Datasets:
athlete_id stringlengths 5 5 | level stringclasses 6
values | sex stringclasses 4
values | age float64 18 59 ⌀ | language stringclasses 2
values | attendance_pattern stringclasses 2
values | base_freq_per_week float64 1.5 6 | source stringclasses 1
value | join_date stringdate 2024-10-01 00:00:00 2025-11-04 00:00:00 |
|---|---|---|---|---|---|---|---|---|
A0001 | novato | M | 27 | en | regular | 3.931592 | synthetic | 2024-10-01 |
A0002 | novato | M | 38 | en | regular | 2.514231 | synthetic | 2024-10-01 |
A0003 | novato | F | 45 | es | regular | 3.207469 | synthetic | 2024-11-29 |
A0004 | novato | F | 27 | en | regular | 2.292109 | synthetic | 2024-10-01 |
A0005 | novato | M | 42 | es | regular | 2.739217 | synthetic | 2024-10-01 |
A0006 | novato | F | 31 | en | regular | 3.781977 | synthetic | 2024-10-01 |
A0007 | novato | F | 24 | en | regular | 2.820972 | synthetic | 2024-10-01 |
A0008 | novato | M | 48 | en | erratic | 2.489863 | synthetic | 2024-10-01 |
A0009 | novato | M | 25 | es | regular | 2.719548 | synthetic | 2024-10-01 |
A0010 | novato | F | 31 | en | regular | 3.936246 | synthetic | 2024-10-01 |
A0011 | novato | F | 39 | en | erratic | 1.974934 | synthetic | 2024-10-01 |
A0012 | novato | F | 34 | en | regular | 2.685607 | synthetic | 2024-10-01 |
A0013 | novato | F | 52 | es | regular | 2.284355 | synthetic | 2024-10-01 |
A0014 | novato | M | 41 | en | regular | 3.353598 | synthetic | 2024-10-01 |
A0015 | novato | F | 31 | en | erratic | 1.789906 | synthetic | 2024-10-01 |
A0016 | novato | F | 25 | en | regular | 2.465939 | synthetic | 2025-11-04 |
A0017 | novato | M | 54 | es | erratic | 1.620763 | synthetic | 2024-10-01 |
A0018 | novato | F | 51 | es | regular | 2.163792 | synthetic | 2024-10-01 |
A0019 | novato | F | 47 | es | regular | 2.086782 | synthetic | 2025-06-03 |
A0020 | novato | F | 27 | en | regular | 3.536296 | synthetic | 2024-10-01 |
A0021 | novato | F | 53 | es | regular | 2.665378 | synthetic | 2024-12-26 |
A0022 | novato | M | 42 | es | regular | 2.543824 | synthetic | 2024-10-01 |
A0023 | novato | M | 38 | en | erratic | 1.883738 | synthetic | 2024-10-01 |
A0024 | novato | F | 38 | en | erratic | 1.972354 | synthetic | 2024-10-01 |
A0025 | Novato | f | 24 | es | regular | 2.540679 | synthetic | 2024-10-01 |
A0026 | novato | M | 43 | es | erratic | 1.742398 | synthetic | 2024-10-01 |
A0027 | novato | F | 39 | en | regular | 3.039699 | synthetic | 2024-10-01 |
A0028 | novato | F | 28 | es | regular | 2.884406 | synthetic | 2024-10-01 |
A0029 | Novato | m | 46 | en | regular | 2.137461 | synthetic | 2024-10-01 |
A0030 | novato | F | 38 | en | regular | 3.943585 | synthetic | 2024-10-01 |
A0031 | novato | F | 53 | es | regular | 2.175589 | synthetic | 2024-10-01 |
A0032 | novato | M | 22 | en | regular | 3.195327 | synthetic | 2025-01-12 |
A0033 | novato | F | 31 | en | regular | 2.657808 | synthetic | 2024-10-01 |
A0034 | novato | M | 41 | es | regular | 3.341717 | synthetic | 2024-10-01 |
A0035 | novato | M | 49 | es | regular | 2.968615 | synthetic | 2024-10-01 |
A0036 | novato | F | 33 | en | regular | 3.247293 | synthetic | 2025-01-07 |
A0037 | novato | F | 33 | es | regular | 2.696946 | synthetic | 2024-10-01 |
A0038 | novato | F | 40 | es | regular | 2.683463 | synthetic | 2024-10-01 |
