Welcome to my 2022 Projections for Hitters 6x6

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 - OPS

---

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 = order(order(rank(OPS,ties.method = 'average'),decreasing = FALSE))/n(),
    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] -0.2

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] 0.031

max(df_batting_init2$Worth)

[1] 5.8

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	0.99	0.89	5.8
Ronald Acuna Jr.	1.00	0.98	0.82	0.96	0.99	0.90	5.6
Byron Buxton	1.00	0.99	0.70	0.92	0.99	0.97	5.6
Tyler O'Neill	0.96	0.97	0.86	0.88	0.96	0.91	5.5
Jose Ramirez	0.98	0.93	0.94	0.94	0.94	0.78	5.5
Bryce Harper	0.96	0.95	0.80	0.82	1.00	0.97	5.5
Teoscar Hernandez	0.92	0.90	1.00	0.80	0.93	0.94	5.5
Kyle Tucker	0.87	0.89	0.94	0.86	0.97	0.94	5.5
Shohei Ohtani	0.95	0.99	0.90	0.94	0.98	0.69	5.4
Vladimir Guerrero Jr.	0.99	0.98	0.92	0.49	0.99	0.98	5.3
Frank Schwindel	0.97	0.90	0.95	0.55	0.98	0.99	5.3
Bo Bichette	0.99	0.76	0.85	0.92	0.87	0.95	5.3
Trea Turner	0.96	0.78	0.63	0.97	0.96	1.00	5.3
Nick Castellanos	0.95	0.94	0.97	0.47	0.98	0.97	5.3
Juan Soto	0.96	0.80	0.83	0.70	0.99	0.99	5.3
Brandon Crawford	0.85	0.78	0.94	0.80	0.94	0.95	5.3
Brandon Belt	0.97	0.99	0.89	0.56	0.99	0.85	5.2
Luis Robert	0.84	0.79	0.83	0.81	0.98	1.00	5.2
Javier Baez	0.87	0.93	0.92	0.91	0.85	0.77	5.2
Marcus Semien	0.94	0.96	0.80	0.81	0.93	0.77	5.2
Mike Trout	0.93	0.91	0.67	0.69	1.00	1.00	5.2
Paul Goldschmidt	0.90	0.83	0.84	0.76	0.94	0.94	5.2
Rafael Devers	0.91	0.93	0.97	0.55	0.94	0.88	5.2
George Springer	0.98	0.97	0.84	0.66	0.96	0.76	5.2
Aaron Judge	0.83	0.96	0.88	0.60	0.97	0.92	5.2
A.J. Pollock	0.64	0.87	0.94	0.82	0.94	0.95	5.2
Manny Machado	0.85	0.79	0.95	0.78	0.89	0.88	5.1
Jorge Polanco	0.90	0.88	0.88	0.76	0.87	0.80	5.1
Matt Olson	0.89	0.94	0.94	0.50	0.96	0.83	5.1
Brandon Lowe	0.93	0.97	0.93	0.66	0.92	0.59	5.0
Freddie Freeman	0.98	0.80	0.64	0.66	0.95	0.95	5.0
Ozzie Albies	0.90	0.78	0.88	0.89	0.82	0.71	5.0
Adam Engel	0.89	0.88	0.72	0.97	0.88	0.64	5.0
Yordan Alvarez	0.91	0.91	0.98	0.34	0.93	0.87	5.0
Thairo Estrada	0.85	0.89	0.96	0.55	0.85	0.85	4.9
Jesse Winker	0.93	0.87	0.84	0.36	0.98	0.96	4.9
Avisail Garcia	0.73	0.92	0.96	0.72	0.86	0.74	4.9
Kyle Schwarber	0.95	0.98	0.87	0.36	0.97	0.78	4.9
Joey Votto	0.78	0.98	0.99	0.35	0.97	0.78	4.9
Bryan Reynolds	0.85	0.69	0.79	0.55	0.96	0.96	4.8
Tim Anderson	0.97	0.57	0.54	0.90	0.83	0.98	4.8
Salvador Perez	0.74	0.99	0.99	0.33	0.91	0.84	4.8
Rhys Hoskins	0.86	0.95	0.93	0.53	0.92	0.58	4.8
Jose Altuve	0.98	0.83	0.66	0.54	0.89	0.87	4.8
Max Muncy	0.94	0.95	0.92	0.40	0.95	0.61	4.8
Austin Riley	0.79	0.87	0.93	0.26	0.95	0.96	4.8
Randy Arozarena	0.92	0.62	0.59	0.91	0.85	0.86	4.8
Xander Bogaerts	0.89	0.70	0.73	0.57	0.92	0.94	4.7
Kris Bryant	0.88	0.77	0.68	0.75	0.88	0.78	4.7
Patrick Wisdom	0.85	0.99	0.94	0.64	0.87	0.44	4.7
Starling Marte	0.96	0.41	0.46	1.00	0.89	0.98	4.7
Hunter Renfroe	0.92	0.91	0.96	0.35	0.85	0.71	4.7
C.J. Cron	0.67	0.88	0.96	0.35	0.95	0.89	4.7
Will Smith	0.83	0.87	0.87	0.50	0.91	0.70	4.7
Franmil Reyes	0.59	0.97	0.99	0.58	0.90	0.66	4.7
Trevor Story	0.88	0.73	0.70	0.91	0.83	0.63	4.7
LaMonte Wade Jr.	0.78	0.84	0.84	0.73	0.84	0.65	4.7
Cedric Mullins II	0.77	0.80	0.29	0.95	0.93	0.93	4.7
Darin Ruf	0.72	0.88	0.78	0.51	0.95	0.82	4.7
Chris Owings	0.99	0.34	0.41	0.94	1.00	0.99	4.7
Chas McCormick	0.88	0.79	0.90	0.68	0.72	0.69	4.7
Ketel Marte	0.80	0.69	0.74	0.48	0.96	0.99	4.7
Justin Turner	0.84	0.79	0.81	0.46	0.88	0.87	4.6
Evan Longoria	0.92	0.81	0.91	0.41	0.88	0.73	4.6
Nelson Cruz	0.77	0.91	0.85	0.47	0.88	0.77	4.6
Jake Meyers	0.77	0.68	0.98	0.77	0.72	0.72	4.6
Mookie Betts	0.96	0.76	0.47	0.77	0.91	0.76	4.6
Pete Alonso	0.65	0.94	0.85	0.46	0.92	0.74	4.6
