SRBG Baseball – Photo Credit: Steve Etzel

Published Seasons – 1961 to 2024.

SBRG Baseball (“SRBG”) is a classic-style baseball sim that uses a 20-sided die and three 6-sided dice to drive the action. In a manner similar to Statis Pro Baseball, the d20 is used to determine if the pitcher or batter card is used to resolve the plate appearance. Each pitcher has a unique “dominator” rating, expressed as a range, such as 7-20. If the d20 roll falls within the range the pitcher controls the plate appearance. Otherwise, the batter card is used. The play result is read from the appropriate player card based on the 3d6 dice roll. The white die determines the column to be used and the two black d6 dice are summed to determine the row. The cards have a somewhat unconventional design, with the columns having uneven probabilities: 1-3, 4-5, and 6.

SBRG has basic runner advancement rules as well as left-right platoon split effects.

The game was shipped in a colorful shrink-wrapped box and included a play mat. Each team comes with 27 player cards. There was also a “light” version that did not have the game box or the playmat.

The game was initially launched through a Kickstarter campaign. Unfortunately, the designer no longer produces SRBG due to a natural disaster that destroyed the equipment that was used to produce the game. There is a Facebook group dedicated to SRBG but it is inactive: https://www.facebook.com/profile.php?id=61556348115741

– Steve Etzel

SRBG Baseball – Photo Credit: Steve Etzel

Methodology

I devised a series of rules that govern the creation of my “Actuals” leagues. For OOTP, there are a number of settings that I enable or disable to achieve my desired rules and league structure.

Initial setup:

New Game -> New Historical Game

• Default Mode enabled
• Uncheck Import complete history up to XXXX
• No Minor Leagues
• No Fictional Pictures
• Game Name: HBRetro-Actuals–YYYY-ZZZZ where YYYY is the first year in the half-decade and ZZZZ is the last year.
• Starting Year -> use the last year (ZZZZ), Auto-Save daily
• GM & Manager role, Play in Commissioner Mode and Cannot be fired
• Game Settings -> League Settings -> Functions -> Under Roster Functions, select Clear Rosters.
• MLB -> Transactions -> Free Agents -> All Free Agents. Click the Actions drop-down and select Delete all free agents.
• optional: In certain expansion seasons, it is necessary to delete some of the Actuals half-decade teams from the league as there are not enough player-seasons to field a competitive team.
• Game Settings -> Global Settings -> Uncheck Use Coaching System and uncheck Enable Storylines.
• Players & Team -> Place a checkmark next to Disable Player Development Lab and Disable Player Development Focus.
• FaceGen -> Change Picture Search Mode to Search for photo first.
• League Settings -> Rules -> Place checkmark next to AL Designated Hitter Rule and NL Designated Hitter Rule.
• Change the Active Roster size to 25. and the Expanded Roster Size to 40.
• Uncheck Enable Trading and Enable Amateur Draft. Ensure that Disable automatic creation of free agents is checked.
• League Settings -> Historical -> Uncheck Automatically Expand League, Automatically adjust league strategy when advancing to next season, Retire players according to history, Players miss seasons according to history and Automatically import historical rookies.
• League Settings -> Stats & AI -> change Typical Starting Rotation Size to 4 Man Rotation for seasons between 1900-1970.
• Manager – Manager Options – Set Active Roster Moves (Demotions, Promotions, IL, etc) to Computer AI. Set all other Team Control Settings to the human manager.
• Update the team ID’s in the text file that you will use for importing historical players to use the Abbreviation listed in Team Settings for each team.
• MLB -> Transactions -> Free Agents -> All Free Agents. Click the Actions drop-down and select Import Historical Player(s). Keep the defaults on the Select the historical_database.odb file dialog box, then click CONFIRM.
• Import Historical Players from text file -> In the field to the right of OR select text file containing numerous entries, click on Click here to select my CSV file. Change Ratings Calculation Base to 1 year, and Base Fielding Ratings and Base Pitching Stamina each to Imported Season. Change Double weight on current year stats to No, then click Import Player to initiate the import process.

I configure the active and reserve rosters, starting lineups vs. RHP and LHP, starting rotations, and bullpen assignments for every team, then make a backup copy before starting the season replay.

• DH is used for all teams
• 25-man active roster, 15 players in reserves
• must have 2 active catchers
• active rosters consist of 15 batters/10 pitchers or 14 batters/11 pitchers
• vsRHP, vsLHP and starting rotations are generally based on the highest Win Shares single-season totals for each position
• schedules generated using the format utilized during that timeframe

The following videos include a recap of the regular season standings and league leaders, American and National League playoff recap, and individual World Series game videos.

Regular Season Recap

Standings and League Leaders – coming soon!

World Series

Game 1

Game 2

Game 3

Game 4

Game 5

Game 6

Game 7

available now at Amazon for $44.99 (paperback) and $19.99 (Kindle)

Features 589 full-color pages with 150+ baseball tabletop sims and 16 interviews with game designers!

