ISACE 2026

Welcome to the International Sports Analytics Conference and Exhibition (ISACE) series, 2026

Sports analytics is the application of AI, data science, psychology, and smart devices to improve sports performance, strategy, and decision-making. It involves collecting, processing, and interpreting data from various sources such as video footage, performance metrics, and scouting reports, and using this information to gain insights into player and team performance, prevent injury, and help coaches and team managers to make more informed decisions and optimize their resources to achieve better results.

There are various sports analytics seminars, satellite workshops (associated with major AI and Data Science conferences), and regional conferences (e.g., MIT Sloan Sports Analytics). The vision of this new International Sports Analytics Conference and Exhibition (ISACE) series is to bring leading academics, researchers, coaches, psychologists, managers and technologists together to advance state-of-the-art of sports analytics.

The third ISACE (June 2026) will be in Vancouver, Canada (before The 2026 Soccer World Cup). The conference proceedings will be published in Springer LNCS series. Top papers will be awarded a Best Paper Certificate and a book voucher from Springer-Nature. Selected papers will be invited to submit an extended version to a special issue of the Springer-Nature Computer Science journal.

The location of ISACE series is to move around the world's major cities with a timing that is likely to be 1-2 weeks before or after a major sporting event which may form one of the themes of the conference.

Previous and future events:

The pre-conference workshop was hosted in Oct 2023, Singapore.

Singapore F1 2023

The first ISACE (July 2024) was held in Paris one week before The 2024 Summer Olympics. Video for the conference.

Pairs Summer Olympic 2024

The second ISACE (September 2025) was held in Shanghai few days before The 2025 Rolex Shanghai Masters tennis tournament. Video for the conference.

Pairs Summer Olympic 2024

The fourth ISACE (2027). Details will be updated later.

Download the PDF poster.

Program

Download the program here.

Keynote Speakers

Professor Jin-Song Dong

Reasoning Beyond LLMs and a Vision for Life After Superintelligence: The Player Era

Abstract: Large Language Models (LLMs) are increasingly used in sports analytics for tasks such as coaching recommendations, video analysis, and automated commentary generation. However, their outputs are not inherently reliable due to well-known hallucination issues. Probabilistic Model Checking (PMC), by contrast, has long been employed for rigorous reliability analysis in safety-critical systems. For example, the reliability of an aircraft can be systematically derived from the reliability of its constituent components, such as engines, wings, and sensors. We extend PMC to a new domain: sports analytics. Specifically, we model a player’s overall performance (e.g., winning probability) as a function of the success rates of individual sub-skills, such as serve, forehand, and backhand in tennis. The first part of the talk highlights the limitations of LLMs in complex decision-making and video analytics, and presents our recent work integrating PMC, LLMs, and computer vision to enable principled and explainable sports analysis. The second part introduces a forward-looking vision for life after superintelligence, termed the Player Era. In this vision, human society evolves into four interconnected roles: Player, Explorer, Co-Creator, and Gatekeeper, forming the foundation of a civilization centered on meaning, creativity, and responsibility.

Bio: Jin-Song Dong is a Professor at the National University of Singapore. His research spans formal methods for LLM-based agents, safety and security systems, trusted AI, probabilistic reasoning, sports analytics, and verified LLM-driven code synthesis. He is a co-founder of the PAT verification system, a widely adopted formal analysis platform with thousands of users across more than 150 countries. He also co-founded Silas (http://www.depintel.com), a commercialized trusted machine learning system with over 50,000 downloads. He has served on the editorial boards of ACM Transactions on Software Engineering and Methodology, Formal Aspects of Computing, and Innovations in Systems and Software Engineering (a NASA journal). He has supervised 34 PhD students, many of whom now hold tenured academic positions at leading institutions worldwide. He is a Fellow of the Institute of Engineers Australia. In the domain of sports analytics, he developed Markov Decision Process (MDP) models for tennis strategy analysis using PAT, supporting professional players with pre-match preparation at the highest level of competition. He also founded ISACE, a new conference series on sports analytics. Outside academia, he is a tennis coach and enjoys mentoring his students and his three children, all of whom have achieved #1 national junior rankings in Singapore and Australia. Two of his children have received full NCAA Division I scholarships in the United States.

