Repository: jldbc/pybaseball (language: python)

Issue:
Major breaking change to FG Team leaderboard
FG removed names from the team leaderboard. This means that a whole bunch of related code breaks, and hence the unit tests for teamid_lookup.py.


Discussion / hints:

Fix patch:
diff --git a/Makefile b/Makefile
new file mode 100644
index 00000000..4a03a8b5
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,48 @@
+ACT_FLAGS :=
+LOG_LEVEL := INFO
+MYPY_RUN_AGAINST_DEFAULT := *.py pybaseball tests
+MYPY_RUN_AGAINST := $(MYPY_RUN_AGAINST_DEFAULT)
+ONLY_MODIFIED := 1
+TEST_RUN_AGAINST := tests
+TEST_FLAGS := -n auto
+
+
+ifeq ($(LOG_LEVEL), DEBUG)
+	TEST_FLAGS += -v --log-cli-level=DEBUG
+endif
+
+ifeq ($(ONLY_MODIFIED), 1)
+	_MODIFIED_FILES := $(shell git status | grep 'modified\|new' | xargs -n1 echo | grep -v ':' | grep [.]py$)
+	_UNIQUE_MODIFIED_FILES := $(shell echo $(_MODIFIED_FILES) | sort | uniq)
+	ifeq ($(MYPY_RUN_AGAINST), $(MYPY_RUN_AGAINST_DEFAULT))
+		MYPY_RUN_AGAINST := $(_UNIQUE_MODIFIED_FILES)
+	endif
+endif
+
+generate-team-ids:
+	python -c "from pybaseball.teamid_lookup import _generate_teams; _generate_teams()"
+
+install: upgrade-pip
+	pip install -e .[test]
+
+mypy:
+	mypy --ignore-missing-imports --follow-imports silent $(MYPY_RUN_AGAINST)
+
+test:
+	pytest $(TEST_RUN_AGAINST) $(TEST_FLAGS) --doctest-modules --cov=pybaseball --cov-report term-missing
+
+# The test-github-actions is here to allow any local developer to test the GitHub actions on their code
+# before pushing and creating a PR. Just install act from https://github.com/nektos/act and run
+# make test-github-actions
+ACT_EXISTS := $(shell act --help 2> /dev/null)
+
+ifeq ($(ACT_EXISTS),)
+test-github-actions:
+	@echo "Testing GitHub actions requires act to be installed. See: https://github.com/nektos/act"
+else
+test-github-actions:
+	act pull_request -P ubuntu-latest=nektos/act-environments-ubuntu:18.04 $(ACT_FLAGS)
+endif
+
+upgrade-pip:
+	pip install --upgrade pip
diff --git a/mypy.ini b/mypy.ini
index d0cfb175..5ad4197d 100644
--- a/mypy.ini
+++ b/mypy.ini
@@ -2,7 +2,7 @@
 python_version = 3.6
 warn_return_any = True
 warn_unused_configs = True
-disallow_untyped_defs =True
+disallow_untyped_defs = True
 
 [mypy-altair]
 ignore_missing_imports = True
diff --git a/pybaseball/data/fangraphs_teams.csv b/pybaseball/data/fangraphs_teams.csv
index 169ce317..91ac006c 100644
--- a/pybaseball/data/fangraphs_teams.csv
+++ b/pybaseball/data/fangraphs_teams.csv
@@ -1,2944 +1,2956 @@
 ,yearID,lgID,teamID,franchID,teamIDfg,teamIDBR,teamIDretro
 0,1871,,BS1,BNA,16,BOS,BS1
 1,1871,,CH1,CNA,17,CHI,CH1
-2,1871,,PH1,PNA,1002,ATH,PH1
-3,1871,,CL1,CFC,1020,CLE,CL1
-4,1871,,RC1,ROK,1020,ROK,RC1
-5,1871,,FW1,KEK,1042,KEK,FW1
-6,1871,,NY2,NNA,1053,NYU,NY2
-7,1871,,WS3,OLY,1057,OLY,WS3
-8,1871,,CL1,CFC,1069,CLE,CL1
-9,1871,,RC1,ROK,1069,ROK,RC1
-10,1871,,TRO,TRO,1080,TRO,TRO
-11,1872,,BS1,BNA,16,BOS,BS1
-12,1872,,PH1,PNA,1002,ATH,PH1
-13,1872,,BL1,BLC,1005,BAL,BL1
-14,1872,,BR2,BRA,1010,BRA,BR2
-15,1872,,CL1,CFC,1020,CLE,CL1
-16,1872,,BR1,ECK,1032,ECK,BR1
-17,1872,,MID,MAN,1045,MAN,MID
-18,1872,,WS4,NAT,1050,NAT,WS4
-19,1872,,NY2,NNA,1053,NYU,NY2
-20,1872,,WS3,OLY,1057,OLY,WS3
-21,1872,,TRO,TRO,1080,TRO,TRO
-22,1873,,BS1,BNA,16,BOS,BS1
-23,1873,,PH1,PNA,1002,ATH,PH1
-24,1873,,BL1,BLC,1005,BAL,BL1
-25,1873,,BR2,BRA,1010,BRA,BR2
