DESC:RESERVATION OF RIGHTS
[0001] A portion of the disclosure of this patent document contains material, which is subject to intellectual property rights such as but are not limited to, copyright, design, trademark, integrated circuit (IC) layout design, and/or trade dress protection, belonging to Jio Platforms Limited (JPL) or its affiliates (hereinafter referred as owner). The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the owner.

FIELD OF INVENTION
[0002] The embodiments of the present disclosure generally relate to systems and methods for sports analytics using artificial intelligence and machine learning. More particularly, the present disclosure relates to a system and a method for an optimum simulator.

BACKGROUND OF THE INVENTION
[0003] The following description of the related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section is used only to enhance the understanding of the reader with respect to the present disclosure, and not as admission of the prior art.
[0004] Conventional gaming simulation techniques are derived from a number of rules, variables, and interactions between them. Multiple decisions have to be processed and the depth of the decisions may vary depending upon the number of variable players for a given set of decisions. Further, conventional gaming includes algorithmic complexity, which relates to both the calculability of the system and the quantity of information necessary to describe the system.
[0005] There is, therefore, a need in the art to provide a system and a method that can mitigate the problems associated with the prior arts.

OBJECTS OF THE INVENTION
[0006] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are listed herein below.
[0007] It is an object of the present disclosure to provide a system and a method that generates an optimized set of players based on multiple factors originating from sport analytics.
[0008] It is an object of the present disclosure to provide a system and a method that performs a pre-game simulation between cricket practitioners and the opponent team to recommend an optimized bowler mapping and computes a lowest simulation score.
[0009] It is an object of the present disclosure to provide a system and a method that performs real-time simulation during the match to recommend an optimized set of bowlers to be mapped to the remaining overs of the match.
[0010] It is an object of the present disclosure to provide a system and a method that generates mappings with respect to economy and wicket taking and further predicts bowler to over mappings.
[0011] It is an object of the present disclosure to provide a system and a method that utilizes the mapping with the lowest simulation score.
[0012] It is an object of the present disclosure to provide a system and a method that utilizes the wicket taking criteria and the economy criteria for every over and formulates a best approach for optimization.
[0013] It is an object of the present disclosure to provide a system and a method that is applicable to various cricketing formats like twenty overs cricket (T20), one day internationals (ODI), etc. as the system considers respective cricketing format’s rules and tweaks the simulation accordingly.

SUMMARY
[0014] This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
[0015] In an aspect, the present disclosure relates to a system for an optimum simulator. The system includes a processor and a memory operatively coupled with the processor, wherein said memory stores instructions which, when executed by the processor, cause the processor to receive a list of a first set of players for playing a sports event for a predetermined period. The processor determines an order of a second set of players to be mapped with the first set of players for playing the sports event based on one or more parameters. The processor simulates the sports event based on the mapping for the predetermined period. The processor generates a simulation score associated with the sports event based on the mapping. The processor generates recommendations, via an artificial intelligence (AI) engine, of an optimized order of the second set of players to be mapped with the first set of players based on the simulation score.
[0016] In an embodiment, the one or more parameters may include at least one of a wicket taking parameter and an economy parameter.
[0017] In an embodiment, the processor may generate the optimized order by being configured to generate one or more mappings associated with the one or more parameters. The processor may merge the one or more mappings to determine the order of second set of players to be mapped with the first set of players for playing the sports event for the predetermined period. The processor may compute the simulation score for the one or more mappings. The processor may generate the recommendations of the optimized order of the second set of players to be mapped with the first set of players based on a lowest simulation score associated with the one or more mappings.
[0018] In an embodiment, the first set of players may include one or more batsmen selected for playing the sports event and the second set of players may include one or more bowlers to be mapped with the one or more batsmen based on the one or more parameters for the predetermined period.
