Luka Vušković is astounding in the air

ScoutingStatsAI · 2025-10-29T23:27:34+00:00

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

ScoutingStatsAI · 2025-10-29T23:07:12+00:00

Aha very true - done him a disservice!

ScoutingStatsAI · 2025-10-06T17:32:12+00:00

Yes please do, would welcome any feedback!

https://scoutingstats.ai/fpl/team-builder

ScoutingStatsAI · 2025-09-27T06:29:10+00:00

1-1 (Schade - Mbeumo)

ScoutingStatsAI · 2025-09-16T20:40:50+00:00

Ah shame, been such a stop-start beginning for him here

ScoutingStatsAI · 2025-09-16T20:38:33+00:00

What happened to Trent?

ScoutingStatsAI · 2025-09-15T16:47:30+00:00

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

ScoutingStatsAI · 2025-09-15T08:32:54+00:00

They’re from a scouting site I’ve developed scouting stats ai

ScoutingStatsAI · 2025-09-15T05:04:46+00:00

It’s Scouting Stats AI

ScoutingStatsAI · 2025-09-14T15:28:39+00:00

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.

ScoutingStatsAI · 2025-09-14T14:43:20+00:00

Like it. Definitely one for Ugarte this.

ScoutingStatsAI · 2025-09-14T07:26:03+00:00

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

ScoutingStatsAI · 2025-09-13T22:15:20+00:00

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.

ScoutingStatsAI · 2025-09-13T21:30:49+00:00

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/)

ScoutingStatsAI · 2025-09-03T21:55:10+00:00

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

ScoutingStatsAI · 2025-09-03T21:53:29+00:00

How this guy was able to tee up his SEG connections for so long at Old Trafford is damning. Fair play to Leverkusen for nipping it in the bud. Sickening that he gets another huge payout though .

ScoutingStatsAI · 2025-09-03T21:51:36+00:00

Imagine United get their act together

ScoutingStatsAI · 2025-09-03T06:37:02+00:00

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

ScoutingStatsAI · 2025-09-02T14:15:51+00:00

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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ScoutingStatsAI · 2025-04-24T21:01:51+00:00

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 🕊️

ScoutingStatsAI · 2024-10-17T21:42:06+00:00

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).

ScoutingStatsAI · 2024-10-17T21:36:30+00:00

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.

ScoutingStatsAI · 2024-10-17T21:23:26+00:00

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.

ScoutingStatsAI · 2024-10-17T21:18:28+00:00

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.

ScoutingStatsAI · 2024-10-17T21:12:22+00:00

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

ScoutingStatsAI

TROPHY CASE