Welcome to my 2022 Projections for Hitters 5x5

Projections using Hypertuned model through XGboost

All data is from FanGraphs. I have no affiliation with FanGraphs, but please consider contributing to their website if you found this project informative.

1 Project Scope

1.1 Objective

This project is designed to showcase how Using a Percentile Based Worth System values Fantasy Baseball Players through a Inning Pitched (IP) weighted projection

The Categories used for prediction valuation are year-end rankings for the following metrics: - HRs - Runs - RBIs - Batting Average - Stolen Bases

---

2 Processing the Data

2.1 Getting Data Into R

2.1.1 Load Libraries

First we need to load the packages that R needs to run the analysis

library(sqldf) #SQL in R
library(skimr) #Summaries and useful for removing low % data
library(ggplot2) #Plotting Functions
library(plyr) #slightly deprecated data cleaning
library(dplyr) #slightly updated data cleaning
library(tidyverse) #tidyverse data cleaning universe
library(caret) #wrapper for creating, tuning and validating models
library(xgboost) #package for creating regression tree model
library(vtreat) # useful package for treating data before modeling 
library(Matrix)
library(Boruta)
library(mgcv)
library(moments) #for measuring skewness
library(data.table) #alternative to dplyr we use to create lags
library(pdp) #partial dependence graphs
library(vip) #variable importance 
library(grid) #put multiple plots on one grid
library(gridExtra) #additional grid functionality
library(janitor) #one function used to clean transposed data set
library(ggpubr) #for qq plot
library(tableHTML)
library(kableExtra)

The # comments generally explain what additional functionality each library adds to R

2.1.2 Load in Data

All data is downloaded from Fan Graphs. From this location. The data is also available on my Github here. There are player level and team data sets

#data read-in
Batter_data <- read_csv("FanGraphs Leaderboard_Hitting50PA.csv")

#Team datasets
FDG_Team = read_csv("FanGraphs Leaderboard_Team.csv")

#Create a prefix for all team stats that starts with T_
FDG_Team2 <- FDG_Team %>% 
  rename_with( ~ paste0("T_", .x))

2.1.3 Checking Team Data

str give information about an object, while skim provides a customizable summary

#Output not shown for space
#str(FDG_Team2)

skim(FDG_Team2) %>%  
  tibble::as_tibble()

---

2.2 Understanding the Dataset

2.2.1 Exploring the dataset

skim let’s us see how the data was imported into R. Documentation can be found here

#Full Dataset dimensions

skimr::skim(Batter_data) %>% 
  tibble::as_tibble() %>% 
  select(skim_type,skim_variable,complete_rate) %>% 
  filter(complete_rate >0.30) #288 Variables

#skim_type - character or numeric
#skim_variable - name of variable
#complete_rate - % of data that is not missing
#filter - only keep variables that have 30% of data populated

Additionally let’s look at how variables vary by year to see if there are any discrepancies there

#It looks like one year, there were fewer games played, and there is a clear drop off in home runs
Batter_data_dist =
Batter_data %>% 
 group_by(Season) %>% 
  summarize (Games_played = max(G),
             Avg_HR= mean(HR)
             )
Batter_data_dist

ggplot(Batter_data_dist, aes(Season, Avg_HR)) +
  geom_col()+
  ggtitle("Average Home Runs by Year")+
  theme(plot.title = element_text(hjust = 0.5,size = 22,color ="steel blue"))

---

2.3 Cleaning and Creating Initial Dataset for Model

What are some issues with the data?

1. Many of Variables, such as K%, are being read in as characters

   • Only Team and Player Name should be characters

2. There is spotty data coverage in some of the variables (~Variables have less than 30% Coverage)

3. 2020 Data only includes 60 games worth of data

   • This was a season shortened due to Covid-19

4. Team Data needs to be appended to Batter Data by Team Name

---

2.3.1 Cleanly Changing all Variables that are characters to numeric.

There are several ways to do this, we will identify the variables we want to change that are mis-identified. parse_number can be used to pull numbers from these variables. Additional ways to tackle this can be found here

#Select Column names that are characters but not Team or Name, These should be percentages
Batter_data_chars_to_convert <- Batter_data %>% 
  select_if(is.character)%>% select(-Team,-Name) %>% 
  mutate_all (function(x) as.numeric(readr::parse_number(x))/100)
#Note : There are additional ways to do this, this is just one solution


#We can exclude the variables we converted and reintroduce them
Batter_data_num <- Batter_data %>% select(-colnames(Batter_data_chars_to_convert))

Batter_data2 = cbind(Batter_data_num,Batter_data_chars_to_convert) %>% 
  select (colnames(Batter_data)) %>%  #preserve original order 
  dplyr::rename(flyball_perc = `FB%...46`,fastball_perc = `FB%...73`) #rename two ambiguous columns
  
skim(Batter_data2) %>% 
  as_tibble() %>% 
  group_by(skim_type) %>% 
  count()

#Logical variables are R's best guess, in our case they are all NA's and will be removed

The same can be done for the Team Data that is loaded

#Select Column names that are characters but not Team or Name, These should be percentages
FDG_Team2_chars_to_convert <- FDG_Team2 %>% 
  select_if(is.character)%>% select(-T_Team) %>% 
  mutate_all (function(x) as.numeric(readr::parse_number(x))/100)
#Keep in mind, parse number may make actual characters into numerical variables so carefully check your data before using

#We can exclude the variables we converted and reintroduce them
FDG_Team2_num <- FDG_Team2 %>% select(-colnames(FDG_Team2_chars_to_convert))

FDG_Team3 = cbind(FDG_Team2_num,FDG_Team2_chars_to_convert) %>% 
  select (colnames(FDG_Team2)) %>%  #preserve original order
dplyr::rename(T_flyball_perc = `T_FB%...45`,T_fastball_perc = `T_FB%...72`) 

skim(FDG_Team3) %>% 
  as_tibble() %>% 
  group_by(skim_type) %>% 
  count()

---

2.3.2 Filtering Data with Low Coverage

I choose 30% coverage of data necessary but this can be adjusted up or down. This will also get rid of columns that are all NA.

# Keep variables with enough values (Need 30% data coverage rate here)
Player_cols_to_keep =
skim(Batter_data2) %>% 
  dplyr::select(skim_type, skim_variable, complete_rate) %>% 
  filter (complete_rate > 0.30)

#Transpose Rows to get column names as skim melts the data
Player_cols_to_keep_transpose = t(Player_cols_to_keep) 

#extract the colnames we would like to keep
Player_cols_to_keep = colnames(janitor::row_to_names(Player_cols_to_keep_transpose,row_number = 2))

#Only keep the columns designated to have over 30% of their data populated or greater
Batter_data3 = Batter_data2 %>% 
  select(one_of(Player_cols_to_keep)) 

Repeat the process for Team Variables

Team_cols_to_keep =
skim(FDG_Team3) %>% 
  dplyr::select(skim_type, skim_variable, complete_rate) %>% 
  filter (complete_rate > 0.30)


#Transpose Rows to get column names as skim melts the data
Team_cols_to_keep_transpose = t(Team_cols_to_keep) 

#extract the colnames we would like to keep
Team_cols_to_keep = colnames(janitor::row_to_names(Team_cols_to_keep_transpose,row_number = 2))

#Only keep the columns designated to have over 30% of their data populated or greater
FDG_Team4 = FDG_Team3 %>% 
  select(one_of(Team_cols_to_keep)) 

---

2.3.3 Creating Variables Normalized by Year

Some Variables will need to be normalized by Innings_Pitched (IP) if they aren’t a percentage already. Remaining Variables are percentages or indices so will not need to be transformed. The full data dictionary for these variables can be found on FanGraph’s website here. for pitching variables and here. for hitting variables.

Batter_data4 = Batter_data3 %>% 
  mutate( #create new variables based on existing variables
    H_PA = H/PA,
    x1B_PA = `1B`/PA, #note: R can't have variables start with a number
    x2b_PA = `2B`/PA,
    x3b_PA = `3B`/PA,
    HR_PA = HR/PA,
    R_PA = R/PA,
    RBI_PA = RBI/PA,
    BB_PA = BB/PA,
    IBB_PA = IBB/PA,
    SO_PA=SO/PA,
    HBP_PA=HBP/PA,
    SF_PA=SF/PA,
    SH_PA=SH/PA,
    GDP_PA= GDP/PA,#ground into double play
    SB_PA=SB/PA,
    CS_PA=CS/PA,
    GB_PA = GB/PA,   #Groundballs
    FB_PA =  FB/PA,  #FlyBalls
    LD_PA = LD/PA,   #LineDrives
    IFFB_PA = IFFB/PA,  #Infield Fly balls
    Pitches_PA= Pitches/PA,
    Balls_PA= Balls/PA,
    Strikes_PA= Strikes/PA,
    IFH_PA= IFH/PA,
    BU_PA= BU/PA,
    BUH_PA= BUH/PA,
    PH_PA= PH/PA,
    Barrels_PA= Barrels/PA,
    HardHits_PA= HardHit/PA
  ) %>% select(-(H:CS),-(GB:BUH),-PH,-Barrels,-HardHit,-Events) #Drop the old variables

#skim(Batter_data4) %>% as_tibble()

Repeat the process for Team Variables

FDG_Team5 = FDG_Team4 %>% 
  mutate( #create new variables based on existing variables
    T_H_T_PA = T_H/T_PA,
    T_x1B_T_PA = T_1B/T_PA, #note: R can't have variables start with a number
    T_x2b_T_PA = T_2B/T_PA,
    T_x3b_T_PA = T_3B/T_PA,
    T_HR_T_PA = T_HR/T_PA,
    T_R_T_PA = T_R/T_PA,
    T_RBI_T_PA = T_RBI/T_PA,
    T_BB_T_PA = T_BB/T_PA,
    T_IBB_T_PA = T_IBB/T_PA,
    T_SO_T_PA=T_SO/T_PA,
    T_HBP_T_PA=T_HBP/T_PA,
    T_SF_T_PA=T_SF/T_PA,
    T_SH_T_PA=T_SH/T_PA,
    T_GDP_T_PA= T_GDP/T_PA,#ground into double play
    T_SB_T_PA=T_SB/T_PA,
    T_CS_T_PA=T_CS/T_PA,
    T_GB_T_PA = T_GB/T_PA,   #Groundballs
    T_FB_T_PA =  T_FB/T_PA,  #FlyBalls
    T_LD_T_PA = T_LD/T_PA,   #LineDrives
    T_IFFB_T_PA = T_IFFB/T_PA,  #Infield Fly balls
    T_Pitches_T_PA= T_Pitches/T_PA,
    T_Balls_T_PA= T_Balls/T_PA,
    T_Strikes_T_PA= T_Strikes/T_PA,
    T_IFH_T_PA= T_IFH/T_PA,
    T_BU_T_PA= T_BU/T_PA,
    T_BUH_T_PA= T_BUH/T_PA,
    T_PH_T_PA= T_PH/T_PA,
    T_Barrels_T_PA= T_Barrels/T_PA,
    T_HardHits_T_PA= T_HardHit/T_PA
  ) %>% select(-(T_H:T_CS),-(T_GB:T_BUH),-T_PH,-T_Barrels,-T_HardHit,-T_Events) #Drop the old variables


#skim(FDG_Team5) %>% as_tibble()

---

2.3.4 Creating Lagged Variables

There are several ways to lag a dataset BY GROUP.
* Dplyr way is here..
* The data.table (the method used below) is here.

