Statistical Report
Tactical Adjustment
Expert
Coaching
System
Winning
Game Video
Design of an Expert
System Coach for Complex Team Sports
A10 Champs
Group Lead:
Brice Colcombe
Group Members:
Lindsay Horton
Muhammad Ommer
Julia Teng
Sponsor:
Dr. Lance Sherry
2
Agenda
•
•
•
•
•
•
•
•
•
Context
Stakeholder Analysis
Problem/ Need Statements
Concept of Operations
Mission/Functional/Design Requirements
Simulation
Risks
Project Plan
Next Steps
Design of an Expert System Coach for Complex Team Sports
3
Objective of Game
• Soccer: Game between two teams of ten field players and one
goalie per team
• Main Focus: George Mason University Men’s Soccer Team
• Field: A standard NCAA regulated field is 115-120 yds (length)
by 70-75 yds (width) including two goals that are defended on
either side of the field.
• Objective: The object of the game is to score more goals than
the other team
Design of an Expert System Coach for Complex Team Sports
4
Strategy
• Offense:
▫ Connect a series of passes to get around and
through the opposing team’s defense by
using numerical advantage
• Defense:
▫ Win the ball back from opposing team
▫ Do not allow the opposing team to get near
your goal with the ball causing them to not
score any goals
5
Strategies: Common Formations
4-3-3
4-4-2
Coaching Rules
Inverting the
Pyramid: A History
of Football Tactics
(Jonathan Wilson)
Figure 4: 4-3-3 Formation
Figure 3: 4-4-2 Formation
Types:
Numerical Advantages:
Types:
Numerical Advantages:
•
•
•
•
•
•
•
•
•
•
•
Flat
Wide Diamond
Narrow Diamond
Numbers out wide
Support for attacking
Disadvantages:
•
Flat
2 Holding Midfielders
2 Attack Midfielders
High/Low Wings
Outnumbered in middle
Design of an Expert System Coach for Complex Team Sports
Attack-centric
Strong central
midfield presence
Disadvantages:
•
•
Open to counterattack
Open on outside
6
Complexity of Soccer
• Baseball vs. Soccer: Discrete vs. Flowing
• Players have to make own decisions in order to
benefit the team
• Players have to adapt to the situations around them
• No instructions/stoppages until halftime
• Network Complexity of Soccer: Netcentricity
Design of an Expert System Coach for Complex Team Sports
7
How Coaching is Done
• Traditional
▫ Use coaching expertise
▫ Watch practices/games, make changes accordingly
▫ Cannot ‘see’ full complexity of game
• New Generation
▫ Use data analysis to find holes in strategy, make
changes accordingly
▫ Able to ‘see’ patterns and complexity of game
• Hybrid Mix of Traditional and New Generation
▫ Use a mix of the two coaching strategies
Design of an Expert System Coach for Complex Team Sports
8
Current Process
Design of an Expert System Coach for Complex Team Sports
9
NCAA Tournament
• It is a single elimination tournament with a total of 48
teams
• 22 teams get an automatic bid from winning their
conference championships
• The remaining 26 teams get an at large bid based on
their teams rating percentage index (RPI) score
Source: NCAA (2015)
Design of an Expert System Coach for Complex Team Sports
10
Rating Percentage Index [RPI]
• Consists of Three Parts:
1. Teams Winning Percentage (Expert Coaching System)

𝑊+ 𝑇 2
𝑊+𝐿+𝑇
W = win, L = loss, and T = tie
2. Opponents Average Winning Percentage (Schedule Planning)

𝑂𝑊+ 𝑂𝑇 2
𝑂𝑊+ 𝑂𝑇 −1 2
(𝑂𝑊−1)+ 𝑂𝑇 2
,
,
𝑂𝑊+ 𝑂𝐿−1 +𝑂𝑇 𝑂𝑊+𝑂𝐿+(𝑂𝑇−1) (𝑂𝑊−1)+𝑂𝐿+𝑂𝑇
(win , tie, loss)
OW = Opponent’s win, OL = opponents loss, and OT = opponents tie
3. Teams A’s Opponent’s Opponents Average Winning Percentage
(Schedule Planning)
 𝑥 of Team A’s opponents’ part 2
Combine the three parts:
𝑝𝑎𝑟𝑡 1 + 2 ∙ 𝑝𝑎𝑟𝑡 2 + 𝑝𝑎𝑟𝑡 3
𝑹𝑷𝑰 =
4
Design of an Expert System Coach for Complex Team Sports
11
Historical Atlantic 10 Data (2004-2014)
GMU vs SLU Winning Percentage
A-10 Historic Winning Percentages
80%
74%
100%
90%
60%
50%
44%
51% 52% 52%
57%
37% 37%
40%
30%
46% 48%
55%
23%
20%
60%
80%
Winning Percentage
Winning Percentage (past ten years)
70%
70%
60%
50%
40%
30%
20%
10%
10%
0%
0%
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
GMU
Winning Percentage:
𝑊+ 𝑇 2
𝑊+𝐿+𝑇
Saint Louis
GMU Average
Saint Louis
Source: Atlantic 10 Team Archives (2015)
Design of an Expert System Coach for Complex Team Sports
12
What a Win is Worth
School
UCLA
Maryland
UI
UNC
URI
VCU
UVA
GMU
UMASS
Salary Win %
205,000 75%
206,000 71%
176,225 62%
88,044 58%
72,500 55%
90,640 55%
115,400 52%
90,855 51%
108,681 47%
Conference
Wins
4
4
1
1
2
0
2
2
1
NCAA
Years Coaching
Total
Championships
at School
Experience
4
12
14
2
23
25
3
6
12
2
5
23
0
3
12
0
6
15
2
20
26
0
11
24
0
1
24
Average Salary: $158,133.80
Average Atlantic 10 Coach: $90,676.50
GMU Head Coach worth per win: $9,085
A national championship level coach gets paid on average $67,457.30 per year more than
an Atlantic 10 coach.
Design of an Expert System Coach for Complex Team Sports
13
Gap Analysis
Replicate Saint Louis Success By:
1. Win Atlantic 10 Conference Championship 2 times
every 5 years
2. Receive an NCAA Bid 6 times every 10 years
3. Average RPI Score of .56
Resulting success will help close head coaches salary gap
of $67,000
Design of an Expert System Coach for Complex Team Sports
14
Agenda
•
•
•
•
•
•
•
•
•
Context
Stakeholder Analysis
Problem/ Need Statements
Concept of Operations
Mission/Functional/Design Requirements
Simulation
Risks
Project Plan
Next Steps
Design of an Expert System Coach for Complex Team Sports
15
Design of an Expert System Coach for Complex Team Sports
16
Stakeholder Analysis
Class
Stakeholder
Goals
Tensions
Head Coach
Produce a team that gives highest
probability of winning
Keep job, get next big job
Assistant Coaches
Scout best players for the team
Prepare the team for games
Become head coach
Player
Perform at highest level
Get to next level (pro)
Pressure from University to
win
Old school coaching ideals
Disagreements with head
coach
Doesn’t always have a say in
decisions
Not getting played by coach
Playing on a losing team
Trainers
Have as few injuries as possible
Rehab injured players
Can’t always control how
hard coach pushes players
Investors (University)
Fund a winning team
Make money through ticket and
memorabilia sales
Market the University in a positive fashion
Want immediate success
(not always possible)
Budget is only so big
Primary
Secondary
Parents
Academies
Tertiary
Data Analytic Companies
Invest money in youth development in
hopes of college scholarships and
professional contracts
Train youth players in hopes of signing with
professional clubs
Earn revenue from data software sales
Design of an Expert System Coach for Complex Team Sports
Can’t be involved with
coaches like at the youth
level
Players move, go onto
college
Multiple data companies
competing with one
another
17
Problem Statement
George Mason University Men’s Soccer Team is not
consistently achieving NCAA Tournament bids at a high
rate (2 bids out of last 10 years).
