Diamond Dollars:
Finding Baseball’s MVPitcher
The University of Chicago Booth School of Business
Sean McCluskey, Andrew Un...
Executive Summary
2
4) Identification – 3 Most Valuable Pitchers
5) Case Study – In-Depth on Each Pitcher
3) Analysis – St...
Value in the Eyes of the Beholder
3
Yankees Cubs
Pirates Marlins
Financial
Resources
Contending Rebuilding
Low
Discount
Ra...
Veteran vs. Prospect
4
Veteran:
Current
Production
Surplus
Prospect:
Delayed
Gratification
Budget and proximity to content...
The Preliminary Process
5
Phase 1 Phase 2 Phase 3
Objective: Identify Baseball’s Most Valuable Pitchers
Identify target
po...
Population Comparables Contracts
6
• Started with PECOTA’s top 40 pitchers for projected 2014
Wins Above Replacement (WAR)...
Population Comparables Contracts
• Select comparables for each pitcher in order to project
future performance, based on:
o...
Population Comparables Contracts
• Pulled contract data from Cot’s Baseball Contracts
• Projected arbitration salaries usi...
9
• Generated projected performance for each pitcher
based on the performance of the 10 most comparable
pitchers. Projecte...
10
• Example: WAR and Value Surplus simulation results
0
50
100
150
1 2 3 4 5
Control Years
Jose Fernandez
100 = WAR of 5....
11
• WAR dollars using agreed-upon ($6 M/win with 5%
inflation)
• Less that season’s compensation (known or estimated)
• E...
12
Statistical Analysis
0
40
80
120
160
Surplus Value ($M)
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
Ratio of Surplus Value to Expec...
13
• Sourced mechanics ranks from Doug Thorburn
(Baseball Prospectus) and DL assessments from Jeff
Zimmerman (FanGraphs) t...
14
• Kershaw and Wainwright paced the field in terms of
both endurance and durability
• Though projected to be a solid ret...
15
• Fernandez’s ability to attack the zone early on in his
career bodes well for confidence and projections of
stuff
• Da...
The Three Most Valuable
Pitchers in Baseball
16
#3
17
Pitcher #3 Pitcher #2 Pitcher #1
18
Yu Darvish – SP Texas Rangers
Bats: R Height: 6’ 5”
Throws: R Weight: 225lb
Age: 27 Se...
Pitcher #3 Pitcher #2 Pitcher #1
19
Yu Darvish – SP Texas Rangers
- Very consistent velocity despite
increased usage in ML...
#2
20
Pitcher #3 Pitcher #2 Pitcher #1
21
Chris Sale – SP Chicago White Sox
Bats: L Height: 6’ 6”
Throws: L Weight: 180lb
Age: 2...
Pitcher #3 Pitcher #2 Pitcher #1
22
Chris Sale – SP Chicago White Sox
Sale, at age 20, after being drafted, asked about wh...
#1
23
Pitcher #3 Pitcher #2 Pitcher #1
24
Jose Fernandez – SP Miami Marlins
Bats: R Height: 6’ 2”
Throws: R Weight: 240lb
Age: 2...
Pitcher #3 Pitcher #2 Pitcher #1
25
Qualitative Analysis:
• Prototypical workhorse build (6’2‛,
240)
• Outstanding makeup ...
Risk Factors & Pitfalls
26
Potential flaws in our approach:
• Scope of comp set
• Model ran with 10 comps per player; it w...
Thank you – Questions?
27
Joe Puccio
Mike Velcich
Sean McCluskey
Andrew Ungerer
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Who are the 3 Most Valuable Pitchers in MLB? A Pitcher Projection and Valuation Study

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2nd Place Winners, 2014 SABR Diamond Dollars Case Competition

My team and I designed a pitcher projection and valuation model that allowed us to put a dollar value on the surplus generated by each of MLB's top pitchers.

