Design of an Expert. Group Leader: Brice Colcombe. Sponsor: Dr. Lance Sherry. Group Members: Lindsay Horton Muhammad Ommer Julia Teng

Transcription

1 Statistical Report Tactical Adjustment Game Video Expert Coaching System Winning Design of an Expert A10 Champs System Coach for Complex Team Sports Group Leader: Brice Colcombe Group Members: Lindsay Horton Muhammad Ommer Julia Teng Sponsor: Dr. Lance Sherry

2 2 Agenda Context Stakeholder Analysis Problem/ Need Statements Concept of Operations Requirements Simulation Results Business Case

3 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 yds (length) by 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

4 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 Coaching Rules 5 Inverting the Pyramid: A History of Football Tactics (Jonathan Wilson) Types: Flat Wide Diamond Narrow Diamond Figure 3: Formation Numerical Advantages: Numbers out wide Support for attacking Disadvantages: Outnumbered in middle Types: Figure 4: Formation Flat 2 Holding Midfielders 2 Attack Midfielders High/Low Wings Numerical Advantages: Attack-centric Strong central midfield presence Disadvantages: Open to counterattack Open on outside

6 6 Challenges in coaching Soccer Baseball vs. Soccer: Discrete vs. Flowing No instructions/stoppages until halftime Players have to make own decisions in order to benefit the team Players have to adapt to the situations around them Autonomous distributed agents Network Complexity of Soccer: Netcentricity

7 How Coaching is Done 7 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

8 Current Coaching Process

9 9 How Coaches are Measured Recruiting Classes Collegiate Tournaments Atlantic 10 Conference Wins NCAA Tournament Appearances Team s Academic Performance

10 10 Collegiate Tournaments Atlantic 10 Conference 13 teams Top 8 teams advance Each team play each other during season Team s record determine tournament participation Use a weighting system to keep track of ranking Winner gets an automatic bid into NCAA tournament NCAA Single elimination 22 teams get an automatic bid from winning their conference championships Remaining 26 teams with highest rating percentage index (RPI) score Source: NCAA (2015)

11 11 Rating Percentage Index [RPI] Consists of Three Parts: 1. Teams Winning Percentage (Expert Coaching System) W+ T 2 W = win, L = loss, and T = tie W+L+T 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 part 2 + part 3 RPI = 4

12 Goals per Shot 0.15 Men s A-10 Conference (2015) goal per game 2 URI 2 goals per game 1 Dayton 3 SLU GWU GMU 10 La Salle 5 Duq Fordham 11 Davidson 4 VCU 9 St. Josephs Mass 13 St. Bonaventure Shots per Game Source: Atlantic 10 Team Archives (2015)

13 % Wins 13 Men s A-10 Conference ( ) Saint Louis Rhode Island George Washington Massachusetts Saint Joseph's George Mason La Salle Davidson Fordham 0.3 VCU Duquesne Dayton 0.2 St. Bonaventure Goal Difference y = x R² = Source: Atlantic 10 Team Archives

14 14 What a Win is Worth to Coaches Salary A national championship level coach gets paid on average $67, per year more than an Atlantic 10 coach. University Salary Win % Conference Wins NCAA Championships Years Coaching at School Total Experience UCLA 205,000 75% Maryland 206,000 71% UI 176,225 62% UNC 88,044 58% URI 72,500 55% VCU 90,640 55% UVA 115,400 52% GMU 90,855 51% UMASS 108,681 47% Average Salary: $158, Average Atlantic 10 Coach: $90, GMU Head Coach worth per win: $9,085

15 # of Appearances 15 Gap Analysis Replicate Saint Louis Success By: 1. Win Atlantic 10 Conference Championship 3 times every 5 years 2. Receive an NCAA Bid 6 times every 10 years 3. Average annual target RPI Score of NCAA tournament Appearances GMU vs Saint Louis Resulting success will help close head coaches salary gap of $67, Past 10 years Saint Louis GMU overall

