The present disclosure is generally related to systems and methods for assessing the decision making ability and learning potential of a user during computer simulation participations.
Many aspects of the present disclosure can be better understood with reference to the following drawings. The components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Moreover, in the drawings, like reference numerals designate corresponding parts throughout the several views.
The present disclosure relates to systems and methods for analyzing a user's cognitive abilities (e.g., learning potential) related to decision-making in fast-paced scenarios, such as within an American football computer simulation.
This interactive solution is designed to measure, teach, and improve a team athlete's decision-making skills, whose role depends on his/her ability to make smart, fast decisions. Accordingly, an exemplary teaching and evaluation application is adapted for an American football quarterback position. Therefore, users can use this tool to improve their cognitive ability to quickly break down a situation into the key components that drive the best decision by a user in the quarterback role. By using the training tool repetitively, a user can improve his or her decision making skills, which will translate to improved field performance.
In one embodiment, an exemplary teaching and evaluation application 115 (
Additionally, a unique formula has been created to determine the cognitive skill level of a player in this position. The inputs (e.g., matrix metrics) to this scoring formula include various data components collected through the Gameplay module including the level of play, demographic player information, decision, decision time, situation, situational importance, playtime, and scenario. For example, in evaluating the gameplay of a user, the user can be shown a simulation of a football play and be enabled to identify an action for the quarterback position, such as throwing to a first wide receiver, a second wide receiver, a running back, tight end, etc. that are eligible receivers for the play being executed. Accordingly, the user may have several options to choose from and based on which decision is made, the user will be evaluated, since certain option(s) are considered to be better than others based on the play being run, how the play unfolds, and how the opposing team is defending the play. As such, the variables that need to be considered by the user and used to identify the best option are dynamic.
In addition, an additional factor or variable that contributes to computing the outcome score (e.g., QBi) is a game context to which the play is being run, such as a first down in the first quarter with the game having a 0 to 0 score versus a fourth down in the last 20 seconds of the fourth quarter with the user's team down by 6 points. Accordingly, the decision making of a user when executing a certain football play may be considered to be more critical or importance in the context of the game and may be weighted more than other less critical plays. Correspondingly, the time it takes for a user to make a decision is another factor to be considered in computing an outcome score, since there is a limited amount of time that a quarterback generally has to make a decision before the play ends or before the opportunity closes to choose the correct option.
Thus, in various embodiments, the various variables or factors present a matrix of options that can be attributed to user where each selected option or measured parameter (e.g., a matrix metric, such as throwing to a covered receiver or taking 5 seconds before throwing the ball) can be evaluated and scored, since each has a different value that can contribute to a final score for the individual play. For the quarterback position, this will be their quarterback intellect (QBi) score. The QBi score, gameplay, learning, and comparison data may then be used to provide a predictive analysis of players' cognitive abilities (e.g., learning potential) related to decision-making in fast-paced scenarios. Such predictive analysis can be used by current coaches, future coaches, scouts, or other evaluators who desire an objective evaluation of a user's cognitive abilities or aptitude within a particular field, such as the field of playing the quarterback position. Since, in practice, quarterbacks play at different sized schools at different skill levels and using different offensive schemes, it is difficult to assess an individual's current quarterback decision making abilities in addition to their potential in learning and developing their quarterbacking skills. As such, the evaluations provided by the exemplary teaching and evaluation application can provide an objective basis for assessing and comparing the cognitive decision making skills of quarterback prospects.
Memory elements 120 include one or more physical memory devices such as, for example, a local memory and one or more storage devices. Local memory refers to random access memory (RAM) or other non-persistent memory device(s) generally used during actual execution of the program code. Storage device may be implemented as a hard disk drive (HDD), solid state drive (SSD), or other persistent data storage device. Computing system 100 may also include one or more cache memories (not shown) that provide temporary storage of at least some program code in order to reduce the number of times program code must be retrieved from storage device during execution.
Stored in the memory 120 are both data and several components that are executable by the processor 110. In particular, stored in the memory 120 and executable by the processor 110 are code for simulating plays within an American football game (142) as part of the teaching and evaluation application 115, code for computing a QBi score based on a user's gameplay within the simulation (144), code for a predictive model of the learning potential of the user (146) and code for interfacing with the predictive model and outputting a predictive learning potential outcome from the predictive model (150). Also stored in the memory 120 may be a data store 125 and other data. The data store 125 can include an electronic repository or database relevant to predictive model results. In addition, an operating system may be stored in the memory 120 and executable by the processor 110. In an embodiment, predictive model data are stored in the data store 125, such as model parameters.
For example, a predictive model may include a digitally constructed model that refers to an electronic digitally stored set of executable instructions and data values, associated with one another, which are capable of receiving and responding to a programmatic or other digital call, invocation, or request for resolution based upon specified input values, to yield one or more stored output values that can serve as the basis of computer-implemented recommendations, output data displays, or machine control, among other things. Persons of skill in the field find it convenient to express models using mathematical equations, but that form of expression does not confine the models disclosed herein to abstract concepts; instead, each model herein has a practical application in a computer in the form of stored executable instructions and data that implement the model using the computer. The model may include a model of past events on the one or more fields, a model of the current status of the one or more fields, and/or a model of predicted events on the one or more fields. Model and field data may be stored in data structures in memory, rows in a database table, in flat files or spreadsheets, or other forms of stored digital data.
Input/output (I/O) devices 160 such as a keyboard, a display device, and a pointing device may optionally be coupled to computing system 100. The I/O devices may be coupled to computing system 100 either directly or through intervening I/O controllers. A network adapter may also be coupled to computing system to enable computing system to become coupled to other systems, computer systems, remote printers, and/or remote storage devices through intervening private or public networks. Modems, cable modems, Ethernet cards, and wireless transceivers are examples of different types of network adapter that may be used with computing system 100.
An exemplary application is designed for the user to create and edit the play content used in the game. Users can create a play from templates, previous plays, or from scratch. Each play has many customizable components, including receiver speed, run type, optimal receiver, optimal decision time, and others. After completing a play, the user can view how the play will be seen in the game in 3D and make any adjustments the user wants. A coach can use the setup wizard to configure plays for their team, school, etc. These are then only accessible to the users associated with that team.
Thus, there are various Learning Center options for each level, including basic playbooks, team-specific playbooks, situational specific, and others. For example,
As discussed, the Practice mode or module can be used in conjunction with the Learning Center module to practice concepts and situations. For example,
Computer program code for carrying out operations of the present disclosure may be written in a variety of computer programming languages. The program code may be executed entirely on at least one computing device (or processor), as a stand-alone software package, or it may be executed partly on one computing device and partly on a remote computer. In the latter scenario, the remote computer may be connected directly to the one computing device via a LAN or a WAN (for example, Intranet), or the connection may be made indirectly through an external computer.
It will be understood that each block of the flowchart illustrations and block diagrams and combinations of those blocks can be implemented by computer program instructions and/or means. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, application specific integrated circuit (ASIC), or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowcharts or block diagrams.
It should be emphasized that the above-described embodiments of the present disclosure are merely possible examples of implementations, merely set forth for a clear understanding of the principles of the disclosure. Many variations and modifications may be made to the above-described embodiment(s) without departing substantially from the principles of the disclosure. All such modifications and variations are intended to be included herein within the scope of this disclosure and protected by the following claims.
This application claims priority to co-pending U.S. provisional application entitled, “Quarterback Decision Making and Learning Potential Analyses and Predictions,” having application No. 63/354,981, filed Jun. 23, 2022, which is entirely incorporated herein by reference.
Number | Date | Country | |
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63354981 | Jun 2022 | US |