Data Analysis on Predicting the Top 12 Fantasy Football Players by Position

Fantasy football enthusiasts rely on rankings populated by their platform of choice to draft winning teams and make strategic roster decisions. This study presents a comprehensive analysis of player performance data to forecast the top 12 fantasy points performers per position for the upcoming season. Leveraging machine learning techniques and historical data, our model identifies key performance indicators and trends to inform player evaluations. Insights gleaned from positional trends, breakout candidates, risk assessment, and matchup analysis offer a competitive edge. By addressing limitations, ethical considerations, and avenues for future research, this study contributes to the advancement of fantasy sports analysis and enhances fan engagement with the NFL and other professional leagues.

Publication Details

Journal
SMU Scholar (Southern Methodist University)
Published
2024-09-04
Primary Topic
Sports Analytics and Performance
Type
article
Field-Weighted Citation Impact
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article

Data Analysis on Predicting the Top 12 Fantasy Football Players by Position

SMU Scholar (Southern Methodist University)
Sports Analytics and Performance
article

Data Analysis on Predicting the Top 12 Fantasy Football Players by Position

article en

Abstract

Fantasy football enthusiasts rely on rankings populated by their platform of choice to draft winning teams and make strategic roster decisions. This study presents a comprehensive analysis of player performance data to forecast the top 12 fantasy points performers per position for the upcoming season. Leveraging machine learning techniques and historical data, our model identifies key performance indicators and trends to inform player evaluations. Insights gleaned from positional trends, breakout candidates, risk assessment, and matchup analysis offer a competitive edge. By addressing limitations, ethical considerations, and avenues for future research, this study contributes to the advancement of fantasy sports analysis and enhances fan engagement with the NFL and other professional leagues.

SMU Scholar (Southern Methodist University)
Openalex Percentile: Top 32%
Sports Analytics and Performance
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Data Analysis on Predicting the Top 12 Fantasy Football Players by Position · SMU Scholar (Southern Methodist University) (2024) | TGRS Research Map | TGRS