Spending Optimization of MLB Pitching Staff IAN KWON Purpose How should teams constitute their starting rotation? Contents 1. Intro: How teams value strikeouts 2. Calculation & Analogy 3. Application and Examples 4. Conclusion EXAMPLE (Notable FA Starting Pitcher in 2018) PATRICK CORBIN NATHAN EOVALDI GARRET RICHARDS • 5.15 ERA / 87 ERA+ 3 seasons ago • 44-53 Pitcher (.454) & ERA+ 95 • Unable to pitch until September • Missed in 2014 due to T.J. Surgery • Pitched 1 full season only • Only 137.5 innings from ‘16 to ‘18 Received 6yr/$140 million Received 4/$68 million Received 2/$15.5 million EXAMPLE (What do they have in common?) K/9 in first 4 seasons and last 4 seasons before FA Have an ‘upper trend’ of Strikeouts & decent K: 10 8.85 8.875 9 8 1) P. Corbin 7.45 K/9 in first 4 years 7.45 7.43 8.85 K/9 in last 4 years 7 6.725 6.225 2) N. Eovaldi 6.23 K/9 in first 4 years 6 7.43 K/9 in last 4 years 5 4 3) G. Richards 6.73 K/9 in first 4 years K/9 per Season 8.88 K/9 in last 4 years 3 2 Eovaldi’s K/9 seems lacking, 1 But his fastball velocity is in the 99th percentile. 0 Corbin Eovaldi Richards Yearly trend EXAMPLE (STRIKE OUTS?) K/9 per season in last 10 seasons of all 30 teams 1. The evolution of Sabermetrics : 10 More Strikeouts 9 8 7 6 5 2. 8.47 K/9 K/9 4 3 3. K/9 rates for pitchers are escalating 2 1 throughout the league. 0 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Year EXAMPLE (Strikeouts get paid) 1) Pitchers with high K rates and/or with good recent trends get PAID 2) Teams keep composing their pitching staff with high K pitchers (escalating K/9 rate in MLB) 3) Simple theory: higher the demand, higher the price. = Pitchers who can K are wanted, and so are paid QUESTION How can we create a financially optimal rotation? Calculation & Analogy (Basic Conditions) 1) Starting pitchers’ data from 2016-2018 - At least started 10 games - Not labeled as ‘relievers’ by Baseball Reference. Leaves out openers 2) Players traded midseason were left out ex) Cole Hamels from Rangers to Cubs * Assumption : • 501 SP from 30 teams in 3 seasons. • Not necessarily the ‘perfect’ calculations - AL: 240 SP • Only dependent on 1 pitching stat: ERA • ‘Projected Full’ model assumes to have 32+= starts - NL: 261 SP • Marketable sides of a ‘big name’ pitcher • Rookie Contracts Calculation & Analogy (Model Equation) • ERA: WinPct = 0.51 + 0.09 * Run Support – 0.10 * ERA ; R-Square = 70.0% • ERA+: WinPct = -0.26 + 0.08 * Run Support + 0.004 * ERA+ ; R-Square = 64.6% • FIP: WinPct = 0.5 + 0.09 * Run Support – 0.10 * FIP ; R-Square = 57.4% •R-Square for model with Run Support & ERA is nearly identical to R-Square for model with Run Support & all 3 metrics : at 70.4% < Correlation Table > Combined (n=501) AL (n=240) NL(n=261) ERA -0.7 -0.72 -0.69 ERA+ 0.68 0.7 0.67 FIP -0.58 -0.59 -0.6 Run Support 0.53 0.59 0.47 Calculation & Analogy (Definitions) • League average run support and the pitcher’s ERA to calculate predicted win percentage. → ERA: WinPct = 0.51 + 0.09 * Run Support – 0.10 * ERA ; R-Square = 70.0% • Predicted win percentage * Number of decisions = Predicted wins. • Observe the difference between predicted wins and observed wins i) If the difference yields a positive number: pitcher was negatively affected by below average run support. ii) If the difference yields a negative number: pitcher was beneficiary of a better run support • Predicted win percent * ‘Full season starts’ (of 32) = Projected full Calculation & Analogy ( EXAMPLE ) Had started 32 games in 2016 and have the same Run Support, • Clayton Kershaw: 23.8 projected wins • Kyle Hendricks: 22.5 projected wins. • Kershaw: $34.5 million ( + 0.9 ) • Hendricks: $541,000 ( - 0.1 ) Calculation & Analogy (Division Winners) Financially, Statistically, • 26 pitchers in 40-man rosters • At least 2 starters who can give 29+= starts. • 45% of the team’s payroll • About 2 starters who can give anywhere from • About 11 to 12 pitchers were paid over $1Mil 19~28 starts • About 4 pitchers’ salary is • 1 Starter who gives you anywhere from More than 4% of the team’s payroll 10 to 18 starts. • About 1 starter gave you an ERA between 2~3 • 2 starters gave you an ERA between 3~4 • About 2 gave you anywhere in the 4~5 Calculation & Analogy (‘Small Market’ Teams) • Milwaukee, Kansas City, Cincinnati, 1) What is 45% of $92 Mil. and $70 Mil? Pittsburgh, Tampa Bay have the least people. 2) What is an appropriate range? • Baltimore, Miami, Oakland, Tampa Bay and : 5% of the 45% to 30% of the 45%? Pittsburgh have the 5 lowest payroll salary. i) 2.07 Mil. to 12.42 Mil. (Average of 8 teams) <3.68 Mil.> In 2018, a) The 8 teams’ average payroll is $ 92 Mil. ii) 1.575 Mil. to 9.45 Mil. b) Tampa Bay & Pittsburgh average $70 Mil. (Tampa and Pittsburgh) <2.8 Mil.> < >: 4% threshold of $ 92 Mil. and $ 70 Mil. Calculation & Analogy ( ‘ACE’ in Small Markets ) The range suggested the ‘most’ a franchise could spend on their starting rotation was $13.365 Mil. The highest paid starter for each 8 teams in 2018 were Salary ($) of Team Ace • MIL: Jhoulys Chacin at $8.75 Mil. 25,000,000 • KC: Ian Kennedy at $16 Mil. 20,000,000 • CIN: Homer Bailey at $21 Mil. • MIA: Wei-Yin Chen at $10 Mil. 15,000,000 • PIT: Ivan Nova at $9.41 Mil 10,000,000 • TB: Blake Snell at $558,200 (…Archer was paid $4.17 Mil.) 5,000,000 • BAL: Andrew Cashner at $9.5 Mil. • OAK: Brett Anderson at $3.3 Mil. 0 Tampa Bay Oakland Milwaukee Pittsburgh Baltimore Miami Kansas City Cincinnati Application & Example ( Choose 1 ) A) ERA Below 3 ( 9 pitchers of the 19 starting are satisfied to have salary below $2.8 Mil. ) • Blake Snell: 31 GS, 1.89 ERA, 23 projected wins • Clay Buchholz 16 GS, 2.01 ERA, 22.7 projected wins • Aaron Nola: 33 GS, 2.37 ERA, 21.5 projected wins • Wade Miley: 16 GS, 2.57 ERA, 20.9 p wins • Walker Buehler: 23 GS, 2.62 ERA, 20.8 p wins • Dereck Rodriguez: 19 GS, 2.81 ERA, 20.2 p wins • Anibal Sanchez: 24 GS, 2.83 ERA, 20.1 p wins • Kyle Freeland: 33 GS, 2.85 ERA, 20 p wins • Mike Foltynewicz: 31 GS, 2.85 ERA, 20 p wins In 2018 Application & Example ( Choose 2 ) B) ERA BETWEEN 3 & 4 C) ERA BETWEEN 4 & 5 • Clevinger: 32 GS, 3.02 ERA, 19.5 projected wins • M.Gonzales: 29 GS, 4 ERA, 16.4 projected wins • Stripling: 28 GS, 3.02 ERA, 19.5 projected wins • Luchessi: 26 GS, 4.08 ERA, 16.2 projected wins • Williams: 31 GS, 3.11 ERA, 19.2 projected wins • Heaney: 30 GS, 4.15 ERA, 16 projected wins • Hardy: 13 GS, 3.56 ERA, 17.8 projected wins • Smith: 16 GS, 4.19 ERA, 15.8 projected wins • Barria: 26 GS, 3.41 ERA, 18.3 projected wins • Bieber: 19 GS, 4.55 ERA, 14.7 projected wins • LeBlanc: 27 GS, 3.72 ERA, 17.3 projected wins • Weaver: 25 GS, 4.95 ERA, 13.4 projected wins 22 pitchers with salary less than $2.8 Mil. out of 55 who 39 pitchers who had a salary less than $2.8 Mil out of 60 had an ERA in between 3 and 4 pitchers who had an ERA in between 4 and 5 Application & Example ( Comparison ) STARTING 5 (2018) INSTEAD… • Severino: 32 GS, 3.39 ERA, 18.4 p win (10.3 K/9) • D.Rodriguez: 19 GS, 2.81 ERA, 20.2 p win (6.8 K/9) • Tanaka: 27 GS, 3.75 ERA, 17.2 p win (9.2 K/9) • Williams: 31 GS, 3.11 ERA, 19.2 p win (6.6 K/9) • Sabathia: 29 GS, 3.65 ERA, 17.5 p win (8.2 K/9) • Barria: 26 GS, 3.41 ERA, 18.3 p win (6.8 K/9) • Gray: 23 GS, 4.9 ERA, 13.6 p win (8.5 K/9) • Urena: 31 GS, 3.98 ERA, 16.5 p win (6.7 K/9) • German: 14 GS, 5.57 ERA, 11.5 p win (10.7 K/9) • Blach: 13 GS, 4.25 ERA, 15.6 p win (5.7 K/9) Total : $39,952,721. Total : $3,002,160 (13th of the price) - 78.1 projected wins - 90 projected wins (12 more projected wins.) - 9.38 K/9 - 6.52 K/9 (3 less) Application & Example ( Comparison ) STARTING 5 (2018) INSTEAD… • Porcello: 33 GS, 3.77 ERA, 17.1 p win (8.9 K/9) • Clevinger: 32 GS, 3.02 ERA, 19.5 p win;-0.2 (9.3 K/9) • Price: 30 GS, 3.58 ERA, 17.7 p win (9.1) • Boyd: 31 GS, 4.39 ERA, 15.2 p win;1.5 (8.4) • Sale: 27 GS, 2.11 ERA, 22.4 p win (13.5) • Sanchez: 24 GS, 2.83 ERA, 20.1 p win;1.2 (8.9) • E.Rod: 23 GS, 3.82 ERA, 17 p win (10.1) • Hellickson: 22 GS, 3.74 ERA, 17.2 p win;-0.8 (6.4) • Pomeranz: 11 GS, 6.08 ERA, 9.9 p win (8) • Miley: 16 GS, 2.57 ERA, 20.9 p win;-0.4 (5.6) Total : $74,502,500.