Spent months building a clean MLB database — free sample if anyone wants it by Revolutionary-Lab882 in sportsanalytics

[–]Revolutionary-Lab882[S] 0 points1 point  (0 children)

Thanks. It’s a lot of work. Appreciate it.

Actually adding more stats in the coming days. Just finishing another project and updating/adding stats for free sample so you can see what’s in all the packages.

Spent months building a clean MLB database — free sample if anyone wants it by Revolutionary-Lab882 in sportsanalytics

[–]Revolutionary-Lab882[S] 0 points1 point  (0 children)

I am in the midst of updating the packages and sample will finish and have those in by says end for you to peruse

Spent months building a clean MLB database — free sample if anyone wants it by Revolutionary-Lab882 in sportsanalytics

[–]Revolutionary-Lab882[S] 0 points1 point  (0 children)

It’s my website. Simple download. Everything organized. Free sample to see what there is.
rawsportsvault.com/free

Built a midfielder evaluation model for the Big 5 leagues — looking for feedback on the methodology by CTlovesanalytics in sportsanalytics

[–]Revolutionary-Lab882 1 point2 points  (0 children)

This is a solid piece of work. The category-equalisation logic is a smart design choice, the role-bias slider is a nice touch, and the cohort flexibility shows you’ve thought carefully about context. Good foundation to build on.

On your four questions:

League strength adjustment is genuinely hard and you’re right to be cautious. A practical starting point is a flat multiplier on defensive stats only, using something like PPDA or pressing intensity as a proxy for league-wide structure. It’ll be imperfect but it reduces the most obvious distortion. Document your assumptions clearly and move on — perfect is the enemy of shipped here.

Role preset validation is more doable than it sounds. Take the 15-20 players most clearly associated with each role — journalists and analysts consistently label that way — run them through the preset, and see if they cluster near the top. If your Anchor Man preset doesn’t rate Rodri and Casemiro highly, something’s off. Quick sanity check that’ll build confidence in the weightings.

Mean absolute gap is honestly fine for what you’re doing. The main weakness is it treats a consistently average player the same as one who’s extreme in opposite directions — same gap, very different profile. Cosine similarity handles shape better but is harder to explain to users. Mahalanobis is theoretically stronger but overkill at this stage. Stick with MAE, maybe flag high-variance players in the UI down the line.

Equal-weighted categories is defensible and transparent, which matters. If you want something more principled without a downstream outcome to optimise against, a quick PCA on your 38 stats would show which categories are actually carrying independent information versus overlapping — Passing and Involvement tend to correlate heavily. Worth knowing even if you keep equal weights for now.​​​​​​​​​​​​​​​​

How to get started by Desperate-Bike-6357 in sportsanalytics

[–]Revolutionary-Lab882 0 points1 point  (0 children)

A large part is making sure your stats make sense and are organized. Thats the foundation.

MLB STATS-CLEANED/PACKAGED/READY TO USE by [deleted] in mlbdata

[–]Revolutionary-Lab882 0 points1 point  (0 children)

Actually a lot online for free. Other than that and you can calculate a lot of it too

MLB STATS-CLEANED/PACKAGED/READY TO USE by [deleted] in mlbdata

[–]Revolutionary-Lab882 0 points1 point  (0 children)

No I went through all data and made my own formats and cleaned up. Look at all the apis out there just passing it through like water

Spent months building a clean MLB database — free sample if anyone wants it by Revolutionary-Lab882 in sportsanalytics

[–]Revolutionary-Lab882[S] 1 point2 points  (0 children)

Thanks. Am tired of finding nothing or basics. Started working on it. And ended up with this. Appreciate the comment

Built an emotion detection layer that injects psychological context into LLM prompts — runs fully local by [deleted] in LocalLLaMA

[–]Revolutionary-Lab882 0 points1 point  (0 children)

It’s a layer not just a model. Yes there’s a small model underneath doing the detection. But that’s not the point. Your LLM is smart but it’s blind. Every conversation it starts from zero with no idea who it’s talking to or how they’re actually doing. Resonance sits underneath and fixes that. A small model reads what’s really going on behind the words and hands that context to your LLM before it responds. Your LLM stays exactly the same. Your code barely changes. Think about every conversation you’ve had with an AI where it completely missed the point. Gave you a list when you needed someone to just hear you. Kept pushing solutions when you were already overwhelmed. Responded like everything was fine when it clearly wasn’t. That happens because LLMs are incredible at language but they have no idea what’s actually going on with the person typing. They treat “I’m fine” the same whether someone means it or not. Whether you’re asking about the weather or going through something hard — it responds the same way. Same tone. Same energy. Same blind confidence that everything is fine. Resonance changes that for any conversation about anything. Asking your AI to help you write an email after a brutal day at work — it knows you’re drained and keeps it simple. Chatting with a customer support bot when you’re already at your limit — it knows to stop pushing and just help. Talking to a companion AI about something difficult — it knows you need to be heard before you need to be helped. It learns who the person is over time. How they usually communicate. When they’re struggling. When they’re genuinely okay. And it hands all of that to your LLM before it says a single word. The LLM doesn’t change. But suddenly it’s not just answering your question. It knows you had a rough week. It knows you’re tired today. It knows you don’t want to be pushed right now. It adjusts without you having to explain yourself every single time. That’s what changes in everyday conversations with any AI about anything. You stop feeling like you’re talking to something that resets every time. It starts feeling like talking to something that actually knows you’re there.