By Ivan Ramler on behalf of the SCORE Network
In 2023, approximately 159.9 million people in the U.S. watched live sports at least once per month1. Sports statistics and analytics offer an exciting, real-world application of data science, capturing the interest of millions. Many students are sports enthusiasts, which creates a natural and accessible context immersing them in data science problems that impacts something they care about.
For students who might not naturally gravitate toward data-oriented careers, sports can be the motivation toward them. Additionally, the abundance of publicly available sports data makes it an ideal resource for teaching statistics and data science due to the wide range of problems for students to solve. Moreover, the knowledge and skills developed through sports analytics, such as statistical modeling and data visualization, are transferable to non-sports fields like business, healthcare, finance, and marketing, where data-driven decision-making is crucial for optimizing outcomes and improving performance.
To provide a hub that inspires and engages students in sports analytics as well as develops pedagogical resources and supports for educators, this is where the NSF-funded SCORE Network (Sports Content for Outreach, Research, and Education) steps up to the plate.
SCORE Network’s Mission and Structure
The SCORE Network is built on three primary goals:
- Create a national network of academic, industry, and government partners to elevate statistics and data science education.
- Develop case-based learning materials focused on sports analytics to teach students how to “think with data” by working with real-world datasets.
- Study and improve educational delivery methods, including in-person, virtual, and hybrid learning models to better reach diverse student populations.
GOAL #1: Create national network
SCORE Network has set up regional hubs in Pittsburgh, Texas, and New York, which includes over 80 confirmed partners from academia, industry, and media. Each hub is responsible for developing and delivering educational content, as well as hosting workshops and outreach events. By partnering academic institutions with professional sports organizations and media outlets, the network is working to build a platform for delivering educational resources that make learning statistics both fun and meaningful.
GOAL #2: Case-based learning materials
SCORE Module Repository
The main component of SCORE’s educational materials is case-based learning modules. SCORE centralizes these materials into a module repository, making it easier for educators to access a wide variety of sports analytics examples while still benefiting from the flexibility of decentralized content creation. These modules teach students foundational data analysis concepts—like data visualization, statistical inference, and statistical modeling—through the lens of sports. Students work with real data from baseball, lacrosse, and esports games—such as League of Legends—to explore topics like win probability, player ratings, and shot success prediction. Whether students are working with marathon data or analyzing trends in hockey, these modules offer a dynamic real-world learning experience.
Currently, the SCORE Network offers over a dozen published modules, with many more in pre-print and under review, highlighting the ongoing expansion of its educational resources. Some of the modules are software-specific (e.g., R), and others allow for flexibility in analysis tools (e.g., no software, point-and-click software (e.g., ISLE, Minitab), or code-based software). Designed to be plug-and-play, each module includes motivational text or videos from experts in the field, data sets, lecture notes, and activities. These are ready to use in the classroom or in workshops, providing flexibility for educators.
SCORE Data Repository
In addition to the modules, SCORE also provides a data repository currently featuring over 70 datasets spanning more than 30 different sports. Educators and students can search for datasets by sport or by statistical and data science topics, making it easy to find data for class projects, workshops, or research. All datasets are publicly shareable and include:
- A descriptive title and brief background of the sports problem and statistical situation.
- A list of relevant statistics and data science tasks or questions.
- A detailed README file serving as a data dictionary, explaining what each row and column represents.
GOAL #3: Study and improve educational delivery methods
Inclusive and Thorough Review Process
Each SCORE module is subjected to a multi-faceted review to ensure high-quality content. A diverse group of experts from academia, industry, and students collaborate to review submissions, focusing on:
- Industry feasibility: Ensuring that the modules make realistic claims and are grounded in practical sports analytics, so that the analysis accurately reflects the real-world dynamics of the sport.
- Educational integrity: Modules are checked for sound statistical methods and effective teaching strategies, helping students develop essential analysis skills.
- Uniformity and clarity: The review process helps standardize the format and quality across all modules, making them easy for educators to adopt and adapt to various learning environments. Additionally, optional student reviewers provide feedback on clarity, ensuring that the content is accessible and understandable for the intended audience.
Moreover, SCORE supports an open and flexible content creation process, where contributors can submit either full modules or initial concepts. If a submission is in its early stages, the contributor is assigned a mentor (called a coach) from the network to help guide the development of the module. This mentorship approach ensures that contributors receive constructive feedback and support, helping them refine their ideas into polished final products.
The review process is designed to be iterative, allowing for continuous improvement while focusing on collaboration rather than critique to ensure that every module meets SCORE’s high standards. Additionally, all modules that are published on the SCORE Module Repository will be given a DOI, or Digital Object Identifier, hosted by the Open Science Framework (OSF). This provides a persistent link to SCORE Network modules. There are several benefits to having an DOI for a publication including making research uniquely identifiable, ensuring authorship recognition, and facilitating easier search and citation2.
For more information on the submission and review process of modules, visit https://scorenetwork.org/submissions.html.
Opportunities for involvement
SCORE’s success relies on collaboration! There are many ways to get involved:
- For educators: The SCORE Network offers ready-to-use educational materials that can be easily integrated into your classroom. Whether you’re teaching a course or leading a workshop, these modules provide everything from data sets to activities, along with supporting resources like handouts and solutions.
- For students: Students can access SCORE’s curated sports data sets and educational modules to develop hands-on experience with real-world data. These resources help students build technical skills in data analysis, statistical modeling, and data visualization, all within the context of sports. By working with SCORE’s materials, students can deepen their understanding of how data science is applied in a professional sports setting.
- For industry professionals: Industry professionals in sports, media, and analytics play an important role within the SCORE network. Their expertise helps shape the overall direction of SCORE’s educational materials, ensuring that the sports problems and datasets used in the modules are grounded in real-world applications.
To access these resources or to learn more about how to get involved, visit scorenetwork.org.
Conclusion
The SCORE Network is enhancing data science education by using sports to inspire engagement. Whether you’re an educator, a student, or an industry professional, SCORE offers a wealth of resources to help you get involved. Be a part of the winning strategy and help us level the playing field for all aspiring data scientists and statisticians.
Acknowledgment
This material is based upon work supported by the National Science Foundation under Grant No. 2142705. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Contributing author Ivan Ramler is an Associate Professor of Statistics in the Department of Mathematics, Computer Science, and Statistics at St. Lawrence University. He teaches undergraduate courses in statistics and data science. His research interests include the application of statistics to sports (both traditional and electronic), statistics education, and interdisciplinary collaborations with faculty in the environmental and biological sciences. You can reach Ivan at iramler@stlawu.edu.
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