ArticlePDF Available

Business Intelligence: How Sport Scientists Can Support Organisation Decision Making in Professional Sport

January 2019
International Journal of Sports Physiology and Performance 14(4):1-10

DOI:10.1123/ijspp.2018-0903

Authors:

Patrick Ward

Liverpool John Moores University

Johann Windt

Vancouver Whitecaps FC

Thomas Kempton

University of Technology Sydney

The application of scientific principles to inform practice has become increasingly common in professional sports, with increasing numbers of sport scientists operating in this area. The authors believe that in addition to domain-specific expertise, effective sport scientists working in professional sport should be able to develop systematic analysis frameworks to enhance performance in their organization. Although statistical analysis is critical to this process, it depends on proper data collection, integration, and storage. The purpose of this commentary is to discuss the opportunity for sport-science professionals to contribute beyond their domain-specific expertise and apply these principles in a business-intelligence function to support decision makers across the organization. The decision-support model aims to improve both the efficiency and the effectiveness of decisions and comprises 3 areas: data collection and organization, analytic models to drive insight, and interface and communication of information. In addition to developing frameworks for managing data systems, the authors suggest that sport scientists' grounding in scientific thinking and statistics positions them to assist in the development of robust decision-making processes across the organization. Furthermore, sport scientists can audit the outcomes of decisions made by the organization. By tracking outcomes, a feedback loop can be established to identify the types of decisions that are being made well and the situations where poor decisions persist. The authors have proposed that sport scientists can contribute to the broader success of professional sporting organizations by promoting decision-support services that incorporate data collection, analysis, and communication.

Content uploaded by Patrick Ward

Content may be subject to copyright.

“Business Intelligence: How Sport Scientists Can Support Organisation Decision Making in Professional Sport”

by Ward P, Windt J, Kempton T

International Journal of Sports Physiology and Performance

Note. This article will be published in a forthcoming issue of the

International Journal of Sports Physiology and Performance. The

article appears here in its accepted, peer-reviewed form, as it was

provided by the submitting author. It has not been copyedited,

proofread, or formatted by the publisher.

Section: Invited Commentary

Article Title: Business Intelligence: How Sport Scientists Can Support Organisation

Decision Making in Professional Sport

Authors: Patrick Ward1,4, Johann Windt2, and Thomas Kempton3, 4

Affiliations: 1 Seattle Seahawks, Seattle, United States. 2 University of British Columbia,

Vancouver, Canada. 3Carlton Football Club, Melbourne, Australia. 4University of

Technology Sydney, Australia.

Journal: International Journal of Sports Physiology and Performance

Acceptance Date: January 13, 2019

DOI: https://doi.org/10.1123/ijspp.2018-0903

“Business Intelligence: How Sport Scientists Can Support Organisation Decision Making in Professional Sport”

by Ward P, Windt J, Kempton T

International Journal of Sports Physiology and Performance

Business Intelligence: how sport scientists can support organisation decision making in

professional sport

Patrick Ward1,4, Johann Windt2, Thomas Kempton3, 4

1 Seattle Seahawks, Seattle, United States

2 University of British Columbia, Vancouver, Canada

3Carlton Football Club, Melbourne, Australia

4University of Technology Sydney, Australia

Submission type: Invited commentary

Running head: Business Intelligence in professional sport

Abstract word count: 249

Word count: 1398

Figures: 1

Correspondence to:

Patrick Ward

Seattle Seahawks, Seattle, United States

pward2@gmail.com

Downloaded by sam.leahey@gmail.com on 02/01/19

“Business Intelligence: How Sport Scientists Can Support Organisation Decision Making in Professional Sport”

by Ward P, Windt J, Kempton T

International Journal of Sports Physiology and Performance

Abstract

The application of scientific principles to inform practice has become increasingly common in

professional sports, with increasing numbers of sport scientists operating in this area. We believe that

in addition to domain specific expertise, effective sport scientists working in professional sport should

be able to develop systematic analysis frameworks to enhance performance within their organisation.