Projection: Our model was built on accurate identification of age-specific comparable players, whose career trajectories informed a series of monte carlo simulations that forecast each individual component of FIP 10,000 times. These independent events were automatically combined into 10,000 instances of FIP, which we then translated into WAR.

Valuation: Using a $6M/year value for WAR that was provided in the case prompt, and taking inflation, injury risk premiums and projection uncertainty into account through inflation and discount rates, we proceeded to build a valuation model that computed the present-day surplus (or deficit) represented by that pitcher's performance through each year of team control.

Sabermetrics and Scouting: We then took our stack-ranked list of top pitchers and applied a detailed series of sabermetric and qualitative filters, to seek either confirmation or denial of the model's results. Our study included grades on pitching mechanics, probabilities of DL-visits in 2014, analyses of makeup and the pitchers' unique pitching characteristics.

Examples of the features we uncovered include Jose Fernandez's 55% zone rate, Chris Sale's unique horizontal movement (3 of the 10 largest horizontal movements in our sample), and Yu Darvish's 3 of the top 7 whiff rate pitches in our sample.

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Who are the 3 Most Valuable Pitchers in MLB? A Pitcher Projection and Valuation Study

  1. 1. Diamond Dollars: Finding Baseball’s MVPitcher The University of Chicago Booth School of Business Sean McCluskey, Andrew Ungerer, Mike Velcich, and Joe Puccio
  2. 2. Executive Summary 2 4) Identification – 3 Most Valuable Pitchers 5) Case Study – In-Depth on Each Pitcher 3) Analysis – Statistical Modeling 2) Preliminary Process – Pool, Comps, Contracts 1) Situation – Understanding ‚Value‛ 6) Risks –Potential Pitfalls
  3. 3. Value in the Eyes of the Beholder 3 Yankees Cubs Pirates Marlins Financial Resources Contending Rebuilding Low Discount Rate, Low Payroll High Discount Rate, High Payroll
  4. 4. Veteran vs. Prospect 4 Veteran: Current Production Surplus Prospect: Delayed Gratification Budget and proximity to contention is key in identifying the most valuable pitching assets for each specific team. Our analysis is focused on identifying the best pitching values, agnostic but mindful of team situations. Performance projections, age, years of control, injury risks, makeup and pitching profile are the key components to a proper analysis.
  5. 5. The Preliminary Process 5 Phase 1 Phase 2 Phase 3 Objective: Identify Baseball’s Most Valuable Pitchers Identify target population • Build database of 75 target pitchers based on performance • Used last 2 years of performance data, also included top minor leaguers Determine comparable pitchers • Identify 10 comparable pitchers for each candidate • Comps based on Bill James’ Similarity Scores Analyze contract data and project arbitration • Aggregate salaries from Cot’s Baseball contracts • Create dynamic arbitration salary projections Population Comparables Contracts N = 75 N = 16 N = 3
  6. 6. Population Comparables Contracts 6 • Started with PECOTA’s top 40 pitchers for projected 2014 Wins Above Replacement (WAR) • Cross-referenced against top 80 pitchers in 2012 & 2013 o Added top prospects (Taijuan Walker, Gerrit Cole, etc.) o Kept relievers (Craig Kimbrel, Aroldis Chapman, etc.) in order to test values $0.00 $5.00 $10.00 $15.00 $20.00 $25.00 $30.00 0 1 2 3 4 5 6 7 8 9 2014 Salary v 2013 WAR
