Columbia Case Competition

1. The following analysis was presented at the Columbia Sports Analytics
Baseball Case Competition by an organization I founded at the
University of Florida - the Florida Sports Analytics Association (FSAA).
The goal of this competition was to name the three most valuable
position players in baseball, based on future value “surplus”
projections. Our team conducted quantitative performance, salary, and
variability analysis (mostly done through programming in R) as well as
qualitative strike zone and strategic baseball knowledge considerations.
Our team of four presented to a wide group of baseball executives,
including strategists from the MLB League Office and Toronto Blue Jays
GM Ben Cherington.
PROJECT BACKGROUND

2. THETHREE MOSTVALUABLE
POSITION PLAYERS IN MLB
University of Florida
Ronnie Socash
RJ Walsh
Tanner Crouch
Danny Lueck

3. TABLE OF CONTENTS
1.Preliminary Process – Find the original pool of players
2.Statistical Analysis – Projecting Future Performance
3.Market Value – Comparable players; predict future contracts
4.Risks – Identify potential pitfalls
5.Identification – Five Most Valuable Position Players
6.Case Study – Analysis of the final cut
7.The Final Three

4. • Step 1: Identify the original pool of players
– Position player WAR leaders
– Future and recent top prospects
• Step 2: Build a database of 20 possible players
– Name, Team, Age, Seasons Played, Games Played, WAR
over last three seasons (if applicable)
PRELIMINARY PROCESS

5. THE TOP CANDIDATES

6. R2
= .91
Database of players
from 1986 - 2016
WAR = -3.9352 + 36*(age) - .0062*(age2
)
PROJECTING FUTURE WAR

7. • We found the average performance
adjustment curve (by age) across the
entire MLB
• Apply the percentage change from year to
year to each player
• Use the average of the last two seasons of
WAR for each player
STATISTICAL ANALYSIS

8. Age at 2017 Opening Day: 25
2015 WAR: 4.5
2016 WAR: 5.2
(5.2 + 4.5) / 2 = 4.9 WAR
• 2017 projection: 4.9 * (slope change from age 24 to 25)
= 4.9 * (1.049073) = 5.1 WAR
• 2018 projection: 5.1 * (slope change from age 25 to “26)
= 5.1 * (1.036205) = 5.3 WAR
• Process repeated through the 2021 season
EXAMPLE: NOLAN ARENADO

9. $7 million per marginal win in 2017 (based on the calculated league average)
 5% increase each year.
Nolan Arenado:
–2017: 5.1 predicted WAR * ($7,000,000) = $35,616,028.35
–2018: 5.3 predicted WAR * ($7,350,000) = $38,750,766.66
–Continue through 2021
–Add all five dollar figures to calculate total 5-year future value in terms
of WAR
–Over next 5 seasons: 26.7 WAR and $207,276,574.20 Production Value
CONVERTING WAR INTO DOLLARS

10. Step 1: Identify Comparable Players
Comparable Player Qualifications:
-Same Position
-Within 3 years of age at time of breakout/peak
- Peak/Breakout seasons within 1.5 WAR
PROJECTING FUTURE SALARY

11. • Similar to the Qualifying Offer, we
used the average of the top 125
contracts over the last 10 years
• We then created a linear model to
predict future increases
• R2
value of .96
• Avg. Salary = -1.14B + 573,300*(year)
FUTURE SALARY INCREASE

12. • Comparable Players: Pablo Sandoval and Kyle Seager
• Machado entered 1st
year of arbitration after 6.8 WAR season
• Sandoval entered 1st
year of arbitration after 5.3 WAR season
• Seager entered 1st
year of arbitration after 5.4 WAR season
• Machado (24), Sandoval (25), Seager (27)
• All play third base
EXAMPLE: MANNY MACHADO

13. • We used the comparable player
salaries during similar career
points
• Calculated the percentage of the
Qualifying Offer that players
made in those years
• Adjust the current player’s salary
to reflect the percent change
PROJECTING ARBITRATION

14. ADJUSTED FOR SALARY INCREASE

15. • If Machado’s production was equal to Seager or Sandoval, we would expect Machado to
receive same percentage of the Qualifying Offer.
• In order to adjust for the difference in production of the current player, we then multiplied
the projected salary by the percent difference in WAR over the three previous seasons.
FINAL ADJUSTMENT

