3 Assessment Two
3.1 Introduction
The second assessment for the module is due due at the end of University Week 20 (20th December 2024). This assessment is worth 60% of your overall grade for the module.
For this assessment, you’ll be asked to submit a 3,000 word critical review of how sport data analytics are currently employed within professional sports settings, focusing on the strengths and weaknesses of current approaches and the challenges involved in data collection, analysis, and preparation.
You are expected to show evidence of reading in the academic literature, as well as drawing evidence from wider reading, your own professional knowledge, and discussions with others on the programme/in your wider network.
3.2 Outcomes
The critical review will be graded according to the extent it meets each of the following outcomes:
Outcome One: Content and Understanding
Your review should:
demonstrate a clear and thorough understanding of sport data analytics in professional sports settings.
critically analyse the strengths and weaknesses of current approaches in data collection, analysis, and preparation.
identify and discuss challenges involved in sport data analytics, offering potential solutions or areas of improvement.
Outcome Two: Integration of Academic Literature
Your review should:
demonstrate a comprehensive engagement with relevant academic literature.
appropriately cite and reference sources to support arguments.
provide a balance between academic sources and practical evidence.
Outcome Three: Use of Practical Examples and Case Studies
Your review should:
effectively integrate insights from practical case-studies and other sources.
provide real-world examples to illustrate points and support the critical review.
analyse practical examples critically, comparing and contrasting with academic literature.
Outcome Four: Structure and Coherence
Your review should:
be well-organised with a clear introduction, body, and conclusion.
contain arguments that are presented logically, and each section transition smoothly to the next.
have main points that are effectively highlighted and supported by relevant evidence.
Outcome Five: Presentation and Writing Style
Your review should:
meets the word limit (3,000 words) and free from grammatical and typographical errors.
uses academic language and tone appropriately.
contains citations and references that are formatted consistently following IEEE format.
3.3 Grading
For each of these five aspects, your review will be graded on a scale from 0 to 100. This grade will reflect the extent to which you have met the outcomes listed above.
92, 100: Exceptional demonstration of the learning outcomes
84: Outstanding demonstration of the learning outcomes
72, 75, 78: Excellent demonstration of the learning outcomes
62, 65, 68: Comprehensive demonstration of the learning outcomes
52, 55, 58: Satisfactory demonstration of the learning outcomes
42, 45, 48: Unsatisfactory demonstration of the learning outcomes
32, 35, 38: Inadequate demonstration of the learning outcomes
20: Clear fail. Weak demonstration of the learning outcomes
10: Minimal demonstration of the learning outcomes
0: No relevant work submitted
The grade awarded for each of the five aspects will be equally-weighted at 20% of the overall grade for the assessment.
Your final grade for the module will be your result for Assessment One (weighted at 40%) + your result for Assessment Two (weighted at 60%). You require an overall total of >=50% to pass the module and must achieve >=50% in both elements to pass.
3.4 Expectations
This section is intended to provide some detailed guidance on how you might approach the assignment. It is not prescriptive.
Learning Outcome One: Demonstrate Engagement with, and Understanding of, Module Content
Characteristic One: Clear and Thorough Understanding of Module Content
Comment: You should demonstrate a deep comprehension of the core topics discussed within the module. It’s important to connect the theory to practical examples from professional sports settings.
For example, you could:
Begin by explaining some key concepts of sport data analytics, such as predictive modeling or player performance analysis, and connect them to the module content.
Highlight specific real-world instances where these principles have been applied in professional sport.
Make reference to module reading.
Characteristic Two: Critical Analysis of Strengths and Weaknesses in Data Approaches
Comment: Critical thinking is vital here. It involves not just describing methods of data collection and analysis but also examining their effectiveness and potential limitations. This could be technical, ethical, or logistical aspects.
For example, you could:
Compare different data collection tools or software used in sport and discuss their effectiveness in various contexts (e.g., tracking player movement vs. performance data).
Discuss both the benefits (e.g., accuracy, real-time analytics) and drawbacks (e.g., privacy concerns, data integrity) of these approaches.
Make reference to module reading.
Characteristic Three: Discussion of Challenges in Sports Data Analytics
Comment: An important aspect of this task is to identify current or emerging challenges that professionals face in sports data analytics. This shows engagement with evolving trends and difficulties in the field.
For example, you could:
Explore the challenges of integrating data from different sources, or issues with data accessibility (e.g., in smaller sports leagues).
Discuss technological limitations, such as the difficulty in analysing unstructured data or problems with data security/missing data/outliers.
Make reference to module reading.
Characteristic Four: Offering Solutions and Areas for Improvement
Comment: The final characteristic focuses on proactivity. Identifying a problem is only part of the process; you could also suggest viable solutions or improvements, showing understanding of the field’s future direction.
