1. BUILDING A BULLPEN
David Higgins, Kyle Jankowski, Eric DeStefano, Grant Flick, Robert Nanna

2. The Case
• Develop the most effective bullpen for a given NL team,
given the imposed constraints
Our Goal
• To forecast expected SIERA for each pitcher, given the
2016 schedule of his proposed team
• Use our expected SIERAs and player profiles to
construct the optimal NL bullpen

3. 1. Evaluation of Pitchers
2. Gathering Data
3. Data Analysis
4. Pitcher Selection
5. Pitcher Profiles

4. 1. Evaluation of Pitchers
2. Gathering Data
3. Data Analysis
4. Pitcher Selection
5. Pitcher Profiles

5. SIERA
• Skill – Interactive ERA
• Accounts for factors pitchers can directly influence
• Strikeouts, Walks, Ground Balls, Fly Balls
• Adjusted to be on the same scale as ERA
• Most predictive among popular DIPS
𝑆𝐼𝐸𝑅� = −16.986ሺSO%ሺ+ 7.653ሺSO%ሺ2
+ 11.434ሺBB%ሺ− 1.858ሺnetGB%ሺ− 6.664ሺ±netGB%ሺ2
+ 10.130ሺSO% ∙ netGB%ሺ− 5.195ሺBB% ∙ netGB%ሺ+ 6.145

6. 1. Evaluation of Pitchers
2. Gathering Data
3. Data Analysis
4. Pitcher Selection
5. Pitcher Profiles

7. Gathering Data
• Analysis of every game event from 2003-2013
• Over 2 million observations!
• SQL queries used to return:
• Game ID
• Batter + Handedness
• Pitcher + Handedness
• Outcome (K, BB, etc.)
• Hit Type (GB, LD, FB, PU)

9. 1. Evaluation of Pitchers
2. Gathering Data
3. Data Analysis
4. Pitcher Selection
5. Pitcher Profiles

10. Analyzing Data
• Breakdown of each matchup based on yearly
pitcher and batter splits
• Count of:
• Identified frequency of each matchup
• Identified outcomes related to SIERA inputs
• Strikeouts, Walks, Ground Balls, Fly Balls

11. Resulting Analysis

12. Resulting Analysis

13. Regression Results

14. 1. Evaluation of Pitchers
2. Gathering Data
3. Data Analysis
4. Pitcher Selection
5. Pitcher Profiles

15. Translating Matchup Data into SIERA
• Using batter and pitcher expectancies
• Regression equations could proportionately gauge both batter and
pitcher input to determine expected outcomes
• Predicted outcomes for Strikeouts, Walks, Groundballs, and
Fly Balls by opposing split
• These figures could then be used for SIERA inputs

16. Determining Best Pitchers
• Each potential pitcher could be run through every
NL schedule
• SIERA projected for each individual player
• Weighted based on batter plate appearances and
schedule proportions
• Resulted in an expected total SIERA for each
pitcher on any NL team

17. 1. Evaluation of Pitchers
2. Gathering Data
3. Data Analysis
4. Pitcher Selection
5. Pitcher Profiles

18. Group One•Andrew Miller

19. Andrew Miller
Breakdown:
•Age: 30
•6’7” - 210 lbs.
•“The Sweeper”
Pitch Arsenal (2015):
•Four-Seam Fastball - 94.3 MPH - 45.7%
•Slider - 83.8 MPH - 54.3%

20. Andrew Miller

21. Andrew Miller

22. Andrew Miller
Our Take:
•Experience
•Command
•Versatility in the Bullpen

23. Group Two• Ken Giles

24. Ken Giles
Breakdown:
•25 years old
•6’2” - 205 lbs.
•“The Maniac”
Pitch Arsenal (2015):
•Fastball - 62%
• Four Seam 96.5 MPH
• Two Seam - 94.8 MPH
•Slider - 86 MPH - 38%

25. Ken Giles
Strengths:
•Great velocity on fastball at a 96.5 MPH average in
2015
•Quick delivery to home out of the stretch
• Tough for hitters to get a good load
• Hard to steal on
•Non-traditional Slider = Unhittable slider