  7. 7. Population Comparables Contracts • Select comparables for each pitcher in order to project future performance, based on: o Age at time of comparison o Performance o Volume of work that year o Handedness • Comparables were based on Bill James’ Similarity Scores 7
  8. 8. Population Comparables Contracts • Pulled contract data from Cot’s Baseball Contracts • Projected arbitration salaries using: o Previous year’s performance / projection o Recent comps o 5% WAR Inflation 8 Analyze Contract Terms Arbitration Projection Example Important Note: Our arbitration salaries are calculated dynamically. When a player performs better in the simulation, his pay increases!
  9. 9. 9 • Generated projected performance for each pitcher based on the performance of the 10 most comparable pitchers. Projected statistics informed FIP, which was then park-adjusted to calculate WAR o IP o IP/GS o BB/9 o HBP/9 o K/9 o HR/9 • Ran 10,000 iterations per pitcher Statistical Analysis
  10. 10. 10 • Example: WAR and Value Surplus simulation results 0 50 100 150 1 2 3 4 5 Control Years Jose Fernandez 100 = WAR of 5.6 and 2014 Surplus of $32.8m Mean WAR Mean Surplus (PV) 0 50 100 150 200 1 2 3 4 5 Control Years Jose Fernandez 100 = 2014 Surplus of $32.8m Mean Surplus (PV) 0.95 Percentile 0.05 Percentile Statistical Analysis
  11. 11. 11 • WAR dollars using agreed-upon ($6 M/win with 5% inflation) • Less that season’s compensation (known or estimated) • Equals excess value • Used risk-adjusted discounted rates to calculate present value of excess… • BUT – What about Return on Investment? o If two players generated the same excess, wouldn’t we place higher value on the lower cost player? YES! o But where’s the trade-off between Gross Surplus and ROI? • What are assumptions made about return on other salary commitments? • Portfolio theory is an incomplete framework- only 5 rotation slots available! • Need to look at multiple measures of value and layer in team-specific situation Statistical Analysis
  12. 12. 12 Statistical Analysis 0 40 80 120 160 Surplus Value ($M) 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 Ratio of Surplus Value to Expected Salary
  13. 13. 13 • Sourced mechanics ranks from Doug Thorburn (Baseball Prospectus) and DL assessments from Jeff Zimmerman (FanGraphs) to support qualitative research • Mechanics ratings helped inform player discount rates used in PV calculation o Hurt: Miller and Sale o Helped: Bumgarner, Fernandez and Darvish Scouting & Risk Analysis Pitching Mechanics & DL Risk Assessment Pitcher Balance Momentum Torque Posture Release Distance Consistency Overall 2014 DL Risk 1 Bumgarner 70 45 65 80 65 70 A 26.2% Fernandez 65 65 70 65 65 60 A 28.6% Darvish 60 60 60 80 65 60 A- 37.3% Teheran 55 65 60 65 65 60 B+ 30.2% Moore 60 55 70 60 60 35 B+ 35.1% Strasburg 65 55 70 50 55 55 B 39.9% Kershaw 55 55 60 50 50 70 B 28.0% Ryu 55 60 55 55 60 60 B 35.1% Wainwright 65 45 50 55 55 70 B 42.9% Sale 30 50 60 70 60 65 B 30.3% Miller 45 55 65 55 60 55 B- 30.4% 1 League Avg DL Risk = 38.1%
