2.1 Methods based on BTL distributions

Most work related to rank-clustering utilizes the BTL family, which comprises the Bradley–Terry and Plackett–Luce distributions and their extensions. The Bradley–Terry model, proposed by Zermelo (Reference Zermelo1929) and discovered independently by Bradley & Terry (Reference Bradley and Terry1952), is parameterized by the vector $\omega \in \mathbb {R}_{>0}^J$ , in which each $\omega _j$ corresponds to the worth of object j. Specifically, the Bradley-Terry model specifies the probability that object i will be ranked above object j in pairwise tournament as

(1) $$ \begin{align} P[i \prec j|\omega_i,\omega_j] = \frac{\omega_i}{\omega_i+\omega_j}. \end{align} $$

The Plackett–Luce model (Plackett, Reference Plackett1975) extended the Bradley–Terry to allow for multiple comparisons, partial rankings, and incomplete rankings, and has been justified under Luce’s Choice Axiom (Luce, Reference Luce1959) and Thurstone’s theory of comparative judgment (Thompson Jr. & Singh, Reference Thompson and Singh1967; Thurstone, Reference Thurstone1927; Yellott Jr., Reference Yellott1977). In this model, a ranking $\pi =\{1\prec 2\prec \dots \prec J\}$ of J objects is assigned probability

(2) $$ \begin{align} P[\Pi=\pi|\omega_1,\dots,\omega_J] &= \prod_{j=1}^J \frac{\omega_j}{\sum_{j^{\prime}=j}^J \omega_{j^{\prime}}}, \end{align} $$

where often one sets $\sum _{j} \omega _j=1$ for identifiability. Rankings drawn from the Plackett–Luce model may be interpreted as being created sequentially, where in the first stage an object is selected among all the options, in the second stage an object is selected among all the remaining, and so on. Extensions of distributions in the BTL family have been proposed to capture intricacies in ranked preferences such as order of presentation effects, ties, and covariates (Chapman & Staelin, Reference Chapman and Staelin1982; Critchlow & Fligner, Reference Critchlow and Fligner1991; Gormley & Murphy, Reference Gormley and Murphy2010; Rao & Kupper, Reference Rao and Kupper1967). Importantly, the BTL family can handle partial and incomplete rankings by exploiting its reliance on Luce’s Choice Axiom.

Since BTL distributions have continuous parameters, rank-clusters may be estimated by employing parameter fusion or shrinkage. Parameter fusion is the process of simultaneously estimating parameter values and groups of parameters that should be set equal in value (i.e., “fusing” parameters together). Masarotto & Varin (Reference Masarotto and Varin2012) analyze pairwise comparison data from sports tournaments with parameter fusion techniques under the Bradley–Terry model. Masarotto & Varin (Reference Masarotto and Varin2012) estimate an overall ranking of teams with rank-clusters by applying the frequentist fused lasso (Tibshirani et al., Reference Tibshirani, Saunders, Rosset, Zhu and Knight2005), in which the absolute difference between every pair of worth parameters is penalized after some data-driven normalization. In this approach, the fused parameters are made equal and thus create a rank-cluster among the corresponding objects. The approach of Masarotto & Varin (Reference Masarotto and Varin2012) was extended to additional datasets in sports (Tutz & Schauberger, Reference Tutz and Schauberger2015) and academic journal rankings (Vana et al., Reference Vana, Hochreiter and Hornik2016; Varin et al., Reference Varin, Cattelan and Firth2016). Jeon & Choi (Reference Jeon and Choi2018) argued that shrinkage methods like those proposed by Masarotto & Varin (Reference Masarotto and Varin2012) and Tutz & Schauberger (Reference Tutz and Schauberger2015) were developed specifically for pairwise comparisons, and thus have inappropriate penalty functions for application to richer kinds of ordinal data like partial or complete rankings. As a result, Jeon & Choi (Reference Jeon and Choi2018) proposed a modified regularization penalty that may be applied to partial or complete rankings under the Plackett–Luce model. Relatedly, Hermes et al. (Reference Hermes, van Heerwaarden and Behrouzi2024) consider sparse estimation of a Plackett–Luce model with object-level covariates under judge heterogeneity. In their setting, the number of heterogeneous preference groups and the group membership of each judge are assumed fixed and known. To improve efficiency of estimation across groups and predictive performance, they impose a lasso penalty on group-specific covariate coefficients and a simultaneous fused lasso penalty between each pair of group-specific covariate coefficients. We note that the setting studied by Hermes et al. (Reference Hermes, van Heerwaarden and Behrouzi2024) is fundamentally different to ours, in that they assume (known) preference heterogeneity among the judges and the presence of object-specific covariates.

Parameter fusion methods for rank-clustering exhibit four distinct disadvantages: First, maximum likelihood estimation of models in the BTL family, even in their simplest forms, often suffers from numerical instability and slow computational speed. As a result, numerous authors have proposed complex algorithms to improve estimation accuracy or speed (Hunter et al., Reference Hunter2004; Maystre & Grossglauser, Reference Maystre and Grossglauser2015; Nguyen & Zhang, Reference Nguyen and Zhang2023; Turner et al., Reference Turner, van Etten, Firth and Kosmidis2020). Second, uncertainty quantification is challenging and theoretically tenuous in lasso-based methods (Fan & Li, Reference Fan and Li2001; Tibshirani, Reference Tibshirani1996). Third, lasso penalty parameters may be difficult to select, requiring data-driven or ad hoc techniques (Masarotto & Varin, Reference Masarotto and Varin2012; Tibshirani, Reference Tibshirani1996). Thus, interpretation of the resulting parameter estimates and associated uncertainty is reliant on the specific choice of penalty parameter. Fourth, prior knowledge on the amount and size of rank-clusters cannot be directly incorporated into the frequentist framework: Although the penalty parameter influences estimation of rank-clusters, the specific meaning of various possible choices is not directly interpretable.