A0039 | novato | F | 42 | en | regular | 2.326765 | synthetic | 2024-10-01 |
A0040 | novato | F | 52 | es | erratic | 1.883715 | synthetic | 2025-07-29 |
A0041 | novato | M | 56 | en | regular | 2.598374 | synthetic | 2024-10-01 |
A0042 | novato | F | 34 | en | regular | 2.129013 | synthetic | 2024-10-01 |
A0043 | novato | F | 41 | en | regular | 2.313775 | synthetic | 2024-10-01 |
A0044 | novato | F | 26 | en | regular | 2.351561 | synthetic | 2024-10-01 |
A0045 | novato | M | 36 | es | regular | 2.608126 | synthetic | 2025-05-19 |
A0046 | novato | F | 48 | en | erratic | 1.910251 | synthetic | 2024-10-01 |
A0047 | novato | F | 28 | es | regular | 2.347486 | synthetic | 2024-10-01 |
A0048 | novato | M | 18 | en | erratic | 1.919277 | synthetic | 2024-10-01 |
A0049 | novato | M | 36 | en | regular | 3.146806 | synthetic | 2024-10-01 |
A0050 | novato | M | 37 | es | regular | 3.446071 | synthetic | 2024-10-01 |
A0051 | novato | M | 40 | es | regular | 2.389918 | synthetic | 2025-01-16 |
A0052 | novato | F | 50 | en | regular | 3.419539 | synthetic | 2024-10-01 |
A0053 | novato | F | 39 | en | regular | 2.508227 | synthetic | 2024-11-18 |
A0054 | novato | F | 39 | en | regular | 2.325474 | synthetic | 2024-10-01 |
A0055 | novato | M | 33 | es | regular | 2.017904 | synthetic | 2024-10-01 |
A0056 | Novato | m | 35 | en | regular | 3.795023 | synthetic | 2024-10-01 |
A0057 | novato | M | 24 | es | regular | 2.58392 | synthetic | 2024-10-01 |
A0058 | novato | F | 24 | en | regular | 3.032768 | synthetic | 2025-05-27 |
A0059 | novato | M | 24 | en | regular | 3.325004 | synthetic | 2025-08-18 |
A0060 | novato | M | 48 | es | regular | 2.697473 | synthetic | 2024-10-01 |
A0061 | novato | F | 33 | es | regular | 2.880437 | synthetic | 2024-10-01 |
A0062 | novato | M | 47 | en | regular | 2.069415 | synthetic | 2024-10-01 |
A0063 | Novato | m | 31 | en | erratic | 2.040149 | synthetic | 2024-10-01 |
A0064 | novato | M | 19 | en | erratic | 1.5 | synthetic | 2024-10-01 |
A0065 | novato | F | 32 | en | regular | 2.759087 | synthetic | 2024-12-10 |
A0066 | novato | M | 21 | en | regular | 2.364619 | synthetic | 2024-10-01 |
A0067 | novato | F | 59 | en | regular | 2.538241 | synthetic | 2024-10-01 |
A0068 | novato | F | 39 | en | regular | 2.906651 | synthetic | 2025-08-03 |
A0069 | novato | M | 40 | en | regular | 2.357938 | synthetic | 2024-10-01 |
A0070 | novato | F | 30 | es | erratic | 2.038928 | synthetic | 2024-11-25 |
A0071 | novato | F | 28 | en | regular | 3.468436 | synthetic | 2024-10-01 |
A0072 | novato | F | 27 | es | regular | 2.973737 | synthetic | 2024-10-01 |
A0073 | novato | F | 25 | en | regular | 2.760414 | synthetic | 2024-10-03 |
A0074 | novato | M | 34 | es | regular | 2.915041 | synthetic | 2024-10-01 |
A0075 | novato | M | null | en | regular | 2.410152 | synthetic | 2024-10-01 |
A0076 | novato | M | 34 | es | regular | 2.331984 | synthetic | 2024-10-01 |
A0077 | novato | F | 39 | es | regular | 2.162884 | synthetic | 2024-10-01 |
A0078 | novato | F | 31 | en | erratic | 1.862518 | synthetic | 2024-10-01 |
A0079 | novato | F | 33 | es | regular | 2.275993 | synthetic | 2025-01-20 |
A0080 | novato | F | 22 | es | regular | 3.307957 | synthetic | 2024-10-01 |
A0081 | Novato | m | 34 | en | regular | 3.59331 | synthetic | 2024-10-01 |
