Objective:

• Prediction of the winner of an international matches Prediction results are "Win / Lose / Draw" or "goal difference"
• Apply the model to predict the result of FIFA world cup 2018.

Data: Data are assembled from multiple sources, most of them are from Kaggle, others come from FIFA website / EA games.

Feature Engineering: To determine who will more likely to win a match, based on my knowledge, I come up with 4 main groups of features as follows:

1. head-to-head match history between 2 teams
2. recent performance of each team (10 recent matches), aka "form"
3. bet-ratio before matches
4. squad strength (from FIFA video game)

Feature list reflects those factors.

Lifecycle

Check the Full Report to gain more insight about this Project. The report contains:

• Exploratory Data Analysis: Investigate correlations, importance of features to results, hypothesis interesting
• Methodology: How I carried out this project, which experiments I did.
• Models: baseline model, logistic regression, random forest, gradient boosting tree, ADA boost tree, Neural Network.
• Evaluation Criteria: F1, 10-fold cross validation accuracy
• Results and Conclusion

1. EDA: Data Exploratory Analysis
2. LE: saved model for Label Encoder
3. data: completed dataset
4. save_model: saved Machine Learning model after training

The dataset are from all international matches from 2000 - 2018, results, bet odds, ranking, squad strengths

1. FIFA World Cup 2018
2. International match 1872 - 2018
3. FIFA Ranking through Time
4. Bet Odd
5. Bet Odd 2
6. Squad Strength - Sofia
7. Squad Strength - FIFA index

• *difference: team1 - team2
• *form: performance in 10 recent matches

python experiment1-W-D-L.py
python experiment2-GoalDiff.py
python experiment3-WorldCup.py

1. A machine learning framework for sport result prediction
2. t-test definition
3. Confusion Matrix Multi-Label example
4. Precision-Recall Multi-Label example
5. ROC curve example
6. Model evaluation
7. Tuning the hyper-parameters of an estimator
8. Validation curves
9. Understand Bet odd format
10. EURO 2016 bet odd

Complete

• Add prediction for Matchday 2
• Add feature Importance
• Add feature of squad and player info
• Build a web crawler for Squad each team
• Build a web crawler for FIFA game player
• Add a simple classification based on "bet odd".
• Add feature group 1
  • Add h_win_diff, h_draw
  • Add rank_diff, title_diff
• Add features group 2
• Add features group 3
• Simple EDA and a small story
• Add features group 4
• Prepare framework for running classifiers
• Add evaluation metrics and plot
  • Add accuracy, precision, recall, F1
  • Add ROC curves
• Build a data without player rating and squad value
• Generate data and preform prediction for EURO 2016, ok now my story is more interesting
• Create more data, "teamA vs teamB -> win" is equivalent to "teamB vs teamA -> lose"