#Note we will only be lagging the player level data, as the previous year's team performance shouldn't impact current performance


#Order the dataset by lag columns
Batter_data5 =  arrange(Batter_data4, playerid,Season) #playerid is the Fangraph id assigned to each player

# Convert dataframe to data.table format
DT_batter = data.table(Batter_data5)

#designate columns to lag - which is all of them
cols1 = colnames(Batter_data5)
anscols = paste("lag", cols1, sep="_")
DT_batter[, (anscols) := data.table::shift(.SD, 1, NA, "lag"),by ='playerid', .SDcols=cols1] #Create 1 period lags by year

Batter_data6 = as.data.frame(DT_batter) %>% select(-lag_playerid, -lag_Team, -lag_Season, -lag_Age,-lag_Name)

ncol(Batter_data5) #287 - no lags

[1] 259

ncol(Batter_data6) #574 - lagged data ~ (287 * 2)-5

[1] 513

---

2.3.5 Merging Team and Player Data

We can use either the merge function or the SQL functionality provided by the sqldf package to join the lagged player level data to the Team level data

df_batting_init = sqldf(
  "
  select a.*, b.*
  from Batter_data6 a
  left join FDG_Team5 b
  on a.Team = b.T_Team and a.Season = b.T_Season
  
  "
)  %>% select(-T_Team,-T_Season,T_Age,T_G,T_AB)# Unncessary Team Variables


nrow(df_batting_init) - nrow(Batter_data6) #check if any rows are duplicated

[1] 0

---

3 Creating Rankings for Players Based On Percentiles

We can use Percentile based ranking to get rankings for players from the 2021 season.

3.1 Worth of each stat

3.1.1 Calculating past performance

Each player goes from a 0% to 100% on each percentile stat that is used for creating a scoring opportunity. Data is not yet normalized by PA as certain stats such as HRs and SBs will be worth more when we do.

#Categories I include are:
#Runs (R), Home Runs (HR), Runs Batted In (RBI), Stolen Bases (SB), Batting Average (AVG)
df_batting_init2 =  df_batting_init %>%
#  arrange(player_id,year) %>% 
  group_by(Season) %>% 
  mutate(
    Runs_share = order(order(rank(R_PA,ties.method = 'average'),decreasing = FALSE))/n(),
     HR_share = order(order(rank(HR_PA,ties.method = 'average'),decreasing = FALSE))/n(),
     RBI_share = order(order(rank(RBI_PA,ties.method = 'average'),decreasing = FALSE))/n(),
     SB_share = order(order(rank(SB_PA,ties.method = 'average'),decreasing = FALSE))/n(),
     AVG_share = order(order(rank(AVG,ties.method = 'average'),decreasing = FALSE))/n(),
    OPS_share = 0,
    Worth = Runs_share+HR_share+RBI_share+SB_share+AVG_share+OPS_share
    ) %>% 
  ungroup() 

Chart of the Distribution of initial percentiles
As the chart below shows, the data is roughly normal.

skewness((df_batting_init2$Worth))

[1] -0.25

ggplot2::qplot(df_batting_init2$Worth, main="Total Dataset") + geom_histogram(colour="black", fill="lime green")

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

min(df_batting_init2$Worth)

[1] 0.029

max(df_batting_init2$Worth)

[1] 4.8

ggpubr::ggqqplot(df_batting_init2$Worth)

shapiro.test(df_batting_init2$Worth)

    Shapiro-Wilk normality test

data:  df_batting_init2$Worth
W = 1, p-value <0.0000000000000002

---

3.2 2021 Player Rankings - Per PA performance

3.2.1 2021 Player Rankings - Top Worth Player

There are per PA rankings. Players like Byron Buxton which had a great per PA score but can’t stay healthy for a season will be adjusted down.

options(digits=2)

df_batting_init2021 =
df_batting_init2 %>% 
  group_by(Name) %>% 
  filter(Season == 2021) %>% 
  arrange(desc(Worth)) %>% 
  select(Name,Runs_share,HR_share,RBI_share, SB_share,OPS_share,AVG_share,Worth)


df_batting_init2021 %>%
  filter (Worth>3.9) %>% 
  kbl() %>% 
 kable_material(c("striped", "hover","condensed","responsive"),full_width = F,fixed_thead = T)

Name	Runs_share	HR_share	RBI_share	SB_share	OPS_share	AVG_share	Worth
Fernando Tatis Jr.	0.99	1.00	0.98	0.96	0	0.89	4.8
Ronald Acuna Jr.	1.00	0.98	0.82	0.96	0	0.90	4.7
Byron Buxton	1.00	0.99	0.70	0.92	0	0.97	4.6
Tyler O'Neill	0.96	0.97	0.86	0.88	0	0.91	4.6
Jose Ramirez	0.98	0.93	0.94	0.94	0	0.78	4.6
Teoscar Hernandez	0.92	0.90	1.00	0.80	0	0.94	4.6
Kyle Tucker	0.87	0.89	0.94	0.86	0	0.94	4.5
Bryce Harper	0.96	0.95	0.80	0.82	0	0.97	4.5
Bo Bichette	0.99	0.76	0.85	0.92	0	0.95	4.5
Shohei Ohtani	0.95	0.99	0.90	0.94	0	0.69	4.5
Javier Baez	0.87	0.93	0.92	0.91	0	0.77	4.4
Frank Schwindel	0.97	0.90	0.95	0.55	0	0.99	4.4
Vladimir Guerrero Jr.	0.99	0.98	0.92	0.49	0	0.98	4.4
Trea Turner	0.96	0.78	0.63	0.97	0	1.00	4.3
Brandon Crawford	0.85	0.78	0.94	0.80	0	0.95	4.3
Nick Castellanos	0.95	0.94	0.97	0.47	0	0.97	4.3
Marcus Semien	0.94	0.96	0.80	0.81	0	0.77	4.3
Juan Soto	0.96	0.80	0.83	0.70	0	0.99	4.3
Luis Robert	0.84	0.79	0.83	0.81	0	1.00	4.3
Brandon Belt	0.97	0.99	0.89	0.56	0	0.85	4.3
Paul Goldschmidt	0.90	0.83	0.84	0.76	0	0.94	4.3
Rafael Devers	0.91	0.93	0.97	0.55	0	0.88	4.2
Manny Machado	0.85	0.79	0.95	0.78	0	0.88	4.2
Jorge Polanco	0.90	0.88	0.88	0.76	0	0.80	4.2
A.J. Pollock	0.64	0.87	0.94	0.82	0	0.95	4.2
George Springer	0.98	0.97	0.84	0.66	0	0.76	4.2
Mike Trout	0.93	0.91	0.67	0.69	0	1.00	4.2
Aaron Judge	0.83	0.96	0.88	0.60	0	0.92	4.2
Ozzie Albies	0.90	0.78	0.88	0.89	0	0.71	4.2
Matt Olson	0.89	0.94	0.94	0.50	0	0.83	4.1
Adam Engel	0.89	0.88	0.72	0.97	0	0.64	4.1
Thairo Estrada	0.85	0.89	0.96	0.55	0	0.85	4.1
Brandon Lowe	0.93	0.97	0.93	0.66	0	0.59	4.1
Avisail Garcia	0.73	0.92	0.96	0.72	0	0.74	4.1
Freddie Freeman	0.98	0.80	0.64	0.66	0	0.95	4.0
Yordan Alvarez	0.91	0.91	0.98	0.34	0	0.87	4.0
Tim Anderson	0.97	0.57	0.54	0.90	0	0.98	4.0
Jesse Winker	0.93	0.87	0.84	0.36	0	0.96	4.0
Kyle Schwarber	0.95	0.98	0.87	0.36	0	0.78	3.9
Chas McCormick	0.88	0.79	0.90	0.68	0	0.69	3.9
Jake Meyers	0.77	0.68	0.98	0.77	0	0.72	3.9

---

4 Creating Model File

4.1 Additional Data Prep

4.1.1 Remove Variables which are based off current hitting numbers

Not all variables can be used for predictive modeling. Variables that go into the percentile ranking or are non-normalized metrics created after the fact (such as WAR - Wins above Replacement or RE24) should be removed. However, metrics that are normalized by a per pitch basis (such as HR/FB%+) can remain as we expect batters to have similar performance in these metrics one year out.

#Creating a new dataset to keep original intact
df_batting_init3 = df_batting_init2

Lagged Percentile (_share) Variables can be used for predictive modeling. However since these variables were created for the Worth metric they must also be removed for modeling purposes.

