The document explores the application of network mathematics to analyze tactical patterns in youth basketball, particularly focusing on the interactions among players during offensive phases. It utilizes social network analysis to quantify these interactions, emphasizing the importance of understanding player roles and their betweenness in gameplay. The study aims to enhance performance indicators and tactical decision-making through a theoretical and ecological framework.

1. Player interactions as performance
indicator in basketball
Applying network mathematics to tactical aspects of sport

2. Preliminary
What will you see?
Exploratory study after tactical patterns in youth
basketball (age < 20) from an ecological perspective.
1. Visualization of perspective
2. Theoretical background
How are tactical patterns measured?
Social network analysis provides mathematical
models to quantify the interactions between players.
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3. Visualization
11 offensive phases
of Gasterra Flames
U-20 basketball team
Analyze interactions:
passes, screens and
releases between
players of the same
team
Note: player positions
in field are not the
actual positions
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4. The network of offensive phases
●
RED arrows are high interactions
●
BLUE arrows are low
interactions
Circle size is
the betweenness
of players
●
Betweenness shows how often
the player is involved in the
sequence of interactions during
an offensive phase
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5. How do I benefit?
Defend 1:1 or in the
zone between the
most frequently
interacting players?
Which player is most
frequently interacting
(in agreement with
the position)?
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6. One step further!
Did substitutions
cause the desired
pattern/tactical
change?
Graph of one quarter
and all 9 players that
participated
Note: the graph was
rotated to fit
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7. Background
Current physiological analysis
Mostly individual characteristics, e.g. heart rate,
frequency of actions, time in different intensity zones,
Team ball games are dynamical
systems, consisting out of many
different interacting parts. Araújo, et al. (2002)
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8. Emergent interaction
Player continuously have to overcome problems
related to space and time, information and
organization. Grehaigne, et al. (1997)
Invasive team ball games require interpersonal
interaction to score points or prevent them from
being scored. Schmidt, et al. (1999)
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9. Patterns matter
Skilled performers are better
Skilled performers of ball team sports are faster and
more accurate in recognizing structured patterns
than novice peers. North, et al. (2006)
How to utilize these findings in team ball games?
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10. Mix of theories
Ecological Network
psychology analysis
(theoretical framework) (mathematical approach)
Personal Game
performance outcome
(athletic profiles) (performance
indicators)
Results coming
soon...
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11. Martijn Swaagman
Sports Field Lab
Zernikeplein 17
9747 AS Groningen
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