Patent Publication Number: US-2022215718-A1

Title: Systems and methods for generating multidimensional data structures based on fantasy sports account activity

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is a continuation of and claims the benefit and priority to U.S. Non-Provisional application Ser. No. 16/437,782, filed on Jun. 11, 2019, which claims the benefit of and priority to U.S. Provisional Application No. 62/683,398, filed on Jun. 11, 2018. The entire disclosure of each of which is incorporated herein by reference in entirety. 
    
    
     BACKGROUND OF THE DISCLOSURE 
     Content management systems allocate and use a lot of computing resources to transmit content to a very large number of remote computing devices. Similarly, remote computing devices also allocate and use a lot of computing resources to receive and display the content received from the content management systems. In the case of mobile devices where memory, processing power and power are all finite resources, the receipt and display of content that is not contextually relevant to a user can adversely affect the device&#39;s performance and life as well the overall user experience. As such, content management systems should utilize appropriate resource management policies to reduce the amount of contextually irrelevant content being delivered to the remote computing devices. 
     BRIEF SUMMARY OF THE DISCLOSURE 
     The present disclosure relates to one or more systems and methods for generating a multidimensional data structure to provide content for one or more users from a plurality of users having similar profile attributes. The multidimensional data structure can be used to identify similarities between multiple user profiles based in part on attributes of players lineups in the respective user&#39;s profile and/or one or more contests (e.g., fantasy contests, betting contests) the respective user has participated in. The multidimensional data structure can correlate the similarities between the user profiles to provide for optimization of the content provided to each of the plurality of users. For example, one or more user profiles can be modified or otherwise updated using the multidimensional data structure to identify contests that the user is more likely to be interested in and participate in based in part on their similarities with other user profiles in the plurality of user profiles. 
     In at least one aspect, a method for generating a multidimensional data structure using fantasy sports account activity is provided. The method can include maintaining, by one or more processors, a plurality of user profiles, each of the user profiles having one or more player lineups associated with at least one fantasy sports contest. The method can include identifying, by the one or more processors, for a first user profile of the plurality of user profiles, one or more player lineups of the first user profile and one or more respective contests for which the player lineups were entered, each of the player lineups including a plurality of players. Each of the plurality of players can include one or more player attributes. Each of the contests can include one or more contest attributes. The method can include generating, by the one or more processors, for the first user profile, a multidimensional data structure including a plurality of features. Each feature can include a respective value that is based on the player attributes corresponding to the players included in the one or more player lineups and the contest attributes corresponding to the one or more respective contests for which the player lineups were entered. The method can include selecting, by the one or more processors using the multidimensional data structure, content for presentation to a device associated with the user profile. The content can be selected using an association between at least one player attribute and at least one contest attribute identified by the multidimensional data structure. The method can include providing, by the one or more processors, using the generated multidimensional data structure, the content to the device associated with the user profile. 
     In embodiments, the one or more player attributes can include one or more of the following: a name, a sport category, a location, a team value, a position value, a price parameter and one or more future contests specific to the respective player. The one or more contest attributes can include one or more of the following: a sport category, a price parameter, a prize parameter, a registrant threshold, a registrant limit, a current registrant count, a day value, a time value, a duration value, a contest type, and an experience level. Each user profile in the plurality of user profiles can include data corresponding to user interactions with one or more fantasy sports contests executing on a device associated with the respective user. 
     In embodiments, the method can include determining a user type for each user profile in the plurality of user profiles based on a frequency of use and determining a skill level for each user profile in the plurality of user profiles based on an outcome value corresponding to one or more fantasy sports contests the respective user participated in. 
     The method can include generating an activity profile for each user profile having data associated with one or more fantasy sports contests the respective user participated in. The method can include modifying the multidimensional data structure responsive to the activity profile of the first user profile. The method can include selecting subsequent content for the first user profile based on the modification to the multidimensional data structure. The method can include updating a second user profile based on the activity profile of the first user profile. 
     In embodiments, the method can include generating, using a clustering algorithm, a user cluster based on a plurality of multidimensional data structures corresponding to the plurality of user profiles. Each user profile in the user cluster can include multiple player attributes in common and multiple contest attributes in common. The method can include receiving, by the one or more processors, one or more contest attributes data from a third party server and determining, by the one or more processors using the multidimensional data structure, the association between the at least player attribute and the at least one contest attributes using the one or more contest attributes from the third party server. The method can include determining, by the one or more processors, a profile association between the user profile and the multidimensional data structure and storing, by the one or more processors, the profile association in the user profile. The method can include modifying, by the one or more processors, at least one player attribute or at least one contest attribute associated with the user profile using a user behavior metric from the one or more respective contests for which the player lineups were previously entered. The method can include generating, by the one or more processors, one or more features of the plurality of features to include the at least one modified player attribute or the at least one modified contest attribute. 
     In at least one aspect, a system for generating a multidimensional data structure using fantasy sports account activity is provided. The system can include one or more processors and memory. The memory can include computer-readable instructions stored in memory that, when executed by the one or more processors, cause the one or more processors to maintain a plurality of user profiles. Each of the user profiles can include one or more player lineups associated with at least one fantasy sports contest. The instructions can cause the one or more processors to identify, for a first user profile of the plurality of user profiles, one or more player lineups of the first user profile and one or more respective contests for which the player lineups were entered. Each of the player lineups can include a plurality of players. Each of the plurality of players can include one or more player attributes. Each of the contests can include one or more contest attributes. The instructions can cause the one or more processors to generate, for the first user profile, a multidimensional data structure including a plurality of features. Each feature can include a respective value that is based on the player attributes corresponding to the players included in the one or more player lineups and the contest attributes corresponding to the one or more respective contests for which the player lineups were entered. The instructions can cause the one or more processors to select, using the multidimensional data structure, content for presentation to a device associated with the user profile. The content can be selected using an association between at least one player attribute and at least one contest attribute identified by the multidimensional data structure. The instructions can cause the one or more processors to provide, using the generated multidimensional data structure, the content to the device associated with the user profile. 
     In embodiments, the instructions can cause the one or more processors to determine a user type for each user profile in the plurality of user profiles based on a frequency of use and determine a skill level for each user profile in the plurality of user profiles based on an outcome value corresponding to one or more fantasy sports contests the respective user participated in. The instructions can cause the one or more processors to generate an activity profile for each user profile having data associated with one or more fantasy sports contests the respective user participated in, modify the multidimensional data structure responsive to the activity profile of the first user profile, and select subsequent content for the first user profile based on the modification to the multidimensional data structure. The instructions can cause the one or more processors to update a second user profile based on the activity profile of the first user profile. 
     In embodiments, the instructions can cause the one or more processors to generate, using a clustering algorithm, a user cluster based on a plurality of multidimensional data structures corresponding to the plurality of user profiles, wherein each user profile in the user cluster having multiple player attributes in common and multiple contest attributes in common. The instructions can cause the one or more processors to receive, by the one or more processors, one or more contest attributes data from a third party server, and determine, by the one or more processors using the multidimensional data structure, the association between the at least player attribute and the at least one contest attributes using the one or more contest attributes from the third party server. The instructions can cause the one or more processors to determine a profile association between the user profile and the multidimensional data structure and store the profile association in the user profile. The instructions can cause the one or more processors to modify at least one player attribute or at least one contest attribute associated with the user profile using a user behavior metric from the one or more respective contests for which the player lineups were previously entered and generate one or more features of the plurality of features to include the at least one modified player attribute or the at least one modified contest attribute. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The foregoing and other objects, aspects, features, and advantages of the disclosure will become more apparent and better understood by referring to the following description taken in conjunction with the accompanying drawings, in which: 
         FIG. 1A  is a block diagram depicting an embodiment of a network environment comprising client devices in communication with server devices via a network; 
         FIG. 1B  is a block diagram depicting a cloud computing environment comprising client device in communication with cloud service providers; 
         FIGS. 1C and 1D  are block diagrams depicting embodiments of computing devices useful in connection with the methods and systems described herein; 
         FIG. 2A  depicts a contest lobby showing contests and a corresponding allocation of computer resources; 
         FIG. 2B  depicts a contest lobby showing contests and a corresponding allocation of computer resources; 
         FIG. 3  is a block diagram depicting one or more embodiments of a contest ranking system; 
         FIG. 4A  is a block diagram depicting one or more embodiments of a historical contest database; 
         FIG. 4B  is a block diagram depicting one or more embodiments of an active contest database; 
         FIG. 5  is a flow chart depicting one or more embodiments of a process for determining one or more contests that are similar to a target contest; 
         FIG. 6A  depicts one or more embodiments of a multidimensional data structure linking a first user profile of a plurality of user profiles; 
         FIG. 6B  depicts one or more embodiments of a data structure having a features with a user behavior metric applied to the respective features; 
         FIG. 7  depicts one or more embodiments of the first user profile of  FIG. 6A  including player attributes and contest attributes associated with the first user profile; 
         FIG. 8  depicts one or more embodiments of a block diagram of a vector management system; and 
         FIGS. 9A-9B  shows a flow diagram of a method for generating a multidimensional data structure. 
     
    
    
     DETAILED DESCRIPTION 
     For purposes of reading the description of the various embodiments below, the following descriptions of the sections of the specification and their respective contents may be helpful: Section A describes a network environment and computing environment which may be useful for practicing embodiments described herein. 
     Section B describes embodiments of systems and methods for monitoring a pace of registrations of contests for improved allocation of computer resources and for ranking contests. 
     Section C describes embodiments of systems and method for generating a multidimensional data structure to generate content for one or more users from a plurality of users having similar profile attributes. 
     A. Computing and Network Environment 
     Prior to discussing specific embodiments of the present solution, it may be helpful to describe aspects of the operating environment as well as associated system components (e.g., hardware elements) in connection with the methods and systems described herein. Referring to  FIG. 1A , an embodiment of a network environment is depicted. In brief overview, the network environment includes one or more clients  102   a - 102   n  (also generally referred to as local machine(s)  102 , client(s)  102 , client node(s)  102 , client machine(s)  102 , client computer(s)  102 , client device(s)  102 , endpoint(s)  102 , or endpoint node(s)  102 ) in communication with one or more servers  106   a - 106   n  (also generally referred to as server(s)  106 , node  106 , or remote machine(s)  106 ) via one or more networks  104 . In some embodiments, a client  102  has the capacity to function as both a client node seeking access to resources provided by a server and as a server providing access to hosted resources for other clients  102   a - 102   n.    
     Although  FIG. 1A  shows a network  104  between the clients  102  and the servers  106 , the clients  102  and the servers  106  may be on the same network  104 . In some embodiments, there are multiple networks  104  between the clients  102  and the servers  106 . In one of these embodiments, a network  104 ′ (not shown) may be a private network and a network  104  may be a public network. In another of these embodiments, a network  104  may be a private network and a network  104 ′ a public network. In still another of these embodiments, networks  104  and  104 ′ may both be private networks. 
     The network  104  may be connected via wired or wireless links. Wired links may include Digital Subscriber Line (DSL), coaxial cable lines, or optical fiber lines. The wireless links may include BLUETOOTH, Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), an infrared channel or satellite band. The wireless links may also include any cellular network standards used to communicate among mobile devices, including standards that qualify as 1G, 2G, 3G, or 4G. The network standards may qualify as one or more generation of mobile telecommunication standards by fulfilling a specification or standards such as the specifications maintained by International Telecommunication Union. The 3G standards, for example, may correspond to the International Mobile Telecommunications-2050 (IMT-2050) specification, and the 4G standards may correspond to the International Mobile Telecommunications Advanced (IMT-Advanced) specification. Examples of cellular network standards include AMPS, GSM, GPRS, UMTS, LTE, LTE Advanced, Mobile WiMAX, and WiMAX-Advanced. Cellular network standards may use various channel access methods e.g. FDMA, TDMA, CDMA, or SDMA. In some embodiments, different types of data may be transmitted via different links and standards. In other embodiments, the same types of data may be transmitted via different links and standards. 
     The network  104  may be any type and/or form of network. The geographical scope of the network  104  may vary widely and the network  104  can be a body area network (BAN), a personal area network (PAN), a local-area network (LAN), e.g. Intranet, a metropolitan area network (MAN), a wide area network (WAN), or the Internet. The topology of the network  104  may be of any form and may include, e.g., any of the following: point-to-point, bus, star, ring, mesh, or tree. The network  104  may be an overlay network which is virtual and sits on top of one or more layers of other networks  104 ′. The network  104  may be of any such network topology as known to those ordinarily skilled in the art capable of supporting the operations described herein. The network  104  may utilize different techniques and layers or stacks of protocols, including, e.g., the Ethernet protocol, the internet protocol suite (TCP/IP), the ATM (Asynchronous Transfer Mode) technique, the SONET (Synchronous Optical Networking) protocol, or the SDH (Synchronous Digital Hierarchy) protocol. The TCP/IP internet protocol suite may include application layer, transport layer, internet layer (including, e.g., IPv6), or the link layer. The network  104  may be a type of a broadcast network, a telecommunications network, a data communication network, or a computer network. 
     In some embodiments, the system may include multiple, logically-grouped servers  106 . In one of these embodiments, the logical group of servers may be referred to as a server farm  38  or a machine farm  38 . In another of these embodiments, the servers  106  may be geographically dispersed. In other embodiments, a machine farm  38  may be administered as a single entity. In still other embodiments, the machine farm  38  includes a plurality of machine farms  38 . The servers  106  within each machine farm  38  can be heterogeneous—one or more of the servers  106  or machines  106  can operate according to one type of operating system platform (e.g., WINDOWS NT, manufactured by Microsoft Corp. of Redmond, Wash.), while one or more of the other servers  106  can operate on according to another type of operating system platform (e.g., Unix, Linux, or Mac OS X). 
     In one embodiment, servers  106  in the machine farm  38  may be stored in high-density rack systems, along with associated storage systems, and located in an enterprise data center. In this embodiment, consolidating the servers  106  in this way may improve system manageability, data security, the physical security of the system, and system performance by locating servers  106  and high performance storage systems on localized high performance networks. Centralizing the servers  106  and storage systems and coupling them with advanced system management tools allows more efficient use of server resources. 
