It Right?
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Realignment in the National Football League: Did They Do It Right? John E. Mitchell Mathematical Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180 Received 5 March 2001; accepted 18 February 2003 DOI 10.1002/nav.10084 Abstract: The National Football League (NFL) in the United States expanded to 32 teams in 2002 with the addition of a team in Houston. At that point, the league was realigned into eight divisions, each containing four teams. We describe a branch-and-cut algorithm for minimizing the sum of intradivisional travel distances. We consider first the case where any team can be assigned to any division. We also consider imposing restrictions, such as aligning the AFC (American Football Conference) and the NFC (National Football Conference) separately or maintaining traditional rivalries. We show that the alignment chosen by the NFL does not minimize the sum of intradivisional travel distances, but that it is close to optimal for an alignment that aligns the NFC and AFC separately and imposes some additional restrictions. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 683–701, 2003. Keywords: realignment; graph equipartition; branch-and-cut 1. INTRODUCTION The National Football League (NFL) in the United States expanded to 32 teams in 2002 with the addition of a team in Houston. At that point, the league was realigned into eight divisions, each containing four teams [30]. Each team plays 16 games during the regular season. Each team plays each other team in its division twice, playing its remaining games against a subset of the teams outside its division. A team’s opponents from outside its division depend on the team’s results in the previous season and on a rotating choice of one other division. Many arrangements of the divisions were under consideration. Since the choice of outside teams varies from season to season, one possibility was to choose the divisions so as to minimize the total intradivisional travel distance. This objective has the benefit of placing nearby cities in the same division, and such an arrangement will create natural rivalries. If this objective had been used, we show that the NFL could have reduced the sum of intradivisional travel distances by 45%. One advantage of our solution is that no two teams separated by more than one time zone appear in the same division. This optimal solution is shown in Figure 1, and it is detailed in Table 1 in Section 3.1 Computational results are presented in Section 3. First, we consider the case where any team can be assigned to any division, in Section 3.1. The realignment chosen by the NFL was © 2003 Wiley Periodicals, Inc. 684 Naval Research Logistics, Vol. 50 (2003) Figure 1. The realignment that minimizes the sum of intradivisional travel distances. designed to maintain various historical rivalries (for example, between the Dallas Cowboys and the Washington Redskins). The NFL is divided into two conferences. Prior to the 2002 season, the AFC (American Football Conference) had 16 teams and the NFC (National Football Conference) had 15 teams. The Houston Texans could have been placed in the NFC, so each conference had 16 teams, and then each conference could be aligned separately. Instead, Houston was placed in the AFC and the Seattle Seahawks were moved from the AFC to the NFC, again with each conference being aligned separately. We consider aligning the NFL in the presence of various combinations of restrictions in Section 3.2. We show that the realignment chosen by the NFL in optimal if several historical rivalries are respected. We model the realignment problem as a k-way equipartition problem, where the nodes of a graph are divided into k equally sized sets in order to minimize an appropriate objective function (see Section 2 for details). Other application areas of the k-way equipartition problem include telecommunications [26], 3-dimensional integrated circuit design [7], and microaggregation for statistical disclosure control [12]. The network design application considered in [26] is to cluster customers into equally sized sets and then build a SONET ring for each cluster, in order to provide redundancy for communication between two customers that exchange a large amount of data. Currently, printed circuit boards are almost exclusively fabricated on a single board, with transistors placed on each side of the board. It is relatively expensive to connect transistors on opposite sides of the board, so it is desirable to partition the set of transistors into two sets of approximately equal size. There is interest in developing 3-dimensional integrated circuits, Mitchell: Realignment in the National Football League 685 consisting of many layers of transistors [7]. It would then be desirable to partition the transistors into k sets of approximately equal cardinality. Many datasets