16 16 Agenda Context Stakeholder Analysis Problem/ Need Statements Concept of Operations Requirements Simulation Results Business Case

17 Stakeholder Interaction Diagram 17

18 18 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 Coaching tool that uses coaching expertise Uses soccer game data to understand the complexity of soccer Coaches seek a competitive advantage NCAA Tournament bid to 3 bids out of every 5 years

19 19 Solution Concept System Operations

20 20 Mission/Functional Requirements Mission Requirement # Requirement Description MR. 1.0 MR. 2.0 MR. 3.0 Functional Requirement # FR. 1.0 The Expert Coaching System (ECS) shall recommend at least 5 possible strategies based on game data gathered in real time. The ECS shall recommend 3 possible strategies based on gameplay data gathered by halftime The ECS shall recommend 2 possible strategies based on gameplay data gathered by overtime. 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 in-between 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.

21 21 Design Requirements (Input/Output) Input Requirement # IR. 1.0 Requirement Description The ECS shall input statistics from sport analytic providers. IR. 2.0 IR. 3.0 The ECS shall input probability map passing and shot percentages from InStat to run the simulation. The ECS shall input 12 coaching rules to be used to make adjustments. Output Requirement # OR. 1.0 OR. 2.0 OR. 3.0 Requirement Description The ECS shall output a table showing the relationship between current formations utilized and possible adjustments in formations recommended by the system. The ECS shall output tactical coaching adjustments that result in 30% greater chance of winning The ECS shall output tactical adjustments within 10 minutes of gathering the necessary passing and shot data from sports analytics companies (assuming can happen in real time).

22 22 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 SR 4.0 SR 5.0 The simulation shall follow 1 set of probability maps per strategy. The simulation shall update the probability map of successful passing rate percentages once the ball moves from one zone to another. 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.

23 23 Agenda Context Stakeholder Analysis Problem/ Need Statements Concept of Operations Simulation Results Business Case

24 24 Simulation Overview Objective: Simulate a soccer game based on game data and determine which of the coaching system rules will impact the outcome of the game Inputs Outputs 1. Probability Pass Map (PPM) Data: Movement Probability Maps Success Probability Maps 2. Expert Coaching Rules 1. Passing % and strategy message Excel files: (1) Gameplay Summary (2) Recorded Gameplay Passes *Resulting Goal Differential Simulates a 90 min. game (two 45 minute halves) using passing probability maps (PPM s) and various ball movements Simulation reverse-engineered from refereeing senior design project: Assessment of soccer Referee Proficiency in Time-Sensitive Decision Making Jones et.al.(2013) Approx. 4,000 lines of code

25 25 Simulation Interface (GUI)

26 26 Data Collection (e.g. Instat, Opta, Prozone, other) 5 games from 2015 season 2 types of play: pass or shot Pass: fail or success Shot: fail, on goal, goal Total 3000 data points Instat pass Diagram Zone Grids Probability Maps

27 27 Probability Pass Maps (PPM s) Total of 30 PPM s per strategy 14 - zone grid 13 possible passing (movement) opportunities per map 13 possible success opportunities per map Once the ball has successfully been passed to a new zone the probability map will change

28 28 Netcentricity (Flow Centrality) SHOT *Data collected from 5 games played in 2015 season

29 29 Movement Map Diamond

30 30 Success Map Diamond shot

31 31 Ball Movement Interaction Diagram *Passing and success rates are determined by a uniform random number generator

32 32 Field Zones Offensive Zone Left Zone Middle Zone Right Zone Midfield Zone Defensive Zone