     The servers  106  of each machine farm  38  do not need to be physically proximate to another server  106  in the same machine farm  38 . Thus, the group of servers  106  logically grouped as a machine farm  38  may be interconnected using a wide-area network (WAN) connection or a metropolitan-area network (MAN) connection. For example, a machine farm  38  may include servers  106  physically located in different continents or different regions of a continent, country, state, city, campus, or room. Data transmission speeds between servers  106  in the machine farm  38  can be increased if the servers  106  are connected using a local-area network (LAN) connection or some form of direct connection. Additionally, a heterogeneous machine farm  38  may include one or more servers  106  operating according to a type of operating system, while one or more other servers  106  execute one or more types of hypervisors rather than operating systems. In these embodiments, hypervisors may be used to emulate virtual hardware, partition physical hardware, virtualize physical hardware, and execute virtual machines that provide access to computing environments, allowing multiple operating systems to run concurrently on a host computer. Native hypervisors may run directly on the host computer. Hypervisors may include VMware ESX/ESXi, manufactured by VMWare, Inc., of Palo Alto, Calif.; the Xen hypervisor, an open source product whose development is overseen by Citrix Systems, Inc.; the HYPER-V hypervisors provided by Microsoft or others. Hosted hypervisors may run within an operating system on a second software level. Examples of hosted hypervisors may include VMware Workstation and VIRTUALBOX. 
     Management of the machine farm  38  may be de-centralized. For example, one or more servers  106  may comprise components, subsystems and modules to support one or more management services for the machine farm  38 . In one of these embodiments, one or more servers  106  provide functionality for management of dynamic data, including techniques for handling failover, data replication, and increasing the robustness of the machine farm  38 . Each server  106  may communicate with a persistent store and, in some embodiments, with a dynamic store. 
     Server  106  may be a file server, application server, web server, proxy server, appliance, network appliance, gateway, gateway server, virtualization server, deployment server, SSL VPN server, or firewall. In one embodiment, the server  106  may be referred to as a remote machine or a node. In another embodiment, a plurality of nodes  290  may be in the path between any two communicating servers. 
     Referring to  FIG. 1B , a cloud computing environment is depicted. A cloud computing environment may provide client  102  with one or more resources provided by a network environment. The cloud computing environment may include one or more clients  102   a - 102   n , in communication with the cloud  108  over one or more networks  104 . Clients  102  may include, e.g., thick clients, thin clients, and zero clients. A thick client may provide at least some functionality even when disconnected from the cloud  108  or servers  106 . A thin client or a zero client may depend on the connection to the cloud  108  or server  106  to provide functionality. A zero client may depend on the cloud  108  or other networks  104  or servers  106  to retrieve operating system data for the client device. The cloud  108  may include back end platforms, e.g., servers  106 , storage, server farms or data centers. 
     The cloud  108  may be public, private, or hybrid. Public clouds may include public servers  106  that are maintained by third parties to the clients  102  or the owners of the clients. The servers  106  may be located off-site in remote geographical locations as disclosed above or otherwise. Public clouds may be connected to the servers  106  over a public network. Private clouds may include private servers  106  that are physically maintained by clients  102  or owners of clients. Private clouds may be connected to the servers  106  over a private network  104 . Hybrid clouds  108  may include both the private and public networks  104  and servers  106 . 
     The cloud  108  may also include a cloud based delivery, e.g. Software as a Service (SaaS)  110 , Platform as a Service (PaaS)  112 , and Infrastructure as a Service (IaaS)  114 . IaaS may refer to a user renting the use of infrastructure resources that are needed during a specified time period. IaaS providers may offer storage, networking, servers or virtualization resources from large pools, allowing the users to quickly scale up by accessing more resources as needed. Examples of IaaS can include infrastructure and services (e.g., EG-32) provided by OVH HOSTING of Montreal, Quebec, Canada, AMAZON WEB SERVICES provided by Amazon.com, Inc., of Seattle, Wash., RACKSPACE CLOUD provided by Rackspace US, Inc., of San Antonio, Tex., Google Compute Engine provided by Google Inc. of Mountain View, Calif., or RIGHTSCALE provided by RightScale, Inc., of Santa Barbara, Calif. PaaS providers may offer functionality provided by IaaS, including, e.g., storage, networking, servers or virtualization, as well as additional resources such as, e.g., the operating system, middleware, or runtime resources. Examples of PaaS include WINDOWS AZURE provided by Microsoft Corporation of Redmond, Wash., Google App Engine provided by Google Inc., and HEROKU provided by Heroku, Inc. of San Francisco, Calif. SaaS providers may offer the resources that PaaS provides, including storage, networking, servers, virtualization, operating system, middleware, or runtime resources. In some embodiments, SaaS providers may offer additional resources including, e.g., data and application resources. Examples of SaaS include GOOGLE APPS provided by Google Inc., SALESFORCE provided by Salesforce.com Inc. of San Francisco, Calif., or OFFICE 365 provided by Microsoft Corporation. Examples of SaaS may also include data storage providers, e.g. DROPBOX provided by Dropbox, Inc. of San Francisco, Calif., Microsoft SKYDRIVE provided by Microsoft Corporation, Google Drive provided by Google Inc., or Apple ICLOUD provided by Apple Inc. of Cupertino, Calif. 
     Clients  102  may access IaaS resources with one or more IaaS standards, including, e.g., Amazon Elastic Compute Cloud (EC2), Open Cloud Computing Interface (OCCI), Cloud Infrastructure Management Interface (CIMI), or OpenStack standards. Some IaaS standards may allow clients access to resources over HTTP, and may use Representational State Transfer (REST) protocol or Simple Object Access Protocol (SOAP). Clients  102  may access PaaS resources with different PaaS interfaces. Some PaaS interfaces use HTTP packages, standard Java APIs, JavaMail API, Java Data Objects (JDO), Java Persistence API (JPA), Python APIs, web integration APIs for different programming languages including, e.g., Rack for Ruby, WSGI for Python, or PSGI for Perl, or other APIs that may be built on REST, HTTP, XML, or other protocols. Clients  102  may access SaaS resources through the use of web-based user interfaces, provided by a web browser (e.g. GOOGLE CHROME, Microsoft INTERNET EXPLORER, or Mozilla Firefox provided by Mozilla Foundation of Mountain View, Calif.). Clients  102  may also access SaaS resources through smartphone or tablet applications, including, e.g., Salesforce Sales Cloud, or Google Drive app. Clients  102  may also access SaaS resources through the client operating system, including, e.g., Windows file system for DROPBOX. 
     In some embodiments, access to IaaS, PaaS, or SaaS resources may be authenticated. For example, a server or authentication server may authenticate a user via security certificates, HTTPS, or API keys. API keys may include various encryption standards such as, e.g., Advanced Encryption Standard (AES). Data resources may be sent over Transport Layer Security (TLS) or Secure Sockets Layer (SSL). 
     The client  102  and server  106  may be deployed as and/or executed on any type and form of computing device, e.g. a computer, network device or appliance capable of communicating on any type and form of network and performing the operations described herein.  FIGS. 1C and 1D  depict block diagrams of a computing device  100  useful for practicing an embodiment of the client  102  or a server  106 . As shown in  FIGS. 1C and 1D , each computing device  100  includes a central processing unit  121 , and a main memory unit  122 . As shown in  FIG. 1C , a computing device  100  may include a storage device  128 , an installation device  116 , a network interface  118 , an I/O controller  123 , display devices  124   a - 124   n , a keyboard  126  and a pointing device  127 , e.g. a mouse. The storage device  128  may include, without limitation, an operating system, software, and a software of a contest management system  206 . As shown in  FIG. 1D , each computing device  100  may also include additional optional elements, e.g. a memory port  103 , a bridge  170 , one or more input/output devices  130   a - 130   n  (generally referred to using reference numeral  130 ), and a cache memory  140  in communication with the central processing unit  121 . 
     The central processing unit  121  is any logic circuitry that responds to and processes instructions fetched from the main memory unit  122 . In many embodiments, the central processing unit  121  is provided by a microprocessor unit, e.g.: those manufactured by Intel Corporation of Mountain View, Calif.; those manufactured by Motorola Corporation of Schaumburg, Ill.; the ARM processor and TEGRA system on a chip (SoC) manufactured by Nvidia of Santa Clara, Calif.; the POWER7 processor, those manufactured by International Business Machines of White Plains, N.Y.; or those manufactured by Advanced Micro Devices of Sunnyvale, Calif. The computing device  100  may be based on any of these processors, or any other processor capable of operating as described herein. The central processing unit  121  may utilize instruction level parallelism, thread level parallelism, different levels of cache, and multi-core processors. A multi-core processor may include two or more processing units on a single computing component. Examples of multi-core processors include the AMD PHENOM IIX2, INTEL CORE i5 and INTEL CORE i7. 
     Main memory unit  122  may include one or more memory chips capable of storing data and allowing any storage location to be directly accessed by the microprocessor  121 . Main memory unit  122  may be volatile and faster than storage  128  memory. Main memory units  122  may be Dynamic random access memory (DRAM) or any variants, including static random access memory (SRAM), Burst SRAM or SynchBurst SRAM (BSRAM), Fast Page Mode DRAM (FPM DRAM), Enhanced DRAM (EDRAM), Extended Data Output RAM (EDO RAM), Extended Data Output DRAM (EDO DRAM), Burst Extended Data Output DRAM (BEDO DRAM), Single Data Rate Synchronous DRAM (SDR SDRAM), Double Data Rate SDRAM (DDR SDRAM), Direct Rambus DRAM (DRDRAM), or Extreme Data Rate DRAM (XDR DRAM). In some embodiments, the main memory  122  or the storage  128  may be non-volatile; e.g., non-volatile read access memory (NVRAM), flash memory non-volatile static RAM (nvSRAM), Ferroelectric RAM (FeRAM), Magnetoresistive RAM (MRAM), Phase-change memory (PRAM), conductive-bridging RAM (CBRAM), Silicon-Oxide-Nitride-Oxide-Silicon (SONOS), Resistive RAM (RRAM), Racetrack, Nano-RAM (NRAM), or Millipede memory. The main memory  122  may be based on any of the above described memory chips, or any other available memory chips capable of operating as described herein. In the embodiment shown in  FIG. 1C , the processor  121  communicates with main memory  122  via a system bus  150  (described in more detail below).  FIG. 1D  depicts an embodiment of a computing device  100  in which the processor communicates directly with main memory  122  via a memory port  103 . For example, in  FIG. 1D  the main memory  122  may be DRDRAM. 
       FIG. 1D  depicts an embodiment in which the main processor  121  communicates directly with cache memory  140  via a secondary bus, sometimes referred to as a backside bus. In other embodiments, the main processor  121  communicates with cache memory  140  using the system bus  150 . Cache memory  140  typically has a faster response time than main memory  122  and is typically provided by SRAM, BSRAM, or EDRAM. In the embodiment shown in  FIG. 1D , the processor  121  communicates with various I/O devices  130  via a local system bus  150 . Various buses may be used to connect the central processing unit  121  to any of the I/O devices  130 , including a PCI bus, a PCI-X bus, or a PCI-Express bus, or a NuBus. For embodiments in which the I/O device is a video display  124 , the processor  121  may use an Advanced Graphics Port (AGP) to communicate with the display  124  or the I/O controller  123  for the display  124 .  FIG. 1D  depicts an embodiment of a computer  100  in which the main processor  121  communicates directly with I/O device  130   b  or other processors  121 ′ via HYPERTRANSPORT, RAPIDIO, or INFINIBAND communications technology.  FIG. 1D  also depicts an embodiment in which local busses and direct communication are mixed: the processor  121  communicates with I/O device  130   a  using a local interconnect bus while communicating with I/O device  130   b  directly. 
     A wide variety of/O devices  130   a - 130   n  may be present in the computing device  100 . Input devices may include keyboards, mice, trackpads, trackballs, touchpads, touch mice, multi-touch touchpads and touch mice, microphones, multi-array microphones, drawing tablets, cameras, single-lens reflex camera (SLR), digital SLR (DSLR), CMOS sensors, accelerometers, infrared optical sensors, pressure sensors, magnetometer sensors, angular rate sensors, depth sensors, proximity sensors, ambient light sensors, gyroscopic sensors, or other sensors. Output devices may include video displays, graphical displays, speakers, headphones, inkjet printers, laser printers, and 3D printers. 
     Devices  130   a - 130   n  may include a combination of multiple input or output devices, including, e.g., Microsoft KINECT, Nintendo Wiimote for the WII, Nintendo WII U GAMEPAD, or Apple IPHONE. Some devices  130   a - 130   n  allow gesture recognition inputs through combining some of the inputs and outputs. Some devices  130   a - 130   n  provides for facial recognition which may be utilized as an input for different purposes including authentication and other commands. Some devices  130   a - 130   n  provides for voice recognition and inputs, including, e.g., Microsoft KINECT, SIRI for IPHONE by Apple, Google Now or Google Voice Search. 
     Additional devices  130   a - 130   n  have both input and output capabilities, including, e.g., haptic feedback devices, touchscreen displays, or multi-touch displays. Touchscreen, multi-touch displays, touchpads, touch mice, or other touch sensing devices may use different technologies to sense touch, including, e.g., capacitive, surface capacitive, projected capacitive touch (PCT), in-cell capacitive, resistive, infrared, waveguide, dispersive signal touch (DST), in-cell optical, surface acoustic wave (SAW), bending wave touch (BWT), or force-based sensing technologies. Some multi-touch devices may allow two or more contact points with the surface, allowing advanced functionality including, e.g., pinch, spread, rotate, scroll, or other gestures. Some touchscreen devices, including, e.g., Microsoft PIXELSENSE or Multi-Touch Collaboration Wall, may have larger surfaces, such as on a table-top or on a wall, and may also interact with other electronic devices. Some I/O devices  130   a - 130   n , display devices  124   a - 124   n  or group of devices may be augmented reality devices. The I/O devices may be controlled by an I/O controller  123  as shown in  FIG. 1C . The I/O controller may control one or more I/O devices, such as, e.g., a keyboard  126  and a pointing device  127 , e.g., a mouse or optical pen. Furthermore, an I/O device may also provide storage and/or an installation medium  116  for the computing device  100 . In still other embodiments, the computing device  100  may provide USB connections (not shown) to receive handheld USB storage devices. In further embodiments, an I/O device  130  may be a bridge between the system bus  150  and an external communication bus, e.g. a USB bus, a SCSI bus, a FireWire bus, an Ethernet bus, a Gigabit Ethernet bus, a Fibre Channel bus, or a Thunderbolt bus. 