#Order the dataset by lag columns
df_batting_init4 =  arrange(df_batting_init3, playerid,Season) #playerid is the Fangraph id assigned to each player

# Convert dataframe to data.table format
DT_batter2 = data.table(df_batting_init4)

#designate columns to lag - which is all of them
cols1 = (c('Runs_share','HR_share','RBI_share', 'SB_share','OPS_share','AVG_share','Worth'))
anscols = paste("lag", cols1, sep="_")
DT_batter2[, (anscols) := data.table::shift(.SD, 1, NA, "lag"),by ='playerid', .SDcols=cols1] #Create 1 period lags by year

#names(DT_batter2)

df_batting_final = as.data.frame(DT_batter2) %>% 
  select(-c(Runs_share,HR_share,RBI_share, SB_share,OPS_share,AVG_share))%>% 
  select(-(G:AVG),-(OBP:BABIP),-(wOBA:Dol),-(`wRC+`:REW),-(`WPA/LI`),-(wFB:wSF),-BsR,-(Def:wGDP),-(`wCH (pi)`:`wCH/C (pi)`),-(`AVG+`),-(`OBP+`:`BABIP+`),-(H_PA:PH_PA)) %>% select (-Name)

4.1.2 Creating Training/Test Split

We split the data into Training Data (which is used to create the model) and test data (which is used to validate the model)

set.seed(15674)  # For reproducibility
# Create index for testing and training data
inTrain <- createDataPartition(y = df_batting_final$Worth, p = 0.80, list = FALSE)
# subset pitching data for training
tr_2021 <- df_batting_final[inTrain,]
# subset the rest to test and validate trained model
te_2021 <- df_batting_final[-inTrain,]

nrow(tr_2021)/nrow(df_batting_final) #check if split is 0.8

[1] 0.8

4.1.3 Treat Missing Data by Imputing Mean Value

Vtreat Package in R is excellent for treating data before using for modeling. Additional documentation can be found here.
Note: The treatment plan also fixes variables names likeHR/FB%+ (which R doesn’t always handle the best) to HR_slash_FB_percent_plus_

treat_plan_2021 <- vtreat::designTreatmentsZ(
  dframe = tr_2021, # training data
  varlist = colnames(tr_2021) %>% .[. != "hitting_score1"], # input variables = all training data columns, except random
  codeRestriction = c("clean", "isBAD", "lev"), # derived variables types (drop cat_P)
  verbose = FALSE) # suppress messages

#clean stands for cleaned numerical variable, isBAD indicates that a value replacement has occurred (which indicates a missing value in this case), and lev is a binary indicator whether a particular value of that categorical variable was present.  

#### Checking Scoreframe

score_frame <- treat_plan_2021$scoreFrame %>% 
  select(varName, origName, code)

head(score_frame)

tr_treated_2021 <- vtreat::prepare(treat_plan_2021, tr_2021)
te_treated_2021 <- vtreat::prepare(treat_plan_2021, te_2021)


Total_dataset1_untreat = as.data.frame(DT_batter2) %>% select(-Name)

treat_plan_2021 <- vtreat::designTreatmentsZ(
  dframe = Total_dataset1_untreat, # training data
  varlist = colnames(Total_dataset1_untreat) %>% .[. != "hitting_score1"], # input variables = all training data columns, except random
  codeRestriction = c("clean", "isBAD", "lev"), # derived variables types (drop cat_P)
  verbose = FALSE) # suppress messages


total_treated_2021_hitting <- vtreat::prepare(treat_plan_2021, Total_dataset1_untreat)



#tr_treated = tr
#te_treated = te

dim(tr_treated_2021) #note there are dummies for each player and team

[1] 3424 1396

---

4.1.4 Check Distribution of Training Population

The population used for Training should be indicative of Total Population

ggplot2::qplot(tr_treated_2021$Worth, main="Training Set") + geom_histogram(colour="black", fill="limegreen") + theme_bw()

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

skewness(tr_treated_2021$Worth) #The skewness is the same as the overall

[1] -0.24

5 Running XGboost Model

To keep things simple with modeling, we’ll turn the training data into simple input variables for caret::train, dropping the response variable and converting the data frame to a matrix. Documentation for this approach to XGboost can be found here.

5.1 Tuning the Model

5.1.1 Initial Non-Tuned Model

Break the data set into x and y inputs with x being a matrix

input_x <- as.matrix(((tr_treated_2021))%>%
   select(-Worth) %>%                      
   select(!ends_with ("_isBAD")))

input_y <- tr_treated_2021$Worth

XGBoost with Default Hyperparameters
The Variable Importance (caret::varImp(xgb_base_2021, scale = F )) from the caret package shows the contribution of each variable to the initial model. As you can see SLG_plus_ (SLG+) takes up much of the importance as it is derived from SLG (one of the key contributors to Worth). These types of variables will be removed during variable selection in the next step.
XGBoost documentation can be found for more general models here.

#Defaults for xgboost model
grid_default <- expand.grid(
  nrounds = 100,
  max_depth = 6,
  eta = 0.3,
  gamma = 0,
  colsample_bytree = 1,
  min_child_weight = 1,
  subsample = 1
)

#This is a blank train_control set, this will be updated after
train_control <- caret::trainControl(
  method = "none",
  verboseIter = FALSE, # no training log
  allowParallel = TRUE # FALSE for reproducible results 
)

xgb_base_2021 <- caret::train(
  x = input_x,
  y = input_y,
  trControl = train_control,
  tuneGrid = grid_default,
  method = "xgbTree",
  verbose = TRUE
)

caret::varImp(xgb_base_2021, scale = F  )

xgbTree variable importance

  only 20 most important variables shown (out of 764)

5.2 Further Variable Selection

5.2.1 Remove redundant and highly correlated variables

Selection Removal Step 1: Check for high correlations
Normally, this step is done early, but those steps were reserved for preparing the data

dep_cor1 <- t(as.data.frame(cor(tr_treated_2021[ , colnames(tr_treated_2021) != "Worth"],
                tr_treated_2021$Worth)))
dep_cor1 <-
as.data.frame(t(as.data.frame(dep_cor1)%>% 
  select(!starts_with("lag")) %>% #remove lag variables
  select(!contains("_isBAD")))) 

dep_cor1 <- tibble::rownames_to_column(dep_cor1,"VARIABLES")%>% #remove indicators for missing data
  filter(V1 > 0.70|V1 < -0.5)

dep_cor1

dep_cor2 <- colnames(row_to_names(t(dep_cor1),row_number = 1))

Let’s Remove variables with high correlation to worth metric (such as wFB/C)

input_x <- as.matrix(((tr_treated_2021))%>%
   select(-Worth) %>% #Remove dependent variable
     select (-all_of(dep_cor2) ) %>%      
select(!ends_with ("_isBAD"))) #indicator variable for missing data

input_y <- tr_treated_2021$Worth

Run the model on the new dataset to make sure the variable importances look fine

#Note Training parameters were set in initial model set up
xgb_base_2021 <- caret::train(
  x = input_x,
  y = input_y,
  trControl = train_control,
  tuneGrid = grid_default,
  method = "xgbTree",
  verbose = TRUE
)

caret::varImp(xgb_base_2021, scale = F  )

xgbTree variable importance

  only 20 most important variables shown (out of 763)

5.3 Model with new data

5.3.1 Tuning All Hyperparameters

A tune grid allows us to test a large amount of hyper-parameters and find the model with the lowest RMSE for predictions.
However, The more values you want to test and the greater the amount of Cross-Fold Validations (method = "cv"), the greater the computational time it will take. More information on the specific parameters can be found here.

# maximum number of trees
nrounds <- 1000

# note to start nrounds from 200, as smaller learning rates result in errors so
# big with lower starting points that they'll mess the scales
tune_grid <- expand.grid(
  nrounds = seq(from = 100, to = nrounds, by = 50),
  eta = c(0.01, 0.025, 0.05, 0.1),
  max_depth = c(2, 4, 6, 8),
  gamma = 0,
  colsample_bytree = 1,
  min_child_weight = 1,
  subsample = 1
)

tune_control <- caret::trainControl(
  method = "cv", # cross-validation
  number = 5, # with n folds 
  ## Note this was # out in the original code
  #index = createFolds(tr_treated$Id_clean), # fix the folds
  verboseIter = FALSE, # no training log
  allowParallel = TRUE # FALSE for reproducible results 
)

Running the initial tuning model

#Note I will be timing these runs to give an estimate on how long this model takes to run
start_time <- Sys.time()

xgb_tune_2021 <- caret::train(
  x = input_x,
  y = input_y,
  trControl = tune_control,
  tuneGrid = tune_grid,
  method = "xgbTree",
  verbose = FALSE
  ,verbosity = 0
)

end_time <- Sys.time()

end_time - start_time

Time difference of 12 mins

Tuning Plot and Variable Importance

varImp(xgb_tune_2021, scale = F  ) 

xgbTree variable importance

  only 20 most important variables shown (out of 763)

# helper function for the plots
tuneplot <- function(x, probs = .90) {
  ggplot(x) +
    coord_cartesian(ylim = c(quantile(x$results$RMSE, probs = probs), min(x$results$RMSE))) +
    theme_bw()
}

tuneplot(xgb_tune_2021)

5.3.2 Fine Tuning Model

5.3.2.1 Second Tuning: Maximum Depth and Minimum Child Weight

After fixing the learning rate to 0.1 and we’ll also set maximum depth to 3 +-1 (or +2 if max_depth == 2) to experiment a bit around the suggested best tune in previous step. Then, well fix maximum depth and minimum child weigh

tune_grid2 <- expand.grid(
  nrounds = seq(from = 50, to = nrounds, by = 50),
  eta = xgb_tune_2021$bestTune$eta,
  max_depth = ifelse(xgb_tune_2021$bestTune$max_depth == 2,
    c(xgb_tune_2021$bestTune$max_depth:4),
    xgb_tune_2021$bestTune$max_depth - 1:xgb_tune_2021$bestTune$max_depth + 1),
  gamma = 0,
  colsample_bytree = 1,
  min_child_weight = c(1, 2, 3),
  subsample = 1
)

xgb_tune2_2021 <- caret::train(
  x = input_x,
  y = input_y,
  trControl = tune_control,
  tuneGrid = tune_grid2,
  method = "xgbTree",
  verbose = TRUE
)

tuneplot(xgb_tune2_2021)

xgb_tune2_2021$bestTune

varImp(xgb_tune2_2021, scale = F  ) 

xgbTree variable importance

  only 20 most important variables shown (out of 763)