Jared Walsh	0.55	0.87	0.96	0.40	0.91	0.87	4.5
Chris Taylor	0.93	0.65	0.69	0.83	0.78	0.66	4.5
Corey Seager	0.73	0.72	0.79	0.37	0.97	0.97	4.5
Matt Beaty	0.89	0.56	0.97	0.57	0.72	0.82	4.5
Jonathan India	0.92	0.62	0.52	0.78	0.89	0.81	4.5
Willy Adames	0.80	0.81	0.73	0.59	0.86	0.74	4.5
Ryan Mountcastle	0.72	0.92	0.88	0.53	0.81	0.67	4.5
Mitch Haniger	0.94	0.92	0.83	0.33	0.83	0.65	4.5
Rafael Ortega	0.74	0.62	0.41	0.92	0.86	0.93	4.5
Tyrone Taylor	0.59	0.80	0.91	0.82	0.77	0.58	4.5
Brett Phillips	0.97	0.80	0.87	0.97	0.60	0.26	4.5
Jose Abreu	0.70	0.82	0.98	0.33	0.88	0.73	4.4
Carlos Correa	0.95	0.74	0.81	0.15	0.90	0.88	4.4
J.D. Martinez	0.86	0.79	0.90	0.05	0.92	0.91	4.4
Tyler Naquin	0.49	0.76	0.88	0.65	0.84	0.81	4.4
Gavin Sheets	0.67	0.96	1.00	0.29	0.87	0.62	4.4
Yasmani Grandal	0.94	0.96	0.95	0.09	0.97	0.51	4.4
Seth Brown	0.82	0.97	0.90	0.69	0.69	0.32	4.4
Adolis Garcia	0.62	0.87	0.82	0.87	0.66	0.55	4.4
Wander Franco	0.98	0.41	0.71	0.52	0.85	0.92	4.4
Sam Hilliard	0.76	0.95	0.81	0.81	0.70	0.33	4.4
Ryan McMahon	0.76	0.71	0.82	0.62	0.77	0.66	4.3
Nolan Arenado	0.62	0.88	0.93	0.39	0.84	0.67	4.3
Adalberto Mondesi	0.81	0.79	0.69	1.00	0.58	0.44	4.3
Adam Duvall	0.57	0.98	1.00	0.59	0.74	0.42	4.3
Austin Meadows	0.75	0.83	0.99	0.53	0.74	0.47	4.3
Mike Zunino	0.97	1.00	0.95	0.13	0.91	0.34	4.3
Harrison Bader	0.46	0.73	0.69	0.83	0.80	0.79	4.3
Matt Vierling	0.84	0.47	0.21	0.87	0.90	0.99	4.3
Steven Duggar	0.90	0.49	0.62	0.85	0.73	0.69	4.3
J.T. Realmuto	0.54	0.58	0.77	0.85	0.78	0.75	4.3
Kolten Wong	0.84	0.52	0.43	0.85	0.79	0.83	4.3
Mitch Garver	0.54	0.90	0.79	0.43	0.93	0.68	4.3
Lorenzo Cain	0.81	0.51	0.70	0.96	0.60	0.69	4.3
Austin Hays	0.79	0.75	0.76	0.55	0.74	0.68	4.3
Buster Posey	0.89	0.72	0.67	0.07	0.94	0.96	4.2
Luis Urias	0.77	0.73	0.73	0.58	0.81	0.61	4.2
Jake Rogers	0.75	0.85	0.75	0.56	0.83	0.50	4.2
Akil Baddoo	0.70	0.52	0.64	0.94	0.73	0.72	4.2
Yuli Gurriel	0.78	0.45	0.75	0.34	0.90	0.99	4.2
Josh Bell	0.73	0.85	0.89	0.12	0.86	0.73	4.2
Garrett Cooper	0.56	0.67	0.73	0.42	0.90	0.90	4.2
Andrew McCutchen	0.78	0.84	0.79	0.63	0.76	0.38	4.2
Brad Miller	0.82	0.89	0.72	0.56	0.75	0.42	4.2
Joey Gallo	0.87	0.96	0.69	0.61	0.84	0.21	4.2
Eduardo Escobar	0.67	0.84	0.87	0.34	0.80	0.64	4.2
Andrew Benintendi	0.51	0.58	0.76	0.71	0.72	0.86	4.2
Giancarlo Stanton	0.44	0.95	0.96	0.03	0.92	0.84	4.1
Jake Cronenworth	0.87	0.61	0.54	0.51	0.83	0.79	4.1
Lourdes Gurriel Jr.	0.49	0.71	0.89	0.35	0.80	0.86	4.1
Jose Rondon	0.86	0.62	0.41	0.83	0.63	0.75	4.1
Connor Joe	0.42	0.70	0.95	0.22	0.90	0.91	4.1
Bobby Dalbec	0.44	0.92	0.98	0.45	0.81	0.51	4.1
Eric Haase	0.64	0.94	0.92	0.47	0.67	0.44	4.1
Jean Segura	0.75	0.45	0.44	0.73	0.79	0.93	4.1
Mike Yastrzemski	0.83	0.84	0.74	0.54	0.73	0.40	4.1
Eddie Rosario	0.34	0.64	0.87	0.88	0.65	0.71	4.1
Jazz Chisholm Jr.	0.79	0.66	0.46	0.95	0.60	0.60	4.1
Dansby Swanson	0.55	0.75	0.76	0.70	0.71	0.60	4.1
Alex Bregman	0.77	0.56	0.78	0.38	0.76	0.81	4.1
Daulton Varsho	0.70	0.66	0.65	0.79	0.70	0.57	4.1
Taylor Ward	0.80	0.63	0.78	0.44	0.74	0.62	4.0
Francisco Lindor	0.81	0.70	0.64	0.79	0.63	0.43	4.0
Austin Slater	0.66	0.72	0.46	0.97	0.67	0.52	4.0
Alex Verdugo	0.86	0.37	0.46	0.62	0.76	0.93	4.0
Wilmer Flores	0.71	0.75	0.65	0.37	0.78	0.74	4.0
Tony Kemp	0.78	0.35	0.35	0.80	0.82	0.88	4.0
Miguel Sano	0.67	0.92	0.80	0.42	0.77	0.39	4.0
Danny Jansen	0.92	0.90	0.77	0.22	0.75	0.39	4.0
Christian Arroyo	0.58	0.62	0.78	0.48	0.73	0.74	3.9
Wil Myers	0.46	0.64	0.70	0.73	0.73	0.67	3.9
Willson Contreras	0.65	0.78	0.62	0.62	0.77	0.49	3.9
Joey Wendle	0.87	0.38	0.50	0.73	0.66	0.77	3.9
Max Schrock	0.83	0.39	0.46	0.54	0.76	0.92	3.9
Dustin Garneau	0.74	1.00	0.93	0.07	0.87	0.29	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 1397