     In some embodiments, display devices  124   a - 124   n  may be connected to I/O controller  123 . Display devices may include, e.g., liquid crystal displays (LCD), thin film transistor LCD (TFT-LCD), blue phase LCD, electronic papers (e-ink) displays, flexile displays, light emitting diode displays (LED), digital light processing (DLP) displays, liquid crystal on silicon (LCOS) displays, organic light-emitting diode (OLED) displays, active-matrix organic light-emitting diode (AMOLED) displays, liquid crystal laser displays, time-multiplexed optical shutter (TMOS) displays, or 3D displays. Examples of 3D displays may use, e.g. stereoscopy, polarization filters, active shutters, or autostereoscopy. Display devices  124   a - 124   n  may also be a head-mounted display (HMD). In some embodiments, display devices  124   a - 124   n  or the corresponding I/O controllers  123  may be controlled through or have hardware support for OPENGL or DIRECTX API or other graphics libraries. 
     In some embodiments, the computing device  100  may include or connect to multiple display devices  124   a - 124   n , which each may be of the same or different type and/or form. As such, any of the I/O devices  130   a - 130   n  and/or the I/O controller  123  may include any type and/or form of suitable hardware, software, or combination of hardware and software to support, enable or provide for the connection and use of multiple display devices  124   a - 124   n  by the computing device  100 . For example, the computing device  100  may include any type and/or form of video adapter, video card, driver, and/or library to interface, communicate, connect or otherwise use the display devices  124   a - 124   n . In one embodiment, a video adapter may include multiple connectors to interface to multiple display devices  124   a - 124   n . In other embodiments, the computing device  100  may include multiple video adapters, with each video adapter connected to one or more of the display devices  124   a - 124   n . In some embodiments, any portion of the operating system of the computing device  100  may be configured for using multiple displays  124   a - 124   n . In other embodiments, one or more of the display devices  124   a - 124   n  may be provided by one or more other computing devices  100   a  or  100   b  connected to the computing device  100 , via the network  104 . In some embodiments software may be designed and constructed to use another computer&#39;s display device as a second display device  124   a  for the computing device  100 . For example, in one embodiment, an Apple iPad may connect to a computing device  100  and use the display of the device  100  as an additional display screen that may be used as an extended desktop. One ordinarily skilled in the art will recognize and appreciate the various ways and embodiments that a computing device  100  may be configured to have multiple display devices  124   a - 124   n.    
     Referring again to  FIG. 1C , the computing device  100  may comprise a storage device  128  (e.g. one or more hard disk drives or redundant arrays of independent disks) for storing an operating system or other related software, and for storing application software programs such as any program related to the software for the contest management system  206 . Examples of storage device  128  include, e.g., hard disk drive (HDD); optical drive including CD drive, DVD drive, or BLU-RAY drive; solid-state drive (SSD); USB flash drive; or any other device suitable for storing data. Some storage devices may include multiple volatile and non-volatile memories, including, e.g., solid state hybrid drives that combine hard disks with solid state cache. Some storage device  128  may be non-volatile, mutable, or read-only. Some storage device  128  may be internal and connect to the computing device  100  via a bus  150 . Some storage devices  128  may be external and connect to the computing device  100  via an I/O device  130  that provides an external bus. Some storage device  128  may connect to the computing device  100  via the network interface  118  over a network  104 , including, e.g., the Remote Disk for MACBOOK AIR by Apple. Some client devices  100  may not require a non-volatile storage device  128  and may be thin clients or zero clients  102 . Some storage device  128  may also be used as an installation device  116 , and may be suitable for installing software and programs. Additionally, the operating system and the software can be run from a bootable medium, for example, a bootable CD, e.g. KNOPPIX, a bootable CD for GNU/Linux that is available as a GNU/Linux distribution from knoppix.net. 
     Client device  100  may also install software or application from an application distribution platform. Examples of application distribution platforms include the App Store for iOS provided by Apple, Inc., the Mac App Store provided by Apple, Inc., GOOGLE PLAY for Android OS provided by Google Inc., Chrome Webstore for CHROME OS provided by Google Inc., and Amazon Appstore for Android OS and KINDLE FIRE provided by Amazon.com, Inc. An application distribution platform may facilitate installation of software on a client device  102 . An application distribution platform may include a repository of applications on a server  106  or a cloud  108 , which the clients  102   a - 102   n  may access over a network  104 . An application distribution platform may include application developed and provided by various developers. A user of a client device  102  may select, purchase and/or download an application via the application distribution platform. 
     Furthermore, the computing device  100  may include a network interface  118  to interface to the network  104  through a variety of connections including, but not limited to, standard telephone lines LAN or WAN links (e.g., 802.11, T1, T3, Gigabit Ethernet, Infiniband), broadband connections (e.g., ISDN, Frame Relay, ATM, Gigabit Ethernet, Ethernet-over-SONET, ADSL, VDSL, BPON, GPON, fiber optical including FiOS), wireless connections, or some combination of any or all of the above. Connections can be established using a variety of communication protocols (e.g., TCP/IP, Ethernet, ARCNET, SONET, SDH, Fiber Distributed Data Interface (FDDI), IEEE 802.11a/b/g/n/ac CDMA, GSM, WiMax and direct asynchronous connections). In one embodiment, the computing device  100  communicates with other computing devices  100 ′ via any type and/or form of gateway or tunneling protocol e.g. Secure Socket Layer (SSL) or Transport Layer Security (TLS), or the Citrix Gateway Protocol manufactured by Citrix Systems, Inc. of Ft. Lauderdale, Fla. The network interface  118  may comprise a built-in network adapter, network interface card, PCMCIA network card, EXPRESSCARD network card, card bus network adapter, wireless network adapter, USB network adapter, modem or any other device suitable for interfacing the computing device  100  to any type of network capable of communication and performing the operations described herein. 
     A computing device  100  of the sort depicted in  FIGS. 1B and 1C  may operate under the control of an operating system, which controls scheduling of tasks and access to system resources. The computing device  100  can be running any operating system such as any of the versions of the MICROSOFT WINDOWS operating systems, the different releases of the Unix and Linux operating systems, any version of the MAC OS for Macintosh computers, any embedded operating system, any real-time operating system, any open source operating system, any proprietary operating system, any operating systems for mobile computing devices, or any other operating system capable of running on the computing device and performing the operations described herein. Typical operating systems include, but are not limited to: WINDOWS 2050, WINDOWS Server 2022, WINDOWS CE, WINDOWS Phone, WINDOWS XP, WINDOWS VISTA, and WINDOWS 7, WINDOWS RT, WINDOWS 8, and WINDOWS 10, all of which are manufactured by Microsoft Corporation of Redmond, Wash.; MAC OS and iOS, manufactured by Apple, Inc. of Cupertino, Calif.; and Linux, a freely-available operating system, e.g. Linux Mint distribution (“distro”) or Ubuntu, distributed by Canonical Ltd. of London, United Kingdom; or Unix or other Unix-like derivative operating systems; and Android, designed by Google, of Mountain View, Calif., among others. Some operating systems, including, e.g., the CHROME OS by Google, may be used on zero clients or thin clients, including, e.g., CHROMEBOOKS. 
     The computer system  100  can be any workstation, telephone, desktop computer, laptop or notebook computer, netbook, ULTRABOOK, tablet, server, handheld computer, mobile telephone, smartphone or other portable telecommunications device, media playing device, a gaming system, mobile computing device, or any other type and/or form of computing, telecommunications or media device that is capable of communication. The computer system  100  has sufficient processor power and memory capacity to perform the operations described herein. In some embodiments, the computing device  100  may have different processors, operating systems, and input devices consistent with the device. The Samsung GALAXY smartphones, e.g., operate under the control of Android operating system developed by Google, Inc. GALAXY smartphones receive input via a touch interface. 
     In some embodiments, the computing device  100  is a tablet e.g. the IPAD line of devices by Apple; GALAXY TAB family of devices by Samsung; or KINDLE FIRE, by Amazon.com, Inc. of Seattle, Wash. In other embodiments, the computing device  100  is an eBook reader, e.g. the KINDLE family of devices by Amazon.com, or NOOK family of devices by Barnes &amp; Noble, Inc. of New York City, N.Y. 
     In some embodiments, the communications device  102  includes a combination of devices, e.g. a smartphone combined with a digital audio player or portable media player. For example, one of these embodiments is a smartphone, e.g. the IPHONE family of smartphones manufactured by Apple, Inc.; a Samsung GALAXY family of smartphones manufactured by Samsung, Inc.; or a Motorola DROID family of smartphones. In yet another embodiment, the communications device  102  is a laptop or desktop computer equipped with a web browser and a microphone and speaker system, e.g. a telephony headset. In these embodiments, the communications devices  102  are web-enabled and can receive and initiate phone calls. In some embodiments, a laptop or desktop computer is also equipped with a webcam or other video capture device that enables video chat and video call. 
     In some embodiments, the status of one or more machines  102 ,  106  in the network  104  are monitored, generally as part of network management. In one of these embodiments, the status of a machine may include an identification of load information (e.g., the number of processes on the machine, CPU and memory utilization), of port information (e.g., the number of available communication ports and the port addresses), or of session status (e.g., the duration and type of processes, and whether a process is active or idle). In another of these embodiments, this information may be identified by a plurality of metrics, and the plurality of metrics can be applied at least in part towards decisions in load distribution, network traffic management, and network failure recovery as well as any aspects of operations of the present solution described herein. Aspects of the operating environments and components described above will become apparent in the context of the systems and methods disclosed herein. 
     B. Systems and Methods for Monitoring a Pace of Registrations of Contests for Improved Allocation of Computer Resources 
     As discussed above, systems and methods of the present solution are directed to generating contests to improve computer resource allocation. A contest generating system can avoid or can delay generating new contests and thus avoid registrant dispersion issues, and can help to ensure that fewer contests with more registrants are generated, thus conserving and better allocating computer resources. 
     According to one aspect, a method for generating contests includes maintaining, by a contest management system including one or more processors, for each first contest of a plurality of first contests managed by the contest management system, historical registration data. The historical registration data identifies, for each time of a plurality of times during which registration of the first contest was open, a number of registrants at the time. Each first contest has respective parameter values of one or more parameters used to generate the first contest. The method further includes identifying, by the contest management system, for a second contest that has an open registration status, respective parameter values of one or more parameters used to generate the second contest, and registration data identifying, for each time of a plurality of times that has lapsed since registration for the second contest was opened, a current number of registrants at the time. The method further includes computing, by the contest management system, a similarity score between the second contest and each first contest of the plurality of first contests. The similarity score can be computed using i) the respective parameter values used to generate the second contest and generate the first contest and ii) the registration data of the second contest and the historical registration data of the first contest. The method further includes selecting, by the contest management system, for the second contest, a subset of the plurality of first contests based on the similarity score exceeding a threshold similarity score value. The method further includes generating, by the contest management system, for the second contest, a projected number of registrants based on i) a current number of registrants of the second contest and ii) the historical registration data of one or more first contests included in the selected subset. The method further includes determining, by the contest management system, a time at which to generate a fourth contest based on the projected number of registrants, and generating, by the contest management system, the fourth contest at the determined time. 
     Referring now to  FIG. 2A , a representation of a lobby  202  including contests C 1  through C 7  is shown. Data or computer resources to allow a client device to render or otherwise present the lobby  202  may be transmitted to the client device by the contest management system  206 . The lobby  202  shows seven contests that are open for registration on a first page or first screen, but more contests (including one or more that are open for registration) can be maintained by the contest management system  206  and may be displayable responsive to the client device transmitting a request for more contests to the contest management system  206 . 
     The lobby  202  may show, for one or more of the contests C 1  through C 7  (e.g. for each contest), a sport category, a contest name or identifier, a contest style, an entry fee, a total prize pool for the contest, a total number of entries and a maximum registrant cap (which may be a dynamic cap that increases or decreases over time, or increases or decreases based on other factors such as current registrant data and/or projected registrant data), and a time corresponding to a registration deadline (e.g. a countdown to the contest going “live,” or a time of the registration deadline). Other information (e.g. contest relevant information) may be displayed in place of, or in addition to, any of the information shown in  FIG. 2A , and any of the information shown in  FIG. 2A  may be omitted as appropriate. As can be seen in  FIG. 2A , the contests C 1  through C 7  do not have many registrants relative to their registrant cap. The registrants are dispersed over the seven contests, even though each contest has significant capacity to include or register more registrants such that all registrants could be accommodated with a smaller number of contest. 
     A block figure showing computer resource allocation  204  is also shown in  FIG. 2A . In some embodiments, the computer resource allocation  204  can represent the total resources the system has allocated to hosting contests. As shown in  FIG. 2A , each of the contests C 1 -C 7  utilize or consume certain amounts of computer resources, which are indicated by the respective blocks shown in  FIG. 2A . The computer resource allocation  204  corresponds to resources allocated by the contest management system  206  to maintain and/or update the contests C 1  through C 7  (e.g. processing power, processing time, time on a cloud or otherwise remote computing system, or other computer resources). As can be seen in  FIG. 2A , a “free” or available amount of computer resources is small relative to the resources required to maintain or update all seven contests. As such, if the contest management system  206  needs to create or generate additional contests, the contest management system  206  may not have sufficient computer resources to do so until additional computer resources are made available to the contest management system or contests utilizing existing computer resources are terminated and free up the computer resources they were consuming. 
