Kawhi Leonard’s Impact on the Toronto Raptors’ 2019 Playoff Run as a Markov Chain
Bibliographic record
Abstract
In the summer of 2018, the Toronto Raptors engineered a trade that would forever change the history of their franchise. The blockbuster trade saw NBA superstar Kawhi Leonard in a Raptors uniform in exchange for then franchise cornerstone DeMar Derozan. The trade was heavily criticized with fans and analysts alike claiming the organization gave up its future for a small chance at a championship. The Raptors went on to win the championship with Kawhi as their centerpiece. By studying their performance in the playoffs as two separate Markov chains, when Kawhi was playing and when he was resting, his contribution can be analyzed. It was assumed that his presence would account for more defensive stops and a more efficient offense. Upon analyzing the collected data, it was seen that his presence accounts for more points per game and offensive rebounds per game and a decreased number of defensive stops. In the future this type of analysis can be applied to data from any team at any level where relevant statistics are tracked. By analyzing one player’s impact on games, organizations will have a better idea of which players to trade away or trade for as well as how to distribute minutes.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".