Many of these disadvantages may be addressed using spike-and-slab priors, a Bayesian approach to variable selection (George & McCulloch, Reference George and McCulloch1997; Ishwaran & Rao, Reference Ishwaran and Rao2005; Mitchell & Beauchamp, Reference Mitchell and Beauchamp1988). Spike-and-slab priors assign weight to both a point-mass at 0 (“spike”) and a continuous density function (“slab”). Although the specific formulations of these priors vary, they estimate parameters which are precisely zero in a probabilistic framework that incorporates prior knowledge via interpretable hyperparameters, as opposed to opaque penalty parameters. However, we are aware of only one variant of this prior class for parameter fusion: Wu et al. (Reference Wu, Shimamura, Yoshikawa, Murayama and Kawano2021) apply spike-and-slab to differences in successive parameters in a linear regression. In their method, the order of parameters from least to greatest in coefficient value must be known in advance (as in the fused lasso). This is not practical in the canonical ordinal data setting because the parameter order is equivalent to the overall ranking, whose estimation is a primary goal. Thus, no Bayesian parameter fusions methods exist which may be directly applied to ordinal data analyses with rank-clustering. Alternatively, one may consider the class of continuous shrinkage priors, which include Bayesian variants of the lasso (Park & Casella, Reference Park and Casella2008) and fused lasso (Casella et al., Reference Casella, Ghosh, Gill and Kyung2010) among others (e.g., Bhattacharya et al., Reference Bhattacharya, Pati, Pillai and Dunson2015; Carvalho et al., Reference Carvalho, Polson and Scott2010; Griffin & Brown, Reference Griffin and Brown2005). However, continuous shrinkage priors do not place positive probability on coefficients (or their differences) being precisely zero. Thus, parameter fusion must be performed via thresholding the posterior distribution, which is often ad-hoc (Porwal & Rodriguez, Reference Porwal and Rodriguez2024) and will not be considered in this work.

2.2 Methods based on Mallows distributions

Alternatively, one may consider rank-clustering under the Mallows family of ranking models (Mallows, Reference Mallows1957). The Mallows family is parameterized by the overall ranking, $\pi _0$ , and a scale parameter $\theta \geq 0$ that dictates how likely rankings of a given distance to $\pi _0$ are to be drawn. Specifically, the probability of drawing a ranking $\pi $ from a Mallows $(\pi _0,\theta )$ distribution is

(3) $$ \begin{align} P[\Pi=\pi|\pi_0,\theta] &= \frac{e^{-\theta d(\pi,\pi_0)}}{\psi(\theta)}, \end{align} $$

where $d(\cdot ,\cdot )$ is a distance metric and $\psi (\theta )$ is a function which provides an appropriate normalizing constant. Foundational models in the family are defined by their distance metric, with common choices being the Kendall’s $\tau $ (Kendall, Reference Kendall1938) and Spearman’s $\rho $ (Spearman, Reference Spearman1904).

To our knowledge, the Clustered Mallows Model (CMM) proposed by Piancastelli & Friel (Reference Piancastelli and Friel2025) is the only rank-clustering method based on the Mallows model. Their work, proposed concurrently and independently to ours, models item indifference (i.e., rank-clusters) by permitting the overall ranking parameter $\pi _0$ to include groups of objects that are tied in rank. The model is estimated in a Bayesian framework from the observed ranking data. However, there are 3 major limitations to their work: First and most importantly, the model requires both the number of rank-clusters and the number of objects per cluster to be pre-specified. Although the authors propose sensible and efficient tools for model selection, the requirement opens the possibility of model misspecification. For example, given seven objects there are 127 model specifications; given 10 objects there are 1,023 model specifications. In addition, pre-specifying the rank-clustering structure removes any uncertainty in the number of rank-clusters and their sizes from the inference task, which we believe to be of key interest in many applications. Second, Bayesian inference of a Clustered Mallows model is in the class of doubly-intractable problems since the proposed model’s normalizing constant is not available in closed form. As a result, exact inference may be computationally slow, or approximation methods may need to be used that require an inexact pseudolikelihood approach. Third, the Mallows model is best suited for ordinal data in the form of complete or partial rankings, meaning the CMM cannot handle pairwise or groupwise comparisons. As will be shown in Section 3, our proposed model avoids all three issues by incorporating parameter fusion in the continuously-parameterized BTL model family.

3.1 PSSF prior

Suppose data are drawn exchangeably from a model, $\mathcal {M}$ , parameterized by the vector $\omega $ . We suppose $\omega $ is of length J and let each $\omega _j\in \Omega $ , $\Omega \subseteq \mathbb {R}$ . Our goal is to estimate $\omega $ under the belief that some pairs or groups of parameters in $\omega $ may be clustered (i.e., fused). We say that two parameters $m,n\in \{1,\dots ,J\}$ , $m\neq n$ , are clustered precisely when $\omega _m=\omega _n$ . Clustered parameters may take on any value in their domain, $\Omega $ .

Before specifying the prior, we provide some notation on partitions. A partition of an object set $\mathcal {J}=\{1,2,\dots ,J\}$ is a collection $g=\{C(1),C(2),\dots ,C(K)\}$ of K disjoint nonempty subsets (henceforth referred to as “clusters”) of $\mathcal {J}$ such that their union forms $\mathcal {J}$ . Let $C^{-1}(j)$ represent the cluster that contains object $j\in \mathcal {J}$ . We let $S(k) = \big |\{C(k)\}\big |$ be the size of the subset $C(k)$ , and denote by K the number of clusters in g. To emphasize dependence on g, we often write $K_g$ , $C_g(k)$ , etc. Lastly, we let $\mathcal {G}$ represent the collection of all partitions g of $\mathcal {J}$ , and let $\mathcal {G}_k = \{g\in \mathcal {G} : K_g = k\}$ .

We are now ready to specify the PSSF prior. Under PSSF, $\omega $ is assumed to be generated via the following hierarchical model:

(4) $$ \begin{align} G &\sim f_G \nonumber\\ \nu_k | G=g &\overset{iid}{\sim} f_\nu && k=1,2,\dots,K_g\\ \omega_j &= \nu_{C^{-1}_g(j)} && j\in\mathcal{J}.\nonumber \end{align} $$

In Equation (4), $f_G(\cdot )$ is a probability mass function on $\mathcal {G}$ and $f_\nu (\cdot )$ is a probability density function on $\Omega $ . In words, the prior generates a partition g, and then assigns a unique value $\nu _k$ to each cluster $C(k)\in g$ . Last, each parameter in $\omega $ is assigned the value of $\nu $ corresponding to its cluster in g.