       FIG. 2B  shows a lobby  202  that includes contests C 1  through C 4 . Contests for the lobby  202  shown in  FIG. 2B  are generated by a contest ranking system  302  (which can be included in the contest management system  206 ) as described herein. As can be seen in  FIG. 2B , fewer contests are open for registration and as a result fewer computer resources are being utilized allowing the contest management system  206  sufficient computer resources to generate new contests. In addition to utilizing fewer computer resources, the total number of registrants in each contest, and the number of registrants as a percentage of the registrant cap of each contest, is increased as compared to the example shown in  FIG. 2A . The increased number of registrations represents better computer resource utilization per registrant, enabling the contest management system to scale up without needing to increase the computer resources. As there is still room in each contest C 1  through C 4 , further registrants can still be accommodated. 
     As can be seen in the computer resource allocation  204  shown in  FIG. 2B , resources need only be allocated for four contests. Maintaining and updating each contest may have a cost in terms of computer resources, and by lowering the total number contests to be maintained or updated, computer resources can be preserved. The computer resource allocation  204  shown in  FIG. 2B  shows that there are more “free” computer resources than in the example shown in  FIG. 2A , in which seven contests C 1  through C 7  were generated. 
       FIG. 3  is a block diagram showing an embodiment of a contest ranking system  302 . The contest ranking system  302  can be included in the contest management system  206  depicted in  FIG. 1C . The contest ranking system  302  can include or be executed on one or more servers, such as the servers  106  shown in  FIG. 1A . The contest ranking system  302  can include one or more applications, services, routines, servers, daemons, or other executable logics for generating a contest, including one or more of a contest generator  304 , a contest registration tracker  306 , a contest similarity score generator  308 , a registrant projector  310 , a pace tracker  312 , and a contest display manager  313 . The contest ranking system  302  can also include, access, maintain or manage one or more data structures, including but not limited to a historical contest database  314  (historical contest DB  314 ), and an active contest database  318  (active contest DB  318 ). 
     The contest generator  304  can include components, subsystems, modules, scripts, applications, or one or more sets of computer-executable instructions and can be configured to generate a contest. The contest generator  304  can be configured to generate a contest by generating a contest object based on one or more contest-generating parameter values. The contest-generating parameter values can include, for example, values for parameters including one or more of a league (e.g. NFL, MLB, NHL, NBA, a non-US league, or another sports organization), a game type (e.g. classic (with salary constraints) or pick&#39;em (without salary constraints)), a registrant cap (e.g. a fixed cap, no cap, or a dynamic cap that changes based on demand or based on an instruction to change from another component, subsystem, module, script, or application of the contest ranking system  302 ), one or more games, a registration close time (e.g. a particular date and time, such as the date and time of the start of a first game of the contest), an entry fee, an eligibility specification requirement (e.g. a specification that only users having a certain experience level (e.g. as determined by a user profile) be permitted to enter the contest), a prize type (e.g. a satellite (a contest having a fixed number of entries to another contest as at least part of a prize pool), a super-satellite (a contest having a dynamic number of entries to another contest as at least part of a prize pool (e.g. the dynamic number based on number of registrants), or a normal prize type (including monetary prizes as at least part of a prize pool). The contest-generating parameter values can include any other appropriate parameter for generating a contest. 
     The contest generator  304  can be further configured to generate a contest at a particular timing or according to a schedule or based on upcoming games. For example, the contest generator  304  can be configured to generate one or more contests corresponding to an upcoming game, or corresponding to a set of games played on one or more days (e.g. on a particular day or in a particular week). The contest generator may be configured to generate a contest when an active contest hits its fixed registrant cap (has a number of registrants equal to a fixed registrant cap). 
     The contest generator  304  can be further configured to deviate from a schedule, or to generate a contest at a determined time, based on data received from another component, subsystem, module, script, or application of the contest ranking system  302 . For example, the contest generator  304  can be configured to determine a time at which to generate a contest based on a pacing determined by the pace tracker  312  (e.g. a pacing of one or more active contests). The contest generator  304  can be configured to deviate from a schedule (e.g. to delay generation of a scheduled contest, or to restrict generation of a scheduled contest) based on a pacing determined by the pace tracker  312 . The contest generator  304  can determine a timing for generating a new contest based on pacing information for more than one contest, and may weigh the pacing information based on features of the one or more contests (e.g. based on respective total numbers of registrants for the contests, and/or based on respective registrant caps for the contests). For example, as is described in more detail below with reference to the pace tracker  312 , the pace tracker  312  may determine that one or more active contests are under-saturated (e.g. are below a projected or desired pace for the active contest, or below a projected or desired number of registrants). The contest generator  304  may be configured such that, based on or responsive to this determination, the contest generator  304  restricts generation of a scheduled contest. 
     The contest generator  304  may determine a time at which to generate a contest based on a policy for generating new contest. The policy may include a schedule for generating new contests including candidate times for generating a new contest. The policy may include one or more rules for determining, at one of the candidate times for generating a new contest, via the pace tracker  312  of the contest ranking system  302 , whether one or more contests is under-saturated, and responsive to determining that the one or more contests is under-saturated, restricting generating a new contest at the one of the candidate times for generating a new contest. This can result in conserving computer resources by restricting the contest generator  304  from generating new contests when a number of currently active contests is determined to be sufficient to satisfy registrant needs. 
     The contest registrant tracker  306  can include components, subsystems, modules, scripts, applications, or one or more sets of computer-executable instructions and can be configured to track registrants for one or more contests. For example, the contest registrant tracker  306  may track, for each of one or more contests, how many users are registered for the respective active contest at a plurality of times. The contest registrant tracker  306  may maintain or reference a registrant profile that includes a plurality of time-registrant pairs for one or more contests. A registrant-time pair may include a pair of associated data that indicates a time and a corresponding number of registrants. The contest registrant tracker  306  may reference the active contest DB  318  to track registrants for active contests. The contest registrant tracker  306  may also determine registrant profile parameter values related to characteristics of registrant profiles for contests. Registrant profile data, for example, may be parametrized as registrant profile parameters (such as, for example, a maximum number of registrants, an integral of a number of registrants (e.g. between a first time and a second time), a number of registrants at a particular time (e.g. a time based on a time at which registration opened or a time based on a time at which registration closes), or parameters of a linear, exponential, or other function representing the registrant profile). 
     The contest similarity score generator  308  can include components, subsystems, modules, scripts, applications, or one or more sets of computer-executable instructions and can be configured to generate a similarity score for a pair of contests, or a similarity score for a first contest and a set of one or more second contests. The contest similarity score generator  308  can determine a similarity score between a first contest and one or more second contests based on respective parameter values for parameters of the contests (e.g. contest-generating parameters of the contests and/or registrant profiles of the contests). The contest similarity score generator  308  can determine a similarity score based on determining a similarity between parameter values of parameters of the pair of contests as described herein with respect to  FIG. 5 . The contest similarity score generator  308  can determine the similarity score based on applying weights (e.g. pre-determined weights) to the determined similarity between parameter values. The contest similarity score generator  308  may determine a similarity score for each of a set of second contests relative to a first contest. The contest similarity score generator  308  may generate a subset of contests of the set of second contests (for which, for example, each contest of the subset of contests has a similarity score above a threshold, or for which the subset of contests includes a predetermined number of contests having the highest similarity score of the set of second contests). An example embodiment of the contest similarity score generator  308  generating a similarity score for each of a set of second contests relative to a first contest is shown in  FIG. 5 . 
     The registrant projector  310  can include components, subsystems, modules, scripts, applications, or one or more sets of computer-executable instructions and can be configured to generate a projection of a future number of registrants for a contest. The registrant projector  310  can determine one or more registrant-profile parameters for a contest. For example, the registrant projector  310  can determine one or more registrant-profile parameters for a target contest based on registrant-profile parameters of contests determined by the contest similarity score generator  308  to be similar to the target contest. The determined registrant-profile parameters for the target contest can be used to project a number of registrants for the target contest at a point in time between opening of registration and close of registration for the target contest. The registrant-profile parameters may include parameters for a linear equation that is a function of time that outputs a projected number of registrants at a given time. A “projection profile” can be a set of such outputs across a span of time. 
     In other embodiments, the registrant projector  310  need not determine registrant-profile parameters for the target contest. The registrant projector  310  can determine a projected number of registrants for the target contest at a point in time relative to opening of registration or close of registration based on a number of registrants at a corresponding point in time for one or more similar contests (e.g. determined to be similar by the contest similarity score generator  308 ). 
     The pace tracker  312  can include components, subsystems, modules, scripts, applications, or one or more sets of computer-executable instructions and can be configured to track a pace of a contest. The pace tracker  312  can determine a projected or desired number of registrants for a target contest at a particular time (e.g. a particular time relative to opening of registration or close of registration). The projected or desired number of registrants can be determined based on registrant data or a registrant profile for one or more contests similar to the target contest. The projected or desired number of registrants can be determined based on registrant-profile parameters determined for the target contest by the registrant projector  310 . 
     The pace tracker  312  can determine a difference between a number of registrants for the target contest at a point in time (e.g. a current number of registrants) and the projected or desired number of registrants for the point in time. In this manner, the pace tracker  312  can determine if the target contest is below pace or above pace, and to what degree the target contest is below pace or above pace. The contest generator  304  can use this determination to determine a timing for generating a new contest. For example, if the target contest is below pace, the contest generator  304  may delay or restrict generating a new contest as described above. One or more embodiments of methods of using the pace tracker  312  are described herein with reference to  FIG. 7A  and  FIG. 7B . 
     The pace tracker  312  can further rank contests based on respective pace statuses. For example, the pace tracker  312  can determine, for each contest of a plurality of contests, to what degree the contest is under-saturated or below pace (e.g. by how many participants the contest is below pace, or by a percentage difference between an “on pace” number of participants (e.g. the projected or desired number of participants) and the projected pace, either at the current time or at a future time (e.g. at close of registration)). The pace tracker  312  can determine a rank for each of the plurality of contests (or for a sub-set of the plurality of contests having being under-saturated or below pace to a degree that is equal to or greater than a threshold) based on the pace statuses. The pace tracker  312  can determine a high rank for a contests that is most under-saturated or below pace. 
     The contest display manager  313  can include components, subsystems, modules, scripts, applications, or one or more sets of computer-executable instructions and can be configured to manage display of a contest on a client device (such as a client  102 ). The contest display manager  313  can provide data corresponding to a layout of a contest display, such as a lobby  202  as shown in  FIG. 2A . 
     The contest display manager  313  can determine a layout of the contest display that includes one or more predetermined locations for displaying contests based on projections or rankings determined by the pace tracker  312 . For example, the contest display manager  313  can determine a layout that includes displaying, on a page or tab of the contest display, only contests that are below pace or that are below pace to a degree that is equal to or above a threshold, or displaying such below pace contests in a predetermined location (e.g. in a prominent position in the lobby, such as at the top or start of a list of contests), or can display such below-pace contests according to one or more features, such as a stylistic feature (e.g. a particular text style (which can specify a size, a font, underlining, bold, italics, or another style, and in some embodiments the particular text style is different than another style used in the contest display), a visual indicator associated with such below pace contests (e.g. a box, circle, or other visual indicator that surrounds or is otherwise positioned relative to the below pace contests), or any other appropriate feature. 
     The contest display manager  313  can determine a layout that includes prominently displaying (e.g. on a front page or tab of the display, or high up in a list of contests) contests based on a display score or a contest match score. The display score or match score may be determined by a contest recommendation system, such as the contest recommendation system  1002  described herein in reference to  FIG. 7 . The contest recommendation system  1002  may determine the display score or match score based on the projections or the rankings determined by the pace tracker  312  (e.g. may include an under-saturation score as a sub-score for a total contest match score). The contest display manager  313  may use rankings determined by the contest recommendation system  1002  to determine the layout for the contest display. Thus, a contest that is determined to be below pace may be displayed more prominently than would otherwise be the case. 
     The historical contest DB  314  can include one or more data structures that store one or more historical contests  316 . A historical contest  316  can be a contest for which registration is closed. For example, the historical contest DB  314  can store a historical contest  316   a ,  316   b ,  316   c , or more historical contests. The historical contest  316   a  can include data  322 , including contest-generating parameters  322   a , context parameters  322   b , and registrant-time data  322   c . Each of the historical contests stored in the historical contest DB  314  can include contest-generating parameters, context parameters, and registrant-time data. The historical contest DB  314  is described in more detail below with respect to  FIG. 4A . 
     The active contest DB  318  can include one or more data structures that store one or more active contests  320 . An active contest  320  can be a contest for which registration is open, or a contest for which registration has not yet opened. For example, the active contest DB  318  can store an active contest  320   a ,  320   b ,  320   c , or more active contests. The active contest  320   a  can include data  324 , including contest-generating parameters  324   a , context parameters  324   b , and registrant-time data  324   c . Each of the active contests stored in the active contest DB  318  can include contest-generating parameters, context parameters, and registrant-time data. The active contest DB  318  is described in more detail below with respect to  FIG. 4B . 
     Referring now to  FIG. 4A ,  FIG. 4A  shows a historical contest DB  314  including a historical contest  316   a , a historical contest  316   b , and a historical contest  316   c . The historical contest  316   a  includes data  322  including contest-generating parameters  322   a , context parameters  322   b , and registrant-time data  322   c . The contest-generating parameters can be, or can be based on, any parameters used (e.g. by the contest generator  304 ) to generate the historical contest  316   a . As described above, the contest-generating parameters can include, for example, one or more of a league, a game type, a registrant cap, one or more games, a registration close time, an entry fee, an eligibility specification requirement, a prize type, or any other parameters used for generating a contest. 
     The context parameters relate to a context for the historical contest  316   a . The context can include, for example, whether sports other than the sport of the historical contest  316   a  were in season during the registration period of the historical contest  316   a . If other sports were in session, the number of registrants for the historical contest  316   a  may have been depressed, and using a context parameter related to this to account for such information (e.g. by the registrant projector  310 ) would be useful. Another context parameter may indicate, for example, a day of the week on which registration for the historical contest  316   a  closed, or whether registration for the historical contest  316   a  closed on a weekend or on a weekday. 