33 33 Expert Coaching Rules Formations Right Wing Zone Passing % Input Left Wing Zone Passing % Middle Zone Passing % Message Flat 30% 30% 40% Get ball wide > 30% > 30% > 40% Continue Strategy Diamond 25% 25% 50% Find central players Output Tactical Change Diamond None Triangle > 25% > 25% > 50% Continue Strategy Flat 40% 40% 20% Get ball wide > 40% > 40% > 20% Continue Strategy Triangle 30% 30% 40% Find central players > 30% > 30% > 40% Continue Strategy None Triangle None Flat None

34 34 Expert Coaching Rules Formations Flat Defense Zone Passing % Input Midfield Zone Passing % Forward Zone Passing % Message Output 20% 50% 30% Get ball higher Tactical Change Diamond < 20% > 50% > 30% Continue Strategy None Diamond 20% 55% 25% Find central players Flat < 20% > 55% > 25% Continue Strategy None Flat 20% 50% 30% Get ball high Flat < 20% > 50% > 30% Continue Strategy None Triangle 25% 55% 20% Find higher players Diamond < 25% > 55% > 20% Continue Strategy None

35 35 Simulation Conclusion Collected From 200 Trials The only formation where a switch was made was from a 433 Triangle to a 442 Diamond 25 Comparative Score Differential v. American First Half Second Half Score Differential Above phenomenon occurs because the data is sparse The data needs to be supplemented by having a base of 10 games of collected data

36 36 Simulation Conclusion Cont d. Through simulation testing, the current data is too sparse Need to have a database of at least ten games to make accurate adjustments Additional data such as goal kicks, throw ins, and fouls could be added to provide a more realistic representation of the game Everything comes full circle to monetary roadblocks i.e. soccer budgets at the NCAA level

37 37 Agenda Context Stakeholder Analysis Problem/ Need Statements Concept of Operations Simulation Results Business Case

38 38 Results While the data did not provide accurate results for simulating games, we were able to create probability maps to give insight into the sport and familiar patterns surrounding the sport By creating our own probability maps by combining all 5 games and changing the success rates, several conclusions could be made.

39 Average Shots per Game Average Goals per Game.5 Success Map (Team B) vs. Changing Success Map (Team A) Average Goals Per Game Sensitivity Team A Goals Team B Goals Average Shots per Game Sensitivity Analysis Team A Shots Team B Shots Success Rate

40 40 Team C vs. Team D Simulation Trials By simulating games between teams which had the same exact probability maps, the following results were made: Team C Win Prob: 0.4 Team D Win Prob: 0.4 Tie Prob: 0.2 Team C Avg Poss: 50.46% Team D Avg Poss: 49.54% Team C Avg Goals:.87 Team D Avg Goals: Team C vs Team D Goal Difference Goal Difference

41 41 Team C vs. Team D Conclusions Simulating Team C vs. Team D provided necessary results surrounding the game of soccer: Many Events are Random Contributing to this randomness is lucky bounces, unlucky touches, etc. These things are a part of the game and can not be affected by coaching. When two teams play each other that are very evenly matched over and over, intangibles which can t be quantified are often what determine the winner and loser. Coaches can only do so much to predict the outcome of a soccer match.

42 42 Implications for Stakeholders Coaches They can only control so much of the game Athletic Director/University Should not put as much pressure on coaches based solely on final results. Player Players are not always at fault for unfavorable results

43 43 Agenda Context Stakeholder Analysis Problem/ Need Statements Concept of Operations Requirements Simulation Results Business Case

44 44 SoccerLytics Business Plan Total Market Size: 206 Division 1 men s and 310 women s teams fighting for 1 championship each Total Market Value: 7,740,000 1 st year: 5 Division 1 Men/Women teams (1%) 2 nd year: 31 total teams (5%) 5 th year: 108 total teams (20%) 8 th year: 186 total teams (35%) Business Model: The NCAA individual soccer program will buy vis subscription Cover all games throughout the season Access to online interactive dashboard

45 45 Business Costs and ROI Startup costs (non-recurring): $250,000 Operational Costs (recurring): $796,630 per year ROI: 5% Year Team % Market Cumulative Revenue Profit $ - $ (250,000) 1 5 1% $ 75,000 $ (971,630) % $ 537,000 $ (1,306,260) % $ 1,386,000 $ (1,253,890) % $ 2,622,000 $ (814,520) % $ 4,245,000 $ 11,850

46 Money 46 Break-even $14,000,000 $12,000,000 Total Revenue: $11,436,000 Subscription: $15,000 per unit $10,000,000 $8,000,000 $6,000,000 Cumulative Net: $4,812,960 Cumulative Cost Cumulative Revenue $4,000,000 Cumulative Net Profit Break Even $2,000,000 $ $(2,000,000) year

47 Only those who can see the 47 invisible can do the impossible -Frank L. Gaines Special Thanks 2014 Referee Proficiency Senior Design Project (Jones, Cann, Almashhadi, Popal) Tom Morrell

48 Back-Up

49 49 References [1] Virginiasports.com, 'VIRGINIASPORTS.COM - The University of Virginia Official Athletic Site - UVa Men's Soccer', [Online]. Available: [Accessed: 25- Oct- 2015]. [2] Uclabruins.com, 'Men's Soccer - Schedule - UCLA Bruins Official Athletic Site UCLABruins.com', [Online]. Available: =2013. [Accessed: 25- Oct- 2015]. [3] Atlantic10.com, 'Men's Soccer - News - Atlantic 10 Conference Official Athletic Site', [Online]. Available: [Accessed: 25- Oct- 2015]. [4] Soccer-training-guide.com, 'The is a Highly Popular Formation', [Online]. Available: [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, [6] J. Perez, Concepts and Coaching Guidelines, 1st ed. U.S Soccer Curriculum, 2015 [Online]. Available: %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: [Accessed: 20- Oct- 2015] [8] Sites.google.com, 'RPI: Formula - RPI for Division I Women's Soccer', [Online]. Available: [Accessed: 20- Oct- 2015] [9] Budget, 'Fiscal Year ', [Online]. Available: [Accessed: 16- Nov- 2015].

50 50 Next Steps Research on more reliable data tracking at the collegiate and professional levels (within budgets) Comparatively research cutting edge technology in NFL and its funding ($13 billion a year profits) RFID Chips in pads started in season Microsoft funding for NFL app (Next Gen Stats)

51 51 Ideal Field-Zone Passing Percentages Function Zones Number Flat Passing % Triangle Passing % Flat Passing % narrow Diamond Passing % Right Wing Zone Left Wing Zone % 30% 30% 25% % 30% 30% 25% Middle Zone % 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%

52 52 Agenda Context Stakeholder Analysis Problem/ Need Statements Concept of Operations Mission/Functional/Design Requirements Simulation Risks Results Next Steps Project Plan

53 53 Score Differential from Monte Carlo Simulation Opponent GMU

54 % Wins 54 Equal Probability Maps % Wins VS GD GD y = x R² =

55 Shots/Game 55 Equal Probability Maps 0.09 Shots/Game VS Goals/Shot Goals/Shot y = x R² =

56 56 Solution Concept of Operations

57 Stakeholder Analysis Class Stakeholder Goals Tensions Primary Secondary Head Coach Assistant Coaches Player Trainers Investors (University) Parents Academies Produce a team that gives highest probability of winning Keep job, get next big job Scout best players for the team Prepare the team for games Become head coach Perform at highest level Get to next level (pro) Have as few injuries as possible Rehab injured players Fund a winning team Make money through ticket and memorabilia sales Market the University in a positive fashion Invest money in youth development in hopes of college scholarships and professional contracts Train youth players in hopes of signing with professional clubs Tertiary Data Analytic Companies Earn revenue from data software sales 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 Can t always control how head coach pushes players Want immediate success (not always possible) Budget is only so big Can t be involved with coaches like at the youth level Players move, go onto college Multiple data companies competing with one another 57

