Predicting player performance in sports is a critical challenge with significant implications for team success,fan engagement,and financial outcomes.Although,inMajor League Baseball(MLB),statistical methodologies such...Predicting player performance in sports is a critical challenge with significant implications for team success,fan engagement,and financial outcomes.Although,inMajor League Baseball(MLB),statistical methodologies such as sabermetrics have been widely used,the dynamic nature of sports makes accurate performance prediction a difficult task.Enhanced forecasts can provide immense value to team managers by aiding strategic player contract and acquisition decisions.This study addresses this challenge by employing the temporal fusion transformer(TFT),an advanced and cutting-edge deep learning model for complex data,to predict pitchers’earned run average(ERA),a key metric in baseball performance analysis.The performance of the TFT model is evaluated against recurrent neural network-based approaches and existing projection systems.In experimental results,the TFT based model consistently outperformed its counterparts,demonstrating superior accuracy in pitcher performance prediction.By leveraging the advanced capabilities of TFT,this study contributes to more precise player evaluations and improves strategic planning in baseball.展开更多
Purpose Understanding the desired attributes for talented soccer players may give insight into the process of(de)selection and player development.This study aimed to explore soccer academy personnel’s perceptions of ...Purpose Understanding the desired attributes for talented soccer players may give insight into the process of(de)selection and player development.This study aimed to explore soccer academy personnel’s perceptions of attributes associated with talent and development.Methods Thirty English and Scottish academy personnel(managers,coaches,recruitment,sports scientists)provided per-ceptions into what attributes contribute to‘talent’,via an online survey.Utilising an E-Delphi method,seven experts refined these inputs over several rounds until a consensus was reached,resulting in 82 agreed terminology.This terminology was resubmitted via a second online survey,where 45 academy personnel rated each using a Likert scale.Results A ranking of attributes by importance was produced,finding Psychological and Technical/Tactical attributes con-sidered of greatest importance.Differences were observed,whereby recruitment personnel consistently over-emphasised the importance of several attributes compared to other personnel(P=0.02-0.04).When analyzed within each age phase,11 variations in the perceived importance of attributes were demonstrated in the youth phase(11-16 years,P=0.01)compared with 5 in the professional phases(17-23 years,P=0.01-0.05)Conclusion The present study demonstrates that‘talent’requires multifaceted developments,with academy personnel per-ceiving psychological attributes the most important contributor to development.展开更多
基金supported by SKKU Global Research Platform Research Fund,Sungkyunkwan University,2024-2025.
文摘Predicting player performance in sports is a critical challenge with significant implications for team success,fan engagement,and financial outcomes.Although,inMajor League Baseball(MLB),statistical methodologies such as sabermetrics have been widely used,the dynamic nature of sports makes accurate performance prediction a difficult task.Enhanced forecasts can provide immense value to team managers by aiding strategic player contract and acquisition decisions.This study addresses this challenge by employing the temporal fusion transformer(TFT),an advanced and cutting-edge deep learning model for complex data,to predict pitchers’earned run average(ERA),a key metric in baseball performance analysis.The performance of the TFT model is evaluated against recurrent neural network-based approaches and existing projection systems.In experimental results,the TFT based model consistently outperformed its counterparts,demonstrating superior accuracy in pitcher performance prediction.By leveraging the advanced capabilities of TFT,this study contributes to more precise player evaluations and improves strategic planning in baseball.
文摘Purpose Understanding the desired attributes for talented soccer players may give insight into the process of(de)selection and player development.This study aimed to explore soccer academy personnel’s perceptions of attributes associated with talent and development.Methods Thirty English and Scottish academy personnel(managers,coaches,recruitment,sports scientists)provided per-ceptions into what attributes contribute to‘talent’,via an online survey.Utilising an E-Delphi method,seven experts refined these inputs over several rounds until a consensus was reached,resulting in 82 agreed terminology.This terminology was resubmitted via a second online survey,where 45 academy personnel rated each using a Likert scale.Results A ranking of attributes by importance was produced,finding Psychological and Technical/Tactical attributes con-sidered of greatest importance.Differences were observed,whereby recruitment personnel consistently over-emphasised the importance of several attributes compared to other personnel(P=0.02-0.04).When analyzed within each age phase,11 variations in the perceived importance of attributes were demonstrated in the youth phase(11-16 years,P=0.01)compared with 5 in the professional phases(17-23 years,P=0.01-0.05)Conclusion The present study demonstrates that‘talent’requires multifaceted developments,with academy personnel per-ceiving psychological attributes the most important contributor to development.
