Balitmore Orioles Application Research
Johnathan Crone
2022-09-15
All Data from 2022 season was collected on September 18 2022 from FanGraphs.com.
Orioles Team Data from 2002 to 2021
Orioles_Data2023 Free Agent Position Players with corresponding Stats and Salary
Free_Agents_Batters2023 Free Agent Pitchers with corresponding Stats and Salary
Free_Agents_PitchersScatterplot and Correlation Matrix to determine which offensive stats have the greatest correlation with winning percentage
OriolesOffensivematrix = Orioles_Data[,c(2:10)]
plot(OriolesOffensivematrix, pch = 20)
round(cor(OriolesOffensivematrix),3)## WPct BA OBP SLG OPS wRC wRC. wOBA wRAA
## WPct 1.000 0.251 0.129 0.608 0.466 0.355 0.789 0.480 0.774
## BA 0.251 1.000 0.943 0.438 0.800 0.814 0.490 0.848 0.446
## OBP 0.129 0.943 1.000 0.392 0.801 0.853 0.413 0.868 0.374
## SLG 0.608 0.438 0.392 1.000 0.865 0.762 0.833 0.726 0.779
## OPS 0.466 0.800 0.801 0.865 1.000 0.961 0.767 0.946 0.711
## wRC 0.355 0.814 0.853 0.762 0.961 1.000 0.691 0.933 0.632
## wRC. 0.789 0.490 0.413 0.833 0.767 0.691 1.000 0.747 0.936
## wOBA 0.480 0.848 0.868 0.726 0.946 0.933 0.747 1.000 0.704
## wRAA 0.774 0.446 0.374 0.779 0.711 0.632 0.936 0.704 1.000Scatterplot and Correlation Matrix to determine which pitching stats have the greatest correlation with winning percentage
OriolesPitchingMatrix = Orioles_Data[,c(2,11:16)]
plot(OriolesPitchingMatrix, pch = 20)
round(cor(OriolesPitchingMatrix),3)## WPct WHIP ERA ERA. FIP xFIP SIERA
## WPct 1.000 -0.734 -0.866 0.892 -0.836 -0.747 -0.671
## WHIP -0.734 1.000 0.870 -0.841 0.819 0.851 0.881
## ERA -0.866 0.870 1.000 -0.956 0.939 0.916 0.869
## ERA. 0.892 -0.841 -0.956 1.000 -0.881 -0.814 -0.784
## FIP -0.836 0.819 0.939 -0.881 1.000 0.945 0.894
## xFIP -0.747 0.851 0.916 -0.814 0.945 1.000 0.968
## SIERA -0.671 0.881 0.869 -0.784 0.894 0.968 1.000Final Chosen Model which included ERA+, wRAA, and SLG stats as the best indicators to predict the teams winning percentage, however we will be disregarding SLG as it is not statistically significant in predicting winning percentage
summary(Orioles_Total_final)##
## Call:
## lm(formula = WPct ~ ERA. + wRAA + SLG, data = Orioles_Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.034954 -0.021394 0.000265 0.016450 0.036552
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.4750707 0.2764015 -1.719 0.1062
## ERA. 0.0059766 0.0007416 8.059 7.86e-07 ***
## wRAA 0.0005157 0.0002447 2.108 0.0523 .
## SLG 0.8797675 0.6101302 1.442 0.1699
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02444 on 15 degrees of freedom
## Multiple R-squared: 0.9247, Adjusted R-squared: 0.9096
## F-statistic: 61.37 on 3 and 15 DF, p-value: 1.184e-08A list of the 2023 hitting free agents with wRAA stat accompanied by salary and wRAA per salary in millions to give the top 30 players that provide the players with the best wRAA per salary
Best_Free_Agent_Batters = Free_Agents_Batters[with(Free_Agents_Batters, order(-wRAA.Cost..Millions.)),]
Best_Free_Agent_Batters = Best_Free_Agent_Batters[1:40,]
Best_Free_Agent_Batters = Best_Free_Agent_Batters[c(1:4,8,9)]
print(Best_Free_Agent_Batters)## PLAYER POS. AGE Salary Average.wRAA wRAA.Cost..Millions.
