What are the changes in basketball shooting pattern and accuracy in National Basketball Association in the past decade?
Bibliographic record
Abstract
The main research question addressed in this study is if and how the shooting pattern and field-goal accuracy have changed in the NBA league in the past decade. This study analyzes NBA game data from the 2011–2012 regular season to the 2020–2021 season. Field goal attempts are grouped into five categories by the shooting distance. The Mann-Kendall trend test was employed to examine if changes are statistically significant (p < 0.05) over the years. Sixteen equal segments in one basketball game, each with 3 min, were analyzed to examine the shooting pattern in different game segments. Results reveal an increasing trend in the percentage of 3-pointer shooting, which has nearly doubled from 22 to 39%. Meanwhile, the percentage of field goals within the range of 16–24 ft has decreased from 20 to 10%. Field-goal accuracy has shown an increasing trend for all shooting distances except for the 3-pointer shooting. The second and fourth 3-min within each quarter have the highest number of field goals. The first quarter has a higher shooting accuracy than the rest three quarters. In addition, results reveal that the last 3-min in each quarter has the lowest shooting accuracy. Reasons for the patterns of field goals in different segments are discussed from the perspective of game rule changes, the fatigue effects, and coaches’ game strategies. The reasons for changes in activity level and performance in different quarters are also discussed. This study offers new insights into the changes in basketball shooting patterns and accuracy in NBA games in the past decade. Practical meanings of this study for basketball players, coaches, and sports psychologists, as well as the strength and limitations of this study, are discussed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".