Uppsats

Simulating Football Games Two Seconds into the Future

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Uppsala universitet/Avdelningen för systemteknik

Publicerad: 2024

Språk: Engelska

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⌕Machine Learning ⌕football ⌕fotboll ⌕Object detection ⌕maskininlärning ⌕Deep Learning ⌕djupinlärning ⌕neural networks ⌕Neurala Nätverk ⌕Model Evaluation ⌕positioning ⌕Velocity ⌕positionering ⌕feature engineering ⌕Allsvenskan ⌕Tracking data ⌕Prediktiv Modellering ⌕Event data ⌕Football analytics ⌕predictive modelling ⌕Hastighet ⌕Data-driven models ⌕Datadrivna modeller ⌕Computational resources ⌕Objektigenkänning ⌕LSTM architecture ⌕Player movement ⌕Predictive accuracy ⌕Sequential Data ⌕Team-based model ⌕GRNN ⌕Position prediction ⌕Sports simulation ⌕Player coordinates ⌕Football Manager ⌕Movement prediction ⌕Spatial dynamics ⌕LSTM-arkitektur ⌕Fotbollsanalys ⌕Spelarrörelse ⌕Spårningsdata ⌕Prediktiv noggrannhet ⌕Sekventiella data ⌕Händelsedata ⌕Lagbaserad modell ⌕Positionsprediktion ⌕Sportssimulering ⌕Spelarpositioner ⌕Rörelseprediktion ⌕Rumslig dynamik ⌕Modellevärdering ⌕Beräkningsresurser ⌕Funktionsutveckling

Sammanfattning

Developing predictive models that can accurately simulate future positions in sports could revolutionise current modelling practices. This thesis endeavours to create a model capable of simulating football games two seconds into the future. By utilising tracking data, in combination with a player database from Football Manager, the goal of the project is to create sophisticated deep learning models with accurate predictions. The data-driven approach learns the movement of football players using neural networks (NN), and particularly networks with the long short-term memory (LSTM) architecture. By combining these complex models with features such as velocity, distance to ball, and tiredness, we achieve the desired results. The most complex model is an LSTM model, averaging a prediction error of 1.59 meters for the two-second time-frame. The results vary based on player position, with the movement of central midfielders and defensive midfielders being the hardest to predict accurately, while the movement of goalkeepers, forwards, and centre-backs tends to be more predictable. The findings of this thesis demonstrate the potential of machine learning in sport analytics, with the predictive modelling offering valuable insights in the realm of analytics. Future work could further enhance the predictive accuracy of the models, by the integration of event-data and the utilisation of team-based modelling.