Dynamically optimized stoppage time media content

1. A computer-implemented method comprising:

receiving past performance data;

collecting a set of observations about a live event;

generating, based on the past performance data and the set of observations about the live event, a stoppage time prediction;

generating, based on the stoppage time prediction and a respective duration of each of a plurality of preexisting media files, an optimized content sequence, wherein the optimized content sequence comprises a sequence of one or more media files of the plurality of preexisting media files;

collecting a second set of observations about the live event at a second time, wherein the second time occurs after the first time;

generating a revised stoppage time prediction based on the second set of observations about the live event and the past performance data; and

revising the optimized content sequence based on the revised stoppage time prediction,

wherein at least a subset of the first set of observations and the second set of observations are based on signals that are received from one or more sensors disposed at a venue hosting the live event and interpreted by a machine learning model.

2. The computer-implemented method of claim 1 further comprising causing the one or more media files to be displayed by a display device.

3. The computer-implemented method of claim 1 , wherein generating the optimized content sequence comprises selecting the one or more media files such that a total duration of the one or more media files matches a duration of the stoppage time prediction.

4. The computer-implemented method of claim 1 , wherein revising the optimized content sequence comprises adjusting a duration of a selected media file of the one or more media files.

5. The computer-implemented method of claim 4 , wherein adjusting the duration of the selected media file comprises adding or removing one or more of a plurality of predetermined segments of the media file.

6. The computer-implemented method of claim 4 , wherein adjusting the duration of the selected media file comprises slowing up or speeding down a portion of the selected media file.

7. The computer-implemented method of claim 4 , wherein adjusting the duration of the selected media file comprises:

generating a new frame of the selected media file, wherein the new frame combines a first visual element from a first frame of the selected media file with a second visual element from a second frame of the selected media file; and

removing the first frame and the second frame from the selected media file.

8. The computer-implemented method of claim 1 , wherein generating a stoppage time prediction comprises, using cognitive data analytics to:

identify one or more stoppage delay paths;

determine, based on the one or more stoppage delay paths, a critical path; and

estimating, a duration of the critical path.

9. The computer-implemented method of claim 1 further comprising:

responsive to determining that a duration of the stoppage time prediction is greater than or equal to a predetermined threshold duration, causing a display device to cease a display of the live event and display the one or more media files; and

responsive to determining that a duration of the stoppage time prediction is less than the predetermined threshold, refraining from causing the display device to cease the display of the live event.

10. A system comprising:

a memory having computer readable instructions; and

one or more processors for executing the computer readable instructions, the computer readable instructions controlling the one or more processors to perform operations comprising:

receiving past performance data;

collecting a set of observations about a live event;

generating, based on the past performance data and the set of observations about the live event, a stoppage time prediction;

generating, based on the stoppage time prediction and a respective duration of each of a plurality of preexisting media files, an optimized content sequence, wherein the optimized content sequence comprises a sequence of one or more media files of the plurality of preexisting media files;

collecting a second set of observations about the live event at a second time, wherein the second time occurs after the first time;

generating a revised stoppage time prediction based on the second set of observations about the live event and the past performance data; and

revising the optimized content sequence based on the revised stoppage time prediction,

wherein at least a subset of the first set of observations and the second set of observations are based on signals that are received from one or more sensors disposed at a venue hosting the live event and interpreted by a machine learning model.

11. The system of claim 10 wherein the operations further comprise causing the one or more media files to be displayed by a display device.

12. The system of claim 10 , wherein generating the optimized content sequence comprises selecting the one or more media files such that a total duration of the one or more media files matches a duration of the stoppage time prediction.

13. The system of claim 10 , wherein revising the optimized content sequence comprises adjusting a duration of a selected media file of the one or more media files.

14. The system of claim 13 , wherein adjusting the duration of the selected media file comprises adding or removing one or more of a plurality of predetermined segments of the media file.

15. A computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by one or more computer processors to cause the one or more computer processors to perform operations comprising:

receiving past performance data;

collecting a set of observations about a live event;

generating, based on the past performance data and the set of observations about the live event, a stoppage time prediction;

generating, based on the stoppage time prediction and a respective duration of each of a plurality of preexisting media files, an optimized content sequence, wherein the optimized content sequence comprises a sequence of one or more media files of the plurality of preexisting media files;

collecting a second set of observations about the live event at a second time, wherein the second time occurs after the first time;

generating a revised stoppage time prediction based on the second set of observations about the live event and the past performance data; and

revising the optimized content sequence based on the revised stoppage time prediction,

wherein at least a subset of the first set of observations and the second set of observations are based on signals that are received from one or more sensors disposed at a venue hosting the live event and interpreted by a machine learning model.

16. The computer program product of claim 15 wherein the operations further comprise causing the one or more media files to be displayed by a display device.

17. The computer program product of claim 15 , wherein generating the optimized content sequence comprises selecting the one or more media files such that a total duration of the one or more media files matches a duration of the stoppage time prediction.