5.3.2.2 Third Tuning: Column and Row Sampling

tune_grid3 <- expand.grid(
  nrounds = seq(from = 50, to = nrounds, by = 50),
  eta = xgb_tune_2021$bestTune$eta,
  max_depth = xgb_tune2_2021$bestTune$max_depth,
  gamma = 0,
  colsample_bytree = c(0.4, 0.6, 0.8, 1.0),
  min_child_weight = xgb_tune2_2021$bestTune$min_child_weight,
  subsample = c(0.5, 0.75, 1.0)
)

xgb_tune3_2021 <- caret::train(
  x = input_x,
  y = input_y,
  trControl = tune_control,
  tuneGrid = tune_grid3,
  method = "xgbTree",
  verbose = TRUE
)

tuneplot(xgb_tune3_2021, probs = .95)

xgb_tune3_2021$bestTune

varImp(xgb_tune3_2021, scale = F  ) 

xgbTree variable importance

  only 20 most important variables shown (out of 763)

5.3.2.3 Fourth Tuning: Gamma

Next, we again pick the best values from previous step, and now will see whether changing the gamma has any effect on the model fit:

tune_grid4 <- expand.grid(
  nrounds = seq(from = 50, to = nrounds, by = 50),
  eta = xgb_tune_2021$bestTune$eta,
  max_depth = xgb_tune2_2021$bestTune$max_depth,
  gamma = c(0, 0.05,0.1, 0.2,0.4, 0.5, 0.7, 0.9, 1.0),
  colsample_bytree = xgb_tune3_2021$bestTune$colsample_bytree,
  min_child_weight = xgb_tune2_2021$bestTune$min_child_weight,
  subsample = xgb_tune3_2021$bestTune$subsample
)

xgb_tune4_2021 <- caret::train(
  x = input_x,
  y = input_y,
  trControl = tune_control,
  tuneGrid = tune_grid4,
  method = "xgbTree",
  verbose = TRUE
)

tuneplot(xgb_tune4_2021)

Warning: The shape palette can deal with a maximum of 6 discrete values because more than 6 becomes difficult
to discriminate; you have 9. Consider specifying shapes manually if you must have them.
Warning: Removed 60 rows containing missing values (geom_point).

xgb_tune4_2021$bestTune

varImp(xgb_tune4_2021, scale = F  ) 

xgbTree variable importance

  only 20 most important variables shown (out of 763)

5.3.2.4 Fifth Tuning: Reducing the Learning Rate

Now, we have tuned the hyperparameters and can start reducing the learning rate to get to the final model:

start_time <- Sys.time()

tune_grid5 <- expand.grid(
  nrounds = seq(from = 100, to = 10000, by = 75),
   eta = c(0.01, 0.015, 0.025,0.035, 0.05,0.75, 0.1),
  max_depth = xgb_tune2_2021$bestTune$max_depth,
  gamma = xgb_tune4_2021$bestTune$gamma,
  colsample_bytree = xgb_tune3_2021$bestTune$colsample_bytree,
  min_child_weight = xgb_tune2_2021$bestTune$min_child_weight,
  subsample = xgb_tune3_2021$bestTune$subsample
)



xgb_tune5_2021 <- caret::train(
  x = input_x,
  y = input_y,
  trControl = tune_control,
  tuneGrid = tune_grid5,
  method = "xgbTree",
  verbose = TRUE
)

#tuneplot(xgb_tune5_2021)

end_time <- Sys.time()

end_time - start_time

Time difference of 19 mins

xgb_tune5_2021$bestTune

varImp(xgb_tune5_2021, scale = F  ) 

xgbTree variable importance

  only 20 most important variables shown (out of 763)

5.3.2.5 Fitting Final Model

(final_grid_2021 <- expand.grid(
  nrounds = xgb_tune5_2021$bestTune$nrounds,
  eta = xgb_tune5_2021$bestTune$eta,
  max_depth = xgb_tune5_2021$bestTune$max_depth,
  gamma = xgb_tune5_2021$bestTune$gamma,
  colsample_bytree = xgb_tune5_2021$bestTune$colsample_bytree,
  min_child_weight = xgb_tune5_2021$bestTune$min_child_weight,
  subsample = xgb_tune5_2021$bestTune$subsample
))

(xgb_model_2021 <- caret::train(
  x = input_x,
  y = input_y,
  trControl = train_control,
  tuneGrid = final_grid_2021,
  method = "xgbTree",
  verbose = TRUE
))

eXtreme Gradient Boosting 

3424 samples
 763 predictor

No pre-processing
Resampling: None 

varImp(xgb_model_2021, scale = F  ) 

xgbTree variable importance

  only 20 most important variables shown (out of 763)

5.3.3 Model Performance

5.3.3.1 Checking Model on Test Split Data

y_pred_test <- predict(xgb_model_2021, data.matrix(te_treated_2021))

test_stats= cbind((te_treated_2021$Worth),y_pred_test)

test_statsR2 = cor(test_stats[,1],test_stats[,2])^2

print(test_statsR2)

[1] 0.89

y_pred_train <- predict(xgb_model_2021, data.matrix(tr_treated_2021))

train_stats = cbind((tr_treated_2021$Worth),y_pred_train)

train_statsR2 = cor(train_stats[,1],train_stats[,2])^2

print(train_statsR2)

[1] 0.96

#test dataset
x <- select(te_treated_2021, -Worth)
y <- (te_treated_2021$Worth)

(xgb_model_rmse <- ModelMetrics::rmse(y, predict(xgb_model_2021, newdata = x)))

[1] 0.34

holdout_x <- select(tr_treated_2021, -Worth)
holdout_y <- tr_treated_2021$Worth

(xgb_model_rmse <- ModelMetrics::rmse(holdout_y, predict(xgb_model_2021, newdata = holdout_x)))

[1] 0.21

5.3.3.2 Graphical Representation of Model

ggplot2::ggplot() +
  aes(x = test_stats[,1], y = test_stats[,2]) +
  geom_jitter() +
  xlab("Predicted Values") +
  ylab("Actual Values") +
  ggtitle("Results of Hitting Model on Test Data")+
  theme(plot.title = element_text(hjust = 0.5,size = 22,color ="steel blue"))+
  geom_smooth(method = "lm")

`geom_smooth()` using formula 'y ~ x'

6 Creating 2022 Projections from Model

6.1 Re-fit model for Important Variables

Now that we have an acceptable model, we can use it to create projections for how well we think players should do in 2022 based on their hitting statistics in 2021. First let’s reduce

1. Only keep variables with high enough importance in model

vip(xgb_model_2021, num_features = 30)  # 10 is the default, 30 gives a visual on the top 30 most important features of the model

unscalevi = vi(xgb_model_2021, method="model") #shows the numbers behind the plot

unscalevi$Importance_perc = with(unscalevi,Importance/sum(Importance)) #adds percentages 

unscalevi # importance by variables

variables_to_keep_2021 = subset(unscalevi, Importance_perc > 0.0010) %>% select(Variable) #Keep Variables that explain at least a small amount [0.1%] of the model. This is a low threshold for inclusion ,but you can adjust this

variables_to_keep_2021b = t(variables_to_keep_2021)

variables_to_keep_2022 = colnames(row_to_names(variables_to_keep_2021b,row_number = 1))

tr_treated_2022 = tr_treated_2021 %>%  select(Worth,one_of(variables_to_keep_2022),starts_with("Team_lev_x_")) #keep modeled important variables along with team indicator variables

te_treated_2022 = te_treated_2021 %>%  select(Worth,one_of(variables_to_keep_2022),starts_with("Team_lev_x_"))

input_x_2022 = as.matrix(select(tr_treated_2022, -Worth))

input_y_2022 = tr_treated_2022$Worth

2. Re-fit model with reduced variable scope

(final_grid_2021 <- expand.grid(
  nrounds = xgb_tune5_2021$bestTune$nrounds,
  eta = xgb_tune5_2021$bestTune$eta,
  max_depth = xgb_tune5_2021$bestTune$max_depth,
  gamma = xgb_tune5_2021$bestTune$gamma,
  colsample_bytree = xgb_tune5_2021$bestTune$colsample_bytree,
  min_child_weight = xgb_tune5_2021$bestTune$min_child_weight,
  subsample = xgb_tune5_2021$bestTune$subsample
))

(xgb_model_2022 <- caret::train(
  x = input_x_2022,
  y = input_y_2022,
  trControl = train_control,
  tuneGrid = final_grid_2021,
  method = "xgbTree",
  verbose = TRUE
))

eXtreme Gradient Boosting 

3424 samples
  76 predictor

No pre-processing
Resampling: None 

vip(xgb_model_2022, num_features = 30)

unscalevi24 = vi(xgb_model_2022, method="model")

unscalevi24$Importance_perc = with(unscalevi24,Importance/sum(Importance)) 

unscalevi24

save(xgb_model_2022,file = '2022_Hitting5x5_Model.Rdata')

hitting5x5 = xgb_model_2022

hittinginput = input_x_2022

#For anything above breaking_IP we need to create projection table by age or age bucket

#write_csv(unscalevi24,"unscalevi24.csv")

