Sports Games Attendance Forecast Using Machine Learning

Du, Peiyuan; Liao, Chengcheng; Xian, Teli

doi:10.1109/icdsca56264.2022.9987748

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…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

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(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.

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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

show abstract

Exploring CrossFit performance prediction and analysis via extensive data and machine learning

LIM,

SONG

2024

J Sports Med Phys Fitness

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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 & 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 & jerk (R2 = 0.93). Across exercises, clean & 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.

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Forecasting Stadium Attendance Using Machine Learning Models: A Case of the National Football League

Pang,

Wang

2024

StS

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The study examines the use of machine learning models to forecast attendance at sports stadiums, specifically analyzing National Football League (NFL) games from 2000 to 2019, with over 5,055 regular-season games. The models, including Linear Regression, Classification and Regression Trees (CART), Random Forest, CatBoost, and XGBoost, integrate a diverse set of variables such as team performance, economic indicators, stadium characteristics, and weather conditions. Each model's accuracy and effectiveness are assessed using five statistical metrics. With a Mean Absolute Error (MAE) of 0.02 and a Root Mean Squared Error (RMSE) of 0.04, the models display high precision in predicting stadium attendance. The coefficient of determination (R²) reaches 77.27% after optimization. These figures suggest that the models, particularly Random Forest and CatBoost, are highly effective in forecasting attendance rates for NFL games. Key influences on game attendance include factors like 'stadium_name,' 'personal_income,' 'stadium_age,' and 'home_club_age', which emerge as significant predictors. This study fills a theoretical gap in the limited research on the NFL and provides valuable insights for strategic planning and decision-making in professional sports management.

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Sports Games Attendance Forecast Using Machine Learning

Cited by 3 publications

References 29 publications

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

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

Exploring CrossFit performance prediction and analysis via extensive data and machine learning

Forecasting Stadium Attendance Using Machine Learning Models: A Case of the National Football League

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