As an example, suppose $\mathcal {J}=\{1,2,3\}$ and we draw $g=\{C(1),C(2)\}$ such that $C(1)=\{2\}$ and $C(2)=\{1,3\}$ , and draw $\nu = [5,10]$ . Then, $\omega = [10,5,10]$ because,

$$ \begin{align*} \omega_1 &= \nu_{C^{-1}_g(1)} = \nu_{2}=10,\\ \omega_2 &= \nu_{C^{-1}_g(2)} = \nu_1=5, \text{ and}\\ \omega_3 &= \nu_{C^{-1}_g(3)} = \nu_2 = 10. \end{align*} $$

3.1.1 Marginal prior probabilities

A useful feature of the PSSF prior is that, regardless of $f_G$ , the marginal distribution of each $\omega _j$ follows $f_\nu $ . This is because,

(5) $$ \begin{align} P[\omega_j] &= \sum_{k=1}^J P[\nu_k|j\in C(k)]P[j\in C(k)]\end{align} $$

(6) $$ \begin{align} &= P[\nu_{1}]\sum_{k=1}^J P[j\in C(k)] \end{align} $$

(7) $$ \begin{align} &= f_\nu(\cdot). \end{align} $$

Equation (5) holds as there cannot be more than J clusters and each object belongs to precisely one cluster, Equation (6) holds by the exchangeability of $\nu _{k}$ , and Equation (7) holds since $P[\nu _1] = f_\nu (\cdot )$ by definition and the Law of Total Probability.

3.1.2 Relationship to spike-and-slab

We have not yet explained the proposed PSSF prior’s relationship to the spike-and-slab. It is easiest to understand their connection by considering the joint prior distribution on two arbitrary component parameters, $\omega _m$ and $\omega _n$ , such that $m\neq n$ . Due to the partitioning structure of parameters in the PSSF prior, there is prior probability associated with a parameter cluster. Thus, their joint prior distribution contains a “spike” component along the line $\omega _m=\omega _n$ , with density of that line determined by $f_\nu $ . Oppositely, given $\omega _m\neq \omega _n$ their joint prior distribution reflects independent draws from $f_\nu $ .

Figure 1 gives examples of the PSSF prior under varying choices of $f_G$ and $f_\nu $ . In all panels, we let $\mathcal {J} = \{1,2\}$ and display the joint prior distribution of $(\omega _1,\omega _2)$ . In this setting, there are only two unique partitions, $g=\{1,1\}$ and $g=\{1,2\}$ . Thus, we specify the prior $f_G$ by stating the so-called “cluster probability,” i.e., the probability that $g=\{1,1\}$ . Columns correspond to cluster probabilities $0.1, 0.5$ , and $0.9$ , respectively. Rows correspond to $f_\nu =\text {Normal}(0,1)$ and $\text {Gamma}(5,3)$ , respectively. We notice that as the cluster probability increases, so does the density of points in the spike component. Regardless of $f_G$ , marginal distributions of each parameter follow $f_\nu $ . The marginal relationships seen in Figure 1 hold identically even as $\mathcal {J}$ grows.

Figure 1 Joint distribution of $(\omega _1,\omega _2)$ under the PSSF prior with varying combinations of $f_G$ and $f_\nu $ .

Note: In all cases, $\mathcal {J}=\{1,2\}$ , and plots show 20,000 sampled values with marginal density estimates along the axes. Rows correspond to the choice of $f_\nu $ and columns to $f_G$ .

Furthermore, we show the difference between parameters, $\omega _2-\omega _1$ , between different scenarios in Figure 2. The rows and columns are identical to that in Figure 1 and make clear the relationship between the PSSF prior and the traditional spike-and-slab, which has a spike component at 0 and a background slab density.

Figure 2 Distribution of $\omega _2-\omega _1$ under the PSSF prior with varying combinations of $f_G$ and $f_\nu $ .

Note: In all cases, $\mathcal {J}=\{1,2\}$ . Rows correspond to the choice of $f_\nu $ and columns to $f_G$ .

3.2 Rank-Clustered BTL model

We now introduce the Rank-Clustered BTL model for ordinal data. Let I be the number of judges who assess J objects. Let $\Pi _i$ represent the ordinal preference provided by judge i, which may be a partial ranking, complete ranking, pairwise comparison, or groupwise comparison. Let $R_i$ be the number of objects ranked by judge i, i.e., $R_i= |\Pi _i|$ . When $R_i<J$ , his/her ranking is partial. Let $\mathcal {S}_i$ denote the objects considered by judge i when forming his/her ranking, such that $\mathcal {S}_i\subseteq \mathcal {J}$ . When $\mathcal {S}_i\subset \mathcal {J}$ , his/her ranking is incomplete. $R_i$ and $\mathcal {S}_i$ are assumed known.

Under the Rank-Clustered BTL model, we assume ordinal data is generated via the following Bayesian model:

(8) $$ \begin{align} \begin{split} \omega &\sim \text{PSSF}(f_G \propto \text{Poisson}(K_g|\lambda),f_\nu = \text{Gamma}(\nu_k|a_\gamma,b_\gamma))\\ \Pi_i|\omega &\overset{iid}\sim \text{BTL}(\omega|\mathcal{S}_i,R_i)\hspace{4.5cm}i=1,\dots,I. \end{split} \end{align} $$

Rank-Clustered BTL applies the proposed PSSF prior under specific choices of $f_G$ and $f\nu $ to the BTL family of distributions for ordinal data. Note that the data-generating BTL distribution is identifiable up to scalar multiplication of $\omega $ . However, the proposed Bayesian model does not suffer from identifiability issues due to the non-uniform prior on $\omega $ (Johnson et al., Reference Johnson, Henderson and Boys2022). We emphasize that unlike existing rank-clustering methods, the proposed model does not pre-specify the number of clusters, a specific rank-clustering structure, or the order of objects. These are treated as random variables and estimated simultaneously.

3.2.1 Prior selection

We now discuss the selection of priors and hyperparameters. We set $f_G$ according to

(9) $$ \begin{align} f_G(g) \propto \text{Poisson}(K_g | \lambda). \end{align} $$

In words, the prior probability of drawing a specific partition g depends only on how many unique clusters, $K_g$ , it contains. This prior is intentionally vague to permit a variety of rank-clustering patterns. Note that every partition with the same $K_g$ has equal prior probability. As a consequence, cluster sizes do not explicitly impact the prior probability of each g.Footnote ² Still, there is an implicit connection between cluster size and $K_g$ . For example, if $K_g=J$ , every cluster must be a singleton. In this setup, one could set $\lambda \approx 1$ to encourage rank-clustering, or $\lambda \approx J$ to discourage rank-clustering. Next, we set $f_\nu $ according to

(10) $$ \begin{align} f_\nu(\nu_k) = \text{Gamma}(\nu_k|a_\gamma,b_\gamma). \end{align} $$

This Gamma prior has been used in Bayesian estimation of BTL models as it allows for closed-form Gibbs sampling via data augmentation (Caron & Doucet, Reference Caron and Doucet2012; Mollica & Tardella, Reference Mollica and Tardella2017). The hyperparameters $a_\gamma $ and $b_\gamma $ control the prior distribution on the worth parameters. Since $\omega $ is invariant to multiplicative transformations, $a_\gamma $ and $b_\gamma $ are generally non-influential. Nonetheless, because the ratios between worth parameters could become very large when one object is strongly preferred over another, $(a_\gamma ,b_\gamma )$ should be chosen to give some density to values near 0 to allow for such extreme ratios.

