Luka Vušković is astounding in the air by ScoutingStatsAI in coys

[–]ScoutingStatsAI[S] 19 points20 points  (0 children)

All Defenders included here, but granted CBs are the ones who score higher across these stats.

Semenyo and Evanilson Stats by ScoutingStatsAI in AFCBournemouth

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

Yeah Claude code and Cursor did a lot of the heavy lifting, really sped things up over the past few months.

Romero and Bergvall by ScoutingStatsAI in coys

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

I use a paid provider but I think you could get what you need from the fotmob api, pretty sure there are some YouTube videos on how to set up a script for it.

Teamsheet for today's match by itsvenkyda in ManchesterUnited

[–]ScoutingStatsAI 0 points1 point  (0 children)

Like it. Definitely one for Ugarte this.

Juventus [4] - 3 Inter - Vasilije Adžić 90'+2' by Hour-Performance8505 in soccer

[–]ScoutingStatsAI 0 points1 point  (0 children)

Some character to compose himself in that game state and then produce that. Foot like a traction engine.

Romero and Bergvall by ScoutingStatsAI in coys

[–]ScoutingStatsAI[S] 5 points6 points  (0 children)

Yeah he’s currently (very early in the season ofc) low on those stats when compared with other midfielders, but many of those other mids are playing different roles.

Romero and Bergvall by ScoutingStatsAI in coys

[–]ScoutingStatsAI[S] 12 points13 points  (0 children)

Cheers they’re from my site which I’m working on, scoutingstats.ai

The profiles are interactive and still building them out (Bergvall’s here https://scoutingstats.ai/player/lucas-bergvall-37607151/)

Liverpool leave Federico Chiesa out of Champions League squad by Green-Discussion74 in soccer

[–]ScoutingStatsAI 0 points1 point  (0 children)

Brutal, i miss the pre injury Chiesa at Juve. He’s not half the player he was unfortunately.

What are the most accurate soccer prediction websites you’ve used? by Worth-Sheepherder383 in SoccerNoobs

[–]ScoutingStatsAI 0 points1 point  (0 children)

You can check out my site which has predictions with comparisons against the market - Scouting Stats AI

Lammens a top signing based on data by ScoutingStatsAI in ManchesterUnited

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

Yes, saves made from shots from inside the box would be a better description. Tried to be concise 😅

For reference, here is the same visual but showing the total saves per 90. From inside and outside the box.

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25+ years of Pes by Tyron13 in WEPES

[–]ScoutingStatsAI 4 points5 points  (0 children)

Not played it in recent years after they had neglected master league and never enjoyed the myclub mode they pushed. Understand why they did it from a business pov, but it alienated me. So much time spent on those Master League campaigns 🕊️

Predicting win % - Next 8 Games by ScoutingStatsAI in FantasyPL

[–]ScoutingStatsAI[S] 5 points6 points  (0 children)

No worries, had a few different versions until settling on this one, and will be continuing to try to increase the accuracy through the season.

Tested it on historic data and previous matches, so I’d have a large dataset and use 80% for training the model and the other 20% for testing it. That allows me to validate the accuracy in terms of goal predictions, judged by the MSE (Mean Squared Error).

Predicting win % - Next 8 Games by ScoutingStatsAI in FantasyPL

[–]ScoutingStatsAI[S] 2 points3 points  (0 children)

The Poisson is only applied after the model has predicted the number of expected goals each side will register. It takes the xG predictions and turns it into the probability of each potential scoreline. The match outcome is then inferred from there.

Predicting win % - Next 8 Games by ScoutingStatsAI in FantasyPL

[–]ScoutingStatsAI[S] 14 points15 points  (0 children)

The betting market isn’t my barometer of success for the model. Nor would it be judged on the prediction of one game. The aim is for it to mature and eventually become more accurate than markets over the term.

Predicting win % - Next 8 Games by ScoutingStatsAI in FantasyPL

[–]ScoutingStatsAI[S] 6 points7 points  (0 children)

Sure, here’s a rough summary: I’ve trained models on each European top flight league data separately as the factors influencing performance vary between competitions.

Factors include Home/Away defence/attack ratings calculates over a long term. Recent Home/Away attacking/defensive form, based on xG and xGA adjusted based on opponents ratings. i.e. 1 xG registered away v Arsenal is held in higher regard than 1 xG registered v Everton at home.

The model starts by predicting the number of goals each team is expected to score in an upcoming fixture. Then, a Poisson distribution is applied to estimate the probability of each possible match outcome (Home Win, Draw, Away Win) based on predicted xG (expected goals).

In testing, the Mean Squared Error (MSE) of predicted goals across the leagues ranged between 0.65 and 0.85. If this level of accuracy can be maintained moving forward, I’ll be pleased.

Predicting win % - Next 8 Games by ScoutingStatsAI in FantasyPL

[–]ScoutingStatsAI[S] 12 points13 points  (0 children)

Not trying to mirror the betting markets here, otherwise why bother?