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Raw Talent Acquired Through Crowdsourcing on the Ball Diamond Jason PowersMBA PresentationsApril & December 2013 The A’s Need.

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Presentation on theme: "Raw Talent Acquired Through Crowdsourcing on the Ball Diamond Jason PowersMBA PresentationsApril & December 2013 The A’s Need."— Presentation transcript:

1 Raw Talent Acquired Through Crowdsourcing on the Ball Diamond Jason PowersMBA PresentationsApril & December 2013 The A’s Need

2  People MGMT  Money (Players’ Salaries)  Macrosabermetric Models  Financial Model  Fan Model  Graphs that fuel both models Wiki Prospect: FOUTS Program

3 Billionaire OwnersMillionaire PlayersSports AgentsGM/Field Managers Coaches/ScoutsMedia: Cynical, DivisiveFans: Obsessive

4 MLB Players Salaries Profile (2010)

5 Two Models: Finances & Fans  Graphs of Revenue Model  Graphs of Exponential Talent Acquisition

6 Financial Model in MLB Total Revenues /Asset Forming Gate Receipts* Local TV Market (Media Contracts) MLB National Media Deals Concessions* Advertising Signage Parking Fees* Merchandising* Sponsorship Deals Fan-base Size Popularity Predicts Revenues Attendance-driven Non-baseball Events* Business /Baseball Expenses Pooled Revenue Sharing (34%) MLB Player Salaries Baseball FO Personnel 1 st Year Rookie Draft International Draft/Spending Scouting Player Development (Minors Staff) Ballpark Operations Facilities Maintenance Debt Service Marketing Public Relations Ticket Office Negotiated Contracts (CBA) Deferred Payments Deprec./Amort. Large Market Team Liability Small Market Team Asset Business FO Personnel Fan Model Park Naming Rights Minor League Prospects

7 Total Revenues /Asset Forming Gate Receipts 1 Advertising/Signage 1 Home Attendance, Popularity and Wins Park Naming Rights 2 Concessions 1 Parking Fees 1 Merchandising 1 Non-baseball Events 1 Local TV Market (Media Contracts) 2 MLB National Media Deals 2 Sponsorship Deals 2 2 Negotiated by MLB team or MLB Proper Over 85% of Revenues can be explained by Attendance, TV Market, and Popularity




11 Fan Model : Just Win Baby WS Championships Season Win % Runs Allowed: Pitching/Defense R 2 >=.75 Runs Scored: Offense R 2 <=.90+ Slugging Ability 1x On-Base Ability 1.5x Base Stealing: Little correlation to Runs Pitching: Keep it in the park Fielding: Catch The baseball Velocity Control Command Variation Endurance Sequencing Positioning Quickness Decisions Arm Glove Communication R 2 =.88+ Runs Scored x (Runs Scored x +Runs Allowed x ) Pythagorean Expectation X = 1.83 Make Playoffs Financial Model & Branding Team A Few, Sabermetric Analysts of Note* Clay Davenport, Bill James: Pythagorean Dan Szymborski, Nate Silver: ZIPS, PECOTA John Dewan, Mitchel Litchman: Fielding Analysis Sean Forman, Sean Lahman: Database Develop & MGMT Voros McCracken: DIPS, BABIP Pete Palmer: Linear Weights Tom Tango: FIP/LI * Many others are noteworthy for contributions in the field GM Trades Field MGMT (Micro) Roster MGMT Minor League Call Ups Player Development Player Valuation DIPS/DICE BABIP FIP ERA LI UZR DER +/- System Talent Acquisition Ballpark Factors

12 Winning Roster MGMT GM TradesPlayer ValuationPlayer Development Time June Draft, International Players FA Signings Traditional Scouting, Psychological & Sabermetric Evaluations Rickey, McLaughlin, among others developed tools and innovations 3-4 Seasons; 22 and 25 years old a key marker to player potential Excess Value can be $100M for players; players fit in to age curves profiles Best franchises leverage prospect value and financial power together to win; do it better than their peers Talent is exponentially distributed; batters a better bet than pitchers Trades are a matter of money; peak value; evaluations of talent and fit for the team Ties to Financial Model

13 *Top 100 Baseball America Prospects were involved in the study

14 90-14 fWAR LevelPlayer TypeAll Pitcher BatterPitcher %Batter %Avg. All Team 60+HOF2151623.8%76.2%0.7 40+All Star3392427.3%72.7%1.1 25+Very Good83265731.3%68.7%2.8 15+Every Day135528338.5%61.5%4.5 7+Productive159897056.0%44.0%5.3 2+Bench22110811348.9%51.1%7.4 2 or lessBusts76538538050.3%49.7%25.5 Total fWAR= 10808Total Players1413674743 P. Roster14.4 7.62Avg. Player Bench7.4 13.7St. Dev. Busts25.5


