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UnravelSports [JB]
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‎README.md‎

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kloppy_dataset=kloppy_dataset
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)
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```
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| | period_id | timestamp | frame_id | ball_state | id | x | y | z | team_id | position_name | game_id | vx | vy | vz | v | ax | ay | az | a | ball_owning_team_id | is_ball_carrier |
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|---:|------------:|:----------------|-----------:|:-------------|:---------------|-------:|------:|----:|:---------------|:----------------|:---------------|-------:|-------:|-----:|------:|-----:|-----:|-----:|----:|:----------------------|:------------------|
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| 0 | 1 | 0 days 00:00:00 | 10000 | alive | DFL-OBJ-00008F | -20.67 | -4.56 | 0 | DFL-CLU-000005 | RCB | DFL-MAT-J03WPY | 0.393 | -0.214 | 0 | 0.447 | 0 | 0 | 0 | 0 | DFL-CLU-00000P | False |
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| 1 | 1 | 0 days 00:00:00 | 10000 | alive | DFL-OBJ-0000EJ | -8.86 | -0.94 | 0 | DFL-CLU-000005 | UNK | DFL-MAT-J03WPY | -0.009 | 0.018 | 0 | 0.02 | 0 | 0 | 0 | 0 | DFL-CLU-00000P | False |
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| 2 | 1 | 0 days 00:00:00 | 10000 | alive | DFL-OBJ-0000F8 | -2.12 | 9.85 | 0 | DFL-CLU-00000P | RM | DFL-MAT-J03WPY | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | DFL-CLU-00000P | False |
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| 3 | 1 | 0 days 00:00:00 | 10000 | alive | DFL-OBJ-0000NZ | 0.57 | 23.23 | 0 | DFL-CLU-00000P | RB | DFL-MAT-J03WPY | 0.179 | -0.134 | 0 | 0.223 | 0 | 0 | 0 | 0 | DFL-CLU-00000P | False |
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| 4 | 1 | 0 days 00:00:00 | 10000 | alive | DFL-OBJ-0001HW | -46.26 | 0.08 | 0 | DFL-CLU-000005 | GK | DFL-MAT-J03WPY | 0.357 | 0.071 | 0 | 0.364 | 0 | 0 | 0 | 0 | DFL-CLU-00000P | False |
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$^1$ <small>Open data available through kloppy.</small>
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‎examples/0_quick_start_guide.ipynb‎