This compendium celebrates 100 years of baseball tabletop simulation games. Our goal with this book is to memorialize as many of these simulation games as possible and document the richness of the hobby we love. Game descriptions are supplemented by articles, game designer interviews, and comments and ratings from over 250 individuals from the tabletop baseball community. The authors wish to encourage readers to explore these games, both past and present, create their own teams and leagues, and share those results with fellow baseball gaming and simulation enthusiasts!

Baseball games are almost as old as the game of baseball itself. What started out as mechanical devices to emulate the batted ball, evolved into tabletop games designed to simulate the entire baseball game experience. Eventually, randomizer mechanisms entered the fray in the form of dice and spinners. Most early games were generic in nature. However, in 1925 we saw what is arguably the first true baseball tabletop simulation with the introduction of National Pastime by Clifford Van Beek. More tabletop games followed, including Ethan Allen’s All Star Baseball Game in the 1940s, the APBA Major League Baseball Game in the 1950s, and Strat-O-Matic Baseball in the early 1960s. Other game designers followed with creations of their own, a trend that continues today.

Book Discussions / Promotional Appearances

Praise for Hardball Retro’s Compendium of Baseball Tabletop Simulations

“This is really a first class effort by the authors. Love the color photos! Derek Bain contacted me to do the interview for the book and you are going to learn many things you didn’t know about DYNASTY League Baseball Powered By Pursue the Pennant.” – Mike Cieslinski

“[Hardball Retro’s Compendium of Baseball Tabletop Simulations] is beyond wonderful! It is truly the encyclopedia of baseball gaming.” – Joe Shaw

“If you play Baseball tabletop sims, this book is a must-purchase. It is an incredible read and deeply educational. I purchased both paperback and Kindle versions. Post reading, it will remain a major reference for me.” – BB Edison, Amazon review

“A must-have for any baseball card and dice player’s library!” – Paul Gardner

“The book is an excellent contribution to keeping the history of tabletop baseball alive. The designer interviews give you interesting insight into the how and why of tabletop baseball creation.” – David Becker

“The book does a great job of quickly telling you what to expect from a game engine, the product’s components, and a brief history of the Sim… [Hardball Retro’s Compendium of Baseball Tabletop Simulations] is well-researched, and taps into the Baseball Sim community for their “takes” on the games, both positive and critical. A must have addition for any gamer’s bookshelf.” – M Pate, Amazon review

“These guys did an amazing job on giving a brief history of tabletop baseball gaming, and reviewed a lot of past and present games… The book is an instant classic!” – C Bauer, Amazon review

“Great book about the history and design of tabletop baseball simulation games, and includes comments from the designers themselves… a must have reference in this hobby.” – Robert Genova, Amazon review

“The Hardball Retro’s of Baseball Tabletop Simulations was everything I hoped for. Since retiring playing baseball sims is all I do. I have 38 baseball Sims and seeing every one of those in the book gives me an idea of how other people think of certain Sims. Authors did a fantastic job compiling the information. So if you enjoy the hobby or thinking of trying certain games out this is the book for you.” – Doug Wahl, Amazon review

“A vast treasure trove of baseball gaming history and fun! [Hardball Retro’s Compendium of Baseball Tabletop Simulations] will be the definitive reference source on all the tabletop baseball games we all grew up playing, as well as those we only knew through magazine ads, and many we never heard of at all!” – Roland Chapdelaine

“An excellent book about tabletop baseball games. Great history on the subject! Read it cover to cover right away!” – Russell D. Esperson – Amazon review

“This book is fantastic! I cannot stop reading it – every gamer should get this book!” – Anthony Giacobbe Jr.

The SeasonTool utility was created by Daniel Guenther in the late-1990’s to facilitate the creation of season disks for Earl Weaver Baseball 1.5 for the IBM PC (EWB). SeasonTool relies on CSV files derived from the Lahman baseball database. However, modern Lahman releases (including SABR Lahman 2025) differ significantly from the Lahman 5.1 schema that SeasonTool’s DOS-era executables were hard-coded to parse.

Attempting to use the current Lahman database files results in schema mismatches which cause SeasonTool to:
• Misread player and team records
• Generate corrupted MASTER files
• Produce empty ROS files
• Crash in STEP2REM and BATTINGR modules
• Fail to import seasons

This project reconstructed the exact, byte-level schemas SeasonTool expects and produced a modern R-based converter that transforms Lahman 2025 data into fully SeasonTool-compatible CSVs.

I developed the initial script using R Studio to import the Lahman csv files and clean up some known issues (playerID and stintID errors).

Here’s a step-by-step video tutorial that outlines the process.

For a deeper dive, keep reading!

Identifying the Problem

I opened a session in Microsoft CoPilot to assist me in the troubleshooting. CoPilot proved to be very beneficial in analyzing the various text files that SeasonTool generates, pinning down the potential causes for a number of issues encountered along the way, and suggesting fixes to implement in my R script. It was necessary to “rein in” CoPilot at times. After being led on a few wild-goose chases, you become more familiar with CoPilot’s limitations and in turn, adjust your responses accordingly. The majority of the SeasonTool’s failures were traced to several incompatibilities between modern Lahman CSVs and the legacy Lahman 5.1 format:
• Missing fields (e.g., managerID, hofID, nameNote, college, lahman40ID, lahman45ID, holtzID, ZR)
• Reordered or renamed fields
• Additional fields in modern Lahman (e.g., WP/SB/CS in Fielding)
• UTF-8 encoding and accented characters
• Incorrect quoting and NA handling
• Incorrect column counts
Because SeasonTool’s executables use fixed-position parsing, even a single missing or shifted field caused cascading corruption.