While statistical analysis is critical to this process, it depends on proper data collection, integration and

storage. The purpose of this commentary is to discuss the opportunity for sport science professionals to

contribute beyond their domain-specific expertise and apply these principles in a business intelligence

function to support decision-makers across the organisation. The decision support model aims to

improve both the efficiency and effectiveness of decisions, and comprises of three areas; (1) data

collection and organisation, (2) analytic models to drive insight, and (3) interface and communication

of information. In addition to developing frameworks for managing data systems, we suggest that the

sport scientist’s grounding in scientific thinking and statistics positions them to assist in the

development of robust decision making processes across the organisation. Furthermore, sport scientists

can also audit the outcomes of decisions made by the organisation. By tracking outcomes, a feedback

loop can be established to identify the types of decisions that are being made well, and the situations

where poor decisions persist. We have proposed that sport scientists can contribute to the broader

success of professional sporting organisations by promoting decision support services that incorporates

data collection, analysis and communication.

Downloaded by sam.leahey@gmail.com on 02/01/19

“Business Intelligence: How Sport Scientists Can Support Organisation Decision Making in Professional Sport”

by Ward P, Windt J, Kempton T

International Journal of Sports Physiology and Performance

Sport science – the application scientific principles to inform practice1 – has become

increasingly common as professional sporting organizations seek to gain a performance advantage.

These organizations increasingly employ sport scientists from varying backgrounds, including

physiology, strength and conditioning, biomechanics, performance analysis, biostatistics, and data

science. Regardless of their foundation and specific job title, we believe that effective sport scientists

working in professional sport should be able to develop systematic analysis frameworks to enhance

performance within their organization.1 While statistical analysis is critical to this process, it depends

on rigorous data collection, integration and storage.2 The purpose of this commentary is to discuss the

opportunity for sport science professionals to contribute beyond their domain-specific expertise and

apply these principles in a business intelligence function to support decision-makers across the

organization.

Key personnel in professional sporting organizations are often faced with complex decisions,

which can range from regular process decisions to infrequent strategic decisions. By definition,

decisions arise when there are multiple realistic alternatives, with the risk of negative outcomes from

taking the wrong position.3 A prerequisite for effective decision making is reducing uncertainty

surrounding the best course of action. Business Intelligence units turn data into knowledge and have

become an important mechanism to remove uncertainty and enhance organizational decision across

many industries.4 While individuals from other disciplines – such as economics – could also serve this

function, we suggest that sport science professionals are positioned to lead this business intelligence

service in professional sporting organizations due to their scientific training and knowledge of human

performance. Assisting other departments within the organization – such as medical, coaching, physical

preparation and recruitment – with their decision making processes offers sport scientists an opportunity

to contribute more broadly to improving sport performance.

Principles of decision support services

Using a decision support services framework, we will outline how sport scientists can not only

embed these processes in their own sphere, but also support other major decision making departments

across the organization. The decision support model aims to improve both the efficiency and

Downloaded by sam.leahey@gmail.com on 02/01/19

“Business Intelligence: How Sport Scientists Can Support Organisation Decision Making in Professional Sport”

by Ward P, Windt J, Kempton T

International Journal of Sports Physiology and Performance

effectiveness of decisions, and comprises of three areas; (1) data collection and organization, (2)

analytic models to drive insight, and (3) interface and communication of information.5

(1) The collection of performance data derived from an ever-expanding array of technology is a

primary role for applied sport scientists. While these data permit novel insights, it is critical that

there are sound structures in place to process the range of data streams. It is becoming necessary

to learn from other disciplines such as computer science to manage the increased volume of

data that is now being generated in professional sporting organizations.6 A key component of

the data collection process is preventing data silos forming within the organization, as the

integration of data sources permits a holistic understanding of performance. For example, when

match analysis data is drawn from multiple sources (such as player tracking, physiological,

player technical involvement and tactical strategy) the combination of these data allows for a

deeper understanding than trying to analyse match performance through a single lense.7

Rigorous data collection processes and maintaining a data legacy permits the transformation of

this operational data into an important asset, which we believe has often previously been

undervalued.