[0019] In an aspect, the present disclosure relates to a method for an optimum simulator. The method includes receiving, by a processor, associated with a system, a list of a first set of players for playing a sports event for a predetermined period. The method includes determining, by the processor, an order of a second set of players to be mapped with the first set of players for playing the sports event based on one or more parameters. The method includes simulating, by the processor, the sports event based on the mapping for the predetermined period. The method includes generating, by the processor, a simulation score associated with the sports event based on the mapping. The method includes generating, by the processor, recommendations, via an AI engine, of an optimized order of the second set of players to be mapped with the first set of players based on the simulation score.
[0020] In an embodiment, the one or more parameters may include at least one of a wicket taking parameter and an economy parameter.
[0021] In an embodiment, generating the optimized order comprises generating, by the processor, one or more mappings associated with the one or more parameters. The method may include merging, by the processor, the one or more mappings to determine the order of second set of players to be mapped with the first set of players for playing the sports event for the predetermined period. The method may include computing, by the processor, the simulation score for the one or more mappings. The method may include generating, by the processor, the recommendations, of the optimized order of the second set of players to be mapped with the first set of players based on a lowest simulation score associated with the one or more mappings.
[0022] In an embodiment, the first set of players may include one or more batsmen selected for playing the sports event and the second set of players may include one or more bowlers to be mapped with the one or more batsmen based on the one or more parameters for the predetermined period.
[0023] In an aspect, a user equipment (UE) for sending requests includes one or more processors communicatively coupled to a processor associated with a system. The one or more processors are coupled with a memory, where said memory stores instructions, which when executed by the one or more processors, cause the one or more processors to transmit a list of a first set of players to the processor via a network. The first set of players are for playing a sports event for a predetermined period. The processor is configured to receive the list of the first set of players. The processor is configured to determine an order of a second set of players to be mapped with the first set of players for playing the sports event based on one or more parameters. The processor is configured to simulate the sports event based on the mapping for the predetermined period. The processor is configured to generate a simulation score associated with the sports event based on the mapping. The processor is configured to generate recommendations, via an AI engine, of an optimized order of the second set of players to be mapped with the first set of players based on the simulation score.

BRIEF DESCRIPTION OF DRAWINGS
[0024] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes the disclosure of electrical components, electronic components, or circuitry commonly used to implement such components.
[0025] FIG. 1 illustrates an exemplary network architecture (100) of a proposed system (108), in accordance with an embodiment of the present disclosure.
[0026] FIG. 2 illustrates an exemplary representation (200) of a proposed system (108), in accordance with an embodiment of the present disclosure.
[0027] FIG. 3 illustrates an exemplary simulation framework (300) of the system (108), in accordance with an embodiment of the present disclosure.
[0028] FIG. 4 illustrates an exemplary optimized solution (400) provided by the system (108), in accordance with an embodiment of the present disclosure.
[0029] FIG. 5 illustrates an exemplary result of the system (108), in accordance with an embodiment of the present disclosure.
[0030] FIG. 6 illustrates an exemplary computer system (600) in which or with which the proposed system (108) may be implemented, in accordance with an embodiment of the present disclosure.
[0031] The foregoing shall be more apparent from the following more detailed description of the disclosure.

BRIEF DESCRIPTION OF THE INVENTION
[0032] In the following description, for explanation, various specific details are outlined in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0033] The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.
[0034] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.
[0035] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[0036] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive like the term “comprising” as an open transition word without precluding any additional or other elements.
[0037] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0038] The terminology used herein is to describe particular embodiments only and is not intended to be limiting the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any combinations of one or more of the associated listed items.
[0039] The present disclosure generates a bowling line up for a predetermined period. Match ups between a selected number of players/batsmen and bowlers is performed by determining their respective head-to-head statistics and bowling history. Further, based on an economy and a wicket taking capability of the bowlers, simulations are performed for determining the best bowler for that particular over such an optimum result is achieved. A pre-game simulation is used to recommend the optimized bowler mapping for the predetermined period. A simulation score is computed by end of the predetermined period based on the mapping. Furthermore, a real-time simulation is performed during the game for recommending the optimized set of bowlers to be mapped for the remaining overs of the match/game. Further, a simulation score based on the real-time simulation is observed based on the mapping.