Reconstructing the Correct Schemas

Using my working SeasonTool installation (circa 2003) as the authoritative reference, I worked with CoPilot to reverse-engineer the exact schemas SeasonTool expects:
• MASTER.csv — 32 columns
• TEAMS.csv — 48 columns
• BATTING.csv — 22 columns
• PITCHING.csv — 27 columns
• FIELDING.csv — 15 columns
• FIELDINGOF.csv — 6 columns
These schemas differed from both Lahman 5.1 and Lahman 2025; SeasonTool uses a custom hybrid that must be matched exactly.

Handling the Missing Fields

The modern Lahman Baseball Database omits several fields SeasonTool requires. We added them back as blank columns to preserve the correct structure:
• managerID
• hofID
• nameNote
• college
• lahman40ID
• lahman45ID
• holtzID
• ZR (Zone Rating)
This ensured that every CSV matched the expected column count and order.

Enforcing ASCII-Only Text

SeasonTool and EWB cannot handle accented characters or extended Unicode. As a workaround, I implemented a sanitization step in the R script to convert all player names and city names to pure ASCII.

Enforcing Correct Quoting and NA Handling

SeasonTool requires:
• Text fields quoted
• Numeric fields unquoted
• Empty numeric fields as blank (“”)
• CRLF line endings
• No UTF-8 BOM
The final writer function enforces all of these.

Final Result

After applying all of the schema corrections and sanitization:
1893 imported successfully
2005 imported successfully
2025 imported successfully
This confirms the pipeline is now fully SeasonTool-compatible.

The Final SeasonTool-Compatible R Script

library(dplyr)
library(stringi)
library(readr)
library(dplyr)
library(stringr)
library(purrr)

CONFIG

src_dir <- "C:/stats/baseballdatabank-master/core"
out_dir <- "C:/ewb15/seasonTool/lahman"
if (!dir.exists(out_dir)) { 
dir.create(out_dir, recursive = TRUE, showWarnings = FALSE) }

LOGGING

log_msg <- function(…) { ts <- format(Sys.time(), "%Y-%m-%d %H:%M:%S") 
cat(ts, " ", paste0(…, collapse=" "), "\n") }

log_msg("=== START LAHMAN 2025 → 5.1 CONVERSION ===")

SAFE CSV READER

safe_read_csv <- function(path, …) {
if (!file.exists(path)) {
log_msg("WARNING: file not found: ", path) return(NULL) }
log_msg("Reading: ", path) tryCatch( readr::read_csv(path, show_col_types = FALSE, progress = FALSE, …), error = function(e) {  
log_msg("ERROR reading ", path, ": ", conditionMessage(e)) NULL } ) }

SeasonTool TeamID Mapping Module

# Handles post-2003 Lahman teamID changes:

# ATH → OAK
# LAA → ANA
# MIA → FLO
# WAS → MON
# Applies to all tables: Teams, Batting, Pitching, Fielding
# Includes validation + logging
# ============================================================


Mapping function

map_teamid_seasontool <- function(teamID, yearID) { 
if (is.na(teamID) || is.na(yearID)) return(teamID) 
if (yearID > 2003) { 
if (teamID == "ATH") return("OAK") 
if (teamID == "LAA") return("ANA") 
if (teamID == "MIA") return("FLO") 
if (teamID == "WAS") return("MON") } return(teamID)}

Vectorized wrapper

map_teamid_vec <- function(df) { 
if (!("teamID" %in% names(df)) || !("yearID" %in% names(df))) { 
stop("Data frame must contain teamID and yearID columns.") } 
df$teamID_original <- df$teamID 
df$teamID <- mapply(map_teamid_seasontool, df$teamID, df$yearID, USE.NAMES = FALSE) df}



Validation function

validate_teamid_mapping <- function(df, table_name = "Unknown Table") { 
unmapped <- df[df$teamID_original != df$teamID, ] 
if (nrow(unmapped) == 0) { message(paste0("[OK] No teamID replacements needed for ", table_name)) return(invisible(df)) } 
message(paste0("[INFO] TeamID replacements in ", table_name, ":")) print(unique(unmapped[, c("yearID", "teamID_original", "teamID")])) 
invisible(df)}


Apply to all Lahman tables

apply_teamid_mapping_all <- function(teams, batting, pitching, fielding, fldOF_old = NULL, fldOF_split = NULL) { 
teams <- map_teamid_vec(teams) 
batting <- map_teamid_vec(batting) 
pitching <- map_teamid_vec(pitching) 
fielding <- map_teamid_vec(fielding) 
# if (!is.null(fldOF_old)) {# fldOF_old <- map_teamid_vec(fldOF_old)# } 
if (!is.null(fldOF_split)) { fldOF_split <- map_teamid_vec(fldOF_split) } 


