Predicting the Winning Football Team
Can we design a predictive model capable of accurately predicting if the home team will win a football match?

Steps

We will clean our dataset
Split it into training and testing data (12 features & 1 target (winning team (Home/Away/Draw))
Train 3 different classifiers on the data
-Logistic Regression
-Support Vector Machine
-XGBoost
Use the best Classifer to predict who will win given an away team and a home team

History
Sports betting is a 500 billion dollar market (Sydney Herald)

Kaggle hosts a yearly competiton called March Madness
https://www.kaggle.com/c/march-machine-learning-mania-2017/kernels
Several Papers on this
https://arxiv.org/pdf/1511.05837.pdf
"It is possible to predict the winner of English county twenty twenty cricket games in almost two thirds of instances."
https://arxiv.org/pdf/1411.1243.pdf
"Something that becomes clear from the results is that Twitter contains enough information to be useful for
predicting outcomes in the Premier League"
https://qz.com/233830/world-cup-germany-argentina-predictions-microsoft/
For the 2014 World Cup, Bing correctly predicted the outcomes for all of the 15 games in the knockout round.
So the right questions to ask are
-What model should we use?
-What are the features (the aspects of a game) that matter the most to predicting a team win? Does being the home team give a team the advantage?
Dataset

Football is played by 250 million players in over 200 countries (most popular sport globally)
The English Premier League is the most popular domestic team in the world
Retrived dataset from http://football-data.co.uk/data.php



Football is a team sport, a cheering crowd helps morale
Familarity with pitch and weather conditions helps
No need to travel (less fatigue)

Acrononyms- https://rstudio-pubs-static.s3.amazonaws.com/179121_70eb412bbe6c4a55837f2439e5ae6d4e.html
Other repositories

https://github.com/rsibi/epl-prediction-2017 (EPL prediction)
https://github.com/adeshpande3/March-Madness-2017 (NCAA prediction)

football

a9dd0cf22dfe9b69669ed75d5e57f4a3_preview.png

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