16 Defense Spectrum: SS-2B-CF-3B-RF- LF-1B-DH (C = separate breed) The Talent Diamond:  Catchers – call game, deter base stealers, strong mentally and physically  Shortstops – quick, instinctual, most opportunities outside catcher on defense  Centerfield – can turn RUNS into outs; often leaders on team  2 nd Base – ability to turn double plays, work with SS, speed position  Pitchers – Without them, impossible to win 12 3 4 5 6 7 Defense Spectrum Talent Diamond


18 * Scouts or Statistics: not just stop watches/ radar guns/video now * 5 Tools: Power, Average, Speed, Arm, Glove  Pitchers hard to project; lefties are usually at a premium in MLB  Hitters develop by 22 (HS); 25(College), pitchers usually later (injury) * Patience at plate improves w/age, power wanes after 35 * June Draft: 40 Rounds fills rosters on 6 Minor league teams * International Pool: changing landscape, need next “Blue Ocean” * Minor League talent fuels trade offers, else:  Huge ($100 to 200 mil+) long-term contracts  pitchers fail more frequently than hitters  Both tend to underperform when past peak age of 27-29 (graphs below)

19 Athletes peak performance occurs at 27-28 years old; fall off varies by sport Improving Factors Batting Contact Skills Confidence Patience/ Feel for game Declining Factors Foot Speed Arm Strength (velocity) Power (sluggers) Injury Recovery & Risk of it


21 Define Problem (Solved with talent) Statistical measurements lacking Measure Create New BIG Data Track New statisticVideo MLBAM (F/X products) Analyze Regression AnalysisGraph DataRank PlayersIdentify Outliers Improve ID players w/better results Determine Value of statistic Economic Benefits Control Acquire by Trade Monitor Minor Leaguers Create /Redefine WIN Model

22 Trades = Arbitrage Franchise Marketing Manage Owner’s Wishes: Models Discussed Trust Scouts/Player Development Devise FOUTS Wiki System Negotiate Risks w/ Type- A Free Agents: Long-term no- nos Decline Measurable Develop Minor League Talent: Lower-cost draft Long-incubation Need ID process The Pool of Money Is only so large: Ask Yankees now.

23 Chances of Finding a Successful Player in the Draft (1st Rd)[7] Year of DraftPicks 1-5Picks 6-10Picks 11-15Picks 16-20Picks 21-25Picks 26-30 1990-1995131116753 1996-20001295941 2001-2006151310493 Ratio40/8433/8531/8520/8518/847/67 Success%47.6%38.8%36.5%23.5%21.4%10.4% Potential Acquisition Costs by Various Methods Top Price (2013)Example June Draft :1st Year Player Draft (40 rounds) [11] $7,790,400 Mark Appel, SP International Prospects Caribbean Region (16-17 yrs) [2] $4,943,700 Houston Astros Player Trades Between TeamsProspects + Cash ConsiderationsDoug Fister, SP Designated for Assignment/Waiver WireLeague MinimumGeorge Kottaras, C Rule 5 December Draft of Minor League FA40-man roster spot- U.S MLB Free Agents [1] $240,000,000 Robinson Cano, 2B International Free Agents from Korea & Japan $175,000,000Masahiro Tanaka, SP

24 Contributes to winning via WAR: Wins Above Replacement player[7] Calculation is based on factors such as:  Plate Appearances  Innings Pitched  Runs Created (Linear Weights of On-field Events)  Positional Factors (SS >>>1B)  Runs Allowed (Fielding Independent Pitching)  Base Running (SB, CS, Advance to 3rd Base )  Fielding (Compared to LG% of Team Outs) Below 0 – any minor league replacement 0.0 – Minor league minimal 0.0. to 0.5 – Bench/role player 0.5 to 1.5 – Marginal starting player 1.5 to 2.5 – Starter 2.5 to 4.0 – Excellent starter Greater that 4.0 – All-Star level Greater than 8.0 – HOF level for a season

25 How often do teams draft well in the 1 st round of the draft? The players paid the most lucrative signing bonuses for the privilege to play baseball? Players that often take more than 3-5 years to reach the Majors? And underachieve projections?


27 FOUTS Approach: Why Wiki Prospect? Problem Statement/Obstacles : The A’s, must find, and cultivate, cheaper talent [12] through the 1 st year player draft from the 1 st round, on down. Scouting evaluations done; professional department is of limited size Old-school scouts are antagonistic to current trends (sabermetric field) Old-school scouts see new ways as a threat to their traditional methods [10] Saber-guys: sarcastic; and arrogant quips have not help them sell their cause Recent Conflict Example [3]: Seattle Mariners GM (Jack Zduriencik) & ownership (Nintendo of America /Lincoln) were at cross-purposes with their scouting department, field manager’s decisions, and direct assistants over player acquisitions and development. One assistant (Blengino) wrote his GM’s application for Seattle job. “But Jack never has understood one iota about statistical analysis. To this day, he evaluates hitters by homers, RBI and batting average and pitchers by wins and ERA. Statistical analysis was foreign to him. But he knew he needed it to get in the door.”

