Chapter 10 Quiz: Passing Networks and Analysis

Instructions

This quiz contains 30 questions covering passing network concepts, metrics, implementation, and interpretation. Select the best answer for each question.

Time Limit: 45 minutes Passing Score: 70% (21/30)

Section A: Graph Theory Foundations (Questions 1-8)

Question 1

In a passing network, what do nodes represent?

A) Passes B) Players C) Positions on the pitch D) Match events

Question 2

For a team of 11 players, what is the maximum number of directed edges in a passing network?

A) 55 B) 110 C) 121 D) 100

Question 3

Network density is calculated as:

A) Total edges / number of nodes B) Actual edges / maximum possible edges C) Average edge weight / total edges D) Number of nodes / number of edges

Question 4

What is the adjacency matrix entry $A_{ij}$ in a weighted passing network?

A) 1 if player i passed to player j, 0 otherwise B) The number of passes from player i to player j C) The average distance of passes from player i to player j D) The probability that player i passes to player j

Question 5

Why are passing networks typically represented as directed graphs rather than undirected?

A) Directed graphs are computationally simpler B) A pass from A to B is distinct from a pass from B to A C) Undirected graphs cannot be weighted D) FIFA regulations require directed analysis

Question 6

In a passing network with reciprocity of 0.8, what does this indicate?

A) 80% of passes were successful B) 80% of passing relationships are bidirectional C) The network has 80% density D) 80% of players passed at least once

Question 7

If player A has out-degree 5 and in-degree 3, what can we conclude?

A) Player A is a target player B) Player A distributes more than receives C) Player A made 5 passes total D) Player A received passes from 3 unique players

Question 8

What does network density measure in a passing network context?

A) How physically dense the team formation is B) How connected the passing network is relative to maximum possible connections C) The concentration of passes in the final third D) Pass completion percentage

Section B: Centrality Measures (Questions 9-16)

Question 9

A player with high betweenness centrality in a passing network is:

A) The player who makes the most passes B) Located on many shortest paths between other players C) The player nearest to all other players D) Connected to the most teammates

Question 10

Which centrality measure would best identify a deep-lying playmaker who connects defense to attack?

A) Degree centrality B) Betweenness centrality C) Closeness centrality D) Clustering coefficient

Question 11

PageRank centrality differs from degree centrality because it:

A) Only counts outgoing edges B) Ignores edge weights C) Values connections to important nodes more highly D) Is calculated only for the goalkeeper

Question 12

Closeness centrality measures:

A) How close a player stands to the goal B) How efficiently a player can reach all other players in the network C) How many players are within one pass D) Pass completion rate

Question 13

A player has high degree centrality but low betweenness centrality. What might this indicate?

A) The player passes a lot but isn't a critical connector between groups B) The player is the most important in the team C) The player is isolated from teammates D) This combination is mathematically impossible

Question 14

In eigenvector centrality, a player's score depends on:

A) Only their own connections B) The number of goals they score C) The centrality of players they connect to D) Their position on the pitch

Question 15

Which player type would typically have the highest betweenness centrality?

A) Goalkeeper B) Central midfielder C) Striker D) Wide defender

Question 16

If the damping factor in PageRank is set to 1.0, what happens?

A) The algorithm ignores all edges B) Random jumps are eliminated, potentially causing convergence issues C) All nodes get equal PageRank D) The algorithm runs faster

Section C: Network-Level Metrics (Questions 17-22)

Question 17

The clustering coefficient of a node measures:

A) How many clusters exist in the network B) The proportion of the node's neighbors that are connected to each other C) The network's overall density D) Distance to the nearest cluster center

Question 18

High network centralization indicates:

A) The team plays a possession-based style B) Passing is concentrated through one or few players C) All players have equal involvement D) The team has many triangular passing combinations

Question 19

Network entropy in passing analysis measures:

A) The disorder or chaos of the match B) The unpredictability of passing patterns C) Energy expenditure of players D) Temperature of the stadium

Question 20

A team has high global clustering coefficient. This suggests:

A) The team plays long balls B) The team has many triangular passing patterns C) Players stand close together D) The team is losing

Question 21

What does a high network density combined with low centralization suggest?

A) Balanced, distributed passing among all players B) Heavy reliance on one key playmaker C) Long-ball playing style D) Low possession percentage

Question 22

If Team A has higher network entropy than Team B, Team A is likely:

A) More predictable in passing patterns B) Less predictable in passing patterns C) Winning the match D) Making fewer passes

Section C: Visualization and Implementation (Questions 23-26)

Question 23

When visualizing a passing network, player node positions are typically determined by:

A) Random assignment B) Alphabetical order of names C) Average touch locations on the pitch D) Jersey numbers

Question 24

What is the purpose of setting a minimum pass threshold when visualizing networks?

