In many real-world complex systems, the behavior can be observed as a collection of discrete events generated by multiple interacting agents. Analyzing the dynamics of these multi-agent systems, especially team sports, often relies on understanding the movement and interactions of individual agents. However, while providing valuable snapshots, event-based positional data typically lacks the continuous temporal information needed to directly calculate crucial properties such as velocity. This absence severely limits the depth of dynamic analysis, preventing a comprehensive understanding of individual agent behaviors and emergent team strategies. To address this challenge, we propose a new method to simultaneously complete the velocity of all agents using only the event-based positional data from team sports. Based on this completed velocity information, we investigate the applicability of existing team sports analysis and evaluation methods. Experiments using soccer event data demonstrate that neural network-based approaches outperformed rule-based methods regarding velocity completion error, considering the underlying temporal dependencies and graph structure of player-to-player or player-to-ball interaction. Moreover, the space evaluation results obtained using the completed velocity are closer to those derived from complete tracking data, highlighting our method's potential for enhanced team sports system analysis.

This paper draws correlations between several challenges and opportunities within the area of team sports analytics and key research areas within multiagent systems (MAS). We specifically consider invasion games, defined as sports where players invade the opposing team's territory and can interact anywhere on a playing surface such as ice hockey, soccer, and basketball. We argue that MAS is well-equipped to study invasion games and will benefit both MAS and sports analytics fields. Our discussion highlights areas for MAS implementation and further development along two axes: short-term in-game strategy (coaching) and long-term team planning (management).

We're seeing AI projects shift from hype to impact, largely because the right roles are getting involved to provide the business context that has been missing previously. Domain expertise is key; machines don't have the depth of context that people have, and people need to know the business and data well enough to understand which actions to take based on any insights or recommendations that are surfaced. When it comes to scaling AI, many leaders think they have a people problem--specifically, not enough data scientists. But not every business problem is a data science problem. Or at least, not every business challenge should be thrown at your data science team.

Predicting the results of matches in sport is a challenging and interesting task. In this paper, we review a selection of studies from 1996 to 2019 that used machine learning for predicting match results in team sport. Considering both invasion sports and striking/fielding sports, we discuss commonly applied machine learning algorithms, as well as common approaches related to data and evaluation. Our study considers accuracies that have been achieved across different sports, and explores whether evidence exists to support the notion that outcomes of some sports may be inherently more difficult to predict. We also uncover common themes of future research directions and propose recommendations for future researchers. Although there remains a lack of benchmark datasets (apart from in soccer), and the differences between sports, datasets and features makes between-study comparisons difficult, as we discuss, it is possible to evaluate accuracy performance in other ways. Artificial Neural Networks were commonly applied in early studies, however, our findings suggest that a range of models should instead be compared. Selecting and engineering an appropriate feature set appears to be more important than having a large number of instances. For feature selection, we see potential for greater inter-disciplinary collaboration between sport performance analysis, a sub-discipline of sport science, and machine learning.

Editor's note: This article originally appeared in Forbes. What does it mean to think of AI as a team sport? We're seeing AI projects shift from hype to impact, largely because the right roles are getting involved to provide the business context that has been missing previously. Domain expertise is key; machines don't have the depth of context that people have, and people need to know the business and data well enough to understand which actions to take based on any insights or recommendations that are surfaced. When it comes to scaling AI, many leaders think they have a people problem--specifically, not enough data scientists.

Building deep and ongoing data science capabilities isn't an easy process: it takes the right people, processes and technology. Finding the right people for the right roles -- as employers and job seekers alike can attest to -- is an ongoing challenge. In this special feature, ZDNet examines how advances in AI, visualization and cloud technology are shaping modern data analytics, and how businesses are addressing data governance and a potential data science skills gap. "The people part is probably the least well-understood aspect of this entire equation," John Thompson, global head of advanced analytics & AI at CSL Behring, said during a virtual panel discussion on Thursday. As the head of analytics at one of the leading international biotechnology companies, Thompson oversees data science teams that tackle a wide range of initiatives.

IDC analysts recommend that companies develop comprehensive guidelines for ethical artificial intelligence and an ongoing review process. Companies using artificial intelligence should start thinking about ethical AI as make or break, not nice to have, according to IDC research. In a webinar on Thursday, March 4, analysts explained why the lack of guidelines for AI is holding back implementation as well as how companies can address this problem. Analysts Bjoern Stengel, Ritu Jyoti and Jennifer Hamel shared new research at the session, "Increasing Trust and Accountability Through Responsible AI and Digital Ethics." Hamel, a research manager of analytics and intelligent automation services, said that ethical AI is a team sport.

Understanding the principles of real-world biological multi-agent behaviors is a current challenge in various scientific and engineering fields. The rules regarding the real-world biological multi-agent behaviors such as team sports are often largely unknown due to their inherently higher-order interactions, cognition, and body dynamics. Estimation of the rules from data, i.e., data-driven approaches such as machine learning, provides an effective way for the analysis of such behaviors. Although most data-driven models have non-linear structures and high prediction performances, it is sometimes hard to interpret them. This survey focuses on data-driven analysis for quantitative understanding of invasion team sports behaviors such as basketball and football, and introduces two main approaches for understanding such multi-agent behaviors: (1) extracting easily interpretable features or rules from data and (2) generating and controlling behaviors in visually-understandable ways. The first approach involves the visualization of learned representations and the extraction of mathematical structures behind the behaviors. The second approach can be used to test hypotheses by simulating and controlling future and counterfactual behaviors. Lastly, the potential practical applications of extracted rules, features, and generated behaviors are discussed. These approaches can contribute to a better understanding of multi-agent behaviors in the real world.

Organizations with an interdisciplinary team have a "far higher ratio of success" when deploying AI projects, said Arun Chandrasekaran, distinguished VP analyst at Gartner, speaking at a Gartner IT Symposium/Xpo Americas session last week. Interdisciplinary teams that blend roles across business and data science have a higher ratio of success with AI projects, as well as a faster time to production. This trend "clearly tells us that AI needs to be a team sport, said Chandrasekaran. "However, in reality what we see in most organizations is data scientists wearing too many hats, because there's a dearth of skills across other areas," he said. Organizations with an interdisciplinary team have a "far higher ratio of success" when deploying AI projects, said Arun Chandrasekaran, distinguished VP analyst at Gartner, speaking at a Gartner IT Symposium/Xpo Americas session last week. Interdisciplinary teams that blend roles across business and data science have a higher ratio of success with AI projects, as well as a faster time to production. This trend "clearly tells us that AI needs to be a team sport, said Chandrasekaran.

The purpose of this course is to teach about how to use Python and machine learning in order to predict sports outcomes. It takes you through through all the steps, from collecting data using a web crawler to making profitable bets based on your predicted results. The course is built around predicting tennis games, but the things taught can be extended to any sport, including team sports. The course includes: 1) Intro to Python and Pandas. This course is geared towards people that have some interest in data science and some experience in Python.