28 3 rd Alternative:Wiki Prospect System Use Fan-Scouts (FOUTS) Fan Oriented Underlying Talent System A crowdsourcing mechanism to address this conflict creatively. Crowdsourcing Definition (Merriam)[9] “the practice of obtaining needed services, ideas, or content by soliciting contributions from a large group of people and especially from the online community rather than from traditional employees or suppliers” FOUTS will create more data points on young and raw players. VIDEOSTOPWATCH RADAR GUNGoogle Hangout UploadsAUDIO FILES Relevant Tech Example[13] Microsoft attempted to buy up a then profitable encyclopedia market (Funk&Wagnalls, Collier’s, Britannica (failed)) to improve on MS Encarta. 2001, Wikipedia was formally launched. Wiki used 43 million plus users to create a product than is the 7 th most popular website. Encarta is DEAD. Approach: What Is Wiki Prospect?

29 Approach: FOUTS Prospecting Cycle Recruit Passion Train Scout Skills Assign Area Evaluate Prospects Data Collect Data Analysis New Draft Method Recruits: Degree-seeking people/biz majors; intense passion for baseball (160 FOUTS) Training: 40-60 hours plus certification exam (held at spring training) Area: Based near several baseball talent mills (college, HS, or low-minors) Evaluate: Collect many times more observations on either random or assigned players Data: Database generates Wiki-like profile with detailed ratings/individuals scouted Draft: Clarified; due to more data seen; more accessible in sabermetric formats

30 FOUTS: Wiki Example Corey Seager, SS B:L T:R H:6’3” DOB 4/1994 Current Video Compilation Follow Up Schedule: 4/14, 4/21, 4/25, 5/12-14 Video Upload Audio Upload DB Upload Wiki Modify ToolsGradeTime(s)PotentialDate Arm6 6 12/13/2013 Glove4 5.5 Batting Average/Contact7 8 On-Base Patience6 7 Power (Gap)5 6 Power (Op. Field)5 5 Speed (1st-3rd)56.55 Speed (1st)54.15 Catching Pop-to-PopN-A Positions GradedSS, 3B Notes: Good carry on throws Fluid 1motion swing Scouting Personnel: Dan Fouts

31 Benefits: Wiki Prospect A’s Benefits Low investment vs. high reward (1 WAR = $6M on FA market 2013)[5] More data: quantity creates quality (Branch Rickey farm model did in 1920s-30s)[4] Supports traditional scouting, or: redesigns it for the A’s from the ground up Advantages technology and modernizes scouting FOUTS Hiring Benefits  FOUTS will create/mold the new system  Build a skill set (resume) that equals long-term job opportunities for them  Pay commensurate with skill, level of accuracy, and decision-influencing information  Project results: World Series participation and winning it all

32 Costs: Wiki Prospect Athletics Initial Costs/Projected 1 st Year Outlays: FOUTS Program Costs Total Games Leagues/ Conferences FOUTS Assigned $/Game scouted/ Course Total Cost /Season Max Earnings Per FOUT High School/Traveling Teams 35-96 $50 $168,000 $1,750 College: Div I NCAA 653264 $60 $249,600 $3,900 Scouting Coordinator/Trainer[8] $80,000 Data Entry Personnel (2) $80,000 Data Analyst $75,000 Training Course + Materials 160 $550 $88,000 FOUTS Tech (iPad, etc.) 160 $800 $128,000 Travel Expenses and Misc. 160 $350 $56,000 Total Costs $924,600

33 Competition/Opportunity Cost/ Weaknesses/Threats to Wiki Prospect: FOUTS Competitive Market & Emulation Can Reduce Gains: 29 other teams, looking for market inefficiencies Emulate or duplicate: once success is apparent – success will last due to being:  Dedicated to approach  People-oriented, not just numbers-obsessed  Seek next steps to improve the system (modeling with psychological factors explored)  Expanded investment: after 2-4 year trial period ($6M =1 WAR) Initial money spent (about TWO replacement-level MLB salaries of $490,000) could be used for 10-12 top scouts’ salaries (Op Cost) FOUTS lack experience: requires a steep learning curve (Weakness) Advance/Area Scouts: may backlash and quit (Threat) Derision/lack support: media, fans, scouts, ownership (Weakness) If it fails, the A’s are no worse off than before

34 [1] Adams, Steve. 2013. Mariners to Sign Robinson Cano. Web. 9 December 2013. { "@context": "", "@type": "ImageObject", "contentUrl": "", "name": "[1] Adams, Steve. 2013. Mariners to Sign Robinson Cano.", "description": "Web. 9 December 2013.

35 [8] Kibilko, John. 2013. How much does an MLB scout make? Web. 12 December 2013. [9] Merriam Webster, Inc. 2013. Crowdsourcing. Web. 9 December 2013. { "@context": "", "@type": "ImageObject", "contentUrl": "", "name": "[8] Kibilko, John. 2013. How much does an MLB scout make.", "description": "Web. 12 December 2013. [9] Merriam Webster, Inc. 2013. Crowdsourcing. Web. 9 December 2013.

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