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Fantasy Basketball Draft Using Minimax
Chris Dietsch
Department of Computer Science
University of Massachusetts Lowell
1 University Ave Lowell MA 01854
[email protected]
ABSTRACT
In this paper we describe our findings and implementation
of a fantasy basketball artificial intelligence web program
designed to aid users first hand in making intelligent
choices for a fantasy basketball league. In order to employ
the adversarial style of a fantasy draft, we used a minimax
algorithm to anticipate the choices of other players in the
league. We assumed the other players to be knowledgeable
and reasonable. The result of our implementation is an
interactive website drawing real stats and rankings for real
NBA players and accurate player photos and links to player
files from [4].
Bruce Malley
Department of Computer Science
University of Massachusetts Lowell
1 University Ave Lowell MA 01854
[email protected]
PROJECT DESCRIPTION
Author Keywords
Artificial Intelligence, AI, searches, adversarial search,
minimax, game theory
INTRODUCTION
Without extensive knowledge, determining the best players
to pick in a fantasy basketball draft is not an easy task. It
takes knowledge of each player’s statistics, injuries, rivals,
and streaks in order to create a winning team. During the
fantasy draft, you typically also have a limited amount of
time to make your picks. This can adversely affect your
ability to choose wisely.
Fortunately, using minimax, a theoretical “best team” will
be created, taking all of the above information into account.
From statisticians, reporters, team owners/managers, to
fantasy players, a method for reliably predicting team
selections in some fashion based on certain circumstances
(pay grade, seniority, skill, etc) can make this solution very
valuable to a multitude of people.
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For our work, we focused on the National Basketball
Association, or NBA, and its players. This allows us to
limit the player selection to at most at any one given time,
450 players (30 teams, 15 roster players [12 who dress for
the game] gives 450 maximum players). The work is easily
extensible to the WNBA, NCAA, and any other level of
play or league that has access to stats.
Using minimax, making the best choices for a fantasy
basketball team is a simple task. Each player in the NBA is
rated based on their performance. For each player, this
rating takes into account the number of games the player
has played this season, the average number of minutes
played, rebounds, and assists per game, field goal
percentage, and free throw percentage. The rating is an
overall grade of the player at a snapshot during the season.
This project will use this rating to determine each player’s
value to the team. The minimax algorithm will use the
rating of each player to make choices. The max agent,
which represents the user’s team, will attempt to maximize
the user’s team rating. It will pick the best player it can to
fill the five basketball positions. On the other hand, the min
agents represent all of the user’s opponents. These agents
will attempt to minimize your team rating, and, in turn,
create the best possible team they can.
The project was a joint effort between the two authors. The
work was split approximately into back end and front end
work.
The back end includes getting outside ranking data for
players into useable form and translating it into a useable
format for the algorithm. Alongside this is making a
relation between player photos and entries in the player
files for output in the front-end. Implementing all
interactive features, display capabilities, and deciding how
to read the data before and after the minimax algorithm has
modified it also falls into this category.
The front end, including implementing the core minimax
algorithm in JavaScript, is the heart of the project. The
algorithm is based on an input number of teams (players)
and team names. It outputs the choice of players that each
player chose on their team with pictures attached to the
respective athletes and links to the NBA’s player files for
actual stats for that player.
We built an interactive webpage (available at [2]) using
these two core pieces of the project. The user is able to
enter a team name for their team, choose the number of
lines similar to an actual NBA team’s roster (lines are full
sets of 5 players in all positions for a basketball team:
center, two guards and two forwards. We split guards into
their technical positions: shooting guard and point guard,
and forwards into theirs: power forward and shooting
forward), and how many opponents (other players in the
league) to simulate.
The data used pertains to the backend of the project. The
rating of each player in the NBA can be found at [2].
Having downloaded the data, we then put the data into
comma separated value (CSV) form. The values are ID,
FirstName, LastName, position, and rating. Using CSV
allowed us then to convert into JavaScript arrays,
specifically JavaScript Object Notation format (JSON)
based on this input CSV file. With the file in a JavaScript
useable form, we also have downloaded appropriate player
photos to enhance usability and aesthetics. To tie the photos
with the players, we will keep the ID field in a one to one
correspondence with the players of the NBA, so one
number will be assigned to each player and so we can call
on this number to find the picture matching this player. We
went one step further during development and split the list
of all players into 5 CSV (and thus, 5 JSON arrays), one file
(/array) per position. So the centers were grouped up, sorted
by ID, and put into a centers file, which was then converted
to a centers JSON array, and similarly for the other 4
positions.
The front end and the algorithm itself were mainly written
in JavaScript. The web page was deployed with XHTML
compliant markup and styled with CSS. This was done in
order to speed up a deliverable of a final product in the
required timeline. It also makes for an easy way to track
user input and produce output with photos and web links, as
we wanted to do.
The algorithm works by iterating through nested for loops.