# 2022 Projections Full

First let’s prepare a file for predicting based on our model object

```r

variableslag5xb= row_to_names(as.data.frame(t(variables_to_keep_2022)),row_number = 1) %>% select (starts_with(“lag”))

variables_nolag5xb = (owmr::remove_prefix(variableslag5xb,“lag” , sep = “_“))

Data_Predict_2022a5xb = total_treated_2021_hitting %>% select (one_of(colnames(variables_nolag5xb)),Season,playerid)

colnames(Data_Predict_2022a5xb) <- paste0(“lag_”, colnames(Data_Predict_2022a5xb))

Data_Predict_2022b5xb = total_treated_2021_hitting %>% select (one_of(colnames(variables_nolag5xb))) colnames(Data_Predict_2022b5xb) = colnames(variableslag5xb)

variables_to_keep_2022_nolag5xb = total_treated_2021_hitting %>% select(one_of(variables_to_keep_2022),Season,playerid,starts_with(“Team_lev_x_”))%>% select(-one_of(colnames(Data_Predict_2022b5xb)))

Data_predict_20225xb = sqldf( ” select a.,b. from Data_Predict_2022a5xb a, variables_to_keep_2022_nolag5xb b on b.playerid = a.lag_playerid and b.Season = a.lag_Season ” ) %>% select(-lag_playerid,lag_Season) %>% filter(Season == 2021) %>% select(one_of(variables_to_keep_2022),starts_with(“Team_lev_x_”))

```

6.2 Create Predictions for Model

6.2.1 Run Projections on Players who Played in 2021

This is the raw prediction score per IP for each pitcher

hitting_predictions5xb = as.data.frame(predict(xgb_model_2022,Data_predict_20225xb))

names(hitting_predictions5xb) = c("Predict_Score")

Data_predict_2022_w_hitting_Predictions5xb = cbind(Data_predict_20225xb,hitting_predictions5xb) %>% select(playerid,Predict_Score)

head(Data_predict_2022_w_hitting_Predictions5xb)

NA

---

Latest_2022_hittingdata_FP = read_csv("FanGraph_Fantasy_Baseball_Hitting.csv")

Rows: 625 Columns: 28
-- Column specification -------------------------------------------------------------------------------------------
Delimiter: ","
chr  (3): Name, Team, playerid
dbl (25): G, PA, AB, H, 2B, 3B, HR, R, RBI, BB, SO, HBP, SB, CS, AVG, OBP, SLG, OPS, wOBA, wRC+, WAR, ADP, Inte...

i Use `spec()` to retrieve the full column specification for this data.
i Specify the column types or set `show_col_types = FALSE` to quiet this message.

Latest_2022_hittingdata_FP

NA
NA

---

hitting_Data_NonAdj_Projections5xb = sqldf(
  "
  select a.*,b.Predict_Score
  from Latest_2022_hittingdata_FP a 
  left join 
  Data_predict_2022_w_hitting_Predictions5xb b
  on a.playerid = b.playerid
  "
) %>% filter(ADP<370 | is.na(Predict_Score)==F)


hitting_Data_Adj_Projections5xb =
hitting_Data_NonAdj_Projections5xb %>% 
  mutate(
    Avg_PA = 300,
    AdjPredict_Score_raw = ifelse(is.na(Predict_Score),NA,Predict_Score*(PA/Avg_PA)),
    max_predscore= max(AdjPredict_Score_raw,na.rm = T),
    AdjPredict_Score = ifelse (is.na(AdjPredict_Score_raw),NA,AdjPredict_Score_raw *100/max_predscore),
    WAR_rank = order(order(rank(WAR,ties.method = 'average'),decreasing = TRUE)),
    AdjPredict_Score_Rank = order(order(rank(AdjPredict_Score,ties.method = 'average'),decreasing = TRUE))-sum(is.na(AdjPredict_Score)),
        Ranks_Above_ADP = ADP - AdjPredict_Score_Rank
  ) %>% select (Name,ADP,WAR, WAR_rank,AdjPredict_Score ,AdjPredict_Score_Rank,Ranks_Above_ADP)


  

ggplot2::qplot(hitting_Data_Adj_Projections5xb$AdjPredict_Score, main="Predictions") + geom_histogram(colour="black", fill="grey") + theme_bw()

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

---

7 2022 Projections Full

7.1 Table of hitting Projections (Players who Didn’t Play in 2021 - Recieve an NA)

AdjPredict_Score are normalized to 100

tableexport =
hitting_Data_Adj_Projections5xb %>%
  arrange (ADP,WAR) %>% 
  kbl() %>% 
 kable_material(c("striped", "hover","condensed","responsive"),full_width = F,fixed_thead = T)

save_kable(tableexport,file = "hitting5x5.html")

#tableexport

This is a better formatted Table

ft_dt <- hitting_Data_Adj_Projections5xb[1:nrow(hitting_Data_Adj_Projections5xb), 1:ncol(hitting_Data_Adj_Projections5xb)] %>% 
  filter(AdjPredict_Score_Rank>0)%>%  arrange((AdjPredict_Score_Rank))

ft_dt$ADP <- color_tile("white", "red")(ft_dt$ADP)

ft_dt$WAR <- color_bar("lightblue")(ft_dt$WAR)

ft_dt$AdjPredict_Score<- color_bar("lightblue")(ft_dt$AdjPredict_Score)

ft_dt$WAR_Rank <- color_tile("green","orange")(ft_dt$WAR_rank)

ft_dt$Predict_Rank <- color_tile("green","orange")(ft_dt$AdjPredict_Score_Rank) 


ft_dt$Ranks_Above_ADP <- 
  ifelse(
  ft_dt$Ranks_Above_ADP < 0,
  cell_spec(round(ft_dt$Ranks_Above_ADP,2), color = "red", italic = T),
  cell_spec(round(ft_dt$Ranks_Above_ADP,2), color = "green", italic = T)
)


ft_dt2 <- ft_dt[c("Name", "ADP", "WAR", "AdjPredict_Score", "WAR_Rank","Predict_Rank","Ranks_Above_ADP")]



table_export = 
kbl(ft_dt2, escape = F) %>% 
 kable_material(c("striped", "hover","condensed","responsive"),full_width = F,fixed_thead = T) %>%   column_spec(6, width = "3cm") %>%
  add_header_above(c(" ", "Scores" = 3, "Ranks" = 2," "))

save_kable(table_export,file = "Hitting5x5_updated.html")
  
