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This case exemplifies leading-edge practice in the coaching and management of elite sports teams in the UK, USA and Australia. Professor Bill Gerrard is one of the first to apply statistical analysis to the management of `invasion'/tactical team sports. This original contribution has been to practices in both the boardroom and the locker room. The approach affects day-to-day decision-making in a range of areas, including recruitment of players, training priorities, team selection and game tactics. Application of the approach now extends to three continents and is applicable to all invasion team sports including football/soccer, rugby union and rugby league.
Physical asset management is a major cost for many organisations and is measured in £billions in the regulated industries within the UK. Methods developed by researchers in the University of Edinburgh Business School have enabled managers to estimate the effectiveness of maintenance interventions, and to build optimal maintenance strategies for large and complex assets, providing a scientific basis for major budgetary allocations. Users have deployed these methods to: achieve optimal asset management (Scottish Water); support regulatory assessments (Yorkshire Water Services); assist Severn Trent Water Company with its regulatory submissions (Cap Gemini); and inform procurement decisions across major platforms in maritime and air environments (Ministry of Defence).
The School of Mathematics at Cardiff University has developed important statistical and mathematical models for forecasting consumer buying behaviour. Enhancements to classical models, inspired by extensively studying their statistical properties, have allowed us to exploit their vast potential to benefit the sales and marketing strategies of manufacturing and retail organisations. The research has been endorsed and applied by Nielsen, the #1 global market research organisation that provides services to clients in 100 countries. Nielsen has utilised the models to augment profits and retain their globally leading corporate position. This has led to a US$30 million investment and been used to benefit major consumer goods manufacturers such as Pepsi, Kraft, Unilever, Nestlé and Procter & Gamble. Therefore the impact claimed is financial. Moreover, impact is also measurable in terms of public engagement since the work has been disseminated at a wide range of national and international corporate events and conferences. Beneficiaries include Tesco, Sainsbury's, GlaxoSmithKline and Mindshare WW.
Now-casting is the prediction of the present, the very near future, and the very recent past. It has been developed within a research programme led by Lucrezia Reichlin at LBS. It is relevant because key economic statistics, particularly quarterly measures such as GDP, are available only with a delay. Now-casting exploits information which is published early and at higher frequencies than the target variable and generates early estimates before the offb01cial fb01gures become available.
Now-casting has signifb01cant infb02uence and impact. The techniques reported in this case study are in widespread use by central banks and policy institutions. Furthermore, this research has achieved successful commercial impact via Now-Casting Economics Limited.
This impact case study describes the development and application of models of training and performance in elite cycling. These models have been used by elite medal winning teams in their search for competitive advantage in the UK (by British Cycling and British Triathlon, including the GB Olympic Cycling and British Triathlon Teams and the British Paralympic Team) and internationally (by the Australian Institute of Sport). These new cycling models have provided the basis for the development of new training processes that are influencing the way in which many nations prepare their elite riders. This work has contributed directly to enhance elite sports science practice in the field of cycling and the competitive advantage for British teams to which it contributes is envied around the world. The adoption of the underlying algorithms for the `Wattbike' software has given our work a wider impact on sports practice and training methods, and it has been adapted for the `Map My Tracks' website which is used by sports enthusiasts worldwide.
This impact case study is based on a Knowledge Transfer Partnership (KTP) between the School of Mathematics, Statistics and Actuarial Science, University of Kent and KROHNE Ltd, a world leading manufacturer of industrial measuring instruments. These precision instruments (typically flow meters and density meters) need to be calibrated accurately before being used and this is an expensive and time-consuming process.
The purpose of the KTP was to use Bayesian methodology developed by Kent statisticians to establish a novel calibration procedure that improves on the existing procedure by incorporating historical records from calibration of previous instruments of the same type. This reduces substantially the number of test runs needed to calibrate a new instrument and will increase capacity by up to 50%.
The impact of the KTP, which was graded as `Outstanding', has been to change the knowledge and capability of the Company, so that they can improve the performance of their manufacturing process by implementing this novel calibration method. This has been achieved by adapting the underpinning Kent research to the specific context of the calibration problem, by running many calibrations to demonstrate the effectiveness of the method in practice, and by supporting the implementation of the new calibration method within the Company's core software.
Moreover, the project has changed the Company's thinking on fundamental science, particularly industrial mathematics. The value of historical data, and the usefulness of Bayesian methods, is now widely appreciated and training for staff in Bayesian Statistics is being introduced. Thus the project has not only changed the protocols of the Company, it has also changed their practice.
Sports Integrity is focused on supporting the development of infrastructure by governments, sports bodies, betting operators and law enforcers, internationally, for identifying and addressing vulnerability to corruption in professional sport, demonstrating the following impact:
Since 2008, statistical research at the University of Bristol has significantly influenced policies, practices and tools aimed at evaluating and promoting the quality of institutional and student learning in the education sector in the UK and internationally. These developments have also spread beyond the education sector and influence the inferential methods employed across government and other sectors. The underpinning research develops methodologies and a much-used suite of associated software packages that allows effective inference from complicated data structures, which are not well-modelled using traditional statistical techniques that assume homogeneity across observational units. The ability to analyse complicated data (such as pupil performance measures when measured alongside school, classroom, context and community factors) has resulted in a significant transformation of government and institutional policies and their practices in the UK, and recommendations in Organisation for Economic Co-operation and Development (OECD) policy documents. These techniques for transforming complex data into useful evidence are well-used across the UK civil service, with consequent policy shifts in areas such as higher education admissions and the REF2014 equality and diversity criteria.
Through a close collaboration with Ford Motor Company, simulation modelling software developed at the University of Southampton has streamlined the design of the car giant's engine production lines, increasing efficiency and delivering significant economic benefits in three key areas. Greater productivity across Ford Europe's assembly operations has generated a significant amount [exact figure removed] in direct cost savings since 2010. Automatic analysis of machine data has resulted in both a 20-fold reduction in development time, saving a large sum per year [exact figure removed], and fewer opportunities for human error that could disrupt the performance of production lines costing a large sum [exact amount removed] each to program.
This case study describes impact resulting from research on assessing the performance of credit scoring models conducted by the Consumer Credit / Retail Banking Research Group of the Mathematics Department at Imperial College. The group's work has influenced both high-level industry strategies for developing scoring models, and also low-level performance measures for which such models are developed, refined and evaluated. We describe examples of companies or bodies that have benefitted from improved credit scoring models, including Prescient Models (a US credit scoring company), Experian and the US Office of the Comptroller of Currency. The group has established a very significant reputation for a wide range of commercially valuable work in this area — to the extent that the group received the major Credit Collections and Risk industry award for Contributions to the Credit Industry in 2012.