     The context parameters can also relate to information related to other contests, such as a number of contests running concurrently with, or in close temporal proximity to (e.g. within one hour of, within 2 hours of, within 5 hours of, within one day of, within two days of, or within a week of) the historical contest  316   a . Running concurrently with, or in close temporal proximity to, the historical contest  316   a  can refer to contests that have an overlapping registration period, or that have registration periods that are separated by a small amount of time (such as one hour, 2 hours, 5 hours, one day, two days, or one week). For example, such context parameters can include an average number of concurrent active contests during the registration period of the historical contest  316   a , or an average number of concurrent active contests during a time period of interest within the registration period of the historical contest  316   a  (e.g. within the last hour of registration of the historical contest  316   a , or at close of registration). These averages may be averages of contests determined to be similar to the historical contest  316   a  by the contest similarity score generator  308 . Another context parameter may be an indicator of a number of registrants of contests running concurrently with, or in close temporal proximity to, the historical contest  316   a.    
     The historical contest  316   a  further includes registrant-time data  324   c . The registrant-time data  324   c  can include time—number-of-registrant pairs (pairs of associated data) indicating a number of registrants at a plurality of times T1 through T_END. T1 may correspond to a time at which registration of the historical contest  316   a  opened, and time T_END may correspond to a time at which registration of the historical contest  316   a  closed. The registrant-time data  324   c  may also include registrant profile parameters for the historical contest  316   a.    
     Referring now to  FIG. 4B ,  FIG. 4B  shows an active contest DB  318  including an active contest  320   a , an active contest  320   b , and an active contest  320   c . The active contest  320   a  include contest-generating parameters  324   a , context parameters  324   b , and registrant-time data  324   c . The active contest DB  318  may include contests for which registration is currently open, or contests for which registration has not yet begun (and may not yet be presented in the lobby  202 ). The contest-generating parameters  324   a , context parameters  324   b , and registrant-time data  324   c  may be similar to the contest-generating parameters  322   a , context parameters  322   b , and registrant-time data  322   c  of the historical contest  316   a , except that the registrant-time data  322   c  of the active contest  320   a  may only include time—number-of-registrant pairs up to a current time (T_Current), rather than up to T_END, as registration has not yet closed for the active contest  320   a.    
     Referring now to  FIG. 5 ,  FIG. 5  shows an example embodiment of a process for generating a similarity score between a first contest and a second contest. This can be used to determine a set of first contests that are similar to a second (target) contest, and the registrant projector  310  can perform analysis using the set of similar first contests to project a number of registrants for the second contest, as described herein. The process can be performed by the contest similarity score generator  308 . In a brief overview, the contest similarity score generator  308  can set a parameter index “n” to one, or some initial value (BLOCK  502 ). The contest similarity score generator  308  can select an nth parameter for a first contest (e.g. a historical or active contest), and an nth parameter for a second contest (e.g. a target contest) (BLOCK  504 ). The contest similarity score generator  308  can calculate a similarity between the parameter values of the nth parameter for the first contest and the nth parameter for the second contest (BLOCK  506 ). The contest similarity score generator  308  can apply a parameter weight to the calculated similarity, and can add the result to a total similarity score (BLOCK  508 ). The contest similarity score generator  308  can determine whether the index n is equal to N (a total number of parameters being considered by the contest similarity score generator  308 ) (BLOCK  510 ). 
     If the contest similarity score generator  308  determines that the index n is not equal to N, the process proceeds to BLOCK  512 , and the contest similarity score generator  308  can then increment n, and the process can proceed to BLOCK  504 . If the contest similarity score generator  308  determines that the index n is equal to N, the process proceeds to BLOCK  514 , and the contest similarity score generator  308  can determine whether the similarity score is less than (or, in other embodiments, equal to) a threshold, such as a pre-determined threshold. If the contest similarity score generator  308  determines that the similarity score is below the threshold, the process proceeds to BLOCK  516 , and the first contest is added to a sub-group of contests (a sub-group of the contests being analyzed) that are similar to the second (target) contest. The process then proceeds to BLOCK  518 , and a next contest is analyzed for similarity with respect to the second contest, or the process may end. If, at BLOCK  514 , the contest similarity score generator  308  determines that the similarity score is not below the threshold, the process proceeds directly to BLOCK  518 , or ends. 
     In more detail, at BLOCK  504 , the contest similarity score generator  308  can select an nth parameter of a first contest (e.g. a historical contest). The nth parameter may be any of the contest parameters described herein, such as, for example, a contest-generating parameter or a context parameter. The nth parameter may be a registrant profile parameter. The contest similarity score generator  308  may further select an nth parameter of a second contest. The nth parameter may correspond to the nth parameter of the first contest (e.g. may be the same parameter). 
     At BLOCK  506 , the contest similarity score generator  308  may calculate a similarity between the value for the nth parameter of the first contest and the value for the nth parameter of the second contest. This similarity may be based on a direct similarity (e.g. if the parameter is an entry fee, the similarity may correspond to a difference between the respective entry fees of the first contest and the second contest), or may be a similarity determined in some other manner, such as by reference to a look-up table (LUT). The LUT may be accessible to the contest similarity score generator  308 , and may describe a number of “similarity points” that correspond to parameter value pairs for particular parameters. For example, if the parameter is a day of the week, and the first parameter value correspond to Saturday and the second parameter value corresponds to Sunday, the LUT may indicate that a first number of similarity points corresponds to this difference in parameter values for the day-of-the-week parameter. If the first parameter value correspond to Wednesday and the second parameter value corresponds to Sunday, the LUT may indicate that a second number of similarity points smaller than the first number corresponds to this difference in parameter values for the day-of-the-week parameter, indicating a greater similarity between a Saturday-Sunday pair than between a Wednesday-Sunday pair. Parameter values may correspond to parameter value groups (e.g. to a “small entry fee” group, or to a “large entry fee” group), and similarities between corresponding groups may be determined rather than directly determining similarities between the parameter values. 
     At BLOCK  508 , the contest similarity score generator  308  may apply a weight corresponding to the nth parameter to the similarity (e.g. to the similarity points) calculated at BLOCK  506 . The weight may be, for example, a predetermined weight. The weight may be a weight determined by a machine learning algorithm trained on a set of contests (e.g. a set of contests including annotations indicating similarity between the contests). This can provide for weighing parameter value similarities according to the importance of the corresponding parameter, and can provide for an improved method of generating a similarity score. 
     As described above, at BLOCKs  514 - 516 , the contest similarity score generator  308  may determine that a similarity score is greater than a pre-determined threshold, and responsive to this determination may assign the first contest to a sub-group, the sub-group including contests similar to the second contest. This sub-group of contests similar to the second contest can be used in subsequent analysis, as described herein. Using the sub-group of similar contests, rather than analyzing every contest for which data is stored, can provide for more efficient and less computer-resource-intensive analysis. For example, the registrant projector  310  may perform analysis on the group of contests to generate a projected number of registrants for the second contest. By limiting the analysis to only those contests that are similar to the second contest and omitting other contests, the registrant projector  310  may use less computing resources to perform the analysis. 
     C. Systems and Methods for Generating a Multidimensional User Vector Based on Fantasy Sports Account Activity 
     The following description relates to one or more systems and methods for generating a multidimensional data structure (e.g., user vector) based on fantasy sports account activity for a user of a fantasy sports platform. A vector management system can be configured to use such multidimensional data structures to select and/or generate content for users of the fantasy sports platform. In addition, the vector management system can utilize the multidimensional data structures to provide recommendations to users based on their fantasy sports account activity. Moreover, the vector management system can utilize the multidimensional data structures to provide personalized experiences to users, for instance, providing personalized lobbies based on the contests they are most likely to participate in based on the type of sport, the entry fee of the contest, the current or maximum number of registrants, the day of the week, among others. 
     The present disclosure can also use the multidimensional data structures generated by vector management system to identify similar users and to provide recommendations based on the activity of other users. In some embodiments, the vector management system can be configured to generate user clusters that include subsets of the data structures that are determined to be similar to one another. The vector management system can then provide content or recommendations based on the activities and/or responses of other users that have user vectors that are included in the same user cluster. 
     As will be described herein, the multidimensional user vector of a user generated by the vector management system can be used to identify similarities between multiple user profiles based in part on attributes of player lineups in the respective user&#39;s profile and/or one or more contests (e.g., fantasy contests) the respective user has participated in. The vector management system can correlate the similarities between the user profiles, based on their respective multidimensional user vectors, to provide for optimization of the content provided to each of the plurality of users. For example, the content management system  206  can identify contests that a user is more likely to be interested in and participate in based on a similarity between the user&#39;s multidimensional user vector and the multidimensional user vectors of other users. 
     Now referring to  FIG. 6A ,  FIG. 6A  shows a block diagram identifying a plurality of user profiles  610   a - n  and a multidimensional data structure  605  (hereinafter “data structure  605 ”). In embodiments, the multidimensional data structure  605  can include or correspond to a multidimensional user vector or a user vector. The first user profile  610  can correspond to a first user of a fantasy sports platform, which can be executed by one or more processors. Each user profile can correspond to a respective user of the fantasy sports platform. 
     In the illustrative embodiment of  FIG. 6A , the data structure  605  can be a first data structure  605  generated based on the first user profile  610   a . The first data structure  605  includes one or more features  635   a - 635   n . The features  635   a - 635   n  correspond to player attributes, contest attributes or a combination of both associated with each user profile linked in the data structure  605 . The features  635   a - 635   n  can include a user behavior metric  650  (e.g., user behavior metric  650  of  FIG. 6B ). The user behavior metric  650  can include a value generated based on a data from a previous contest a user profile  610  participated in, a past performance of one or more fantasy lineups of the user profile  610  in one or more previous contests, or selections made by the user profile  610  in one or more previous contests. In some embodiments, the user behavior metric  650  can include an association between a user behavior in a previous contest and at least one player attribute, at least one contest attribute or a combination of one or more player attributes and/or one or more contest attributes. For example, in one embodiment, the user behavior metric  650  can include an association between the user profile  610  and an entry fee for at least one previous contest the user profile  610  participated in. The user behavior metric  650  can include an association between the user profile  610  and a sport type (e.g., football contest, baseball contest, NASCAR contest) of at least one previous contest the user profile  610  participated in. The user behavior metric  650  can include an association between the user profile  610 , a contest attribute (e.g., entry fee, sport type, duration) and a frequency of or a commonality of the respective contest attribute. For example, in one embodiments, the user behavior metric  650  can identify a unique, rare or uncommon contest type that the respective user profile  610  has previously participated in. 
     The player attributes can include one or more of the following: a name, a sport category, a location, a team value, a position value, a price parameter or one or more future contests specific to the respective player. The contest attributes can include one or more of the following: a sport category, a price parameter, a prize parameter, a registrant threshold, a registrant limit, a current registrant count, a day value, a time value, a duration value, a contest type, and an experience level. In addition, the features  635   a - 635   n  can correspond to other attributes associated with a user profile, such as a user device associated with the profile, geographical location associated with the profile, preferences associated with the profile, activities associated with the profile, interests associated with the profile, among others. The features  635  can be based on activity that can be detected by an application executing on the user device associated with the profile, including responses to notifications provided to the user device, content provided to the user device and other activity performed on the user device. 
     Each of the features  635   a - 635   n  can be assigned a value that can be determined in part on a frequency value indicating a frequency that the respective feature appears in the user profiles linked in the first data structure  605 . For example, a first player attribute can correspond to the first feature  635   a  and the first player attribute may include a location or team (e.g., Boston professional teams) that a particular player on a player lineup in the first user profile  1530  plays for. In an embodiment, six player lineups included in the first user profile may include players from the same Boston team, thus, the first feature  635   a  can be assigned a value of six. Each of the other features  635   b - 635   n  can correspond to different player attributes, contest attributes or a combination of both associated the user profile corresponding to the first data structure  605   a.    
     The multidimensional data structure  605  can be computed based on the values of each feature  635   a - 635   n . The vector management system  800  can assign different weights to each feature  635  such that certain features may more importance than others when determining an impact of the multidimensional user vector. For example, the user behavior metric can be used to modify a player attribute or a contest attribute to generate at least one feature  635  for a data structure  605 . In embodiments, a feature  635  can include a player attribute or contest attribute modified based on the associated between the respective player attribute or contest attribute and the user profile  610  to form a weighted player attribute or weighted contest attribute. The player attribute or contest attribute can be weighted based on a frequency the user profile. 
     In one embodiment, a player attribute can be weighted based on a frequency the user profile  610  used or selected players having the respective player attribute (e.g., common team, common position). In one embodiment, a contest attribute can be weighted based on a frequency the user profile  610  participated in contest having the respective contest attribute (e.g., contest duration, sport type, value of entry fee). In some embodiments, a higher weighed value can correspond to a higher frequency and a lower weighted value can correspond to a lower frequency. In embodiments, a higher weighed value can correspond to a lower frequency (e.g.,  10  corresponds to a least favorite or uncommon choice) and a lower weighted value can correspond to a higher frequency (e.g., 1 corresponds to a favorite or most common choice). 
     The first data structure  605  can be generated by a vector management system (e.g., vector management system  800  of  FIG. 8 ) using be enhanced, modified or otherwise updated using a clustering algorithm. The vector management system  800  can implement the clustering algorithm to identify user profiles that are similar, such as but not limited to, by comparing the player lineups of user profiles, contests that the users are participating in, geographical information associated with the user profile, online activity monitored via the application of the content management system, among others. In some embodiments, the vector management system  800  can use the clustering algorithm to generate a plurality of user clusters. Each user cluster can include a plurality of user data structures  605  (e.g., user vectors) corresponding to particular user profiles  610 . It should be appreciated that one or more known clustering techniques can be used to generate user clusters based on a distance between respective multidimensional data structures  605  of user profiles  610 . It should be appreciated that one or more known clustering techniques can be used to generate user clusters based on an association or commonality between respective multidimensional data structures  605  of user profiles  610 . The clustering algorithm can generate statistics on each of the user profiles and their respective attributes (e.g., user behavior metrics, player profile attributes, contest attributes) to group user profiles to one or more user clusters. The statistics can include the historical data included in the activity profiles of the user profiles, the player attributes included in the player lineups in the user profiles and the contest attributes for contests associated with the user profiles. 