(2) Once data has been collected and aggregated, it can be analysed to provide insights that inform

decisions. A conceptual model for analysis comprising two complementary functions – fast and

slow analysis – reflects the range of decision tasks in professional sporting organizations.8 Fast

analysis is suited where decisions are required immediately, often for repeated process

problems that arise in the high-pressure daily training environment. Basic dashboards and

reports underpinned by heuristics may be most effective in this area.9 The slow approach more

closely reflects critical scientific inquiry – leading to higher order understanding of the

problems faced in the professional sports environment.8 While this approach is often

overlooked in professional organizations due to time constraints or the lack of analytical

expertise, these slow activities may help solve complex problems. They can also guide key

strategic decisions and organizational philosophies which are paramount to the long-term

Downloaded by sam.leahey@gmail.com on 02/01/19

“Business Intelligence: How Sport Scientists Can Support Organisation Decision Making in Professional Sport”

by Ward P, Windt J, Kempton T

International Journal of Sports Physiology and Performance

success of the program. Finally, insights derived from slow analyses can improve the system

approaches employed in the day-to-day work flow of fast analysis.8

(3) Communicating knowledge derived from analyses is the critical step in enhancing decision

making processes.10 Data visualisation and performance dashboards that utilise commercially

available business intelligence software have become common. These visualisations allow data

to be displayed in simple, attractive formats and provide coaches and managers with a powerful

decision making tool to address specific problems.11 Dashboards can also be structured to allow

interaction with the data, moving us away from structured report formats and allowing intuitive

investigation of data.11 While software now provides an avenue for visualising information,

effective verbal communication between the sport scientist and decision-makers remains

critical to provide context to the information. Further, the sport scientist requires both subject

matter knowledge and strong inter-personal relationships to ensure that information is

effectively incorporated into the decision making process. These relationships are cultivated

through time and effort spent within the organization and require sport scientists to be selfless,

humble and open minded in these interactions.12

Enhancing decision making processes

In addition to managing data systems, we suggest that the sport scientist’s grounding in

scientific thinking and statistics positions them to assist in the development of robust decision making

processes across the organization. While the broad stream of decision making research falls beyond the

scope of this commentary, we will draw attention to some important concepts that can guide the sport

scientist in this area.

Fallibility theories in medical science suggest that failures in decision making processes can

arise due to ignorance or ineptitude.13 Failures of ignorance arise when limitations of current scientific

understanding restrict our ability to fully comprehend an entity. These situations are common in the

complex world of human physiology and sporting performance. In this domain, sport scientists should

aspire to produce quality research which builds our understanding in important areas of sport

performance. Further, constructing advanced models – utilising large data sets that are now being

Downloaded by sam.leahey@gmail.com on 02/01/19

“Business Intelligence: How Sport Scientists Can Support Organisation Decision Making in Professional Sport”

by Ward P, Windt J, Kempton T

International Journal of Sports Physiology and Performance

collected – and applying sophisticated analyses such as machine learning and artificial intelligence can

improve our understanding. Paradoxically, even with growing data sets that permit advanced models,

we risk drawing false confidence in our understanding of complex phenomena if we rely on models that

are overfit to the data. Rather, when the underlying relationships between factors are not well

understood, simple heuristics should instead be employed to assist decision making.14

Failures of ineptitude occur when knowledge exists but it is failed to be applied correctly. This

type of decision failure can arise when memory and attention are diverted from routine processes by

the strain of stress or other distractions.15 Failures of ineptitude can also arise when important steps are

missed or ignored in favour of less relevant information. Sport scientists can proactively reduce failures

of ineptitude by developing robust decision making processes. For example, the development of

checklists, which have become a crucial method for reducing errors in medicine and aviation, have been

advocated as an effective strategy to protect against these types of failures.15 They work by explicitly

outlining the key steps in a process and drawing attention to important factors, which helps fortify

decision making processes.

Finally, sport scientists can audit the outcomes of decisions made by the organization. By

tracking outcomes, a feedback loop can be established to identify the types of decisions that are being

made well, and the situations where poor decisions persist. A secondary audit layer can examine the

decision-making process, irrespective of outcome. The nature of uncertainty dictates that not all

decisions will lead to the optimal outcome. However, bringing scientific rigour to the decision making

process and analysing its effectiveness can ensure better outcomes over the long-term.