Validation logs 

validate_teamid_mapping(teams, "teams") 
validate_teamid_mapping(batting, "batting") 
validate_teamid_mapping(pitching, "pitching") 
validate_teamid_mapping(fielding, "fielding")

# if (!is.null(fldOF_old)) {# validate_teamid_mapping(fldOF_old, "fldOF_old")# } 
if (!is.null(fldOF_split)) { validate_teamid_mapping(fldOF_split, "fldOF_split") } list( teams = teams, batting = batting, pitching = pitching, fielding = fielding, fldOF_old = fldOF_old, fldOF_split = fldOF_split )}

LOAD TABLES

master <- safe_read_csv(file.path(src_dir, "People.csv"))
batting <- safe_read_csv(file.path(src_dir, "Batting.csv"))
pitching <- safe_read_csv(file.path(src_dir, "Pitching.csv"))
fielding <- safe_read_csv(file.path(src_dir, "Fielding.csv"))
fldOF_old <- safe_read_csv(file.path(src_dir, "FieldingOF.csv"))
fldOF_split <- safe_read_csv(file.path(src_dir, "FieldingOFsplit.csv"))
teams <- safe_read_csv(file.path(src_dir, "Teams.csv"))
required <- list( master=master, batting=batting, pitching=pitching, fielding=fielding, fldOF_old=fldOF_old, fldOF_split=fldOF_split, teams=teams )

missing <- names(required)[vapply(required, is.null, logical(1))] if (length(missing) > 0) stop("Missing required tables: ", paste(missing, collapse=", "))

FIX TEAMID MAPPING FOR Team Changes post-2003

mapped <- apply_teamid_mapping_all( teams, batting, pitching, fielding,# fldOF_old, fldOF_split)

teams <- mapped$teams
batting <- mapped$batting
pitching <- mapped$pitching
fielding <- mapped$fielding
fieldingOF <- mapped$fieldingOF
fieldingOFsplit<- mapped$fieldingOFsplit
required <- list( master=master, batting=batting, pitching=pitching, fielding=fielding, fldOF_old=fldOF_old, fldOF_split=fldOF_split, teams=teams)
missing <- names(required)[vapply(required, is.null, logical(1))]
if (length(missing) > 0) stop("Missing required tables: ", paste(missing, collapse=", "))

FIXES: PLAYER ID CORRECTIONS + VENDITTE

# There is an incorrect playerID for Yuli Gurriel

log_msg("Applying ID fixes…")
fix_id <- function(tbl, from, to) { 
if ("playerID" %in% names(tbl)) { 
tbl$playerID <- ifelse(tbl$playerID == from, to, tbl$playerID) } tbl }
master <- fix_id(master, "gourryu01", "gurriyu01")
batting <- fix_id(batting, "gourryu01", "gurriyu01")
pitching <- fix_id(pitching, "gourryu01", "gurriyu01")
fielding <- fix_id(fielding, "gourryu01", "gurriyu01")
fldOF_old<- fix_id(fldOF_old,"gourryu01", "gurriyu01")
fldOF_split<-fix_id(fldOF_split,"gourryu01","gurriyu01")

Pat Venditte: THROWS = “S” → “L”

# Earl Weaver Baseball 1.5 (along with most computer baseball sims from that era)
# only allow pitchers to be "R" (right-hander) or "L" (left-hander)
# Since there is no option for a "switch-pitcher", I elected to assign an "L" for
# Pat Venditte's throwing handedness. The code below is not specifically looking for
# Venditte; however he is the only player assigned as "S" (switch-pitcher) in the 
# Lahman database as of the 2025 edition. 
 
if ("throws" %in% names(master)) { master$throws <- ifelse(master$throws == "S", "L", master$throws) }

MLB-ONLY FILTER (AL/NL ONLY)

log_msg("Filtering to MLB-only players (AL/NL)…")
mlb_leagues <- c("AL","NL") 
players_bat <- batting %>% filter(lgID %in% mlb_leagues) %>% distinct(playerID) players_pit <- pitching %>% filter(lgID %in% mlb_leagues) %>% distinct(playerID) players_fld <- fielding %>% filter(lgID %in% mlb_leagues) %>% distinct(playerID)
mlb_players <- bind_rows(players_bat, players_pit, players_fld) %>% distinct(playerID) %>% pull(playerID)
master <- master %>% filter(playerID %in% mlb_players)

log_msg("Master rows after MLB filter: ", nrow(master))

