The University of Southampton
×
Electronics and Computer Science

Published: 27 February 2020
Illustration
The new approach can form efficient teams based on the value of players’ teamwork.

Machine learning experts from the University of Southampton are optimising football team selection by using AI to value teamwork between pairs of players.

The new approach uses historic performance data to identify which player combinations are most important to a team, generating insights that can help select teams' most efficient line-ups and identify suitable transfer targets.

The study, led by PhD student Ryan Beal in the Agents, Interaction and Complexity (AIC) Group, has developed a number of teamwork metrics that can accurately predict team performance statistics, including passes, shots on target and goals.

Researchers presented their findings and hosted an AI in Team Sports workshop at this month’s Association for the Advancement of Artificial Intelligence (AAAI) Conference in New York.

"We have tested our methods from games in the 2018 FIFA World Cup and the last two seasons of the English Premier League," Ryan says. "We found that we could select teams using the AI in a similar fashion to human managers and then also suggest changes that would improve the team.

"When looking at the results for the Premier League, the teamwork analysis identified Aymeric Laporte as one of the key players for Manchester City. He has been injured for much of this season which may explain their downturn in form compared to last season."

The Southampton team have used a number of machine learning techniques to assess teamwork values from the historic data and found that teams with higher teamwork levels are more likely to win. They then trained an optimisation method to assess the teamwork between pairs of players and compute a number of new metrics that they compare in their latest paper.

"While this work could be used as a tool to assist football managers, we think that the approach could also be extended into other domains where teamwork between humans is important, such as emergency response or in security," Ryan says.

Ryan has also presented his work to sporting industry experts at the StatsBomb Innovation in Football Conference at Stamford Bridge in October.

Ryan's work is supported by UK Research and Innovation (UKRI) and AXA Research Fund. The work was done in collaboration with Narayan Changder (NIT Durgapur), Professor Tim Norman and Professor Gopal Ramchurn.

Team sport performance is one of a several innovative AI research topics being explored in the AIC Group. In 2018, Gopal and Dr Tim Matthews revealed how machine learning algorithms can accurately predict team and player performance to finish in the top 1% of the Fantasy Premier League game, outperforming close to six million human players.

Articles that may also interest you

The Cyber Security Academy training courses

Hampshire and Sussex Cyber Cluster launched to strengthen regional cybersecurity

Breathing sensor 'sticker' could help save lives
Share this article FacebookTwitterWeibo
Telephone:
+442380593542
Email:
x.cai@soton.ac.uk

 

Personal homepage

Xiaohao Cai is a Lecturer (Assistant Professor equivalent) in the School of Electronics and Computer Science at the University of Southampton. He received his PhD degree in mathematics from The Chinese University of Hong Kong in 2012. He afterwards was a Postdoctoral Researcher at the Department of Mathematics of the Technische Universitat Kaiserslautern in Germany. After that he was a Wellcome Trust Research Fellow and Issac Newton Trust Research Fellow, affiliated with the Department of Plant Sciences, and Department of Applied Mathematics and Theoretical Physics at the University of Cambridge. Thenceforth, before joining Southampton, he was a Research Fellow in the Mullard Space Science Laboratory (MSSL) at University College London (UCL).

He has broad multi-disciplinary research interests in applied mathematics, statistics, and computer science, with main focus and applications in image/signal/data processing, optimisation, machine learning and computer vision.

He is happy to consider PhD applications, etc.; more details click here.

Teaching

Computer Vision, Image Processing, Artificial Intelligence

Publications

Cai, Xiaohao, Chan, Raymond H., Morigi, Serena and Sgallari, Fiorella (2012) Framelet-based algorithm for segmentation of tubular structures. In, Bruckstein, Alfred M., Haar Romeny, Bart M. ter, Bronstein, Alexander M. and Bronstein, Michael M. (eds.) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6667 LNCS) Third International Conference: Scale Space and Variational Methods in Computer Vision (29/05/11 - 02/06/11) SpringerLink, pp. 411-422. (doi:10.1007/978-3-642-24785-9_35).

