ScorePredict

#DEVLOG 4#
Okay, guys, I’ve finally found the cause of the issue.
It was not in the code, it was in my mind.🤦‍♂️If I wanted the model to predict a draw, it would lose by accuracy.
To understand this I made some calculations:

away: 7341  matches  - 31,6%
draw: 5522  matches  - 23,8%
home: 10368 matches  - 44,6%

SUM: 23231 matches

In a nutshell: It NEVER worth it to predict a draw!

Now that I “solved” this problem, I can focus on increasing accuracy: adding more aspects, finding bugs, etc.

#DEVLOG 3#
This is just a quick devlog from me.
I have some issues. The main thing is what I have already written about: the model NEVER EVER predicts a draw. (It is because of the mechanism of Poisson-model…). I tried different methods to solve this problem, but none of them worked.
There is 2 ways to solve this:

1. Get a bigger dataset. But the API that I’m using provides only 100 calls/day which is a really small number. So I would need to pay, which I don’t really want. (In the very unexpected case you want to fund my project, DM me @be.the.nike)
2. There is a model that corrigates the torsion of Poisson-model. BUT AI said to me that to achive this I will have to kind of write my own AI model training algorithm. And this sounds really scary to me

Cheer for me!

Some good news at the end: my model is a tiny little bit better in predicting the winner of a match than betting offices!

#DEVLOG 2#

I haven’t posted devlog for a while. But I was working hard.
I ran the secind evaluation of the Poisson regression model (the first model was so unreliable that it don’t desire to mention).

Results:

• Outcome (H/D/A) accuracy: 54.1% ± 2.1%
• Goal-count prediction (Poisson deviance): 1.13
• Feature importances look sensible: team_attack, opp_defense, is_home are the strongest signals
• Weakness: model almost never predicts draws I tried a “draw margin” trick, but it didn’t work

Next options (ideas only, none started yet):

• Add Elo rating (instead of current attack/defense strength method)
• Dixon-Coles correction to address draw underestimation
• Incorporate missing key players as a feature (it costs too many API calls)
• Tune alpha hyperparameter

This is my first project written in Python so I’m still learning pretty much. I’ll push my progress to Github as soon as I can produce accurate enough predictions. Be patient, I’m doing my best!

Arrivederci, Auf Wiedersehen, Goodbye, A viszontlátásra!!

#DEVLOG 1#

I started to work on a website that predicts the scores of a football match. I’ll make my own AI model to create it.
To do so my first task is to get a database. There are a lot of APIs on the internet but just a few of them provides a service without any limitations. I’ve finally find one on Github. So I started to work on it and format the data. BUT after ~2 hours I realized, it wouldn’t work.
The reason why it wouldn’t work: I wrote some criteria that this AI model should consider. And the Github repo did not fit to it.
Sooo I’ll start over with a new provider.
By the way, here are the aspects I was talking about: