Chapter 23: Quiz - Network Analysis in Football

Instructions

Choose the best answer for each question. Questions cover network fundamentals, centrality metrics, and football-specific applications.

Section 1: Network Fundamentals (Questions 1-10)

Question 1

In a passing network, nodes typically represent:

A) Plays or formations B) Players (QB and receivers) C) Games or drives D) Yards or statistics

Question 2

Edges in a passing network usually represent:

A) Physical distance between players B) Pass attempts or completions C) Player relationships off the field D) Time between plays

Question 3

A directed graph is appropriate for passing networks because:

A) All passes are equal B) Passes have a direction (from thrower to receiver) C) It's easier to visualize D) It uses less memory

Question 4

Network density measures:

A) The average node size B) The ratio of actual edges to possible edges C) The total number of nodes D) The maximum edge weight

Question 5

A passing network with high density indicates:

A) The offense uses many different receiver combinations B) The QB focuses on one receiver C) The offense runs the ball frequently D) The defense is weak

Question 6

Edge weight in a passing network typically represents:

A) Physical weight of players B) Importance or frequency of connection C) Distance of passes D) Player salary

Question 7

An undirected graph would be appropriate for:

A) Passing networks B) Coach-player relationships C) Blocking assignments (mutual relationship) D) Recruiting flows

Question 8

A node with no incoming edges in a passing network is likely:

A) A receiver B) The quarterback C) An injured player D) A defensive player

Question 9

The adjacency matrix of a passing network:

A) Lists all players alphabetically B) Shows connections between nodes as 0s and 1s (or weights) C) Calculates player statistics D) Stores game scores

Question 10

A bipartite graph in football networks would separate:

A) Offense and defense B) Home and away teams C) QBs and receivers (two distinct node types) D) Running and passing plays

Section 2: Centrality Metrics (Questions 11-18)

Question 11

In-degree centrality in a passing network measures:

A) How many passes a player throws B) How many times a player is targeted C) A player's physical speed D) Contract value

Question 12

PageRank in a passing network indicates:

A) Web search ranking B) Importance based on who passes to you and their importance C) Page number in the playbook D) Social media following

Question 13

A receiver with high betweenness centrality:

A) Catches the most touchdowns B) Connects different parts of the passing game C) Plays the most snaps D) Has the highest salary

Question 14

Eigenvector centrality differs from degree centrality by:

A) Using different software B) Considering the importance of connected nodes C) Only counting completions D) Ignoring edge weights

Question 15

A player with high closeness centrality:

A) Sits close to the coach B) Can reach all other nodes in few steps C) Has the shortest career D) Lives near the stadium

Question 16

Target share is calculated as:

A) Receiver targets / Total team targets B) Completions / Attempts C) Yards / Games D) Touchdowns / Red zone opportunities

Question 17

The Herfindahl-Hirschman Index (HHI) for targets measures:

A) Historical performance B) Concentration of targets among receivers C) Player health D) Draft position

Question 18

A low HHI for target distribution indicates:

A) Targets concentrated on one receiver B) Targets spread evenly across receivers C) High completion percentage D) Strong rushing attack

Section 3: Coaching Networks (Questions 19-23)

Question 19

In a coaching tree network, edges represent:

A) Physical distance between schools B) Mentor-protégé relationships C) Game outcomes D) Salary differences

Question 20

A coach with many descendants in the coaching tree:

A) Has won many games B) Has influenced many other coaches' careers C) Has the highest salary D) Has been coaching the longest

Question 21

Community detection in coaching networks can reveal:

A) Coaching salary structures B) Clusters of coaches with similar philosophies C) Game scheduling patterns D) Player injuries

Question 22

The "Bill Walsh coaching tree" is notable for:

A) Its small size B) Producing many successful head coaches C) Only including defensive coaches D) Being limited to one conference

Question 23

Analyzing coaching network structure can help:

A) Predict hiring decisions B) Identify potential assistants C) Understand scheme evolution D) All of the above

Section 4: Recruiting Networks (Questions 24-28)

Question 24

In a recruiting network, a "pipeline school" is:

A) A school with good plumbing B) A high school that regularly sends players to a specific college C) A college with many NFL draft picks D) A school in a pipeline-shaped state

Question 25

Geographic reach in recruiting networks refers to:

A) How far coaches travel B) Diversity of states/regions from which recruits come C) Stadium capacity D) Travel budget

Question 26

Two colleges recruiting from the same high schools indicates:

A) They are in the same state B) They may be recruiting rivals C) They have the same mascot D) They share coaches

Question 27

Weighted edges in recruiting networks often represent:

A) Player weight B) Number of recruits or average star rating C) Distance between schools D) Scholarship amounts

Question 28

Analyzing recruiting networks can help identify:

A) Pipeline schools to maintain B) Recruiting competition C) Geographic expansion opportunities D) All of the above

Section 5: Advanced Analysis (Questions 29-35)

Question 29

Community detection algorithms find:

A) Individual player statistics B) Groups of densely connected nodes C) The oldest players D) Contract details

Question 30

The Louvain algorithm is commonly used for:

A) Calculating player salaries B) Detecting communities in large networks C) Predicting game outcomes D) Measuring player speed

Question 31

Temporal network analysis examines:

A) How networks change over time B) The temperature during games C) Player retirement dates D) Historical game scores only

Question 32

A play sequence network connects:

A) Players who celebrate together B) Plays that frequently occur in sequence C) Coaches and players D) Different stadiums

Question 33

Link prediction in coaching networks could help:

A) Predict future hiring relationships B) Calculate game scores C) Measure player speed D) Design stadiums

Question 34

Multi-layer networks in football might combine:

A) Passing, blocking, and personnel layers B) Multiple game scores C) Different seasons only D) Player heights and weights

Question 35

Network visualization is important because:

A) It creates pretty pictures B) It helps communicate complex relationships C) It's required by the NFL D) It increases player salaries

Answer Key

Section 1: Fundamentals

  1. B - Players (QB and receivers)
  2. B - Pass attempts or completions
  3. B - Passes have direction
  4. B - Ratio of actual to possible edges
  5. A - Many receiver combinations used
  6. B - Importance or frequency
  7. C - Blocking (mutual relationship)
  8. B - The quarterback (only throws, doesn't receive)
  9. B - Connection matrix
  10. C - QBs and receivers as distinct types

Section 2: Centrality

  1. B - Times targeted
  2. B - Importance from who passes to you
  3. B - Connects different parts of passing game
  4. B - Considers importance of connections
  5. B - Reaches all nodes in few steps
  6. A - Receiver targets / Total targets
  7. B - Target concentration
  8. B - Spread evenly across receivers

Section 3: Coaching

  1. B - Mentor-protégé relationships
  2. B - Influenced many coaches
  3. B - Similar philosophy clusters
  4. B - Produced many successful HCs
  5. D - All of the above

Section 4: Recruiting

  1. B - Regularly sends players to a college
  2. B - Diversity of recruit origins
  3. B - May be recruiting rivals
  4. B - Number of recruits or star rating
  5. D - All of the above

Section 5: Advanced

  1. B - Densely connected groups
  2. B - Detecting communities
  3. A - Network changes over time
  4. B - Plays in sequence
  5. A - Predict future hiring
  6. A - Passing, blocking, personnel layers
  7. B - Communicates complex relationships

Scoring Guide

  • 31-35 correct: Excellent! Ready for advanced network analysis
  • 25-30 correct: Good understanding, review specific areas
  • 19-24 correct: Solid foundation, more practice needed
  • Below 19: Review chapter material before proceeding