16. ADJUSTED SALARY PROJECTION

17. OUR TOP EIGHT

18. THE FIVE FINALISTS

19. MOOKIE BETTS – RED SOX CF
TOTAL SURPLUS
VALUE: $221,626,813.59

20. MIKE TROUT – ANGELS CF
TOTAL SURPLUS
VALUE: $236,765,429.42

21. FRANCISCO LINDOR - INDIANS SS
TOTAL SURPLUS
VALUE: $247,817,480.85

22. KRIS BRYANT – CUBS 3B/OF
TOTAL SURPLUS
VALUE: $258,096,445.52

23. COREY SEAGER – DODGERS SS
TOTAL SURPLUS
VALUE: $311,915,858.51

24. • A balancing act between younger and older players
• Younger – can be paid at a discount, but, less of a track record
• Older – command more money, but, proven track record
• Variance formula
• Mean = (career WAR) / (career seasons played)
• Divided by (# of games played) – 1
SAMPLE SIZE ANALYSIS

25. Player UZR DRS
Lindor 20.8 17
Betts 17.8 32
Bryant 5.3 (3B) / 6.2 (OF) 9
Trout -0.3 6
Seager 10.6 0
The players that derive the most value from their defense are Francisco
Lindor and Mookie Betts.
FanGraphs uses UZR as its main component of WAR, and all five players
are within 20 runs of each other.
Assuming the value of one win is 10 runs, the most WAR defense would
likely account for is two wins.
FIELDING PROFILE

26. • According to the FanGraphs glossary, there is a high level
of variability in UZR. For example, UZR is given a five-
run error range in either direction. Therefore, a UZR of
+10 could be either +5 or +15.
• Because FanGraphs’ WAR rating uses defensive metrics
that are less exact to evaluate, we believe that offensive
performance should be weighted more heavily than
defense in terms of predicting future performance.
DEFENSIVE METRIC VARIABILITY

27. • Mike Trout has the best wRC+, Hard Hit %, and wOBA
• There seems to be a slight correlation between
Hard Hit % and wRC+
Player Soft % Med % Hard % wRC+ wOBA
Trout 12.0 46.3 41.7 171 .418
Bryant 17.0 42.7 40.3 149 .396
Seager 12.7 47.6 39.7 137 .372
Betts 17.4 49.2 33.4 135 .379
Lindor 17.2 55.2 27.5 112 .340
2016 PLAYER PROFILES

28. • In late May, ESPN reported that MLB’s Competition Committee
agreed to raise the strike zone.
• The Strike Zone is to be moved to the top of the hitter’s knees
from “the hollow beneath the kneecap” currently.
• With fastball velocity continuing to increase, we believe that this
will result in more pitchers challenging hitters up in the zone.
• The “bottom of the zone” will no longer belong to pitchers.
ACCOUNTING FOR CHANGES IN THE GAME ITSELF

34. • We believe that Corey Seager and Kris Bryant are going to be the most
positively affected by an upward shift in the strike zone.
• Francisco Lindor is the player most likely to be negatively affected by the
strike zone shift.
• We do not believe Mike Trout will be particularly affected in any drastic way
due to his five full seasons of high-level performance. If pitchers have not
figured him out by now, there is no indication that they will.
TYING IT ALL TOGETHER

35. MIKE TROUT
#3
1. TOTAL SURPLUS VALUE:
$236,765,429.42
2. Four years of Team Control
3. Best Hard Hit %, wOBA, and
wRC+
4. Projected 50.7 WAR between
2017-2021

36. 1. TOTAL SURPLUS VALUE:
$258,096,445.52
2. Five years of Team Control
3. 2nd
best Hard Hit % and 2nd
highest wRC+
4. Projected 41.4 WAR from 2017-
2021
Kris Bryant
#2
KRIS BRYANT

37. 1. TOTAL SURPLUS VALUE:
$311,915,858.51
2. Five years of Team Control
3. 3rd
best Hard Hit % and 3rd
highest wRC+
4. Projected 47.2 WAR from 2017-
2021
#1
COREY SEAGER

38. QUESTIONS?