U.S. patent application number 13/527117 was filed with the patent office on 2012-12-27 for method and device for fantasy sports auction recommendations.
Invention is credited to David Michael Fishel, J. Nathaniel SLOAN.
Application Number | 20120329542 13/527117 |
Document ID | / |
Family ID | 47362356 |
Filed Date | 2012-12-27 |
United States Patent
Application |
20120329542 |
Kind Code |
A1 |
SLOAN; J. Nathaniel ; et
al. |
December 27, 2012 |
METHOD AND DEVICE FOR FANTASY SPORTS AUCTION RECOMMENDATIONS
Abstract
A device and method groups sport players into tiers for a
fantasy sports auction and generates bid recommendations. The
method includes receiving at least one parameter value for each of
a plurality of sport players. The method includes determining a
score value for each of the sport players as a function of the at
least one parameter value. The method includes determining a
corresponding tier value of a plurality of tier values for each of
the sport players, each of the tier values being indicative of a
respective range of score values. The method includes providing
first player data for one of the plurality of sport players
including at least identity data and the corresponding tier value.
The method includes accepting the nomination of a second player,
and generating recommended bid data using the at least one
parameter value, and optionally modifying the recommendation
thereupon.
Inventors: |
SLOAN; J. Nathaniel;
(Bellevue, WA) ; Fishel; David Michael;
(Farmington, CT) |
Family ID: |
47362356 |
Appl. No.: |
13/527117 |
Filed: |
June 19, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61500018 |
Jun 22, 2011 |
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Current U.S.
Class: |
463/9 ;
463/42 |
Current CPC
Class: |
A63F 13/828 20140902;
A63F 13/798 20140902; A63F 13/00 20130101; A63F 13/65 20140902;
A63F 2300/558 20130101; A63F 13/79 20140902; A63F 13/85 20140902;
A63F 13/44 20140902; A63F 13/46 20140902; A63F 2300/575
20130101 |
Class at
Publication: |
463/9 ;
463/42 |
International
Class: |
A63F 13/12 20060101
A63F013/12; A63F 9/24 20060101 A63F009/24 |
Claims
1. A method for a fantasy sports application, comprising: receiving
at least one parameter value for each of a plurality of sport
players; determining a score value for each of the sport players as
a function of the at least one parameter value; determining a
corresponding tier value of a plurality of tier values for each of
the sport players, each of the tier values being indicative of a
respective range of score values; and providing first player data
for one of the plurality of sport players including at least
identity data and the corresponding tier value.
2. The method of claim 1, further comprising: generating a list of
the sport players, the sport players being presented on the list
according to the tier values for each of the sport players.
3. The method of claim 1, further comprising: determining the sport
players having a first tier value; determining recommendation data
for a first sport player having the first tier value to be
nominated at an auction draft as a function of a remaining number
of sport players in the first tier value.
4. The method of claim 3, further comprising: adjusting the
recommendation data as a function of a second remaining number of
sport players in a second tier value, the sport players in the
second tier value representing players assigned a greater value to
an owner's team than the sport players remaining in the first
tier.
5. The method of claim 3, wherein the recommendation data is
comprised of at least one of an affirmative indication value, a
negative indication value, and a maximum bid value for the first
sport player.
6. The method of claim 3, wherein the affirmative indication value
is determined when a current bid value is less than the maximum bid
value and wherein the negative indication value is determined when
the current bid value is greater than the maximum bid value.
7. The method of claim 5, further comprising: determining an
initial bid value; and adjusting the initial bid value as a
function of the maximum bid value and a comparison of the remaining
number of sport players in the corresponding tier value of the
first sport player to a threshold value indicative of a percentage
of a total number of sport players in the corresponding tier
value.
8. The method of claim 1, wherein the parameter values are one of
projected statistic values, current statistic values, past
statistic values, and a combination thereof associated with each of
the sport players.
9. The method of claim 7, wherein the initial bid value is
determined for a first owner as a function of prior bid values
placed by at least one second owner on the first sport player.
10. The method of claim 1, wherein the identity data includes at
least name data and position data.
11. A device, comprising: a communication arrangement configured to
communicate via a communication network; a display device; a memory
arrangement; and a processor for a fantasy sports application,
wherein the processor receives at least one parameter value for
each of a plurality of sport players, wherein the processor
determines a score value for each of the sport players as a
function of the at least one parameter value, wherein the processor
determines a corresponding tier value of a plurality of tier values
for each of the sport players, each of the tier values being
indicative of a respective range of score values, and wherein first
player data for one of the plurality of sport players including at
least identity data and the corresponding tier value is shown on
the display device.
12. The device of claim 11, wherein the processor generates a list
of the sport players, the sport players being presented on the list
according to the tier values for each of the sport players.