[0040] The various embodiments throughout the disclosure will be explained in more detail with reference to FIGs. 1-6.
[0041] FIG. 1 illustrates an exemplary network architecture (100) of a proposed system (108), in accordance with an embodiment of the present disclosure. As illustrated in FIG. 1, one or more computing devices (104-1, 104-2…104-N) may be connected to the proposed system (108) through a network (106). A person of ordinary skill in the art will understand that the one or more computing devices (104-1, 104-2…104-N) may be collectively referred as computing devices (104) and individually referred as a computing device (104). One or more users (102-1, 102-2…102-N) may operate the computing devices (108). A person of ordinary skill in the art will understand that the one or more users (102-1, 102-2…102-N) may be collectively referred as users (102) and individually referred as user (102).
[0042] In an embodiment, the computing device (104) may include, but not be limited to, a mobile, a laptop, etc. Further, the computing device (104) may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as a camera, audio aid, microphone, or keyboard. Further, the computing device (104) may include a mobile phone, smartphone, virtual reality (VR) devices, augmented reality (AR) devices, a laptop, a general-purpose computer, a desktop, a personal digital assistant, a tablet computer, and a mainframe computer. Additionally, input devices for receiving input from a user such as a touchpad, touch-enabled screen, electronic pen, and the like may be used. In an embodiment, users/customers may submit their complaints through the computing devices (104) as shown in FIG. 1.
[0043] In an embodiment, the network (106) may include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. The network (106) may also include, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof.
[0044] In an embodiment, the system (108) may receive a list of first set of players selected by the users (102) via the computing device (104). The users (102) may include cricket practitioners that generate one or more simulations for a host team. The first set of players may be selected for playing a sports event for a predetermined period. The predetermined period may include overs set for a cricket match.
[0045] In an embodiment, the system (108) may determine an order of second set of players to be mapped with the first set of players for playing the sports event based on one or more parameters. The one or more parameters may include but not limited to a wicket taking parameter and an economy parameter. Further, the system (108) may simulate the sports event based on the mapping for the predetermined period.
[0046] In an embodiment, the first set of players may include one or more batsmen selected for playing the sports event and the second set of players may include one or more bowlers to be mapped with the one or more batsmen based on the one or more parameters for the predetermined period.
[0047] In an embodiment, the system (108) may generate a simulation score associated with sports event based on the mapping. The system (108) may generate recommendations via an artificial intelligence (AI) engine, of an optimized order of the second set of players to be mapped with the first set of players based on the simulation score.
[0048] In an embodiment, the system (108) may generate the optimized order by being configured to generate one or more mappings associated with the one or more parameters. The system (108) may merge the one or more mappings to determine the order of second set of players to be mapped with the first set of players for playing the sports event for the predetermined period. The system (108) may compute the simulation score for the one or more mappings. The system (108) may generate recommendations, of the optimized order of the second set of players to be mapped with the first set of players based on a lowest simulation score associated with the one or more mappings.
[0049] In an embodiment, the system (108) may further perform one or more simulations that may extended to a 50 over format with the one or more configurations. The system (108) may on the playing 11 players, generate a bowling line up for all the 50 overs. The 50 overs may be bifurcated into 1-10, 11-40, and 41-50 overs. Each bowler may ball 10 overs maximum i.e. a bowling over constraint may be changed to 10 overs and further changed from 50 to 20 overs based on the one or more configurations.
[0050] Although FIG. 1 shows exemplary components of the network architecture (100), in other embodiments, the network architecture (100) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 1. Additionally, or alternatively, one or more components of the network architecture (100) may perform functions described as being performed by one or more other components of the network architecture (100).
[0051] FIG. 2 illustrates an exemplary representation (200) of a proposed system (108), in accordance with an embodiment of the present disclosure.
