|Kurzendörfer, David: Tennis Match Outcome Prediction using LSTM Networks and Historical Averaging. |
Universität Stuttgart, Fakultät Informatik, Elektrotechnik und Informationstechnik, Bachelorarbeit Nr. 92 (2020).
51 Seiten, englisch.
In this thesis, a deep learning model has been created with the goal to predict tennis match outcome probabilities for ATP men's tennis matches. Leveraging state of the art architectures in sequence modelling and prediction power of hand engineered features, a model has been built combining Long-Short-Term-Memory (LSTM) and averaged statistics of historical tennis matches. New features have been created for example reflecting surprise in expectation of players when they tend to win as underdogs or lose as favorites. Likewise, an Elo rating model has been added to the set of features. The proposed model achieved 69.95% accuracy on a test set of 14770 tennis matches.
|Abteilung(en)||Universität Stuttgart, Institut für Parallele und Verteilte Systeme, Scientific Computing|
|Betreuer||Pflüger, Prof. Dirk; Leiteritz, Raphael|
|Eingabedatum||9. April 2021|