13. The device of claim 11, wherein the processor determines the
sport players having a first tier value and determines
recommendation data for a first sport player having the first tier
value to be nominated at an auction draft as a function of a
remaining number of sport players in the first tier value.
14. The device of claim 13, wherein the processor adjusts the
recommendation data as a function of a second remaining number of
sport players in a second tier value, the sport players in the
second tier value representing players assigned a greater value to
an owner's team than the sport players remaining in the first
tier.
15. The device of claim 13, wherein the recommendation data is
comprised of at least one of an affirmative indication value, a
negative indication value, and a maximum bid value for the first
sport player.
16. The device of claim 13, wherein the affirmative indication
value is determined when a current bid value is less than the
maximum bid value and wherein the negative indication value is
determined when the current bid value is greater than the maximum
bid value.
17. The device of claim 15, wherein the processor determines an
initial bid value and adjusts the initial bid value as a function
of the maximum bid value and a comparison of the remaining number
of sport players in the corresponding tier value of the first sport
player to a threshold value indicative of a percentage of a total
number of sport players in the corresponding tier value.
18. The device of claim 11, wherein the parameter values are one of
projected statistic values, current statistic values, past
statistic values, and a combination thereof associated with each of
the sport players.
19. The device of claim 17, wherein the processor determines the
initial bid value for a first owner as a function of prior bid
values placed by at least one second owner on the first sport
player.
20. A computer readable storage medium including a set of
instructions executable by a processor, the set of instructions
operable to: receive at least one parameter value for each of a
plurality of sport players; determine a score value for each of the
sport players as a function of the at least one parameter value;
determine a corresponding tier value of a plurality of tier values
for each of the sport players, each of the tier values being
indicative of a respective range of score values; and provide first
player data for one of the plurality of sport players including at
least identity data and the corresponding tier value.
Description
PRIORITY CLAIM
[0001] This invention claims priority to U.S. Provisional
Application Ser. No. 61/500,018 entitled "Automated Fantasy Draft
Player Recommendations", filed Jun. 22, 2011, the disclosure of
which is incorporated, in its entirety, herein.
INCORPORATION BY REFERENCE
[0002] The entire disclosures of U.S. patent application Ser. No.
13/331,894, filed Dec. 20, 2011, U.S. patent application Ser. No.
12/760,277, filed Apr. 14, 2010, U.S. patent application Ser. No.
12/760,422, filed Apr. 14, 2010, U.S. patent application Ser. No.
12/760,384, filed Apr. 14, 2010, and U.S. patent application Ser.
No. 12/760,269, filed Apr. 14, 2010, including the specification,
claims, and abstract, all of which share at least one common
inventor and are assigned to a common assignee with the present
application, are hereby expressly incorporated by reference
herein.
FIELD OF THE INVENTION
[0003] This present invention pertains to the field of fantasy
sports games. The exemplary embodiments relate to a method and
system for providing recommended bid amounts for use during an
auction-style draft, while also breaking players up into tiers to
inform a user's player selection process.
BACKGROUND INFORMATION
[0004] A fantasy sports game is a game where users act as managers
or owners of simulated sport teams called "fantasy teams," where
each team comprises a number of "players." Thus, the term "owner"
is used to refer to a participant in the fantasy sports game. An
owner may be a natural person or a computer-controlled opponent. A
"user" is a fantasy owner who is also a natural person. Thus, the
term "user" and "owner" are used interchangeably in their roles in
the fantasy sports game. In contrast, the term "player" refers to
one of the selectable fantasy characters. In certain fantasy sports
games, each player corresponds to an athlete in a professional
sport league.
[0005] Features for conventional fantasy sports games are already
known in the art. In a first example, a player evaluation system
uses historical data to predict player performance through the end
of the season using a blending function. The system is also applied
in a draft context by assigning average performance values to the
slots on the owner's team that have not yet been filled with
players yet to be drafted. In calculating team points, one version
weighs certain statistics more heavily than others.
[0006] Conventional recommendation engines are also known in the
art. In one conventional recommendation engine, player analyzing
software queries a sports statistics system to analyze the relevant
players and delivers the analysis to a roster move recommending
software component that delivers to the user roster move
recommendations based on the results of the player analysis. The
player analysis may be based on actual statistics or projected
statistics.
[0007] Some fantasy sports owners, when participating in an
auction-style draft, will compute players' auction values before
the draft and then approximate the necessary adjustments as the
draft goes along. However, this requires that the owners perform
many manual calculations with regard to the entire pool of
available players. This is often very time-consuming and may result
in poor decisions, particularly when a player is overlooked but
would otherwise be optimal to be nominated.