[0052] Referring to FIG. 2, the system (108) may include one or more processor(s) (202). The one or more processor(s) (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that process data based on operational instructions. Among other capabilities, the one or more processor(s) (202) may be configured to fetch and execute computer-readable instructions stored in a memory (204) of the system (108). The memory (204) may be configured to store one or more computer-readable instructions or routines in a non-transitory computer readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory (204) may comprise any non-transitory storage device including, for example, volatile memory such as random-access memory (RAM), or non-volatile memory such as erasable programmable read only memory (EPROM), flash memory, and the like.
[0053] In an embodiment, the system (108) may include an interface(s) (206). The interface(s) (206) may comprise a variety of interfaces, for example, interfaces for data input and output devices (I/O), storage devices, and the like. The interface(s) (206) may facilitate communication through the system (108). The interface(s) (206) may also provide a communication pathway for one or more components of the system (108). Examples of such components include, but are not limited to, processing engine(s) (208), a database (210), a data ingestion engine (212) and an AI engine (214).
[0054] The processing engine(s) (208) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) (208). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) (208) may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) (208) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) (208). In such examples, the system may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system and the processing resource. In other examples, the processing engine(s) (208) may be implemented by electronic circuitry.
[0055] In an embodiment, the processor (202) may receive a list of first set of players via the data ingestion engine (212) selected by the users (102) through the computing device (104). Further, the processor (202) may store the received list of the first set of players in the database (210). The users (102) may include cricket practitioners that generate one or more simulations for a host team. The first set of players may be selected for playing a sports event for a predetermined period and the predetermined period may include overs set for a cricket match.
[0056] In an embodiment, the processor (202) may determine an order of a second set of players to be mapped with the first set of players for playing the sports event based on one or more parameters. The one or more parameters may include but not limited to a wicket taking parameter and an economy parameter. Further, the processor (202) may simulate the sports event based on the mapping for the predetermined period.
[0057] In an embodiment, the first set of players may include one or more batsmen selected for playing the sports event and the second set of players may include one or more bowlers to be mapped with the one or more batsmen based on the one or more parameters for the predetermined period.
[0058] In an embodiment, the processor (202) may generate a simulation score associated with sports event based on the mapping. The processor (202) may generate recommendations via an AI engine (214), of an optimized order of the second set of players to be mapped with the first set of players based on the simulation score.
[0059] In an embodiment, the processor (202) may generate the optimized order by being configured to generate one or more mappings associated with the one or more parameters. The processor (202) may merge the one or more mappings to determine the order of second set of players to be mapped with the first set of players for playing the sports event for the predetermined period. The processor (202) may compute the simulation score for the one or more mappings. The processor (202) may generate recommendations, of the optimized order of the second set of players to be mapped with the first set of players based on a lowest simulation score associated with the one or more mappings.
[0060] FIG. 3 illustrates an exemplary simulation framework (300) of the system (108), in accordance with an embodiment of the present disclosure.
[0061] As illustrated in FIG. 3, the user (102) may select the players he wants to select in the team. After selecting, the user (102) may run a simulation based on the selected players. For example, the user (102) may want to generate a bowling line up for all 20 overs. Hence, the simulation framework (300) may create the matchups based on the selected players by taking their head-to-head statistics and bowling history. Further, based on the economy and wicket criteria, the simulation framework (300) may generate the matchup and simulation, i.e., the order of bowling for the bowling team.
[0062] In an embodiment, the simulation framework (300) may include an input (304). The input may include data including opposite playing batsmen (310). Further, the simulation framework (300) may include intermediate compensations (306). The intermediate compensations (306) may include generation of matchup data against bowling options (312) provided by the cricket practitioners, where the bowling options may be based on economy and wicket taking. Further, users (102)/ cricket practitioners (314) may enter the required players which may include 11 players. Further, the simulation framework (300) may regenerate matchup data (316) using the selected players. The simulation framework (300) may combine bowler recommendations (318) from both economy and wicket taking criteria for each opponent batsmen. The simulation framework (300) may generate an output (308) that may include cricket practitioner’s bowler to over mapping (320) based on two different approaches that may include but not limited to logical and optimum opposition team’s batting simulation score.
