05-02-2013, 12:33 PM
Using Neural Networks in Preparing and Analysis of Basketball Scouting
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Introduction
“I don't believe in keeping statistics. Only statistics that is important is the final result.”
This used to be true, mostly because opponents also did not keep or use statistics. However,
the times have changed. Final result is still the most important thing, but a way in which
such result is obtained is also of great importance.
Basketball is the one of most popular sports, in Serbia and in the world. It is a team sport.
Actors of a basketball game are the players from two opposing teams, their team officials
with coaches, assistant coaches, doctors and officials (commissioner, referees, table officials,
statisticians). Every team, depending on a league or competition, may have no more than 12
or 10 players per game, and of those 5 are actively engaged in the game [1]. Basketball game
would not start without 5 players from each game on the court. In Europe, regular
basketball game is divided into 4 quarters of 10 minutes each, while in NBA every quarter is
2 minutes longer. If a result is draw, after regular time additional time of 5 minutes is
played, as many times as necessary to decide a winner of a game.
There is no limitations regarding a number of substitutions of players during a game, but
there is a limitation regarding personal fouls. If a player gets fifth personal foul, he must
leave the court and may not play any more.
Scouting and data mining techniques
The main reason for scouting is to know the opponent in all stages of basketball. Scouting is
done at team and individual level. Team level reviews opponents systems of playing the
game in offense, defense and transition; how the team acts in all kinds of defense, how it
attacks after outs and how it transits from defense to offence. Every stage may be
statistically shown as number of tries, lines of offense and percentage. In addition, good and
bad sides of team's and individual game may be shown. Individual scouting reviews
performance of every individual player in all game stages, his statistical performance, his
good and bad sides. For example, from which action he attacks most frequently and most
successfully or in which actions he has lower performance, as well as in how he (or she)
performs in different kinds of defense (what he defends worst?)
All in all, scouting shows how to attack the opponent in most efficient way and how to
handle defense. Therefore, statistics and scouting are important part of every analysis
required in order to prepare for future games. A good scouting often requires to follow
several games of the opponent team, mostly last four (two at home and two away). This
requires exceptional knowledge about basketball and computers as well. Scouts often need
several days to prepare players and coach for the next opponent. With present rhythm,
playing games twice a week (Wednesday – Sunday), scouts often don’t have enough time to
cover all opponents, so some teams have two or even more scouts in order to analyze every
next opponent. Naturally, there is a question of a mean to shorten the time for the scout, but
in such a way that he still obtains good quality information that will provide advantage over
the opponent. There is a powerful and good-quality tool today: “data mining in sport”
techniques. Data mining techniques in sports, especially in basketball, are in rise recently.
Those tools and techniques are developed with the aim to measure performances of
individual players and of the team as a whole.
Neural network
The modern discipline of neural networks was created as a combination of several quite
different ways of research: signal processing, neurobiology and physics [20] and therefore is a typical interdisciplinary branch of science [21]. It is basically an effort to comprehend
intricacies of a human brain, as well as to apply new insightsto processing complex
information [22]. There is a number of progressive, non-algorithmic systems, as learning
algorithms, genetic algorithms, adaptive memory, associative memory, fuzzy logic. General
opinion is that neural networks are presently the most mature and most applicable
technology [23].
Conventional computers’ work is based on logic: deterministic, sequential or with a very
low level of parallelism. Software written for such computers must be literally perfect in
order to work properly. This requires a long and therefore expensive process of perpetual
design and testing.
Backward propagation
Neural network is a controlled learning method, demanding a large training set of complete
records, including target variable. Since every observation from training set is conducted
through the network, output value is obtained at the output node. This value is compared to
the real value of target variable for given observation in training set, and the difference
between the real and the predicted value is calculated.
Sensitivity analysis
One of downsides of neural networks is their vagueness. The same exquisite flexibility that
enables a neural network to model a wide range of nonlinear behaviors, at the same time
limits our ability to interpret results using easily formulated rules. Unlike decision trees,
there is no clear procedure for translating complexities of neural network to a compact set of
decision rules.
Data mining in sport
Huge amounts of data are present in all areas of sport. These data may show particular traits
of any player, or events that happened during a game, and/or how a team is performing as a
unit. It is important to determine which data to store and to comprise a way for their best
usage [26]. By finding the best method to obtain new facts from these information and to
transform it to a particular data, sports organizations provide themselves a leverage in
comparison to other teams [27]. Such approach to knowledge seeking may be applied to a
whole organization – from players who may improve their performance using techniques of
video analysis, to scouts who use statistic analysis and projection techniques in order to
identify which talented youth would develop the most and become a good player [28].