syracuse university sabr fall 2016

Most Valuable Players

SYRACUSE UNIVERSITY BASEBALL STATISTICS AND SABERMETRICS CLUB

C.B. Garrett Chris Karasinski Evan Weiss Justin Perline Olivia Lavelle

Table of Contents 1.Methodology

–Aging Curves–WAR Calculation–Arbitration & Pre-Arbitration Projection–Marginal Win Values–Value Calculation

2.Assessment of Risk3.Player Projections

Methodology• Compute a new statistic “WAR+” to measure value to

team

WAR+ = Projected WAR(Salary/ Marginal Value)

Sum of annual WAR+ values for remaining years under contract

- Measure of added value over duration of contract

Aging Curves• Compute Aging curves to breakdown three main components of

WAR–wRAA–UZR (rSB and rPP for catchers)–BsR

• Each aging curve broken down by standard deviation above/below the mean– Account for deviation in

better/worse subsets– 18 total aging curves

Highest Standard Deviation Group

3

Aging Curves (Highest Standard Deviation Groups)

BsR Aging Curve UZR Aging Curve

Recalculating WAR

● Aging Curves extrapolated (1) wRAA (2) UZR (3) BsR through 2028○ Based on 2016 original data

● League Adjustment, Positional Adjustment, Replacement Runs, Runs Per Win kept constant○ Not assuming position switch

Contract Projections● Pre-Arbitration

○ Projections purely based on service time

● Arbitration○ Arbitration model based on arbitration class and projected

WAR totals○ Assembled arbitration database on players from 2012-2016

● Contracts○ Options assumed to be accepted given value

● Marginal Value: Cost per year adjusted for inflation○ Cost of win for 2017: $7,000,000○ Inflation Percentage: 5%

Marginal Value

WAR / $● “WAR+” to measure value to team over duration of contract

WAR+ = Projected WAR (Salary / Marginal Value)

● Large contracts are more burdensome than lower WAR figures

Life of Contract WAR and $• WAR through 2028

–Mike Trout (3rd)–Large Contract + High Production

• Salary owed through duration of contract–Giancarlo Stanton (1st)–Miguel Cabrera (2nd)–Joey Votto (3rd)

Risk Assessment• Three Part Factor (Weighted on strength of the model)

–Injury Risk–Age Risk–Performance Risk

• Injury Risk–DL days projection based on lag and two lags of annual DL days

• Age Risk–Standard deviations of wRAA, UZR, BsR by age

• Performance Risk–Standard deviations of annual WAR performance–Rookies with 1 year of experience projected on average Yr 1 to 2 risk

Risk in Conjunction with Projections

• Applied Error Bars–Upper and lower bounds of projection

• Acceptable level of risk varies by team situation–Lower payroll teams cannot afford as much risk

• Lower bound of risk-associated projection used as final figures–Worst possible likely outcome assumed

Three Most Valuable Players

1.Corey Seager• 39.7 Hard-Hit% • 24.4 Line Drive%• Career wRC+ 142• 7.5 fWAR among SS (1st)

• Owned through 2021• 36.57 Projected WAR• $34,973,000 Projected Salary

2017 WAR+ 2018 WAR+ 2019 WAR+ 2020 WAR+ 2021 WAR+ Total

98.8 104.3 5.9 5.3 4.7 219.0

2. Francisco Lindor• 20.8 UZR among SS (2nd Highest)• 6.3 fWAR• 17 Defensive Runs Saved• 112 wRC+ in 2016



85.4 90.4 5.8 5.2 4.6 191.3

3. Carlos Correa• 37.2 Hard-Hit% • 122 wRC+• 11.4 BB% (2nd among SS)• 201 out of zone (OOZ) plays in MLB

(3rd)



62.7 66.8 5.6 4.8 4.1 144.0

Just MissedKris Bryant

Extra year of arbitration versus pre-arbitrationMookie Betts

One less year of control than those above him

Appendix2017 Salary 2018 Salary 2019 Salary 2020 Salary 2021 Salary

Corey Seager $515,183 $522,280 $9,665,360 $11,156,490 $13,121,404

Francisco Lindor $518,511 $525,791 $8,517,328 $10,008,458 $11,973,373

Carlos Correa $518,758 $526,038 $6,532,678 $8,036,627 $10,014,360

2017 WAR 2018 WAR 2019 WAR 2020 WAR 2021 WAR

Corey Seager 7.27 7.31 7.33 7.34 7.32

Francisco Lindor 6.32 6.37 6.41 6.43 6.44

Carlos Correa 4.65 4.71 4.76 4.80 4.82

Questions?

syracuse university sabr fall 2016

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