# US 2019/0318651 A1 · System and Method for Analysing Sports Performance Data

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- Number: US 2019/0318651 A1
- Document ID: US20190318651A1
- Status: Published application
- Kind: A1
- Application number: 16/474,151
- Filed: 2017-01-26
- Published: 2019-10-17
- Earliest claimed: 2017-01-26
- Assignees: Telefonaktiebolaget LM Ericsson (publ)
- Applicants: Telefonaktiebolaget LM Ericsson (publ)
- Inventors: Nóra Fenyvesi; István Gódor; Zsófia Kallus; József Stéger
- Web page: https://patentgrep.com/patent/US20190318651A1

## CPC

- G09B 19/0038: {Sports}
- G07C 1/28: Indicating playing time
- G06N 20/00: Machine learning
- G07C 1/22: in connection with sports or games
- G09B: EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS (devices for psychotechnics or for testing reaction times A61B5/16; games, sports, amusements A63; projectors, projector screens G03B)
- G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A computer system and computer-automated method for analyzing sports performance data from a game. A data set is provided that contains a log of a first time sequence of game data events which are classified into event types. A second time sequence is generated from the first time sequence by aggregating the events into game patterns, and a third time sequence is generated by aggregating the game patterns into game strategies. A multi-level time sequence event hierarchy is thus created. These multiple time sequence levels are rendered into a visualization in which the different types at each level are visually distinct from each other. The visualization reveals to a game expert player or team behavior in the data set which can be used for player and team improvement.

## Claims

### Claim at position 1

1-19. (canceled)

### Claim 20

A computer-automated method for analyzing sports performance data from a game, the method comprising:
receiving a data set containing a log of a first time sequence of game data events in which each event has been classified into one of a plurality of event types;
creating a second time sequence from the first time sequence by aggregating the events into event patterns, wherein each event pattern is defined as a plurality of events which are in a specific sequence of event types, each event pattern being classified into one of a plurality of event pattern types;
creating a third time sequence from the second time sequence by aggregating the event patterns into event strategies, wherein each event strategy is defined as a plurality of event patterns which are in a specific sequence of event pattern types, each event strategy being classified into one of a plurality of event strategy types; and
rendering into a visualization at least one of the time sequences such that in the visualization each of the types is visually distinct from other types in the same time sequence.

### Claim 21 (depends on 20)

The method of claim 20, wherein the event pattern types are predefined.

### Claim 22 (depends on 21)

The method of claim 21:
wherein the event pattern types are predefined by rules of a game; and
wherein the events are events in a game that has been, or is being, played according to the rules.

### Claim 23 (depends on 22)

The method of claim 22, wherein the event strategy types are predefined so that each specific sequence of event pattern types defined as an event strategy represents implementation of a gaming strategy.

### Claim 24 (depends on 20)

The method of claim 20, wherein the event pattern types are defined as part of creating the second time sequence from the first time sequence and according to the first time sequence.

### Claim 25 (depends on 20)

The method of claim 20, wherein the event strategy types are predefined.

### Claim 26 (depends on 20)

The method of claim 20, wherein the event strategy types are defined as part of creating the third time sequence from the second time sequence and according to the second time sequence.

### Claim 27 (depends on 20)

The method of claim 20, wherein the game is a sport and the events are measured during playing the sport.

### Claim 28 (depends on 27)

The method of claim 27, wherein the sport is a racquet sport, a hand sport, a bat-and-ball sport, a football sport, and/or a cue sport.

### Claim 29 (depends on 27)

The method of claim 27, wherein the events are ball contacts with a playing surface and/or a playing implement or part thereof.

### Claim 30 (depends on 20)

The method of claim 20, further comprising co-rendering a representation of the events into the visualization.

### Claim 31 (depends on 30)

The method of claim 30, wherein the representation is video footage of the events recorded in parallel with logging the first time sequence of events.

### Claim 32 (depends on 30)

The method of claim 30, wherein the representation is video footage rendered at least in part from an automated analysis of the first and second time sequences.

### Claim 33 (depends on 20)

The method of claim 20, wherein the rendering produces a visualization in which the first, second, and third time sequences are presented with a common time base.

### Claim 34 (depends on 20)

The method of claim 20, further comprising:
recognizing a time evolution of events in the data set by matching the current event patterns and/or strategies to a first time period of at least one stored data set in which the same event patterns and/or strategies are present; and
predicting future events and/or patterns based on warping the first time period of the at least one stored data set onto the current data set and using the warped second time period of the at least one stored data set as the prediction.

### Claim 35 (depends on 20)

The method of claim 20, further comprising creating at least one higher order time sequence from the time sequence of the previous highest order by aggregating the groups of the previous highest order, referred to as sub-ordinate groups, into supra-ordinate groups, wherein each supra-ordinate group is defined as a plurality of sub-ordinate groups which are in a specific sequence of sub-ordinate group types, each supra-ordinate group being classified into one of a plurality of event supra-ordinate group types.

### Claim 36

A non-transitory computer readable recording medium storing a computer program product for controlling a computing device for automated analyzing sports performance data from a game, the computer program product comprising software instructions which, when run on processing circuitry of the computing device, causes the computing device to:
receive a data set containing a log of a first time sequence of game data events in which each event has been classified into one of a plurality of event types;
create a second time sequence from the first time sequence by aggregating the events into event patterns, wherein each event pattern is defined as a plurality of events which are in a specific sequence of event types, each event pattern being classified into one of a plurality of event pattern types;
create a third time sequence from the second time sequence by aggregating the event patterns into event strategies, wherein each event strategy is defined as a plurality of event patterns which are in a specific sequence of event pattern types, each event strategy being classified into one of a plurality of event strategy types; and
render into a visualization at least one of the time sequences such that in the visualization each of the types is visually distinct from other types in the same time sequence.

### Claim 37

A computer system for analyzing sports performance data from a game, the system comprising:
a data input operable to receive a data set containing a log of a first time sequence of game data events in which each event has been classified into one of a plurality of event types;
memory operable to store the data set;
processing circuitry operable to analyze the data set by:
creating a second time sequence from the first time sequence by aggregating the events into event patterns, wherein each event pattern is defined as a plurality of events which are in a specific sequence of event types, each event pattern being classified into one of a plurality of event pattern types;
creating a third time sequence from the second time sequence by aggregating the event patterns into event strategies, wherein each event strategy is defined as a plurality of event patterns which are in a specific sequence of event pattern types, each event strategy being classified into one of a plurality of event strategy types; and
rendering into a visualization into a visualization at least one of the time sequences such that in the visualization each of the types is visually distinct from other types in the same time sequence; and
an output operable to output the visualization to a display.