Football-Analytics
December 26, 2022 ยท View on GitHub
In this repository I have explored some concepts of football analytics using Event data and Tracking data from different sources. This includes:
- Player detection and Shirt Number Identification from game footages, using Deep Learning (Yolov3, CNN and VGG models). More information here: https://daniel-azevedo.medium.com/player-detection-using-deep-learning-492122c3bf9
- Creation of a Pitch Control Model (baseline), which reflects the probability of the team getting the ball possession at a given filed position
- Explore different Pitch Control variants, focused on Ball Possession Retention, Vertical Game, of Game by the Flanks
- Computing the value of a given action, using SPADL structure data, based on the probability of a given action will end in goal
- Predicting if a game action will lead to a goal or not, using Machine Learning
- Plots of different game features, like a game move, shots frame and players passing and shooting accuracy
- Analysing the importance of features (like game stats and individual players) in the matches results
- Creation of an Expected Goal Model, using only distance to goal as input to a SVM classifier
- Players Heat Map, using tracking data
- Predict a substitute player rating, based on the game statistics until the moment he enters in the game
- Determination of the best/most suitable position for a given player (using FIFA 20 dataset)
- Implementation of a Genetic Algorithm for finding the best set of players to buy under a given budget, for given positions, considering different criteria, as, for example, the player age (using FIFA 20 dataset)
- Capture Player playing style, using Non Negative Matrix Factorization
- Capture Team passing patterns, using K-Means applied to passing sequences
- Also, a Tableau dashboard of Manchester City season 2018/2019 and beginning of 2019/2020. In this dashboard one can see the team performance in terms of results (home and away), scores, game stats, player individual stats, players importance on the game result, and much more. The data was scraped from web and saved in BigQuery with an ETL job