La-liga

Predict la liga matches for 2023 season.

• Scrap Web for data( 1249 games ranging from 2021 to 2023 seasons )
• Trained a random forest model
• Improved feature selection
• Model paramter tuning
• Varaince importance plot
• Trained descion tree classifier
• Trained a neural net

Predictors for model

Venue- home and away advantage is signiificant in football. so i am going to convert home and away into numbers

Team- give each team a code

Time- do some teams play better at certain times of the day? Night games and mid afternoon games can have different outcomes because of several factors like heat, rain, visibility, crowd energy amanog many others

Day- same as above. Sunday games have more viewers as opposed to midweek. Could such pressure affect performance

Using Random forest model.

Random forest helps pick up non linear trends in data. There is no clear relantionship between hour and winning. This model will help us determine such trends.

Trained on data from previous seasons. Tetsed on current season.

accuracy= 60.7%(3sf)

Improving predictors

• Form- current form is important in determining the outcome of next game. so we can leverage this further imporve our model. in this case past 4 games are being used to detemine form

• Goals For

• Goals Against

• Pentaly kick attempts

• Distance a shot is taken from

• Free kicks

• Shots on target

• Penalty kicks

• Shots

Improved accuracy= 98.1%(3sf) After parameter tuning Improved accuracy= 98.7%(3sf)

Variable importance plot

Made a variable importance plot to determine what factors the random forest model thought were important

Using other models

Lets try a Descion Tree

A decision tree might split the data based on the home team's win/loss record, the away team's win/loss record, etc. Each of these splits creates a subset of the data that is more homogeneous in terms of the outcome (win or loss), allowing the tree to make more accurate predictions based on the remaining features

accuracy = 99.7%(3sf)

Neural Network

Neural networks are a class of algorithms that are inspired by the structure and function of the human brain. They can be used for a wide range of tasks, including classification, and have been shown to be effective at predicting winners and losers in various domains.

accuracy = 85.0%(3sf)