[0063] FIG. 4 illustrates an exemplary optimized solution (400) provided by the system (108), in accordance with an embodiment of the present disclosure.
[0064] As illustrated in FIG. 4, the system (108) may utilize an optimum approach for mapping with respect to economy and wicket taking. The system (108) may merge the mappings to generate multiple bowler to over mappings. Further, the system (108) may compute a bowler simulation score for each of the merged mappings and pick the mapping with the lowest simulation score.
[0065] As illustrated in FIG. 4, in an embodiment, the system (108) may receive inputs (402) such as but not limited to an over, a line-up based on economy, and a line-up based on wicket taking ability.
[0066] In an embodiment, the system (108) may generate multiple mappings (404) by merging economy and wicket taking line-ups. The system (108) may generate bowling simulation scores associated with a bowling line-up utilized based on the economy and the wicket taking ability. The multiple mappings (404) may include mapping of the players based on the bowling simulation score.
[0067] Further, in an embodiment, the system (108) may generate a final mapping with a lowest bowling simulation score (406). The final mapping may include but not limited to an over, an optimized line-up, and a decision with respect to the final mapping. The optimized line-up may include the bowline line-up for a specific over based on the economy or the wicket taking ability.
[0068] FIG. 5 illustrates an exemplary result (500) of the system (108), in accordance with an embodiment of the present disclosure.
[0069] As illustrated in FIG. 5, in an embodiment, the system (108) may generate Project updates/Results/Metrics (502) based on the provided inputs. The users/cricket practitioners (102) may select a match (504) they want to simulate for the host team. Further, in an embodiment, the users (102) may simulate outputs (506) related to a logical, an optimum, and an actual value associated with the generated final mappings.
[0070] FIG. 6 illustrates an exemplary computer system (600) in which or with which the proposed system (108) may be implemented, in accordance with an embodiment of the present disclosure.
[0071] As shown in FIG. 6, the computer system (600) may include an external storage device (610), a bus (620), a main memory (630), a read-only memory (640), a mass storage device (650), a communication port(s) (660), and a processor (670). A person skilled in the art will appreciate that the computer system (600) may include more than one processor and communication ports. The processor (670) may include various modules associated with embodiments of the present disclosure. The communication port(s) (660) may be any of an RS-232 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication ports(s) (660) may be chosen depending on a network, such as a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system (600) connects.
[0072] In an embodiment, the main memory (630) may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (640) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chip for storing static information e.g., start-up or basic input/output system (BIOS) instructions for the processor (670). The mass storage device (650) may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces).
[0073] In an embodiment, the bus (620) may communicatively couple the processor(s) (670) with the other memory, storage, and communication blocks. The bus (620) may be, e.g. a Peripheral Component Interconnect PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), USB, or the like, for connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (670) to the computer system (600).
[0074] In another embodiment, operator and administrative interfaces, e.g., a display, keyboard, and cursor control device may also be coupled to the bus (620) to support direct operator interaction with the computer system (600). Other operator and administrative interfaces can be provided through network connections connected through the communication port(s) (660). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (600) limit the scope of the present disclosure.
[0075] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be implemented merely as illustrative of the disclosure and not as a limitation.

ADVANTAGES OF THE INVENTION
[0076] The present disclosure provides a system and a method that generates an optimized set of players based on multiple factors originating from sport analytics.
[0077] The present disclosure provides a system and a method that performs a pre-game simulation between cricket practitioners and the opponent team to recommend an optimized bowler mapping and computes a lowest simulation score.
[0078] The present disclosure provides a system and a method that performs real-time simulation during the match to recommend an optimized set of bowlers to be mapped to the remaining overs of the match.