     Now referring to  FIG. 6B , a data structure  605  having a features  635   a - 635   n  with a user behavior metric  650  applied to the respective feature  635 . The user behavior metric  650  can correspond to a frequency or affinity of the respective attribute to other attributes included with or associated with the user profile  610 . For example, a first feature  635   a  can include a player attribute corresponding a team the respective player plays for. The team of the player can correspond to a local team or a favorite team of a user associated with the user profile  610 . For example, in some embodiments, the user profile  610  can include many players from the same team as the first feature  635   a . Thus, the first feature  635   a  can be give a user behavior metric of 1 indicating a highest frequency (e.g., most players are from this team) or the user profile  610  selects players from this team the most often as compared to other players selected for one or more fantasy sports lineups of the user profile  610 . 
     In embodiments, a second feature  635   b  can include a player attribute corresponding a position the respective player plays. The position of the player can correspond to at least one positon on a team the player plays on, such as but not limited to, a quarterback for a football team, pitcher for a baseball team or center for a basketball team. In some embodiments, the user profile  610  can include a few players that play the same position as from the same team as the first feature  635   a . Thus, the second feature  635   a  can be give a user behavior metric of 3 indicating a middle or less than middle frequency as compared to positons of other players selected for one or more fantasy sports lineups of the user profile  610 . 
     In embodiments, a third feature  635   c  can include a contest attribute corresponding a sport type (e.g., football, baseball, NASCAR) of the respective contest. In one embodiment, the third feature  635   c  can correspond to a contest attribute, here a type of sport, that the user profile  610  does not typically participate in. The third feature  635   c  can correspond to a type of sport that the user profile  610  participates in less than an average value of the total number of contests the user profile  610  participates in. Thus, the third feature  635   c  can be give a user behavior metric of 3 indicating a less than average frequency as compared to other types of contests the user profile  610  participates in. 
     In embodiments, a fourth feature  635   d  can include a contest attribute corresponding an entry fee (e.g., value of entry fee, size of entry fee) of at least one contest. The entry fee can be grouped into a range of values or be used a distinct value. In one embodiment, the fourth feature  635   d  can correspond to an entry fee that is within a range of values common to a majority of the contests the user profile  610  typically participates in. For example, the fourth feature  635   d  can correspond to an entry fee that falls within a range of values that correspond to entry fees for a high frequency of contests the user profile  610  participates in. Thus, the fourth feature  635   d  can be give a user behavior metric of 2 indicating a higher than average frequency as compared to the entry fees of other contests the user profile  610  participates in. 
     The user behavior metrics can be weighted against or in comparison to each of the attributes included within the user profile  610 . For example, the team player attribute can be weighted as compared to the positon player attribute, the sport type contest attribute, and/or the entry fee contest attribute. The user behavior metrics can be weighted against or in comparison to each of the attributes of the same category. For example, the team player attribute can be weighted as compared to the positon player attribute, and/or one or more additional player attributes. The user behavior metrics can be weighted against or in comparison to attributes of the same type. For example, the team player attribute can be weighted as compared to the teams represented by other players included within one or more fantasy sports lineups of the user profile  610 . 
     Now referring to  FIG. 7 , the first user profile  610   a  is illustrated having one or more lineups (e.g., player lineups)  710   a - 710   n , one or more contests  720   a - 720   n  and an activity profile  730 . The lineups  710   a - 710   n  may include player lineups, such as but not limited to, fantasy sports lineups. Each of the lineups  710   a - 710   n  can be created for a particular contest and can include one or more players selected by the user for the particular contest. The lineups  710   a - 710   n  can include previous lineups (or inactive lineups) for past contests that have closed or have otherwise been completed, active lineups for active contests or lineups for future contests. In some embodiments, the lineups  710   a - 710   n  may include lineups, such as but not limited to, for online gambling sites. The lineups  710   a - 710   n  may be the same as or substantially similar to lineup  1012   c  described above with respect to  FIG. 7 . The contests  720   a - 720   n  can include any contest described herein, such as but not limited to, one or more fantasy sports contests. 
     Each of the lineups  710   a - 710   n  can be associated with one or more contests  720   a - 720   n . For example, each of the lineups  710   a - 710   n  can be generated by a user for a particular contests  720   a - 720   n . In some embodiments, the lineups  710   a - 710   n  can be used in multiple contests  720   a - 720   n . The lineups  710   a - 710   n  may include a list of players the user selected for the particular contest  720   a - 720   n . The number of players included in a respective one of the lineups  710   a - 720   n  can vary and can be based at least in part on the parameters of the particular contest  720   a - 720   n .  FIG. 7  shows a detailed player lineup for the first lineup  710   a , however the remaining lineups  710   b - 710   n  may include similar player lineups having one or more players. 
     The players  712   a - 712   n  can include one or more player attributes  775   a - 775   n . As stated above, the player attributes  775   a - 775   n  can include characteristics of the particular player, such as but not limited to a name, a sport category, a location, a team value, a position value, a price parameter or one or more future contests specific to the respective player. In addition, the player attributes can include statistical information about performances of the player. The player attributes can include additional information relating to injuries of the player, projected performance of the player against a given opponent, as well as any other information that may be determined by the system. The player attributes can also include correlations to statistics associated with the player&#39;s team as well as other teammates of the player or players on an opposing team of the player. 
     Each of the contests  720   a - 720   n  can include one or more contest attributes  740   a - 740   n  corresponding to parameters, entry data, requirements, or any combination of them. For example, the contest attributes can include one or more of the following: a sport category, a price parameter, a prize parameter, a registrant threshold, a registrant limit, a current registrant count, a day value, a time value, a duration value, a contest type, and an experience level. The number of attributes  740   a - 740   n  for a contest  720   a - 720   n  can vary and be based at least in part on the parameters, entry data, or requirements of the particular contest. 
     The activity profile  730  can include a listing of previous contests the user has participated in. For example, the activity profile  730  can include data corresponding to user interactions with one or more fantasy sports contests executing on a device associated with the respective user. The activity profile can be used to determine a type of user based on the activity profile of the user. For instance, depending on the number of contests the user enters or the frequency at which the user is participating in contests or accessing the application, the system can determine whether the respective user profile is a casual fantasy sports player or a serious fantasy sports player. In some embodiments, the system can store predetermined threshold values with which to compare a user&#39;s activity profile to determine the type of user a particular user is. 
     In some embodiments, the activity profile  730  can be used to determine one or more user behavior metrics  650  for the user profile  610  and associations between at least player attribute and the at least one contest attribute. The activity profile  730  can identify common or most frequency profile attributes and/or contest attributes associated with a user profile  610 . The activity profile  730  can be used to determine how often a user profile  610  participates in certain types of contests and the attributes of the respective contests. In one embodiments, the activity profile  730  can be used to identify that the user profile  610  participates in contests that are daily more frequently than contests that are monthly. A user behavior metric  650  can be generated using the activity profile  730  to weight daily contests greater for the user profile  610  as compared to monthly contests. The activity profile  730  can be used to determine how often a user profile  610  uses or selects players having certain types of player attributes. In one embodiments, the activity profile  730  can be used to identify that the user profile  610  selects players from one team as compared other possible teams available to select players. A user behavior metric  650  can be generated using the activity profile  730  to weight players from that respective higher for the user profile  610  as compared to the players from the other possible teams available to select players. 
     Now referring to  FIG. 8 , a block diagram depicting an embodiment of the vector management system  800  is shown. The vector management system  800  can include or be executed on one or more servers, such as the servers  106  shown in  FIG. 1A . The vector management system  800  can be a part of or include the content management system  200  described herein. The vector management system  800  can include one or more user profile manager  810 , user cluster manager  815 , contest data manager  820 , a player list manager  825 , a lobby manager  830 , and a content selector  835 . The vector management system  800  can also include, access, maintain or manage one or more data structures, including but not limited to a vectors  850 , and a user profiles data structure  605 . The vectors  850  can store one or more user clusters  852  and one or more data structures  605   a - n . The user profiles data structure  605  can store one or more user profiles  610   a - n  corresponding to users of the fantasy sports platform. The user profiles  610   a - n  can include player lineups  710   a - n  generated for one or more contests  720   a - n  in which the user submitted a player lineup  710  and an activity profile  730  of the user including information relating to usage of the application executing on a client device as well as other information obtained by the fantasy sports platform via interactions with the user through application, browser, or other data sources to which the fantasy sports platform has access. 
     The user profile manager  810  can comprise components, subsystems, modules, scripts or one or more sets of computer-executable instructions and can be configured to identify and store user profiles  610   a - n  created, registered and/or used to enter one or more contests. The user profile manager  810  can manage completed user profiles and incomplete user profiles (e.g., user profiles missing data). The user profile manager  810  can generate user profiles, such as the user profile  610   a . The user profile manager  810  can access, from a plurality of databases and other data sources, one or more attributes associated with one or more players included in player lineups  710   a  generated by the user and included in the user profile  610   a . In addition, the user profile manager  810  can access, from a plurality of databases and other data sources, one or more attributes associated with one or more contests  720   a - n  for which the user submitted one or more player lineups  710   a . The user profile manager can also use activity information from an activity profile  730  that can include any information accessible to the user profile manager  810  that the user has provided or otherwise helped the vector management system  800  generate or otherwise acquire. 
     The user profile manager  810  can be configured to generate a user vector for each user profile  610  based on the lineups  710 , contests  720  and activity profile  730  associated with the user profile  610 . The user vector can include multiple features. Each feature can include a feature value providing an indication of a strength of a particular feature based on the user&#39;s profile  610 . The user profile manager  810  can generate one or more features for a user vector based on player attributes  775  of the players  712  include in each player lineup  710 . 
     The contest data manager  820  can comprise components, subsystems, modules, scripts or one or more sets of computer-executable instructions and can be configured to store data corresponding to one or more contests, including but not limited to, contest attributes. The contest data manager  820  can include a sport category, a price parameter, a prize parameter, a registrant threshold, a registrant limit, a current registrant count, a day value, a time value, a duration value, a contest type, and an experience level required for a particular contest. The contest data manager  820  may include data corresponding to previous contests, active contests or future contests. 
     The player list manager  825  can comprise components, subsystems, modules, scripts or one or more sets of computer-executable instructions and can be configured to manage player lists for users of the vector management system  800 . The player list manager  800  can be configured to access accounts of users and/or the user profiles  610  of the vector management system  800 . In some embodiments, the accounts of users can be linked to the user profiles  610 . 
     The player list manager  825  is configured to maintain, for each user, one or more player lineups  710 . The player lineups  710  can include a list of all of the players that the user has selected in one or more lineups for one or more contests. The player list manager  825  can be configured to receive updates and modifications to the player lineups  710  including statistics about each player included in the player lineups. In addition, the player list manager  825  can include information about a value assigned to each player as well as a number of fantasy points assigned to each player based on the player&#39;s in-game performance. 
     In some embodiments, the player list manager  825  can be configured to establish a web socket connection with one or more servers of one or more fantasy sports systems and be configured to receive activity updates of the users. In some embodiments, the vector management system  800  may identify a user&#39;s account at the fantasy sports system and link the respective accounts of the user. As described herein, the vector management system  800  can include or be part of a content management system or the fantasy sports platform, and as such, may be configured to receive and access data generated by or otherwise accessible to the content management system or the fantasy sports platform. In this way, lineup changes, additions, or deletions made in an account of a user of the fantasy sports system can be provided to the vector management system  800 , thereby allowing the vector management system  800 , or the player list manager  825 , to update the player lineups  710   a - n  of user profiles  610 . 
     The player list manager  825  can be configured to receive, for a user of the vector management system  800 , from a fantasy sports system, a communication identifying a fantasy lineup and an account of the user maintained by the fantasy sports system. The player list manager  825  can use the account of the user maintained by the fantasy sports system to identify the corresponding account of the user maintained by the vector management system  800  to which the account of the user of the fantasy sports system is linked. The player list manager  825  can then retrieve player lineups  710   a - n  associated with the account of the user maintained by the vector management system  800  and identify each of the players included in the respective player lineups  710   a - n  and maintain a count of the number of times a particular player has been selected or included in the player lineups  710   a - n . In some embodiments, the player list manager  825  can maintain a player list  872  that includes each of the players included in the player lineups  710   a - n.    
     In some embodiments, the player list manager  825  can maintain, for each player in the player list  872 , a number of lineups of the user in which the player is included. The player list manager  825  can also maintain, for each player in the player list  872 , an expiration condition according to which the player will be removed from the player list  872 . For instance, the player is removed from the player list when the player is no longer included in any fantasy lineup of the user for contests that are ongoing or are scheduled for the future. As such, the player list manager  825  can remove the player from the player list  872  once the expiration condition is satisfied. In some embodiments, the expiration condition is satisfied when all of the sporting events of a particular fantasy contest have ended. In some embodiments, the expiration condition is satisfied when all of the sporting events of a particular fantasy contest have begun. In some embodiments, the player list manager  825  can receive score updates from one or more content servers and use the score updates to determine when sporting events end. In this way, the player list manager  825  maintains a list of players that are relevant to a user in that the players are part of lineups submitted for contests having one or more sporting events that have yet to be completed. In some embodiments, players may be removed from a player list after a certain amount of time (for example, 1 week, 1 month, 1 year, etc.) has passed since the player was included in a player lineup that was entered into a contest. 