[0008] FIG. 1 shows a method 100 for executing a fantasy sports
application to draft players as is known in the art. Specifically,
the method 100 relates to the fantasy sports application when
players are selected in a conventional auction format. Thus, a user
is provided a predetermined, overall budget with which to "bid" for
select players; the user who provides the highest "bid" for a
particular player receives that player for the owner's team. In
step 110, the draft is initialized. In step 120, an owner or user
selects a player and the host executing the fantasy sports
application receives the selection. In step 130, the owner or user
provides a bid on the player and the host receives the bid.
[0009] In step 130, further owners or users who are also interested
in drafting the selected player provide bids and the host receives
the respective bids. Thus, in step 140, the host determines the
user who provided the highest bid, and that user drafts the player.
In step 150, a determination is made whether there are empty slots
for a respective position related to the sport in the fantasy
sports application. If the determination in step 150 indicates that
more auctions are to be performed since there are still empty
slots, the method 100 returns to step 120 where further selections
are received. If the determination in step 150 indicates that no
more slots are empty, the method 100 ends.
[0010] Thus, within the conventional auction format for selecting a
fantasy sports team, the users are required to determine, manually,
the players on which to bid, as well as the amount to bid for each
of the players, as all of the available players are amassed into a
common pool of players. Furthermore, the conventional system does
not provide the user with easily-understood information regarding
the relative value of the several players in the game, making it
difficult for users to determine, in the short period of time
provided in an online auction, whether or not a given player would
be a worthy addition to the team.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] FIG. 1 shows a conventional method for executing a fantasy
sports application to draft players, where the method depicted is
an auction-style draft.
[0012] FIG. 2 shows a system in which a fantasy sports application
is executed according to an exemplary embodiment of the present
invention.
[0013] FIG. 3 shows a user device that connects to a host of FIG. 2
for the fantasy sports application according to an exemplary
embodiment of the present invention.
[0014] FIG. 4 shows a recommendation engine according to an
exemplary embodiment of the present invention.
[0015] FIG. 5 shows a method for generating a plurality of tiers of
players in order to provide recommendations for a fantasy sports
auction according to an exemplary embodiment of the present
invention.
[0016] FIG. 6 shows a method 600 for generating a recommended bid
for a player up for bid in a fantasy sports auction according to an
exemplary embodiment of the present invention.
DETAILED DESCRIPTION
[0017] The present invention relates to a method and device for
grouping sport players into tiers for a fantasy sports auction and
generating bid recommendations. The method comprises receiving at
least one parameter value for each of a plurality of sport players;
determining a score value for each of the sport players as a
function of the at least one parameter value; determining a
corresponding tier value of a plurality of tier values for each of
the sport players, each of the tier values being indicative of a
respective range of score values; and providing first player data
for one of the plurality of sport players including at least
identity data and the corresponding tier value; accepting the
nomination of a second player, and generating a recommended bid
value, using the at least one parameter value, and optionally
modifying that recommendation based upon the tier value of the
second sport player.
[0018] The exemplary embodiments may be further understood with
reference to the following description of the exemplary embodiments
and the related appended drawings, wherein like elements are
provided with the same reference numerals. The exemplary
embodiments are related to systems and methods for providing
recommendations for players who are drafted in a fantasy sports
application in which the drafting is performed using an auction.
Specifically, the players are grouped into tiers in which the
players of a tier are statistically similar. Furthermore, the
recommendations relate to a bidding amount for a player being
auctioned and whether a potential bid is recommended. While
drafting a fantasy sports team, a fantasy team owner must consider
a multitude of factors to determine the best possible selection.
The exemplary embodiments of the present invention assist in the
drafting by providing a set of recommendations that help guide the
decision-making process, in a manner useful during a (potentially
time-limited) draft.
[0019] Initially, it is noted that the terminology used herein for
the exemplary embodiments of the present invention are consistent
with what was described above. Accordingly, the terms of an "owner"
and a "user" may be used interchangeably to refer to a common
person or computer who runs a fantasy team. On the other hand, the
term of "a player" relates to an actual sport athlete participating
in the respective live sport of the fantasy sports application.
[0020] The fantasy sports application may be an interface provided
on a client. Accordingly, the client may be executed on an
electronic device that is configured with a transceiver to connect
the device to a network. FIG. 2 shows a system 200 in which the
fantasy sports application may be executed. As shown in FIG. 2, a
plurality of users 240 may each have a user device 230 that is
configured to communicate with a communication network 220, for
example, via a wired or wireless connection. The network 220 may
include or connect to a host device 210 that is configured to
execute the fantasy sports application. As will be described in
further detail below, the fantasy sports application may be
configured to provide recommendations to the users 240.
Accordingly, the host 210 may be connected or have access to a
plurality of different sources of data that is used to provide the
recommendations. It should be noted that the discussed embodiment
with three users 240 and respective user devices 230 is only
exemplary. Those skilled in the art will understand that the system
200 may include any number of users 240 and user devices 230 who
participate in the fantasy sports application.
