A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes

Basketball games evolve continuously in space and time as players constantly interact with their teammates, the opposing team, and the ball. However, current analyses of basketball outcomes rely on discretized summaries of the game that reduce such interactions to tallies of points, assists, and similar events. In this paper, we propose a framework for using optical player tracking data to estimate, in real time, the expected number of points obtained by the end of a possession. This quantity, called \textit{expected possession value} (EPV), derives from a stochastic process model for the evolution of a basketball possession; we model this process at multiple levels of resolution, differentiating between continuous, infinitesimal movements of players, and discrete events such as shot attempts and turnovers. Transition kernels are estimated using hierarchical spatiotemporal models that share information across players while remaining computationally tractable on very large data sets. In addition to estimating EPV, these models reveal novel insights on players' decision-making tendencies as a function of their spatial strategy.Comment: 31 pages, 9 figure

Use of Machine Learning to Automate the Identification of Basketball Strategies Using Whole Team Player Tracking Data

The use of machine learning to identify and classify offensive and defensive strategies in team sports through spatio-temporal tracking data has received significant interest recently in the literature and the global sport industry. This paper focuses on data-driven defensive strategy learning in basketball. Most research to date on basketball strategy learning has focused on offensive effectiveness and is based on the interaction between the on-ball player and principle on-ball defender, thereby ignoring the contribution of the remaining players. Furthermore, most sports analytical systems that provide play-by-play data is heavily biased towards offensive metrics such as passes, dribbles, and shots. The aim of the current study was to use machine learning to classify the different defensive strategies basketball players adopt when deviating from their initial defensive action. An analytical model was developed to recognise the one-on-one (matched) relationships of the players, which is utilised to automatically identify any change of defensive strategy. A classification model is developed based on a player and ball tracking dataset from National Basketball Association (NBA) game play to classify the adopted defensive strategy against pick-and-roll play. The methodology described is the first to analyse the defensive strategy of all in-game players (both on-ball players and off-ball players). The cross-validation results indicate that the proposed technique for automatic defensive strategy identification can achieve up to 69% accuracy of classification. Machine learning techniques, such as the one adopted here, have the potential to enable a deeper understanding of player decision making and defensive game strategies in basketball and other sports, by leveraging the player and ball tracking data

Proceedings of Mathsport international 2017 conference

Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017. MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet. Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports

A Bayesian marked spatial point processes model for basketball shot chart

The success rate of a basketball shot may be higher at locations where a player makes more shots. For a marked spatial point process, this means that the mark and the intensity are associated. We propose a Bayesian joint model for the mark and the intensity of marked point processes, where the intensity is incorporated in the mark model as a covariate. Inferences are done with a Markov chain Monte Carlo algorithm. Two Bayesian model comparison criteria, the Deviance Information Criterion and the Logarithm of the Pseudo-Marginal Likelihood, were used to assess the model. The performances of the proposed methods were examined in extensive simulation studies. The proposed methods were applied to the shot charts of four players (Curry, Harden, Durant, and James) in the 2017--2018 regular season of the National Basketball Association to analyze their shot intensity in the field and the field goal percentage in detail. Application to the top 50 most frequent shooters in the season suggests that the field goal percentage and the shot intensity are positively associated for a majority of the players. The fitted parameters were used as inputs in a secondary analysis to cluster the players into different groups

TRACKING FORMATION CHANGES AND ITS EFFECTS ON SOCCER USING POSITION DATA

This study investigated the application of advanced machine learning methods, specifically k-means clustering, k-Nearest Neighbors (kNN), and Support Vector Machines (SVM), to analyze player tracking data in soccer. The primary hypothesis posits that such data can yield a standalone, in-depth understanding of soccer matches. The study revealed that while k-means and spatial analysis are promising in analyzing player positions, kNN and SVM show limitations without additional variables. Spatial analysis examined each team’s convex hull and studied the correlation between team length, width, and surface area. Results showed team length and surface area have a strong positive correlation with a value of 0.8954. This suggested that teams with longer team length have a more direct style of play with players more spread out which led to larger surface areas. k-means clustering was performed with different k values derived from different approaches. The silhouette method recommended a k value of 2 and the elbow recommended a k value of 4. The context of the sport suggested additional analysis with a k value of 11. The results from k-means suggested natural data partitions, highlighting distinct player roles and field positions. kNN was performed to find similar players with the model of k = 19 showing the highest accuracy of 8.61%. The SVM model returned a classification of 55 classes which indicated a highly granular level of categorization for player roles. The results from kNN and SVM indicated the necessity of further contextual data for more effective analysis and emphasized the need for balanced datasets and careful model evaluation to avoid biases and ensure practical application in real-world scenarios. In conclusion, each algorithm offers unique perspectives and interpretations on player positioning and team formations. These algorithms, when combined with expert knowledge and additional contextual data, can significantly enrich the scope of analysis in soccer. Future work should consider incorporating event data and additional variables to enhance the depth of analytical insights, enabling a more comprehensive understanding of how formations evolve in response to various in-game situations