---

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] -0.2

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 765)

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 764)

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 13 mins

Tuning Plot and Variable Importance

varImp(xgb_tune_2021, scale = F  ) 

xgbTree variable importance

  only 20 most important variables shown (out of 764)

# 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 764)

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 764)

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 764)

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 29 mins

xgb_tune5_2021$bestTune

varImp(xgb_tune5_2021, scale = F  ) 

xgbTree variable importance

  only 20 most important variables shown (out of 764)

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
 764 predictor

No pre-processing
Resampling: None 

varImp(xgb_model_2021, scale = F  ) 

xgbTree variable importance

  only 20 most important variables shown (out of 764)

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] 0.92

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] 0.99

#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] 0.37

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] 0.11

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),playerid,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),playerid,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
  79 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_Hitting6x6_Model.Rdata')

hitting6x6 = xgb_model_2022

hittinginput6X6 = 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

variableslag6xb= row_to_names(as.data.frame(t(variables_to_keep_2022)),row_number = 1) %>% select (starts_with(“lag”))

variables_nolag6xb = (owmr::remove_prefix(variableslag6xb,“lag” , sep = “_“))

Data_Predict_2022a6xb = total_treated_2021_hitting %>% select (one_of(colnames(variables_nolag6xb)),Season,playerid)

colnames(Data_Predict_2022a6xb) <- paste0(“lag_”, colnames(Data_Predict_2022a6xb))

Data_Predict_2022b6xb = total_treated_2021_hitting %>% select (one_of(colnames(variables_nolag6xb))) colnames(Data_Predict_2022b6xb) = colnames(variableslag6xb)

variables_to_keep_2022_nolag6xb = total_treated_2021_hitting %>% select(one_of(variables_to_keep_2022),Season,playerid,starts_with(“Team_lev_x_”))%>% select(-one_of(colnames(Data_Predict_2022b6xb)))

Data_predict_20226xb = sqldf( ” select a.,b. from Data_Predict_2022a6xb a, variables_to_keep_2022_nolag6xb 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),playerid,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_predictions6xb = as.data.frame(predict(xgb_model_2022,Data_predict_20226xb))

names(hitting_predictions6xb) = c("Predict_Score")

Data_predict_2022_w_hitting_Predictions6xb = cbind(Data_predict_20226xb,hitting_predictions6xb) %>% select(playerid,Predict_Score)

head(Data_predict_2022_w_hitting_Predictions6xb)

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, InterSD, InterSK, IntraSD

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_Projections6xb = sqldf(
  "
  select a.*,b.Predict_Score
  from Latest_2022_hittingdata_FP a 
  left join 
  Data_predict_2022_w_hitting_Predictions6xb b
  on a.playerid = b.playerid
  "
) %>% filter(ADP<370 | is.na(Predict_Score)==F)


hitting_Data_Adj_Projections6xb =
hitting_Data_NonAdj_Projections6xb %>% 
  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_Projections6xb$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_Projections6xb %>%
  arrange (ADP,WAR) %>% 
  kbl() %>% 
 kable_material(c("striped", "hover","condensed","responsive"),full_width = F,fixed_thead = T)

save_kable(tableexport,file = "hitting6x6.html")

#tableexport

This is a better formatted Table

ft_dt <- hitting_Data_Adj_Projections6xb[1:nrow(hitting_Data_Adj_Projections6xb), 1:ncol(hitting_Data_Adj_Projections6xb)] %>% 
  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 = "Hitting6x6_updated.html")
  