3.2.2 Goodness-of-fit

To assess the adequacy of an estimated Bayesian model to observed data, we use a posterior predictive p-value (Gelman et al., Reference Gelman, Carlin, Stern, Dunson, Vehtari and Rubin2013, p. 146),

$$ \begin{align*}p = P\big(T(\Pi^{rep};g,\nu)\geq T(\Pi^{obs};g,\nu)\ \big| \ \Pi^{obs}\big),\end{align*} $$

where $\Pi ^{\text {rep}}$ is a draw from the posterior predictive distribution, $\Pi ^{\text {obs}}$ is the observed data, $T(\Pi ;g,\nu )$ is a discrepancy measure chosen to test a specific quality of the assumed model, and the probability is taken over the posterior distribution of parameters $g,\nu $ and the posterior predictive distribution of $\Pi $ . Based on Yao & Böckenholt (Reference Yao and Böckenholt1999) and Mollica & Tardella (Reference Mollica and Tardella2017), we employ a discrepancy measure that considers the number of times item j beats item $j^{\prime }$ , denoted $\tau _{jj^{\prime }}$ , for $j,j^{\prime }=1,\dots ,J$ . Specifically,

$$ \begin{align*} T(\Pi;g,\nu) &= \sum_{j<j^{\prime}}\frac{(\tau_{jj^{\prime}}-\tau^{*}_{jj^{\prime}})^2}{\tau^{*}_{jj^{\prime}}}, \end{align*} $$

where $\tau ^*_{jj^{\prime }}$ is the theoretical frequency expected under an assumed model with parameters $g,\nu $ . Under a well-fitting model, the posterior predictive p-value would be near 0.5, with small values indicating inadequate model fit.

4.1 Gibbs sampler

Equation (4) defines $\omega $ by the pair $(\nu ,g)$ . Thus, to estimate $\omega $ , we sample from the joint posterior distribution of $(\nu ,g)$ . We do so using a RJMCMC Gibbs sampler that alternates between updating g and $\nu $ via their full conditionals after data augmentation. The sampler is summarized in Algorithm 1.

Based on our experience fitting Rank-Clustered BTL models to real and simulated data, we recommend initializing $g^{(0)} = \{1,2,\dots ,J\}$ (and thus $K_{g^{(0)}}=J$ ) as it allows rank-clusters to be formed during the estimation process (as opposed to being imposed by the analyst during initialization). For Step 2, $T_1$ should be sufficiently large to allow for convergence of the MCMC chain, although specific choices are context-dependent. Step 2(a) performs RJMCMC on clusters of objects. Since RJMCMC can be slow to converge in high dimensions, it is important to run multiple chains and assess for mixing and convergence (Gelman et al., Reference Gelman, Carlin, Stern, Dunson, Vehtari and Rubin2013). Step 2(b) relies on a closed-form Gibbs sampler. We find $T_2\leq 5$ is usually sufficient for posterior sampling.

4.1.1 Details of Step 2(a)

We now detail Step 2(a), which proposes a new partition $g^{\prime }$ based on the current partition g. Since $(\nu, g)$ are intricately tied, $\nu $ must simultaneously be updated to an appropriate $\nu ^{\prime }$ . The sampling of discrete partitions is challenging to perform efficiently. In a seminal paper on RJMCMC, Green (Reference Green1995) provided a method for sampling partitions. We adapt that work for the Rank-Clustered BTL model.

Following Green (Reference Green1995), we only propose $g^{\prime }$ which are slight modifications of g: Precisely, we allow only for “births” splitting one cluster into two, or “deaths” merging two clusters into one. Since all partitions have positive probability, this process is irreducible, as required. There is no need to propose $g^{\prime }$ that shuffle the partitions but maintain the number of clusters, as these partitions may be obtained by successive birth and death moves.

Births are attempted with probability $b_g = 0.5$ .Footnote ³ In this case, we select a cluster k at random among those with at least two objects. The cluster is split “binomially”, meaning that each object is placed independently into one of the “child” subgroups, $k_1$ or $k_2$ , with equal probability, conditional on each subgroup ultimately containing at least one object. Deaths are attempted with probability $d_g = 1-b_g = 0.5$ . In a death, two adjacent clusters are merged at random. Adjacency means that $\not \exists k : \nu _k\in (\nu _{k_1},\nu _{k_2})$ .

Births and deaths require updating $\nu $ by increasing or decreasing its dimension by 1, respectively. In a birth, we split a cluster’s worth $\nu _k$ into $(\nu ^{\prime }_{k_1},\nu ^{\prime }_{k_2})$ using,

(11) $$ \begin{align} \nu^{\prime}_{k_1} = u\nu_k, \ \ \ \nu^{\prime}_{k_2} = u^{-1}\nu_k, \end{align} $$

where $u\sim \text {Unif}(0.5,1.5)$ . The corresponding death solves these equations simultaneously:

(12) $$ \begin{align} \nu_k = \sqrt{\nu^{\prime}_{k_1}\nu^{\prime}_{k_2}}. \end{align} $$

For reversibility, we automatically reject proposed births where $\nu ^{\prime }_{k_1},\nu ^{\prime }_{k_2}$ are not adjacent.