Cai, Xiaohao and Steidl, Gabriele (2013) Multiclass segmentation by iterated ROF thresholding. In, Heyden, Anders, Kahl, Fredrik, Olsson, Carl, Oskarsson, Magnus and Tai, Xue-Cheng (eds.) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8081) Energy Minimization Methods in Computer Vision and Pattern Recognition: 9th International Conference (19/08/13 - 21/08/13) SpringerLink, pp. 237-250. (doi:10.1007/978-3-642-40395-8_18).

Cai, Xiaohao, Chan, Raymond and Zeng, Tieyong (2013) A two-stage image segmentation method using a convex variant of the Mumford-Shah model and thresholding. SIAM Journal on Imaging Sciences, 6 (1), 368-390. (doi:10.1137/120867068).

Cai, Xiaohao, Chan, Raymond, Morigi, Serena and Sgallari, Fiorella (2013) Vessel segmentation in medical imaging using a tight-frame-based algorithm. SIAM Journal on Imaging Sciences, 6 (1), 464-486. (doi:10.1137/110843472).

Cai, Xiaohao, Fitschen, Jan Henrik, Nikolova, Mila, Steidl, Gabriele and Storath, Martin (2014) Disparity and optical flow partitioning using extended Potts priors. Information and Inference: A Journal of the IMA, 4 (1), 43-62. (doi:10.1093/imaiai/iau010).

Lee, Juheon, Cai, Xiaohao, Schonlieb, Carola Bibiane and Coomes, David (2015) Mapping individual trees from airborne multi-sensor imagery. In, 2015 International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, pp. 5411-5414. (doi:10.1109/IGARSS.2015.7327059).

Cai, Xiaohao, Pereyra, Marcelo and McEwen, Jason D. (2019) Quantifying uncertainty in high dimensional inverse problems by convex optimisation. In, 2019 27th European Signal Processing Conference (EUSIPCO). (HASH(0xe7657e0), 2019-September) IEEE. (doi:10.23919/EUSIPCO.2019.8903038).

Scaife, Jessica Elizabeth, Harrison, Karl, Drew, Amelia, Cai, Xiaohao, Lee, Juheon, Schonlieb, Carola-Bibiane, Sutcliffe, Michael, Parker, M. Andy, Freeman, Sue, Romanchikova, Marina, Thomas, Simon, Jena, Raj, Bates, Amy and Burnet, Neil (2015) Accuracy of manual and automated rectal contours using helical tomotherapy image guidance scans during prostate radiotherapy. Journal of Clinical Oncology, 33 (7_suppl), 94-94. (doi:10.1200/jco.2015.33.7_suppl.94).

Cai, Xiaohao (2015) Variational image segmentation model coupled with image restoration achievements. Pattern Recognition, 48 (6), 2029-2042. (doi:10.1016/j.patcog.2015.01.008).

Cai, Xiaohao, Schönlieb, Carola Bibiane, Lee, Juheon, Scaife, J., Harrison, Karl, Sutcliffe, Michael, Parker, M. Andy and Burnet, Neil G. (2016) Automatic contouring of soft organs for image-guided prostate radiotherapy. Radiotherapy and Oncology, 119 (Supplement 1), S895-S896. (doi:10.1016/S0167-8140(16)33144-9).

Lee, Juheon, Cai, Xiaohao, Lellmann, Jan, Dalponte, Michele, Malhi, Yadvinder, Butt, Nathalie, Morecroft, Mike, Schonlieb, Carola Bibiane and Coomes, David A. (2016) Individual tree species classification from airborne multisensor imagery using robust PCA. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9 (6), 2554-2567, [7500049]. (doi:10.1109/JSTARS.2016.2569408).

