#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!!