table_export  

	Scores			Ranks
Name	ADP	WAR	AdjPredict_Score	WAR_Rank	Predict_Rank	Ranks_Above_ADP
Vladimir Guerrero Jr.	5.6	5.6	100.00	5	1	4.6
Bryce Harper	9.1	5.2	99.88	8	2	7.1
Bo Bichette	4.9	4.7	99.41	13	3	1.9
Marcus Semien	39.0	4.7	99.38	14	4	35
Luis Robert	16.2	4.6	97.61	16	5	11.2
Trea Turner	1.4	5.5	97.41	6	6	-4.6
Rafael Devers	14.5	4.8	96.67	11	7	7.5
Nick Castellanos	60.6	2.5	95.83	81	8	52.6
Freddie Freeman	19.2	5.1	95.71	9	9	10.2
Shohei Ohtani	9.2	3.9	94.98	34	10	-0.8
José Ramírez	3.4	5.9	94.93	3	11	-7.6
Teoscar Hernández	27.8	2.3	94.24	98	12	15.8
Juan Soto	3.9	7.6	94.00	1	13	-9.1
Cedric Mullins	32.8	3.3	94.00	52	14	18.8
Kyle Tucker	12.7	4.6	93.76	15	15	-2.3
Paul Goldschmidt	47.6	3.5	92.99	44	16	31.6
Jorge Polanco	77.8	3.1	90.47	58	17	60.8
Mookie Betts	15.4	5.8	89.60	4	18	-2.6
Jose Altuve	73.3	3.8	89.15	36	19	54.3
Tim Anderson	30.9	3.2	89.03	55	20	10.9
Mike Trout	13.0	6.6	88.43	2	21	-8
Manny Machado	22.1	4.5	88.38	20	22	0.1
Matt Olson	39.0	4.2	87.89	28	23	16
Aaron Judge	36.0	5.3	87.77	7	24	12
George Springer	57.8	4.3	87.01	26	25	32.8
Pete Alonso	46.2	3.5	86.25	43	26	20.2
Tyler O'Neill	47.1	2.8	86.06	67	27	20.1
Bryan Reynolds	93.0	4.2	85.88	30	28	65
Byron Buxton	48.0	4.4	85.84	23	29	19
Xander Bogaerts	45.8	4.5	84.61	19	30	15.8
Brandon Lowe	75.4	4.3	84.56	24	31	44.4
Ozzie Albies	17.6	4.1	83.75	32	32	-14.4
Ronald Acuña Jr.	10.4	5.0	83.51	10	33	-22.6
Jonathan India	90.7	3.8	83.07	35	34	56.7
José Abreu	78.4	2.1	82.26	114	35	43.4
Trevor Story	36.8	4.2	82.16	29	36	0.8
Javier Báez	62.5	2.8	82.11	68	37	25.5
Frank Schwindel	233.8	0.8	81.23	262	38	195.8
Starling Marte	31.2	2.4	80.74	84	39	-7.8
Yordan Alvarez	26.3	4.1	80.67	31	40	-13.7
Austin Riley	51.8	3.1	80.32	59	41	10.8
Mitch Haniger	109.0	2.4	80.11	89	42	67
Kris Bryant	84.9	2.4	79.90	86	43	41.9
Corey Seager	78.3	4.5	79.66	22	44	34.3
Jared Walsh	119.8	2.2	79.24	101	45	74.8
Ketel Marte	77.2	3.7	79.16	37	46	31.2
Jesse Winker	111.8	3.4	79.11	45	47	64.8
Joey Votto	141.9	1.9	78.73	130	48	93.9
Dansby Swanson	119.1	2.7	78.12	72	49	70.1
Carlos Correa	100.9	4.7	78.00	12	50	50.9
Brandon Crawford	219.2	2.7	77.49	70	51	168.2
Randy Arozarena	65.4	2.1	77.17	113	52	13.4
Wander Franco	58.9	4.5	77.16	21	53	5.9
Kyle Schwarber	114.5	2.7	77.12	71	54	60.5
Salvador Perez	32.2	3.7	76.97	38	55	-22.8
Nolan Arenado	67.7	3.7	76.69	39	56	11.7
Avisaíl García	170.2	2.3	76.66	93	57	113.2
J.D. Martinez	88.9	2.0	76.37	116	58	30.9
Rhys Hoskins	126.3	3.0	75.91	61	59	67.3
Franmil Reyes	120.9	1.8	74.37	132	60	60.9
Brandon Belt	226.3	2.5	73.93	80	61	165.3
Ryan Mountcastle	119.7	1.6	73.85	161	62	57.7
Willy Adames	123.2	3.3	73.61	49	63	60.2
J.T. Realmuto	48.8	3.6	73.05	40	64	-15.2
C.J. Cron	124.7	1.7	72.21	154	65	59.7
Francisco Lindor	53.5	4.2	72.18	27	66	-12.5
Hunter Renfroe	161.4	1.7	71.37	153	67	94.4
Max Muncy	134.9	3.2	71.16	54	68	66.9
Jake Cronenworth	127.1	3.4	70.56	48	69	58.1
Jazz Chisholm Jr.	76.0	1.8	70.47	138	70	6
Josh Bell	127.6	1.9	70.46	129	71	56.6
Justin Turner	152.7	3.0	69.78	62	72	80.7
Nelson Cruz	171.9	1.4	69.41	181	73	98.9
Joey Gallo	177.0	3.5	68.97	42	74	103
Whit Merrifield	32.5	2.3	68.82	97	75	-42.5
Eduardo Escobar	200.5	1.9	68.03	131	76	124.5
Kolten Wong	190.8	2.5	66.98	79	77	113.8
Lourdes Gurriel Jr.	145.2	1.4	66.53	187	78	67.2
Anthony Rizzo	193.6	2.1	66.08	109	79	114.6
Chris Taylor	139.9	2.3	65.76	95	80	59.9
Giancarlo Stanton	97.7	2.3	65.62	99	81	16.7
Ryan McMahon	161.2	2.0	65.58	119	82	79.2
Alex Verdugo	168.6	2.4	65.55	85	83	85.6
Will Smith	53.2	4.6	65.26	17	84	-30.8
Luis Urías	173.3	2.5	65.19	75	85	88.3
Mark Canha	283.4	1.8	65.16	141	86	197.4
Austin Meadows	145.5	1.5	65.05	168	87	58.5
Jean Segura	202.3	2.4	64.95	91	88	114.3
Adolis García	166.0	1.3	64.94	201	89	77
Mike Yastrzemski	286.5	2.2	64.74	108	90	196.5
Austin Hays	243.2	1.7	64.28	150	91	152.2
Ian Happ	213.2	2.2	63.86	104	92	121.2
Tommy Edman	90.2	2.3	63.28	94	93	-2.8
Max Kepler	294.6	2.5	63.24	76	94	200.6
AJ Pollock	218.6	1.8	63.02	136	95	123.6
Jonathan Schoop	213.2	1.7	62.96	145	96	117.2
Miguel Sanó	280.4	0.8	62.56	271	97	183.4
Yasmani Grandal	101.1	4.0	62.55	33	98	3.1
Dylan Carlson	172.0	2.5	62.54	77	99	73
Tyler Naquin	352.7	0.7	61.98	274	100	252.7
Nathaniel Lowe	251.7	1.8	61.88	133	101	150.7
Ramón Laureano	247.0	2.6	61.76	74	102	145
Enrique Hernández	226.7	2.8	61.48	66	103	123.7
Trent Grisham	143.5	3.1	61.47	57	104	39.5
Yoán Moncada	158.7	3.4	61.27	47	105	53.7
Jeimer Candelario	239.6	3.2	61.21	56	106	133.6
Ty France	154.6	2.4	61.21	88	107	47.6
Brendan Rodgers	169.9	1.6	61.10	160	108	61.9
LaMonte Wade Jr.	343.8	1.0	60.95	233	109	234.8
Adam Duvall	212.8	1.7	60.72	148	110	102.8
Amed Rosario	170.7	2.0	60.56	118	111	59.7
Akil Baddoo	156.9	1.6	60.33	158	112	44.9
Jesús Sánchez	226.3	2.0	60.19	115	113	113.3
Andrew Benintendi	192.7	1.9	59.98	125	114	78.7
Jorge Soler	192.2	1.7	59.05	147	115	77.2
Charlie Blackmon	236.7	0.6	58.78	301	116	120.7
Adalberto Mondesi	57.3	2.0	58.71	123	117	-59.7
Bobby Dalbec	229.2	1.6	58.46	157	118	111.2
Christian Yelich	97.2	2.9	58.33	64	119	-21.8
Trey Mancini	205.0	1.4	57.55	180	120	85
Eloy Jiménez	65.1	2.2	57.36	105	121	-55.9
Adam Frazier	400.3	2.0	57.26	117	122	278.3
Yuli Gurriel	210.6	1.4	57.17	189	123	87.6
Robbie Grossman	191.3	1.7	57.15	149	124	67.3
Patrick Wisdom	358.3	1.2	56.75	214	125	233.3
Willson Contreras	109.9	2.7	56.64	69	126	-16.1
Andrew McCutchen	348.9	0.9	56.56	254	127	221.9
Matt Chapman	181.5	3.6	56.49	41	128	53.5
Alex Bregman	88.3	4.5	56.21	18	129	-40.7
Harrison Bader	235.8	2.9	56.03	63	130	105.8
Eddie Rosario	181.7	0.9	55.86	251	131	50.7
Wil Myers	288.9	1.0	55.59	231	132	156.9
Josh Donaldson	187.1	3.3	55.07	51	133	54.1
Brandon Nimmo	324.3	3.2	54.97	53	134	190.3
Nicky Lopez	250.4	1.8	54.62	140	135	115.4
César Hernández	403.0	1.7	54.18	152	136	267
Seth Brown	502.2	0.6	53.86	293	137	365.2
Fernando Tatis Jr.	27.5	3.4	53.75	46	138	-110.5
Rafael Ortega	337.3	1.3	53.19	193	139	198.3
Miguel Rojas	475.9	2.3	52.60	96	140	335.9
Josh Rojas	247.6	1.5	52.36	167	141	106.6
Jo Adell	228.4	0.6	52.31	300	142	86.4
Joey Wendle	403.7	1.3	52.24	194	143	260.7
Eugenio Suárez	198.5	2.2	51.30	107	144	54.5
Michael Brantley	251.4	2.0	51.19	122	145	106.4
Tony Kemp	459.8	1.6	50.89	155	146	313.8
Alex Kirilloff	177.6	1.5	50.70	165	147	30.6
Daulton Varsho	93.3	2.1	50.36	110	148	-54.7
Anthony Santander	298.7	1.3	49.90	197	149	149.7
Tyler Stephenson	138.3	2.9	49.84	65	150	-11.7
Tommy Pham	274.8	1.9	49.83	126	151	123.8
DJ LeMahieu	119.2	2.4	49.39	87	152	-32.8
Ke'Bryan Hayes	142.6	3.3	49.38	50	153	-10.4
Evan Longoria	455.5	1.5	49.25	174	154	301.5
Gleyber Torres	154.9	2.5	48.80	78	155	-0.1
Jonathan Villar	253.1	1.1	48.75	225	156	97.1
Andrew Vaughn	250.3	1.2	48.73	207	157	93.3
Raimel Tapia	273.9	0.1	48.60	384	158	115.9
Randal Grichuk	419.6	0.6	48.42	292	159	260.6
Michael Conforto	207.5	2.7	48.19	73	160	47.5
Isiah Kiner-Falefa	296.8	1.6	48.16	163	161	135.8
Mike Zunino	253.5	2.0	47.69	124	162	91.5
J.P. Crawford	452.2	2.5	46.58	83	163	289.2
Myles Straw	127.0	2.3	46.58	100	164	-37
Mitch Garver	166.1	2.4	46.28	90	165	1.1
David Fletcher	417.0	1.3	46.27	191	166	251