Cai, Xiaohao, Chan, Raymond, Nikolova, Mila and Zeng, Tieyong (2017) A three-stage approach for segmenting degraded color images: Smoothing, Lifting and Thresholding (SLaT). SIAM Journal on Scientific Computing, 72 (3), 1313-1332. (doi:10.1007/s10915-017-0402-2).

Lee, Juheon, Coomes, David, Schonlieb, Carola-Bibiane, Cai, Xiaohao, Lellmann, Jan, Dalponte, Michele, Malhi, Yadvinder, Butt, Nathalie and Morecroft, Mike (2017) A graph cut approach to 3D tree delineation, using integrated airborne LiDAR and hyperspectral imagery. arXiv.

Burnet, Neil G., Scaife, Jessica E., Romanchikova, Marina, Thomas, Simon J., Bates, Amy, Wong, Emma, Noble, David J., Shelley, Leila, Bond, Simon, Forman, Julia, Hoole, Andrew, Barnett, Gillian, Brochu, Frederic, Simmons, Michael, Jena, Raj, Harrison, Karl, Yeap, Ping Lin, Drew, Amelia, Silvester, Emma, Elwood, Patrick, Pullen, Hannah, Sultana, Andrew, Seah, Shannon, Wilson, Megan, Russell, Simon, Benson, Richard, Rimmer, Yvonne, Jefferies, Sarah, Taku, Nicolette, Gurnell, Mark, Powlson, Andrew, Schönlieb, Carola-Bibiane, Cai, Xiaohao, Sutcliffe, Michael and Parker, Michael (2017) Applying physical science techniques and CERN technology to an unsolved problem in radiation treatment for cancer: the multidisciplinary 'VoxTox' research programme. CERN IdeaSquare Journal of Experimental Innovation, 1 (1), 3-12. (doi:10.23726/cij.2017.457).

Bauer, Benjamin, Cai, Xiaohao, Peth, Stephan, Schladitz, Katja and Steidl, Gabriele (2017) Variational-based segmentation of bio-pores in tomographic images. Computers and Geosciences, 98, 1-8. (doi:10.1016/j.cageo.2016.09.013).

LSST Dark Energy Science Collaboration (2021) Sparse Bayesian mass mapping with uncertainties: hypothesis testing of structure: hypothesis testing of structure. Monthly Notices of the Royal Astronomical Society, 506 (3), 3678-3690. (doi:10.1093/mnras/stab1983).

Cai, Xiaohao, Chan, Raymond, Nikolova, Mila and Zeng, Tieyong (2018) The synergy between different colour spaces for degraded colour images segmentation. Science Trends. (doi:10.31988/scitrends.10714).

Cai, Xiaohao, Pereyra, Marcelo and McEwen, Jason D. (2018) Uncertainty quantification for radio interferometric imaging - I. Proximal MCMC methods. Monthly Notices of the Royal Astronomical Society, 480 (3), 4154-4169. (doi:10.1093/MNRAS/STY2004).

Cai, Xiaohao, Pereyra, Marcelo and McEwen, Jason D. (2018) Uncertainty quantification for radio interferometric imaging: II. MAP estimation. Monthly Notices of the Royal Astronomical Society, 480 (3), 4170-4182. (doi:10.1093/MNRAS/STY2015).

Cai, Xiaohao, Chan, Raymond, Xie, Xiaoyu and Zeng, Tieyong (2019) A two-stage classification method for high-dimensional data and point clouds. arXiv.

Pratley, Luke, McEwen, Jason D., d'Avezac, Mayeul, Cai, Xiaohao, Perez-Suarez, David, Christidi, Ilektra and Guichard, Roland (2019) Distributed and parallel sparse convex optimization for radio interferometry with PURIFY. arXiv.

Mai, Keith Ka Ki, Yeung, Wai Tsun, Han, Sang Yun, Cai, Xiaohao, Hwang, Inhwan and Kang, Byung Ho (2019) Electron tomography analysis of thylakoid assembly and fission in chloroplasts of a single-cell C4 plant, Bienertia sinuspersici. Scientific Reports, 9 (1), [19640]. (doi:10.1038/s41598-019-56083-w).

