Identifying team style in soccer using formations learned from spatiotemporal tracking data

To the trained-eye, experts can often identify a team based on their unique style of play due to their movement, passing and interactions. In this paper, we present a method which can accurately determine the identity of a team from spatiotemporal player tracking data. We do this by utilizing a formation descriptor which is found by minimizing the entropy of role-specific occupancy maps. We show how our approach is significantly better at identifying different teams compared to standard measures (i.e., shots, passes etc.). We demonstrate the utility of our approach using an entire season of Prozone player tracking data from a top-tier professional soccer league.

Representing team behaviours from noisy data using player role

Due to their unobtrusive nature, vision-based approaches to tracking sports players have been preferred over wearable sensors as they do not require the players to be instrumented for each match. Unfortunately however, due to the heavy occlusion between players, variation in resolution and pose, in addition to fluctuating illumination conditions, tracking players continuously is still an unsolved vision problem. For tasks like clustering and retrieval, having noisy data (i.e. missing and false player detections) is problematic as it generates discontinuities in the input data stream. One method of circumventing this issue is to use an occupancy map, where the field is discretised into a series of zones and a count of player detections in each zone is obtained. A series of frames can then be concatenated to represent a set-play or example of team behaviour. A problem with this approach though is that the compressibility is low (i.e. the variability in the feature space is incredibly high). In this paper, we propose the use of a bilinear spatiotemporal basis model using a role representation to clean-up the noisy detections which operates in a low-dimensional space. To evaluate our approach, we used a fully instrumented field-hockey pitch with 8 fixed high-definition (HD) cameras and evaluated our approach on approximately 200,000 frames of data from a state-of-the-art real-time player detector and compare it to manually labeled data.

The innovation imperative: The relationships between team climate, innovation, and performance in research and development teams

A team’s climate for innovation has been shown to be important for innovation in management and work teams. This article investigates the relationship of team climate with project team innovation and performance in research and development organizations. It is argued that the relationship between team climate and innovation will be stronger for research teams than development teams as research teams have greater scope for creating novel and innovative ideas. A sample of 193 scientists and technologists in 20 research teams and 18 development teams were measured on their team’s climate for innovation, team performance, and six indicators of innovation. Research and development teams showed similar ratings for team climate and for measures of innovation. However, the relationships between team climate and individual and team innovation were stronger for research teams than development teams. These findings are significant for fostering innovativeness and innovation in knowledge work teams.

LunaRoo: A Proposal for the Google Lunar XPrize Payload Opportunity with the Part Time Scientists team

This proposal describes the innovative and competitive lunar payload solution developed at the Queensland University of Technology (QUT)–the LunaRoo: a hopping robot designed to exploit the Moon's lower gravity to leap up to 20m above the surface. It is compact enough to fit within a 10cm cube, whilst providing unique observation and mission capabilities by creating imagery during the hop. This first section is deliberately kept short and concise for web submission; additional information can be found in the second chapter.

Is member-rated project success influenced by leadership team emotional intelligence?

Large complex projects often fail spectacularly in terms of cost overruns and delays; witness the London Olympics and the Airbus A380. In this project, we studied the emotional intelligence (EI) of leadership teams involved in such projects. We collected our data from 370 employees in 40 project teams working on large Australian defense contracts. We asked leadership team members to complete a scale measuring their EI, and project team members to rate the success of the projects. We found it was not the mean score, but the highest EI score in the leadership team that predicted members’ project success ratings.