This collection of words highlights some of the most important concepts and terms needed to
understand the world of sports data analytics. Hopefully, you find this helpful and can improve both your understanding and literacy.
- Advanced Analytics: The autonomous or semi-autonomous examination of data or
content using sophisticated techniques and tools, typically beyond those of traditional
business intelligence (BI). It is used to discover deeper insights, make predictions, or
2. Algorithm: Refers to a mathematical formula which is output from the tools. The formula summarizes the model
3. Analytics: The process of breaking a problem into simpler parts and using inferences
based on data to drive decisions, a way of thinking and acting.
4. Anonymization: The process of removing or obfuscating indicators in the data which
show who it specifically refers to.
5. Antitrust: A law that maintains market competition by regulating anti-competitive conduct by companies. In sports, a “labor exemption” was created to allow player unions to enter into potentially monopolistic agreements so long as they are collectively bargained.
6. Audience identification analytics: Analytics fueled by data specifically pertaining to
information about your audience, such as location, industry and business.
7. Behavioral analytics: The use of data on a person or object’s behavior to make
predictions on how it might change in the future.
8. Benchmark: A measure of comparison, where businesses put their results again industry best practice results.
9. Big Data: Refers to the huge amounts of data that large businesses and other
organizations collect and store.
10. Big data analytics: When analytics is performed on large data sets with huge volume, variety and velocity of data it can be termed as big data analytics.
11. Classification: The ability to use data to determine which of a number of predetermined groups an item belongs in
12. Clickstream analytics: Analysis of the way humans interact with computers or use
13. Cookies: Refers to a small file that is stored on a user’s browser or computer when they visit a website. Cookies (often called Tracking Cookies) allow marketers to track the behavior of visitors while on their site, retarget visitors while on other sites and map the path visitors take once landing on their site.
14. Conversion Rate: Conversion rate is calculated when users take a particular, defined
action that you want them to take. The most common measurement for conversion is the number of unique website visitors that convert to paying customers.
- 15. Clustering: Clustering is also about grouping objects together but it differs because it is used when there are no predetermined groups. Objects (or events) are clustered
together due to similarities they share and algorithms determine what that common
relationship between them may be. Clustering is a data science technique which makes
unsupervised learning possible.
16. Dashboard: The presentation of easy-to-read data, offering a performance overview
within an analytics or management tool. Some tools offer customized dashboards, so
users can access their most important results first.
17. Data: Facts and statistics collected for reference or analysis.
18. Data mining: The automated searching of large databases and the collection of
statistical and machine learning methods used with those databases.
19. Data warehousing: The process of managing a database and involves extraction,
transformation and loading (ETL) of data. Data warehousing precedes analytics. The
data managed in a data warehouse is usually taken out and used for business analytics.
20. Direct Traffic: Anyone who has come to your site by typing your domain name in their browser or used a saved bookmark to access your site will be attributed to direct traffic.
21. Decision Trees: A basic decision-making structure which can be used by a computer to understand and classify information. By asking a series of questions about each data
item fed into them, outputs are channeled along different branches leading to different
outcomes, typically labelling or classification of the piece of data.
22. Descriptive analytics: A set of techniques used to describe or explore or profile any kind of data.
23. Fan Equity: What a fan believes he deserves for his loyalty to the team and the
emotional bond tying a fan to a team. Though fans have no actual ownership of a team,
many speak as though they do because of the connection a team makes to its city and
24. Heatmap: A visual representation of user clicks across a webpage, the more clicks on a specific area (usually button or images), the more intense the colour. This helps
businesses easily understand how users interact with their website.
25. Hit: A recognition of server access (when a visitor lands on/loads your website).
26. Hit counter: A basic counter of how many hits your website receives. Many web analytics tools include a hit counter as part of their data analysis.
27. Landing page: A webpage specifically designed to start the visitor journey when
responding to a marketing campaign (such as PPC or email). This page aims to convert
visitors instantly, though can also spark brand interest and a longer website journey.