FIELDINGOF: 1871–1955 FROM OLD FILE + 1956+ FROM FIELDINGOF_split

log_msg("Building FieldingOF (1871–1955 old file, 1956+ split file)…")
stop_if_missing <- function(tbl, cols, name) { 
miss <- setdiff(cols, names(tbl)) 
if (length(miss) > 0) stop("Missing columns in ", name, ": ", paste(miss, collapse=", ")) }
stop_if_missing(fldOF_old, c("playerID","yearID","stint","Glf","Gcf","Grf"), "FieldingOF") 
stop_if_missing(fldOF_split, c("playerID","yearID","stint","POS","G"), "FieldingOFsplit")
fldOF_old_51 <- fldOF_old %>% filter(yearID <= 1955) %>% transmute(playerID, yearID, stint, Glf, Gcf, Grf)
fldOF_new_51 <- fldOF_split %>% filter(POS %in% c("LF","CF","RF"), yearID >= 1956) %>% mutate( Glf = if_else(POS=="LF", as.integer(G), 0L), Gcf = if_else(POS=="CF", as.integer(G), 0L), Grf = if_else(POS=="RF", as.integer(G), 0L) ) %>% group_by(playerID, yearID, stint) %>% summarise( Glf=sum(Glf), Gcf=sum(Gcf), Grf=sum(Grf), .groups="drop" )
fldOF_51 <- bind_rows(fldOF_old_51, fldOF_new_51) %>% arrange(yearID, playerID, stint)

log_msg("FieldingOF rows: ", nrow(fldOF_51))

NORMALIZE TO STRICT LAHMAN 5.1 SCHEMAS

log_msg("Normalizing tables to Lahman 5.1 schemas…")
master_51 <- master %>% transmute(
ID = as.integer(ID),
playerID = as.character(playerID),

# Missing in 2025 → must be blank

managerID = "",
hofID = "",
birthYear = as.integer(birthYear),
birthMonth = as.integer(birthMonth),
birthDay = as.integer(birthDay),
birthCountry = as.character(birthCountry),
birthState = as.character(birthState),
birthCity = as.character(birthCity),
deathYear = as.integer(deathYear),

deathMonth = as.integer(deathMonth),
deathDay = as.integer(deathDay),
deathCountry = as.character(deathCountry),
deathState = as.character(deathState),
deathCity = as.character(deathCity),
nameFirst = as.character(nameFirst),
nameLast = as.character(nameLast),
# Missing in 2025 → must be blank
nameNote = "",
nameGiven = as.character(nameGiven),
# Missing in 2025 → must be blank
nameNick = "",
weight = as.numeric(weight),
height = as.numeric(height),
bats = as.character(bats),
throws = as.character(throws),
debut = as.character(debut),
# Missing in 2025 → must be blank
college = "",
lahman40ID = "",
lahman45ID = "",
retroID = as.character(retroID),
# Missing in 2025 → must be blank
holtzID = "",
bbrefID = as.character(bbrefID))

batting_51 <- batting %>% transmute(
playerID = as.character(playerID),
yearID = as.integer(yearID),
stintID = as.integer(stint),
teamID = as.character(teamID),
lgID = as.character(lgID),
G = as.integer(G),
AB = as.integer(AB),
R = as.integer(R),
H = as.integer(H),
2B = as.integer(2B),
3B = as.integer(3B),
HR = as.integer(HR),
RBI = as.integer(RBI),
SB = as.integer(SB),
CS = as.integer(CS),
BB = as.integer(BB),
SO = as.integer(SO),
IBB = as.integer(IBB),
HBP = as.integer(HBP),
SH = as.integer(SH),
SF = as.integer(SF),
GIDP = as.integer(GIDP))

pitching_51 <- pitching %>% transmute(
playerID = as.character(playerID),
yearID = as.integer(yearID),
stintID = as.integer(stint),
teamID = as.character(teamID),
lgID = as.character(lgID),
W = as.integer(W),L = as.integer(L),G = as.integer(G),GS = as.integer(GS),CG = as.integer(CG),SHO = as.integer(SHO),SV = as.integer(SV),
IPOuts = as.integer(IPouts),H = as.integer(H),ER = as.integer(ER),HR = as.integer(HR),BB = as.integer(BB),SO = as.integer(SO),
BAOpp = as.numeric(BAOpp), # may be blank for early yearsERA = as.numeric(ERA),IBB = as.integer(IBB),WP = as.integer(WP),HBP = as.integer(HBP),BK = as.integer(BK),BFP = as.integer(BFP),GF = as.integer(GF),R = as.integer(R))
fielding_51 <- fielding %>% transmute(
playerID = as.character(playerID),
yearID = as.integer(yearID),
stintID = as.integer(stint),
teamID = as.character(teamID),
lgID = as.character(lgID),
POS = as.character(POS),
G = as.integer(G),
GS = as.integer(GS),
InnOuts = as.integer(InnOuts),
PO = as.integer(PO),
A = as.integer(A),
E = as.integer(E),
DP = as.integer(DP),
PB = as.integer(PB),
# ZR removed from modern Lahman → must be synthesized
ZR = 0L )

teams_51 <- teams %>% filter(lgID %in% mlb_leagues) %>% transmute(
yearID = as.integer(yearID),
lgID = as.character(lgID),
teamID = as.character(teamID),
franchID = as.character(franchID),
divID = as.character(divID),
Rank = as.integer(Rank),
G = as.integer(G),
Ghome = na_if(as.integer(Ghome), 0),
W = as.integer(W),
L = as.integer(L),
DivWin = as.character(DivWin),
WCWin = as.character(WCWin),
LgWin = as.character(LgWin),
WSWin = as.character(WSWin),

#Batting totals (SeasonTool order)