[0021] The network 220 may be any type of network configuration
capable of connecting the plurality of user devices 230. In a first
exemplary embodiment of the present invention, the host 210 may be
a website. Accordingly, the network 220 may be the Internet (e.g.,
WAN). In this exemplary embodiment, the network 220 may include a
plurality of network components such as a server, a database, a
network management arrangement, a plurality of access points, etc.
In a second exemplary embodiment of the present invention, the host
210 may be an electronic device (e.g., server terminal) operated by
a user. Accordingly, the network 220 may be a local area network
(LAN). In this exemplary embodiment, the network 220 may include a
hub that is configured to connect the user devices 230 to the host
210 for data to be exchanged thereamong.
[0022] FIG. 3 shows one of the user devices 230 that connects to
the host 210 and shows the interface for the fantasy sports
application according to an exemplary embodiment of the present
invention. The user device 230 may be any electronic device such as
a computer terminal, a laptop, a personal digital assistant, a
tablet, a cellular phone, etc. The user device 230 may also operate
using any operating system such as Windows, Mac OS, Linux, Android,
iOS, etc. That is, the recommendation engine according to the
exemplary embodiments of the present invention may be adapted for
any platform used by the user device 230. It should also be noted
that the recommendation engine according to the exemplary
embodiments of the present invention may also be adapted to any
fantasy sports game that is run on the host 210. The user device
230 may include a processor 310, a memory arrangement 320, an input
device 330, a display 340, and a transceiver 350. It should be
noted that the user device 230 may include further components; for
example, when the user device is a portable electronic device, a
power supply may be included. It should also be noted that the
input device 330 and the display 340 may be embodied together, for
example, in a touch screen configured to perform both
functionalities.
[0023] The processor 310, the memory 320, the input device 330, the
display 340, and the transceiver 350 may all provide conventional
functionalities for the user device 230. For example, the processor
310 may execute the interface for the fantasy sports application.
In another example, the processor 310 may execute a browser
application in which the fantasy sports application is executed
thereon. The transceiver 350 may exchange data through the network
220 with the host 210, in particular to receive data related to the
fantasy sports application as well as the recommendations generated
by the recommendation engine, as will be discussed in further
detail below.
[0024] While performing the draft as described in one of the
methods above, the host 210 may include a recommendation engine
that provides one or more recommendations for the users to
determine an optimal selection of one or more players. FIG. 4 shows
a recommendation engine 400 that is configured to provide the
recommendations according to an exemplary embodiment of the present
invention. The recommendation engine 400 may be incorporated as
part of the host 210 or may be a separate utility of the fantasy
sports application. The recommendation engine 400 may also be
configured to access or be connected to a plurality of data
sources. For example, the data may be related to past ranking
values of a player as a function of performance from previous
seasons. In another example, the data may be related to current
ranking values of a player as assessed by other leagues in the
sport of the fantasy sports application. In yet another example,
the data may be related to ranking values as determined by
"experts" in the sport. It should be noted that there are many
other sources of data that provide ranking values of players that
the recommendation engine may consider prior to generating the
recommendations for the users 240.
[0025] For the recommendation engine 400 to ultimately generate
recommendations, the recommendation engine 400 may utilize a
plurality of processors that provide data thereto. Specifically,
each of the plurality of processors may be sources of analyzed data
that the recommendation engine 400 uses to generate the
recommendations. As illustrated in FIG. 4, the plurality of
processors may include a recommendation provider 410 connected to a
first data storage 420 providing a first set of analyzed data, a
recommendation filter 430 connected to a second data storage 440
providing a second set of analyzed data, a recommendation score
provider 450 connected to a third data storage 460 providing a
third set of analyzed data, and an auction value provider 470
connected to a fourth data storage 480 providing a fourth set of
analyzed data. The first, second, third, and fourth analyzed data
may be received by the recommendation engine 400. Because the
recommendation engine is designed with multiple componentized
interfaces, additional ranking techniques and weights to the system
may be added easily by, e.g., an administrator. If required,
further processors may also be incorporated for the recommendation
engine 400. By receiving the first, second, third, and fourth
analyzed data from the plurality of processors listed above, the
recommendation engine 400 may further analyze the analyzed data to
generate the recommendations according to the exemplary embodiments
of the present invention. Note that the separation into various
processors 410, 430, 450, and 470, and various data storages 420,
440, 460, 480, is merely exemplary. Those skilled in the art will
understand that any of these processing functions may be
subcomponents of a single processor, and that any number of the
data storages may be physically maintained in a single unit.
[0026] According to the exemplary embodiments of the present
invention, the recommendation engine 400 may separate players into
tiers to subsequently display the player data (e.g., on the display
340) such as name and position with a corresponding tier value that
is determined. The recommendation engine 400 may also be configured
to generate a list of players arranged by the tier values. The
tiers may be based upon a variety of factors. In a preferred
exemplary embodiment of the present invention, the tiers may be
based upon projected statistics such as an expected score players
in the tier are calculated to provide to the team of the owner.
