Bibliographic record
Abstract
This thesis focuses on quantifying the true talent of each individual player on the court of a Canadian USports basketball game.In this pursuit, the ultimate measure of success is whether or not the quantification of player value can be used to project future outcomes of players and the teams which they comprise.The paper explores both bottom-up and top-down approaches to assigning value to individual players.These assignments of value are then tested and contrasted to observe what measures should to be used to understand properly what a player can contribute to a team.The name of the game in basketball is to score more points than the other team and thus a player who can contribute to this end is valuable.However, due to the smaller number of games played by individual players at the Canadian University level as compared to professional levels of basketball, more noise is present in measures that focus on team outcomes, which obscures player's true talent.To combat this, a deeper examination is undertaken to understand the processes which predict better outcomes and tell a more salient picture of a player's true talent.I would like to sincerely thank all the people who made the research of sport statistics possible for me and that starts with my supervisor Dr. Shirley Mills.This thesis is born of work I did as a full-time intern of the Carleton University Ravens Women's and Men's basketball teams and if it were not for Dr.Mills I would have never have been in this position.The position too would never have existed without the generosity and vision of an incredible donor.Additionally, the person who made the partnership work between the teams and the School of Mathematics and Statistics, and the Director of Recreation & Athletics at Carleton University, Jennifer Brenning.I also add my thanks to all the people on these teams who I worked with everyday and put up with my incessant questions in trying to learn the intricacies of the game, truly experts in their field and absolute honour to work with: Director of Basketball Operations Dave Smart, Men's Head Coach Taffe Charles, Women's Head Coach Brian Cheng, Men's Assistant Coach Rob Smart, Men's Assistant Coach Jamie Campbell and Women's Assistant Coach Michelle Abella.And lastly, I thank all the players, who are the reason why we all do this.Your incredible
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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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".