58 58 Comparative Score Differential v. American First Half Second Half Score Differential Collected From 200 Trails The only formation where a switch was made was a 433 Triangle to a 442 Diamond

59 59 Data Collection 5 games from 2015 season 2 types of play: pass or shot Play outcome: Pass: fail or success Shot: fail, on goal, goal About 600 passes per game

60 60 System Risks Risk Category What is the risk Risk Mitigation Safety Will this system hurt someone? Verify the system doesn t put human life into risk Money Will this system lose money? Compare win probability using system with how much each win is worth Bad Advice Will this system give bad advice? Test system with 100 scenarios and verify accurate results with coaches Expert System Rules Simulation Will accurate rules input into the system? Will the simulation of the system be accurate? Talk to head and assistant coaches to get various opinions of accurate rules Replicate 100 scenarios to test simulation in Monte Carlo mode

61 61

62 62 Work Breakdown Structure

63 63 Budget Task Number Tasks Predicted Hours Cost 1 Expert Coaching System 1950 $ 97, Management 140 $ 7, Research 260 $ 13, CONOPS 40 $ 2, Originating Requirements 50 $ 2, Design Alternatives 30 $ 1, Design Of Experiment 30 $ 1, Simulation 700 $ 35, Analysis 130 $ 6, Conclusion 100 $ 5, Presentations 200 $ 10, Documentation 270 $ 13, Total Cost = (Average Junior SE salary per hour) * Overhead Cost * Predicted Hours Source : GMU Office of Budget and Planning

64 Cost (Thousands) 64 Project Cost Analysis (week 1-28) $90.00 $80.00 $70.00 $60.00 $50.00 $40.00 $30.00 $20.00 $10.00 $ Time (weeks) Planned Value Actual Cost Earned Value

65 Performance Index 65 Performance Indices (week 1-28) Time (weeks) Cost Performance Index (CPI) Schedule Performance Index (SPI)

66 66 Soccer Overview ECS helps soccer coach to see patterns to increase the success of a coach and a team Problems: $67, salary gap between returning Division I NCAA tournament playoff coaches and other NCAA coaches 206 Division 1 men s and 310 women s teams fighting for 1 championship each Solution: Provide real-time analysis and expert system tactical advisories of game data from the first half of the game, in order to suggest tactical changes for the second half. Customer: NCAA coaches

67 67 Project Risks Critical Tasks Foreseeable Risks Mitigation Data Collection Coaching Rules Unable to get GMU Men s Soccer Team data Inaccurate rules input in the system Use Instat dashboard to look at other college Team s data Talk to XX coaches to get various opinions of accurate rules Develop Simulation 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 Cost Analysis Schedule Variance is negative Cost Variance is negative Work more hours than planed to ensure project is on schedule Work more hours than planned to ensure actual cost is under budget

68 68 Complexity of Coaching Figure 2: Coaches role in sports team dynamics Source : Concepts and Coaching Guidelines

69 69 Soccer Analytics 10/21/2015 Not much is known about Soccer Analytics before 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

70 70 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)

71 71 Current Process Challenges: Patterns are inconsistent from game to game A lot of pressure on coaches to make successful decisions Limited time between games 71

72 RPI Score % Win 72 Historical Atlantic 10 Data ( ) GMU vs SLU RPI GMU vs Saint Louis Win Percentage Saint Louis GMU Saint Louis Average GMU Average Saint Louis GMU Saint Louis Average GMU Average Winning Percentage: W+ T 2 W+L+T Source: Atlantic 10 Team Archives

73 Coference Win Percentage 73 What a Win is Worth 10/21/ % Winning Percentage vs. Salary 70% 60% 50% 40% 30% y = 1E-06x R² = % 10% 0% 0 50, , , , ,000 Head Coaches Salary