Jesús Aguilar	300.5	1.2	46.26	215	167	133.5
Odúbel Herrera	583.2	1.1	45.65	228	168	415.2
Luke Voit	253.5	1.3	45.65	199	169	84.5
Joc Pederson	480.8	0.8	45.62	270	170	310.8
Lorenzo Cain	451.3	1.5	45.51	176	171	280.3
Paul DeJong	491.4	2.1	45.43	112	172	319.4
Eric Hosmer	470.1	0.4	45.09	329	173	297.1
Jake Meyers	593.2	1.1	45.06	217	174	419.2
Luis Arraez	351.3	1.5	44.88	177	175	176.3
Gavin Sheets	525.8	0.8	44.21	265	176	349.8
Jarred Kelenic	141.5	1.5	44.10	169	177	-35.5
Brad Miller	551.8	1.4	44.07	179	178	373.8
Nick Madrigal	342.1	1.8	43.69	139	179	163.1
Manuel Margot	416.6	1.1	43.57	229	180	236.6
Sam Hilliard	477.8	0.5	43.54	309	181	296.8
David Peralta	518.1	1.1	43.01	221	182	336.1
Wilmer Flores	472.5	1.2	42.95	208	183	289.5
Alcides Escobar	586.4	0.2	42.92	374	184	402.4
Lane Thomas	269.2	1.1	42.87	226	185	84.2
Pavin Smith	476.9	0.6	42.59	286	186	290.9
Bobby Bradley	491.0	0.5	42.42	308	187	304
Darin Ruf	454.5	0.9	42.25	247	188	266.5
Connor Joe	341.2	0.8	42.23	255	189	152.2
Jeff McNeil	323.0	1.9	42.18	128	190	133
Ramón Urías	529.7	1.6	41.95	162	191	338.7
Adam Engel	597.3	0.6	41.80	290	192	405.3
Gio Urshela	306.8	1.3	41.80	192	193	113.8
Ben Gamel	579.0	0.9	41.63	242	194	385
José Iglesias	547.7	1.2	41.53	210	195	352.7
Josh Harrison	412.3	1.1	41.53	224	196	216.3
Yandy Díaz	464.5	1.6	41.40	156	197	267.5
Cody Bellinger	98.9	3.1	41.03	60	198	-99.1
Tyrone Taylor	526.6	1.1	41.03	222	199	327.6
Danny Jansen	403.1	1.9	40.25	127	200	203.1
Kyle Farmer	528.0	1.1	40.14	220	201	327
Alec Bohm	311.6	1.4	40.08	188	202	109.6
Michael A. Taylor	516.5	1.2	40.06	212	203	313.5
Hunter Dozier	443.8	0.6	39.85	288	204	239.8
Garrett Cooper	527.1	1.0	39.51	238	205	322.1
Carlos Santana	511.8	0.7	39.08	272	206	305.8
Chas McCormick	495.8	1.1	39.04	218	207	288.8
Elias Díaz	229.7	1.3	38.97	198	208	21.7
Kyle Lewis	399.0	1.5	38.79	170	209	190
Christian Walker	506.6	0.7	38.58	276	210	296.6
Steven Duggar	597.8	0.7	38.56	275	211	386.8
Brian Anderson	495.9	1.8	38.53	137	212	283.9
Ji-Man Choi	577.3	0.7	38.52	282	213	364.3
Kevin Kiermaier	588.0	1.2	38.43	203	214	374
Andrés Giménez	282.5	1.7	38.34	146	215	67.5
Chad Pinder	570.3	0.6	38.01	291	216	354.3
Eric Haase	302.9	0.7	37.96	278	217	85.9
Max Stassi	300.6	1.8	37.64	135	218	82.6
Jake Fraley	547.6	0.9	37.51	241	219	328.6
Sean Murphy	252.4	2.5	37.39	82	220	32.4
Yadiel Hernandez	583.5	0.1	37.37	400	221	362.5
Bradley Zimmer	505.3	0.7	36.89	280	222	283.3
Gavin Lux	266.4	1.5	36.79	178	223	43.4
Tommy La Stella	561.3	1.2	36.41	213	224	337.3
Rowdy Tellez	342.4	0.8	36.24	268	225	117.4
Nick Ahmed	581.5	1.1	36.05	223	226	355.5
Marcell Ozuna	174.3	2.2	35.95	103	227	-52.7
Leury García	536.2	0.7	35.69	281	228	308.2
Garrett Hampson	299.6	0.3	35.60	340	229	70.6
Gary Sánchez	245.3	1.3	35.50	196	230	15.3
Christian Vázquez	201.1	1.7	35.34	142	231	-29.9
Edmundo Sosa	501.9	1.5	35.19	172	232	269.9
Anthony Rendon	110.2	4.3	35.15	25	233	-122.8
Keibert Ruiz	145.4	2.2	35.06	106	234	-88.6
Austin Slater	575.2	1.0	34.51	232	235	340.2
Bryan De La Cruz	534.7	0.9	34.25	240	236	298.7
Didi Gregorius	513.0	0.9	34.20	252	237	276
Abraham Toro	323.7	1.4	34.19	190	238	85.7
Brandon Marsh	418.4	1.2	34.01	209	239	179.4
Ha-Seong Kim	349.8	1.5	33.80	173	240	109.8
Luis García	556.8	0.7	33.71	277	241	315.8
Colin Moran	582.8	-0.1	33.51	494	242	340.8
Anthony Alford	555.4	0.0	33.46	468	243	312.4
Yadier Molina	322.4	1.2	33.36	211	244	78.4
Cavan Biggio	319.3	1.4	33.14	184	245	74.3
Justin Upton	565.1	0.3	32.89	349	246	319.1
Jurickson Profar	540.1	0.3	32.83	341	247	293.1
Andy Ibáñez	563.1	1.5	32.79	175	248	315.1
Miguel Cabrera	539.9	-0.4	32.78	500	249	290.9
Willie Calhoun	451.5	0.2	32.74	359	250	201.5
Kevin Pillar	590.7	0.4	32.61	314	251	339.7
Elvis Andrus	566.4	0.9	32.57	248	252	314.4
Kyle Isbel	542.5	0.6	32.48	299	253	289.5
Kole Calhoun	522.9	1.1	32.47	227	254	268.9
Yoshi Tsutsugo	431.9	0.3	32.33	336	255	176.9
Carson Kelly	280.7	2.0	32.29	120	256	24.7
Michael Chavis	569.3	0.0	31.86	477	257	312.3
Nick Solak	494.8	0.7	31.77	284	258	236.8
Josh Naylor	587.4	0.5	31.66	306	259	328.4
Jordan Luplow	600.6	0.8	31.43	263	260	340.6
Nick Senzel	422.0	1.0	31.42	234	261	161
Alejandro Kirk	237.1	1.7	31.14	144	262	-24.9
Omar Narváez	251.8	1.7	31.04	143	263	-11.2
Nico Hoerner	506.9	1.4	30.98	182	264	242.9
Ryan Jeffers	480.5	1.6	30.97	159	265	215.5
Corey Dickerson	581.1	0.1	30.57	388	266	315.1
Dominic Smith	456.4	0.5	30.47	304	267	189.4
Santiago Espinal	557.5	0.7	30.41	273	268	289.5
Victor Robles	474.9	1.0	30.29	237	269	205.9
Jorge Mateo	472.9	0.5	30.25	312	270	202.9
Jed Lowrie	597.2	0.8	30.16	267	271	326.2
Oscar Mercado	593.3	0.7	29.78	285	272	321.3
Mike Moustakas	374.8	0.9	29.69	245	273	101.8
Travis d'Arnaud	224.4	1.4	29.59	183	274	-49.6
Aristides Aquino	585.4	0.4	29.59	333	275	310.4
Yan Gomes	442.1	1.5	29.49	166	276	166.1
Lars Nootbaar	572.6	0.2	29.24	375	277	295.6
Rougned Odor	504.9	0.6	29.18	289	278	226.9
Thairo Estrada	600.3	0.8	29.16	259	279	321.3
Luis Torrens	446.4	0.7	27.97	279	280	166.4
Kevin Newman	597.1	0.5	27.93	303	281	316.1
J.D. Davis	511.6	0.9	27.78	253	282	229.6
Alex Dickerson	597.9	0.2	27.55	357	283	314.9
Niko Goodrum	571.4	0.7	27.34	283	284	287.4
James McCann	351.5	1.4	27.26	186	285	66.5
Jake McCarthy	600.8	0.0	26.71	429	286	314.8
Roberto Pérez	545.7	1.3	26.49	195	287	258.7
Victor Reyes	592.8	0.4	26.39	316	288	304.8
Jacob Stallings	421.9	2.2	26.38	102	289	132.9
Jason Heyward	594.8	0.8	26.23	269	290	304.8
Aledmys Díaz	573.3	0.9	26.14	243	291	282.3
Stephen Piscotty	594.4	-0.2	25.66	497	292	302.4
Brett Gardner	598.6	0.6	25.65	297	293	305.6
Jorge Alfaro	454.9	0.0	25.42	474	294	160.9
Francisco Mejía	492.9	1.3	25.33	202	295	197.9
Jarren Duran	497.2	0.4	24.88	327	296	201.2
Martín Maldonado	562.4	1.2	24.43	216	297	265.4
Donovan Solano	594.0	0.4	24.18	324	298	296
Daniel Vogelbach	586.6	0.6	23.98	298	299	287.6
Austin Nola	377.8	1.5	23.41	171	300	77.8
Aaron Hicks	524.7	1.6	23.21	164	301	223.7
Brett Phillips	590.7	0.3	23.18	337	302	288.7
Tucker Barnhart	471.5	1.3	22.87	200	303	168.5
Cole Tucker	586.6	0.4	22.32	325	304	282.6
Tyler Wade	505.0	0.4	22.32	331	305	200
Manny Piña	570.7	1.2	22.05	205	306	264.7
Yu Chang	596.6	0.4	21.85	334	307	289.6
DJ Stewart	600.3	0.5	21.81	305	308	292.3
Matt Vierling	578.6	0.5	21.71	313	309	269.6
Austin Hedges	587.0	0.5	21.43	307	310	277
Keston Hiura	465.4	0.2	21.21	364	311	154.4
Trevor Larnach	565.1	0.1	21.12	390	312	253.1
Kyle Higashioka	555.9	1.0	21.04	236	313	242.9
Josh VanMeter	597.6	0.4	20.98	323	314	283.6
Kelvin Gutierrez	597.6	0.3	20.96	351	315	282.6
Dom Nuñez	586.9	0.5	20.74	311	316	270.9
Jose Barrero	478.7	1.2	20.72	206	317	161.7
Carter Kieboom	539.8	0.8	20.67	258	318	221.8
Lewin Díaz	585.3	0.3	20.62	344	319	266.3
Mitch Moreland	600.9	0.3	20.58	342	320	280.9
Clint Frazier	472.5	0.3	20.27	345	321	151.5
Taylor Walls	587.7	1.1	19.70	219	322	265.7
Matt Duffy	586.2	0.5	19.68	310	323	263.2
Jace Peterson	579.4	0.2	19.64	361	324	255.4
Jackie Bradley Jr.	589.7	0.2	19.56	379	325	264.7
Pedro Severino	522.6	0.9	19.13	244	326	196.6
Brent Rooker	597.9	0.3	18.49	348	327	270.9
Tom Murphy	539.9	1.2	18.38	204	328	211.9
