Sports and entertainment data encompasses information about everything related to sports and entertainment industries. This includes the players and entertainers, the fans, the administrators, the managers, the coaches, the facilities, and so on.
This data comes from many sources. First, the players and entertainers themselves: their abilities, record on the field, their demographics, the public perception of them. Sports teams and leagues (and sport) have similar data.
Second is data from the management, agencies, and coaches. They have records of wins and losses as well as play styles and connections to studios, venues, or sponsors. Venue administrators may also use the data to plan exhibitions, events, or activities that draw the public.
Finally, there is data about the entertainment facilities themselves. Particularly with the advent of smart facilities, the ability of administrators, economists, and other professionals to determine whether venues meet audience demands or fail to pay for expensive upgrades.
The data attributes you will encounter depends on the aspect of the data you are looking at. Marketers will look at fans’ behavioral data, online sentiment data, tourism data, and intent data. They may also look at external factors like meteorological data (fewer people go out in adverse weather) or the health of the local economy. Of course, all this information may be presented by data vendors such as the ones on our site as audience segments rather than long columns and rows of statistics.
In contrast, coaches planning diet and training schedules for their players will use long rows of historical data like number of goals and assists per season per club, injuries, weight, etc.
There are several uses for this data. Sports team managers and coaches can optimize each player’s diet and training, decide on player trades, determine the best plays to run against their rivals, and more.
Entertainer agents and managers can decide on marketing strategies and PR campaigns as well as determine which studio or movie would be the best for their career.
Finally, municipal planners and agencies can argue for new facilities to be built. Then city planners can determine the best location for the new facilities and supporting transportation routes, restaurants, and stores.
Because so many external factors affect this data, it can be hard to determine whether a dataset is really complete. For instance, weather, politics, economics and fan behavior (like a preference for streaming) affect event attendance. And that is only one aspect of this category.
However, with careful forethought, you can incorporate as much relevant data as you need to meet your goals. You could also consider reaching out to one of the data vendors on our site.
Beyond this, make sure that you use frequently updated data, and take care to keep it as accurate and internally consistent as possible.
“CAA uses analytics to match talent to opportunities – a matching system can help make recommendations for people in CAA’s talent portfolio would be a fit. The matching algorithm takes into account demographics, past roles, social media sentiment, talent availability, and many other factors.”
X-Byte’s dataset – ‘X-Byte | Streaming & OTT Data Global Netflix, Hulu, Apple TV, Amazon Prime Video, HBO Now’ provides TV Streaming Rating Data and Sports & Entertainment Data that can be used in and Hedge Fund Management
TL1’s dataset – ‘TL1- DMP LICENSE DATA PLATFORM’ provides , Individual Data, Online/Mobile Data, Web Data and Sports & Entertainment Data that can be used in Online and Social Media Performance Tracking and
Throtle’s dataset – ‘Throtle – Connected TV (CTV) Identity Data’ provides TV Streaming Rating Data, Individual Data, and Sports & Entertainment Data that can be used in , Targeted Marketing and Trend Forecasting