74 74 Statistical Data (InStat) Individual Player Stats Challenges, lost ball, recoveries 130 Participating Attacks 52 Set pieces 13 Passes 13 Action Maps 12 Action Graph (time x actions) 12 Successful Actions Graph (time x actions) 12 Average Position Player Maps 3 Pass Distribution Table 1 Pass Distribution Maps 1 Dangerous attack maps 10 Overall Player tables 104 Overall Player Maps 16 Overall Player Zones 97 Total of 707 Statistics for each team evaluated Team Total actions 25 Challenges 9 Indexes 4 Passes 11 Ball Possession 10 Attacks 14 Above data time in game (15min) 102 Zones Actions Successful 14 Passes Accurate 14 Challenges won 14 % Challenges won 14 74

75 InStat Data 75

76 Input Data 76

77 77 Expert Coaching System (ECS) The ECS sees things that coaches can t, due to the netcentricity involved in soccer Being able to spot these patterns is crucial to the success of a coach and a team

78 78 Primary Customers The expert coaching system has been designed to help head NCAA coaches make tactical decisions to help improve their chances of winning

79 79 Solution Provide real-time analysis and simulation of game data from the first half of the game, in order to suggest tactical changes for the second half.

80 80 Monetary Gaps The average yearly salary gap between returning Division I NCAA tournament playoff coaches and other NCAA coaches $67, Coaches need to have a competitive advantage over other coaches in order to close this gap 206 Division 1 men s and 310 women s teams fighting for 1 championship each

81 81 Project Milestones First year we want two DC area Division I teams Second year 5 teams (VA, MD, DC) Five years 50 teams (national NCAA teams)

82 82 Business Competitors Competitors: Data Analytics Companies (e.g Prozone, Opta, InStat) Our solution works in conjunction with these providers and goes above and beyond their simple data collection! NCAA regulations are the major concern when implementing new technology.

83 83 Team Subscriptions The NCAA individual soccer programs (i.e. GMU soccer team) will buy via subscription. Different packages will have different features Basic: basic simulation that will cover minimum 10 games Advanced: basic simulation that will cover all games Prime: basic simulation, access to dashboard (with other teams data) and will provide team with future statistic projections

84 84 Business costs Startup costs (non-recurring): $250,000 is the project cost This is the development cost Operational Costs (recurring): Costs will include facility and equipment Employee Salaries, utilities, maintenance and travel. $498,000 per year

85 85 Sales Profile Will need 10 teams a year to purchase the Advanced Subscription: First subscription $60, Subscribe again $42, Revenue per team: $5,500 per subscription Basic Subscription: $3,200 per game (minimum 10 games)

86 Money 86 Return-on-Investment and Break-even $6,000, $5,000, $4,000, $3,000, $2,000, $1,000, Cumulative Cost Cumulative Revenue Cumulative Net Break even $- $(1,000,000.00) $(2,000,000.00) year

87 87 Management Team Current Team: CEO- Very knowledgeable of soccer and has an overall view of what the ECS goals and customers are Data Analyst- Knowledgeable about what the input and output data is and able to display it in a meaningful way Project Manager Makes sure the team is on schedule and establishes major milestones/ deadlines Simulation Expert Created simulation and continually working to make the next model. Open Positions: Computer Programmer/Software Engineer

88 88 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.

89 Cost (Thousands) 89 Cost Analysis $ $ $ $80.00 $60.00 $40.00 $20.00 $ Time (weeks) Planned Value Actual Cost Earned Value

90 Critical Path 90

91 Critical Path 91

92 Critical Path 92

93 Critical Path 93

94 94 Next Steps (WBS) Design of Experiment (1.6) Continue to add complexity to simulation ( ) Analysis (1.8) Validation and Verification (1.9.2) Recommendations (1.9.4) Documentation (1.11)

95 Passes Passes 95 First Half Second Half Trials Passes Home Offense Passes Home Defense Passes Home Offense Passes Home Defense Trial