Conclusion

Navigating complex problems in professional sport requires making decisions in the face of

uncertainty. We have proposed that sport scientists can contribute to the success of these organizations

by promoting decision support services that incorporates data collection, analysis and communication.

Further, sport scientists can facilitate the application of rational scientific principles to develop robust

decision making processes and provide feedback on the efficacy of these processes by auditing the

outcomes. The ultimate success of this service requires the sport scientist to build meaningful

Downloaded by sam.leahey@gmail.com on 02/01/19

“Business Intelligence: How Sport Scientists Can Support Organisation Decision Making in Professional Sport”

by Ward P, Windt J, Kempton T

International Journal of Sports Physiology and Performance

relationships with key personnel across the organization and to demonstrate the benefits of this decision

support.

Downloaded by sam.leahey@gmail.com on 02/01/19

“Business Intelligence: How Sport Scientists Can Support Organisation Decision Making in Professional Sport”

by Ward P, Windt J, Kempton T

International Journal of Sports Physiology and Performance

References

1. Bishop D. An applied research model for the sport sciences. Sports Med. 2008;38(3):253-263.

2. Lacome M, Simpson, B., & Buchheit, M. Monitoring training status with player-tracking

technology. Part 1: Traditional practices and new concepts. Aspetar Sports Med J. 2018;7:54-

63.

3. Hubbard DW. How to measure anything. Third Edition ed. New Jersey: Wiley; 2014.

4. Negash S. Business intelligence. Communications of the association for information systems.

2004;13(1):15.

5. Shim JP, Warkentin M, Courtney JF, Power DJ, Sharda R, Carlsson C. Past, present, and

future of decision support technology. Decision support systems. 2002;33(2):111-126.

6. Coutts AJ. In the age of technology, Occam’s razor still applies. International Journal of

Sports Physiology & Performance. 2014;9:741.

7. Impellizzeri F, Marcora S. Test validation in sport physiology: Lessons learned from

clinimetrics. Int J Sports Physiol Perform. 2009;4(2):269-277.

8. Coutts AJ. Working fast and working slow: the benefits of embedding research in high-

performance sport. International Journal of Sports Physiology & Performance. 2016;11:1-2.

9. Robertson S, Bartlett JD, Gastin PB. Red, amber, or green? Athlete monitoring in team sport:

the need for decision-support systems. Int J Sports Physiol Perform. 2017;12(Suppl 2):73-79.

10. Buchheit M. Chasing the 0.2. International Journal of Sports Physiology & Performance.

2016;11:417-418.

11. Lacome M, Simpson, B., & Buchheit, M. Monitoring training status with player-tracking

technology. Part 2: Increasing coach 'buy-in' with good data visualisation Aspetar Sports Med

J. 2018;7:64-66.

12. Buchheit M. Outside the box. International Journal of Sports Physiology & Performance.

2017;12:1001-1002.

13. Gorovitz S, MacIntyre A. Toward a theory of medical fallibility. The journal of medicine and

philosophy. 1976;1(1):51-71.

14. Sull D, & Eisenhardt, K. M. Simple Rules: How to thrive in a complex world. New York:

Houghton Mifflin Harcourt; 2015.

15. Gawande A. The Checklist Manifesto. London: Profile Books; 2011.

Downloaded by sam.leahey@gmail.com on 02/01/19

“Business Intelligence: How Sport Scientists Can Support Organisation Decision Making in Professional Sport”

by Ward P, Windt J, Kempton T

International Journal of Sports Physiology and Performance

Figure 1: Model outlining how sport scientists can support the decisions of multiple departments across

the sporting organization by providing a business intelligence service.

