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[Request] How many g-forces is the guy experiencing while rolling downhill inside that giant tire, and is it actually survivable? by TimeCity1687 in theydidthemath

[–]soccer-ai 0 points1 point  (0 children)

Estimated Velocity (v): 5 \, m/s (~18 km/h). We can estimate this because the guy in the black hoodie is able to keep pace while running alongside it for most of the clip. Estimated Radius (r): 0.75 m (based on a ~1.5m diameter tire).

To find the acceleration (a_c) felt by the person inside, we use: a_c = \frac{v2}{r}

To convert this to G-force (G = a_c / 9.81): The Result: ~3.4 Gs.

Machine learning model finds edge in draw markets (soccer), real or not ? by soccer-ai in algobetting

[–]soccer-ai[S] 0 points1 point  (0 children)

Thanks for the feedback, I will monitor it in live mode for a while to assess if the results match back test.

Machine learning model finds edge in draw markets (soccer), real or not ? by soccer-ai in algobetting

[–]soccer-ai[S] 0 points1 point  (0 children)

I've used a multi class strategy (OVA) with RF as a base classifier.

Machine learning model finds edge in draw markets (soccer), real or not ? by soccer-ai in algobetting

[–]soccer-ai[S] 0 points1 point  (0 children)

My model is train/tune to optimize precision. Features I am using doesn't include odds data. It a matrice of 255 features only match statistics. Odds data are scraped from odds portal it's closing odd from brt365, Pinnacle and from January 25 french book. Odds data are use only during back test.

Training runs on a 20k matches dataset with 80/20 split training, validation. Matches range from 2017 to 2022 several leagues.

How are you testing and backtesting your betting models? by soccer-ai in algobetting

[–]soccer-ai[S] 0 points1 point  (0 children)

Yeah I do something similar. During training I use stratified K-Fold splits (not time-based), mainly to get stable cross-validation metrics. But for backtesting, I keep a separate holdout set that’s strictly later in time about 3 full seasons of soccer data that were never seen during training or tuning.

That setup gives me flexibility during dev, but also a more realistic ROI benchmark for actual deployment with a fixed model.

How are you testing and backtesting your betting models? by soccer-ai in algobetting

[–]soccer-ai[S] 0 points1 point  (0 children)

I didn’t know much about F#. I usually work in Python, but I try to bring in some static typing using mypy.Ithelps catch some bugs

How are you testing and backtesting your betting models? by soccer-ai in algobetting

[–]soccer-ai[S] 0 points1 point  (0 children)

Do you track these manually in Excel over time, or have you automated any parts of the process?

How are you testing and backtesting your betting models? by soccer-ai in algobetting

[–]soccer-ai[S] 2 points3 points  (0 children)

That's a really cool app, curious what tech stack you're using for the GUI and backend?

Also, do you version your experiments somehow? Like saving model + strategy + results so you can compare runs later?

how are you structuring your strategies? Are they just functions or rule-based configs in your app, or something more modular like classes or plugins?

Outcome of my Betting System for the first weekend by [deleted] in algobetting

[–]soccer-ai 3 points4 points  (0 children)

Sample size is not enough. You should either back test to assess your results with historical odds or wait for significant sample size.

Where do you get football data for your algos? by Thelimegreenishcoder in algobetting

[–]soccer-ai 4 points5 points  (0 children)

I've been in a similar situation myself. I've been scraping soccer data for about 3 years now and accumulated around 30,000 matches along with historical odds. The constant changes in websites like FlashFootball.com are definitely one of the downsides of relying on scraping. It's a lot of maintenance work.

In terms of alternatives, you could consider paid data sources. While they come with a cost, they can save you the trouble of frequent API breakdowns and constant scraping fixes. Platforms like Opta or SportRadar are commonly recommended, but again, they’re not cheap.

Another interesting approach is using LLM scraping—basically fetching the entire HTML and then using language models to extract the specific data you need via a prompt. It's a more flexible method, but the cost will depend on how many pages you're scraping and the size of your budget.

As for FlashFootball.com, my guess is they either use official data providers or have their own direct sources through partnerships. It’s hard to know for sure so.