u/gsus_21

Estoy buscando un telefono para mi mamá, que cumpla con los básico, por ejemplo:

- Ver youtube (es lo que más hace), tener sus redes sociales, bancos, etc.
- Que tenga una cámara normal, que no sea la vaina más arrecha, pero que tampoco sea un asco, algo aceptable.
- Una memoria ram que le permita un rendimiento aceptable y fluido.
- Espacio de almacenamiento para que pueda guardar bastantes fotos y videos (ya saben como son las mamás), sus cursos de costura, repostería, etc, y que ws no la este jodiendo dentro de 1 año con que no tiene almacenamiento. Algo duradero.

Quiero algo que le dure al menos unos 2-3 años. Mi presupuesto no es muy alto, estaba pensando entre unos 80-140. No puedo gastar más de ahí, ojala pudiera pero no.

Si tienen alguna recomendación para mi, estaré leyendo. Si no tienes una recomendación y me vas a decir que ponga un poquito más de plata, sigue de largo, que no tengo más.

Si conocen al menos 2 o 3 teléfonos en ese rango mejor, para tener varias opciones por si no consigo algún modelo en la zona.

Gracias!!!

Hi everyone. I'm developing a Python ETL pipeline to feed a predictive Machine Learning model (XGBoost) for MLB.

It's worth noting that I'm a beginner at this. I have some background because I'm studying systems engineering, but I'm building this almost entirely through "vibe coding." This is my first time building a prediction system.

Currently, I'm using Python and SQLite. My automated pipeline already extracts raw physical data from Baseball Savant/Statcast (allowed xwOBA, Barrel%, K%, BB%, etc.) and merges it with scheduled games using StatsAPI. I've already solved the lookahead bias by using a strict backward pd.merge_asof, ensuring the model only sees metrics available the day before the game. The base model is already running, evaluating hitting, splits, and Park Factors.

The Problem: To improve my model's Brier Score and Log Loss, I need to inject the full spectrum of advanced pitching metrics (all variables from the 'Advanced', 'Batted Ball', and 'Plate Discipline' dashboards, including SIERA, FIP, xFIP, LOB%, SwStr%, K-BB%, etc.). I need this bulk extraction at two levels: individual starters and grouped by team (to isolate the collective performance of the bullpen).

FanGraphs is the standard source for these consolidated dashboards, but I've hit a hard technical roadblock:

• Direct export of CSV files is locked behind their premium subscription (FanGraphs+).
• I tried extracting the data by directly consuming their backend API (JSON endpoints) passing the splits and dates parameters, but their anti-bot system (Cloudflare) constantly throws a 403 Error.
• To bypass Cloudflare, I implemented cloudscraper and then tried TLS Spoofing using the curl_cffi library (impersonating Chrome 120), but the server still rejects the connection or data request due to lack of authentication.
• I also tried using the pybaseball library (pitching_stats), but it breaks or fails when trying to extract short daily date ranges and specific bullpen splits in bulk.

What I'm looking for: Since I want to maintain the script's automation without relying on a manual "copy-paste" process for tables, or paying hundreds of dollars for a commercial API, I'm looking for your technical recommendations:

1. Do you know of any specific headers/cookies configuration, or any Python scraping tool that is currently successfully bypassing FanGraphs' Cloudflare for bulk data requests?
2. Is there a robust alternative source (free API or less protected website) where I can automate the daily download of all these sabermetric pitching metrics?
3. Alternatively, does anyone have experience or a reference repository calculating this entire block of advanced metrics (SIERA, FIP, xFIP, etc.) locally in SQLite/Python using only raw play-by-play (Pitch-by-Pitch) data from Statcast/Retrosheet? (I have some of the formulas, but calculating the league constant coefficients on the fly for the entire pool of metrics seems error-prone and computationally expensive).

I'd appreciate any guidance on data architecture, evasive scraping techniques, or applied sabermetrics.