  14. 14. 14 • Kershaw and Wainwright paced the field in terms of both endurance and durability • Though projected to be a solid return on investment for the Cardinals, Miller has yet to establish himself pitching late into games. Additionally, his low ground ball rate and high FB reliance raise questions Pitcher Profiles Pitcher Age Height Weight Throws FIP IP IP/GS GB% Bumgarner 24 6'5" 235 Left 3.05 201.1 6.5 46.8% Fernandez 21 6'2" 240 Right 2.86 172.2 6.2 45.1% Darvish 27 6'5" 225 Right 3.17 209.2 6.6 41.0% Teheran 23 6'2" 175 Right 3.69 185.2 6.2 37.8% Moore 25 6'3" 210 Left 3.95 150.1 5.6 39.4% Strasburg 25 6'4" 200 Right 3.21 183.0 6.1 51.5% Kershaw 25 6'3" 220 Left 2.39 236.0 7.2 46.0% Ryu 27 6'2" 255 Left 3.24 192.0 6.4 50.6% Wainwright 32 6'7" 235 Right 2.55 241.2 7.1 49.1% Sale 25 6'6" 180 Left 3.17 214.1 7.1 46.6% Miller 23 6'3" 215 Right 3.67 173.1 5.6 38.4% 2013 Headline StatsProfile
  15. 15. 15 • Fernandez’s ability to attack the zone early on in his career bodes well for confidence and projections of stuff • Darvish shows elite strikeout ability but struggled with HRs in ’13 • Ryu relies on command, not velocity to achieve results Pitcher Profiles Pitcher Avg FB Velo K% BB% HR% SwStr% Fastball % Breaking Ball % Off Speed % Zone % Bumgarner 91.2 24.8% 7.7% 1.9% 11.1% 39.6% 50.1% 9.7% 51.4% Fernandez 94.9 27.5% 8.5% 1.5% 10.1% 57.3% 34.0% 8.7% 55.0% Darvish 92.9 32.9% 9.5% 3.1% 12.6% 38.2% 59.9% 1.9% 46.8% Teheran 91.5 22.0% 5.8% 2.8% 10.5% 63.8% 31.5% 5.3% 53.2% Moore 92.4 22.3% 11.8% 2.2% 9.5% 62.2% 19.6% 18.4% 44.3% Strasburg 95.3 26.1% 7.7% 2.2% 10.6% 61.0% 22.9% 16.1% 49.4% Kershaw 92.6 25.6% 5.7% 1.2% 11.4% 60.7% 36.9% 2.4% 50.2% Ryu 90.3 19.7% 6.3% 1.9% 8.1% 54.2% 23.9% 22.3% 51.1% Wainwright 91.1 22.9% 3.7% 1.6% 9.6% 40.5% 58.0% 3.8% 48.9% Sale 93.1 26.1% 5.3% 2.7% 10.8% 51.4% 29.8% 19.0% 52.4% Miller 93.7 23.4% 7.9% 2.8% 9.0% 71.3% 18.8% 6.3% 53.1% Stuff Approach
  16. 16. The Three Most Valuable Pitchers in Baseball 16
  17. 17. #3 17
  18. 18. Pitcher #3 Pitcher #2 Pitcher #1 18 Yu Darvish – SP Texas Rangers Bats: R Height: 6’ 5” Throws: R Weight: 225lb Age: 27 Service Time: 2 yr Acquired: post ‘12 Awards: 2x ASG Contract Terms 2014 2015 2016 2017 2018 2019 Yu Darvish $10.00 $10.00 $10.00 $11.00 FA FA Year W L G IP ERA WHIP CG SHO H R ER HR BB K K/BB K/9 GB/FB ERA+ FIP WAR 2012 16 9 29 191.1 3.9 1.28 0 0 156 89 83 14 89 221 2.48 10.4 1.46 112 3.29 3.9 2013 13 9 32 209.2 2.83 1.073 0 0 145 68 66 26 80 277 3.46 12 1.08 145 3.28 5.8 2 Yrs 29 18 61 401 3.34 1.17 0 0 301 157 149 40 169 498 2.95 11.2 1.25 127 3.28 9.6 Elite Stuff: Three of the top Seven Whiff Rate pitches in our sample belong to Yu and his 32 K% paces the field
  19. 19. Pitcher #3 Pitcher #2 Pitcher #1 19 Yu Darvish – SP Texas Rangers - Very consistent velocity despite increased usage in ML year 2 - Showcased elite production in ’13 despite an elevated HR rate - Reduced fastball effectiveness and usage in ’13 is trend to monitor though no visible signs of velo or movement loss - Sheer variety of offerings is a unique attribute
  20. 20. #2 20