Downloaded by sam.leahey@gmail.com on 02/01/19

A survey of organizational structure and operational practices of elite youth football academies and national federations from around the world: A performance and medical perspective

Article

Full-text available

Nov 2022

Aim Medical and performance units are integral components of player development programmes in elite football academies. Nevertheless, the nature of the operational processes implemented by practitioners within clubs and national federations remains unexplored. The aim of the present study, therefore, was to survey elite youth professional football academies from around the world regarding the operational processes adopted by their medical and performance units. Methods Of the 50 organizations invited, 10 national federations and 25 clubs took part in the survey resulting in a response rate of 70% (95% confidence interval, 56%−81%). The respondents represented three groups: senior club and academy management, performance, and medical staff. Results The majority (60%−90%) of clubs and national federations reported strategic alignment between senior and academy medical and performance units as well as between academy medical and performance units. Survey responses indicated substantial heterogeneity in the composition and number of medical and performance professionals employed in academies. The majority of respondents agreed their medical and performance departments were effective in utilizing staff knowledge and external sources of knowledge to inform their practice (56%−80%). Performance staff (40%−50%) and physiotherapists (30%−32%) were deemed most influential in injury prevention programmes. During the return-to-play process, the influence of specific practitioners in the medical and performance units was dependent upon the phase of return-to-play. Shared decision-making was common practice amongst performance and medical staff in injury prevention and return-to-play processes. Medical and performance data were generally centralized across the first team and academy in majority (50%−72%) of clubs and national federations. Data were integrated within the same data management system to a higher degree in clubs (68%) vs. national federations (40%). Research and development activity were reported for most academies (50%−72%), and generally led by the head of performance (37%) or team doctor (21%). Research activities were largely undertaken via internal staff (~100%), academic collaborations (50%−88%) and/or external consultants and industry partnerships (77%−83%) in the national federation and clubs. Conclusion Collectively, these findings provide a detailed overview regarding key operational processes delivered by medical and performance practitioners working in elite football academies.

Sport Science Model to Support the Professional Sports Organization Decision Making

Conference Paper

Full-text available

Aug 2020

Making everyone’s job easier. How do data scientists fit as a critical member of integrated support teams?

Article

Aug 2020

Capturing the ‘expert’s eye’: A perspective on developing a better understanding and implementation of subjective performance evaluations in team sports

Article

Jun 2022

Subjective evaluations of athletic performance are an important part of decision making across sporting organisations. Based on their expertise and intuition, coaches select their starting line-ups, scouts recommend or discourage teams from signing new potential players, and academy directors decide which players should move up or out of a team’s academy system. While this intuitive evaluation of performance occurs constantly throughout sport, little attention has been given to how this process can be more formally designed, implemented, and assessed to capture these expert evaluations of performance more effectively and thereby better inform decision making within sports organisations. In this opinion paper, the discrepancy between how often subjective evaluations are used in practice and how little research exists regarding their development, implementation, and effectiveness is examined. To assist in moving the sport science field forward, this paper will attempt to define subjective performance evaluations, describe the potential benefits that sporting organisations may get from using them, detail existing and potential uses for these measures, provide a framework for understanding how subjective data collection, and call upon the academic and applied sport community to drive the subjective evaluation field further.

Áreas de aplicación de big data e inteligencia de negocios.

Conference Paper

Full-text available

Mar 2022

Big data, es una tecnología que se refiere a grandes conjuntos de datos complejos tanto estructurados como no estructurados, que los sistemas de procesamiento convencionales no pueden gestionar. Por otro lado, la inteligencia de negocios busca apoyar el proceso de la toma de decisiones a través de la extracción de conocimiento de los datos almacenados. Estas dos áreas del conocimiento están siendo muy importantes para la solución de problemas que se presentan en diferentes contextos del conocimiento en donde tengan como insumo los datos y/o se busque obtener información relevante para la toma de decisiones. Se presentan distintas aplicaciones de big data e inteligencia de negocios en áreas como la agricultura, ciencias sociales, empresa, salud, educación, marketing, ecoturismo, deporte, entre otras. Asimismo, se observa como estas dos áreas pueden resolver problemas de tratamiento de datos para la toma de decisiones, sin importar la profesión que presente dicho problema.