Cai, Xiaohao, Chan, Raymond, Schönlieb, Carola Bibiane, Steidl, Gabriele and Zeng, Tieyong (2019) Linkage between piecewise constant Mumford-Shah model and Rudin-Osher-Fatemi model and its virtue in image segmentation. SIAM Journal on Scientific Computing, 41 (6), B1310-B1340. (doi:10.1137/18M1202980).

Cai, Xiaohao, Pratley, Luke and McEwen, Jason D. (2019) Online radio interferometric imaging: Assimilating and discarding visibilities on arrival. Monthly Notices of the Royal Astronomical Society, 485 (4), 4559-4572. (doi:10.1093/mnras/stz704).

Price, Matthew A., McEwen, Jason D., Cai, Xiaohao and Kitching, Thomas D. (2019) Sparse Bayesian mass mapping with uncertainties: peak statistics and feature locations. Monthly Notices of the Royal Astronomical Society, 489 (3), 3236-3250. (doi:10.1093/mnras/stz2373).

Price, Matthew A., Cai, Xiaohao, McEwen, Jason D., Pereyra, Marcelo and Kitching, Thomas D. (2020) Sparse Bayesian mass mapping with uncertainties: local credible intervals. Monthly Notices of the Royal Astronomical Society, 492 (1), 394-404. (doi:10.1093/mnras/stz3453).

Williams, Jonathan, Schonlieb, Carola Bibiane, Swinfield, Tom, Lee, Juheon, Cai, Xiaohao, Qie, Lan and Coomes, David A. (2020) 3D segmentation of trees through a flexible multiclass graph cut algorithm. IEEE Transactions on Geoscience and Remote Sensing, 58 (2), 754-776, [8854321]. (doi:10.1109/TGRS.2019.2940146).

Lee, Juheon, Cai, Xiaohao, Schönlieb, Carola Bibiane and Coomes, David A. (2015) Nonparametric image registration of airborne LiDAR, hyperspectral and photographic imagery of wooded landscapes. IEEE Transactions on Geoscience and Remote Sensing, 53 (11), 6073-6084. (doi:10.1109/TGRS.2015.2431692).

Cai, Xiaohao, Wallis, Christopher G.R., Chan, Jennifer Y.H. and McEwen, Jason D. (2020) Wavelet-based segmentation on the sphere. Pattern Recognition, 100, 1-15, [107081]. (doi:10.1016/j.patcog.2019.107081).

Cai, Xiaohao, Chan, Raymond and Zeng, Tieyong (2021) An Overview of SaT Segmentation Methodology and Its Applications in Image Processing. In, Chen, Ke (ed.) Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging. SpringerLink, pp. 1-27. (doi:10.1007/978-3-030-03009-4).

Du, Mingyang, He, Xikai, Cai, Xiaohao and Bi, Daping (2021) Balanced Neural Architecture Search and Its Application in Specific Emitter Identification. IEEE Transactions on Signal Processing, 69, 5051–5065. (doi:10.1109/TSP.2021.3107633).

Mallios, Dimitrios and Cai, Xiaohao (2021) Deep Rectum Segmentation for Image Guided Radiation Therapy with Synthetic Data. In 29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings. vol. 2021-August, European Signal Processing Conference, EUSIPCO. pp. 975-979 . (doi:10.23919/EUSIPCO54536.2021.9616115).

Du, Mingyang, Zhong, Ping, Cai, Xiaohao and Bi, Daping (2022) DNCNet: deep radar signal denoising and recognition. IEEE Transactions on Aerospace and Electronic Systems. (doi:10.1109/TAES.2022.3153756).

Cai, Xiaohao, Pratley, Luke and McEwen, Jason D. (2020) Offline and online reconstruction for radio interferometric imaging. In 2020 33rd General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2020. Institute of Electrical and Electronics Engineers Inc. 4 pp . (doi:10.23919/URSIGASS49373.2020.9232233).