Accordingly, players having projected statistics within a
predetermined range may be grouped into a particular tier. In a
second example, the tiers may be based upon past statistics such as
scores that the players have produced in the last year, in the past
several years, since entering the professional league, etc.
Accordingly, players having prior statistics within a predetermined
range may be grouped into a particular tier.
[0027] As discussed above, the recommendation engine 400 may
receive data related to the players available in the draft from a
variety of sources. The recommendation engine 400 may receive this
data for consideration in determining how the players are to be
arranged into the different tiered groups. As discussed above, the
fantasy sports application may be different from one league to
another in a variety of ways such as which factors are considered
in determining a score for the team of the owner or for each player
of the team. Accordingly, the recommendation engine 400 may be
configured to determine the parameters that the league with which
the owner is associated utilizes for calculating the scores.
[0028] Upon receiving the data of the players and the parameters
for the score calculations, the recommendation engine 400 may
initially determine a score value for the sport players to
determine the tier in which the player is to be grouped. The
recommendation engine may further separate the players according to
a playing position within the respective sport (e.g., in football,
the playing positions may be running back, quarterback, wide
receiver, tight end, etc.) to further narrow an ultimate
recommendation. For example, in a most simplified example, if the
group of players relates to running backs when the fantasy sports
application is football, the parameter may be projected touchdowns
that the player is expected to score during the season. The
recommendation engine 400 may generate thresholds indicating a
range of score values that determine whether a player is to be
placed into a respective tier so that a player having a greater
number than the threshold is placed into a higher tier whereas a
player having a lower number than the threshold is placed into a
lower tier group. Accordingly, depending on the number of tiered
groups that the recommendation engine 400 is to generate, there may
be n-1 thresholds separating the tiered groups, n being the total
number of tiered groups. According to a preferred exemplary
embodiment of the present invention, the thresholds may be
generated dynamically. For example, as is known the art, the method
of least squares may be used to determine the n points i.sub.1,
i.sub.2, . . . , i.sub.n which best characterize the data; these n
points then define n-1 thresholds t.sub.1, t.sub.2, . . . ,
t.sub.n-1, where each threshold t.sub.x is the midpoint of two
successive points i.sub.x and i.sub.x+1. However, it should be
noted that the thresholds may also be generated in a predetermined
manner to separate the players into the tiers where the n-1
thresholds are provided by calculation before the beginning of the
draft.
[0029] It should be noted that the above example of the running
back and expected touchdowns scored is only exemplary. The
recommendation engine 400 may be configured to consider a wide
variety of parameters that the league is designed to include in the
score calculation. For example, the recommendation engine 400 may
further consider receptions, yards from scrimmage, yards after
catch, fumbles, etc. Through consideration of all the parameters
the league is designed to use, the recommendation engine may
generate thresholds for expected scores that the players are
predicted to provide. Accordingly, the recommendation engine 400
may generate tiered groups as a function of the predicted score,
rather than for only a single parameter.
[0030] According to the exemplary embodiments of the present
invention, the recommendation engine 400 may further provide
recommendations to the users with regard to drafting a player
during an auction type draft. Furthermore, the recommendation
engine 400 may utilize the tier groups previously generated prior
to running the draft to determine the recommendations. As will be
described in further detail below, the recommendation engine 400
may generate the recommendations as whether or not an owner should
offer a bid (e.g., an affirmative indication value being 1 and a
negative indication value being 0), a maximum bid value for a
player up for bid, or both. The recommendations that are generated
may be shown to the owners, for example, via the display 340. Thus,
when the fantasy sports application includes a graphical user
interface, each player may be displayed with the associated
recommendations, a window may be created for each player of
interest (e.g., a pop up window when a player name is hovered over
by a user input device such as a mouse), an input may be received
by the owner that indicates a request for the recommendations on
the interface, etc.
[0031] In a first exemplary embodiment of the present invention,
the recommendation engine 400 may determine an initial bid value
for a player. For example, through the data received by the
plurality of processors of FIG. 4, in particular the auction value
provider 470, the recommendation engine 400 may calculate a
reasonable bid value should the owner wish to draft that player.
This calculation may be based upon any repeatable mechanism
desired. In a preferred embodiment of the present invention, this
reasonable bid value is based upon the amount of money remaining
for the team in question, the number of players already drafted to
the team in question, the total number of teams in the league and
the composition of each, the total amount of money available to
other players, the projected value of the player, scoring rules for
the league, and other related factors. As discussed above, the
owner may receive this initial bid value before any bidding is done
for this player. The initial bid value may also be based upon the
tier of the group in which the player is associated. For example, a
higher tier group may have an initial bid value that is greater
than a lower tier group. The recommendation engine 400 may adjust
the initial bid value of the player as a function of the tier
group. In a preferred embodiment of the present invention, this
tier value may be an additional piece of data used in the
calculation of a reasonable bid value.