## 62 Brandon Drury 3B 30 $900,000 8.95 9.944
## 64 Rob Refsnyder RF 31.4 $800,000 2.40 3.000
## 4 Aaron Judge RF 30.3 $19,000,000 54.25 2.855
## 50 Matt Carpenter 3B 36.8 $2,000,000 5.15 2.575
## 28 Brandon Nimmo RF 29.4 $7,000,000 16.20 2.314
## 47 Albert Pujols DH 42.7 $2,500,000 4.80 1.920
## 17 Josh Bell 1B 30.1 $10,000,000 18.80 1.880
## 39 Tyler Naquin LF 31.3 $4,025,000 7.40 1.839
## 3 Trea Turner SS 29.2 $21,000,000 31.60 1.505
## 29 Joc Pederson LF 30.3 $6,000,000 9.00 1.500
## 18 Willson Contreras C 30.3 $9,625,000 13.45 1.397
## 8 Jose Abreu 1B 35.6 $19,666,668 25.15 1.279
## 51 Jace Peterson 3B 32.3 $1,825,000 2.15 1.178
## 22 Andrew Benintendi LF 28.2 $8,500,000 9.55 1.124
## 27 Yulieski Gurriel 1B 38.2 $8,000,000 7.65 0.956
## 15 Dansby Swanson SS 28.6 $10,000,000 9.40 0.940
## 2 J.D. Martinez DH 35 $19,350,000 18.05 0.933
## 10 Anthony Rizzo 1B 33.1 $16,000,000 14.90 0.931
## 25 Mitch Haniger RF $32 $7,750,000 6.35 0.819
## 7 Brandon Belt 1B 34.3 $18,400,000 13.60 0.739
## 12 A.J. Pollock LF 34.8 $10,000,000 7.15 0.715
## 1 Nolan Arenado 3B 31.4 $35,000,000 23.35 0.667
## 9 Michael Brantley LF 35.3 $16,000,000 10.55 0.659
## 36 Donovan Solano 2B 34.7 $4,500,000 2.55 0.567
## 23 Andrew McCutchen DH 35.9 $8,500,000 4.80 0.565
## 13 Joey Gallo LF $29 $10,275,000 5.50 0.535
## 35 Corey Dickerson LF $33 $5,000,000 2.15 0.430
## 34 Robbie Grossman RF 33 $5,000,000 1.95 0.390
## 44 Mike Zunino C 31.4 $7,000,000 2.35 0.336
## 52 Ben Gamel RF 30.2 $1,800,000 0.55 0.306
## 5 Charlie Blackmon DH 36.2 $18,333,334 4.70 0.256
## 26 David Peralta LF 35.1 $8,000,000 1.85 0.231
## 38 Aledmys Diaz SS 32.1 $4,450,000 0.70 0.157
## 6 Eric Hosmer 1B 32.8 $13,000,000 1.55 0.119
## 11 Jorge Soler DH 30.5 $15,000,000 1.50 0.100
## 19 Adam Duvall RF $34 $9,275,000 0.30 0.032
## 24 Adam Frazier 2B 30.8 $8,000,000 -0.45 -0.056
## 31 Jose Iglesias SS 32.7 $5,000,000 -0.35 -0.070
## 14 Jurickson Profar LF 29.5 $8,333,333 -1.30 -0.156
## 55 Jake Lamb 1B 31.9 $1,500,000 -0.40 -0.267A list of the 2023 pitching free agents with ERA+ stat accompanied by salary and ERA+ per salary in millions to give the top 40 players that provide the best ERA+ per salary
Best_Free_Agent_Pitchers = Free_Agents_Pitchers[with(Free_Agents_Pitchers, order(-ERA..Cost..Millions.)),]
Best_Free_Agent_Pitchers = Best_Free_Agent_Pitchers[1:40,]
Best_Free_Agent_Pitchers = Best_Free_Agent_Pitchers[c(1,2,4,7,8)]
print(Best_Free_Agent_Pitchers)## Player POS. Salary Average.ERA. ERA..Cost..Millions.
## 62 Jason Adam RP $900,000.00 157.5 175.00
## 63 Erasmo Ramirez SP $700,000.00 101.5 145.00
## 60 Jesse Chavez RP $1,250,000.00 164.0 131.20
## 64 Jose Urena SP $700,000.00 77.0 110.00
## 65 Daniel Norris RP $700,000.00 68.5 97.86
## 58 Hunter Strickland RP $1,825,000.00 165.0 90.41
## 61 Jose Alvarez RP $1,500,000.00 126.0 84.00
## 53 Justin Wilson RP $2,300,000.00 143.5 62.39
## 59 Steve Cishek RP $1,750,000.00 109.0 62.29
## 54 Matt Moore SP $2,500,000.00 129.5 51.80
## 56 Jose Quintana SP $2,000,000.00 97.0 48.50
## 55 Chris Martin RP $2,500,000.00 116.0 46.40
## 57 Jeurys Familia RP $2,000,000.00 85.0 42.50
## 51 Rafael Montero RP $2,725,000.00 114.5 42.02
## 52 Miguel Castro RP $2,620,000.00 107.0 40.84
## 47 Carlos Estevez RP $3,025,000.00 121.0 40.00
## 45 David Robertson RP $3,500,000.00 134.5 38.43
## 38 Adam Ottavino RP $4,000,000.00 152.0 38.00
## 40 Chad Green RP $4,000,000.00 135.5 33.88
## 49 Matthew Strahm RP $3,000,000.00 94.5 31.50
## 41 Seth Lugo RP $3,925,000.00 117.5 29.94
## 43 Ross Stripling SP $3,790,000.00 112.5 29.68
## 39 Martin Perez SP $4,000,000.00 118.5 29.63
## 48 Chad Kuhl SP $3,000,000.00 88.5 29.50
## 16 Andrew Chafin RP $7,000,000.00 197.0 28.14
## 35 Craig Stammen RP $4,000,000.00 112.5 28.13
## 42 Trevor Williams RP $3,900,000.00 108.5 27.82
## 29 Brad Hand RP $6,000,000.00 166.5 27.75
## 36 Johnny Cueto SP $4,200,000.00 114.0 27.14
## 44 Archie Bradley RP $3,750,000.00 99.0 26.40
## 34 Michael Fulmer RP $4,950,000.00 127.0 25.66
## 37 Alex Colome RP $4,100,000.00 97.0 23.66
## 50 Vincent Velasquez SP $3,000,000.00 70.5 23.50
## 32 Matt Boyd SP $5,200,000.00 105.5 20.29
## 33 Rich Hill SP $5,000,000.00 98.0 19.60
## 17 Edwin Diaz RP $10,200,000.00 195.5 19.17
## 30 Jameson Taillon SP $5,800,000.00 99.5 17.16
## 27 Michael Wacha SP $7,000,000.00 119.5 17.07
## 15 Taijuan Walker SP $6,000,000.00 102.0 17.00
## 22 Tyler Anderson SP $8,000,000.00 124.0 15.50After evaluating the presented data and comparing to the Orioles current roster and future prospects, I have come to the conclusion that Matt Boyd from the San Francisco Giants would be the best sign for the Orioles in the 2023 free agent class. As the Orioles weakest component is their starting pitching staff, it would be best to increase the value of the starting pitching staff. With this, I would expect a contract around $5.5 million AAV for Matt Boyd