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

Here’s a rundown of the requirements:

• Micro League Baseball 6.0 – Game
• optional, but recommended – Micro League Baseball 6.0 – Season Disk(s)

Micro League Baseball 6.0 was developed by Micro League Sports Assocation. David Holt, designer of the Pro League Baseball series, was the executive producer. Micro League Baseball 6.0, the last of five titles in this series, was published in 1997 for the IBM PC and compatibles.

The tutorial includes the Draft (using players from the 1996 MLB season disk) and an exhibition game utilizing two of the newly-drafted rosters.

View the tutorial videos here:

Season Disk Downloads:

coming soon

References:

Mobygames entry for Micro League Baseball 6.0

Here’s a rundown of the requirements:

• Action!PC Baseball – Game
• Action!PC Baseball – Season Disk(s)

Action!PC Baseball was developed by Dave Koch. The first edition was published by Dave Koch Sports for the IBM PC personal computer in 2001.

I divided the tutorial into two sections: the Draft (copying the All-Time Greats league, releasing players into the free agent pool, conducting the draft), Simulate Season (review manager profiles for each team, simulate an entire season, reviewing the final standings, league leaders and team-by-team rundown).

View the tutorial videos here:

Part 1 – Draft

Part 2 – Lineups & Pitching Staffs, Simulate Season, Final Standings, League Leaders

Season Disk Downloads:

coming soon

References:

SABR Stay at Home – Dave Koch

Here’s a rundown of the requirements:

• Out of the Park Baseball 24 – Game

Out of the Park Baseball was developed by Markus Heinsohn. The first edition was published for the IBM PC personal computer in 1999.

I divided the tutorial into two sections: the Draft (copying the All-Time Greats league, releasing players into the free agent pool, conducting the draft), Simulate Season (review manager profiles for each team, simulate an entire season, reviewing the final standings, league leaders and team-by-team rundown).

View the tutorial videos here:

Part 1 – Draft

Part 2 – Lineups & Pitching Staffs, Simulate Season, Final Standings, League Leaders

Season Disk Downloads:

coming soon

References:

Here’s a rundown of the requirements:

• Diamond Mind Baseball 12 – Game
• optional, but recommended: Diamond Mind Baseball 12 – Season Disk(s)

Diamond Mind Baseball was developed by Tom Tippett (original title: Pursue the Pennant). The first edition was published for the IBM PC personal computer in 1987.

I divided the tutorial into two sections: the Draft (copying the All-Time Greats league, releasing players into the free agent pool, conducting the draft), Simulate Season (review manager profiles for each team, simulate an entire season, reviewing the final standings, league leaders and team-by-team rundown).

View the tutorial videos here:

Part 1 – Draft

Part 2 – Manager Profiles, Simulate Season, Final Standings, Statistical Review

Season Disk Downloads:

coming soon

References:

Retro Computer Baseball Game Review – Pursue the Pennant – Diamond Mind Baseball

Here’s a rundown of the requirements:

• Front Page Sports Baseball ’94 (’96 or ’98 editions will also work) – Game Disk

• optional, but recommended: Front Page Sports Baseball – Season Disk(s)

Front Page Sports Baseball ’94 was developed by Dynamix and published in 1994 by Sierra for the IBM PC personal computer. Please note that while the tutorial utilizes Front Page Sports Baseball ’94, you can perform the same steps in Front Page Sports Baseball ’96 or ’98 editions.

View the tutorial video here:

Season Disk Downloads:

coming soon

References:

Front Page Sports Baseball ’94:

https://archive.org/details/msdos_Front_Page_Sports_Baseball_94_1994

Retro Computer Baseball Game Review – Front Page Sports: Baseball ’94 – ’96 – ‘98

SABR Stay at Home – Glen Wolfram

Here’s a rundown of the requirements:

• Front Page Sports Baseball ’94 (’96 or ’98 editions will also work) – Game Disk
• optional, but recommended: Front Page Sports Baseball – Season Disk(s)

Front Page Sports Baseball ’94 was developed by Dynamix and published in 1994 by Sierra for the IBM PC personal computer. Please note that while the tutorial utilizes Front Page Sports Baseball ’94, you can perform the same steps in Front Page Sports Baseball ’96 or ’98 editions. Alternatively, you can create / edit teams and conduct a draft using the players that you create.

View the tutorial video here:

Season Disk Downloads:

coming soon

References:

https://archive.org/details/msdos_Front_Page_Sports_Baseball_94_1994

Retro Computer Baseball Game Review – Front Page Sports: Baseball ’94 – ’96 – ‘98

SABR Stay at Home – Glen Wolfram

Here’s a rundown of the requirements:

• Old Time Baseball (or Tony La Russa Baseball 3 or 4) – Game Disk
• optional, but recommended: Tony La Russa Baseball 3 or 4 – Season Disk(s)

Old Time Baseball was developed and published in 1995 by Stormfront Studios for the IBM PC personal computer. Please note that while the tutorial utilizes Old Time Baseball, you can perform the same steps in Tony La Russa Baseball 3 or 4. The only additional steps would be to import the season in OTBB first, then import into TLBB 3 or 4. Alternatively, you can create / edit teams and conduct a draft using the players that you create.