table_export  

	Scores			Ranks
Name	ADP	WAR	AdjPredict_Score	WAR_Rank	Predict_Rank	Ranks_Above_ADP
Marcus Semien	39.0	4.7	100.00	14	1	38
Bryce Harper	9.1	5.2	99.95	8	2	7.1
Vladimir Guerrero Jr.	5.6	5.6	98.11	5	3	2.6
Trea Turner	1.4	5.5	97.08	6	4	-2.6
Paul Goldschmidt	47.6	3.5	96.51	44	5	42.6
José Ramírez	3.4	5.9	96.06	3	6	-2.6
Bo Bichette	4.9	4.7	94.81	13	7	-2.1
Freddie Freeman	19.2	5.1	94.63	9	8	11.2
Rafael Devers	14.5	4.8	94.54	11	9	5.5
Shohei Ohtani	9.2	3.9	94.39	34	10	-0.8
Juan Soto	3.9	7.6	94.04	1	11	-7.1
Nick Castellanos	60.6	2.5	93.49	81	12	48.6
Luis Robert	16.2	4.6	93.25	16	13	3.2
Kyle Tucker	12.7	4.6	93.18	15	14	-1.3
Teoscar Hernández	27.8	2.3	92.29	98	15	12.8
Mookie Betts	15.4	5.8	89.86	4	16	-0.6
Jorge Polanco	77.8	3.1	89.62	58	17	60.8
Cedric Mullins	32.8	3.3	89.55	52	18	14.8
Aaron Judge	36.0	5.3	89.23	7	19	17
Matt Olson	39.0	4.2	88.97	28	20	19
Ozzie Albies	17.6	4.1	88.83	32	21	-3.4
Pete Alonso	46.2	3.5	88.13	43	22	24.2
Mike Trout	13.0	6.6	87.97	2	23	-10
Jose Altuve	73.3	3.8	87.86	36	24	49.3
Manny Machado	22.1	4.5	87.73	20	25	-2.9
Bryan Reynolds	93.0	4.2	87.46	30	26	67
George Springer	57.8	4.3	87.14	26	27	30.8
Tim Anderson	30.9	3.2	87.03	55	28	2.9
Tyler O'Neill	47.1	2.8	86.93	67	29	18.1
Xander Bogaerts	45.8	4.5	85.46	19	30	15.8
Ronald Acuña Jr.	10.4	5.0	84.52	10	31	-20.6
Yordan Alvarez	26.3	4.1	84.50	31	32	-5.7
Trevor Story	36.8	4.2	84.37	29	33	3.8
Javier Báez	62.5	2.8	83.60	68	34	28.5
Jonathan India	90.7	3.8	83.60	35	35	55.7
Brandon Lowe	75.4	4.3	82.47	24	36	39.4
Kris Bryant	84.9	2.4	82.30	86	37	47.9
Austin Riley	51.8	3.1	82.28	59	38	13.8
Frank Schwindel	233.8	0.8	81.82	262	39	194.8
Byron Buxton	48.0	4.4	80.75	23	40	8
Jesse Winker	111.8	3.4	80.71	45	41	70.8
José Abreu	78.4	2.1	80.56	114	42	36.4
Ketel Marte	77.2	3.7	80.38	37	43	34.2
Mitch Haniger	109.0	2.4	80.23	89	44	65
Starling Marte	31.2	2.4	80.18	84	45	-13.8
Salvador Perez	32.2	3.7	79.95	38	46	-13.8
Joey Votto	141.9	1.9	79.69	130	47	94.9
Nolan Arenado	67.7	3.7	79.03	39	48	19.7
Kyle Schwarber	114.5	2.7	79.00	71	49	65.5
J.D. Martinez	88.9	2.0	78.49	116	50	38.9
Rhys Hoskins	126.3	3.0	77.54	61	51	75.3
Randy Arozarena	65.4	2.1	77.53	113	52	13.4
Franmil Reyes	120.9	1.8	77.34	132	53	67.9
Avisaíl García	170.2	2.3	77.03	93	54	116.2
Wander Franco	58.9	4.5	76.70	21	55	3.9
Ryan Mountcastle	119.7	1.6	76.37	161	56	63.7
Brandon Crawford	219.2	2.7	75.82	70	57	162.2
Carlos Correa	100.9	4.7	75.22	12	58	42.9
Brandon Belt	226.3	2.5	74.80	80	59	167.3
Corey Seager	78.3	4.5	74.79	22	60	18.3
Jared Walsh	119.8	2.2	74.41	101	61	58.8
Willy Adames	123.2	3.3	73.96	49	62	61.2
Dansby Swanson	119.1	2.7	73.62	72	63	56.1
C.J. Cron	124.7	1.7	73.33	154	64	60.7
Max Muncy	134.9	3.2	72.27	54	65	69.9
Jake Cronenworth	127.1	3.4	72.23	48	66	61.1
Hunter Renfroe	161.4	1.7	71.92	153	67	94.4
Francisco Lindor	53.5	4.2	71.53	27	68	-14.5
J.T. Realmuto	48.8	3.6	71.20	40	69	-20.2
Joey Gallo	177.0	3.5	71.12	42	70	107
Josh Bell	127.6	1.9	71.02	129	71	56.6
Justin Turner	152.7	3.0	70.54	62	72	80.7
Nelson Cruz	171.9	1.4	70.54	181	73	98.9
Eduardo Escobar	200.5	1.9	69.73	131	74	126.5
Austin Meadows	145.5	1.5	69.72	168	75	70.5
Whit Merrifield	32.5	2.3	69.04	97	76	-43.5
Giancarlo Stanton	97.7	2.3	68.42	99	77	20.7
Kolten Wong	190.8	2.5	68.12	79	78	112.8
Anthony Rizzo	193.6	2.1	67.38	109	79	114.6
Ty France	154.6	2.4	67.17	88	80	74.6
Jean Segura	202.3	2.4	67.02	91	81	121.3
Jazz Chisholm Jr.	76.0	1.8	66.56	138	82	-6
Dylan Carlson	172.0	2.5	66.20	77	83	89
Will Smith	53.2	4.6	65.75	17	84	-30.8
Chris Taylor	139.9	2.3	65.74	95	85	54.9
Austin Hays	243.2	1.7	65.74	150	86	157.2
Adolis García	166.0	1.3	65.73	201	87	79
Enrique Hernández	226.7	2.8	65.73	66	88	138.7
Ryan McMahon	161.2	2.0	65.39	119	89	72.2
Mark Canha	283.4	1.8	65.26	141	90	193.4
Luis Urías	173.3	2.5	64.82	75	91	82.3
Alex Bregman	88.3	4.5	64.79	18	92	-3.7
Lourdes Gurriel Jr.	145.2	1.4	64.25	187	93	52.2
Jonathan Schoop	213.2	1.7	64.03	145	94	119.2
Andrew Benintendi	192.7	1.9	64.02	125	95	97.7
Alex Verdugo	168.6	2.4	63.99	85	96	72.6
Yuli Gurriel	210.6	1.4	63.75	189	97	113.6
Akil Baddoo	156.9	1.6	63.45	158	98	58.9
Yasmani Grandal	101.1	4.0	63.38	33	99	2.1
Jesús Sánchez	226.3	2.0	62.86	115	100	126.3
Mike Yastrzemski	286.5	2.2	62.81	108	101	185.5
AJ Pollock	218.6	1.8	62.28	136	102	116.6