     Although the player list manager  825  as described herein may receive a list of players associated with a user from the fantasy sports system, the player list manager  825  can be configured to receive the list of players from the client device of the user. In some embodiments the player list manager  825  can be configured to determine the list of players from the request for content received from the user. In some embodiments, the request for content can include the list of players that the user is interested in, which can be based on players that are included in one or more fantasy lineups. In some embodiments, the request for content generated by the client device or the application executing on the client device can include the list of players responsive to determining that a configuration setting for filtering content based on the players included in one or more fantasy lineups of the user. In some embodiments, the request for content can include a flag or other indicator indicating that the status of the configuration setting for filtering the content. For instance, the flag can be set to a first value if the configuration setting is enabled and the flag can be set to a second value if the configuration setting is disabled. 
     Referring again to the user profile manager  810 , the user profile manager  810  can be configured to generate the user vector of a user profile based on input from the contest data manager  820  and the player list manager  825 . The user profile manager  810  can generate the multidimensional vector based on the players selected by the user in one or more lineups as well as the contests for which the user has registered. The multidimensional vector can apply weights to different features or values of features based on additional information relating to the user, for instance, a user&#39;s geographical location, a user&#39;s declared interests, one or more inferred interests or preferences of the user, among others. Furthermore, the user profile manager  810  can import information from other users to further enhance a user&#39;s multidimensional data structure  605 . For instance, the user cluster manager  815  can identify one or more users that are similar to the user based on their respective multidimensional user vectors and can determine information common across some or many users in the cluster of users that the user profile manager  810  may import into the user. For instance, if users in the cluster of users prefer a certain player or players having a certain attribute, the user profile manager  810  may adjust the multidimensional vector of the user to indicate that the user also prefers the certain player or players having the certain attribute. 
     The lobby manager  830  can comprise components, subsystems, modules, scripts or one or more sets of computer-executable instructions and can be configured to manage one or more lobbies that can be provided to one or more users. A lobby is a data structure that can include one or more contests arranged according to a particular order and can be provided to a device to enable a user to select a contest from the lobby. The user can select a contest to enter the contest or to learn more about the contests. In some embodiments, the lobby manager  830  can be configured to generate a lobby including one or more contests. The lobby can include one or more active contests that are currently in progress, one or more future contests for which registration is still open, and one or more past contests for which the contests have already ended. The lobby manager  830  can be configured to generate a lobby that can be transmitted to a plurality of users or can generate a personalized lobby based on the user vector of a given user. Additional details regarding the lobby manager  830  are described below. 
     The content selector  835  can comprise components, subsystems, modules, scripts or one or more sets of computer-executable instructions and can be configured to select content to transmit to devices of users associated with user profiles maintained by the vector management system  800 . The content selector  835  can be configured to transmit content items responsive to the vector management system  800  or the content management system  206  receiving requests from applications executing on such devices of users. 
     The content selector  835  can be configured to determine the type of content items to transmit to the application executing on a device of a user responsive to the request received from the application as well as based on state information associated with the application at the time the request was transmitted. The state information associated with the application at the time the request was transmitted can be determined by establishing a persistent connection, such as a web socket connection with the application to determine which page of the application the user is on, one or more configuration settings set on the application, among others. The content selector  835  can determine, from the request or the state information, the type of content to transmit to the client device on which the application is executing. 
     The content selector  835  can identify, from a plurality of content items accessible to the vector management system  800 , a subset of content items to transmit to the application. In some embodiments, the content selector  835  can maintain a queue of content items to transmit to the application. The content items can be selected using the user vector associated with the user. In some embodiments, the queue of content items can be ordered in accordance to chronological order or reverse chronological order. For instance, the content items can be ordered based on a timestamp of the content item such that the content item that the content management system most recently received is placed at the top of the queue of content items. The content selector  835  can be configured to rearrange the content items included in the queue of content items as the user adjusts one or more content filtering parameters on the application. The content selector  835  can prioritize one or more content items over other content items included in the queue of content items. The content filtering parameters can include parameters that affect which content is selected by the content selector  835  for transmission and/or display to the client device. The content filtering parameters may change as the user navigates to different pages on the application or as the user changes the type of content items the user would like to view in the content feed. 
     The content selector  835  can be configured to assign a priority score to each content item included in the queue. The priority score can be determined based on the tags associated with the content item. The priority score can be determined based on the tags associated with the content item and based on the players included in the player list of the user maintained by the player list manager  825 . As players are removed from the player list manager, the priority score of content items having player tags associated with such players can be decreased. Conversely, as players are added to the player list manager, the priority score of content items having player tags associated with players that have been added can be increased. In some embodiments, the content selector  835 , or the vector management system  800 , may transmit content items based on the priority scores of the content items. Stated in another way, the content selector  835 , or the vector management system  800 , may transmit content items that relate to players included in the player list before transmitting content items that relate to players not included in the list. 
     In some embodiments, the content selector  835  can be configured to send all of the content identified or accepted by the vector management system  800 . The content selector  835  may send the content with one or more tags assigned by the content labeler. The content selector  835  can be configured to communicate with the application executing on the client device to transmit all of the content items to the client device. 
     The vector data structure  850  is a data structure that can store one or more user clusters  852  and one or more data structures  605   a - n . The vector data structure can be maintained by the vector management system  800 . In some embodiments, the vector data structure  850  can be stored on a server of the vector management system  800  or can be accessed by the vector management system. 
     In some embodiments, the vector management system  800  can be configured to modify the user clusters  852  or data structures  605   a - n . In some embodiments, as new data structures  605  are generated, the user vectors can be assigned to one or more user clusters  852  based on a similarity between the user vector and the user clusters  852 . In addition, the vector management system can assign one or more tags to each user vector and/or user cluster based on attributes of the user vector or user cluster. These tags can be based on a weight of a particular attribute. For instance, if a user vector includes a lot of players that play football but no players that play basketball, the vector management system  800  can assign a football tag to the user vector. These tags can be used by various modules of the vector management system  800  for selecting content, for arranging lobbies, or for identifying similar users. 
     Now referring to  FIGS. 9A-9B , a method  900  for generating a multidimensional user vector begins at block  905 , by maintaining a plurality of user profiles  610 . A vector management system  800  can maintain user profiles  610  for each user participating in a variety of different contests  720 . The vector management system  800  can include one or more processors. In embodiments, each of the user profiles  610  can include one or more player lineups  710  associated with at least one fantasy sports contest  720 . 
     A user profile  610  can be generated for a user when the user registers and/or creates a profile with a fantasy sports site or a betting site, or when a user enters a particular contest  720  for a fantasy sports site or a betting site. A contest  720  as used herein may refer to and include fantasy sports contests or betting contests (e.g., contests through online gambling sites). The vector management system  800  can include one or more databases that store each of the user profiles created. For example, the databases can include a plurality of tables and/or files to organize the user profiles based on attributes of the particular user. The databases can include a plurality of entry the actual user profiles  610  (e.g., first user profile  610   a  of  FIGS. 15-16 ), an identifier linking the respective user profile  610  to an entry in at least one of the databases, and/or an association linking the respective user profile  610  to an entry in at least one of the databases. 
     The vector management system  800  can maintain user profiles  610  for various types of users, such as casual users and serious users. Each of the user profiles  610  can include an activity profile  730  that includes data corresponding to user interactions with one or more fantasy sports contests executing on a device associated with the respective user. The activity profile  730  can indicate whether the respective user profile  610  corresponds to a casual user or a serious user. Moreover, the activity profile  730  can indicate other types of users, for example, users that only play on weekends, certain days of the week, certain sports or leagues, or certain types of contests, among others. The vector management system  800  can assign one or more tags to each user profile  610  based on the user profile&#39;s associated activity and history playing contests. These tags can then be used to determine which contests to promote to a user, which bets to provide to a user, which content items to provide to a user, among others. 
     Each of the user profiles  610  can include one or more player lineups  710  associated with one or more fantasy sports contests  720 . The player lineups  710  can include a listing of players selected by the user for a particular contest  720 . In some embodiments, such as for betting contests, the player lineups  710  can include a card selection or betting hand. The player lineups  710  can be associated with a single contest  720  or multiple contests  720 . For example, the user can select a player lineup  710  and enter that player lineup  710  into a single contest  720  or enter that player lineup  710  into multiple contests  720 . Each of the player lineups  710  created and/or used can be stored in the user profile  610  of the respective user. Thus, the user can access previous player lineups  710  to enter them into future contests  720  and/or modify previous player lineups  710  to enter them into future contests  720 . 
     The vector management system  800  can determine a user type for each user profile  610  in the plurality of user profiles  610  based on a frequency of use, a total winnings value or winnings ratio. The user type can refer to whether the user is a casual user or more frequent user (e.g., hard-core user, serious user). The frequency of use can refer to how often a user participates in contests  720 . The total winnings can refer to a total amount of points and/or money a user has won while competing in one or more different contests  720  (e.g., fantasy sports contests, betting contests). The winnings ratio can refer a ratio between the total amount a user has won versus a total number of contests  720  the user has participated in. 
     The vector management system  800  can determine a skill level for each user profile  610  in the plurality of user profiles  610  based on an outcome value corresponding to one or more fantasy sports contests  720  the respective user participated in. The outcome value can correspond to how many contests  720  the user has participated in. In some embodiments, the outcome value can correspond to the total number of contests  720  the user has participated in and an average place or level the user finished in during the respective contests  720 . In some embodiments, the vector management system  800  can determine if the user is a beginner level (e.g., novice) user, intermediate level user or expert level user. 
     At block  910 , one or more player lineups  710  and one or more contests for which the player lineups  710  were entered can be identified for a first user profile  610  of the plurality of user profiles  610 . In embodiments, the one or more processors of the vector management system  800  can identify, for a first user profile  610  of the plurality of user profiles  610 , one or more player lineups  710  of the first user profile  610  and one or more respective contests  720  for which the player lineups  710  were entered. Each of the player lineups  710  can include a plurality of players. Each of the plurality of players can have one or more player attributes. Each of the contests  720  having one or more contest attributes. 
     The vector management system  800  can identify at least one user prolife  610  from the plurality of user profiles  610  to generate a multidimensional data structure  605  (e.g., user vector) for. For example, the vector management system  800  can retrieve the first user profile and generate a user vector based on the first user profile. Having selected the first user profile  610 , the vector management system  800  can identify player lineups  710  and the contests  720  that those player lineups  710  were entered into by the first user. Each of the player lineups  710  can include a plurality of players with each of the plurality of players having one or more player attributes and each of the contests having one or more contest attributes. 
     The player attributes can include one or more of the following: a name, a sport category (e.g., type of sport such as but not limited to baseball, football), a location (e.g., home city of the team the player plays for), a team value (e.g., team name), a position value (e.g., the players position), a price parameter (e.g., cost for specific player), and one or more future contests specific to the respective player. The contest attributes include one or more of the following: a sport category, a price parameter (e.g., entry fee), a prize parameter (e.g., winning payout), a registrant threshold, a registrant limit, a current registrant count, a day value (e.g., day of week), a time value (e.g., time of day), a duration value (e.g., single day, week long, season long, etc.), a contest type, and an experience level (e.g., beginner, intermediate, expert), whether the contest is private versus public. 
     The vector management system  800  can identify one or more player attributes or one or more contest attributes and use the player or contest attributes to generate a multidimensional data structure  605 . For example, the contest attribute “sport category” can be used to identify other user profiles  610  that have previously participated in contests  720  of the same sport category. The vector management system  800  can identify other user profiles  610  having players from the same location and participating in the same sport as the player in the player lineup  710  of the first user profile  610 . The vector management system  800  can search and extract the player lineups  710  from one or more user profiles  610  stored a database (e.g., player lineups  710  of  FIG. 7 ) to identify lineups having players from the same team as the first user profile  610 . In some embodiments, these other user profiles can be considered similar to the first user profile due to each of their respective player lineups having players from the same sports team. Thus, the similarities between the player attributes and/or contest attributes for different user profiles can be determined by comparing the data structures  605  (e.g., user vectors) of the respective user profiles. 
     At block  915 , a multidimensional data structure  605  can be generated for the first user profile  610 . In embodiments, the one or more processors of the vector management system  800  can generate, for the first user profile  610 , a multidimensional data structure  605  including a plurality of features  635 . Each feature can include a respective value that is based on the player attributes corresponding to the players included in the one or more player lineups  710  and/or the contest attributes corresponding to the one or more respective contests  720  for which the player lineups  710  were entered. The features  635  can correspond to the particular attributes used to identify similarities between the different user profiles. 
     The vector management system  800  can generate the data structure  605  by identifying associations between two or more player attributes included within a user profile  610 . In some embodiments, the association between two or more player attributes can indicate an importance of the respective player attributes to the user profile  610 . For example, the vector management system  800  can generate the data structure  605  by identifying the player attributes that appear most frequent within a user profile  610 . The vector management system  800  can assign weights to each player attribute such that certain player attributes are given more importance for that particular user profile  610  than other player attributes. For example, the player attributes that appear the most frequent, the top three, the top five or appear over a frequency threshold can be given a greater or higher weight value than those player attributes that appear less frequent, the bottom three, the bottom five or appear in the user profile less than a frequency threshold, which can be given a lower or smaller weight value. 
     The vector management system  800  can generate the data structure  605  by identifying associations between two or more contest attributes included within a user profile  610 . In some embodiments, the association between two or more contest attributes can indicate an importance of the respective contest attributes to the user profile  610 . For example, the vector management system  800  can generate the data structure  605  by identifying the contest attributes that appear most frequent within a user profile  610 . The vector management system  800  can assign weights to each contest attribute such that certain contest attributes are given more importance for that particular user profile  610  than other contest attributes. For example, the contest attributes that appear the most frequent, the top three, the top five or appear over a frequency threshold can be given a greater or higher weight value than those contest attributes that appear less frequent, the bottom three, the bottom five or appear in the user profile less than a frequency threshold, which can be given a lower or smaller weight value. 