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"outputs": [
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{
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"name": "stdout",
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"text": [
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"\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"%pip install unravelsports --quiet"
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"11/11 [==============================] - 1s 17ms/step - loss: 83.5579 - auc_10: 0.5369 - binary_accuracy: 0.5419 - val_loss: 15.6364 - val_auc_10: 0.5000 - val_binary_accuracy: 0.4762\n",
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"11/11 [==============================] - 1s 16ms/step - loss: 21.7806 - auc: 0.5278 - binary_accuracy: 0.5419 - val_loss: 5.1682 - val_auc: 0.5000 - val_binary_accuracy: 0.5000\n",
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"Epoch 2/10\n",
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" 1/11 [=>............................] - ETA: 0s - loss: 60.3926 - auc_10: 0.5569 - binary_accuracy: 0.5625WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 3 batches). You may need to use the repeat() function when building your dataset.\n"
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" 1/11 [=>............................] - ETA: 0s - loss: 9.2846 - auc: 0.3651 - binary_accuracy: 0.5000WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 3 batches). You may need to use the repeat() function when building your dataset.\n"
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"11/11 [==============================] - 0s 6ms/step - loss: 32.2668 - auc_10: 0.5139 - binary_accuracy: 0.5120\n",
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"11/11 [==============================] - 0s 6ms/step - loss: 4.5155 - auc: 0.5366 - binary_accuracy: 0.5449\n",
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"Epoch 3/10\n",
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"11/11 [==============================] - 0s 5ms/step - loss: 10.2167 - auc_10: 0.5079 - binary_accuracy: 0.5030\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 2.0773 - auc: 0.4515 - binary_accuracy: 0.4731\n",
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"11/11 [==============================] - 0s 5ms/step - loss: 4.3491 - auc_10: 0.4934 - binary_accuracy: 0.4940\n",
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"11/11 [==============================] - 0s 5ms/step - loss: 1.1006 - auc: 0.5205 - binary_accuracy: 0.5150\n",
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"11/11 [==============================] - 0s 5ms/step - loss: 2.4990 - auc_10: 0.4782 - binary_accuracy: 0.4910\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.9159 - auc: 0.4915 - binary_accuracy: 0.5180\n",
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"11/11 [==============================] - 0s 5ms/step - loss: 1.7516 - auc_10: 0.4798 - binary_accuracy: 0.4850\n",
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"11/11 [==============================] - 0s 5ms/step - loss: 0.8020 - auc: 0.4873 - binary_accuracy: 0.5060\n",
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"11/11 [==============================] - 0s 5ms/step - loss: 1.1898 - auc_10: 0.5383 - binary_accuracy: 0.5240\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.8067 - auc: 0.4960 - binary_accuracy: 0.5299\n",
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"11/11 [==============================] - 0s 5ms/step - loss: 1.1877 - auc_10: 0.4917 - binary_accuracy: 0.5060\n",
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"11/11 [==============================] - 0s 6ms/step - loss: 0.7808 - auc: 0.5055 - binary_accuracy: 0.5299\n",
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"11/11 [==============================] - 0s 5ms/step - loss: 1.0447 - auc_10: 0.5133 - binary_accuracy: 0.5150\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.7661 - auc: 0.4937 - binary_accuracy: 0.5060\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.9795 - auc_10: 0.5214 - binary_accuracy: 0.5090\n"
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"11/11 [==============================] - 0s 5ms/step - loss: 0.7406 - auc: 0.5098 - binary_accuracy: 0.5329\n"
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{

‎examples/1_kloppy_gnn_train.ipynb‎

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"32/32 [==============================] - 1s 9ms/step - loss: 18.2753 - auc: 0.4842 - binary_accuracy: 0.4910 - val_loss: 1.1902 - val_auc: 0.5000 - val_binary_accuracy: 0.5110\n",
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"32/32 [==============================] - 1s 9ms/step - loss: 51.8502 - auc: 0.5028 - binary_accuracy: 0.5070 - val_loss: 2.7810 - val_auc: 0.5000 - val_binary_accuracy: 0.4890\n",
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"<small>shape: (5, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>frame_id</th><th>period_id</th><th>timestamp</th><th>y_hat</th></tr><tr><td>i64</td><td>i64</td><td>duration[μs]</td><td>f32</td></tr></thead><tbody><tr><td>10300</td><td>1</td><td>12s</td><td>0.566736</td></tr><tr><td>10301</td><td>1</td><td>12s 40ms</td><td>0.566736</td></tr><tr><td>10302</td><td>1</td><td>12s 80ms</td><td>0.566736</td></tr><tr><td>10303</td><td>1</td><td>12s 120ms</td><td>0.566736</td></tr><tr><td>10304</td><td>1</td><td>12s 160ms</td><td>0.566736</td></tr></tbody></table></div>"
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"<small>shape: (5, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>frame_id</th><th>period_id</th><th>timestamp</th><th>y_hat</th></tr><tr><td>i64</td><td>i64</td><td>duration[μs]</td><td>f32</td></tr></thead><tbody><tr><td>10300</td><td>1</td><td>12s</td><td>0.991924</td></tr><tr><td>10301</td><td>1</td><td>12s 40ms</td><td>0.991924</td></tr><tr><td>10302</td><td>1</td><td>12s 80ms</td><td>0.991924</td></tr><tr><td>10303</td><td>1</td><td>12s 120ms</td><td>0.991924</td></tr><tr><td>10304</td><td>1</td><td>12s 160ms</td><td>0.991924</td></tr></tbody></table></div>"
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‎examples/pressing_intensity.ipynb‎

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