DeepHoops: Evaluating Micro-Actions in Basketball Using Deep Feature Representations of Spatio-Temporal Data

by   Anthony Sicilia, et al.

How much is an on-ball screen worth? How much is a backdoor cut away from the ball worth? Basketball is one of a number of sports which, within the past decade, have seen an explosion in quantitative metrics and methods for evaluating players and teams. However, it is still challenging to evaluate individual off-ball events in terms of how they contribute to the success of a possession. In this study, we develop an end-to-end deep learning architecture DeepHoops to process a unique dataset composed of spatio-temporal tracking data from NBA games in order to generate a running stream of predictions on the expected points to be scored as a possession progresses. We frame the problem as a multi-class sequence classification problem in which our model estimates probabilities of terminal actions taken by players (e.g. take field goal, turnover, foul etc.) at each moment of a possession based on a sequence of ball and player court locations preceding the said moment. Each of these terminal actions is associated with an expected point value, which is used to estimate the expected points to be scored. One of the challenges associated with this problem is the high imbalance in the action classes. To solve this problem, we parameterize a downsampling scheme for the training phase. We demonstrate that DeepHoops is well-calibrated, estimating accurately the probabilities of each terminal action and we further showcase the model's capability to evaluate individual actions (potentially off-ball) within a possession that are not captured by boxscore statistics.


Actions Speak Louder Than Goals: Valuing Player Actions in Soccer

Assessing the impact of the individual actions performed by soccer playe...

Measuring Football Players' On-the-ball Contributions From Passes During Games

Several performance metrics for quantifying the in-game performances of ...

Action Completion: A Temporal Model for Moment Detection

We introduce completion moment detection for actions - the problem of lo...

Location analysis of players in UEFA EURO 2020 and 2022 using generalized valuation of defense by estimating probabilities

Analyzing defenses in team sports is generally challenging because of th...

Generalized Action-based Ball Recovery Model using 360^∘ data

Even though having more possession does not necessarily lead to winning,...

"Why Would I Trust Your Numbers?" On the Explainability of Expected Values in Soccer

In recent years, many different approaches have been proposed to quantif...

Automated Offside Detection by Spatio-Temporal Analysis of Football Videos

In this paper, we propose a new automated method to detect offsides from...

Please sign up or login with your details

Forgot password? Click here to reset