Summary of Offensive Lineup Analysis in Basketball with Clustering Players Based on Shooting Style and Offensive Role, by Kazuhiro Yamada et al.
Offensive Lineup Analysis in Basketball with Clustering Players Based on Shooting Style and Offensive Role
by Kazuhiro Yamada, Keisuke Fujii
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This study examines the impact of playing style compatibility on scoring efficiency in basketball lineups, focusing exclusively on offensive plays. The authors employ two methods to quantify playing styles: shooting style clustering using tracking data and offensive role clustering based on annotated playtypes and advanced statistics. The former leverages interpretable hand-crafted shot features and Wasserstein distances between shooting style distributions, while the latter applies soft clustering to playtype data for the first time. By analyzing lineup information derived from these clusterings, Bayesian machine learning models are trained to predict statistics representing scoring efficiency. This approach provides insights into effective combinations of five players and productive pairs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine you’re trying to score points in a basketball game. You need to work well with your teammates to get the ball in the hoop. But how do you know which team members are compatible? This study tries to answer that question by looking at how different playing styles affect scoring efficiency. The researchers use special methods to analyze player styles, including shooting and movement patterns. They then use this information to predict which teams will score more points together. By understanding what makes a good team, coaches can make better decisions about who plays together. |
Keywords
* Artificial intelligence * Clustering * Machine learning * Tracking