Mike Brosseau	597.1	0.4	18.32	320	329	268.1
Matt Beaty	591.6	0.2	18.14	358	330	261.6
Andrew Stevenson	597.8	-0.1	18.02	482	331	266.8
Robinson Chirinos	595.5	0.6	17.93	294	332	263.5
Brian Goodwin	600.6	0.1	17.51	399	333	267.6
Asdrúbal Cabrera	599.6	0.0	17.41	425	334	265.6
Dylan Moore	509.0	0.4	17.40	326	335	174
Riley Adams	593.0	0.8	17.34	257	336	257
Sheldon Neuse	999.0	0.2	17.28	362	337	662
Albert Pujols	597.5	-0.3	17.28	499	338	259.5
Andrelton Simmons	586.5	1.1	17.09	230	339	247.5
Ehire Adrianza	999.0	0.1	16.32	403	340	659
Max Schrock	599.8	0.0	16.21	467	341	258.8
Cristian Pache	573.0	0.8	15.96	266	342	231
Christian Arroyo	594.0	0.2	15.92	354	343	251
Derek Hill	999.0	0.1	15.88	387	344	655
Austin Barnes	592.5	0.8	15.79	264	345	247.5
Jason Castro	599.2	0.8	15.55	256	346	253.2
Nick Gordon	584.3	0.4	15.49	330	347	237.3
Daz Cameron	599.3	0.2	15.45	378	348	251.3
Harold Castro	599.7	-0.1	15.33	492	349	250.7
Yonathan Daza	598.7	-0.1	15.30	480	350	248.7
Danny Santana	999.0	0.1	15.23	406	351	648
Adam Eaton	999.0	0.2	15.22	355	352	647
Jonah Heim	531.7	0.9	14.96	246	353	178.7
Curt Casali	592.1	0.9	14.65	250	354	238.1
Victor Caratini	582.5	0.8	14.63	260	355	227.5
Edward Olivares	584.8	0.3	14.07	346	356	228.8
Tomás Nido	601.0	0.2	13.81	360	357	244
Ben Rortvedt	999.0	1.0	13.72	235	358	641
Leody Taveras	573.9	0.6	13.61	287	359	214.9
Jake Marisnick	999.0	0.2	13.39	381	360	639
Khris Davis	999.0	0.0	13.23	448	361	638
Kevin Plawecki	581.7	0.8	13.07	261	362	219.7
Nomar Mazara	600.4	0.1	13.05	416	363	237.4
Jarrod Dyson	999.0	-0.2	12.97	495	364	635
Taylor Ward	600.9	0.4	12.74	322	365	235.9
Guillermo Heredia	999.0	-0.1	12.52	491	366	633
Shed Long Jr.	999.0	0.2	12.44	367	367	632
Willi Castro	563.7	0.3	12.44	352	368	195.7
Juan Lagares	999.0	0.1	12.31	417	369	630
Charlie Culberson	597.2	0.0	11.99	432	370	227.2
Harold Ramirez	574.0	0.2	11.97	373	371	203
Jon Berti	576.3	0.4	11.84	328	372	204.3
Wilson Ramos	600.9	0.6	11.62	296	373	227.9
Luis Rengifo	600.2	0.4	11.33	317	374	226.2
Shogo Akiyama	600.8	0.0	11.26	449	375	225.8
Hoy Park	600.7	0.4	11.06	315	376	224.7
Jack Mayfield	600.9	0.1	10.56	407	377	223.9
Maikel Franco	590.0	0.0	10.51	444	378	212
David Bote	597.7	0.4	10.50	319	379	218.7
Mauricio Dubón	596.2	0.3	10.46	335	380	216.2
Seby Zavala	598.5	-0.1	10.25	485	381	217.5
Hanser Alberto	600.3	0.2	10.00	356	382	218.3
Michael Perez	999.0	0.4	9.88	318	383	616
Starlin Castro	597.3	0.4	9.81	332	384	213.3
Owen Miller	600.7	0.2	9.73	365	385	215.7
Kurt Suzuki	599.5	0.2	9.73	369	386	213.5
Jose Trevino	596.6	0.3	9.67	343	387	209.6
Ronald Torreyes	600.9	0.0	9.65	453	388	212.9
Eli White	599.7	0.1	9.45	414	389	210.7
Zach McKinstry	596.2	0.1	9.37	382	390	206.2
Andrew Velazquez	600.7	0.2	8.93	368	391	209.7
Orlando Arcia	600.8	0.3	8.90	350	392	208.8
Zack Collins	598.5	0.6	8.90	302	393	205.5
Joe Panik	999.0	0.1	8.69	422	394	605
Wilmer Difo	999.0	-0.1	8.50	487	395	604
Miguel Andújar	595.5	0.0	8.46	439	396	199.5
Travis Shaw	600.5	0.1	8.39	418	397	203.5
Jake Cave	999.0	-0.1	8.36	490	398	601
Josh Reddick	999.0	-0.2	8.21	498	399	600
Ryan O'Hearn	599.8	0.0	7.99	461	400	199.8
Tim Locastro	598.6	0.1	7.86	401	401	197.6
Kyle Garlick	999.0	0.0	7.71	471	402	597
Jake Rogers	600.8	0.3	7.66	347	403	197.8
Marwin Gonzalez	600.4	0.2	7.62	376	404	196.4
Billy McKinney	599.2	0.0	7.54	438	405	194.2
Renato Núñez	599.6	0.2	7.53	380	406	193.6
Cal Raleigh	560.8	0.9	7.34	249	407	153.8
Eric Sogard	999.0	0.0	7.33	430	408	591
Jose Rojas	600.4	0.0	7.32	450	409	191.4
Emmanuel Rivera	999.0	0.1	7.25	393	410	589
Yonny Hernandez	599.8	0.3	7.20	339	411	188.8
Stephen Vogt	999.0	0.1	7.09	409	412	587
Matt Carpenter	597.1	0.0	7.07	458	413	184.1
Danny Mendick	999.0	0.1	7.04	383	414	585
Sergio Alcántara	600.5	0.2	6.86	371	415	185.5
Brock Holt	999.0	0.1	6.75	423	416	583
Billy Hamilton	597.8	-0.1	6.73	489	417	180.8
Daniel Johnson	999.0	0.0	6.63	442	418	581
Rob Refsnyder	999.0	0.0	6.49	447	419	580
Ernie Clement	999.0	0.2	6.31	370	420	579
Lewis Brinson	600.5	0.0	6.25	441	421	179.5
Andrew Knizner	598.8	0.6	6.22	295	422	176.8
Chris Owings	600.7	0.0	6.20	435	423	177.7
Tres Barrera	999.0	0.1	6.13	398	424	575
Isaac Paredes	600.3	0.4	6.10	321	425	175.3
Luis Guillorme	999.0	0.3	5.94	353	426	573
Nick Maton	999.0	0.0	5.87	434	427	572
Phil Gosselin	594.3	0.0	5.87	460	428	166.3
Rodolfo Castro	999.0	0.1	5.81	386	429	570
Rafael Marchan	999.0	0.1	5.62	392	430	569
Cam Gallagher	600.6	0.3	5.50	338	431	169.6
Travis Jankowski	999.0	0.0	5.49	427	432	567
Sandy León	999.0	0.0	5.21	443	433	566
Pablo Reyes	999.0	0.1	5.21	408	434	565
Jordy Mercer	999.0	-0.1	5.19	483	435	564
José Peraza	600.8	0.1	5.19	410	436	164.8
William Contreras	593.2	0.2	5.16	363	437	156.2
Taylor Trammell	600.9	0.1	5.11	396	438	162.9
Alex Jackson	999.0	0.2	4.90	366	439	560
Reese McGuire	599.1	0.2	4.84	372	440	159.1
Taylor Jones	999.0	0.0	4.76	436	441	558
Jahmai Jones	600.7	0.1	4.71	411	442	158.7
Roman Quinn	999.0	0.0	4.40	454	443	556
Dustin Garneau	999.0	0.1	4.26	402	444	555
Jake Bauers	600.5	-0.1	4.22	486	445	155.5
Aramis Garcia	999.0	0.0	4.19	457	446	553
José Rondón	999.0	0.0	4.13	446	447	552
Ryan McKenna	999.0	0.1	4.00	391	448	551
Andrew Knapp	600.9	0.0	3.96	462	449	151.9
DJ Peters	600.5	0.0	3.93	464	450	150.5
Matt Joyce	999.0	-0.1	3.86	484	451	548
Drew Ellis	999.0	0.1	3.79	389	452	547
Luke Williams	999.0	0.0	3.77	455	453	546
Alex Blandino	999.0	-0.2	3.74	496	454	545
Magneuris Sierra	999.0	-0.1	3.69	493	455	544
Yermín Mercedes	596.9	0.1	3.63	415	456	140.9
Jake Lamb	999.0	0.1	3.62	404	457	542
Alan Trejo	999.0	0.0	3.57	426	458	541
Isan Díaz	600.5	0.1	3.56	421	459	141.5
Evan White	600.2	0.0	3.53	437	460	140.2
Richie Martin	999.0	-0.1	3.49	479	461	538
Zack Short	999.0	0.1	3.46	385	462	537
Skye Bolt	999.0	0.2	3.32	377	463	536
Ender Inciarte	600.0	0.0	3.24	465	464	136
David Dahl	599.6	0.0	3.15	476	465	134.6
Donovan Walton	999.0	0.1	3.06	405	466	533
Jason Vosler	999.0	0.1	3.04	419	467	532
Luke Raley	999.0	0.1	3.02	420	468	531
John Nogowski	600.8	0.0	2.95	433	469	131.8
Daniel Robertson	999.0	0.0	2.91	440	470	529
Gilberto Celestino	999.0	0.1	2.82	413	471	528
Grayson Greiner	999.0	0.0	2.76	456	472	527
Delino DeShields	999.0	0.0	2.60	451	473	526
Austin Romine	999.0	0.1	2.59	395	474	525
Tucupita Marcano	600.6	0.0	2.33	428	475	125.6
Abraham Almonte	999.0	0.0	2.25	459	476	523
Rio Ruiz	999.0	0.0	2.10	452	477	522
Pat Valaika	999.0	0.0	2.05	469	478	521
Jonathan Araúz	999.0	0.0	1.85	424	479	520
Webster Rivas	999.0	0.0	1.84	470	480	519
Wyatt Mathisen	999.0	0.0	1.81	431	481	518
Ildemaro Vargas	999.0	0.0	1.80	445	482	517
Patrick Mazeika	999.0	0.1	1.78	412	483	516
Phillip Evans	999.0	0.0	1.73	478	484	515
Chad Wallach	999.0	0.0	1.49	475	485	514
Jonathan Davis	999.0	-0.1	1.49	481	486	513
Franchy Cordero	999.0	0.0	1.47	463	487	512
Justin Williams	999.0	-0.1	1.42	488	488	511
Willians Astudillo	601.0	0.0	1.37	466	489	112
Edwin Ríos	587.4	0.0	1.23	472	490	97.4
Chance Sisco	999.0	0.1	0.97	397	491	508
Albert Almora Jr.	999.0	0.1	0.81	394	492	507
Robel García	999.0	0.0	0.51	473	493	506

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