I divided the tutorial into three sections: Draft (importing the 1954 season from the Old Time Baseball CD-ROM, selecting the player pool, configuring the draft rules, conducting the draft), Simulate Season (create schedule, simulate an entire season, play an All-Star Matchup with Mel Allen performing the play-by-play, reviewing the final standings) and Stat Review (games of note including one-hitters and 3-homer games, league leaderboards, and a team-by-team rundown).

View the tutorial videos here:

Part 1 – Import Universe, Draft, Review Lineups and Pitching Staffs

Part 2 – Simulate Season, All-Star Matchup, Final Standings

Part 3 – Statistical Review (Games of Note, League Leaders and Team-by-Team)

Season Disk Downloads:

coming soon

References:

Retro Computer Baseball Game Review – Tony La Russa Baseball 3 – 4 – Old Time Baseball

SABR Stay at Home – Don Daglow

Here’s a rundown of the requirements:

• APBA Baseball for Windows (version 5.0 or higher) – Game Disk
• optional, but recommended: APBA Baseball for Windows – Season Disk(s)
• optional, but recommended: Baseball Wizard (Windows Edition)
• optional, but recommended: Fanpark / Bill James Baseball Encyclopedia (Windows Edition)

APBA Baseball for Windows was developed and published by Miller Associates for the IBM PC personal computer. Several iterations were released for Windows 3.1 and subsequent editions were built to run on the Windows 95 operating system between 1993 and 1999. I’m utilizing version 5.75 in my tutorial but this feature was implemented in version 5.0.

I divided the tutorial into three sections: Advanced Draft (selecting the player pool, draft rules, conducting the draft), League Manager (setting lineups/rotations, scheduling and injury management rules, simulating a complete season) and StatMaster (reviewing the simulated season standings, league leaders and team-by-team batting and pitching results).

View the tutorial videos here:

Part 1 – Advanced Draft

Part 2 – League Manager

Part 3 – StatMaster

Season Disk Downloads:

coming soon

References:

Miller Associates 1993 catalog:

https://apbagames.com/wp-content/uploads/baseball/Miller_Assoc_1993.pdf

Retro Computer Baseball Game Review – APBA Baseball

on sale now! Kindle edition ($9.99), paperback ($19.99)

order here -> https://www.amazon.com/dp/B09TPYMNSS (Kindle),

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Hardball Architects examines the trades, free agent acquisitions, draft picks and other transactions for the 30 Major League Baseball franchises, divided into a 2-volume set (American League and National League). All key moves are scrutinized for every team and Sabermetric principles are applied to the roster construction throughout the lifetime of the organization to encapsulate the hits and misses by front office executives. Team performances are analyzed based on transaction type with graphs depicting the WAR (Wins Above Replacement) in every decade. Individual results for each player-transaction is charted over the duration of their stint with the franchise. Every team chapter includes All-Time Rosters and Single-Season Leaders based on transaction type. The Team Trade Record chronicles the WAR and WS (Win Shares) accumulated by players acquired in comparison to those traded to opposing teams. The opening chapter is devoted to the Evolution of the General Manager and incorporates a discussion with former GM’s Fred Claire (Dodgers) and Mike Port (Angels, Red Sox) along with current GM Nick Krall (Reds) on a variety of front office topics. Prepare yourself for a journey through baseball history to discover the path to building a contending ball club!

About the Author

I am a New Jersey native with a passion for baseball, statistics, computers and video games who enjoys spending quality time with his family.

“Hardball Retrospective” is available in paperback and digital (Kindle) format at Amazon.com. Supplemental Statistics, Charts and Graphs along with a discussion forum are offered at TuataraSoftware.com.

Don Daglow (Intellivision World Series Major League Baseball, Earl Weaver Baseball, Tony LaRussa Baseball) contributed the foreword for Hardball Retrospective. The foreword and preview of my book are accessible here.

“Hardball Retrospective – Addendum 2014 to 2016” supplements my research for Hardball Retrospective, providing retroactive standings based on Wins Above Replacement (WAR) and Win Shares (WS) for each “original” team over the past three seasons (2014-2016). Team totals from 2010 – 2013 are included for reference purposes. “Addendum” is available in paperback and digital (Kindle) format at Amazon.com. 

“Hardball Retroactive”, published in June 2018, is available in paperback and digital (Kindle) format at Amazon.com. A cross-section of essays that I penned for Seamheads.com along with my Baseball Analytics blog spanning nearly a decade touching on subjects including “Taking the Extra Base”, “General Manager Scorecard”, “Worst Trades”, “BABIP By Location” and “Baseball Birthplaces and the Retro World Baseball Classic”. Rediscover your favorite hardball arcade and simulations in “Play Retro Baseball Video Games In Your Browser” or take a deep dive into every franchise’s minor league successes and failures in relation to their major league operations in “Minors vs. Majors”.

“Hardball Architects – Volume 1 (American League)”, published in July 2020, is available in paperback and digital (Kindle) format at Amazon.com. 

“Hardball Architects – Volume 2 (National League Teams)”, published in April 2022, is available in paperback and digital (Kindle) format at Amazon.com. 

“Hardball Architects” examines the trades, free agent acquisitions, draft picks and other transactions for the 30 Major League Baseball franchises, divided into a 2-volume set (American League and National League). All key moves are scrutinized for every team and Sabermetric principles are applied to the roster construction throughout the lifetime of the organization to encapsulate the hits and misses by front office executives.