ANALISIS SHOOTING FREE THROW KAWHI LEONARD MVP (MOST VALUABLE PLAYER) FINAL NBA 2019 MENGGUNAKAN SOFTWARE KINOVEA
Bibliographic record
Abstract
Laga Final NBA 2019 yang mempertemukan Tim Toronto raptors melawan Golden State Warriors Kawhi Leonard berhasil membawa timnya Toronto Raptors menjadi juara NBA 2019 sekaligus sebagai penerima penghargaan pemain terbaik. Di setiap poin yang diciptakan Kawhi Leonard memiliki tingkat keberhasilan paling tinggi pada percobaan shooting free throw. Tujuan : Untuk mengetahui efektivitas shooting Kawhi Leonard ditinjau dari aspek biomekanika yaitu sudut siku, sudut bahu, sudut elevasi dan kecepatan tembakan. Metode : Penelitian ini menggunakan penelitian non-eksperimen dengan metode penelitian analisis deskriptif kuantitatif. Teknik analisis data menggunakan software Kinovea dan setelah data terkumpul akan dihitung dengan rumus persentase dan rumus gerak parabola. Hasil : tingkat keberhasilan shooting free throw Kawhi Leonard sebesar 88%, Sudut siku yang menghasilkan bola masuk yaitu pada rentang 86o-90o, sudut bahu 124o-130o, sudut elevasi 46o-52o dan rata-rata kecepatan tembakan 7,33 m/s. Kesimpulan : Shooting free throw yang paling efektif yaitu dengan sudut siku mendekati 90o, sudut bahu 129o, sudut elevasi 45o dengan kecepatan 7,69 m/s. Kata Kunci: Analisis biomekanika, software Kinovea, Shooting free throw.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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; both teacher heads agree on what is shown here.
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".