26. Ken Giles
Risks:
•Decrease in productivity from 2014 to 2015
• Fastball declined by 0.7 MPH
• Strikeout-to-walk rate down 11%
•Composure when facing adversity

27. Ken Giles
Our Take
•Powerful fastball-slider pitcher
•Fierce competitor
•Young, healthy arm

28. Group Three
• Trevor Rosenthal
• David Robertson

29. Trevor Rosenthal
Breakdown:
•25 years old
•6’2” - 220 lbs.
•“The Flamethrower”
Pitch Arsenal (2015):
•Four Seam Fastball - 97.6 MPH - 74.3%
•Changeup - 87.8 MPH - 17.2%
•Cutter - 88.3 MPH - 5.6%
•Curveball - 81.1 MPH - 2.1%

30. Trevor Rosenthal
Strengths:
•Deadly Fastball-Changeup combo
•Commands fastball almost perfectly
•Rising action on fastball
• Gets hitters to chase up in the zone

31. Trevor Rosenthal
Weaknesses:
•Became increasingly reliant on fastball as
2015 season progressed
•Falls off to left side of mound
•History of command issues

32. Trevor Rosenthal
Our Take:
•Dominates with Fastball
•Coming off best year of career
• Mechanical adjustment indicates
sustainable success
• Significant decrease in BB%
• 13.6 BB% in 2014
• 8.7 BB% in 2015

33. David Robertson
Background:
•Age: 30
•5’11” - 195 lbs.
•“The Ghost”
Pitch Arsenal (2015):
•Four-seam Fastball - 92.7 MPH - 19.9%
•Cutter - 91.9 MPH - 46.7%
•Knuckle Curve - 82.6 MPH - 30.4%
•Changeup - 86.8 MPH - 2.2%

34. David Robertson
Strengths:
•Fastball-Cutter duo
•Throwing motion hides ball until release
•Generates “whiffs,” ground balls and fly balls at
high rate
• Swing and Miss - 14.1%
• Ground Balls - 35.6%
• Fly Balls - 34.2%

35. David Robertson
Risks:
•“Flat” Fastball
•ERA has steadily increased past 3 years
•Uncomfortable with Changeup

36. David Robertson
Our Take:
•Provides experience out of the bullpen
•Best Curveball of pitchers within Group
•“Junk Ball” pitcher

37. At-Large Selection• Will Smith

38. Will Smith
Breakdown:
•26 years old
•6’5” - 260 lbs.
•“The Deceptionist”
Pitch Arsenal (2015):
•Four Seam Fastball - 93.3 MPH - 48.2%
•Two Seam Fastball - 90.6 MPH - 2%
•Slider - 86 MPH - 43%
•Curveball- 76.7 MPH - 5.6%
•Changeup- 84 MPH - 0.8%

39. Will Smith
Strengths:
•Premier Slider
• 30% swing-and-miss rate
• Glove-side cut
•Four Seam Fastball generates a large percentage of ground balls
when thrown down in the zone
•Five pitches to work from keeps batters guessing

40. Will Smith
Risks:
•Lack of experience in winning environments
• Royals and Brewers
• No playoff experience
•Frequently uses just two of five pitches
•Predictability

41. Will Smith
Our Take:
•Swing-and-miss slider
• 82.82% swing and miss rate
•Hitters will adjust to young pitcher’s tendencies

42. Team Fit
• Cubs rotation
• Rotation members average 6.2 innings per start
• Allows team to maximize strengths, minimize
weaknesses of each member of the bullpen
• Jake Arrieta
• Jon Lester
• John Lackey
• Kyle Hendricks
• Jason Hammel

43. Team Fit
• Strong defense
• Complements pitcher skill
• Ensures further run prevention

45. 1. Evaluation of Pitchers
2. Gathering Data
3. Data Analysis
4. Pitcher Selection
5. Pitcher Profiles
Panel Discussion