A0082 | Novato | f | 50 | es | regular | 2.768094 | synthetic | 2024-10-01 |
A0083 | novato | F | 45 | es | regular | 2.608089 | synthetic | 2024-11-16 |
A0084 | novato | F | null | en | regular | 2.713337 | synthetic | 2024-10-01 |
A0085 | novato | F | 29 | es | regular | 2.879728 | synthetic | 2024-10-01 |
A0086 | novato | M | 45 | en | regular | 2.594909 | synthetic | 2024-10-01 |
A0087 | novato | M | 26 | en | regular | 2.912753 | synthetic | 2024-10-01 |
A0088 | novato | F | 48 | en | regular | 2.633479 | synthetic | 2024-10-01 |
A0089 | novato | F | 29 | es | regular | 2.667108 | synthetic | 2024-10-01 |
A0090 | novato | F | 47 | es | erratic | 2.247515 | synthetic | 2024-10-01 |
A0091 | novato | F | 31 | es | erratic | 1.589053 | synthetic | 2024-11-03 |
A0092 | novato | F | 46 | es | regular | 3.168687 | synthetic | 2024-10-01 |
A0093 | novato | F | 18 | en | regular | 2.866608 | synthetic | 2025-08-10 |
A0094 | novato | F | 24 | es | regular | 2.667542 | synthetic | 2024-10-01 |
A0095 | intermedio | M | 31 | es | regular | 2.93276 | synthetic | 2024-10-01 |
A0096 | Intermedio | m | null | es | erratic | 2.331545 | synthetic | 2024-10-01 |
A0097 | intermedio | F | 30 | es | regular | 4.376908 | synthetic | 2024-10-01 |
A0098 | intermedio | F | 37 | en | regular | 3.702924 | synthetic | 2024-10-01 |
A0099 | intermedio | M | 35 | es | regular | 3.457528 | synthetic | 2024-10-01 |
A0100 | Intermedio | f | 29 | en | regular | 3.956019 | synthetic | 2024-10-01 |
End of preview. Expand in Data Studio
WODPredict-AI sintético
Dataset sintético de 280 atletas de CrossFit (94 novatos, 93 intermedios, 93 élite) entre el 1/10/2024 y el
30/9/2026, para el proyecto WODPredict-AI (Actividad 5, Maestría en IA, Tecmilenio). Objetivo principal: predecir
sobrecarga al día siguiente (overreaching_label) a partir de carga, frecuencia, ACWR y comentarios previos.
| Carpeta | Contenido |
|---|---|
datos_ini/ |
Exportación "tal como llega" que usa el proyecto: sessions.csv (87,258 filas, con duplicados y errores de captura simulados), athletes.csv, workouts_catalog.csv, la exportación real de SugarWOD de un atleta (workouts_sugarwod.csv) y 84 comentarios etiquetados por un coach (notas_etiquetadas_coach.csv). |
sintetico_generador/ |
Salida limpia del generador (semilla 42): sessions.csv (85,548), athletes.csv, components.csv (283,961 componentes de WOD), catálogo y probabilidades de fatiga. |
generador/ |
Código del generador wodpredict-synth, configuración con los supuestos, reporte de validación y simular_captura.py (agrega los errores de captura a datos_ini). |
Versiones: cada versión es un tag de Git del repositorio (v1.0, …). Para fijarla:
hf_hub_download("rick-lastra/wodpredict-synth", "datos_ini/sessions.csv", repo_type="dataset", revision="v1.0").
Supuestos y límites: todo es sintético salvo workouts_sugarwod.csv y notas_etiquetadas_coach.csv. La
dificultad de cada WOD se fija con los resultados del atleta real; la heterogeneidad entre atletas, el ruido de la
etiqueta (5 %) y los errores de captura son supuestos declarados en datos_ini/synth.yaml y en
generador/simular_captura.py. No contiene datos personales.
Código del proyecto: https://github.com/rick-lastra/Actividad_5
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