A) To reduce computational time B) To eliminate noise from infrequent connections and improve clarity C) To make the visualization more colorful D) To comply with data privacy requirements

Question 25

A passing matrix heatmap shows:

A) Physical locations of passes B) Pass volume between each pair of players C) Player movements over time D) Shot locations

Question 26

When comparing passing networks from different matches, why is normalization important?

A) To make graphs look prettier B) Because different matches have different durations and total pass counts C) To satisfy data privacy requirements D) Normalization is not important for network comparison

Section D: Tactical Applications (Questions 27-30)

Question 27

Community detection in a passing network can reveal:

A) Player salary groupings B) Clusters of players who pass frequently among themselves C) Which players live in the same community D) Twitter followers

Question 28

How might a team's passing network differ when protecting a lead versus chasing a goal?

A) No difference expected B) Chasing teams typically have more centralized networks with vertical progression C) Protecting teams always have higher density D) The goalkeeper's centrality increases when chasing

Question 29

An analyst finds that a team's network density drops significantly in the second half. This could indicate:

A) The team is dominating possession more B) The team is tiring or changing tactics to more direct play C) The data is corrupted D) The opponent made a substitution

Question 30

When using network analysis for opponent scouting, an analyst should:

A) Only analyze one match B) Consider multiple matches to identify consistent patterns C) Ignore network metrics and focus only on individual stats D) Only use centrality measures, not network-level metrics

Answer Key

Section A: Graph Theory Foundations

  1. B - Nodes represent players in a passing network
  2. B - 11 × 10 = 110 directed edges possible (each player can pass to 10 others)
  3. B - Density = actual edges / maximum possible edges
  4. B - In weighted networks, $A_{ij}$ equals the number of passes from i to j
  5. B - Direction matters: A→B and B→A are distinct passing actions
  6. B - Reciprocity measures the proportion of bidirectional relationships
  7. D - In-degree represents number of unique predecessors (players who passed to A)
  8. B - Density measures connectivity relative to maximum possible

Section B: Centrality Measures

  1. B - Betweenness counts how often a node lies on shortest paths
  2. B - Betweenness captures "bridge" roles between team areas
  3. C - PageRank weights connections by the importance of neighbors
  4. B - Closeness is based on shortest path distances to all nodes
  5. A - High degree with low betweenness suggests local activity without bridging
  6. C - Eigenvector centrality recursively depends on neighbor centrality
  7. B - Central midfielders typically bridge defensive and offensive players
  8. B - Damping factor < 1 adds random jumps for numerical stability

Section C: Network-Level Metrics

  1. B - Clustering measures triangle formation around a node
  2. B - High centralization means unequal distribution of importance
  3. B - Entropy captures unpredictability in the passing distribution
  4. B - High clustering indicates frequent triangular combinations
  5. A - Dense + decentralized = everyone passes to everyone fairly equally
  6. B - Higher entropy means more diverse, less predictable patterns

Section C: Visualization and Implementation

  1. C - Average positions ensure visual correspondence with tactical placement
  2. B - Threshold filtering removes clutter from rare connections
  3. B - Matrix heatmaps show pairwise pass volumes
  4. B - Different match circumstances require normalization for fair comparison

Section D: Tactical Applications

  1. B - Communities reveal clusters of interconnected players
  2. B - Chasing games typically show more direct, progression-focused networks
  3. B - Lower density often indicates fatigue or tactical changes to directness
  4. B - Multiple matches reveal consistent patterns vs. match-specific variations

Scoring Guide

  • 27-30 correct: Excellent - You have mastered passing network analysis
  • 24-26 correct: Good - Strong understanding with minor gaps
  • 21-23 correct: Passing - Adequate knowledge, review weak areas
  • 18-20 correct: Needs Improvement - Review chapter material
  • Below 18: Review Required - Revisit fundamental concepts

Concepts to Review by Score Range

If you scored below 80%, review: - Graph theory fundamentals (Sections 10.1-10.2) - Centrality measure definitions and interpretations (Section 10.3)

If you scored below 70%, also review: - Network-level metrics (Section 10.4) - Practical implementation examples (Section 10.8)

If you scored below 60%, complete: - All chapter exercises - Hands-on coding practice with provided examples - Review prerequisite graph theory materials