Forecasting in the NBA and other team sports

Abstract

The multi-million sports-betting market is based on the fact that the task of predicting the outcome of a sports event is very hard. Even with the aid of an uncountable number of descriptive statistics and background information, only a few can correctly guess the outcome of a game or a league. In this work, our approach is to move away from the traditional way of predicting sports events, and instead to model sports leagues as networks of players and teams where the only information available is the work relationships among them. We propose two network-based models to predict the behavior of teams in sports leagues. These models are parameter-free, that is, they do not have a single parameter, and moreover are sport-agnostic: they can be applied directly to any team sports league. First, we view a sports league as a network in evolution, and we infer the implicit feedback behind network changes and properties over the years. Then, we use this knowledge to construct the network-based prediction models, which can, with a significantly high probability, indicate how well a team will perform over a season. We compare our proposed models with other prediction models in two of the most popular sports leagues: the National Basketball Association (NBA) and the Major League Baseball (MLB). Our model shows consistently good results in comparison with the other models and, relying upon the network properties of the teams, we achieved a ≈ 14% rank prediction accuracy improvement over our best competitor.

Untangling performance from success

“…Our goal here is to explore in a quantitative manner the relationship between performance, an individual measure, and success, a collective measure capturing the societal acknowledgement of a given level of performance. We do so through sports, an area where performance is accurately recorded in terms of number of wins, place in rankings or career records [25][26][27][28][29][30][31]. Sports is characterized by an equally obsessive focus on popularity and fame, which strongly affects an athlete's market value [32].…”

Provenance Network Analytics

“…isolated nodes or those with few links, not on predicting node attributes as in this work. Also relatedly, Varlamis and Louta (2009) count network links in order to express trustworthiness, as an example application of the principle described by Yu and Singh (2000) that propagation can be considered one of the properties of trust (along with symmetry, transitivity, selfreinforcement, etc.) However, Varlamis and Louta (2009) did not have voting data available in order to assess the accuracy of their model in the way we could as in the CollabMap application.…”

Predicting the Success of NFL Teams using Complex Network Analysis

“…Additionally, the labor relationships between NBA players and teams were also modeled as a complex network but, differently from previous work, no box score statistics are used to predict outcomes and the network evolves over time (Vaz de Melo et al, 2012). The knowledge acquired from the evolving network is applied to build a prediction model, which estimates how well a team will perform in seasons.…”