Alert button
Picture for Alejandro Rodriguez Pascual

Alejandro Rodriguez Pascual

Alert button

Understanding why shooters shoot -- An AI-powered engine for basketball performance profiling

Mar 17, 2023
Alejandro Rodriguez Pascual, Ishan Mehta, Muhammad Khan, Frank Rodriz, Rose Yu

Figure 1 for Understanding why shooters shoot -- An AI-powered engine for basketball performance profiling
Figure 2 for Understanding why shooters shoot -- An AI-powered engine for basketball performance profiling
Figure 3 for Understanding why shooters shoot -- An AI-powered engine for basketball performance profiling
Figure 4 for Understanding why shooters shoot -- An AI-powered engine for basketball performance profiling

Understanding player shooting profiles is an essential part of basketball analysis: knowing where certain opposing players like to shoot from can help coaches neutralize offensive gameplans from their opponents; understanding where their players are most comfortable can lead them to developing more effective offensive strategies. An automatic tool that can provide these performance profiles in a timely manner can become invaluable for coaches to maximize both the effectiveness of their game plan as well as the time dedicated to practice and other related activities. Additionally, basketball is dictated by many variables, such as playstyle and game dynamics, that can change the flow of the game and, by extension, player performance profiles. It is crucial that the performance profiles can reflect the diverse playstyles, as well as the fast-changing dynamics of the game. We present a tool that can visualize player performance profiles in a timely manner while taking into account factors such as play-style and game dynamics. Our approach generates interpretable heatmaps that allow us to identify and analyze how non-spatial factors, such as game dynamics or playstyle, affect player performance profiles.

* 12 pages, 6 figures, to be published at MIT Sloan Sports Analytics Conference, March 4-5 2022 
Viaarxiv icon

BACON: Deep-Learning Powered AI for Poetry Generation with Author Linguistic Style Transfer

Dec 14, 2021
Alejandro Rodriguez Pascual

Figure 1 for BACON: Deep-Learning Powered AI for Poetry Generation with Author Linguistic Style Transfer
Figure 2 for BACON: Deep-Learning Powered AI for Poetry Generation with Author Linguistic Style Transfer
Figure 3 for BACON: Deep-Learning Powered AI for Poetry Generation with Author Linguistic Style Transfer
Figure 4 for BACON: Deep-Learning Powered AI for Poetry Generation with Author Linguistic Style Transfer

This paper describes BACON, a basic prototype of an automatic poetry generator with author linguistic style transfer. It combines concepts and techniques from finite state machinery, probabilistic models, artificial neural networks and deep learning, to write original poetry with rich aesthetic-qualities in the style of any given author. Extrinsic evaluation of the output generated by BACON shows that participants were unable to tell the difference between human and AI-generated poems in any statistically significant way.

* 9 pages, 7 figures, independent high school research project 
Viaarxiv icon