Konstantinos Pelechrinis

Christos Petridis, Konstantinos Pelechrinis

Identifying combinations of players (that is, lineups) in basketball - and other sports - that perform well when they play together is one of the most important tasks in sports analytics. One of the main challenges associated with this task is the frequent substitutions that occur during a game, which results in highly sparse data. In particular, a National Basketball Association (NBA) team will use more than 600 lineups during a season, which translates to an average lineup having seen the court in approximately 25-30 possessions. Inevitably, any statistics that one collects for these lineups are going to be noisy, with low predictive value. Yet, there is no existing work (in the public at least) that addresses this problem. In this work, we propose a regression-based approach that controls for the opposition faced by each lineup, while it also utilizes information about the players making up the lineups. Our experiments show that L-RAPM provides improved predictive power than the currently used baseline, and this improvement increases as the sample size for the lineups gets smaller.

Iasonas Nikolaou, Konstantinos Pelechrinis, Evimaria Terzi

In this work, we develop a graphical model to capture team dynamics. We analyze the model and show how to learn its parameters from data. Using our model we study the phenomenon of team collapse from a computational perspective. We use simulations and real-world experiments to find the main causes of team collapse. We also provide the principles of building resilient teams, i.e., teams that avoid collapsing. Finally, we use our model to analyze the structure of NBA teams and dive deeper into games of interest.

Xin Liu, Konstantinos Pelechrinis, Alexandros Labrinidis

Which area in NYC is the most similar to Lower East Side? What about the NoHo Arts District in Los Angeles? Traditionally this task utilizes information about the type of places located within the areas and some popularity/quality metric. We take a different approach. In particular, urban dwellers' time-variant mobility is a reflection of how they interact with their city over time. Hence, in this paper, we introduce an approach, namely hood2vec, to identify the similarity between urban areas through learning a node embedding of the mobility network captured through Foursquare check-ins. We compare the pairwise similarities obtained from hood2vec with the ones obtained from comparing the types of venues in the different areas. The low correlation between the two indicates that the mobility dynamics and the venue types potentially capture different aspects of similarity between urban areas.

Evangelos Papalexakis, Konstantinos Pelechrinis

During the past few years advancements in sports information systems and technology has allowed us to collect a number of detailed spatio-temporal data capturing various aspects of basketball. For example, shot charts, that is, maps capturing locations of (made or missed) shots, and spatio-temporal trajectories for all the players on the court can capture information about the offensive and defensive tendencies and schemes of a team. Characterization of these processes is important for player and team comparisons, pre-game scouting, game preparation etc. Playing tendencies among teams have traditionally been compared in a heuristic manner. Recently automated ways for similar comparisons have appeared in the sports analytics literature. However, these approaches are almost exclusively focused on the spatial distribution of the underlying actions (usually shots taken), ignoring a multitude of other parameters that can affect the action studied. In this work, we propose a framework based on tensor decomposition for obtaining a set of prototype spatio-temporal patterns based on the core spatiotemporal information and contextual meta-data. The core of our framework is a 3D tensor X, whose dimensions represent the entity under consideration (team, player, possession etc.), the location on the court and time. We make use of the PARAFAC decomposition and we decompose the tensor into several interpretable patterns, that can be thought of as prototype patterns of the process examined (e.g., shot selection, offensive schemes etc.). We also introduce an approach for choosing the number of components to be considered. Using the tensor components, we can then express every entity as a weighted combination of these components. The framework introduced in this paper can have further applications in the work-flow of the basketball operations of a franchise, which we also briefly discuss.

Konstantinos Pelechrinis, Prashant Krishnamurthy, Ke Zhang

The proliferation of location-based social networks (LBSNs) has provided the community with an abundant source of information that can be exploited and used in many different ways. LBSNs offer a number of conveniences to its participants, such as - but not limited to - a list of places in the vicinity of a user, recommendations for an area never explored before provided by other peers, tracking of friends, monetary rewards in the form of special deals from the venues visited as well as a cheap way of advertisement for the latter. However, service convenience and security have followed disjoint paths in LBSNs and users can misuse the offered features. The major threat for the service providers is that of fake check-ins. Users can easily manipulate the localization module of the underlying application and declare their presence in a counterfeit location. The incentives for these behaviors can be both earning monetary as well as virtual rewards. Therefore, while fake check-ins driven from the former motive can cause monetary losses, those aiming in virtual rewards are also harmful. In particular, they can significantly degrade the services offered from the LBSN providers (such as recommendations) or third parties using these data (e.g., urban planners). In this paper, we propose and analyze a honeypot venue-based solution, enhanced with a challenge-response scheme, that flags users who are generating fake spatial information. We believe that our work will stimulate further research on this important topic and will provide new directions with regards to possible solutions.