Per Green (Reference Green1995), the Metropolis–Hastings probabilities for a birth and death, respectively, are $\min (1,A)$ and $\min (1,A^{-1})$ , where

(13) $$ \begin{align} A &= \frac{P(\nu^{\prime},g^{\prime}|\Pi)}{P(\nu,g|\Pi)}\times \frac{q(\nu,g | \nu^{\prime},g^{\prime})}{q(\nu^{\prime},g^{\prime} | \nu,g)P(u)}\times \Big|\frac{\partial(\nu^{\prime}_{k_1},\nu^{\prime}_{k_2})}{\partial(u,\nu_k)}\Big|, \end{align} $$

where $q(\nu ^{\prime },g^{\prime }|\nu ,g)$ is the transition probability of sampling $(\nu ^{\prime },g^{\prime })$ given current parameter set $(\nu ,g)$ . We now calculate each term in A. First,

(14) $$ \begin{align} \frac{P(\nu^{\prime},g^{\prime}|\Pi)}{P(\nu,g|\Pi)} &= \frac{P(\Pi|\nu^{\prime},g^{\prime})P[\nu^{\prime}|g^{\prime}]P[g^{\prime}]}{\sum_{g^{\prime\prime}}\int_{\nu^{\prime\prime}}P(\Pi|\nu^{\prime\prime},g^{\prime\prime})P[\nu^{\prime\prime}|g^{\prime\prime}]d\nu^{\prime\prime}P[g^{\prime\prime}]}\frac{\sum_{g^{\prime\prime}}\int_{\nu^{\prime\prime}}P(\Pi|\nu^{\prime\prime},g^{\prime\prime})P[\nu^{\prime\prime}|g^{\prime\prime}]d\nu^{\prime\prime}P[g^{\prime\prime}]}{P(\Pi|\nu,g)P[\nu|g]P[g]}\nonumber\\ &= \frac{P(\Pi|\nu^{\prime},g^{\prime})P[\nu^{\prime}|g^{\prime}]P[g^{\prime}]}{P(\Pi|\nu,g)P[\nu|g]P[g]}\nonumber\\ &= \frac{P(\Pi|\nu^{\prime},g^{\prime})}{P(\Pi|\nu,g)}\times\frac{\text{Gamma}(\nu^{\prime}_{k_1}|a_\gamma,b_\gamma)\text{Gamma}(\nu^{\prime}_{k_2}|a_\gamma,b_\gamma)}{\text{Gamma}(\nu_{k}|a_\gamma,b_\gamma)}\times\frac{P[g^{\prime}]}{P[g]}, \end{align} $$

where $P(\Pi |\nu ,g)$ and $P[g]$ are defined by Equation (8). Second,

(15) $$ \begin{align} \frac{q(\nu,g | \nu^{\prime},g^{\prime})}{q(\nu^{\prime},g^{\prime} | \nu,g)P(u)} &= \frac{d_{g^{\prime}}\times\frac{1}{K_{g^{\prime}}-1}}{\Big(b_g\times\frac{1}{\#\{l:S_{l}(g)\geq2\}}\times\frac{2}{2^{S_g(k)}-2}\Big)\Big(\frac{1}{1.5-0.5}\Big)}\\ &=\frac{d_{g^{\prime}}\#\{l:S_g(l)\geq2\}(2^{S_g(k)-1}-1)}{b_g(K_{g^{\prime}}-1)}.\nonumber \end{align} $$

The numerator in Equation (15) is the death probability, $d_{g^{\prime }}$ , times the probability of selecting a pair of adjacent partitions given $K_{g^{\prime }}$ total partitions after a split (there are $K_{g^{\prime }}-1$ such pairs). The denominator is the birth probability, $b_g$ , times the probability of selecting a specific cluster k among those with at least two members. This term also includes the probability of dividing the $S_g(k)$ objects in cluster k into two non-empty subsets. There are $(2^{S_g(k)}-2)/2$ such subsets, since there are $2^{S_g(k)}$ total possible partitions, two empty partitions, and two ways to obtain each two-way split. Third and last,

(16) $$ \begin{align} \Big|\frac{\partial(\nu^{\prime}_{k_1},\nu^{\prime}_{k_2})}{\partial(u,\nu_k)}\Big| &= \Bigg|\begin{bmatrix} \frac{\partial}{\partial u}\nu_{k_1}^{\prime} & \frac{\partial}{\partial \nu_k}\nu_{k_1}^{\prime} \\ \frac{\partial}{\partial u}\nu_{k_2}^{\prime} & \frac{\partial}{\partial \nu_k}\nu_{k_2}^{\prime} \end{bmatrix}\Bigg|= \Bigg|\begin{bmatrix} \frac{\partial}{\partial u}u\nu_k & \frac{\partial}{\partial \nu_k}u\nu_k \\ \frac{\partial}{\partial u}\nu_k/u & \frac{\partial}{\partial \nu_k}\nu_k/u \end{bmatrix}\Bigg|=\Bigg|\begin{bmatrix} \nu_k & u \\ -\nu_k/u^2 & 1/u \end{bmatrix}\Bigg|\nonumber\\ &=\frac{2\nu_k}{u}. \end{align} $$

4.1.2 Details of Step 2(b)

To update $\nu $ conditional on a partition g and our data, $\Pi $ , we turn to a clever data augmentation trick for Bayesian estimation of Plackett–Luce models as seen in Caron & Doucet (Reference Caron and Doucet2012) and Mollica & Tardella (Reference Mollica and Tardella2017). Here, we adapt their trick to account for the more general BTL family of distributions and rank-clustering. Let $Y = \{Y_{ir}\}$ be a collection of independent random variables, $i=1,\dots ,I$ and $r=1,\dots ,R_i$ , sampled according to

(17) $$ \begin{align} Y_{ir}\sim \text{Exponential}\Big(\sum_{j\in\mathcal{S}_i} \nu_{g^{-1}(j)} - \sum_{s=0}^{r-1}\nu_{g^{-1}(\pi_i(s))}\Big). \end{align} $$

The exponential rates are precisely the denominator terms from BTL densities that are burdensome to calculate. The full conditional posterior probability $P[\nu |Y,\Pi ,g]$ is then,

