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.

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. 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. 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. 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. 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. 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. 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. 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
• 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
• 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
• 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
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
• 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
• 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
• 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. The Three Most Valuable
Pitchers in Baseball
16

17. #3
17

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. 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. #2
20

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. 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. #1
23

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. 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. 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. Thank you – Questions?
27
Joe Puccio
Mike Velcich
Sean McCluskey
Andrew Ungerer