Nathaniel Lowe	251.7	1.8	62.25	133	103	148.7
Miguel Sanó	280.4	0.8	62.04	271	104	176.4
Robbie Grossman	191.3	1.7	61.68	149	105	86.3
Max Kepler	294.6	2.5	61.62	76	106	188.6
Trent Grisham	143.5	3.1	61.50	57	107	36.5
Jeimer Candelario	239.6	3.2	61.14	56	108	131.6
Ian Happ	213.2	2.2	61.05	104	109	104.2
Yoán Moncada	158.7	3.4	60.86	47	110	48.7
Harrison Bader	235.8	2.9	60.62	63	111	124.8
Tyler Naquin	352.7	0.7	60.57	274	112	240.7
Charlie Blackmon	236.7	0.6	59.83	301	113	123.7
Brendan Rodgers	169.9	1.6	59.65	160	114	55.9
Tommy Edman	90.2	2.3	59.59	94	115	-24.8
Christian Yelich	97.2	2.9	59.12	64	116	-18.8
Ramón Laureano	247.0	2.6	58.58	74	117	130
LaMonte Wade Jr.	343.8	1.0	58.23	233	118	225.8
Patrick Wisdom	358.3	1.2	57.95	214	119	239.3
Amed Rosario	170.7	2.0	57.87	118	120	50.7
Jorge Soler	192.2	1.7	57.55	147	121	71.2
Josh Donaldson	187.1	3.3	57.21	51	122	65.1
Eloy Jiménez	65.1	2.2	57.01	105	123	-57.9
Andrew McCutchen	348.9	0.9	56.86	254	124	224.9
Bobby Dalbec	229.2	1.6	56.81	157	125	104.2
Adam Frazier	400.3	2.0	56.78	117	126	274.3
Trey Mancini	205.0	1.4	56.69	180	127	78
Eddie Rosario	181.7	0.9	56.52	251	128	53.7
Brandon Nimmo	324.3	3.2	56.45	53	129	195.3
Willson Contreras	109.9	2.7	56.44	69	130	-20.1
Adalberto Mondesi	57.3	2.0	56.27	123	131	-73.7
Adam Duvall	212.8	1.7	56.11	148	132	80.8
Wil Myers	288.9	1.0	56.03	231	133	155.9
Rafael Ortega	337.3	1.3	55.28	193	134	203.3
Seth Brown	502.2	0.6	55.16	293	135	367.2
Nicky Lopez	250.4	1.8	55.05	140	136	114.4
Matt Chapman	181.5	3.6	54.39	41	137	44.5
Tony Kemp	459.8	1.6	54.22	155	138	321.8
Fernando Tatis Jr.	27.5	3.4	54.02	46	139	-111.5
Josh Rojas	247.6	1.5	53.63	167	140	107.6
Jo Adell	228.4	0.6	53.30	300	141	87.4
Michael Brantley	251.4	2.0	52.69	122	142	109.4
Miguel Rojas	475.9	2.3	52.48	96	143	332.9
Joey Wendle	403.7	1.3	51.66	194	144	259.7
Jesús Aguilar	300.5	1.2	51.03	215	145	155.5
Gavin Sheets	525.8	0.8	50.88	265	146	379.8
Daulton Varsho	93.3	2.1	50.85	110	147	-53.7
J.P. Crawford	452.2	2.5	50.81	83	148	304.2
Tyler Stephenson	138.3	2.9	50.42	65	149	-10.7
Ke'Bryan Hayes	142.6	3.3	49.79	50	150	-7.4
Tommy Pham	274.8	1.9	49.76	126	151	123.8
Anthony Santander	298.7	1.3	49.58	197	152	146.7
Mike Zunino	253.5	2.0	49.34	124	153	100.5
Eugenio Suárez	198.5	2.2	49.10	107	154	44.5
César Hernández	403.0	1.7	48.99	152	155	248
DJ LeMahieu	119.2	2.4	48.80	87	156	-36.8
Lorenzo Cain	451.3	1.5	47.99	176	157	294.3
Alex Kirilloff	177.6	1.5	47.90	165	158	19.6
Evan Longoria	455.5	1.5	47.66	174	159	296.5
Mitch Garver	166.1	2.4	47.43	90	160	6.1
Isiah Kiner-Falefa	296.8	1.6	47.29	163	161	135.8
Michael Conforto	207.5	2.7	46.72	73	162	45.5
Jonathan Villar	253.1	1.1	46.42	225	163	90.1
Jake Meyers	593.2	1.1	46.36	217	164	429.2
Myles Straw	127.0	2.3	46.30	100	165	-38
Eric Hosmer	470.1	0.4	46.29	329	166	304.1
Raimel Tapia	273.9	0.1	46.23	384	167	106.9
Andrew Vaughn	250.3	1.2	46.13	207	168	82.3
Randal Grichuk	419.6	0.6	45.75	292	169	250.6
Luke Voit	253.5	1.3	45.06	199	170	83.5
Connor Joe	341.2	0.8	44.99	255	171	170.2
Gleyber Torres	154.9	2.5	44.96	78	172	-17.1
Brad Miller	551.8	1.4	44.72	179	173	378.8
Pavin Smith	476.9	0.6	44.58	286	174	302.9
Wilmer Flores	472.5	1.2	44.38	208	175	297.5
Nick Madrigal	342.1	1.8	44.36	139	176	166.1
Yandy Díaz	464.5	1.6	43.94	156	177	287.5
Odúbel Herrera	583.2	1.1	43.70	228	178	405.2
Ramón Urías	529.7	1.6	43.62	162	179	350.7
Lane Thomas	269.2	1.1	43.28	226	180	89.2
Paul DeJong	491.4	2.1	43.27	112	181	310.4
Sam Hilliard	477.8	0.5	43.17	309	182	295.8
Joc Pederson	480.8	0.8	43.03	270	183	297.8
David Peralta	518.1	1.1	42.92	221	184	334.1
Luis Arraez	351.3	1.5	42.86	177	185	166.3
Bobby Bradley	491.0	0.5	42.52	308	186	305
Adam Engel	597.3	0.6	42.42	290	187	410.3
Manuel Margot	416.6	1.1	42.41	229	188	228.6
Josh Harrison	412.3	1.1	42.33	224	189	223.3
Jarred Kelenic	141.5	1.5	41.87	169	190	-48.5
Alcides Escobar	586.4	0.2	41.77	374	191	395.4
David Fletcher	417.0	1.3	41.64	191	192	225
Darin Ruf	454.5	0.9	41.61	247	193	261.5
Jeff McNeil	323.0	1.9	41.47	128	194	129
José Iglesias	547.7	1.2	41.30	210	195	352.7
Keibert Ruiz	145.4	2.2	41.24	106	196	-50.6
Garrett Cooper	527.1	1.0	41.17	238	197	330.1
Ji-Man Choi	577.3	0.7	40.70	282	198	379.3
Ben Gamel	579.0	0.9	40.53	242	199	380