For example, you could:
Suggest improvements to existing data analytics frameworks, such as integrating machine learning techniques to enhance prediction models or proposing a hybrid data collection method.
Recommend strategies for overcoming data challenges, like improved cross-platform data integration or enhanced training for professionals to collect data more effectively.
Learning Outcome Two: Demonstrate Appropriate Integration of Academic Literature
Characteristic One: Comprehensive Engagement with Relevant Academic Literature
Comment: This emphasises the need for thorough research and familiarity with associated reading for the module. It should demonstrate your ability to draw on various studies, theories, and discussions relevant to the module content.
For example, you could:
Integrate key journal articles, books, and other authoritative sources to build a strong theoretical foundation.
Compare and contrast the viewpoints of different authors or studies, providing a nuanced understanding of the subject matter.
Characteristic Two: Balance Between Academic Sources and Practical Evidence
Comment: You should try to blend theoretical insights with practical examples. While academic sources provide the foundation, practical evidence can ensure the relevance of the argument to real-world contexts.
For example, you could:
Discuss a theoretical model from an academic paper and then show how it has been applied or challenged in a real-world sports analytics case, such as a professional team using data to optimise performance.
Use case studies or examples from recent sport analytics projects to support academic literature, showing the practical applications of the theory.
Characteristic Three: Critical Evaluation of Academic Literature
Comment: Beyond just citing sources, it’s important to evaluate them critically. This shows a deeper level of engagement, where you assess the strengths and limitations of the literature.
For example, you could:
Critically discuss the limitations of a particular academic model or theory, explaining how newer research or data might challenge or refine it.
Compare the methodologies used in different studies, analysing how they might impact the results or conclusions.
Learning Outcome Three: Incorporate Practical Examples and Case Studies
Characteristic One: Use Real-World Examples to Illustrate Points
Comment: Real-world examples help ground theoretical discussions in reality, making arguments more relatable and impactful. This characteristic encourages using practical examples to clarify complex ideas.
For example, you could:
Discuss how a specific sports team or athlete used data analytics to improve performance, linking this to relevant concepts from your module, such as predictive analysis or injury prevention.
Highlight innovations in sports analytics that have been successfully implemented in various leagues or teams, showing their direct relevance to your review.
Characteristic Two: Critical Analysis of Practical Examples
Comment: Rather than simply mentioning real-world examples, this focuses on the need for critical evaluation. You should assess how effective these examples are and compare them with theoretical models.
For example, you could:
Critically compare how a sports team’s use of analytics aligns or conflicts with academic theories on data efficiency, drawing on case study results and outcomes.
Analyse the success or failure of a practical application of sports analytics, offering critiques of its methodology or outcomes and comparing it with alternative approaches found in literature.
Characteristic Three: Comparison and Contrast with Academic Literature
Comment: This characteristic is key to showing deeper engagement with both theory and practice. It involves drawing connections between real-world examples and academic literature, evaluating how they inform or contradict each other.
For example, you could:
Contrast a real-world data-driven decision-making process in sports with academic models of decision theory or player performance analysis, discussing any disparities.
Use case studies to highlight where academic theories fall short in practical applications or where they align closely with professional practices.
Learning Outcome Four: Structure and Coherence
Characteristic One: Logical Structure and Smooth Transitions
Comment: Your review should be well-structured, with clear and logical argumentation. Sections should connect smoothly, guiding the reader through the analysis.
For example, you could:
Create an outline before writing to ensure your review has a clear introduction, well-developed body paragraphs, and a concise conclusion.
Use transition phrases between sections and paragraphs to ensure the flow of ideas is smooth (e.g., “Furthermore,” “In contrast,” or “Building upon this idea…”).
Learning Outcome Five: Appropriate Presentation and Writing Style
Characteristic One: Meeting the Word Limit and Free from Errors
Comment: This characteristic stresses the importance of adhering to the word count and ensuring that the writing is polished and professional, free from errors.
For example, you could:
- Edit your draft multiple times to ensure the word count is within the specified range while maintaining the core argument and supporting details.
Characteristic Two: Appropriate Use of Academic Language and Tone
Comment: Academic writing requires formal language and tone. This characteristic ensures the review maintains professionalism throughout by avoiding colloquialisms and maintaining objectivity.
For example, you could:
Use precise, formal language to express complex ideas clearly, avoiding casual language or overly complicated jargon that may confuse the reader.
Use a passive or third-person voice to maintain academic objectivity (e.g., “It is argued that…” instead of “I think…”).
Characteristic Three: Consistent Citation and Referencing in IEEE Format
Comment: Proper citation is essential. This characteristic requires that all sources be consistently formatted in the IEEE citation style, ensuring accuracy and credibility.
For example, you could:
Use citation management software (I recommend Zotero) to organise your references and automatically format them in IEEE style.
Cross-check every citation in the text to ensure they match the corresponding reference in the bibliography.