-26,1873,,BL4,MAR,1046,MAR,BL4
-27,1873,,NY2,NNA,1053,NYU,NY2
-28,1873,,PH2,PWS,1065,PHI,PH2
-29,1873,,ELI,RES,1066,RES,ELI
-30,1873,,WS5,WBL,1083,WAS,WS5
+2,1871,,CL1,CFC,1020,CLE,CL1
+3,1871,,FW1,KEK,1042,KEK,FW1
+4,1871,,NY2,NNA,1053,NYU,NY2
+5,1871,,PH1,PNA,1002,ATH,PH1
+6,1871,,RC1,ROK,1069,ROK,RC1
+7,1871,,TRO,TRO,1080,TRO,TRO
+8,1871,,WS3,OLY,1057,OLY,WS3
+9,1872,,BL1,BLC,1005,BAL,BL1
+10,1872,,BR1,ECK,1032,ECK,BR1
+11,1872,,BR2,BRA,1010,BRA,BR2
+12,1872,,BS1,BNA,16,BOS,BS1
+13,1872,,CL1,CFC,1020,CLE,CL1
+14,1872,,MID,MAN,1045,MAN,MID
+15,1872,,NY2,NNA,1053,NYU,NY2
+16,1872,,PH1,PNA,1002,ATH,PH1
+17,1872,,TRO,TRO,1080,TRO,TRO
+18,1872,,WS3,OLY,1057,OLY,WS3
+19,1872,,WS4,NAT,1050,NAT,WS4
+20,1873,,BL1,BLC,1005,BAL,BL1
+21,1873,,BL4,MAR,1046,MAR,BL4
+22,1873,,BR2,BRA,1010,BRA,BR2
+23,1873,,BS1,BNA,16,BOS,BS1
+24,1873,,ELI,RES,1066,RES,ELI
+25,1873,,NY2,NNA,1053,NYU,NY2
+26,1873,,PH1,PNA,1002,ATH,PH1
+27,1873,,PH2,PWS,1065,PHI,PH2
+28,1873,,WS5,WBL,1083,WAS,WS5
+29,1874,,BL1,BLC,1005,BAL,BL1
+30,1874,,BR2,BRA,1010,BRA,BR2
 31,1874,,BS1,BNA,16,BOS,BS1
 32,1874,,CH2,CNA,17,CHI,CH2
-33,1874,,PH1,PNA,1002,ATH,PH1
-34,1874,,BL1,BLC,1005,BAL,BL1
-35,1874,,BR2,BRA,1010,BRA,BR2
-36,1874,,HR1,HNA,1033,HAR,HR1
-37,1874,,NY2,NNA,1053,NYU,NY2
-38,1874,,PH2,PWS,1065,PHI,PH2
-39,1875,,BS1,BNA,16,BOS,BS1
-40,1875,,SL1,SLR,16,SLR,SL1
-41,1875,,CH2,CNA,17,CHI,CH2
-42,1875,,PH1,PNA,1002,ATH,PH1
-43,1875,,BR2,BRA,1010,BRA,BR2
-44,1875,,PH3,CEN,1019,CEN,PH3
-45,1875,,HR1,HNA,1033,HAR,HR1
-46,1875,,NH1,NHV,1052,NHV,NH1
-47,1875,,NY2,NNA,1053,NYU,NY2
-48,1875,,PH2,PWS,1065,PHI,PH2
-49,1875,,SL2,SNA,1070,STL,SL2
-50,1875,,BS1,BNA,1073,BOS,BS1
-51,1875,,SL1,SLR,1073,SLR,SL1
-52,1875,,KEO,WES,1084,WES,KEO
-53,1875,,WS6,WNT,1088,WAS,WS6
-54,1876,NL,BSN,ATL,16,BSN,BSN
-55,1876,NL,CHN,CHC,17,CHC,CHN
+33,1874,,HR1,HNA,1033,HAR,HR1
+34,1874,,NY2,NNA,1053,NYU,NY2
+35,1874,,PH1,PNA,1002,ATH,PH1
+36,1874,,PH2,PWS,1065,PHI,PH2
+37,1875,,BR2,BRA,1010,BRA,BR2
+38,1875,,BS1,BNA,16,BOS,BS1
+39,1875,,CH2,CNA,17,CHI,CH2
+40,1875,,HR1,HNA,1033,HAR,HR1
+41,1875,,KEO,WES,1084,WES,KEO
+42,1875,,NH1,NHV,1052,NHV,NH1
+43,1875,,NY2,NNA,1053,NYU,NY2
+44,1875,,PH1,PNA,1002,ATH,PH1
+45,1875,,PH2,PWS,1065,PHI,PH2
+46,1875,,PH3,CEN,1019,CEN,PH3
+47,1875,,SL1,SLR,1073,SLR,SL1
+48,1875,,SL2,SNA,1070,STL,SL2
+49,1875,,WS6,WNT,1088,WAS,WS6
+50,1876,NL,BSN,ATL,16,BSN,BSN
+51,1876,NL,CHN,CHC,17,CHC,CHN
+52,1876,NL,CN1,CNR,1028,CIN,CN1
+53,1876,NL,HAR,HAR,1033,HAR,HAR
+54,1876,NL,LS1,LGR,1043,LOU,LS1
+55,1876,NL,NY3,NYU,1053,NYU,NY3
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-57,1876,NL,CN1,CNR,1028,CIN,CN1
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-796,1924,NL,BRO,LAD,22,BRO,BRO
-797,1924,NL,PHI,PHI,26,PHI,PHI
-798,1924,NL,PIT,PIT,27,PIT,PIT
-799,1924,NL,SLN,STL,28,STL,SLN
-800,1924,NL,NY1,SFG,30,NYG,NY1
-801,1925,AL,SLA,BAL,2,SLB,SLA
-802,1925,AL,BOS,BOS,3,BOS,BOS
-803,1925,AL,CHA,CHW,4,CHW,CHA
-804,1925,AL,CLE,CLE,5,CLE,CLE
-805,1925,AL,DET,DET,6,DET,DET
-806,1925,AL,WS1,MIN,8,WSH,WS1
-807,1925,AL,NYA,NYY,9,NYY,NYA
-808,1925,AL,PHA,OAK,10,PHA,PHA
-809,1925,NL,BSN,ATL,16,BSN,BSN
-810,1925,NL,CHN,CHC,17,CHC,CHN
-811,1925,NL,CIN,CIN,18,CIN,CIN
-812,1925,NL,BRO,LAD,22,BRO,BRO
-813,1925,NL,PHI,PHI,26,PHI,PHI
-814,1925,NL,PIT,PIT,27,PIT,PIT
-815,1925,NL,SLN,STL,28,STL,SLN
-816,1925,NL,NY1,SFG,30,NYG,NY1
-817,1926,AL,SLA,BAL,2,SLB,SLA