R = as.integer(R),
AB = as.integer(AB),
H = as.integer(H),
2B = as.integer(2B),
3B = as.integer(3B),
HR = as.integer(HR),
BB = as.integer(BB),
SO = as.integer(SO),
SB = na_if(as.integer(SB), 0),
CS = na_if(as.integer(CS), 0),
HBP = na_if(as.integer(HBP), 0),
SF = na_if(as.integer(SF), 0),

# Pitching totals

RA = as.integer(RA),
ER = as.integer(ER),
ERA = as.numeric(ERA),
CG = as.integer(CG),
SHO = as.integer(SHO),
SV = as.integer(SV),
IPOuts = as.integer(IPouts),
HA = as.integer(HA),
HRA = as.integer(HRA),
BBA = as.integer(BBA),
SOA = as.integer(SOA),

# Fielding totals

E = as.integer(E),
DP = na_if(as.integer(DP), 0),
FP = as.numeric(FP),

# Metadata

name = as.character(name),
park = as.character(park),
attendance = na_if(as.integer(attendance), 0),
BPF = as.integer(BPF),
PPF = as.integer(PPF),
teamIDBR = as.character(teamIDBR),
teamIDlahman45= as.character(teamIDlahman45),
teamIDretro = as.character(teamIDretro))

fieldingOF_51 <- fldOF_51 %>% transmute(
playerID = as.character(playerID),
yearID = as.integer(yearID),
stintID = as.integer(stintID),
Glf = as.integer(Glf),
Gcf = as.integer(Gcf),
Grf = as.integer(Grf) )

Willard Brown (brownwi02) stintID correction

#Negro League data introduced a mismatch:

#batting_51: stintID = 2
#fldOF_51: stintID = 1
#MLB stint for 1947 St. Louis Browns should be stintID = 1

batting_51$stintID <- ifelse( batting_51$playerID == "brownwi02" & batting_51$yearID == 1947, 1, batting_51$stintID )
fielding_51$stintID <- ifelse( fielding_51$playerID == "brownwi02" & fielding_51$yearID == 1947, 1, fielding_51$stintID )

fieldingOF_51$stintID <- ifelse( fieldingOF_51$playerID == "brownwi02" & fieldingOF_51$yearID == 1947, 1, fieldingOF_51$stintID )

FIX NUMERIC NA VALUES FOR SEASONTOOL COMPATIBILITY

fix_numeric_na <- function(df) { 
df %>% mutate(across(where(is.numeric), ~ ifelse(is.na(.x), 0, .x))) }
#Special rule: ERA must be 99.99 instead of 0
pitching_51 <- pitching_51 %>% mutate( ERA = ifelse(is.na(ERA) | IPOuts == 0, 99.99, ERA), IPOuts = ifelse(IPOuts == 0 & ER == 0, 1, IPOuts) )
batting_51 <- fix_numeric_na(batting_51) pitching_51 <- fix_numeric_na(pitching_51) fielding_51 <- fix_numeric_na(fielding_51) fieldingOF_51 <- fix_numeric_na(fieldingOF_51) 
teams_51 <- fix_numeric_na(teams_51) 

ascii_sanitize <- function(x) { 
x <- stri_trans_general(x, "Latin-ASCII") 
# remove accents 
x <- gsub("[^ -~]", "", x) 
# remove any remaining non-ASCII x } 
master_51 <- master_51 %>% mutate( nameFirst = ascii_sanitize(nameFirst), nameLast = ascii_sanitize(nameLast), nameGiven = ascii_sanitize(nameGiven), nameNick = ascii_sanitize(nameNick), birthCity = ascii_sanitize(birthCity), deathCity = ascii_sanitize(deathCity), college = ascii_sanitize(college) )

WRITE CSV (QUOTES AROUND TEXT FIELDS ONLY!)

write_seasontool_csv <- function(df, path) {
write.table(
df,
file = path,
sep = ",",
row.names = FALSE,
col.names = FALSE,
quote = TRUE, # let R quote character fields normally
na = "" # empty fields become ,, not "" ) }

write_seasontool_csv(master_51, file.path(out_dir,"Master.csv"))
write_seasontool_csv(batting_51, file.path(out_dir,"Batting.csv"))
write_seasontool_csv(pitching_51, file.path(out_dir,"Pitching.csv"))
write_seasontool_csv(fielding_51, file.path(out_dir,"Fielding.csv"))
write_seasontool_csv(fieldingOF_51, file.path(out_dir,"FieldingOF.csv"))
write_seasontool_csv(teams_51, file.path(out_dir,"Teams.csv"))

log_msg("=== SEASON-SPECIFIC EXPORT COMPLETE ===")

Generating a Season Disk with SeasonTool

Extract the SeasonTool files to a subfolder of your Earl Weaver Baseball 1.5 directory.

Open a DosBox session and change the directory to your EWB15\SeasonTool subfolder.

Type: GETMASTR This parses the newly-created Lahman MASTER.CSV file for use with SeasonTool.

Type: IMPORT [YYYY] where YYYY is the year of the season that you wish to import.