Need Statement
There is a need for a coaching tool that uses coaching
expertise and uses soccer game data to understand the
complexity of soccer and seek a competitive advantage
in order to increase the probability of an NCAA
Tournament bid to 3 bids out of every 5 years.
Design of an Expert System Coach for Complex Team Sports
18
Solution Concept of Operations
Design of an Expert System Coach for Complex Team Sports
19
Mission Requirements
Mission Requirement # Requirement Description
MR. 1.0
The Expert Coaching System (ECS) shall recommend XX possible
strategies based on game data gathered in real time.
MR. 2.0
The ECS shall recommend XX possible strategies based on gameplay
data gathered by half-time
MR. 3.0
The ECS shall recommend XX possible strategies based on gameplay
data gathered by overtime.
Design of an Expert System Coach for Complex Team Sports
20
Functional Requirements
Functional Requirement #
FR. 1.0
Requirement Description
The ECS shall run a simulated soccer game derived from
collected gameplay data.
FR. 2.0
The ECS shall accurately recognize soccer field patterns using
probability maps 90% of the time.
FR. 3.0
The ECS shall accurately quantify pass rate probabilities inbetween the 14 zones from game data 95% of the time.
FR. 4.0
The ECS shall accurately determine goal probability rates that
can be made from each of the 14 zones.
Design of an Expert System Coach for Complex Team Sports
21
Design Requirements (Input/Output)
Input Requirement #
Requirement Description
IR. 1.0
The ECS shall input statistics from Sports Data company providers.
IR. 2.0
The ECS shall input zone graph percentages from InStat to run the
simulation.
IR. 3.0
The ECS shall input XX coaching rules to be used to make
adjustments.
Output Requirement # Requirement Description
The ECS shall output a table showing the relationship between
OR. 1.0
current formations utilized and possible adjustments that are
recommended by the system.
The ECS shall output correct coaching adjustments that result in
OR. 2.0
30% greater chance of winning
OR. 3.0
The ECS shall output tactical adjustments xx amount of hours after
receiving game data from Data analytics’ providers.
Design of an Expert System Coach for Complex Team Sports
22
Agenda
•
•
•
•
•
•
•
•
•
Context
Stakeholder Analysis
Problem/ Need Statements
Concept of Operations
Mission/Functional/Design Requirements
Simulation
Risks
Next Steps
Project Plan
Design of an Expert System Coach for Complex Team Sports
23
Simulation
• Objective: To simulate a game based on game data and
see if the coaching system rules will impact the outcome
of the game.
• Input: Input passing data that is transformed into
probability maps that are inputted into the simulation
• Output: table of passing percentages and strategy
changes
• Simulates a 90 minute game (two 45 minute halves)
using passing probability maps and different ball
movements
• Simulation based on previous refereeing senior design
project: Assessment of soccer Referee Proficiency in
Time-Sensitive Decision Making Jones et.al.(2013)
• Currently over 10,000 lines of code
Design of an Expert System Coach for Complex Team Sports
24
Simulation Requirements
Simulation Requirements # Requirement Description
The simulation shall input zone graph data consisting of 14 zones of
SR 1.0
a standard NCAA soccer field.
SR 2.0
The simulation shall follow strategies of George Mason University
Men’s Soccer Team formations.
SR 3.0
The simulation shall follow 1 set of probability maps per strategy.
SR 4.0
The simulation shall update the probability map of successful passing
rate percentages once the ball moves from one zone to another.
SR 5.0
The simulation shall change the possession of the ball after a shot or
intercepted pass.
SR 6.0
The simulation shall calculate average player location within 1 of the
14 zones in order to determine feasible pass rates from zone to zone.
Design of an Expert System Coach for Complex Team Sports
25
Probability Maps
• Total of 15 probability maps
per strategy
• 14 cell grid
• 13 possible passing
opportunities per map
• Once the ball has
successfully been passed to
a new zone the probability
map will change
Design of an Expert System Coach for Complex Team Sports
26
Netcentricity – (Flow Centrality)
SHOT
Design of an Expert System Coach for Complex Team Sports
27
Ball Movement Interaction Diagram
Design of an Expert System Coach for Complex Team Sports
28
Simulation Interface
Design of an Expert System Coach for Complex Team Sports
29
Output Table
Time
From Zone
To Zone
Possession
Action
Successful
80:08
7
6
Home
Pass
Yes
80:10
6
7
Home
Pass
Yes
80:11
7
7
Home
Dribble
Yes
80:15
7
10
Home
Pass
No
80:17
10
13
Away
Pass
No
80:21
13
13
Home
Dribble
Yes
80:25
13
14
Home
Pass
Yes
80.29
14
14
Home
SHOT
GOAL
Design of an Expert System Coach for Complex Team Sports
30
Field Functions
Function
Zones
4-3-3 Flat
4-3-3 Triangle
4-4-2 Flat
4-4-2 narrow
Diamond
Right Wing Zone
10 - 13
40%
30%
30%
25%
Left Wing Zone
0-3
40%
30%
30%
25%
Middle Zone
4-9
20%
40%
40%
50%
Defense Zone
0,4,5,10
20%
25%
20%
20%
Midfield Zone
1,2,6,7,11,12
50%
55%
50%
55%
Forward Zone
3,8,9,13
30%
20%
30%
25%
31
Coaching Rules (Numerical Advantage)
Current Strategy 4-3-3 Flat
RWZone <= 40% and LWZone<= 40%
Tactical Change
4-3-3 Triangle
Message: Ball needs to get out wide because passing in wing zones is xx change
formation
Dzone > 20% and Fzone is <= 30%
4-4-2 Flat
Message: Ball is being passed to much in the defensive zone and not enough
attacking is occurring. Switch formation to 422 flat.
Current Strategy meets criteria
Message: continue to play with current strategy.
Current Strategy 4-3-3 Flat
Tactical Change
RWZone <= 40% and LWZone<= 40%
4-3-3 Triangle
Dzone > 20% and Fzone is <= 30%
Message: Ball needs to get out wide because passing in wing zones is xx change
formation
4-4-2 Flat
Current Strategy meets criteria
Message: Ball is being passed to much in the defensive zone and not enough
attacking is occurring. Switch formation to 422 flat.
Message: continue to play with current strategy.
Design of an Expert System Coach for Complex Team Sports
32
Coaching Rules (Numerical Advantage)
Current Strategy 4-4-2 Flat
RWZone <= 30% and LWZone<=
30%
Dzone > 20% and Mzone is <=
50%
Current Strategy meets criteria
Tactical Change
4-2-2 Diamond
Message: Ball needs to get out wide because passing in wing zones is xx change
formation
4-4-2 Diamond
Message: Ball is being passed too much in the defensive zone and not enough
attacking is occurring. Switch formation to 4-4-2 diamond to get the ball in the
midfield and forward.
Message: continue to play with current strategy.
Current Strategy 4-4-2
Narrow Diamond
Tactical Change
MidZone<= 50%
4-3-3 Triangle
Midzone <= 50%
Message: Need to find central midfielders with the ball. Change to a 4-3-3 triangle
and get ball inside.
4-4-2 Flat
Current Strategy meets criteria
Message: Ball needs to go through the middle more. Switch to a 4-4-2 flat to get the
ball to the wider flanks.
Message: continue to play with current strategy.