Michael A. Taylor	516.5	1.2	40.05	212	200	316.5
Kyle Lewis	399.0	1.5	40.01	170	201	198
Kyle Farmer	528.0	1.1	39.87	220	202	326
Chas McCormick	495.8	1.1	39.85	218	203	292.8
Tyrone Taylor	526.6	1.1	39.84	222	204	322.6
Brian Anderson	495.9	1.8	39.79	137	205	290.9
Anthony Rendon	110.2	4.3	39.62	25	206	-95.8
Elias Díaz	229.7	1.3	39.11	198	207	22.7
Max Stassi	300.6	1.8	38.90	135	208	92.6
Jake Fraley	547.6	0.9	38.79	241	209	338.6
Hunter Dozier	443.8	0.6	38.60	288	210	233.8
Gio Urshela	306.8	1.3	38.29	192	211	95.8
Alec Bohm	311.6	1.4	38.25	188	212	99.6
Kevin Kiermaier	588.0	1.2	38.24	203	213	375
Chad Pinder	570.3	0.6	37.37	291	214	356.3
Leury García	536.2	0.7	37.13	281	215	321.2
Eric Haase	302.9	0.7	36.82	278	216	86.9
Christian Walker	506.6	0.7	36.67	276	217	289.6
Carlos Santana	511.8	0.7	36.64	272	218	293.8
Danny Jansen	403.1	1.9	36.18	127	219	184.1
Carson Kelly	280.7	2.0	36.18	120	220	60.7
Steven Duggar	597.8	0.7	35.95	275	221	376.8
Gavin Lux	266.4	1.5	35.79	178	222	44.4
Rowdy Tellez	342.4	0.8	35.59	268	223	119.4
Marcell Ozuna	174.3	2.2	35.55	103	224	-49.7
Yadiel Hernandez	583.5	0.1	35.52	400	225	358.5
Sean Murphy	252.4	2.5	35.18	82	226	26.4
Abraham Toro	323.7	1.4	35.13	190	227	96.7
Andy Ibáñez	563.1	1.5	35.07	175	228	335.1
Edmundo Sosa	501.9	1.5	34.71	172	229	272.9
Miguel Cabrera	539.9	-0.4	34.53	500	230	309.9
Gary Sánchez	245.3	1.3	34.33	196	231	14.3
Tommy La Stella	561.3	1.2	34.17	213	232	329.3
Kyle Isbel	542.5	0.6	34.00	299	233	309.5
Yoshi Tsutsugo	431.9	0.3	33.96	336	234	197.9
Jordan Luplow	600.6	0.8	33.83	263	235	365.6
Justin Upton	565.1	0.3	33.82	349	236	329.1
Andrés Giménez	282.5	1.7	33.51	146	237	45.5
Brandon Marsh	418.4	1.2	33.43	209	238	180.4
Cody Bellinger	98.9	3.1	33.43	60	239	-140.1
Garrett Hampson	299.6	0.3	33.38	340	240	59.6
Austin Slater	575.2	1.0	33.27	232	241	334.2
Christian Vázquez	201.1	1.7	33.26	142	242	-40.9
Bradley Zimmer	505.3	0.7	32.70	280	243	262.3
Nick Solak	494.8	0.7	32.51	284	244	250.8
Yadier Molina	322.4	1.2	32.43	211	245	77.4
Kole Calhoun	522.9	1.1	32.23	227	246	276.9
Anthony Alford	555.4	0.0	32.21	468	247	308.4
Kevin Pillar	590.7	0.4	32.16	314	248	342.7
Alejandro Kirk	237.1	1.7	32.10	144	249	-11.9
Nick Ahmed	581.5	1.1	31.97	223	250	331.5
Jurickson Profar	540.1	0.3	31.88	341	251	289.1
Bryan De La Cruz	534.7	0.9	31.53	240	252	282.7
Colin Moran	582.8	-0.1	31.44	494	253	329.8
Jed Lowrie	597.2	0.8	31.04	267	254	343.2
Cavan Biggio	319.3	1.4	30.99	184	255	64.3
Didi Gregorius	513.0	0.9	30.82	252	256	257
Lars Nootbaar	572.6	0.2	30.61	375	257	315.6
Ha-Seong Kim	349.8	1.5	30.60	173	258	91.8
Nick Senzel	422.0	1.0	30.27	234	259	163
Corey Dickerson	581.1	0.1	30.04	388	260	321.1
Willie Calhoun	451.5	0.2	30.00	359	261	190.5
Jacob Stallings	421.9	2.2	29.89	102	262	159.9
J.D. Davis	511.6	0.9	29.81	253	263	248.6
Omar Narváez	251.8	1.7	29.56	143	264	-12.2
Santiago Espinal	557.5	0.7	29.54	273	265	292.5
Luis García	556.8	0.7	29.43	277	266	290.8
Yan Gomes	442.1	1.5	29.31	166	267	175.1
Nico Hoerner	506.9	1.4	29.19	182	268	238.9
Aristides Aquino	585.4	0.4	29.07	333	269	316.4
Dominic Smith	456.4	0.5	29.02	304	270	186.4
Elvis Andrus	566.4	0.9	29.00	248	271	295.4
Michael Chavis	569.3	0.0	28.96	477	272	297.3
Ryan Jeffers	480.5	1.6	28.59	159	273	207.5
Thairo Estrada	600.3	0.8	28.55	259	274	326.3
Mike Moustakas	374.8	0.9	28.39	245	275	99.8
Josh Naylor	587.4	0.5	28.26	306	276	311.4
Rougned Odor	504.9	0.6	28.11	289	277	227.9
Jorge Mateo	472.9	0.5	28.09	312	278	194.9
Jake McCarthy	600.8	0.0	27.82	429	279	321.8
Travis d'Arnaud	224.4	1.4	27.34	183	280	-55.6
Oscar Mercado	593.3	0.7	26.97	285	281	312.3
Austin Nola	377.8	1.5	26.90	171	282	95.8
Luis Torrens	446.4	0.7	26.55	279	283	163.4
Victor Reyes	592.8	0.4	26.27	316	284	308.8
Aledmys Díaz	573.3	0.9	25.96	243	285	288.3
Niko Goodrum	571.4	0.7	25.90	283	286	285.4
Alex Dickerson	597.9	0.2	25.34	357	287	310.9
Francisco Mejía	492.9	1.3	25.26	202	288	204.9
James McCann	351.5	1.4	24.39	186	289	62.5
Brett Phillips	590.7	0.3	24.18	337	290	300.7
Daniel Vogelbach	586.6	0.6	23.82	298	291	295.6
Aaron Hicks	524.7	1.6	23.74	164	292	232.7
Tucker Barnhart	471.5	1.3	23.71	200	293	178.5
Brett Gardner	598.6	0.6	23.57	297	294	304.6
Tyler Wade	505.0	0.4	23.51	331	295	210
Victor Robles	474.9	1.0	23.47	237	296	178.9