Capturing the "expert’s eye": Towards a better understanding and implementation of subjective performance evaluations in team sports

Preprint

Full-text available

Aug 2021

Feedback of GPS training data within professional English soccer: A comparison of decision making and perceptions between coaches, players and performance staff

Article

May 2020

Purpose: The aim of the study was to examine the perceptions of training data feedback from key stakeholders within the coaching process of professional soccer clubs. A survey assessed the importance of training data towards reflection and decision-making, potential barriers and player preferences. Methods: A total of 176 participants comprising coaches, players and performance staff completed the survey. Results: The training data coaches most commonly identified as wanting to see to support reflection was ‘high-intensity’ actions and variables recognised by the coach as ‘work rate/intensity’. All stake- holders reported training data as at least somewhat important in guiding their coaches’ practices, with lack of a common goal and high volumes of information being the main barriers to effective feedback of training data. Players deemed feedback as positive to change their behaviour, with total distance, high-speed running and sprint distances as the information they would most like to see. It would be likely to be looked at via message or pinned up in the changing room. Conclusion: Training data are seen as an impactful and effective tool for use by all key stakeholders. Despite this, its use can be optimised by increasing opportunities for informal reflection, using less information, and improving communication of data.

The Effect of a Single Session of Plyometric Training Per Week on Fitness Parameters in Professional Female Soccer Players: A Randomized Controlled Trial

Article

Full-text available

Feb 2020

Nonnato, A, Hulton, AT, Brownlee, TE, and Beato, M. The effect of a single session of plyometric training per week on fitness parameters in professional female soccer players. A randomized controlled trial. J Strength Cond Res XX(X): 000-000, 2020-As the interest and popularity of female soccer has increased over the last few decades, there still lacks research conducted with the elite population, specifically ecological training interventions during the competitive season. Therefore, the aim of this study was to compare the effectiveness of 12 weeks (undertaken once a week) of plyometric (PLY) training on physical performance in professional female soccer players during the season. Using a randomized controlled trial design, 16 players were included in the current study (mean ± SD; age 23 ± 4 years, weight 60.3 ± 4.9 kg, height 167 ± 3.7 cm) and randomized in PLY (n = 8) and Control groups (CON, n = 8), respectively. Squat jump (SJ), counter movement jump (CMJ), long jump (LJ), single-leg triple jump distance test (triple jump test), changes of direction 505 test (505-COD), and sprint 10 and 30 m were performed before and after 12 weeks of PLY training. Significant within-group differences were found in triple jump test dominant (p = 0.031, effect size [ES] = moderate) and nondominant limb (p = 0.021, ES = moderate) and sprint 10 m (p = 0.05, ES = large), whereas the CON did not report any positive variation. However, neither group reported significant variation in SJ, CMJ, LJ, 505-COD, and sprint 30 m (underlining the difficulties in obtain meaningful variation in season). These findings have strong practical applications because this study showed for the first time that a single session a week of plyometric training can significantly increase sport-specific fitness parameters in professional female soccer players during the season.

Sport Start-ups: The Role of Networking

Chapter

Aug 2020

Vanessa Ratten

Using interdisciplinary tools to improve anti-doping: Utopia or necessity?

Article

Oct 2019

Marjolaine Viret

This article proposes a reflection on the value of interdisciplinary approaches applied to anti-doping. It aims to show that interdisciplinary research and collaborative work is central to establishing sound anti-doping policies. After presenting some fundamentals of interdisciplinary approaches, the article analyses how these can apply to doping as a “wicked” problem, highlighting the importance of clarifying policy goals as a prerequisite for applying interdisciplinary tools. The article then focuses on some features of “interdisciplinarity” that are critical to anti-doping practice and involve creating common ground on concepts, assumptions and methodologies. Having regard to the challenges involved in integrating scientific research and evidence for policy-making purposes, transparency and procedures may equally come to play a critical part, to which interdisciplinarity can contribute. If anti-doping policies inevitably incorporate some subjectivity rooted in stakeholder values, we should strive for such subjectivity to be an “informed” one. The legal profession can have a key role in this regard. Lawyers—whether academic researcher or practitioner—must evolve into being the (co-)actors of a broader reflection on the finalities and means of anti-doping, one that will encourage the creation of truly interdisciplinary platforms and ensure that the resulting insights are appropriately transposed into regulatory terms.