33
Validation of Concept of Operations
Design of an Expert System Coach for Complex Team Sports
34
Score Differential from Monte Carlo
Simulation
35
Design of Experiment
Formations
Replications
Flat
Right Wing
Zone
Left Wing Zone Middle Zone
Flat
≤ 30%
≤ 40%
Get ball wide
4-4-2
Diamond
> 30%
> 30%
> 40%
Continue
Strategy
None
≤ 25%
≤ 25%
≤ 50%
Find central
players
4-3-3 Triangle
> 25%
> 25%
> 50%
Continue
Strategy
None
≤ 40%
≤ 40%
≤ 20%
> 40%
> 40%
> 20%
Continue
Strategy
None
≤ 30%
≤ 30%
≤ 40%
Find central
players
4-4-2 Flat
> 30%
> 30%
> 40%
Continue
Strategy
None
250
250
250
4-3-3
Triangle
Tactical
Change
≤ 30%
4-4-2
Diamond
Message
250
Get ball wide 4-3-3 Triangle
The above design of experiment focuses on the various formations and the
equivalent right, left and middle zone passing percentages
36
Design of Experiment
Formations
Flat
Replications
Defense
Zone
Midfield
Zone
Forward Zone
Message
Tactical Change
≥ 20%
≤ 50%
≤ 30%
Get ball higher
4-4-2 Diamond
< 20%
> 50%
> 30%
Continue
Strategy
None
≥ 20%
≤ 55%
≤ 25%
Find central
players
4-4-2 Flat
< 20%
> 55%
> 25%
Continue
Strategy
None
≥ 20%
≤ 50%
≤ 30%
Get ball high
4-4-2 Flat
< 20%
> 50%
> 30%
Continue
Strategy
None
≥ 25%
≤ 55%
≤ 20%
Find higher
players
4-4-2 Diamond
< 25%
> 55%
> 20%
Continue
Strategy
None
250
4-4-2
Diamond
Flat
250
250
4-3-3
Triangle
250
The above design of experiment focuses more on the defensive, midfield and
forward zone passing percentages in order to run the various different formations
37
System Risks
Risk Category
Safety
Money
Bad Advice
Expert System Rules
Simulation
What is the risk
Risk Mitigation
Will this system hurt someone?
Verify the system doesn’t put
human life into risk
Will this system lose money?
Compare win probability using
system with how much each
win is worth
Test system with XX scenarios
Will this system give bad advice? and verify accurate results
with coaches
Talk to XX coaches to get
Will accurate rules input into the
various opinions of accurate
system?
rules
Will the simulation of the system Create XX scenarios to test
be accurate?
simulation
Design of an Expert System Coach for Complex Team Sports
38
Agenda
•
•
•
•
•
•
•
•
•
Context
Stakeholder Analysis
Problem/ Need Statements
Concept of Operations
Mission/Functional/Design Requirements
Simulation
Risks
Project Plan
Next Steps
Design of an Expert System Coach for Complex Team Sports
39
Work Breakdown Structure
Design of an Expert System Coach for Complex Team Sports
40
Critical Path
Design of an Expert System Coach for Complex Team Sports
41
Budget
Task Number
Tasks
Predicted Hours
1
Expert Coaching System
2326
$
116,300.00
1.1
Management
70
$
3,500.00
1.2
Research
308
$
15,400.00
1.3
CONOPS
93
$
4,650.00
1.4
Originating Requirements
80
$
4,000.00
1.5
Design Alternatives
85
$
4,250.00
1.6
Design Of Experiment
88
$
4,400.00
1.7
Simulation
995
$
49,750.00
1.8
Analysis
130
$
6,500.00
1.9
1.1
Conclusion
100
$
5,000.00
Presentations
107
$
5,350.00
1.11
Documentation
270
$
13,500.00
Total Cost = (Average Junior SE salary per hour) * Overhead Cost * Predicted Hours
Source : GMU Office of Budget and Planning
Design of an Expert System Coach for Complex Team Sports