Professor Oliver Schulte

Ranking and Representing Hockey Players with Deep Reinforcement Learning

Abstract: This talk develops the idea that reinforcement learning can solve the problems of sports analytics. I focus on ranking player performance. A common approach is to assign a value to each player action and rank a player by their aggregate action values. I propose to measure the value of a player’s action by how much it increases their team’s chance of success, that is, their team’s chance of scoring the next goal. This requires a model that outputs a success probability estimate, given a match context and an action. The talk describes deep reinforcement learning techniques for building success probability models from sports data. The most advanced model incorporates playing style representations for 1K+ NHL players. The resulting action values and player rankings are illustrated with data from the National Hockey League.

Bio: Oliver Schulte is a Professor in the School of Computing Science at Simon Fraser University, Vancouver, Canada. He received his Ph.D. from Carnegie Mellon University in 1997. His current research focuses on machine learning for structured data, such as sports events, networks, and relational databases. He has given several invited talks on sports analytics and spent two years at Sportlogiq, a sports analytics company now part of Teamworks. His publications include papers in leading AI and machine learning venues on a variety of topics, including sports analytics, learning Bayesian networks, game theory, and scientific discovery. While he has won some nice awards, his biggest claim to fame may be a draw against chess world champion Gary Kasparov.

Industrial Talks

Dr. Masoumeh Izadi

From video to victory in different segments of the sports content value chain

Options	Early Bird (before 1 May 2026)	Standard
Individual	Student	500 USD	600 USD
Regular	600 USD	700 USD

Call for Exhibition (Demo Track)

The ISACE Exhibition (Demo Track) is an opportunity to showcase innovation and excellence in sports industry. Companies, institutes, and sports teams are invited to submit a pich deck (slides) and present their technologies and products to a panel of industry experts, potential customers and investors.

Each selected organization may have up to two presenters, and each presenter must register for the conference. Each pitch is 5 minutes followed by Q&A. Submission format must be PDF or PPTX or KEY.

Your submission should include 1) a pitch deck of slides, and 2) responses to a simple survey (download the survey Word doc here).

Submission deadline: 15 April 2026

Selected companies can pay a discounted registration fee equivalent to the student fees.

Graduate Student Symposium

We cordially invite Matser and PhD students to submit their research work to the ISACE 2026 Graduate Student Symposium. This is an excellent opportunity to present your research and receive feedback from experienced researchers. We will select top student presentation and invite them to submit to our Journal special issue after the conference.

Submission deadline: 15 April 2026

Submission requirements: One-page abstract

Submission should be done electronically in PDF format through the EasyChair submission page

Paper Submissions

Conference Paper Submission

Manuscripts may be submitted in Springer LNCS format to one of the following two categories:
(i) Regular Papers and (ii) Practical Experience Papers.

Regular Papers (15 pages max, excluding references and appendices) should describe original research.
Practical Experience Papers (6 pages max, excluding references and appendices) should describe a real-world experience or a case study, such as the design and deployment of a method or a system in practical settings.

Additional material may be placed in an appendix, to be read at the discretion of the reviewers and to be omitted in the final version. Formatting style files and further guidelines for formatting can be found at the Springer website (more details here).

The proceedings will be published in the Springer Lecture Notes in Computer Science series.

Submission should be done electronically in PDF format through the EasyChair submission page.