-818,1926,AL,BOS,BOS,3,BOS,BOS
-819,1926,AL,CHA,CHW,4,CHW,CHA
-820,1926,AL,CLE,CLE,5,CLE,CLE
-821,1926,AL,DET,DET,6,DET,DET
-822,1926,AL,WS1,MIN,8,WSH,WS1
-823,1926,AL,NYA,NYY,9,NYY,NYA
-824,1926,AL,PHA,OAK,10,PHA,PHA
-825,1926,NL,BSN,ATL,16,BSN,BSN
-826,1926,NL,CHN,CHC,17,CHC,CHN
-827,1926,NL,CIN,CIN,18,CIN,CIN
-828,1926,NL,BRO,LAD,22,BRO,BRO
-829,1926,NL,PHI,PHI,26,PHI,PHI
-830,1926,NL,PIT,PIT,27,PIT,PIT
-831,1926,NL,SLN,STL,28,STL,SLN
-832,1926,NL,NY1,SFG,30,NYG,NY1
-833,1927,AL,SLA,BAL,2,SLB,SLA
-834,1927,AL,BOS,BOS,3,BOS,BOS
-835,1927,AL,CHA,CHW,4,CHW,CHA
-836,1927,AL,CLE,CLE,5,CLE,CLE
-837,1927,AL,DET,DET,6,DET,DET
-838,1927,AL,WS1,MIN,8,WSH,WS1
-839,1927,AL,NYA,NYY,9,NYY,NYA
-840,1927,AL,PHA,OAK,10,PHA,PHA
-841,1927,NL,BSN,ATL,16,BSN,BSN
-842,1927,NL,CHN,CHC,17,CHC,CHN
-843,1927,NL,CIN,CIN,18,CIN,CIN
-844,1927,NL,BRO,LAD,22,BRO,BRO
-845,1927,NL,PHI,PHI,26,PHI,PHI
-846,1927,NL,PIT,PIT,27,PIT,PIT
-847,1927,NL,SLN,STL,28,STL,SLN
-848,1927,NL,NY1,SFG,30,NYG,NY1
-849,1928,AL,SLA,BAL,2,SLB,SLA
-850,1928,AL,BOS,BOS,3,BOS,BOS
-851,1928,AL,CHA,CHW,4,CHW,CHA
-852,1928,AL,CLE,CLE,5,CLE,CLE
-853,1928,AL,DET,DET,6,DET,DET
-854,1928,AL,WS1,MIN,8,WSH,WS1
-855,1928,AL,NYA,NYY,9,NYY,NYA
-856,1928,AL,PHA,OAK,10,PHA,PHA
-857,1928,NL,BSN,ATL,16,BSN,BSN
-858,1928,NL,CHN,CHC,17,CHC,CHN
-859,1928,NL,CIN,CIN,18,CIN,CIN
-860,1928,NL,BRO,LAD,22,BRO,BRO
-861,1928,NL,PHI,PHI,26,PHI,PHI
-862,1928,NL,PIT,PIT,27,PIT,PIT
-863,1928,NL,SLN,STL,28,STL,SLN
-864,1928,NL,NY1,SFG,30,NYG,NY1
-865,1929,AL,SLA,BAL,2,SLB,SLA
-866,1929,AL,BOS,BOS,3,BOS,BOS
-867,1929,AL,CHA,CHW,4,CHW,CHA
-868,1929,AL,CLE,CLE,5,CLE,CLE
-869,1929,AL,DET,DET,6,DET,DET
-870,1929,AL,WS1,MIN,8,WSH,WS1
-871,1929,AL,NYA,NYY,9,NYY,NYA
-872,1929,AL,PHA,OAK,10,PHA,PHA
-873,1929,NL,BSN,ATL,16,BSN,BSN
-874,1929,NL,CHN,CHC,17,CHC,CHN
-875,1929,NL,CIN,CIN,18,CIN,CIN
-876,1929,NL,BRO,LAD,22,BRO,BRO
-877,1929,NL,PHI,PHI,26,PHI,PHI
-878,1929,NL,PIT,PIT,27,PIT,PIT
-879,1929,NL,SLN,STL,28,STL,SLN
-880,1929,NL,NY1,SFG,30,NYG,NY1
-881,1930,AL,SLA,BAL,2,SLB,SLA
-882,1930,AL,BOS,BOS,3,BOS,BOS
-883,1930,AL,CHA,CHW,4,CHW,CHA
-884,1930,AL,CLE,CLE,5,CLE,CLE
-885,1930,AL,DET,DET,6,DET,DET
-886,1930,AL,WS1,MIN,8,WSH,WS1
-887,1930,AL,NYA,NYY,9,NYY,NYA
-888,1930,AL,PHA,OAK,10,PHA,PHA
-889,1930,NL,BSN,ATL,16,BSN,BSN
-890,1930,NL,CHN,CHC,17,CHC,CHN
-891,1930,NL,CIN,CIN,18,CIN,CIN
-892,1930,NL,BRO,LAD,22,BRO,BRO
-893,1930,NL,PHI,PHI,26,PHI,PHI
-894,1930,NL,PIT,PIT,27,PIT,PIT
-895,1930,NL,SLN,STL,28,STL,SLN
-896,1930,NL,NY1,SFG,30,NYG,NY1
-897,1931,AL,SLA,BAL,2,SLB,SLA
-898,1931,AL,BOS,BOS,3,BOS,BOS
-899,1931,AL,CHA,CHW,4,CHW,CHA
-900,1931,AL,CLE,CLE,5,CLE,CLE
-901,1931,AL,DET,DET,6,DET,DET
-902,1931,AL,WS1,MIN,8,WSH,WS1
-903,1931,AL,NYA,NYY,9,NYY,NYA
-904,1931,AL,PHA,OAK,10,PHA,PHA
-905,1931,NL,BSN,ATL,16,BSN,BSN
-906,1931,NL,CHN,CHC,17,CHC,CHN
-907,1931,NL,CIN,CIN,18,CIN,CIN
-908,1931,NL,BRO,LAD,22,BRO,BRO
-909,1931,NL,PHI,PHI,26,PHI,PHI
-910,1931,NL,PIT,PIT,27,PIT,PIT
-911,1931,NL,SLN,STL,28,STL,SLN
-912,1931,NL,NY1,SFG,30,NYG,NY1
-913,1932,AL,SLA,BAL,2,SLB,SLA
-914,1932,AL,BOS,BOS,3,BOS,BOS
-915,1932,AL,CHA,CHW,4,CHW,CHA
-916,1932,AL,CLE,CLE,5,CLE,CLE
-917,1932,AL,DET,DET,6,DET,DET
-918,1932,AL,WS1,MIN,8,WSH,WS1
-919,1932,AL,NYA,NYY,9,NYY,NYA
-920,1932,AL,PHA,OAK,10,PHA,PHA
-921,1932,NL,BSN,ATL,16,BSN,BSN
-922,1932,NL,CHN,CHC,17,CHC,CHN
-923,1932,NL,CIN,CIN,18,CIN,CIN
-924,1932,NL,BRO,LAD,22,BRO,BRO