The import process takes a while to complete. You can increase the CPU cycles in DosBox by holding the CTRL key and repeatedly pressing the F12 key. You will see the CPU cycles increase at the top of the DosBox window. Use CTRL-F11 to decrease the cycles. I usually bump my DosBox session up to 40000 during the Import, but your mileage will vary depending on your computer’s processing power. When the Import is complete, you’ll see a “success” message and the command prompt will be visible.

Type: DRAFT

The draft process allows you to select or deselect players to be included on each team’s roster. You have a 25-man roster per team. I usually take care to select a minimum of 2 catchers. For current teams (2000-present) I go with a mix of 12 batters / 13 pitchers or 13 batters / 12 pitchers. Teams from 1920 through late 1990’s – depending on the team, maybe go with 15 batters / 10 pitchers, 14/11, etc. Deadball era teams usually have less than 25 players total, so you’re selecting nearly every player in most instances. There is a built-in help function to see the various keyboard shortcuts. Primarily I’m using A to move up or Z to move down one player at a time. S skips up 15 players at a clip while X skips down by 15. Select a player to be added to the roster with the space bar. You’ll see an “X” to the left of their name. Use the space bar to select or deselect players. Once you select 25 players on a given team, press R and wait a few seconds. A box will appear mid-screen, allowing you to specify the primary and secondary position for players appearing at multiple positions during that season. Use the A and Z keys to move up/down, then press the space bar to mark the currently selected position as the primary. Repeat this step to select the secondary position, at which time the next multiple-position player will appear in the box. If you accidentally select the incorrect position, make a note of it and cycle through the remaining players. Once you return to the main draft screen, move up or down to the player whose position needs to be adjusted, then press P. You can select the primary and secondary position for that player.

When you have all of the 25-man rosters configured and fielding positions assigned, press E to export. This process will run for several minutes. Assuming it is successfully completed, SeasonTool will respond that you need to run EXPORT.EXE at the command prompt.

Type: EXPORT

SeasonTool should respond that it created 3 files: PLAYERS.DAT, LEAGNAME.DAT, PARKS.DAT

Use Windows Explorer (or the DOS command prompt if you prefer) to browse to your Earl Weaver Baseball 1.5 main program folder. Make a backup copy of any .DAT files to a subfolder.

Return to your SeasonTool subfolder and copy the 3 .DAT files into your Earl Weaver Baseball 1.5 main program folder.

At this point, I recommend running the Commissioner’s Disk (STATTOOL from the command prompt). Review the league structure in League/Division Editor:

You’ll notice 14 teams in the American League and 16 teams in the National League. Earl Weaver Baseball 1.5 pre-dates the MLB expansion in the mid-1990’s along with the switch to 3 divisions per league. Another issue that you’ll need to workaround – you can only create a schedule (Schedule A League) with an even number of teams in a league. In this case, I’ve left Houston in the National League. You may recall that the script renames several team ID’s (ATH, LAA, MIA, WAS) that did not exist prior to SeasonTool’s creation. Here, you can edit those team names with the city, abbreviation or team nickname. I recommend using the Advanced Ball Park Editor in conjunction with the Seamheads Ballparks Database to modify the ballparks to reflect the modern-day field names and dimensions.

Future State

There were 30 MLB teams when Daniel Guenther created the SeasonTool utility. If and when MLB expands to 32 teams, I’m not sure if SeasonTool will be able to accommodate them. It may be necessary to modify the R script in order to exclude the new teams from the CSV files. Earl Weaver Baseball 1.5 can accommodate up to 32 teams, so it would at least be possible to import data for the 30 franchises that exist as of 2026, then manually create/edit the two expansion teams using the Commissioner’s Disk. We’ll leave that issue for another day.

Wrap-Up

This project successfully restored full compatibility between modern Lahman data and SeasonTool’s legacy import pipeline. The final R script produces CSVs that match SeasonTool’s expected schemas exactly, enabling successful imports for seasons from 1871 through 2025. We can extend the life of Earl Weaver Baseball 1.5 with a stable, future-proof workflow (well, at least until the next round of expansion) for generating SeasonTool-ready data.

Resources

SeasonTool is no longer available on the Internet (at least I wasn’t able to locate it). The software was freely available and I’m sharing it here for the handful of individuals that might be interested in using this program (the filename is SeasonTool1901-2003.zip). I will be periodically creating season disks for Earl Weaver Baseball 1.5 and uploading them here.

EWB 1.5 Season Disks

Many thanks to Daniel Guenther for creating SeasonTool, and of course to Don Daglow, Eddie Dombrower, Earl Weaver and the teams at Electronic Arts and Mirage Graphics (see the full list of credits here) for the ground-breaking Earl Weaver Baseball game.

Don Daglow – SABR G&S Virtual

Eddie Dombrower – SABR G&S Virtual

Hardball Retrospective Rosters – 1910 Win Shares Leaders

Based on research for “Hardball Retrospective”, “Hardball Retroactive” and “Hardball Architects” –
https://www.amazon.com/kindle-dbs/entity/author/B00S4SXV8S

Identify the 1910 Philadelphia Phillies leaders in total Win Shares based on the Hardball Retrospective “Original” rosters. Each player is listed as a member of the “original” team that he signed his first professional contract with. Ken Griffey Jr. would be a member of the Mariners for the duration of his career in this scenario while Mike Piazza is considered a member of the Dodgers.