Cost
42
Project Cost Analysis (week 1-14)
$60.00
Cost (Thousands)
$50.00
$40.00
$30.00
$20.00
$10.00
$0
2
4
Planned Value
6
8
Time (weeks)
Actual Cost
10
Earned Value
Design of an Expert System Coach for Complex Team Sports
12
14
16
43
Performance Indices (week 1-14)
1.80
1.60
Performance Index
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
0
2
4
6
Cost Performance Index (CPI)
8
Time (weeks)
10
12
Schedule Performance Index (SPI)
Design of an Expert System Coach for Complex Team Sports
14
16
44
Project Risks
Critical Tasks
Data Collection
Coaching Rules
Develop Simulation
Cost Analysis
Foreseeable Risks
Mitigation
•
Unable to get GMU Men’s
Soccer Team data
•
Use Instat dashboard to look
at other college Team’s data
•
Inaccurate rules input in the
system
•
Talk to XX coaches to get
various opinions of accurate
rules
•
Unable to run simulation
•
correctly
The simulation of the system is •
inaccurate
Test and debug while
developing simulation
Using Monte Carlos simulation
to test it XX times
•
Work more hours than planed
to ensure project is on
schedule
Work more hours than
planned to ensure actual cost
is under budget
•
•
•
Schedule Variance is negative
Cost Variance is negative
•
Design of an Expert System Coach for Complex Team Sports
45
Next Steps (WBS)
•
•
•
•
•
•
Design of Experiment (1.6)
Continue to add complexity to simulation (1.7.2.3)
Analysis (1.8)
Validation and Verification (1.9.2)
Recommendations (1.9.4)
Documentation (1.11)
Design of an Expert System Coach for Complex Team Sports
“Only those who can see the
invisible can do the impossible”
46
-Frank L. Gaines
Special Thanks
• 2014 Referee Proficiency Senior Design Project
(Jones, Cann, Almashhadi, Popal)
Tom
Morrell
Design of •
an Expert
System
Coach for Complex Team Sports
47
References
• [1] Virginiasports.com, 'VIRGINIASPORTS.COM - The University of Virginia Official Athletic Site - UVa Men's
Soccer', 2015. [Online]. Available: http://www.virginiasports.com/sports/m-soccer/archive/va-m-soccerarchive.html. [Accessed: 25- Oct- 2015].
• [2] Uclabruins.com, 'Men's Soccer - Schedule - UCLA Bruins Official Athletic Site | UCLABruins.com', 2015.
[Online]. Available:
http://www.uclabruins.com/SportSelect.dbml?SPSID=749841&SPID=126914&DB_OEM_ID=30500&Q_SEASON
=2013. [Accessed: 25- Oct- 2015].
• [3] Atlantic10.com, 'Men's Soccer - News - Atlantic 10 Conference Official Athletic Site', 2015. [Online].
Available: http://www.atlantic10.com/SportSelect.dbml?DB_OEM_ID=31600&SPID=136213&SPSID=799925.
[Accessed: 25- Oct- 2015].
• [4] Soccer-training-guide.com, 'The 4-3-3 is a Highly Popular Formation', 2015. [Online]. Available:
http://www.soccer-training-guide.com/4-3-3.html#.Vi1ul36rTIU. [Accessed: 25- Oct- 2015].
• [5] J. Duch, J. Waitzman and L. Amaral, 'Quantifying the Performance of Individual Players in a Team Activity',
PLoS ONE, vol. 5, no. 6, p. e10937, 2010.
• [6] J. Perez, Concepts and Coaching Guidelines, 1st ed. U.S Soccer Curriculum, 2015 [Online]. Available:
http://resources.ussoccer.com/n7v8b8j3/cds/downloads/Part%202%20%20Concepts%20and%20Coaching%20Guidelines%20U.S.%20Soccer%20Coaching%20Curriculum.pdf.
