Sports Games Attendance Forecast Using Machine Learning

Du, Peiyuan; Liao, Chengcheng; Xian, Teli

doi:10.1109/icdsca56264.2022.9987748

Cited by 2 publications

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the need arises for providing high-quality, interpretable, and comprehensible explanations for a model's predictions in sports, utilizing feature importance. Indeed, feature importance plays a pivotal role in delivering detailed explanations of machine learning models in sports [52,53].…”

Section: Discussionmentioning

confidence: 99%

Exploring CrossFit Performance Prediction and Analysis via Extensive Data and Machine Learning

Lim,

Song

2023

Preprint

View full text Add to dashboard Cite

(1) Background: The analysis of athletic performance has always aroused great interest from sport scientist. This study utilized machine learning methods to build predictive models using a comprehensive CrossFit (CF) dataset, aiming to reveal valuable insights into the factors influencing performance and emerging trends.; (2) Methods: The study used Random Forest (RF) and Multiple Linear Regression (MLR) models to predict performance in four key weightlifting exercises within CF: clean &amp; jerk, snatch, back squat, and deadlift. Performance was evaluated using R-squared (R2) values and Mean Squared Error (MSE). Feature importance analysis was conducted using RF, XGBoost, and AdaBoost models.; (3) Results: The RF model excelled in deadlift performance prediction (R2 = 0.80), while the MLR model demonstrated remarkable accuracy in clean &amp; jerk (R2 = 0.93). Across exercises, clean &amp; jerk consistently emerged as a crucial predictor. The feature importance analysis revealed intricate relationships among exercises, with gender significantly impacting deadlift performance.; (4) Conclusions: This research advances our understanding of performance prediction in CF through machine learning techniques. It provides actionable insights for practitioners, optimize performance, and demonstrates the potential for future advancements in data-driven sports analytics.

show abstract

Section: Discussionmentioning

confidence: 99%