-925,1932,NL,PHI,PHI,26,PHI,PHI
-926,1932,NL,PIT,PIT,27,PIT,PIT
-927,1932,NL,SLN,STL,28,STL,SLN
-928,1932,NL,NY1,SFG,30,NYG,NY1
-929,1933,AL,SLA,BAL,2,SLB,SLA
-930,1933,AL,BOS,BOS,3,BOS,BOS
-931,1933,AL,CHA,CHW,4,CHW,CHA
-932,1933,AL,CLE,CLE,5,CLE,CLE
-933,1933,AL,DET,DET,6,DET,DET
-934,1933,AL,WS1,MIN,8,WSH,WS1
-935,1933,AL,NYA,NYY,9,NYY,NYA
-936,1933,AL,PHA,OAK,10,PHA,PHA
-937,1933,NL,BSN,ATL,16,BSN,BSN
-938,1933,NL,CHN,CHC,17,CHC,CHN
-939,1933,NL,CIN,CIN,18,CIN,CIN
-940,1933,NL,BRO,LAD,22,BRO,BRO
-941,1933,NL,PHI,PHI,26,PHI,PHI
-942,1933,NL,PIT,PIT,27,PIT,PIT
-943,1933,NL,SLN,STL,28,STL,SLN
-944,1933,NL,NY1,SFG,30,NYG,NY1
-945,1934,AL,SLA,BAL,2,SLB,SLA
-946,1934,AL,BOS,BOS,3,BOS,BOS
-947,1934,AL,CHA,CHW,4,CHW,CHA
-948,1934,AL,CLE,CLE,5,CLE,CLE
-949,1934,AL,DET,DET,6,DET,DET
-950,1934,AL,WS1,MIN,8,WSH,WS1
-951,1934,AL,NYA,NYY,9,NYY,NYA
-952,1934,AL,PHA,OAK,10,PHA,PHA
-953,1934,NL,BSN,ATL,16,BSN,BSN
-954,1934,NL,CHN,CHC,17,CHC,CHN
-955,1934,NL,CIN,CIN,18,CIN,CIN
-956,1934,NL,BRO,LAD,22,BRO,BRO
-957,1934,NL,PHI,PHI,26,PHI,PHI
-958,1934,NL,PIT,PIT,27,PIT,PIT
-959,1934,NL,SLN,STL,28,STL,SLN
-960,1934,NL,NY1,SFG,30,NYG,NY1
-961,1935,AL,SLA,BAL,2,SLB,SLA
-962,1935,AL,BOS,BOS,3,BOS,BOS
-963,1935,AL,CHA,CHW,4,CHW,CHA
-964,1935,AL,CLE,CLE,5,CLE,CLE
-965,1935,AL,DET,DET,6,DET,DET
-966,1935,AL,WS1,MIN,8,WSH,WS1
-967,1935,AL,NYA,NYY,9,NYY,NYA
-968,1935,AL,PHA,OAK,10,PHA,PHA
-969,1935,NL,BSN,ATL,16,BSN,BSN
-970,1935,NL,CHN,CHC,17,CHC,CHN
-971,1935,NL,CIN,CIN,18,CIN,CIN
-972,1935,NL,BRO,LAD,22,BRO,BRO
-973,1935,NL,PHI,PHI,26,PHI,PHI
-974,1935,NL,PIT,PIT,27,PIT,PIT
-975,1935,NL,SLN,STL,28,STL,SLN
-976,1935,NL,NY1,SFG,30,NYG,NY1
-977,1936,AL,SLA,BAL,2,SLB,SLA
-978,1936,AL,BOS,BOS,3,BOS,BOS
-979,1936,AL,CHA,CHW,4,CHW,CHA
-980,1936,AL,CLE,CLE,5,CLE,CLE
-981,1936,AL,DET,DET,6,DET,DET
-982,1936,AL,WS1,MIN,8,WSH,WS1
-983,1936,AL,NYA,NYY,9,NYY,NYA
-984,1936,AL,PHA,OAK,10,PHA,PHA
-985,1936,NL,BSN,ATL,16,BSN,BSN
-986,1936,NL,CHN,CHC,17,CHC,CHN
-987,1936,NL,CIN,CIN,18,CIN,CIN
-988,1936,NL,BRO,LAD,22,BRO,BRO
-989,1936,NL,PHI,PHI,26,PHI,PHI
-990,1936,NL,PIT,PIT,27,PIT,PIT
-991,1936,NL,SLN,STL,28,STL,SLN
-992,1936,NL,NY1,SFG,30,NYG,NY1
-993,1937,AL,SLA,BAL,2,SLB,SLA
-994,1937,AL,BOS,BOS,3,BOS,BOS
-995,1937,AL,CHA,CHW,4,CHW,CHA
-996,1937,AL,CLE,CLE,5,CLE,CLE
-997,1937,AL,DET,DET,6,DET,DET
-998,1937,AL,WS1,MIN,8,WSH,WS1
-999,1937,AL,NYA,NYY,9,NYY,NYA
-1000,1937,AL,PHA,OAK,10,PHA,PHA
-1001,1937,NL,BSN,ATL,16,BSN,BSN
-1002,1937,NL,CHN,CHC,17,CHC,CHN
-1003,1937,NL,CIN,CIN,18,CIN,CIN
-1004,1937,NL,BRO,LAD,22,BRO,BRO
-1005,1937,NL,PHI,PHI,26,PHI,PHI
-1006,1937,NL,PIT,PIT,27,PIT,PIT
-1007,1937,NL,SLN,STL,28,STL,SLN
-1008,1937,NL,NY1,SFG,30,NYG,NY1
-1009,1938,AL,SLA,BAL,2,SLB,SLA
-1010,1938,AL,BOS,BOS,3,BOS,BOS
-1011,1938,AL,CHA,CHW,4,CHW,CHA
-1012,1938,AL,CLE,CLE,5,CLE,CLE
-1013,1938,AL,DET,DET,6,DET,DET
-1014,1938,AL,WS1,MIN,8,WSH,WS1
-1015,1938,AL,NYA,NYY,9,NYY,NYA
-1016,1938,AL,PHA,OAK,10,PHA,PHA
-1017,1938,NL,BSN,ATL,16,BSN,BSN
-1018,1938,NL,CHN,CHC,17,CHC,CHN
-1019,1938,NL,CIN,CIN,18,CIN,CIN
-1020,1938,NL,BRO,LAD,22,BRO,BRO
-1021,1938,NL,PHI,PHI,26,PHI,PHI
-1022,1938,NL,PIT,PIT,27,PIT,PIT
-1023,1938,NL,SLN,STL,28,STL,SLN
-1024,1938,NL,NY1,SFG,30,NYG,NY1
-1025,1939,AL,SLA,BAL,2,SLB,SLA
-1026,1939,AL,BOS,BOS,3,BOS,BOS
-1027,1939,AL,CHA,CHW,4,CHW,CHA
-1028,1939,AL,CLE,CLE,5,CLE,CLE
-1029,1939,AL,DET,DET,6,DET,DET
-1030,1939,AL,WS1,MIN,8,WSH,WS1