Contact

Telephone: +44 (0) 23 8059 442380593542

Email: x.cai@soton.ac.uk

Share this profile FacebookTwitterWeibo

- Event

Date:
19th of May, 2020  @  10:00 - 12:00
Venue:
32.4073
Share this event FacebookTwitterWeibo

- Event

Date:
27th of February, 2020  @  14:30 - 15:30
Venue:
100-4011 (Harvard L/TB)

Event details

Presentation Title: Advances in imaging of electrical trees in polymeric insulation

​ 

Abstract: The growth of electrical trees is an important degradation process in polymer dielectrics under high voltages. The growth of an electrical tree can ultimately lead to breakdown and the failure of high voltage equipment. Suppressing and/or predicting tree growth in polymer insulation is therefore an important concern for the power supply industry. This seminar will consider recent advances in the imaging of trees. In particular 3D reconstructions from X-ray computed tomography (XCT), and the use of atomic force microscopy infrared spectroscopy (AFM-IR) will illustrate how improved analytical techniques can help understand the processes behind tree growth.

 

Prof Simon Rowland completed a B.Sc. in physics at The University of East Anglia, and his PhD at Chelsea College, London University. He worked for many years on dielectrics and their applications in multinational companies prior to joining The Dept. of Electrical and Electronic Engineering in The University of Manchester in 2003. He was appointed Professor of Electrical Materials in 2009, and served as Head of School from 2015-19. He was President of the IEEE Dielectric and Electrical Insulation Society in 2011 and 2012. His current research interests include the processes leading to dielectric failure in high voltage DC networks.

 

​

Share this event FacebookTwitterWeibo

- Event

Date:
21st of February, 2020  @  14:00 - 15:00
Venue:
Nuffield Theatre (6) - 1081
View on map

Event details

Presentation Title: Analysis of dielectric data: Challenges and Benefits

Abstract: The information contained in the dielectric response of materials can be used to investigate the micro-structure, charge motion and the ageing of dielectric materials. However there are several issues which may impede the correct interpretation of the data. For example, the measured dielectric spectra are often a superposition of several distinct processes, the electrode contact can change significantly the measured response and last but not least the choice of dielectric function to model the relaxation processes is often based on personal preferences and prior experience. Therefore, for the correct analysis and interpretation of the data, it is necessary to decompose the response into individual processes, split the bulk processes from the electrode contact and select a suitable dielectric response function.

In this talk, Dr Chalashkanov will explore the techniques available for analysis of dielectric data and will focus on the application of equivalent circuit models.  The use of equivalent circuit models offers an effective method to investigate the measured dielectric response data using discrete electrical components representing relaxation and conduction processes. The method of constructing the equivalent circuits and the rationale for using specific components in the circuits will be explained in detail. Some issues related to the application of the equivalent circuit method will be illustrated through examples.

Dr Nikola M. Chalashkanov was born in Sofia, Bulgaria in 1981. He graduated from the Technical University of Sofia in 2003 with a Bachelor’s degree in industrial engineering and gained the Master’s degree there in industrial engineering in 2005. He joined the University of Leicester, UK in 2007 as a Graduate Teaching Assistant and received a Ph.D. degree for his work on charge transport and electrical breakdown in epoxy resins in 2012. In the period 2011-2019, he was a Teaching Fellow in the Electrical Power and Power Electronics Research Group in the Department of Engineering, University of Leicester. He joined the University of Lincoln as a Senior Lecturer in 2019.

Dr Chalashkanov is author and co-author of more than 40 peer reviewed papers. His research interests include partial discharge and electrical treeing phenomena, dielectric properties and charge transport in polymers, chaos theory, statistical analysis and data mining. He is a member of the Institute of Physics, a senior member of the IEEE and a fellow of the HEA.

​

Share this event FacebookTwitterWeibo

Pages