"Machine Learning for Soccer Match Result Prediction", written by myself and co-authored by @keisuke_fj and Calvin Yeung, is a forthcoming book chapter in "Artificial Intelligence, Optimization, and Data Sciences in Sports"
European Sports Economics Conference is over.
Great experience in a stunning university.
Congrats to @Robbiembutler, David Butler, Massey and all staff from @UCC 🇮🇪
5 great days at ESEA 2023 come to an end in Cork. A big thank you to all those that participated. We had almost 70 papers from more than 150 co-authors from around the world including 🇦🇹🇦🇺🇨🇦🇨🇴🇨🇿🇩🇪🇩🇰🏴🇪🇸🇫🇮🇬🇷🇮🇪🇮🇱🇮🇹🇪🇨🇳🇱🇳🇴🇳🇿🇯🇵🇺🇸🏴
Here, lead author Zhang Ziyi, myself, Prof. Kazuya Takeda, & Assoc. Prof @keisuke_fj propose a #deeplearning method using an attention mechanism to detect distinct segments in trajectories of given classes to analyze multi-agent trajectories in ball sports
https://t.co/Xga3K1O1TP
@keisuke_fj 2/ This can be used to understand differences between classes and highlight segmented trajectories.
The effectiveness of the method was verified by comparing various baselines with effective/ineffective attack labels and goal/non-goal labels using different sizes of the dataset.
🚨New research💡
#PLOSONE: "A framework of interpretable match results prediction in #football with #FIFA ratings and team formation"
Lead author: Calvin C. K. Yeung, and co-authored by myself and @keisuke_fj
https://t.co/ixj58kNJrQ
Links to my paper and slides from the 9th #mathsportinternational conference, hosted by
@UniofReading
【Paper】https://t.co/uvXtXZ0onq
【Slides】https://t.co/BNnIArULQv