A doubly self-exciting Poisson model for describing scoring levels in NBA basketball

04/04/2023
by   Álvaro Briz-Redón, et al.
0

In this paper, Poisson time series models are considered to describe the number of field goals made by a basketball team or player at both the game (within-season) and the minute (within-game) level. To deal with the existence of temporal autocorrelation in the data, the model is endowed with a doubly self-exciting structure, following the INGARCH(1,1) specification. To estimate the model at the within-game level, a divide-and-conquer procedure, under a Bayesian framework, is carried out. The model is tested with a selection of NBA teams and players from the 2018-2019 season.

READ FULL TEXT
research
01/22/2019

A Conway-Maxwell-Poisson GARMA Model for Count Data

We propose a flexible model for count time series which has potential us...
research
03/14/2022

UEFA EURO 2020: a "pure game of chance"?

We analysed the distribution of the number of goals scored in each of th...
research
05/20/2021

Poisson Modeling and Predicting English Premier League Goal Scoring

The English Premier League is well-known for being not only one of the m...
research
05/03/2018

NFL Injuries Before and After the 2011 Collective Bargaining Agreement (CBA)

The National Football League's (NFL) 2011 collective bargaining agreemen...
research
02/06/2019

Playing Fast Not Loose: Evaluating team-level pace of play in ice hockey using spatio-temporal possession data

Pace of play is an important characteristic in hockey as well as other t...
research
07/30/2022

'PeriHack': Designing a Serious Game for Cybersecurity Awareness

This paper describes the design process for the cybersecurity serious ga...
research
09/13/2021

Predicting the outcome of team movements – Player time series analysis using fuzzy and deep methods for representation learning

We extract and use player position time-series data, tagged along with t...

Please sign up or login with your details

Forgot password? Click here to reset