View source for Stats LLC (20240342552). DEFENSIVE AND FITNESS PLAYER ANALYSIS USING REMOTE TRACKING IN SPORTS simplified abstract

=DEFENSIVE AND FITNESS PLAYER ANALYSIS USING REMOTE TRACKING IN SPORTS=


==Organization Name==

[[:Category:Stats LLC|Stats LLC]]


[[Category:Stats LLC]]

==Inventor(s)==

[[:Category:Matthew Scott of Chicago IL (US)|Matthew Scott of Chicago IL (US)]][[Category:Matthew Scott of Chicago IL (US)]]

[[:Category:Patrick Joseph Lucey of Chicago IL (US)|Patrick Joseph Lucey of Chicago IL (US)]][[Category:Patrick Joseph Lucey of Chicago IL (US)]]

[[:Category:Joe Dominic Gallagher of Wirral (GB)|Joe Dominic Gallagher of Wirral (GB)]][[Category:Joe Dominic Gallagher of Wirral (GB)]]

[[:Category:Michael St�ckl of Munich (DE)|Michael St�ckl of Munich (DE)]][[Category:Michael St�ckl of Munich (DE)]]

[[:Category:Felix Wei of Melbourne (AU)|Felix Wei of Melbourne (AU)]][[Category:Felix Wei of Melbourne (AU)]]

[[:Category:Michael John Horton of Wellington (NZ)|Michael John Horton of Wellington (NZ)]][[Category:Michael John Horton of Wellington (NZ)]]

==DEFENSIVE AND FITNESS PLAYER ANALYSIS USING REMOTE TRACKING IN SPORTS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240342552 titled 'DEFENSIVE AND FITNESS PLAYER ANALYSIS USING REMOTE TRACKING IN SPORTS

==Simplified Explanation==
The patent application relates to tracking player movements in a video broadcast to determine a defensive influence score based on the player's defensive intensity during a game.

* Uses machine learning models to generate a defensive influence score.
* Estimates defensive pressure a player exerts on another player.
* Captures fitness metrics like sprints and efforts during plays.
* Utilizes event detection outputs for estimating player fitness metrics.
* Provides frame-by-frame predictions for defensive intensity.

==Key Features and Innovation==
* Tracking player movements in a video broadcast.
* Determining defensive influence score based on defensive intensity.
* Implementing machine learning models for score generation.
* Estimating defensive pressure between players.
* Capturing fitness metrics like sprints and efforts during plays.

==Potential Applications==
This technology can be used in sports analytics, coaching, player performance evaluation, and scouting.

==Problems Solved==
This technology addresses the need for quantifying defensive intensity in sports, tracking player movements accurately, and estimating fitness metrics during gameplay.

==Benefits==
* Improved player performance evaluation.
* Enhanced scouting and recruitment processes.
* Better understanding of defensive strategies in sports.
* Enhanced coaching techniques based on defensive influence scores.
* Accurate tracking of player movements and fitness metrics.

==Commercial Applications==
### Sports Analytics: Enhancing performance analysis and strategy development.
### Coaching: Improving training programs and player development.
### Player Scouting: Identifying talent based on defensive influence scores.
### Broadcasting: Enhancing viewer experience with detailed player insights.

==Prior Art==
Readers can explore prior research on player tracking technologies, machine learning in sports analytics, and defensive performance evaluation in sports.

==Frequently Updated Research==
Stay updated on advancements in player tracking technologies, machine learning models in sports analytics, and innovations in defensive performance evaluation.

==Questions about Player Tracking Technology==
===How does player tracking technology impact sports analytics?===
Player tracking technology revolutionizes sports analytics by providing detailed insights into player movements, performance metrics, and strategies.

===What are the potential applications of player tracking technology beyond sports?===
Player tracking technology can be applied in healthcare for monitoring patient movements, in security for tracking suspicious activities, and in entertainment for enhancing virtual reality experiences.


==Original Abstract Submitted==

the present embodiments relate to tracking player movements from a video broadcast and determining a defensive influence score from the tracked movements of the player. the present embodiments can implement one or more models to generate a defensive influence score that quantifies a defensive intensity of a player during the course of a game. the defensive influence score can include a frame-by-frame machine learning prediction that can be used to estimate the defensive pressure a player is having on another player during the course of the game. additionally, the present embodiments can capture and estimate fitness metrics, such as sprints and efforts around detected plays such as pick-and-rolls and off-ball screens, which can be good proxies for player effort. further, event detection outputs (both offensive and defensive metrics), can be used as features to estimate fitness metrics for the player (e.g., player load, sprints, jogs, etc.).



[[Category:A63B24/00]]
[[Category:A63B71/06]]
[[Category:G06V20/40]]
[[Category:CPC_A63B24/0021]]