Submit Conference Paper

Organising Committee

General Co-Chairs

Dr Zhe Hou, Griffith University, Australia
Prof Vishal Misra, Columbia University, US

Program Co-Chairs

Dr Zhaoyu Liu, National University of Singapore, Singapore

Publicity Co-Chair

Asst Prof Mark Huasong Meng, University College Dublin, Ireland
Henry (Yuanheng) Wang, AWS Generative AI Innovation Center, US

Program Committee

A/Prof Bimlesh Wadhwa, National University of Singapore, Singapore
Prof Chunyang Chen, Technical University of Munich, Germany
A/Prof David Saxby, Griffith Centre of Biomedical and Rehabilitation Engineering, Griffith University, Australia
Dr Elizabeth Bradshaw, Deakin University, Australia
Dr Hadrien Bride, Dependable Intelligence, France
Prof Jin Song Dong, National University of Singapore, Singapore
A/Prof John Komar, National Institute of Education, Nanyang Technological University, Singapore
Dr Kan Jiang, National University of Singapore, Singapore
Prof Lijun Guo, Ningbo University, China
Dr Luke Wildman, RGB Assurance, Australia
Dr Masoumeh Izadi, Television Content Analytics Pte. Ltd, Singapore
Prof Oliver Schulte, Simon Fraser University, Canada
Dr Sebastian Binnewies, Griffith University, Australia
Prof Seungbok Lee, Yonsei University, South Korea
A/Prof Teck Khim Ng, National University of Singapore, Singapore
Dr Wei-Yao Wang, National Yang Ming Chiao Tung University, Taiwan
Prof Yamine AIT AMEUR, IRIT - National Polytechnic Institute of Toulouse, France
A/Prof Yun Lin, Shanghai Jiao Tong University, China
Dr Zhaoyu Liu, National University of Singapore, Singapore
Dr Zhe Hou, Griffith University, Australia
A/Prof Zhiyong Huang, National University of Singapore, Singapore
A/Prof Xiaofei Xie, Singapore Management University, Singapore
Prof Mehul Raval, Ahmedabad University, India
A/Prof Srikrishnan Divakaran, Krea University, India
Prof Tolga Kaya, Sacred Heart University, USA
Daniel Bolarinwa, Accenture, UK
Rajdeep Singh Hundal, National University of Singapore, Singapore
Dr Dileepa Fernando, Singapore University of Technology and Design, Singapore
Isuru Supasan, SLTC Research University, Sri Lanka
Karan Gupta, SunPower, USA
Prof Raveendran Paramesran, Monash University, Malaysia
Prof Bessam Abdulrazak, University of Sherbrooke, Canada
Steven Lin, Azra Games, USA
Dr Tao Lin, Ethiqly, USA
A/Prof Robert Moskovitch, Ben Gurion University, Israel
Prof David Clausi, University of Waterloo, Canada
Ruchir Pandya, ex-Disruptive, ex-Meta, ex-NBA, USA
Siqi Hao, Huawei, Finland
A/Prof Willie Harrison, Brigham Young University, USA
Matthew Caron, VfL Wolfsburg, Germany
A/Prof Leili Javadpour, University of the Pacific, USA
A/Prof Jacomine Grobler, Stellenbosch University, South Africa
Charles Danoff, Mr. Danoff’s Teaching Laboratory, USA
Klaus Mueller, Wictory.ai, Austria
Markus Unterweger, Wictory.ai, Austria
Dr Chean Khim Toa, Xiamen University Malaysia, Malaysia
Prof Divya Mehta, Queensland University of Technology, Australia
Dr Haresh Suppiah, La Trobe University Australia
Dr Xian Song, Zhejiang University, China
A/Prof Yu Xin, Ningbo University, China
Prof Ruchika Malhotra, Delhi Technological University, India
Shaobo Cai, Shanghai Jiao Tong University, China
Roland Janos Nemes, Hosei University, Japan
Guoqing Luo, University of Alberta, Canada
A/Prof Yutao Yue, Hong Kong University of Science and Technology (Guangzhou), China

The 3^rd International Sports Analytics Conference and Exhibition (ISACE)

Welcome to the International Sports Analytics Conference and Exhibition (ISACE) series, 2026

Important Dates (AoE)