(18) $$ \begin{align} P[\nu|Y,\Pi,g] &\propto P[Y|\Pi,g,\nu]P[\Pi|g,\nu]P[g|\nu]P[\nu]\nonumber\\&\propto P[Y|\Pi,g,\nu]P[\Pi|g,\nu]P[\nu]\nonumber\\&= \prod_{i=1}^I \prod_{r=1}^{R_i} \Big(\sum_{j\in\mathcal{S}_i} \nu_{g^{-1}(j)} - \sum_{s=0}^{r-1}\nu_{g^{-1}(\pi_i(s))}\Big)e^{-y_{ir}\big(\sum_{j\in\mathcal{S}_i} \nu_{g^{-1}(j)} - \sum_{s=0}^{r-1}\nu_{g^{-1}(\pi_i(s))}\big)}\times \nonumber\\& \quad \prod_{i=1}^I \prod_{r=1}^{R_i} \frac{\nu_{g^{-1}(\pi_i(r))}}{\sum_{j\in\mathcal{S}_i} \nu_{g^{-1}(j)} - \sum_{s=0}^{r-1}\nu_{g^{-1}(\pi_i(s))}}\times \prod_{k=1}^K \nu_k^{a_\gamma-1}e^{-b_\gamma\nu_k}\nonumber\\&= \prod_{i=1}^I \prod_{r=1}^{R_i} \nu_{g^{-1}(\pi_i(r))}e^{-y_{ir}\big(\sum_{j\in\mathcal{S}_i} \nu_{g^{-1}(j)} - \sum_{s=0}^{r-1}\nu_{g^{-1}(\pi_i(s))}\big)}\times\prod_{k=1}^K \nu_k^{a_\gamma-1}e^{-b_\gamma\nu_k}. \end{align} $$

Given these cancellations, we notice a closed-form expression for the posterior:

(19) $$ \begin{align} P[\nu|Y,\Pi,g] &\propto \prod_{i=1}^I\prod_{k=1}^K \nu_k^{c_{ki}}e^{-\nu_k\sum_{r=1}^{R_i}y_{ir}\delta_{irk}}\times\prod_{k=1}^K \nu_k^{a_\gamma-1}e^{-b_\gamma\nu_k}\nonumber\\&= \prod_{k=1}^K \nu_k^{a_\gamma+\sum\limits_{i=1}^I c_{ki}-1}e^{-\nu_k(b_\gamma + \sum\limits_{i=1}^I\sum\limits_{r=1}^{R_i}y_{ir}\delta_{irk})}\nonumber\\&\propto \prod_{k=1}^K \text{Gamma}\Big(\nu_k \ \Big| \ a_\gamma+\sum\limits_{i=1}^I c_{ki}, b_\gamma + \sum\limits_{i=1}^I\sum\limits_{r=1}^{R_i}y_{ir}\delta_{irk}\Big), \end{align} $$

where

(20) $$ \begin{align} c_{ki} &= \big|\big\{j : j\in\pi_i, g^{-1}(j) = k\big\}\big|\end{align} $$

(21) $$ \begin{align} \delta_{irk} &= \big|\big\{j : j\in\mathcal{S}_i, j\not\in \{\pi_i(1),\dots,\pi_i(r-1)\}, g^{-1}(j) = k\big\}\big|. \end{align} $$

Thus, we can sample $\nu $ from a closed-form Gamma distribution after augmentation of the conditioning data $\Pi $ and random variable g with Y.

Now that we have developed an efficient estimation algorithm for Rank-Clustered BTL models, we turn to a numerical simulation to demonstrate estimation accuracy under different rank-clustering regimes.

4.2 Numerical simulation

We now demonstrate accurate estimation of worth parameters and rank-clusters via a Rank-Clustered BTL model in a numerical simulation. We assume there are $J=8$ objects which form K=1, 2, 4, or 8 rank-clusters. When $K=J=8$ , every object is independent; there are only singleton rank-clusters. In the true worth parameter vector, $\omega _0$ , rank-clustered objects have identical values and successive rank-clusters are separated in value by a factor of 4 (see Table 1 for specific values). Fourfold increases induce strong but not absolute separation between objects: For demonstration, in a pairwise tournament between an object with $\omega _1=1$ and $\omega _2=4$ , the probability of selecting object 2 is,

$$ \begin{align*}P[2\prec 1|\omega_1=1,\omega_2=4] = \frac{\omega_2}{\omega_1+\omega_2}=\frac{4}{4+1} = 0.8.\end{align*} $$

We also vary the Poisson hyperparameter on the number of rank-clusters, $\lambda \in \{0.1,2,4,8\}$ , which encourages rank-clustering to different extents and allows us to measure robustness of results when $\lambda $ is somewhat misspecified. To assess consistency in the number of observations, we vary the number of judges $I\in \{50,200,800\}$ . Finally, to assess the influence of partial and incomplete rankings, we vary the tuple $(R,S)\in \{(2,2),(4,4),(2,8),(4,8),(8,8)\}$ , where R is the number of ranked objects and S is the number of objects considered by each judge. When $R<8$ the ranking is partial, when $S<8$ the ranking is incomplete. The set of considered objects, $\mathcal {S}_i$ for each judge i, is selected independently and uniformly at random.

Table 1 Simulation settings for $\omega _0$ under varying numbers of true rank-clusters, K

For each combination of K, $\lambda $ , $(R,S)$ , and I, we generate 20 independent datasets and fit a Rank-Clustered BTL distribution to each, under hyperparameters $a_\gamma =5$ and $b_\gamma =3$ . We set $T_1=5{,}000$ and $T_2=2$ to obtain 10,000 posterior samples in each MCMC chain and remove the first half as burn-in. We note that no MCMC chain of length 10,000 took longer than 20 minutes to run ( $\sim $ 0.12 seconds/iteration); many ran in under 2 minutes. For identifiability, posterior estimates of $\omega _0$ are normalized post-hoc such that $\sum _j \omega _{0j} = 1$ .

We first examine the accuracy of estimation for $\omega _0$ across simulation settings. Figure 3 displays boxplots of mean absolute error (MAE) for $\omega _0$ by number of judges I, true number of rank-clusters K, and the choice of hyperparameter $\lambda $ . In general, estimation is quite accurate. We see that for any specific combination of K and $\lambda $ , MAE decreases as I increases. Estimation error is higher when K is large and I is small, most likely the result of error estimating a complex rank-clustering structure.

Figure 3 Boxplots of posterior mean absolute error for $\omega _0$ across combinations of the number of judges I, true number of rank-clusters K, hyperparameter $\lambda $ , number of ranked objects R, and number of assessed objects S.

Note: Errors are calculated after normalization of posterior samples such that $\sum _{j}\omega _{0j}=1$ .

Figure 4 displays the mean posterior probability of rank-clustering across object pairs which are truly rank-clustered (navy) or independent (gold) in $\omega _0$ .

Figure 4 Boxplots of the mean posterior probability of rank-clustering object pairs which are truly rank-clustered (left) or independent (right) across combinations of I, K, $\lambda $ , R, and S.