     The vector management system  800  can generate the data structure  605  by identifying associations between at least one player attribute and at least one contest attribute included within a user profile  610 . In some embodiments, the association between all attributes (e.g., player attributes, contest attributes) can indicate an importance of the respective attribute to the user profile  610 . The vector management system  800  can generate the data structure  605  by identifying the attributes, whether player attributes or contest attributes, that appear most frequent within a user profile  610 . The vector management system  800  can assign weights to each attribute such that certain attributes are given more importance for that particular user profile  610  than other attributes. For example, the attributes that appear the most frequent, the top three, the top five or appear over a frequency threshold can be given a greater or higher weight value than those attributes that appear less frequent, the bottom three, the bottom five or appear in the user profile less than a frequency threshold, which can be given a lower or smaller weight value. 
     At block  920 , an activity profile  730  can be generated. The vector management system  800  can generate at least one activity profile  730  for each user profile  610  having data associated with one or more fantasy sports contests  720  the respective user participated in. For example, the vector management system  800  can identify and generate a listing of previous contests  720  that the user profile  610  has participated in. The vector management system  800  can determine or generate data corresponding to user interactions with one or more fantasy sports contests  720  executing on a device associated with the respective user profile  610 . The activity profile  730  can be used to determine a type of user based on the activity profile of the user. In some embodiments, the vector management system  800  can determine, based in part on the number of contests  720  the user profile  610  enters or a frequency at which the user profile  610  is participating in contests  720  or accessing an application (e.g., client application) hosting the contests  720 , whether the user profile  610  corresponds to a that is a casual fantasy sports player or a serious fantasy sports player. In some embodiments, the vector management system  800  can store predetermined threshold values with which to compare a user&#39;s activity profile to determine the type of user a particular user is. 
     At block  925 , one or more user behavior metrics  650  can be determined. The vector management system  800  can determine one or more user behavior metrics  650  for each user profile  610 . The user behavior metrics  650  can include or correspond to a weighted or modified player attribute, a weighted or modified contest attribute, and/or a weighted or modified attribute that is a combination of at least one player attribute and at least one contest attribute. The vector management system  800  can determine user behavior metrics  650  based in part on a frequency of the respective player attribute or contest attribute appears in the user profile  610  and/or an importance of the respective player attribute or contest attribute appears in the user profile  610 . For example, a player attribute or contest attribute that appears the most can be given a higher user behavior metric as the respective player attribute corresponds to high percentage behavior of the respective user profile  610  when participating in one or more contests  720 . In some 
     For example, a contest attribute may appear often in a user profile  610  because the contest attribute is a common attribute across all user of a fantasy sports system. In one embodiments, a football contest can correspond to the sport type contest attribute and the football contest may correspond to the most common type of contest for all users. Thus, the fact that the football contest appears most frequently in a user profile  610  may not indicate a true importance to the respective user profile  610  and may instead be based in part on the football contest being generally the most common contest attribute. Thus, the vector management system  800  can determine user behavior metrics  650  for attributes based in part on a frequency the attribute appears in a particular user profile  610  and a frequency or average frequency that the same attribute appears in the plurality of user profiles  610 . For example, the vector management system  800  may reduce a user behavior metric  650  value for attributes that are common or appear in a large percentage of user profiles  610  of the plurality of user profiles  610 . The vector management system  800  may increase a user behavior metric  650  value for attributes that are uncommon, rare, or don&#39;t appear in a large percentage of user profiles  610  of the plurality of user profiles  610 . The uncommon attribute can be highlighted or given greater importance for the respective user profile  610  such that content  840  corresponding to the uncommon attribute can be generated and/or provided to the respective user profile  610 . 
     In some embodiments, the vector management system  800  can use an activity profile  730  to determine one or more user behavior metrics  650  for a user profile  610  and associations between at least player attribute and the at least one contest attribute. The vector management system  800  can use the activity profile  730  to identify common or most frequent profile attributes and/or contest attributes associated with a user profile  610 . The vector management system  800  can generate a user behavior metric  650  using the activity profile  730  to weight particular contests  720  greater for the user profile  610  as compared to other types of contests  720 . 
     At block  930 , content  840  can be selected for presentation. In embodiments, the one or more processors of the vector management system  800  can select, using the multidimensional data structure  605 , content  840  for presentation to a device associated with the user profile  610 . The content  840  can be selected using an association between at least one player attribute and at least one contest attribute identified by the multidimensional data structure  605 . 
     In embodiments, the vector management system  800  can include a content selector  835  having a content matching algorithm to identify content  840  for the first user profile  610  based on the data structure  605  generated for the first user profile  610 . The content selector  835  can execute the content matching algorithm to identify content  840  that a first user associated with the first user profile  610  may be interested in based on the user profile&#39;s  610  historical data, as well as the historical data of users that are included in the cluster  852  to which the data structure  605  of the first user profile  610  is assigned. For example, the content matching algorithm can use the frequency values and weights to identify which features  635  or attributes appear the most frequently in the respective user profiles  610  and then identify content  840  that include those attributes. In some embodiments, the content  840  can include, but not limited to, future or subsequent contests  720 , data for one or more players included within one or more lineups  710  of the user profile  610  and/or advertisements. The vector management system  800  can execute the content selector to identify content  840  the first user or other users similar to the first user are more likely to be interested in. In embodiments, the vector management system  800  can execute the content selector  835  to identify content  840  to provide to a client device associated with the first user profile  610  based in part on the data structure  605  corresponding to the first user profile  610 . The content selector  835  can identify content  840  (e.g., content items) from one or more features  635  of the data structure  605 . In one embodiments, the vector management system  800  can execute the content selector  835  to determine a user&#39;s favorite sport, team or player based on features  635  and user behavior metrics  650  of the data structure  605  for the user profile  610 . The vector management system  800  can generate or retrieve content  840  relevant to the particular favorite sport, team or player for presentation to the user at the user&#39;s client device. 
     In some embodiments, the content selector  835  can execute the content matching algorithm to identify content  840  that a first user associated with the first user profile  610  may be interested in and is provided by a third party server (e.g., servers  106   a - 106   b  of  FIG. 1A ). For example, the content matching algorithm can identify attributes (e.g., player attributes, contest attributes) having a particular user behavior metric or in a range of user behavior metric values to identify attributes of interest to the respective user. The vector management system  800  can request content  840  from one or more third party servers corresponding to the attributes having a particular user behavior metric or in a range of user behavior metric values. For example, in one embodiment, the vector management system  800 , using the content selector  835 , can identify a NASCAR type contest having a particular user behavior metric for the first user profile  610 . The vector management system  800  can transmit a request to one or more third party servers for content  840  (e.g., scores, updates, driver information, advertisements) corresponding to the NASCAR type contest. 
     At block  935 , content  840  can be provided to a device associated with the user profile  610  based on the multidimensional data structure  605 . In embodiments, the one or more processors of the vector management system  800  can provide, using the generated multidimensional data structure, the content  840  to the device associated with the user profile  610 . The vector management system  800  can provide or transmit the content  840  to the device associated with the user profile  610 . In embodiments, the vector management system  800  can provide the content  840  to the device through an application (e.g., client application) executing on the respective device associated with the user profile  610 . The device can include any client device and/or computing device the user uses to receive data related to a contest and/or participate in a contest, such as but not limited to, client devices  102   a - 102   n  of  FIG. 1A-1B . 
     In some embodiments, the content  840  (or content item as also referred to herein) can include recommendations for one or more future contests  720  selected for user profiles  610  using the data structure  605 . The recommendations can include can include a media item (e.g. any combination of text, image, video, or user-interactive content), and the media item can reference a contest  720  selected for the user profiles  610  using the data structure  605 . The subsequent contests  720  can be selected based in part on one or more of the features  635  included in the data structure  605  and/or one or more user behavior metrics  650  included in the data structure  605 . For example, in some embodiments, if a player attribute corresponding to a team location (e.g., hometown, favorite team) and a contest attribute corresponding to a sport type had the highest frequency value in the data structure  605 , one or more future or subsequent contests  720  can be recommended for the first user profile  610 , or any other profile  610  linked by a cluster  852  or data structure  605 , that involves players from that team in that particular sport (e.g., identify contests in which players from the Boston Celtics are involved in). The content  840  can include text that recommends one or more contests  720  to the user profiles  610 . The content  840  can include an object, such as a user-selectable hyperlink that initiates a process to download a webpage, or initiate a process of an application, for registering for the one or more contests  720 . 
     In some embodiments, the vector management system  800  can generate, update or modify an activity profile  730  for one or more of the user profiles  610  linked by the cluster  852  and/or the data structure  605 . The activity profile  730  can include a user history of the respective user profile  610 . For example, the activity profile  730  can include data corresponding to user interactions with one or more fantasy sports contests  720  executing on a device associated with the respective user profile  610 . The user history can include one or more historical contests  720  that are associated with a user (e.g. in which a user has participated). The historical contests can be contests  720  in which the user has registered and which have not yet ended or can be contests  720  that have ended. The contest  720  may be associated with information including values for parameters (e.g. any parameters described herein, including contest-generating parameters and context parameters). The vector management system  800  can update or modify the activity profiles  730  of the user profiles  610  linked by a cluster  852  or one or more data structures  605  when a respective user or user profile  610  registers and/or participates in a new contest  720 . 
     At block  940 , the vector management system  800  can modify the data structure  605 . The vector management system  800  can modify the data structure  605  responsive to a change in an activity profile  730  of a user profile  610  associated with the data structure  605 . The vector management system  800  can modify the data structure  605  responsive to a change in an activity profile  730  of one or more of the user profiles  610  linked by a cluster  852  and/or one or more data structures  605 . For example, when at least one user profile  610  in the cluster  852  participates in a new contest  720  using a new or existing player lineup  710 , the vector management system  800  can update the features  635 , frequency values and/or user behavior metrics  650  of the cluster  852  to reflect the changes. In some embodiments, the vector management system  800  can identify new user profiles  610  to link to with a data structure  605  and/or cluster  852 . The vector management system  800  can add or link the new user profiles  610  to the data structure  605  and/or cluster  852 . The vector management system  800  may remove one or more user profiles  610  from the data structure  605  and/or cluster  852  based on the changes to one or more activity profiles  730 . 
     At block  945 , subsequent content  840  can be selected. The vector management system  800  can identify and select new, different, or subsequent content  840  for the first user profile  610  based on the modifications to the data structure  605 . For example, the vector management system  800  can modify the data structure  605  responsive to changes to the activity profile  730  of the first user profile  610 , changes to one or more lineups  710  of the user profile  610 , and/or new contests  720  entered by the user profile  610 . The vector management system  800  can select subsequent content  840  for the first user profile  610  based on the modification to the multidimensional data structure  605 . 
     In embodiments, the vector management system  800  can receive one or more contest attributes data from a third party server. The vector management system  800  can use the contest attributes (e.g., scores, updates, information) to determine, using the multidimensional data structure  605 , an association between the at least player attribute and/or the at least one contest attributes using the one or more contest attribute from the third party server. For example, the contest attribute from the third party server can include an update (e.g., score update, injury update) to a current contest  720  or a future contest  720  the user profile  610  is registered for. The vector management system  800  can use the contest attribute from the third party server to determine that a player from at least one lineup  710  will participate in the current contest  720  or the future contest  720  the user profile  610  is registered for. The vector management system  800  can update the data structure  605  to include the association or a plurality of associations between the at least player attribute and/or the at least one contest attributes using the one or more contest attribute from the third party server. The vector management system  800  can generate an alert or indication for the user profile  610  to alert the user profile  610  of the update or change to the contest attribute and/or data structure  605 . The vector management system  800  can determine a profile association between the user profile  610  and the multidimensional data structure  605 . The vector management system can store the profile association in the user profile  610 . 
     In some embodiments, the vector management system can identify and select new, different or subsequent content  840  for a second or different user profile  610  based on the modifications to the data structure  605  and/or cluster  852 . For example, the vector management system  800  can update a second or other user profiles  610  linked by the data structure  605  and/or cluster  852  based on the activity profile  730  of the first user or any other activity profile  730  of any other user linked by the data structure  605  and/or cluster  852 . The vector management system  800  can continually update and select content  840  for the first user profile  610  or any other user profile  610  linked by the data structure  605  and/or cluster  852  based on changes or modifications to the data structure  605  and/or cluster  852 . 
     In some embodiments, the vector management system  800  can update a second user profile  610 , different from the first user profile  610 , based on the activity profile  730  of the first user profile  610 . The vector management system  800  can update one or more user profiles  610  linked by the data structure  605  and/or cluster  852  based on changes or modifications to at least one user profile  610  linked by the data structure  605  and/or cluster  852  and/or changes or modifications the data structure  605  and/or cluster  852 . 
     In some embodiments, one or more user clusters for multiple user profiles can be generated in the plurality of user profiles using a clustering algorithm. 
     The vector management system  800  can generate, using a clustering algorithm, a user cluster  852  based on a plurality of multidimensional data structures  605  corresponding to the plurality of user profiles  610 . Each user profile  610  in the user cluster  852  can include multiple player attributes in common and/or multiple contest attributes in common. 
     The vector management system  800  can implement the clustering algorithm to identify user profiles  610  having similarities, such as but not limited to, the player attributes and the contest attributes. In some embodiments, the vector management system  800  can use the clustering algorithm to determine which user profiles  610  should be included in a particular user cluster  852 . The clustering algorithm can generate statistics on each of the user profiles  610  and their respective attributes to group user profiles to one or more user clusters  852 . The statistics can include the historical data included in the activity profiles  730  of the user profiles  610 , the player attributes included in the player lineups  710  in the user profiles  610  and the contest attributes for contests  720  associated with the user profiles  610 . In embodiments, the statistics can include user behavior metrics  650  generated for the data structures  605  of the user profiles  610 . Each of the plurality of user profiles  610  can be included in one or more user vectors and one or more user clusters. 
     It should be appreciated that although the specification and claims refer to fantasy sports, the application is not limited to fantasy sports. Rather, the scope of the application may extend to other contexts where a content management server maintains a dynamic set of resources that can be monitored for projected utilization and actions can be taken based on the projected utilization, including the generation or allocation of new resources or actions that cause traffic to be directed to one or more resources of the existing set of resources.