28. Licensing: Contractual agreement between a sport’s team and another business entity which allows the licensee to use the brand name and logo in exchange for a fee or
royalty. Team branding is licensed on everything from clothes and hats to coffee mugs,
toys and posters.
29. Log-based data collection: This is the process of gathering and analyzing real-time data from servers and devices. The process is often used to identify security breaches but also offered the foundations for early web analytics tools.
- 30. Luxury Tax: A surcharge attached to the total payroll of a team if it exceeds a
predetermined dollar amount set by the league. Used as a deterrent so teams don’t gain
a competitive advantage by outspending other teams in the league.
31. Machine learning: Involves using statistical methods to create algorithms. It replaces explicit programming which can become cumbersome due to the large amounts of data, inflexible to adapt to the solution requirements and also sometimes illegible.
32. Metadata: Data about data, or data attached to other data.
33. Metrics: Numerically fueled values offering objective and reliable results for analysis. Most website analysis remains founded upon metrics due to their definitive nature, ensuring decisions are influenced by high-quality data.
34. Multi-channel: Referring specifically to the use of multiple marketing channels. For
example, an individual receives an email from a recent campaign. Then uses that email
to access your website, causing them to convert – this is a multi-channel approach to
35. OLAP: Online analytical processing refers to descriptive analytic techniques of slicing and dicing the data to understand it better and discover patterns and insights. The term is derived from another term “OLTP” – online transaction processing which comes from the data warehousing world.
36. Omnichannel: Refers to the growing need for a consistent customer experience across multiple channels. For example, if a customer is shopping for your product, their
experience across their tablet, smartphone, desktop or in-store should be relatively the
37. Predictive modeling: When you seek to predict a target variable using records where the target is known. Statistical or machine learning models are “trained” using the known data, then applied to data where the outcome variable is unknown.
38. Properties: A property can be any number of defined data points that you are tracking. For example, a customer property could include age, gender, location, company, email, revenue, etc. You can also define specific properties that you’d like to track such as industry, website, number of Twitter followers, etc.
39. Revenue Report: A report solely focused on revenue generated from specific activities. For example, you might want to pull a revenue report if you’re interested in
understanding exactly how much revenue your event marketing campaigns have
40. SAAS: Stands for “software as a service”. SAAS tools offer businesses software that
provides a fully-fledged solution as part of a subscription package, usually with software hosted in the cloud.
41. Salary Cap: A limit placed on the amount of money each professional team can spend on player salaries. Some leagues, like basketball, enforce a “soft salary cap” where dollars spent over a certain limit above the cap are hit with a luxury tax. Other leagues, like football, have a “hard salary cap” where teams are not allowed to exceed the cap, no matter the circumstance.
42. Secondary Market: Sports tickets are often bought, by individuals or ticket brokers, at face value and then resold, generally at a higher value, on this aftermarket.
- 43. Statistical Modeling: The formalization of relationships between variables in the form of a math equation. More-and-more, statistics are being applied to how teams are built and how on-the-field decisions are made. Modeling helps predict outcomes so management and coaches go off more than basic statistics, gut feelings and naked-eye scouting.
44. Social analysis: The act of measuring and analyzing social media activity and success, looking into how this feeds into website activity.
45. Sunk Cost: Costs that have already been incurred and cannot be recovered.
Management in guaranteed contract sports, like baseball and basketball, often apply the sunk cost theory to players who sign expensive, long-term deals and then way under perform them. The amount an athlete is paid shouldn’t impact the amount of playing time she/he/they receive(s) because the cost cannot be recovered no matter what.
46. Statistics: The study of the collection, organization, and interpretation of data.
47. Taxonomy: With so much data, it can be difficult to break down and analyze the
information you receive to extract the most meaningful insights possible. Taxonomy is a way of organizing your data into categories and subcategories to allow for greater
segmentation and filtering.
48. Text mining: The application of data mining methods to text.
49. Web analytics: The process of gathering data from a website and analyzing it to decipher patterns and trends to indicate how users interact with a site, to understand what can be done to improve site performance.
50. Website: A set of webpages housed under a single domain. These pages all relate to
each other and create a public source of content available on the world wide web.