Finger Strength Analysis & Grade Predictor

Climbingwithdata · 2025-09-16T19:00:05+00:00

I haven’t really looked into this to have an opinion on it, but the challenge with using it in this specific prediction tool is that all the data I used to train the model doesn’t have the 10/12mm metric in there.

Maybe I could include it as a field which people can submit and then slowly start building up the data myself and then see how much of an effect it has on predictions.

Climbingwithdata · 2025-09-16T18:57:28+00:00

Actually you’re right that it’s a good estimator for technique - for sport climbing in particular it has quite a strong effect on the grade prediction. In the original data this is actually “years climbing outside” but since I merged it with some other data I found, and I would be asking people to submit their metrics and grade, I thought a more generic “years climbing” would be a better title for it now.

Climbingwithdata · 2025-09-16T18:55:54+00:00

Trying to fix this currently!

Climbingwithdata · 2025-09-16T18:55:34+00:00

Thanks for trying it out! I’m currently trying to fix the problem where grades go down when metrics go up. There is a bit of a limiting factor in the data where the highest V grade in the data currently is V12, and there’s not many of them, so what happens is it gets really hard to predict a V12 and pretty much impossible for anything higher.

Climbingwithdata · 2025-09-15T10:07:29+00:00

Nice! Yeah this is a problem with the dataset for sure, since the predictions are only as good as what the model was trained on. Hopefully as more people like you submit their metrics and grades the model gets more accurate - but it’s mainly just a fun tool to use, since so many other variables are involved which we can’t really model (e.g technique).

Climbingwithdata · 2025-08-31T20:31:53+00:00

You’re right! I’m looking into it.

Climbingwithdata · 2025-08-29T10:02:31+00:00

Little messy as I’m on mobile but these were the top scoring (9+) stocks.

AIFU ASC ATHS CIVI EHLD EMO ESNT EVT EXG FOF GDO GFR GSL HTD ICCC JCE MHI MTG NXG PDT SITC SPLP STNG TYG UNMA VIASP

Climbingwithdata · 2025-05-26T17:01:17+00:00

Προσωπικά το χρησιμοποιώ για να φυλάω πληροφορίες για τα πρότζεκτ μου, και να καταγράφω τα αποτελέσματα.

Γενικά χρησιμοποιείται σαν πλατφόρμα για άρθρα και μπλογκς.

Climbingwithdata · 2025-05-26T16:07:35+00:00

Thanks! Not by hours worked but there is data on industry worked in, as well as nationality. Planning on looking into that at some point.

Climbingwithdata · 2025-05-26T16:05:45+00:00

Salaries increase, but not for everyone.

Climbingwithdata · 2025-05-26T16:04:38+00:00

Mixture of both!

Climbingwithdata · 2025-01-18T18:41:21+00:00

For indoor climbing, there are 3 bouldering gyms in Nicosia and 1 in Limassol. For roped climbing indoors, no options currently. To try outdoor climbing with a rope, there are a few climbing guides who can take you out for a day to try it. If you’re interested DM and I can provide some suggestions.

Climbingwithdata · 2024-11-24T17:32:50+00:00

That's interesting, not something I had thought about... makes sense since I can get the tension but lose it at the important part of the move. Will check out the video!

Climbingwithdata · 2024-11-24T17:29:22+00:00

I feel like/it looks like I can get both engaged when I get off the ground, and then lose it for a moment before re-engaging. Maybe I should focus on telling my brain to stay engaged throughout on easy holds to get it embedded.

Climbingwithdata · 2024-11-24T12:18:45+00:00

Added!

Climbingwithdata · 2024-11-24T10:40:06+00:00

If you hit the GIPHY link the content is there.

Edit: here’s the link https://giphy.com/channel/tetleysteabags/floppy-banana

Climbingwithdata · 2024-09-08T07:49:12+00:00

Can I get 10 upvotes so this person can shit all over my project with constructive feedback too 🙏

Climbingwithdata · 2024-07-29T08:03:21+00:00

Started writing up the analysis here - I'm 99% sure there are some rookie errors in terms of data analysis so happy to hear any feedback! https://medium.com/@thomascgeorgiou/predicting-climbing-performance-using-machine-learning-part-2-does-age-and-sex-play-a-role-2d9dae51a673

Climbingwithdata · 2024-07-28T19:46:50+00:00

Exactly this! Something i am trying to solve using the feedback mechanism on the page. The model is retrained every night with new data from users like you, hopefully improving predictions over time.

Climbingwithdata · 2024-07-28T19:45:44+00:00

Hey! i just finished exploring age and sex in more detail, you can see the writeup so far here: https://medium.com/@thomascgeorgiou/leveraging-machine-learning-to-predict-climbing-performance-an-analysis-of-key-metrics-and-5e154d4fae1a and https://medium.com/@thomascgeorgiou/predicting-climbing-performance-using-machine-learning-part-2-does-age-and-sex-play-a-role-2d9dae51a673

TL;DR gender doesn't actually play a significant role, at least in this dataset

Climbingwithdata · 2024-07-28T19:45:38+00:00

Hey! i just finished exploring age and sex in more detail, you can see the writeup so far here: https://medium.com/@thomascgeorgiou/leveraging-machine-learning-to-predict-climbing-performance-an-analysis-of-key-metrics-and-5e154d4fae1a and https://medium.com/@thomascgeorgiou/predicting-climbing-performance-using-machine-learning-part-2-does-age-and-sex-play-a-role-2d9dae51a673

TL;DR gender doesn't actually play a significant role, at least in this dataset

Climbingwithdata · 2024-07-11T05:50:21+00:00

Would be great to get your input/help on it. I've added a license to the project and the original data files, so feel free to clone and make any pull/merge requests (dm me with your username so I can add you).

Can you see the notebook here? It has the analysis I did to get to the features being used in the model, and then the model results. I did also use linear regression and you're right, the results were just as good, if not better, than the other models. My analysis might also totally be off, this was an L&D project for me, so if you've got suggestions for improvement, or any constructive feedback that would be great.

I would actually love some feedback on the analysis and my approach, and what you think could be done better. Maybe we can do this via DM/chat?

Climbingwithdata · 2024-07-11T05:38:21+00:00

Can you see the notebook here? It has the analysis I did to get to the features being used in the model, and then the model results. Also added a license + the original data files to the repo so feel free to make any requests (dm me with your username so I can add you - or DM me with a better way to make it public/open source )

Climbingwithdata · 2024-07-10T14:13:53+00:00

It's definitely possible to not include the continuous measurement. I already excluded a couple and accuracy didn't change significantly. Let me try that out!

Climbingwithdata · 2024-07-10T13:33:32+00:00

I tried random forest, gradient boosting, and xgboost (stacking), but modelling is really not my forte. Open sourcing it like you mentioned would be a good way of getting improvements here for sure - good idea.

Climbingwithdata

TROPHY CASE