Stephen Piscotty	594.4	-0.2	23.41	497	297	297.4
Roberto Pérez	545.7	1.3	23.39	195	298	247.7
Donovan Solano	594.0	0.4	23.28	324	299	295
Jason Heyward	594.8	0.8	23.20	269	300	294.8
Kevin Newman	597.1	0.5	22.44	303	301	296.1
Manny Piña	570.7	1.2	22.42	205	302	268.7
Jarren Duran	497.2	0.4	22.24	327	303	194.2
Mitch Moreland	600.9	0.3	21.77	342	304	296.9
Jorge Alfaro	454.9	0.0	21.75	474	305	149.9
Matt Vierling	578.6	0.5	21.62	313	306	272.6
Yu Chang	596.6	0.4	21.45	334	307	289.6
Kyle Higashioka	555.9	1.0	21.33	236	308	247.9
DJ Stewart	600.3	0.5	21.32	305	309	291.3
Cole Tucker	586.6	0.4	20.09	325	310	276.6
Jace Peterson	579.4	0.2	20.02	361	311	268.4
Matt Beaty	591.6	0.2	19.96	358	312	279.6
Dom Nuñez	586.9	0.5	19.74	311	313	273.9
Matt Duffy	586.2	0.5	19.74	310	314	272.2
Pedro Severino	522.6	0.9	19.66	244	315	207.6
Josh VanMeter	597.6	0.4	19.59	323	316	281.6
Martín Maldonado	562.4	1.2	19.36	216	317	245.4
Trevor Larnach	565.1	0.1	19.26	390	318	247.1
Brent Rooker	597.9	0.3	19.16	348	319	278.9
Clint Frazier	472.5	0.3	19.12	345	320	152.5
Lewin Díaz	585.3	0.3	19.00	344	321	264.3
Carter Kieboom	539.8	0.8	18.82	258	322	217.8
Austin Hedges	587.0	0.5	18.76	307	323	264
Mike Brosseau	597.1	0.4	18.20	320	324	273.1
Albert Pujols	597.5	-0.3	17.99	499	325	272.5
Taylor Walls	587.7	1.1	17.93	219	326	261.7
Tom Murphy	539.9	1.2	17.55	204	327	212.9
Brian Goodwin	600.6	0.1	17.21	399	328	272.6
Asdrúbal Cabrera	599.6	0.0	17.10	425	329	270.6
Robinson Chirinos	595.5	0.6	17.08	294	330	265.5
Max Schrock	599.8	0.0	16.79	467	331	268.8
Ehire Adrianza	999.0	0.1	16.42	403	332	667
Derek Hill	999.0	0.1	16.30	387	333	666
Dylan Moore	509.0	0.4	16.21	326	334	175
Jason Castro	599.2	0.8	16.17	256	335	264.2
Keston Hiura	465.4	0.2	16.11	364	336	129.4
Andrew Stevenson	597.8	-0.1	15.79	482	337	260.8
Christian Arroyo	594.0	0.2	15.56	354	338	256
Riley Adams	593.0	0.8	15.52	257	339	254
Jackie Bradley Jr.	589.7	0.2	15.46	379	340	249.7
Kevin Plawecki	581.7	0.8	15.38	261	341	240.7
Austin Barnes	592.5	0.8	15.06	264	342	250.5
Jose Barrero	478.7	1.2	15.06	206	343	135.7
Andrelton Simmons	586.5	1.1	14.85	230	344	242.5
Yonathan Daza	598.7	-0.1	14.83	480	345	253.7
Kelvin Gutierrez	597.6	0.3	14.80	351	346	251.6
Curt Casali	592.1	0.9	14.52	250	347	245.1
Edward Olivares	584.8	0.3	14.31	346	348	236.8
Taylor Ward	600.9	0.4	14.18	322	349	251.9
Danny Santana	999.0	0.1	14.08	406	350	649
Adam Eaton	999.0	0.2	13.97	355	351	648
Nick Gordon	584.3	0.4	13.90	330	352	232.3
Jonah Heim	531.7	0.9	13.76	246	353	178.7
Daz Cameron	599.3	0.2	13.64	378	354	245.3
Harold Castro	599.7	-0.1	13.58	492	355	244.7
Ben Rortvedt	999.0	1.0	12.93	235	356	643
Jake Marisnick	999.0	0.2	12.48	381	357	642
Victor Caratini	582.5	0.8	12.48	260	358	224.5
Guillermo Heredia	999.0	-0.1	12.45	491	359	640
Sheldon Neuse	999.0	0.2	11.95	362	360	639
Shed Long Jr.	999.0	0.2	11.67	367	361	638
Harold Ramirez	574.0	0.2	11.51	373	362	212
Charlie Culberson	597.2	0.0	11.21	432	363	234.2
Jarrod Dyson	999.0	-0.2	11.19	495	364	635
Willi Castro	563.7	0.3	11.01	352	365	198.7
Jon Berti	576.3	0.4	10.83	328	366	210.3
Juan Lagares	999.0	0.1	10.83	417	367	632
Luis Rengifo	600.2	0.4	10.62	317	368	232.2
Wilson Ramos	600.9	0.6	10.62	296	369	231.9
Mauricio Dubón	596.2	0.3	10.62	335	370	226.2
Nomar Mazara	600.4	0.1	10.48	416	371	229.4
Leody Taveras	573.9	0.6	10.47	287	372	201.9
Starlin Castro	597.3	0.4	10.42	332	373	224.3
Jack Mayfield	600.9	0.1	10.36	407	374	226.9
Cristian Pache	573.0	0.8	10.35	266	375	198
Khris Davis	999.0	0.0	10.12	448	376	623
Tomás Nido	601.0	0.2	10.11	360	377	224
Hoy Park	600.7	0.4	9.80	315	378	222.7
Owen Miller	600.7	0.2	9.57	365	379	221.7
Hanser Alberto	600.3	0.2	9.53	356	380	220.3
Maikel Franco	590.0	0.0	9.42	444	381	209
Jose Trevino	596.6	0.3	9.31	343	382	214.6
Josh Reddick	999.0	-0.2	9.29	498	383	616
Zack Collins	598.5	0.6	9.26	302	384	214.5
Shogo Akiyama	600.8	0.0	9.24	449	385	215.8
Zach McKinstry	596.2	0.1	9.22	382	386	210.2
Seby Zavala	598.5	-0.1	9.14	485	387	211.5
David Bote	597.7	0.4	9.11	319	388	209.7
Ronald Torreyes	600.9	0.0	9.06	453	389	211.9
Eli White	599.7	0.1	8.92	414	390	209.7
Michael Perez	999.0	0.4	8.54	318	391	608
Andrew Velazquez	600.7	0.2	8.46	368	392	208.7
Orlando Arcia	600.8	0.3	8.44	350	393	207.8
