Trusted Decision-Making for Sports Analytics

Speaker: Professor Jin-Song Dong
         The National University of Singapore

Title:  "Trusted Decision-Making for Sports Analytics"

Date:   Wednesday, 10 May 2023

Time:   11:00am - 12 noon

Venue:  Room 2463 (via lift 25/26), HKUST


Abstract:

Sports analytics encompasses the utilization of data science, artificial
intelligence (AI), psychology, and advanced Internet of Things (IoT)
devices to enhance sports performance, strategy, and decision-making. This
process involves the collection, processing, and interpretation of
cloud-based data from a variety of sources, such as video recordings,
performance metrics, and scouting reports. The resulting insights aid in
evaluating player and team performance, preventing injuries, and
supporting coaches and team managers in making well-informed decisions to
optimize resources and achieve superior outcomes. One widely recognized
formal method, Probabilistic Model Checking (PMC), has been conventionally
employed in reliability analysis for intricate safety critical systems.
For instance, the reliability of an aircraft can be determined by
evaluating the reliability of its individual components, including the
engine, wings, and sensors. Our groundbreaking approach applies PMC to a
novel domain: Sports Strategy Analytics. As an example, the reliability
(winning percentage) of a sports player can be ascertained from the
reliability (success rate) of their specific sub-skill sets (e.g., serve,
forehand, backhand, etc., in tennis).

In this presentation, we will discuss our recent research work, which
involves the application of PMC, machine learning, and computer vision to
the realm of sports strategy analytics. At the end of the presentation, we
will also discuss the vision of a new international sports analytics
conference series (https://formal-analysis.com/isace/2023/).


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Biography:

Dr. Jin-Song Dong is a professor at the National University of Singapore.
His research spans a range of fields, including formal methods, safety and
security systems, probabilistic reasoning, sports analytics, and trusted
machine learning. He co-founded the commercialized PAT verification
system, which has garnered thousands of registered users from over 150
countries and received the 20-Year ICFEM Most Influential System Award.
Jin Song also co-founded the commercialized trusted machine learning
system Silas (www.depintel.com). He has received numerous best paper
awards, including the ACM SIGSOFT Distinguished Paper Award at ICSE 2020.
He served on the editorial board of ACM Transactions on Software
Engineering and Methodology, Formal Aspects of Computing, and Innovations
in Systems and Software Engineering, A NASA Journal. He has successfully
supervised 28 PhD students, many of whom have become tenured faculty
members at leading universities worldwide. He is also a Fellow of the
Institute of Engineers Australia. In his leisure time, Jin Song developed
Markov Decision Process (MDP) models for tennis strategy analysis using
PAT, assisting professional players with pre-match analysis (outperforming
the world's best). He is a Junior Grand Slam coach and takes pleasure in
coaching tennis to his three children, all of whom have reached the #1
national junior ranking in Singapore/Australia. Two of his children have
earned NCAA Division 1 full scholarship, while his second son, Chen Dong,
played #1 singles for Australia in the Junior Davis Cup and participated
in both the Australian Open and US Open Junior Grand Slams.