[0032] The recommendation engine 400 may then determine whether
players in the same tier remain undrafted, and, if so, how many
such undrafted players exist. The recommendation engine 400 may
then adjust the bid value for the player and eventually the
recommendation for that player accordingly. For example, in the
highest tier group, the number of players may be relatively small;
if the owner does not have any players from this tier group, the
recommendation engine 400 may increase the potential bid value so
that the owner has a higher probability of acquiring the player.
The recommendation engine 400 may utilize a first threshold value,
such as a percentage of players in the group remaining, to
determine whether the bid value should be decreased. For example,
if the tier group has a number of remaining players greater than
the first threshold, the recommendation engine 400 may determine
that the likelihood that the owner is able to draft a player from
this tier group is still very high; consequently, the recommended
bid value may be lowered.
[0033] If the recommendation engine 400 finds that a player is in a
very low tier, it may determine that there is no value in bidding
on that player whatsoever, because another, essentially equivalent
(or better) player will be freely available at the end of the
draft; therefore, a recommendation not to bid may be made.
[0034] If the percentage of remaining players in the tier group is
below the first threshold, the recommendation engine 400 may
determine that the initial bid value should be maintained or even
increased. For example, if the percentage of the remaining players
in the tier group is within a given range, the recommendation
engine 400 may determine that the initial bid value is the optimal
amount that the owner should bid for the player, but if the
percentage is particularly low (e.g., if only a single player at a
high tier remains), the recommended bid may be increased. It should
again be noted that the recommendation engine 400 may include a
variety of other factors to make this determination. For example,
the recommendation engine 400 may review the current roster of the
team of the owner. This data may indicate that the recommendation
engine 400 should increase or lower the initial bid value. In
another example, the recommendation engine 400 may consider the
remaining assets available for bidding, particularly as a function
of the number of remaining players to be bid or the number of empty
slots. If the recommendation engine 400 determines that this player
is the last or one of the last remaining players in the tier group,
the recommendation engine 400 may be configured to increase the
recommended bid value to increase the likelihood that the owner is
able to draft the player should the other circumstances surrounding
the team dictate. That is, the recommendation engine 400 may
utilize a second threshold value, such as a further percentage of
players in the group remaining. At this stage, the recommendation
engine 400 may provide an adjusted bid value to the owner. This
threshold value may be built directly into the system or set by an
administrator.
[0035] The recommendation engine 400 may be configured to further
incorporate previous bids placed on the player in the calculation
to provide a recommendation. For example, if a current bid is
already made on the player, the recommendation engine 400 may
compare the current bid with the adjusted bid values previously
determined. Accordingly, the recommendation engine 400 may be
configured to determine whether or not to recommend that the owner
bid on the player. For example, if the recommendation engine 400
has considered all the other factors of the owner's team, the
recommendation engine 400 may determine that if the current bid is
greater than the adjusted bid, a bid by the owner is not
recommended, or vice versa. On the other hand, if no bids have been
made on the player and the recommendation engine 400 determines
that the player should be drafted, the recommendation engine 400
may recommend placing a bid at the adjusted bid value that was
previously determined.
[0036] In a second exemplary embodiment of the present invention,
the recommendation engine 400 may determine the bid value of the
player dynamically as the player is eventually placed for auction.
For example, the recommendation engine 400 may generate an initial
bid value based upon the projected statistics of the player
regardless of the tier group (as well as the previously described
manner of generating the initial bid value). In another example,
the recommendation engine 400 may not generate a recommended bid
value until a bid has been placed upon a given player by another
owner.
[0037] The recommendations may be shown to the owners in a variety
of manners. In a first example, as discussed above, the player's
name and the associated tier group may be shown. In a second
example, when the bid values are to be displayed, the player's name
and a range of recommended bid values may be displayed. In a third
example, when the recommendation as to whether or not to bid is to
be shown, simplified graphic may be used such as a strikeout
through the player's name to indicate a recommendation to not bid
on the player or a highlight in green lettering to indicate a
recommendation to bid on the player. In a fourth example, any
combination of the above may be shown to the user. Those skilled in
the art will appreciate that this is but a small subset of the
variety of mechanisms by which this information may be conveyed,
and that the most appropriate display may vary depending upon the
remainder of the user interface and other factors.
[0038] FIG. 5 shows a method 500 for generating a plurality of
tiers of players in order to provide recommendations for a fantasy
sports application according to an exemplary embodiment of the
present invention. The method 500 will be described with reference
to the recommendation engine 400 of FIG. 4.