The first for loop goes from zero to the number of players
per team. The second for loop goes from zero to the
number of teams in the league. This allows us to pick the ith
player for the jth team. For each iteration, the code generates
a random number. This random number represents a
basketball position (shooting guard, point guard, power
forward, shooting forward, or center). If the current team
has already filled all the spots for that position, a new
random number will be generated. Once a position has
been decided on, the team makes its pick by adding the
highest rated player that plays the position previously
decided upon to the team’s array and removes that player
from the player pool. This continues until all the teams are
filled.
ANALYSIS OF RESULTS
Our project set out to give users a way to have some
intelligence and planning behind a fantasy basketball draft.
The resulting end product does that, and more. The project
does some expectations of opponent’s picks of players
beyond the trivial first few choices in a draft. It helps to
explore the space of all possible choices and helps you
anticipate an opponent picking a player that you would
never have assumed he would have picked in that scenario.
Our project is easily extensible as well, lending itself to
more serious use in the future if wanted. Things like player
salaries and more stats were on the cutting board for the
deadline, but could easily be added into the backend and
used to enhance the experience.
Because all of the teams will be making the most logical
and best picks, it is expected that the teams will have very
similar team ratings. We expect that the teams will be very
distributed amongst the top players (as any fantasy selection
normally is). To make sure our selections are logical and
worthy of being referred to as useful fantasy teams, we will
compare with the website at [3].
This website allows you to input your fantasy team’s roster
and get a valuation of that team at this moment in the
season. So for our project to achieve success, it will first
have picked five distinct players at five distinct positions
for each team. This team will be comprised of the best
choices possible at any moment in the season against a
chosen number of opponents. Because of this, in the long
run the chosen fantasy team (or actual team of players
picked in real life draft, for the owners of teams using this,
for example) will perform hopefully as well as possible.
The algorithm went from general purpose team of any 5
players per line to dedicated, playable 5 player lines with 1
player required at each of the 5 unique positions. Due to
this, we feel our algorithm is much more realistic and useful
than it was at the onset of the project.
DISCUSSION
The biggest thing we learned about our project was during
the implementation of minimax itself. We learned that there
are key distinctions between a one level depth tree search
for minimax to use, and the entire level of the tree being
played out and evaluated. Our proposal and all deliverables
during development led us to believe our minimax was just
that, minimax searching. After examination from others and
from ourselves, we learned that our experience with the
algorithm and its depth of the tree maxes out at
approximately one level. That is, one player pick is then
followed by n min agents running, where n is the number of
other players chosen by the user. The most important thing
we both learned during this project’s lifespan is that this
should have been not just one level deep, but instead each
min agent should report a value that is the anticipation of its
best possible team selection after the user chose a player,
and this min agent then proceeded to simulate the
remaining picks of every other computer controller player
and the user’s remaining picks. Then, the top level max
agent would recommend pick number two for the user to be
the pick that most damages the rest of the league, that is, the
pick that minimizes the expected value of all the other
players in the league competing against the user.
and hundreds of variables can be simulated with this
minimax algorithm for a draft.
An important thing about our project was that it is a good
framework. It has the ability to be able to be extended upon
in a natural way. This includes salaries of players for a
team-wide cap, team chemistry indicators, approximations
and heuristics and so on. In the sports realm numbers play a
natural role in determining many things so modeling these
things is not a giant leap which lends itself well to AI
approaches and adjustments to our algorithm.
The work done by Smyth and Balfe [5] is intriguing to our
needs for this project and can be seen as an extension. Their
work aims to find ways to improve future results lists of
search engines based on implied preferences of a
community. The community in our case is sports domain
related searches, particularly basketball. What would be
most useful is a way to search players based on statistics
and rankings and other inputs to the minimax algorithm.
These types of results, building off Smyth and Balfe’s
work, can and should be ‘cached’ in a sense, in order to
make more frequent updates of our back end data possible.
Doing this well will lead to a more up to date and realistic
view of the domain and a more useful output.
A big piece of our project that we felt would be a good
place to extend upon is the interactivity of the project. One
player choosing against min agents who are representing
intelligent enemies is useful in its own right, but the ability
to play live against 1 or more human opponents gives the
draft a whole new element of surprise. Probabilities are one
thing in a draft based on one element (player rating) but
when human players make a statistically unlikely choice of
player at some moment (for team chemistry reasons, for
example), the AI may perform poorly as it did not expect
that choice. The implementation details of a two (or more)
player setup ruled out adding this feature in favor of a more
refined one player experience. The idea can be extended to
an internet setting, but clearly out of time frame for this
project.
The idea of automating a process of predictive picking of
players based on some knowledge of their worth (only
estimated skill for this project, but possibly expected cost to
play, team chemistry, age of player, etc) is a very valuable
and lucrative idea to a great number of people. Selecting
players for NBA drafts, or similar sports’ drafts is a hugely
speculative process dependent on many, many variables.
