Analysis of Player Actions in Selected Hockey Game Situations
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
We present a proof of concept system to represent and reason about hockey play. The system takes as input player motion trajectory data tracked from game video and supported by knowledge of hockey strategy, game situation and specific player profiles. The raw motion trajectory data consists of space-time point sequences of player position registered to rink coordinates. The raw data is augmented with knowledge of forward/backward skating, possession of the puck and specific player attributes (e.g., shoots left, shoots right). We use a finite state machine (FSM) model to represent our total knowledge of given situations and develop evaluation functions for primitive hockey behaviours (e.g., pass, shot). Based on the augmented trajectory data, the FSMs and the evaluation functions, we describe what happened in each identified situation, assess the outcome, estimate when and where key play choices were made, and attempt to predict whether better alternatives were available to achieve understood goals. A textual natural language description and a simple ID graphic animation of the analysis are produced as the output. The design is flexible to allow the substitution of different analysis modules and extensible to allow the inclusion of additional hockey situations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| 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