[0039] In step 510, the recommendation engine 400 receives player
data. As discussed above, the recommendation engine 400 may receive
player data from a variety of sources such as the processors 410,
430, 450, 470, each having access to a data storage 420, 440, 460,
480, respectively. The player data may relate to at least one
parameter value as discussed above. In step 520, the recommendation
engine 400 may further determine the parameters that a league
utilizes for calculating scores for players and/or teams. As
discussed above, each league may use different parameters in the
scoring. Therefore, by determining the correct comparison
parameters for the league, the recommendation engine 400 is
configured to provide optimal recommendations according to the
needs of the particular owner.
[0040] In step 530, the recommendation engine 400 utilizes the
player data to determine the tier of the player. As discussed
above, score values of the players may be determined to further
determine the tier of the players. For example, each tier may
include a range of projected statistic values (e.g., from the
analyzed data of the processors 410, 430, 450, 470 that is further
analyzed by the recommendation engine 400) that a player is
expected to provide, thereby implying a threshold value between
adjacent tiers. The recommendation engine 400 may use the projected
statistics from the player data to determine the tier of the
player. In step 540, the recommendation engine 400 may group the
players into the tiers determined in step 530. Thus, in step 550,
the recommendation engine 400 may generate respective displays for
the players including player data such as name data and position
data and further include the respective tier value. The
recommendation engine may further generate a list of the players in
the tier value that may be made available to the owners prior to
and during the draft.
[0041] FIG. 6 shows a method 600 for generating a recommendation on
a player up for bid in a fantasy sports application according to an
exemplary embodiment of the present invention. The method 600 may
be invoked when a draft has started and a player is up for bid.
Furthermore, the method 600 incorporates the tier groups generated
by the recommendation engine 400, for example, as illustrated in
the method 500. The method 600 will be described with reference to
the first exemplary embodiment in which the initial bid value is
used as a basis for adjustment in order to provide the
recommendation. However, as discussed above, the recommendation
engine 400 may determine the recommendation using a variety of
factors that may be independent of any initial bid value, whether
such value was calculated beforehand or not.
[0042] In step 605, the recommendation engine 400 may determine the
initial bid value of the player. As discussed above, the
recommendation engine 400 may receive data regarding the initial
bid value such as from the auction value provider 470. In another
example, the recommendation engine 400 may receive the player data
from the other processors 410, 430, 450 and determine the initial
bid value. In step 610, the recommendation engine 400 determines
the tier group of the player up for bid. As discussed above in the
generation of the tier groups, each player may be placed in one of
the tier groups and may be associated therewith along with the
other players in the tier group.
[0043] In step 615, a determination is made whether the number of
remaining players in the tier group is greater than a first
predetermined threshold. The first predetermined threshold may be
determined as a general value applied for each analysis of the
players (e.g., by the administrator). For example, the
recommendation engine 400 may set the first predetermined threshold
to 50% of the players remaining excluding the player up for bid.
Thus, if a tier group includes ten players, three of whom have
already been drafted, then the percentage value of the remaining
players in step 615 is 60% since six out of ten players remain
undrafted if the player up for bid is excluded.
[0044] If the number of the tier group remaining is greater than
the predetermined threshold, the method 600 continues to step 620.
In step 620, since the recommendation engine 400 has determined
that a sufficient number of players in the tier group remain and
the probability that the owner will be capable of drafting a player
from this tier group is high, the initial bid value that would
otherwise be recommended to the owner is lowered. That is, the
maximum recommended value is adjusted by being lowered. The
lowering value may be determined dynamically, particularly as a
function of the threshold value, the maximum recommended value, a
remaining budget for bidding, etc.
[0045] Returning to step 615, if the number of the tier group
remaining is less than the predetermined threshold, the method 600
continues to step 625. In step 625, a further determination is made
whether the number of remaining players in the tier group is
greater than a second predetermined threshold; in particular,
whether or not the player up for bid is the last or one of the last
players to be bid in the tier group. Accordingly, the second
predetermined threshold may be a smaller percentage value than the
first predetermined threshold. The second predetermined threshold
may also be generated in a substantially similar manner as the
first predetermined threshold. As discussed above, the
determination of step 625 may be a further range or percentage of a
number of remaining players. Thus, using the aforementioned
example, with ten players in the tier group, the recommendation
engine 400 may utilize a range of 10-30% as the second
predetermined threshold so that if the remaining players are within
this range, the recommendation engine 400 is configured to perform
different adjustments.
[0046] It should be noted that the determination for remaining
players within only the respective tier group is only exemplary.
According to a preferred exemplary embodiment of the present
invention, the recommendation engine 400 may evaluate the
recommended bid with players remaining in other tiers, in
particular, a higher tier group. Thus, a subsequent adjustment to
be made to the recommended bid as described in further detail below
may be affected by remaining players in a higher tier group.