Results are further separated by the number of judges, I, true number of clusters, K, and hyperparameter $\lambda $ . For rank-clustered pairs, accuracy of recovery is generally high and increases with the number of judges, I. Accuracy is best when hyperparameter $\lambda \approx K$ , which occurs when prior belief regarding the number of rank-clusters is approximately correct. If there is limited prior knowledge on the number of rank-clusters, we suggest specifying a vague hyperparameter setting such as $\lambda = \frac {J}{2}$ and assessing sensitivity of results to various choices of $\lambda $ . The posterior probability of rank-clustering independent object pairs is near 0 in all simulations, indicating excellent recovery accuracy of objects with distinct worth parameters.

The numerical simulations in this section indicate that the proposed Rank-Clustered BTL model is able to accurately estimate the relative worth of objects in a collection, including in the presence of rank-clustering or partial/incomplete observed rankings. Estimation error decreases to 0 as the number of observations increases. Overall, the model correctly identifies rank-clustered and independent object pairs.

5.1 Sushi preferences in Tohoku

We first study complete preference rankings of 10 sushi types from a benchmarking dataset by Kamishima (Reference Kamishima2003). To allow our results to be comparable with an analysis of the sushi data by Piancastelli & Friel (Reference Piancastelli and Friel2025), we analyze the preferences of survey respondents who lived in Japan’s Tohoku region until at least 15 years of age. There were 280 such respondents. We fit a Rank-Clustered BTL distribution to the data with $a_\gamma =5$ , $b_\gamma =3$ , and $\lambda = 2$ to encourage rank-clustering but permit a wide variety of outcomes. We ran a total of 32,000 MCMC iterations, which took approximately 12 minutes (0.02 seconds/iteration). Figure 5 displays posterior rank-clustering probabilities (left) and parameter posteriors (right). In the left panel, the color of the $(i,j)$ square of the clustering matrix represents the posterior probability that sushi types i and j are equal in rank at the population level. Additional results, including goodness-of-fit and convergence diagnostics, are provided in the Appendices .

Table 2 Summary of applications by subsection

Figure 5 Primary results from Rank-Clustered BTL analysis of Tohoku sushi data.

Note: Left: Posterior rank-clustering probabilities. Main diagonal displays posterior median estimate of worth parameter after normalization. Red squares indicate maximum a posteriori rank-clusters. Right: Posterior distributions of sushi-specific worth parameters.

Sushi types are ordered according to posterior median worth. Based on the left panel in Figure 5, fatty tuna appears to be strictly most preferred in this population, followed by tuna and shrimp rank-clustered in second place. Salmon roe and sea eel exhibit high posterior probability of rank-clustering, as do sea urchin, tuna roll, and squid; these two groups may themselves be rank-clustered. Egg and cucumber roll are rank-clustered in last place. Our results demonstrate the proposed model’s ability to rank-cluster objects with uncertainty under complete rankings in survey data.

We compare our results to those found by Piancastelli & Friel (Reference Piancastelli and Friel2025) in a CMM. They estimate the following ranking: fatty tuna $\prec $ tuna $\prec $ shrimp $\prec $ {salmon roe, sea urchin} $\prec $ {sea eel, tuna roll, squid} $\prec $ {egg, cucumber roll}. Our results are, unsurprisingly, similar, but differ in illuminating ways. Tuna and shrimp are rank-clustered in our model. The rank-clusters {salmon roe, sea eel} and {sea urchin, tuna roll, squid} swap the rank of sea eel and sea urchin. These two rank-clusters exhibit some posterior probability of rank-clustering themselves. These differences showcase how the model pre-specification required by CMM limits the flexibility of results and may not fully show what the data has to offer or fully account for uncertainty in the estimated ranks and rank-clusters. The Rank-Clustered BTL model requires no pre-specification and permits complex posterior summaries of rank-clustering, including uncertainty in the number of rank-clusters and their respective sizes.

5.2 2021 Minneapolis mayoral election

Our second example analyzes real rank-choice votes from the 2021 mayoral election in Minneapolis, Minnesota (Minneapolis Elections and Voter Services, 2021). This election included 17 candidates (excluding write-ins and one who received no votes) and asked voters to rank their top-three choices, in order. A total of 145,337 votes were cast in this election. To mimic exit polling data, we randomly sample 1000 valid votes for analysis, which we treat as a random sample of preferences from the population of Minneapolis voters. We want to estimate the overall preferences of Minneapolis voters regarding mayoral candidates and learn which candidates, if any, are rank-clustered at the population level. Clustering candidates may be of interest to political scientists or local political organizations for the purpose of understanding voter preferences (Dimock et al., Reference Dimock, Doherty, Kiley and Krishnamurthy2014; Gunther & Diamond, Reference Gunther and Diamond2003). For example, if the winner of the election is deemed to be rank-clustered with other candidate(s), their mandate may be considered weak. Conversely, if the winner is a singleton first-place rank-cluster—clearly ranked above all other candidates—their mandate may be considered strong. We fit a Rank-Clustered BTL to the data with $a_\gamma =5$ , $b_\gamma =3$ , and $\lambda = 2$ to encourage few rank-clusters. We ran a total of 80 $,$ 000 MCMC iterations, which took approximately 72.5 minutes (approximately 0.05 seconds/iteration). Figure 6 displays posterior rank-clustering probabilities (left) and parameter posteriors (right). In the left panel, the color of the $(i,j)$ square of the clustering matrix represents the posterior probability that candidates i and j are equal in rank at the population level. Additional results, including goodness-of-fit and convergence diagnostics, are provided in the Appendices.

Figure 6 Primary results from Rank-Clustered BTL analysis of mayoral votes.

Note: Party abbreviations are in parentheses after candidate surnames. Left: Posterior rank-clustering probabilities. Main diagonal displays posterior median estimate of worth parameter after normalization. Red squares indicate maximum a posteriori rank-clusters. Right: Posterior distributions of candidate-specific worth parameters.

In Figure 6, candidates are ordered by their posterior median estimate of worth. Cluster 1 consists of Jacob Frey, the winner and incumbent. We note that Frey is not rank-clustered with other candidates with high posterior probability, suggesting a relatively strong mandate. Cluster 2 consists of Kate Knuth and Sheila Nezhad, both female, non-incumbent DFL candidates. Last, Cluster 7 consists of 6 candidates with minimal support.