[0079] The present disclosure provides a system and a method that generates mappings with respect to economy and wicket taking and further predicts bowler to over mappings.
[0080] The present disclosure provides a system and a method that utilizes the mapping with the lowest simulation score.
[0081] The present disclosure provides a system and a method that save times in deciding an order of bowling and batting for the players.
[0082] The present disclosure provides a system and a method that utilizes the wicket taking criteria and the economy criteria for every over and formulates a best approach for optimization.
,CLAIMS:1. A system (108) for an optimum simulator, the system (108) comprising:
a processor (202); and
a memory (204) operatively coupled with the processor (202), wherein said memory (204) stores instructions which, when executed by the processor (202), cause the processor (202) to:
receive a list of first set of players for playing a sports event for a predetermined period;
determine an order of a second set of players to be mapped with the first set of players for playing the sports event based on one or more parameters;
simulate the sports event based on the mapping for the predetermined period;
generate a simulation score associated with the sports event based on the mapping; and
generate recommendations, via an artificial intelligence (AI) engine, of an optimized order of the second set of players to be mapped with the first set of players based on the simulation score.
2. The system (108) as claimed in claim 1, wherein the one or more parameters comprise at least one of: a wicket taking parameter and an economy parameter.
3. The system (108) as claimed in claim 1, wherein the processor (202) is to generate the optimized order by being configured to:
generate one or more mappings associated with the one or more parameters;
merge the one or more mappings to determine the order of the second set of players to be mapped with the first set of players for playing the sports event for the predetermined period;
compute the simulation score for the one or more mappings; and
generate the recommendations of the optimized order of the second set of players to be mapped with the first set of players based on a lowest simulation score associated with the one or more mappings.
4. The system (108) as claimed in claim 1, wherein the first set of players comprise one or more batsmen selected for playing the sports event, and the second set of players comprise one or more bowlers to be mapped with the one or more batsmen based on the one or more parameters for the predetermined period.
5. A method for an optimum simulator, the method comprising:
receiving, by a processor (202), associated with a system (108), a list of a first set of players for playing a sports event for a predetermined period;
determining, by the processor (202), an order of a second set of players to be mapped with the first set of players for playing the sports event based on one or more parameters;
simulating, by the processor (202), the sports event based on the mapping for the predetermined period;
generating, by the processor (202), a simulation score associated with the sports event based on the mapping; and
generating, by the processor (202), recommendations via an artificial intelligence (AI) engine, of an optimized order of the second set of players to be mapped with the first set of players based on the simulation score.
6. The method as claimed in claim 5, wherein the one or more parameters comprise at least one of: a wicket taking parameter and an economy parameter.
7. The method as claimed in claim 6, wherein generating, by the processor (202), the optimized order comprises:
generating, by the processor (202), one or more mappings associated with the one or more parameters;;
merging, by the processor (202), the one or more mappings to determine the order of the second set of players to be mapped with the first set of players for playing the sports event for the predetermined period;
computing, by the processor (202), the simulation score for the one or more mappings; and
generating, by the processor (202), the recommendations of the optimized order of the second set of players to be mapped with the first set of players based on a lowest simulation score associated with the one or more mappings.
8. A user equipment (UE) for sending requests, the UE comprising:
one or more processors communicatively coupled to a processor (202) associated with a system (108), wherein the one or more processors are coupled with a memory, and wherein said memory stores instructions, which when executed by the one or more processors, cause the one or more processors to:
transmit a list of a first set of players to the processor (202) via a network, wherein the first set of players are for playing a sports event for a predetermined period, and wherein the processor (202) is configured to:
receive the list of the first set of players;
determine an order of a second set of players to be mapped with the first set of players for playing the sports event based on one or more parameters ;
simulate the sports event based on the mapping for the predetermined period;
generate a simulation score associated with the sports event based on the mapping; and
generate recommendations, via an artificial intelligence (AI) engine, of an optimized order of the second set of players to be mapped with the first set of players based on the simulation score.