-answers to be posted here-

baseball card images courtesy of the Trading Card Database – https://www.tcdb.com/

Hardball Retrospective Rosters – 1910 Win Shares Leaders

Based on research for “Hardball Retrospective”, “Hardball Retroactive” and “Hardball Architects” –
https://www.amazon.com/kindle-dbs/entity/author/B00S4SXV8S

Identify the 1910 Philadelphia Athletics leaders in total Win Shares based on the Hardball Retrospective “Original” rosters. Each player is listed as a member of the “original” team that he signed his first professional contract with. Ken Griffey Jr. would be a member of the Mariners for the duration of his career in this scenario while Mike Piazza is considered a member of the Dodgers.

-answers to be posted here-

baseball card images courtesy of the Trading Card Database – https://www.tcdb.com/

Methodology

I imported the players from the Bill James Baseball Encyclopedia (1995 edition) using APBA Baseball for Windows 5.5 running on a Windows 98 SE virtual machine. I configured the starting lineups vs. RHP and LHP, starting rotations, and the adjustments outlined below before migrating the organization files into version 5.75.

• DH is used for all teams
• 25-man active roster, 15 players in the “farm” organization per team
• must have 2 active catchers
• active rosters consist of 15 batters/10 pitchers or 14 batters/11 pitchers
• vsRHP, vsLHP and starting rotations are generally based on the highest Win Shares single-season totals for each position
• schedules generated using the format utilized during that timeframe
• Season Factor and Max. Batters Faced are adjusted using (162/110) for SF and MaxBF for all players from strike-shortened seasons

Why is this organization called the “Actuals”? When I wrote my first book, “Hardball Retrospective”, I placed every player on the first team which they signed a professional MLB contract with. I called those teams the “Originals” while calling their real-life counterparts the “Actuals”. I sometimes refer to the “Actuals” as the “Franchise Best Players”. There will be instances where the same player appears on multiple teams, such as Tom Seaver ’75 (Mets) and Tom Seaver ’78 (Reds), or Rod Carew ’77 (Twins) and Rod Carew ’79 (Angels) . However, you will only find one player-season for a given team on a half-decade roster (i.e. Joe Morgan’s best season from 1975 through 1979 based on Win Shares is his 1975 campaign, so that is the version of Morgan that appears on the 1975-1979 Reds roster.)

The following videos include a recap of the regular season standings and league leaders, American and National League playoff recap, and individual World Series game videos with Ernie Harwell narrating the play-by-play!

Regular Season Recap

Playoff Recap

World Series

Game 1

Game 2

Game 3

Game 4

Season Recap

1975-1979 Actuals – Final Regular Season Standings

1975-1979 Actuals – Individual Statistics

1975-1979 Actuals – League Leaderboards

1975-1979 Actuals – Team Totals

Reference

Retro Computer Baseball Game Review – APBA Baseball

APBA Baseball for Windows – tutorial – create/edit teams

SABR Stay at Home – Kenneth and Roswell Miller

Hardball Retrospective Rosters – 1910 Win Shares Leaders

Based on research for “Hardball Retrospective”, “Hardball Retroactive” and “Hardball Architects” –
https://www.amazon.com/kindle-dbs/entity/author/B00S4SXV8S

Identify the 1910 New York Highlanders leaders in total Win Shares based on the Hardball Retrospective “Original” rosters. Each player is listed as a member of the “original” team that he signed his first professional contract with. Ken Griffey Jr. would be a member of the Mariners for the duration of his career in this scenario while Mike Piazza is considered a member of the Dodgers.

-answers to be posted here-

baseball card images courtesy of the Trading Card Database – https://www.tcdb.com/

Hardball Retrospective Rosters – 1910 Win Shares Leaders

Based on research for “Hardball Retrospective”, “Hardball Retroactive” and “Hardball Architects” –
https://www.amazon.com/kindle-dbs/entity/author/B00S4SXV8S

Identify the 1910 Washington Nationals leaders in total Win Shares based on the Hardball Retrospective “Original” rosters. Each player is listed as a member of the “original” team that he signed his first professional contract with. Ken Griffey Jr. would be a member of the Mariners for the duration of his career in this scenario while Mike Piazza is considered a member of the Dodgers.

-answers to be posted here-

baseball card images courtesy of the Trading Card Database – https://www.tcdb.com/

Hardball Retrospective Rosters – 1910 Win Shares Leaders

Based on research for “Hardball Retrospective”, “Hardball Retroactive” and “Hardball Architects” –
https://www.amazon.com/kindle-dbs/entity/author/B00S4SXV8S

Identify the 1910 Brooklyn Superbas leaders in total Win Shares based on the Hardball Retrospective “Original” rosters. Each player is listed as a member of the “original” team that he signed his first professional contract with. Ken Griffey Jr. would be a member of the Mariners for the duration of his career in this scenario while Mike Piazza is considered a member of the Dodgers.

-answers to be posted here-

baseball card images courtesy of the Trading Card Database – https://www.tcdb.com/