-1031,1939,AL,NYA,NYY,9,NYY,NYA
-1032,1939,AL,PHA,OAK,10,PHA,PHA
-1033,1939,NL,BSN,ATL,16,BSN,BSN
-1034,1939,NL,CHN,CHC,17,CHC,CHN
-1035,1939,NL,CIN,CIN,18,CIN,CIN
-1036,1939,NL,BRO,LAD,22,BRO,BRO
-1037,1939,NL,PHI,PHI,26,PHI,PHI
-1038,1939,NL,PIT,PIT,27,PIT,PIT
-1039,1939,NL,SLN,STL,28,STL,SLN
-1040,1939,NL,NY1,SFG,30,NYG,NY1
-1041,1940,AL,SLA,BAL,2,SLB,SLA
-1042,1940,AL,BOS,BOS,3,BOS,BOS
-1043,1940,AL,CHA,CHW,4,CHW,CHA
-1044,1940,AL,CLE,CLE,5,CLE,CLE
-1045,1940,AL,DET,DET,6,DET,DET
-1046,1940,AL,WS1,MIN,8,WSH,WS1
-1047,1940,AL,NYA,NYY,9,NYY,NYA
-1048,1940,AL,PHA,OAK,10,PHA,PHA
-1049,1940,NL,BSN,ATL,16,BSN,BSN
-1050,1940,NL,CHN,CHC,17,CHC,CHN
-1051,1940,NL,CIN,CIN,18,CIN,CIN
-1052,1940,NL,BRO,LAD,22,BRO,BRO
-1053,1940,NL,PHI,PHI,26,PHI,PHI
-1054,1940,NL,PIT,PIT,27,PIT,PIT
-1055,1940,NL,SLN,STL,28,STL,SLN
-1056,1940,NL,NY1,SFG,30,NYG,NY1
-1057,1941,AL,SLA,BAL,2,SLB,SLA
-1058,1941,AL,BOS,BOS,3,BOS,BOS
-1059,1941,AL,CHA,CHW,4,CHW,CHA
-1060,1941,AL,CLE,CLE,5,CLE,CLE
-1061,1941,AL,DET,DET,6,DET,DET
-1062,1941,AL,WS1,MIN,8,WSH,WS1
-1063,1941,AL,NYA,NYY,9,NYY,NYA
-1064,1941,AL,PHA,OAK,10,PHA,PHA
-1065,1941,NL,BSN,ATL,16,BSN,BSN
-1066,1941,NL,CHN,CHC,17,CHC,CHN
-1067,1941,NL,CIN,CIN,18,CIN,CIN
-1068,1941,NL,BRO,LAD,22,BRO,BRO
-1069,1941,NL,PHI,PHI,26,PHI,PHI
-1070,1941,NL,PIT,PIT,27,PIT,PIT
-1071,1941,NL,SLN,STL,28,STL,SLN
-1072,1941,NL,NY1,SFG,30,NYG,NY1
-1073,1942,AL,SLA,BAL,2,SLB,SLA
-1074,1942,AL,BOS,BOS,3,BOS,BOS
-1075,1942,AL,CHA,CHW,4,CHW,CHA
-1076,1942,AL,CLE,CLE,5,CLE,CLE
-1077,1942,AL,DET,DET,6,DET,DET
-1078,1942,AL,WS1,MIN,8,WSH,WS1
-1079,1942,AL,NYA,NYY,9,NYY,NYA
-1080,1942,AL,PHA,OAK,10,PHA,PHA
-1081,1942,NL,BSN,ATL,16,BSN,BSN
-1082,1942,NL,CHN,CHC,17,CHC,CHN
-1083,1942,NL,CIN,CIN,18,CIN,CIN
-1084,1942,NL,BRO,LAD,22,BRO,BRO
-1085,1942,NL,PHI,PHI,26,PHI,PHI
-1086,1942,NL,PIT,PIT,27,PIT,PIT
-1087,1942,NL,SLN,STL,28,STL,SLN
-1088,1942,NL,NY1,SFG,30,NYG,NY1
-1089,1943,AL,SLA,BAL,2,SLB,SLA
-1090,1943,AL,BOS,BOS,3,BOS,BOS
-1091,1943,AL,CHA,CHW,4,CHW,CHA
-1092,1943,AL,CLE,CLE,5,CLE,CLE
-1093,1943,AL,DET,DET,6,DET,DET
-1094,1943,AL,WS1,MIN,8,WSH,WS1
-1095,1943,AL,NYA,NYY,9,NYY,NYA
-1096,1943,AL,PHA,OAK,10,PHA,PHA
-1097,1943,NL,BSN,ATL,16,BSN,BSN
-1098,1943,NL,CHN,CHC,17,CHC,CHN
-1099,1943,NL,CIN,CIN,18,CIN,CIN
-1100,1943,NL,BRO,LAD,22,BRO,BRO
-1101,1943,NL,PHI,PHI,26,PHI,PHI
-1102,1943,NL,PIT,PIT,27,PIT,PIT
-1103,1943,NL,SLN,STL,28,STL,SLN
-1104,1943,NL,NY1,SFG,30,NYG,NY1
-1105,1944,AL,SLA,BAL,2,SLB,SLA
-1106,1944,AL,BOS,BOS,3,BOS,BOS
-1107,1944,AL,CHA,CHW,4,CHW,CHA
-1108,1944,AL,CLE,CLE,5,CLE,CLE
-1109,1944,AL,DET,DET,6,DET,DET
-1110,1944,AL,WS1,MIN,8,WSH,WS1
-1111,1944,AL,NYA,NYY,9,NYY,NYA
-1112,1944,AL,PHA,OAK,10,PHA,PHA
-1113,1944,NL,BSN,ATL,16,BSN,BSN
-1114,1944,NL,CHN,CHC,17,CHC,CHN
-1115,1944,NL,CIN,CIN,18,CIN,CIN
-1116,1944,NL,BRO,LAD,22,BRO,BRO
-1117,1944,NL,PHI,PHI,26,PHI,PHI
-1118,1944,NL,PIT,PIT,27,PIT,PIT
-1119,1944,NL,SLN,STL,28,STL,SLN
-1120,1944,NL,NY1,SFG,30,NYG,NY1
-1121,1945,AL,SLA,BAL,2,SLB,SLA
-1122,1945,AL,BOS,BOS,3,BOS,BOS
-1123,1945,AL,CHA,CHW,4,CHW,CHA
-1124,1945,AL,CLE,CLE,5,CLE,CLE
-1125,1945,AL,DET,DET,6,DET,DET
-1126,1945,AL,WS1,MIN,8,WSH,WS1
-1127,1945,AL,NYA,NYY,9,NYY,NYA
-1128,1945,AL,PHA,OAK,10,PHA,PHA
-1129,1945,NL,BSN,ATL,16,BSN,BSN
-1130,1945,NL,CHN,CHC,17,CHC,CHN
-1131,1945,NL,CIN,CIN,18,CIN,CIN
-1132,1945,NL,BRO,LAD,22,BRO,BRO
-1133,1945,NL,PHI,PHI,26,PHI,PHI
