# US 2021/0275059 A1 · Systems, Methods, and Computer-Program Products for Assessing Athletic Ability and Generating Performance Data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0275059 A1 - Document ID: US20210275059A1 - Status: Published application - Kind: A1 - Application number: 17/002,331 - Filed: 2020-08-25 - Published: 2021-09-09 - Earliest claimed: 2020-03-04 - Assignees: Recruiting Analytics LLC - Applicants: Recruiting Analytics LLC - Inventors: Corey Leon Yates; Alfonzo Thurman, II - Web page: https://patentgrep.com/patent/US20210275059A1 ## CPC - G06T 7/13: Edge detection - G06V 20/42: {of sport video content} - A61B 5/1118: {Determining activity level} - A61B 5/1128: {using image analysis (A61B5/1127 takes precedence)} - A61B 5/117: Identification of persons (methods or arrangements for recognising patterns, e.g. fingerprints, G06F18/00, G06V40/00; identification of persons by analysing their voice or speech G10L17/00) - A63B 24/0003: {Analysing the course of a movement or motion sequences during an exercise or trainings sequence, e.g. swing for golf or tennis} - A63B 24/0021: {Tracking a path or terminating locations} - A63B 24/0062: {Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance} - G06T 7/181: involving edge growing; involving edge linking - G06T 7/246: using feature-based methods, e.g. the tracking of corners or segments - G06T 7/248: {involving reference images or patches} - G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) - G06V 40/23: {Recognition of whole body movements, e.g. for sport training} - G06V 40/25: {Recognition of walking or running movements, e.g. gait recognition} - A61B 2503/10: Athletes - G06T 2200/24: involving graphical user interfaces [GUIs] - G06T 2207/10032: Satellite or aerial image; Remote sensing - G06T 2207/20061: Hough transform - G06T 2207/30196: Human being; Person - G06T 2207/30221: Sports video; Sports image - G06T 2207/30228: Playing field - G06T 2207/30241: Trajectory - G06V 2201/07: Target detection - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - A63B: APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT (apparatus for passive exercising, massage A61H) ## Abstract Methods, systems, and computer-program products used for assessing athletic ability and generating performance data. In one embodiment, athlete performance data is generated through computer-vision analysis of video of an athletic performing, e.g., during practice or gameplay. The generated performance data for the athlete may include, for example, maximum speed, maximum acceleration, time to maximum speed, transition time (e.g., time to change direction), closing speed (e.g., time to close the distance to another athlete), average separation (e.g., between the athlete and another athlete), play-making ability, athleticism (e.g., a weighted computation and/or combination of multiple metrics), and/or other performance data. This performance data may be used to generate and/or update a profile associated with the athlete, which can be utilized for recruiting, scouting, comparing, and/or assessing athletes with greater efficiency and precision. ## Claims ### Claim 1 A computer-implemented method for generating athlete performance data using computer-vision analysis, the method comprising: accessing a video, the video comprising a plurality of frames depicting an athlete, each of the plurality of frames having an associated time-stamp; identifying, using a computer-vision system, a location of the athlete in each frame of the plurality of frames; identifying, using the computer-vision system, a location of a reference element in each frame of the plurality of frames; identifying, using the computer-vision system, a number of pixels between the athlete and the reference element in each frame of the plurality of frames to thereby determine a pixel-distance between the athlete and the reference element in each frame; converting, using the computer-vision system, each pixel-distance to a corresponding physical distance; generating performance data for the athlete using the converted physical distance at each time-stamp; and updating a profile associated with the athlete to include the generated performance data. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the reference element is a fixed object in an environment. ### Claim 3 (depends on 2) The computer-implemented method of claim 2, wherein the performance data is a maximum speed achieved by the athlete over a period of time. ### Claim 4 (depends on 2) The computer-implemented method of claim 2, wherein the performance data is a maximum acceleration achieved by the athlete over a period of time. ### Claim 5 (depends on 2) The computer-implemented method of claim 2, wherein the performance data comprises a transition time for the athlete over a period of time. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein the reference element comprises one of: a solid line, a dashed line, or a geometric shape. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the athlete is a first athlete, wherein the plurality of frames further depict a second athlete, and wherein the reference element is the second athlete. ### Claim 8 (depends on 7) The computer-implemented method of claim 7, wherein the performance data comprises an average physical separation between the first athlete and the second athlete. ### Claim 9 (depends on 7) The computer-implemented method of claim 7, wherein the performance data is a closing speed of the first athlete relative to the second athlete over a period of time. ### Claim 10 (depends on 1) The computer-implemented method of claim 1, wherein the converting comprises using at least one of: a calibration factor, and a transformation matrix. ### Claim 11 One or more computer-readable media having computer-executable instructions stored thereon that, when executed by one or more computer processors, perform a method for generating athlete performance data through computer-vision analysis, the method comprising: accessing a video, the video comprising a plurality of frames depicting an athlete, each of the plurality of frames having an associated time-stamp; identifying, using a computer-vision system, a location of the athlete in each frame of the plurality of frames; identifying, using the computer-vision system, a location of a reference element in each frame of the plurality of frames; identifying, using the computer-vision system, a number of pixels between the athlete and the reference element in each frame of the plurality of frames to thereby determine a pixel-distance between the athlete and the reference element in each frame; converting, using the computer-vision system, each pixel-distance to a corresponding physical distance; generating performance data for the athlete using the converted physical distance at each time-stamp; and updating a profile associated with the athlete to include the generated performance data. ### Claim 12 (depends on 11) The one or more computer-readable media of claim 11, wherein the reference element is a fixed object in an environment. ### Claim 13 (depends on 12) The one or more computer-readable media of claim 12, wherein the performance data is a maximum speed achieved by the athlete over a period of time. ### Claim 14 (depends on 12) The one or more computer-readable media of claim 12, wherein the performance data is a maximum acceleration achieved by the athlete over a period of time. ### Claim 15 (depends on 12) The one or more computer-readable media of claim 12, wherein the performance data is a transition time of the athlete. ### Claim 16 (depends on 11) The one or more computer-readable media of claim 11, wherein the athlete is a first athlete, wherein the plurality of frames further depict a second athlete, and wherein the reference element is the second athlete. ### Claim 17 (depends on 16) The one or more computer-readable media of claim 16, wherein the performance data is an average separation between the first athlete and the second athlete. ### Claim 18 (depends on 16) The one or more computer-readable media of claim 16, wherein the performance data is a closing speed of the first athlete relative to the second athlete over a period of time. ### Claim 19 (depends on 11) The one or more computer-readable media of claim 11, wherein the converting comprises using at least one of: a calibration factor, and a transformation matrix. ### Claim 20 A computer system configured for generating athlete performance data using computer-vision analysis, the system comprising: at least one processor; and one or more computer-readable media having computer-executable instructions stored thereon that, when executed by the at least one processor, perform a method comprising: accessing a video, the video comprising a plurality of frames depicting an athlete, each of the plurality of frames having an associated time-stamp; identifying, using a computer-vision system, a location of the athlete in each frame of the plurality of frames; identifying, using the computer-vision system, a location of a reference element in each frame of the plurality of frames; identifying, using the computer-vision system, a number of pixels between the athlete and the reference element in each frame of the plurality of frames to thereby determine a pixel-distance between the athlete and the reference element in each frame; converting, using the computer-vision system, each pixel-distance to a corresponding physical distance; generating performance data for the athlete using the converted physical distance at each time-stamp; and updating a profile associated with the athlete to include the generated performance data.