[Accessed: 20- Oct- 2015]
• [7] J. Maxcy and J. Drayer, Sports Analytics: Advancing Decision Making Through Technology and Data, 1st ed.
Philadelphia: Fox School of Business, 2014 [Online]. Available: http://ibit.temple.edu/wpcontent/uploads/2014/04/IBITSportsanalytics.pdf. [Accessed: 20- Oct- 2015]
• [8] Sites.google.com, 'RPI: Formula - RPI for Division I Women's Soccer', 2015. [Online]. Available:
https://sites.google.com/site/rpifordivisioniwomenssoccer/rpi-formula. [Accessed: 20- Oct- 2015]
• [9] Budget, 'Fiscal Year 2015-2016', 2015. [Online]. Available: http://budget.gmu.edu/fiscal-year-2015-2016/.
[Accessed: 16- Nov- 2015].
Back-Up
49
Complexity of Coaching
Figure 2: Coaches role in sports team dynamics
Source : Concepts and Coaching Guidelines
Design of an Expert System Coach for Complex Team Sports
50
Soccer Analytics
10/21/2015
• Not much is known about
Soccer Analytics before 1996.
– Influenced by the movement of
data analytics in other major
sports
– The Sabermetrics model in MLB
got the ball rolling in the 1970’s
(Oakland Athletics first used)
• Using big data to make decisions in
areas such as : pricing, management,
player development, and training.
Figure 5: Percentages of sports that
employ data analysis
Source : Sports Analytic: Advancing Decision Making Through
Technology and Data
Design of an Expert System Coach for Complex Team Sports
51
Soccer Analytics (cont’d)
10/21/2015
• The emergence of modern sports data companies
– Opta – 1996 – began collecting data for English Premier
league teams
• Initial Data Collection approach
– Number of passes
– Number of tackles
– Distance travelled
– Prozone – 1998 – providing multiple performance analysis
packages to major football clubs including:
» Manchester United (1999)
» Real Madrid (2006)
» US Major League Soccer (2008)
Design of an Expert System Coach for Complex Team Sports
52
Current Process
Challenges:
• Patterns are inconsistent from game to game
• A lot of pressure on coaches to make successful decisions
• Limited time between games
52
53
What a Win is Worth
10/21/2015
Winning Percentage vs. Salary
80%
Coference Win Percentage
70%
60%
50%
40%
y = 1E-06x + 0.3963
R² = 0.6994
30%
20%
10%
0%
0
50,000
100,000
150,000
200,000
Head Coaches Salary
Design of an Expert System Coach for Complex Team Sports
250,000
54
Statistical Data (InStat)
Team
Individual Player Stats
Challenges, lost ball, recoveries
130
Total actions
25
Participating Attacks
52
Challenges
9
Set pieces
13
Indexes
4
Passes
13
Passes
11
Action Maps
12
Action Graph (time x actions)
12
Ball Possession
10
Successful Actions Graph (time x actions)
12
Attacks
14
Average Position Player Maps
3
Above data time in game (15min)
Pass Distribution Table
1
Zones
Pass Distribution Maps
1
Actions Successful
14
Dangerous attack maps
10
Passes Accurate
14
Challenges won
14
% Challenges won
14
Overall Player tables
104
Overall Player Maps
16
Overall Player Zones
97
Total of 707 Statistics for each team evaluated
102
54
55
InStat Data
56
Input Data
57
10/21/2015
Statement of Work
• Scope of work includes all planning, design,
implementation, risk mitigation and validation of the
expert coach system
• Responsible for allocating the proper resources to
the proper phases in creating the system.
Design of an Expert System Coach for Complex Team Sports
58
Cost Analysis
$140.00
$120.00
Cost (Thousands)
$100.00
$80.00
$60.00
$40.00
$20.00
$0
5
10
Planned Value
15
20
Time (weeks)
Actual Cost
25
Earned Value
30
35
40
59
Critical Path
60
Critical Path
61
Critical Path
62
Critical Path