-1134,1945,NL,PIT,PIT,27,PIT,PIT
-1135,1945,NL,SLN,STL,28,STL,SLN
-1136,1945,NL,NY1,SFG,30,NYG,NY1
-1137,1946,AL,SLA,BAL,2,SLB,SLA
-1138,1946,AL,BOS,BOS,3,BOS,BOS
-1139,1946,AL,CHA,CHW,4,CHW,CHA
-1140,1946,AL,CLE,CLE,5,CLE,CLE
-1141,1946,AL,DET,DET,6,DET,DET
-1142,1946,AL,WS1,MIN,8,WSH,WS1
-1143,1946,AL,NYA,NYY,9,NYY,NYA
-1144,1946,AL,PHA,OAK,10,PHA,PHA
-1145,1946,NL,BSN,ATL,16,BSN,BSN
-1146,1946,NL,CHN,CHC,17,CHC,CHN
-1147,1946,NL,CIN,CIN,18,CIN,CIN
-1148,1946,NL,BRO,LAD,22,BRO,BRO
-1149,1946,NL,PHI,PHI,26,PHI,PHI
-1150,1946,NL,PIT,PIT,27,PIT,PIT
-1151,1946,NL,SLN,STL,28,STL,SLN
-1152,1946,NL,NY1,SFG,30,NYG,NY1
-1153,1947,AL,SLA,BAL,2,SLB,SLA
-1154,1947,AL,BOS,BOS,3,BOS,BOS
-1155,1947,AL,CHA,CHW,4,CHW,CHA
-1156,1947,AL,CLE,CLE,5,CLE,CLE
-1157,1947,AL,DET,DET,6,DET,DET
-1158,1947,AL,WS1,MIN,8,WSH,WS1
-1159,1947,AL,NYA,NYY,9,NYY,NYA
-1160,1947,AL,PHA,OAK,10,PHA,PHA
-1161,1947,NL,BSN,ATL,16,BSN,BSN
-1162,1947,NL,CHN,CHC,17,CHC,CHN
-1163,1947,NL,CIN,CIN,18,CIN,CIN
-1164,1947,NL,BRO,LAD,22,BRO,BRO
-1165,1947,NL,PHI,PHI,26,PHI,PHI
-1166,1947,NL,PIT,PIT,27,PIT,PIT
-1167,1947,NL,SLN,STL,28,STL,SLN
-1168,1947,NL,NY1,SFG,30,NYG,NY1
-1169,1948,AL,SLA,BAL,2,SLB,SLA
-1170,1948,AL,BOS,BOS,3,BOS,BOS
-1171,1948,AL,CHA,CHW,4,CHW,CHA
-1172,1948,AL,CLE,CLE,5,CLE,CLE
-1173,1948,AL,DET,DET,6,DET,DET
-1174,1948,AL,WS1,MIN,8,WSH,WS1
-1175,1948,AL,NYA,NYY,9,NYY,NYA
-1176,1948,AL,PHA,OAK,10,PHA,PHA
-1177,1948,NL,BSN,ATL,16,BSN,BSN
-1178,1948,NL,CHN,CHC,17,CHC,CHN
-1179,1948,NL,CIN,CIN,18,CIN,CIN
-1180,1948,NL,BRO,LAD,22,BRO,BRO
-1181,1948,NL,PHI,PHI,26,PHI,PHI
-1182,1948,NL,PIT,PIT,27,PIT,PIT
-1183,1948,NL,SLN,STL,28,STL,SLN
-1184,1948,NL,NY1,SFG,30,NYG,NY1
-1185,1949,AL,SLA,BAL,2,SLB,SLA
-1186,1949,AL,BOS,BOS,3,BOS,BOS
-1187,1949,AL,CHA,CHW,4,CHW,CHA
-1188,1949,AL,CLE,CLE,5,CLE,CLE
-1189,1949,AL,DET,DET,6,DET,DET
-1190,1949,AL,WS1,MIN,8,WSH,WS1
-1191,1949,AL,NYA,NYY,9,NYY,NYA
-1192,1949,AL,PHA,OAK,10,PHA,PHA
-1193,1949,NL,BSN,ATL,16,BSN,BSN
-1194,1949,NL,CHN,CHC,17,CHC,CHN
-1195,1949,NL,CIN,CIN,18,CIN,CIN
-1196,1949,NL,BRO,LAD,22,BRO,BRO
-1197,1949,NL,PHI,PHI,26,PHI,PHI
-1198,1949,NL,PIT,PIT,27,PIT,PIT
-1199,1949,NL,SLN,STL,28,STL,SLN
-1200,1949,NL,NY1,SFG,30,NYG,NY1
-1201,1950,AL,SLA,BAL,2,SLB,SLA
-1202,1950,AL,BOS,BOS,3,BOS,BOS
-1203,1950,AL,CHA,CHW,4,CHW,CHA
-1204,1950,AL,CLE,CLE,5,CLE,CLE
-1205,1950,AL,DET,DET,6,DET,DET
-1206,1950,AL,WS1,MIN,8,WSH,WS1
-1207,1950,AL,NYA,NYY,9,NYY,NYA
-1208,1950,AL,PHA,OAK,10,PHA,PHA
-1209,1950,NL,BSN,ATL,16,BSN,BSN
-1210,1950,NL,CHN,CHC,17,CHC,CHN
-1211,1950,NL,CIN,CIN,18,CIN,CIN
-1212,1950,NL,BRO,LAD,22,BRO,BRO
-1213,1950,NL,PHI,PHI,26,PHI,PHI
-1214,1950,NL,PIT,PIT,27,PIT,PIT
-1215,1950,NL,SLN,STL,28,STL,SLN
-1216,1950,NL,NY1,SFG,30,NYG,NY1
-1217,1951,AL,SLA,BAL,2,SLB,SLA
-1218,1951,AL,BOS,BOS,3,BOS,BOS
-1219,1951,AL,CHA,CHW,4,CHW,CHA
-1220,1951,AL,CLE,CLE,5,CLE,CLE
-1221,1951,AL,DET,DET,6,DET,DET
-1222,1951,AL,WS1,MIN,8,WSH,WS1
-1223,1951,AL,NYA,NYY,9,NYY,NYA
-1224,1951,AL,PHA,OAK,10,PHA,PHA
-1225,1951,NL,BSN,ATL,16,BSN,BSN
-1226,1951,NL,CHN,CHC,17,CHC,CHN
-1227,1951,NL,CIN,CIN,18,CIN,CIN
-1228,1951,NL,BRO,LAD,22,BRO,BRO
-1229,1951,NL,PHI,PHI,26,PHI,PHI
-1230,1951,NL,PIT,PIT,27,PIT,PIT
-1231,1951,NL,SLN,STL,28,STL,SLN
-1232,1951,NL,NY1,SFG,30,NYG,NY1
-1233,1952,AL,SLA,BAL,2,SLB,SLA
-1234,1952,AL,BOS,BOS,3,BOS,BOS
-1235,1952,AL,CHA,CHW,4,CHW,CHA
-1236,1952,AL,CLE,CLE,5,CLE,CLE