  21. 21. Pitcher #3 Pitcher #2 Pitcher #1 21 Chris Sale – SP Chicago White Sox Bats: L Height: 6’ 6” Throws: L Weight: 180lb Age: 25 Service Time: 3.06yr Drafted: 13th – 2010 Awards: 2x ASG 2014 2015 2016 2017 2018 2019 Chris Sale $3.50 $6.00 $9.15 $12.00 $12.50* $13.50* * Denotes team options **Denotes time spent as a reliever Contract Terms Unique Movement: In 18 pitcher, 87 pitch sample, Sale has three of the top ten pitches with the most horizontal movement Year W L G IP ERA WHIP CG SHO H R ER HR BB K K/BB K/9 GB/FB ERA+ FIP WAR 2010 2 1 21** 23.1 1.93 1.071 0 0 15 5 5 2 10 32 3.2 12.47 1.39 225 2.74 1.2 2011 2 2 58** 71 2.79 1.113 0 0 52 22 22 6 27 79 2.926 10.01 1.55 156 3.12 2.3 2012 17 8 30 192 3.05 1.135 1 0 167 66 65 19 51 192 3.765 9 1.40 140 3.27 5.9 2013 11 14 30 214.1 3.07 1.073 4 1 184 81 73 23 46 226 4.913 9.5 1.46 140 3.17 6.9 4 Yrs 32 25 139 500 2.97 1.1 5 1 418 174 165 50 134 529 3.95 9.52 1.44 144 3.12 16.3
  22. 22. Pitcher #3 Pitcher #2 Pitcher #1 22 Chris Sale – SP Chicago White Sox Sale, at age 20, after being drafted, asked about what other pitchers he models himself after: "You can't really pitch like anyone," Sale said. "Everyone has own style of pitching, as they do hitting. I don't try to pitch like [Cole] Hamels or [Randy] Johnson, throwing 100 mph or the nastiest breaking ball ever. I pitch my game." - Sale actually slightly increased velo from year 1 to year 2 as a starter and saw gains in already lethal slider’s effectiveness as well. - Concern remains about ‚Inverted ‘W’‛ delivery: Makeup and Personality
  23. 23. #1 23
  24. 24. Pitcher #3 Pitcher #2 Pitcher #1 24 Jose Fernandez – SP Miami Marlins Bats: R Height: 6’ 2” Throws: R Weight: 240lb Age: 21 Service Time: 1yr Drafted: 14th – 2011 Awards: ‘13 RoY, AS Contract Terms 2014 2015 2016 2017 2018 2019 Jose Fernandez $0.64 $0.80 Arb 1 Arb 2 Arb 3 FA Attacks Hitters: 55% of pitches in strike zone, represents highest % in our top-tier sample Pitch Breakdown Year W L G IP ERA WHIP CG SHO H R ER HR BB K K/BB K/9 GB/FB ERA+ FIP WAR 2013 12 6 28 172.2 2.19 0.979 0 0 111 47 42 10 58 187 3.224 9.774 1.36 176 2.73 6.3
  25. 25. Pitcher #3 Pitcher #2 Pitcher #1 25 Qualitative Analysis: • Prototypical workhorse build (6’2‛, 240) • Outstanding makeup – Escaped Cuba as a teen, rescued his mother after she fell overboard • Fernandez, after admiring his first career home run from the batter’s box in a game against the Atlanta Braves: “This is a professional game, and we should be professional players. I think that never should happen. I'm embarrassed, and hopefully that will never happen again." • Fearless mound presence Jose Fernandez – SP Miami Marlins Scouting Analysis: • Pounds the zone • Highly ffective against both Righties and Lefties • Development of secondary pitches could sustain current level
  26. 26. Risk Factors & Pitfalls 26 Potential flaws in our approach: • Scope of comp set • Model ran with 10 comps per player; it was challenging to identify good comps for such young, inexperienced pitchers, and this introduced variation • Risk assessment • Captured projection confidence and injury probability in two places: comps, and discount rate. For example, used higher discount rate on Chris Sale because of his inverted ‚W‛ mechanics. Also used higher rate on Jose Fernandez because of his brief, 1 year track record • Definition of value • We defined value in multiple ways, first by gross excess return over contractual obligations, then by return on investment. These approaches yielded somewhat different results, and so the answer can change based on the desired outcome
  27. 27. Thank you – Questions? 27 Joe Puccio Mike Velcich Sean McCluskey Andrew Ungerer

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