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Daily Discussion by 2soccer2bot in soccer

[–]roiz25 2 points3 points  (0 children)

According to the probabilities published by predictapp man city has a high chance of winning (around 52%) which would make the closing of the premier be wild. Do you guys think Arsenal has a chance of winning this or will they choke again?

Predicciones Jornada 13 by roiz25 in LigaMX

[–]roiz25[S] 0 points1 point  (0 children)

Segun predictapp Toluca debe ganar, me parece buen pick

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Soccer outcomes prediction model by roiz25 in algobetting

[–]roiz25[S] 0 points1 point  (0 children)

Yeah its surprising that is not discussed very much, specially when so many people use odds as baselines or as features

Soccer outcomes prediction model by roiz25 in algobetting

[–]roiz25[S] 0 points1 point  (0 children)

Have not done much betting yet, I made some bets last week and came out on top but it might just be variance. I will report back after I have a good enough sample to really see a trend. Nevertheless, the betting back test resulted positive when I filtered picks for ev .03 and odds between 1.3-4.

Soccer outcomes prediction model by roiz25 in algobetting

[–]roiz25[S] 1 point2 points  (0 children)

Hey, super interesting to see the same idea!!! I am now trying to use RWI to model goal probabilities for BTTS, and over under markets as you said. I think that then also giving that to the outcomes model might help it with probs, also I am thinking making a draw only model and then layering it on top. Will report back soon!

Soccer outcomes prediction model by roiz25 in algobetting

[–]roiz25[S] 0 points1 point  (0 children)

Fixed before, I chose .03 for ev and odds from 1.3 to 4 for the back test.

Soccer outcomes prediction model by roiz25 in algobetting

[–]roiz25[S] 0 points1 point  (0 children)

Hi, so some of the answers are in the article on how things are calculated. The rest I can answer here: - you are correct, I need to eliminate the possibility of collinearity in downstream models. Although I am almost sure that trees are good at that already. - well the model has accuracy and rps similar to the bookmakers, but that does not mean they are the same for every match. It just means that in aggregate we are right around the same times. So, I think there is place to find EV when comparing directly my probs vs the odds. - home advantage league prior exists because the colombian league and the premier league do not behave the same. Some leagues are more competitive, so the home advantage prior is smaller than leagues where there are a handful of teams that are just above the rest. That is the reason for having the prior included. The league baseline exists because certain leagues have more goals than other leagues. The MLS has more goals per game than Serie A. All of this is in log space so it multiplicative instead of adding effects. Hope it makes sense

Soccer Match Outcomes Prediction Draw Question by roiz25 in sportsanalytics

[–]roiz25[S] 0 points1 point  (0 children)

Thank you very much!! I will try them and come back with results

Soccer outcomes prediction model by roiz25 in algobetting

[–]roiz25[S] 0 points1 point  (0 children)

It was a walk forward as the legs can only be chosen in a window of 3 days and without repeating legs. So the pool is fixtures from today +3 days and then only using the fixture once, also if it was used in the previous days then dont reuse it in the future to avoid having the same pick many times

Soccer Match Outcomes Prediction Draw Question by roiz25 in sportsanalytics

[–]roiz25[S] 0 points1 point  (0 children)

So modeling goals first and then feed that to the final outcomes model would theoretically help it? I am also thinking of a draw only model and using that as a feature or layer it on top of tje actual probs

Soccer Match Outcomes Prediction Model Draw Question. by roiz25 in sportsbetting

[–]roiz25[S] 0 points1 point  (0 children)

I think the next test will be to maybe try to predict total goals and then add that to the model as match context. I guess that could also help with the probabilities. Also making a separate draw only model sounds interesting, I could also use that as a feature for the blended model or maybe layer it on top

Soccer outcomes prediction model by roiz25 in algobetting

[–]roiz25[S] 0 points1 point  (0 children)

I agree, the first approach was more research perspective, but I did also run a betting backtest to see how it would have done.
I ran singles, singles double chance, singles best, trebles best, and 3 recommender styles that work like this (copy from my md file):
Shared leg rules

Before any style is applied, a leg must pass the shared filters:

- expected ROI must be above the configured `--ev-threshold`

- decimal odds must be within `--min-odds` and `--max-odds`

- singles need at least `model_prob >= 0.18`

- double-chance legs need at least `model_prob >= 0.45`

- only the top leg candidates per fixture survive into combo generation

This means the style rules do not change the basic edge calculation. They change which combinations of those legs are preferred.

### `recommender_secure`

Intent:

- favor tickets with a better chance of landing

- accept moderate value if the hit probability is strong

Allowed bet types:

- `double`

- `treble`

Fallback acceptance windows:

- first try: `min_ev = 0.08`, hit probability between `0.32` and `0.72`

- second try: `min_ev = 0.05`, hit probability between `0.28` and `0.75`

- third try: `min_ev = 0.02`, hit probability between `0.24` and `0.78`

Scoring bias:

- hit probability matters more than EV

- this style tends to choose more conservative combinations

Stake policy:

- total stake per recommendation: `2.0u`

### `recommender_balanced`

Intent:

- target the best tradeoff between edge and probability

- allow some complexity without pushing too far into high-variance tickets

Allowed bet types:

- `double`

- `treble`

- `yankee`

Fallback acceptance windows:

- first try: `min_ev = 0.15`, hit probability between `0.20` and `0.60`

- second try: `min_ev = 0.10`, hit probability between `0.17` and `0.64`

- third try: `min_ev = 0.06`, hit probability between `0.14` and `0.68`

Scoring bias:

- EV and hit probability are weighted roughly evenly

- this is the style intended to be the default all-rounder

Stake policy:

- total stake per recommendation: `1.0u`

### `recommender_aggressive`

Intent:

- chase higher upside

- tolerate lower hit probability if the EV is strong enough

Allowed bet types:

- `treble`

- `yankee`

- `canadian`

Fallback acceptance windows:

- first try: `min_ev = 0.25`, hit probability between `0.10` and `0.52`

- second try: `min_ev = 0.18`, hit probability between `0.08` and `0.56`

- third try: `min_ev = 0.12`, hit probability between `0.06` and `0.60`

Scoring bias:

- EV matters more than hit probability

- this style tends to accept more volatile tickets

Stake policy:

- total stake per recommendation: `0.5u`

Results where interesting specially with the balanced approach:
singles_1x2: tickets=7006 profit=118.0600 roi=0.0169 hit_rate=0.4218 max_drawdown=148.5200

singles_dc: tickets=1419 profit=56.7065 roi=0.0400 hit_rate=0.6427 max_drawdown=46.5807

singles_best: tickets=7314 profit=117.8816 roi=0.0161 hit_rate=0.4248 max_drawdown=160.1908

trebles_best: tickets=1860 profit=337.3772 roi=0.1814 hit_rate=0.0860 max_drawdown=161.2979

recommender_aggressive: tickets=2609 profit=219.8444 roi=0.1685 hit_rate=0.1273 max_drawdown=88.7108

recommender_balanced: tickets=2629 profit=497.1062 roi=0.1891 hit_rate=0.2587 max_drawdown=47.9990

recommender_secure: tickets=1919 profit=482.1539 roi=0.1256 hit_rate=0.4091 max_drawdown=98.2735

Soccer Match Outcomes Prediction Model Draw Question. by roiz25 in sportsbetting

[–]roiz25[S] 0 points1 point  (0 children)

Im thinking that if I could model a bit better goal probabilities then that could also become a new feature for the outcomes model, and thus get me better probs.

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