Business Intelligence

Article

Full-text available

Jan 2004

Solomon Negash

Business intelligence systems combine operational data with analytical tools to present complex and competitive information to planners and decision makers. The objective is to improve the timeliness and quality of inputs to the decision process. Business Intelligence is used to understand the capabilities available in the firm; the state of the art, trends, and future directions in the markets, the technologies, and the regulatory environment in which the firm competes; and the actions of competitors and the implications of these actions. The emergence of the data warehouse as a repository, advances in data cleansing, increased capabilities of hardware and software, and the emergence of the web architecture all combine to create a richer business intelligence environment than was available previously. Although business intelligence systems are widely used in industry, research about them is limited. This paper, in addition to being a tutorial, proposes a BI framework and potential research topics. The framework highlights the importance of unstructured data and discusses the need to develop BI tools for its acquisition, integration, cleanup, search, analysis, and delivery. In addition, this paper explores a matrix for BI data types (structured vs. unstructured) and data sources (internal and external) to guide research.

Past, Present, and Future of Decision Support Technology

Article

Full-text available

Jun 2002
DECIS SUPPORT SYST

Since the early 1970s, decision support systems (DSS) technology and applications have evolved significantly. Many technological and organizational developments have exerted an impact on this evolution. DSS once utilized more limited database, modeling, and user interface functionality, but technological innovations have enabled far more powerful DSS functionality. DSS once supported individual decision-makers, but later DSS technologies were applied to workgroups or teams, especially virtual teams. The advent of the Web has enabled inter-organizational decision support systems, and has given rise to numerous new applications of existing technology as well as many new decision support technologies themselves. It seems likely that mobile tools, mobile e-services, and wireless Internet protocols will mark the next major set of developments in DSS. This paper discusses the evolution of DSS technologies and issues related to DSS definition, application, and impact. It then presents four powerful decision support tools, including data warehouses, OLAP, data mining, and Web-based DSS. Issues in the field of collaborative support systems and virtual teams are presented. This paper also describes the state of the art of optimization-based decision support and active decision support for the next millennium. Finally, some implications for the future of the field are discussed.

Outside the Box

Article

Sep 2017

Martin Buchheit

Red, Amber or Green? Athlete Monitoring in Team Sport: The Need for Decision Support Systems

Article

Dec 2016

Decision support systems are used in team sport for a variety of purposes including evaluating individual performance and informing athlete selection. A particularly common form of decision support is the traffic light system, where colour coding is used to indicate a given status of an athlete with respect to performance or training availability. However despite relatively widespread use, there remains a lack of standardisation with respect to how traffic light systems are operationalised. This paper addresses a range of pertinent issues for practitioners relating to the practice of traffic light monitoring in team sports. Specifically, the types and formats of data incorporated in such systems are discussed, along with the various analysis approaches available. Considerations relating to the visualisation and communication of results to key stakeholders in the team sport environment are also presented. In order for the efficacy of traffic light systems to be improved, future iterations should look to incorporate the recommendations made here.

Communications of the Association for Information Systems

Article

Jan 2006

Chasing the 0.2

Article

May 2016
Int J Sports Physiol Perform

Martin Buchheit

No abstract available for this article.

Simple rules for a complex world

Article

Sep 2012
HARVARD BUS REV

How do successful companies shape their high-level strategies? A decade ago, London Business School's Suit and Stanford's Eisen-hardt looked for the answer by studying the era's leading high-tech firms. They discovered that such firms relied not on complicated frameworks but on simple rules of thumb. Managers translated corporate objectives into a few straightforward guidelines that helped employees make on-the-spot decisions and adapt to constantly shifting environments, while keeping the big picture in mind. This article describes the authors' subsequent research into why simple rules work and how firms develop them. Typically, after setting its priorities, a company will identify a bottleneck preventing it from making progress toward them and then create rules for managing that bottleneck. At America Latina Logistica, for instance, the problem was capital spending: The railway had only a tenth of the funds it needed to invest in growth and infrastructure. So a cross-functional team came up with rules to guide spending: Any proposal had to remove obstacles to growth, minimize up-front costs, provide immediate benefits, and reuse existing resources. The rules allowed employees across departments to make difficult trade-offs and quickly innovate solutions that put the firm on the fast track. Effective rules, the authors say, are specific, not broad; draw from historical experience; and are made by their users, not the CEO. Moreover, as a company evolves, they evolve with it. HBR Reprint R1209

Working Fast and Working Slow: The Benefits of Embedding Research in High Performance Sport

Article