Wilmer Difo	999.0	-0.1	8.38	487	394	605
Kurt Suzuki	599.5	0.2	8.26	369	395	204.5
Travis Shaw	600.5	0.1	8.16	418	396	204.5
Jake Rogers	600.8	0.3	8.14	347	397	203.8
Renato Núñez	599.6	0.2	8.00	380	398	201.6
Ryan O'Hearn	599.8	0.0	7.85	461	399	200.8
Miguel Andújar	595.5	0.0	7.73	439	400	195.5
Matt Carpenter	597.1	0.0	7.68	458	401	196.1
Kyle Garlick	999.0	0.0	7.67	471	402	597
Jake Cave	999.0	-0.1	7.45	490	403	596
Cal Raleigh	560.8	0.9	7.42	249	404	156.8
Joe Panik	999.0	0.1	7.29	422	405	594
Jose Rojas	600.4	0.0	7.09	450	406	194.4
Andrew Knizner	598.8	0.6	7.00	295	407	191.8
Sergio Alcántara	600.5	0.2	6.99	371	408	192.5
Emmanuel Rivera	999.0	0.1	6.96	393	409	590
Billy McKinney	599.2	0.0	6.74	438	410	189.2
Chris Owings	600.7	0.0	6.73	435	411	189.7
Danny Mendick	999.0	0.1	6.59	383	412	587
Stephen Vogt	999.0	0.1	6.55	409	413	586
Eric Sogard	999.0	0.0	6.49	430	414	585
Daniel Johnson	999.0	0.0	6.46	442	415	584
Ernie Clement	999.0	0.2	6.32	370	416	583
Luis Guillorme	999.0	0.3	6.29	353	417	582
Billy Hamilton	597.8	-0.1	6.28	489	418	179.8
Lewis Brinson	600.5	0.0	6.26	441	419	181.5
Rob Refsnyder	999.0	0.0	6.20	447	420	579
Yonny Hernandez	599.8	0.3	6.19	339	421	178.8
Marwin Gonzalez	600.4	0.2	6.05	376	422	178.4
Phil Gosselin	594.3	0.0	6.04	460	423	171.3
Tim Locastro	598.6	0.1	5.93	401	424	174.6
Jordy Mercer	999.0	-0.1	5.89	483	425	574
Brock Holt	999.0	0.1	5.77	423	426	573
Nick Maton	999.0	0.0	5.73	434	427	572
Sandy León	999.0	0.0	5.67	443	428	571
Isaac Paredes	600.3	0.4	5.55	321	429	171.3
José Peraza	600.8	0.1	5.45	410	430	170.8
Tres Barrera	999.0	0.1	5.38	398	431	568
Travis Jankowski	999.0	0.0	5.38	427	432	567
Taylor Trammell	600.9	0.1	5.18	396	433	167.9
Rodolfo Castro	999.0	0.1	5.14	386	434	565
Pablo Reyes	999.0	0.1	4.97	408	435	564
Taylor Jones	999.0	0.0	4.78	436	436	563
William Contreras	593.2	0.2	4.75	363	437	156.2
Rafael Marchan	999.0	0.1	4.62	392	438	561
Dustin Garneau	999.0	0.1	4.50	402	439	560
José Rondón	999.0	0.0	4.42	446	440	559
Cam Gallagher	600.6	0.3	4.23	338	441	159.6
Reese McGuire	599.1	0.2	4.21	372	442	157.1
DJ Peters	600.5	0.0	4.11	464	443	157.5
Jahmai Jones	600.7	0.1	4.00	411	444	156.7
Yermín Mercedes	596.9	0.1	3.88	415	445	151.9
Luke Williams	999.0	0.0	3.86	455	446	553
Drew Ellis	999.0	0.1	3.84	389	447	552
Roman Quinn	999.0	0.0	3.75	454	448	551
Matt Joyce	999.0	-0.1	3.67	484	449	550
Jake Lamb	999.0	0.1	3.65	404	450	549
Evan White	600.2	0.0	3.59	437	451	149.2
Isan Díaz	600.5	0.1	3.57	421	452	148.5
Richie Martin	999.0	-0.1	3.57	479	453	546
Alan Trejo	999.0	0.0	3.53	426	454	545
Andrew Knapp	600.9	0.0	3.53	462	455	145.9
Alex Jackson	999.0	0.2	3.40	366	456	543
Alex Blandino	999.0	-0.2	3.36	496	457	542
Jake Bauers	600.5	-0.1	3.27	486	458	142.5
Ryan McKenna	999.0	0.1	3.25	391	459	540
Zack Short	999.0	0.1	3.14	385	460	539
Aramis Garcia	999.0	0.0	3.14	457	461	538
Delino DeShields	999.0	0.0	3.13	451	462	537
Magneuris Sierra	999.0	-0.1	3.01	493	463	536
Donovan Walton	999.0	0.1	2.98	405	464	535
Ender Inciarte	600.0	0.0	2.98	465	465	135
Grayson Greiner	999.0	0.0	2.95	456	466	533
Jason Vosler	999.0	0.1	2.85	419	467	532
David Dahl	599.6	0.0	2.65	476	468	131.6
Skye Bolt	999.0	0.2	2.65	377	469	530
Daniel Robertson	999.0	0.0	2.61	440	470	529
John Nogowski	600.8	0.0	2.58	433	471	129.8
Gilberto Celestino	999.0	0.1	2.49	413	472	527
Austin Romine	999.0	0.1	2.47	395	473	526
Luke Raley	999.0	0.1	2.26	420	474	525
Abraham Almonte	999.0	0.0	2.26	459	475	524
Tucupita Marcano	600.6	0.0	2.13	428	476	124.6
Pat Valaika	999.0	0.0	1.99	469	477	522
Rio Ruiz	999.0	0.0	1.80	452	478	521
Jonathan Araúz	999.0	0.0	1.77	424	479	520
Webster Rivas	999.0	0.0	1.71	470	480	519
Wyatt Mathisen	999.0	0.0	1.68	431	481	518
Phillip Evans	999.0	0.0	1.59	478	482	517
Willians Astudillo	601.0	0.0	1.59	466	483	118
Jonathan Davis	999.0	-0.1	1.51	481	484	515
Patrick Mazeika	999.0	0.1	1.49	412	485	514
Ildemaro Vargas	999.0	0.0	1.41	445	486	513
Franchy Cordero	999.0	0.0	1.20	463	487	512
Chad Wallach	999.0	0.0	1.19	475	488	511
Justin Williams	999.0	-0.1	1.03	488	489	510
Edwin Ríos	587.4	0.0	0.91	472	490	97.4
Chance Sisco	999.0	0.1	0.80	397	491	508
Albert Almora Jr.	999.0	0.1	0.64	394	492	507
Robel García	999.0	0.0	0.50	473	493	506

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