Players’ Performance Prediction for Fantasy Premier League, Using Transformer-based Sentiment Analysis on News and Statistical Data
Bibliographic record
Abstract
Abstract Fantasy sports have become increasingly popular, with millions of players engaging in strategic team management and competition. In the realm of Fantasy Premier League (FPL), effective player analysis and performance prediction are crucial for success in each game. This paper presents an innovative approach to enhance FPL analysis and performance prediction by integrating news sentiment and players’ injury with statistical data sources. A dataset of weekly news articles was enriched through pretrained transformer-based sentiment analysis toolkit and combined with different boosting and neural network algorithms for prediction tasks. Our findings demonstrate that integrating these features enhances model performance, with the CNN architecture achieving a reduction in MSE from 6.27 to 5.63 outperforming the state of the art model. These results highlight the potential of leveraging diverse data sources for more accurate predictions and informed decision-making in FPL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".