Figure 7 compares point estimates of rank for each candidate across four methods. The first and second rows display assigned ranks from ranked choice and “first-past-the-post” (FPP) election procedures, respectively. We calculate FPP ranks by ordering candidates by the number of first place votes he/she received (ignoring all second and third place votes).Footnote ⁴ The third and fourth rows display maximum a posteriori ranks from a standard Bayesian BTL and our Rank-Clustered BTL, respectively. Frey wins the election in all methods. The BTL and Rank-Clustered BTL models roughly reflect the deterministic algorithms, although we notice some swaps in candidate ranks which may be attributed to differences between first place and second or third place votes. For example, Conner received fewer first place votes than Turner, but far more second and third place votes (see Appendices for vote totals). As a result, deterministic algorithms rank Turner above Conner, while the BTL model takes into account the additional preference information and ranks Conner above Turner. In summary, the overall ordering estimated by the Rank-Clustered BTL differs from a standard BTL model and two deterministic election procedures. Furthermore, our model confirms that Frey is strictly preferred over the remaining candidates by voters.

Figure 7 Comparison of estimated rank for each candidate across four aggregation methods: Ranked Choice, First-Past-the-Post (FPP), BTL, and Rank-Clustered BTL (RC BTL).

Note: Candidates are ordered by their rank in the actual ranked choice election.

5.3 Eurobarometer 34.1 survey data

We analyze data from the Eurobarometer 34.1 survey (Reif & Melich, Reference Reif and Melich1993), which included the following question:

We subset the data to respondents from Great Britain to avoid heterogeneity and non-proportional sampling among respondents from different European countries. There were 1005 valid responses among this group (out of 1,031 total surveyed), of which 970 were complete rankings and the rest ranked between one and five items (a top-six ranking is inherently equivalent to a complete ranking since all survey options were presented). We seek to identify a population-level ordering of the priorities that accounts for potential equality or indistinguishability among the options based on the survey data. These data were previously studied by Wang et al. (Reference Wang, Matsueda and Erosheva2017) with a mixed-membership model to learn about heterogeneity of opinions among survey respondents. Our analysis, although a simplification of the diverse population’s heterogeneous preferences, provides a simpler interpretation to policy-makers interested in understanding rank-ordering of policy preferences.

We fit a Rank-Clustered BTL model to the data with $a_\gamma =5$ , $b_\gamma =3$ , and $\lambda =2$ to encourage rank-clustering. We ran a total of 16,000 MCMC iterations, which took approximately 12.5 minutes (0.047 seconds/iteration). Figure 8 displays posterior rank-clustering probabilities (left) and parameter posteriors (right). In the left panel, the color of the $(i,j)$ square of the clustering matrix represents the posterior probability that policies i and j are equal in rank at the population level. Additional results, including goodness-of-fit and convergence diagnostics, are provided in the Appendices.

Figure 8 Primary results from Rank-Clustered BTL analysis of Eurobarometer 34.1 data.

Note: Left:Posterior rank-clustering probabilities. Main diagonal displays posterior median estimate of worth parameter after normalization. Red squares indicate maximum a posteriori rank-clusters. Right: Posterior distributions of policy-specific worth parameters.

Policy option 2 (hunting drug pushers) is strictly preferred to the rest among the population of survey respondents from Great Britain, whereas options 5 (funding research) and 3 (legal penalty) are rank-clustered last. The results indicates to policymakers that respondents in Great Britain strongly prioritize Option 2 in comparison to the rest, while pairs of Options 1 and 4 and Options 3 and 5, are, respectively, indistinguishable within each pair, with 1 and 4 being strongly preferred to 3 and 5. By rank-clustering similarly-preferred options, interpretation of constituent preferences is simplified for policymakers.

5.4 2023–2024 NBA game outcomes

Last, we analyze outcomes of 1 $,$ 230 games from the 2023–2024 season of the National Basketball Association (NBA) of the United States of America (National Basketball Association, 2024). In this season, 30 teams each played 82 games, including between two and five games against every other team. We seek to estimate an overall ranking of teams that allows for potential equality in ranking.

We fit a Rank-Clustered BTL model to the data with $a_\gamma =5$ , $b_\gamma =3$ , and $\lambda =1$ to encourage rank-clustering given the limited ordinal comparison data provided by pairwise matchups. We ran a total of 320,000 MCMC iterations, which took approximately 22.6 hours (approximately 0.25 seconds/iteration). Figure 9 displays posterior rank-clustering probabilities (left) and parameter posteriors (right). In the left panel, the color of the $(i,j)$ square of the clustering matrix represents the posterior probability that teams i and j are equal in rank at the population level. Additional results, including goodness-of-fit and convergence diagnostics, are provided in the Appendices.

Figure 9 Primary results from Rank-Clustered BTL analysis of 2023–2024 NBA data.

Note: Left: Posterior rank-clustering probabilities. Main diagonal displays posterior median estimate of worth parameter after normalization. Right: Posterior distributions of team-specific worth parameters.

In this setting, the Rank-Clustered BTL model estimates an ordering of professional basketball teams with uncertain rank-clustering patterns. Uncertain rank-clustering may result from two aspects of this application. First, pairwise comparisons provide little information in relation to partial or complete rankings, by construction. Second, game outcomes provide low signal measurements of team ability (Baumer et al., Reference Baumer, Matthews and Nguyen2023). That is because many factors influence game outcomes, such as skill, home advantage, injuries, roster changes, and luck (Cai et al., Reference Cai, Yu, Wu, Du and Zhou2019). Consistent with the low signal and limited information setting, an 80% posterior credible interval indicates that there are between 6 and 9 rank-clusters. Every team has less than 0.037 posterior probability of belonging to a singleton rank-cluster.

As seen in the left panel of Figure 9, four teams (Boston Celtics, Oklahoma City Thunder, Denver Nuggets, and Minnesota Timberwolves) appear to be rank-clustered for first place. Based on regular season data alone, our model suggests that these 4 teams were of roughly indistinguishable ability. Conversely, we observe that 6 teams (Toronto Raptors, San Antonio Spurs, Portland Trail Blazers, Charlotte Hornets, Washington Wizards, and Detroit Pistons) all have a high posterior probability of rank-clustering in last place. Instead of reporting the uncertain ranking of these teams with some granularity, we recommend to infer that these teams were the worst teams of the league in this season. These rank-clusters, despite not accounting for the complexities of the sport, provide useful and interpretable summaries of the teams’ abilities across the regular season. A similar analysis could be used in the future to predict postseason performance.