-1237,1952,AL,DET,DET,6,DET,DET
-1238,1952,AL,WS1,MIN,8,WSH,WS1
-1239,1952,AL,NYA,NYY,9,NYY,NYA
-1240,1952,AL,PHA,OAK,10,PHA,PHA
-1241,1952,NL,BSN,ATL,16,BSN,BSN
-1242,1952,NL,CHN,CHC,17,CHC,CHN
-1243,1952,NL,CIN,CIN,18,CIN,CIN
-1244,1952,NL,BRO,LAD,22,BRO,BRO
-1245,1952,NL,PHI,PHI,26,PHI,PHI
-1246,1952,NL,PIT,PIT,27,PIT,PIT
-1247,1952,NL,SLN,STL,28,STL,SLN
-1248,1952,NL,NY1,SFG,30,NYG,NY1
-1249,1953,AL,SLA,BAL,2,SLB,SLA
-1250,1953,AL,BOS,BOS,3,BOS,BOS
-1251,1953,AL,CHA,CHW,4,CHW,CHA
-1252,1953,AL,CLE,CLE,5,CLE,CLE
-1253,1953,AL,DET,DET,6,DET,DET
-1254,1953,AL,WS1,MIN,8,WSH,WS1
-1255,1953,AL,NYA,NYY,9,NYY,NYA
-1256,1953,AL,PHA,OAK,10,PHA,PHA
-1257,1953,NL,ML1,ATL,16,MLN,MLN
-1258,1953,NL,CHN,CHC,17,CHC,CHN
-1259,1953,NL,CIN,CIN,18,CIN,CIN
-1260,1953,NL,BRO,LAD,22,BRO,BRO
-1261,1953,NL,PHI,PHI,26,PHI,PHI
-1262,1953,NL,PIT,PIT,27,PIT,PIT
-1263,1953,NL,SLN,STL,28,STL,SLN
-1264,1953,NL,NY1,SFG,30,NYG,NY1
-1265,1954,AL,BAL,BAL,2,BAL,BAL
-1266,1954,AL,BOS,BOS,3,BOS,BOS
-1267,1954,AL,CHA,CHW,4,CHW,CHA
-1268,1954,AL,CLE,CLE,5,CLE,CLE
-1269,1954,AL,DET,DET,6,DET,DET
-1270,1954,AL,WS1,MIN,8,WSH,WS1
-1271,1954,AL,NYA,NYY,9,NYY,NYA
-1272,1954,AL,PHA,OAK,10,PHA,PHA
-1273,1954,NL,ML1,ATL,16,MLN,MLN
-1274,1954,NL,CHN,CHC,17,CHC,CHN
-1275,1954,NL,CIN,CIN,18,CIN,CIN
-1276,1954,NL,BRO,LAD,22,BRO,BRO
-1277,1954,NL,PHI,PHI,26,PHI,PHI
-1278,1954,NL,PIT,PIT,27,PIT,PIT
-1279,1954,NL,SLN,STL,28,STL,SLN
-1280,1954,NL,NY1,SFG,30,NYG,NY1
-1281,1955,AL,BAL,BAL,2,BAL,BAL
-1282,1955,AL,BOS,BOS,3,BOS,BOS
-1283,1955,AL,CHA,CHW,4,CHW,CHA
-1284,1955,AL,CLE,CLE,5,CLE,CLE
-1285,1955,AL,DET,DET,6,DET,DET
-1286,1955,AL,WS1,MIN,8,WSH,WS1
-1287,1955,AL,NYA,NYY,9,NYY,NYA
-1288,1955,AL,KC1,OAK,10,KCA,KC1
-1289,1955,NL,ML1,ATL,16,MLN,MLN
-1290,1955,NL,CHN,CHC,17,CHC,CHN
-1291,1955,NL,CIN,CIN,18,CIN,CIN
-1292,1955,NL,BRO,LAD,22,BRO,BRO
-1293,1955,NL,PHI,PHI,26,PHI,PHI
-1294,1955,NL,PIT,PIT,27,PIT,PIT
-1295,1955,NL,SLN,STL,28,STL,SLN
-1296,1955,NL,NY1,SFG,30,NYG,NY1
-1297,1956,AL,BAL,BAL,2,BAL,BAL
-1298,1956,AL,BOS,BOS,3,BOS,BOS
-1299,1956,AL,CHA,CHW,4,CHW,CHA
-1300,1956,AL,CLE,CLE,5,CLE,CLE
-1301,1956,AL,DET,DET,6,DET,DET
-1302,1956,AL,WS1,MIN,8,WSH,WS1
-1303,1956,AL,NYA,NYY,9,NYY,NYA
-1304,1956,AL,KC1,OAK,10,KCA,KC1
-1305,1956,NL,ML1,ATL,16,MLN,MLN
-1306,1956,NL,CHN,CHC,17,CHC,CHN
-1307,1956,NL,CIN,CIN,18,CIN,CIN
-1308,1956,NL,BRO,LAD,22,BRO,BRO
-1309,1956,NL,PHI,PHI,26,PHI,PHI
-1310,1956,NL,PIT,PIT,27,PIT,PIT
-1311,1956,NL,SLN,STL,28,STL,SLN
-1312,1956,NL,NY1,SFG,30,NYG,NY1
-1313,1957,AL,BAL,BAL,2,BAL,BAL
-1314,1957,AL,BOS,BOS,3,BOS,BOS
-1315,1957,AL,CHA,CHW,4,CHW,CHA
-1316,1957,AL,CLE,CLE,5,CLE,CLE
-1317,1957,AL,DET,DET,6,DET,DET
-1318,1957,AL,WS1,MIN,8,WSH,WS1
-1319,1957,AL,NYA,NYY,9,NYY,NYA
-1320,1957,AL,KC1,OAK,10,KCA,KC1
-1321,1957,NL,ML1,ATL,16,MLN,MLN
-1322,1957,NL,CHN,CHC,17,CHC,CHN
-1323,1957,NL,CIN,CIN,18,CIN,CIN
-1324,1957,NL,BRO,LAD,22,BRO,BRO
-1325,1957,NL,PHI,PHI,26,PHI,PHI
-1326,1957,NL,PIT,PIT,27,PIT,PIT
-1327,1957,NL,SLN,STL,28,STL,SLN
-1328,1957,NL,NY1,SFG,30,NYG,NY1
-1329,1958,AL,BAL,BAL,2,BAL,BAL
-1330,1958,AL,BOS,BOS,3,BOS,BOS
-1331,1958,AL,CHA,CHW,4,CHW,CHA
-1332,1958,AL,CLE,CLE,5,CLE,CLE
-1333,1958,AL,DET,DET,6,DET,DET
-1334,1958,AL,WS1,MIN,8,WSH,WS1
-1335,1958,AL,NYA,NYY,9,NYY,NYA
-1336,1958,AL,KC1,OAK,10,KCA,KC1
-1337,1958,NL,ML1,ATL,16,MLN,MLN
-1338,1958,NL,CHN,CHC,17,CHC,CHN
-1339,1958,NL,CIN,CIN,18,CIN,CIN
