Modeling Offensive Player Movement in Professional Basketball
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The 2013 arrival of SportVU player tracking data in all NBA arenas introduced an overwhelming amount of on-court information—information which the league is still learning how to maximize for insights into player performance and basketball strategy. The data contain the spatial coordinates for the ball and every player on the court for 25 frames per second, which opens up avenues of player and team performance analysis that was not possible before this technology existed. This article serves as a step-by-step guide for how to leverage a data feed from SportVU for one NBA game into visualizable components that can model any player's movement on offense. We detail some utility functions that are helpful for manipulating SportVU data before applying it to the task of visualizing player offensive movement. We conclude with visualizations of the resulting output for one NBA game, as well as what the results look like aggregated across an entire season for three NBA stars with very different offensive tendencies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it