Bibliographic record
Abstract
This thesis examines the use of neural network modelling and ordinal logistic regression on a single season of data to score or rank soccer players.These scores and ranks are then compared with ones from FIFA EASports that are based on a much more extensive data set that includes more variables as well as historical data.Using repeated measures one-way ANOVA and subsequent multiple comparison testing, we conclude that a neural network model with ten nodes in one hidden layer is able to generate scores statistically similar to FIFA EASports, while ordinal logistic regression cannot.We confirm this result be also examining the ranks obtained by both the neural network model and the ordinal logistic regression model.Spearman rank correlations are also examined and confirm that a neural network with 10 nodes in one hidden layer also generates ranks statistically similar to FIFA EASports.We also demonstrate the use of association rule mining on one team's data from the 2015-16 season to identify players and combinations thereof that are associated with winning (or not winning) a match.Analyses are based on data from the Italian Serie A League 2015-2016 season.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".