For example, teams decide if they need a player at a certain
position based on what they currently have at that position,
that player’s age and skill, the new player’s potential ability
to be a difference, etc. With all these variables for just one
choice, the cascading of choices over hundreds of players
Other arenas will clearly be interested in this, for example
websites providing fantasy sports can implement this AI
and let players do mock drafts on their own in order to
spend more time on their site and generate more revenue.
The people using the fantasy league/website will be more
inclined to use a website with more features like this and so
everyone will win.
CONNECTIONS TO EXISTING RESEARCH IN AI
Minimax is a widely used algorithm in artificial intelligence
applications for game play and adversarial searches, by
definition. In preparing and implementing our minimax
algorithm, we saw ways to use existing AI research not just
in minimax speedups and optimizations, but in the entire
process of finding data and modeling it to be useful for a
sports domain application.
An important part of this project was to search the web and
find relevant, useful data to feed into the back end.
Another problem we had to solve that has been researched
is the seemingly simple problem of creating a one to one
relation between players in the league and their pictures.
For our timeframe, we had a brute force approach where we
manually went in and assured player photos matched an
immutable ID field of the CSV and eventually the JSON
array.
The work done by Lin, Oakes and Tait in [6] is similar to
this seemingly innocuous problem. The problem of learning
in the AI sense which pictures match which players is not a
simple one. With new players changing team (and therefore
new photos) and rookies entering and veterans retiring, the
database could become stale rather quickly. Finding a way
to learn which player belongs to which field in the database
automatically as opposed to manually is another large
advantage to the overall effectiveness of the project.
An interesting piece of research that is highly related to our
project’s results and hypothesis is presented in Hurley [7].
Our hypothesis revolves around the idea of a ‘momentum’
in a large sense—the momentum of a player’s career and
their season affects their value to the user and to the min
agents acting as users. This research says that without this
model of momentum and a statistical background (i.e. their
propensity to continue to play well if they won an MVP
award last season), the problem of choosing players could
be just as simple as choosing names at random to fill your
team. In modeling players in a more sophisticated way,
where their rating is not just a number but an indicator of
their playing during the season and previous seasons to an
extent (the numbers gathered from [1] are just this), we
have a much more in depth problem where minimax is a
more suitable choice of attack to the problem of
anticipating performance over a longer period of time (a
season).
CONCLUSIONS
The resulting deliverable of our project satisfies what we
set out to achieve: a working, real-time interactive webpage
complete with working player links and photos for each
player that uses a minimax algorithm to anticipate player
choices for the opponents in the league and generates
expected and recommended choices for the user. An auto
pick option is also included to give a user an idea of what to
reasonably expect on their team given n players in the
league, assuming intelligent opponents.
The most logical additions to this project have been stated:
player salaries as arguments to the minimax routine, along
with other heuristic and complicating factors in order to
more closely mimic reality, multiple user capabilities, team
chemistries, and so on.
ACKNOWLEDGMENTS
As a team of two, the work was divided approximately
equally, or more specifically, into the two domains of front
end and back end. Chris was responsible for the player
picture downloads and the construction of the relation
between photos and player in the CSV and JSON.
Alongside this was the work of gathering player data from
[1] and modifying it to suit our needs. Chris also styled the
webpage with CSS. Bruce was responsible for architecting
the minimax solution in JavaScript, the heart of the project
itself. As he dealt with the outputs of his algorithm he was
also responsible for XHTML development to allow user
input and readable output.
The work described in this paper was conducted as part of a
Fall 2010 Artificial Intelligence course, taught in the
Computer Science Department of the University of
Massachusetts Lowell by Prof. Fred Martin.
REFERENCES
1. CBS Sports player rankings.
http://www.cbssports.com/nba/playerrankings/regularse
ason/OVERALL
2. Interactive Final Project.
http://weblab.cs.uml.edu/~bmalley/ai/
3. Fantasy team analyzer.
http://www.fantasysp.com/team_analyzer/nba/
4. National Basketball Association.
http://www.nba.com
5. Barry Smyth and Evelyn Balfe. 2006. Anonymous
personalization in collaborative web search. Inf. Retr. 9,
2 (March 2006), 165-190. DOI=10.1007/s10791-0067148-z http://dx.doi.org/10.1007/s10791-006-7148-z
6. Wei-Chao Lin, Michael Oakes, and John Tait. 2010.
Improving image annotation via representative feature
vector selection. Neurocomput. 73, 10-12 (June 2010),
1774-1782. DOI=10.1016/j.neucom.2010.01.019
http://dx.doi.org/10.1016/j.neucom.2010.01.019
7. W. J. Hurley. 2002. How Should Team Captains Order
Golfers on the Final Day of the Ryder Cup
Matches? Interfaces 32, 2 (March 2002), 74-77.
DOI=10.1287/inte.32.2.74.64
http://dx.doi.org/10.1287/inte.32.2.74.64