[0047] If there are any remaining players in the tier group, the
method 600 continues to step 630. In step 630, the recommendation
engine 400 may maintain the initial bid. Specifically, the
recommendation engine 400 may determine that the likelihood that
the owner will be able to draft a player in this remaining tier
group is decreasing, and that lowering the initial bid value will
not provide the owner with a good chance to draft a player in this
tier group. However, the maintenance of the initial bid value is
only exemplary. As discussed above, the recommendation engine 400
may still lower or even increase the initial bid value as a
function of the other factors to be considered for the owner and
the team as a whole.
[0048] Returning to step 625, if there are too few available
players in the tier group (less than the second predetermined
threshold value), the method 600 continues to step 635. In step
635, the initial bid value is increased, since there is a decreased
likelihood of drafting a remaining player, as few or even no
players in this tier group remain. Again, as discussed above, a
range may be used for the determination of step 625. Accordingly,
to attempt to guarantee that a player in this tier group is
drafted, the recommendation engine 400 may increase the initial bid
value to improve the odds of the player being drafted. The
increasing value may also be determined dynamically, particularly
as a function of the threshold value, the maximum recommended
value, a remaining budget for bidding, etc.
[0049] It should again be noted that at this stage of the method
600, the recommendation engine may provide the adjusted bid value
to the owner via a graphic display of a bid number, an audio
indication, etc. Using the interface of the fantasy sports
application, the user may be provided with the adjusted bid value.
The recommendation engine 400 may also provide the initial bid
value to allow the owner to see whether the recommended adjusted
bid value has been increased, decreased, or maintained.
[0050] It should also be noted that according to the preferred
embodiment where the recommendation engine 400 considers players
remaining in other tier groups, the adjustments made in steps 620,
630 and 635 may further be adjusted or affected. For example, step
615 may determine that the number of remaining players in the tier
is below the threshold and step 625 may determine that there are no
more remaining players in the tier group. However, the
recommendation engine 400 may further determine that a player in a
higher tier group remains. In such an example, the recommendation
engine 400 may determine that the adjustment should be for
maintaining the recommended bid value at the initial bid value or
even lowering the recommended bid value. It should also be noted
that the recommendation engine 400 may further consider the user's
roster when considering players in other tier groups. For example,
although a player in a higher tier group remains, if the place on
the roster that the player would occupy has already been filled by
other players, this may also affect the manner in which the
recommendation engine 400 adjusts the recommended bid value.
[0051] After steps 620, 630, and 635, the method 600 continues to
step 640. In step 640, a determination is made whether a prior bid
has been made on the player up for bid. That is, another owner may
have placed a bid on the player. As discussed above, the
recommendation engine 400 may further incorporate a current bid in
determining the recommendation. If no prior bid is available, the
method 600 continues to step 645 where the recommendation engine
400 provides a further adjusted bid value. For example, the
recommendation engine 400 may consider all the other factors at
this point (e.g., remaining funds to bid) to adjust the bid value
to an optimal amount for the owner.
[0052] Returning to step 640, if there is a prior bid, the method
600 continues to step 650. In step 650, a further determination is
made whether the prior or current bid made by another owner is
greater than the adjusted bid value determined in steps 620, 630,
or 635. As noted above, the adjusted bid value may be a maximum bid
value provided to the owner so that any bid value under the maximum
indicates a better deal for the owner. Thus, if the current bid is
greater than the adjusted bid (representing a maximum), the method
600 continues to step 660 where a recommendation not to bid for the
player is provided. If the current bid is less than the adjusted
bid, the method 600 continues to step 655 where a recommendation to
bid for the player is provided.
[0053] The exemplary embodiments of the present invention provide a
recommendation engine that receives player data from a plurality of
different sources, such that a recommendation may be determined for
an auction type draft for a fantasy sports application. The
recommendation engine may group the players into tiers, and may
ultimately generate a list of tiers with the players grouped
accordingly. The recommendation engine may also utilize the tier
groups to determine a recommendation for the owner. The
recommendation may include a maximum bid value representing a
highest bid value that the owner should make should the owner wish
to draft the player. The recommendation may also include a basic
affirmative or negative response whether to draft the player or
not.
[0054] Those skilled in the art will understand that the
above-described exemplary embodiments may be implemented in any
number of manners, including, as a separate software module, as a
combination of hardware and software, etc. For example, the
recommendation engine may be a program containing lines of code
that, when compiled, may be executed on a processor.
[0055] It will be apparent to those skilled in the art that various
modifications may be made in the present invention, without
departing from the spirit or the scope of the invention. Thus, it
is intended that the present invention cover modifications and
variations of this invention provided they come within the scope of
the appended claimed and their equivalents.
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