contain confidential data, with the raw material consisting of information from individual respondents. One way to protect confidentiality is to cluster the data from respondents with similar characteristics. In order to protect the confidentiality of the data, it is desirable to impose a minimum size on any of the clusters. In certain situations, the clusters have equal cardinality. This method of statistical disclosure control is known as microaggregation [12]. A different problem in statistical disclosure control is described by Fischetti and Salazar [18], who use an integer programming branch-and-cut approach. Operations research has been used successfully in the past in several applications drawn from sports—we describe just a few examples. Nemhauser and Trick [29] developed a playing schedule for the ACC, a major college basketball conference. Trick [32] has examined the scheduling of other sports tournaments, including minor league baseball. Older work on sports scheduling includes that of Bean and Birge [4] on the NBA (National Basketball Association). The problem of determining which teams can still qualify for the playoffs, based on the remaining schedule, can be formulated as a linear programming problem. For a recent descrip- tion of this problem, see Adler et al. [1]. Duckworth and Lewis [14] proposed a method for accommodating rain delays in determining the result of cricket matches. This method has been adopted by the International Cricket Council and is used throughout the world. Saltzman and Bradford [31] modeled the NFL realignment problem as a modification of the quadratic assignment problem. They also proposed minimizing the sum of intradivisional travel distances. Their formulation leads to a quadratic programming problem with a nonconvex objective function. They used MINOS to find a local minimizer for this quadratic program. When the NFL expanded to 30 teams in 1996, they showed that the alignment resulting from their algorithm would have reduced the total intradivisional travel distance by 40%. Before discussing our results, we describe the algorithm used in Section 2. It should be emphasized that our approach results in an optimal solution in each situation considered; that is, there is no possible arrangement of the cities that has a smaller sum of intradivisional travel distances. 2. THE k-WAY EQUIPARTITION PROBLEM The teams in the NFL can be regarded as the vertices of a graph. An edge between two teams has weight equal to the distance between the two corresponding cities. Any partition of the teams into eight divisions each containing four teams is a valid alignment. The total intradivi- sional travel distance is the sum of the edge weights of all edges that have both endpoints in the same division. The objective we choose is to minimize this sum. This has the effect of placing teams that are close to one another in the same division. The realignment problem is an example of the k-way equipartition problem. Given a graph ϭ ʦ G (V, E) with vertices V and edges E, where each edge e E has weight ce, the aim of the k-way equipartition problem is to divide the graph into k sets of vertices, each of the same size, so as to minimize the total weight of the edges which have both endpoints in one of the sets. We will call these k sets divisions. We assume ͉V͉ is an integer multiple of k. This can be regarded as a clustering of the vertices, with the additional condition that each cluster must contain the same number ͉V͉/k ϭ: S of elements. In what follows we assume G is the complete graph on ͉V͉ vertices, as is the case for the NFL realignment problem. We use a branch-and-cut algorithm to solve the problem. This constructs an integer program- ming formulation of the problem. It then solves a linear programming (LP) relaxation of the 686 Naval Research Logistics, Vol. 50 (2003) problem. The solution to the LP relaxation can be used to generate feasible integer solutions. The LP relaxation is tightened by adding strong cutting planes, that is, inequalities that are high-dimensional faces of the convex hull of feasible solutions to the integer program, and are violated by the solution to the LP relaxation. Once the LP relaxation has been tightened, it can be solved again and the process repeated. If good cutting planes cannot be found and optimality has not yet been proved, then branching can be used to finish off the solution. The algorithm is described in more detail in Section 2.3. First, in Section 2.2, we discuss the polyhedral theory of the k-way equipartition problem, and the inequalities that are used as cutting planes. We define a binary variable xij, which takes the value 1 if i and j are in the same division and 0 otherwise. Our formulation is min ¥eʦE cexe ¥ ϭ Ϫ ᭙ ʦ subject to eʦ␦͑v͒ xe S 1 v V, x is the incidence vector of a clustering, where ␦(v) denotes the set of edges incident to vertex v.