-1340,1958,NL,LAN,LAD,22,LAD,LAN
-1341,1958,NL,PHI,PHI,26,PHI,PHI
-1342,1958,NL,PIT,PIT,27,PIT,PIT
-1343,1958,NL,SLN,STL,28,STL,SLN
-1344,1958,NL,SFN,SFG,30,SFG,SFN
-1345,1959,AL,BAL,BAL,2,BAL,BAL
-1346,1959,AL,BOS,BOS,3,BOS,BOS
-1347,1959,AL,CHA,CHW,4,CHW,CHA
-1348,1959,AL,CLE,CLE,5,CLE,CLE
-1349,1959,AL,DET,DET,6,DET,DET
-1350,1959,AL,WS1,MIN,8,WSH,WS1
-1351,1959,AL,NYA,NYY,9,NYY,NYA
-1352,1959,AL,KC1,OAK,10,KCA,KC1
-1353,1959,NL,ML1,ATL,16,MLN,MLN
-1354,1959,NL,CHN,CHC,17,CHC,CHN
-1355,1959,NL,CIN,CIN,18,CIN,CIN
-1356,1959,NL,LAN,LAD,22,LAD,LAN
-1357,1959,NL,PHI,PHI,26,PHI,PHI
-1358,1959,NL,PIT,PIT,27,PIT,PIT
-1359,1959,NL,SLN,STL,28,STL,SLN
-1360,1959,NL,SFN,SFG,30,SFG,SFN
-1361,1960,AL,BAL,BAL,2,BAL,BAL
-1362,1960,AL,BOS,BOS,3,BOS,BOS
-1363,1960,AL,CHA,CHW,4,CHW,CHA
-1364,1960,AL,CLE,CLE,5,CLE,CLE
-1365,1960,AL,DET,DET,6,DET,DET
-1366,1960,AL,WS1,MIN,8,WSH,WS1
-1367,1960,AL,NYA,NYY,9,NYY,NYA
-1368,1960,AL,KC1,OAK,10,KCA,KC1
-1369,1960,NL,ML1,ATL,16,MLN,MLN
-1370,1960,NL,CHN,CHC,17,CHC,CHN
-1371,1960,NL,CIN,CIN,18,CIN,CIN
-1372,1960,NL,LAN,LAD,22,LAD,LAN
-1373,1960,NL,PHI,PHI,26,PHI,PHI
-1374,1960,NL,PIT,PIT,27,PIT,PIT
-1375,1960,NL,SLN,STL,28,STL,SLN
-1376,1960,NL,SFN,SFG,30,SFG,SFN
-1377,1961,AL,LAA,ANA,1,LAA,LAA
-1378,1961,AL,BAL,BAL,2,BAL,BAL
-1379,1961,AL,BOS,BOS,3,BOS,BOS
-1380,1961,AL,CHA,CHW,4,CHW,CHA
-1381,1961,AL,CLE,CLE,5,CLE,CLE
-1382,1961,AL,DET,DET,6,DET,DET
-1383,1961,AL,MIN,MIN,8,MIN,MIN
-1384,1961,AL,NYA,NYY,9,NYY,NYA
-1385,1961,AL,KC1,OAK,10,KCA,KC1
-1386,1961,AL,WS2,TEX,13,WSA,WS2
-1387,1961,NL,ML1,ATL,16,MLN,MLN
-1388,1961,NL,CHN,CHC,17,CHC,CHN
-1389,1961,NL,CIN,CIN,18,CIN,CIN
-1390,1961,NL,LAN,LAD,22,LAD,LAN
-1391,1961,NL,PHI,PHI,26,PHI,PHI
-1392,1961,NL,PIT,PIT,27,PIT,PIT
-1393,1961,NL,SLN,STL,28,STL,SLN
-1394,1961,NL,SFN,SFG,30,SFG,SFN
-1395,1962,AL,LAA,ANA,1,LAA,LAA
-1396,1962,AL,BAL,BAL,2,BAL,BAL
-1397,1962,AL,BOS,BOS,3,BOS,BOS
-1398,1962,AL,CHA,CHW,4,CHW,CHA
-1399,1962,AL,CLE,CLE,5,CLE,CLE
-1400,1962,AL,DET,DET,6,DET,DET
-1401,1962,AL,MIN,MIN,8,MIN,MIN
-1402,1962,AL,NYA,NYY,9,NYY,NYA
-1403,1962,AL,KC1,OAK,10,KCA,KC1
-1404,1962,AL,WS2,TEX,13,WSA,WS2
-1405,1962,NL,ML1,ATL,16,MLN,MLN
-1406,1962,NL,CHN,CHC,17,CHC,CHN
-1407,1962,NL,CIN,CIN,18,CIN,CIN
-1408,1962,NL,HOU,HOU,21,HOU,HOU
-1409,1962,NL,LAN,LAD,22,LAD,LAN
-1410,1962,NL,NYN,NYM,25,NYM,NYN
-1411,1962,NL,PHI,PHI,26,PHI,PHI
-1412,1962,NL,PIT,PIT,27,PIT,PIT
-1413,1962,NL,SLN,STL,28,STL,SLN
-1414,1962,NL,SFN,SFG,30,SFG,SFN
-1415,1963,AL,LAA,ANA,1,LAA,LAA
-1416,1963,AL,BAL,BAL,2,BAL,BAL
-1417,1963,AL,BOS,BOS,3,BOS,BOS
-1418,1963,AL,CHA,CHW,4,CHW,CHA
-1419,1963,AL,CLE,CLE,5,CLE,CLE
-1420,1963,AL,DET,DET,6,DET,DET
-1421,1963,AL,MIN,MIN,8,MIN,MIN
-1422,1963,AL,NYA,NYY,9,NYY,NYA
-1423,1963,AL,KC1,OAK,10,KCA,KC1
-1424,1963,AL,WS2,TEX,13,WSA,WS2
-1425,1963,NL,ML1,ATL,16,MLN,MLN
-1426,1963,NL,CHN,CHC,17,CHC,CHN
-1427,1963,NL,CIN,CIN,18,CIN,CIN
-1428,1963,NL,HOU,HOU,21,HOU,HOU
-1429,1963,NL,LAN,LAD,22,LAD,LAN
-1430,1963,NL,NYN,NYM,25,NYM,NYN
-1431,1963,NL,PHI,PHI,26,PHI,PHI
-1432,1963,NL,PIT,PIT,27,PIT,PIT
-1433,1963,NL,SLN,STL,28,STL,SLN
-1434,1963,NL,SFN,SFG,30,SFG,SFN
-1435,1964,AL,LAA,ANA,1,LAA,LAA
-1436,1964,AL,BAL,BAL,2,BAL,BAL
-1437,1964,AL,BOS,BOS,3,BOS,BOS
-1438,1964,AL,CHA,CHW,4,CHW,CHA
-1439,1964,AL,CLE,CLE,5,CLE,CLE
-1440,1964,AL,DET,DET,6,DET,DET
-1441,1964,AL,MIN,MIN,8,MIN,MIN
-1442,1964,AL,NYA,NYY,9,NYY,NYA
-1443,1964,AL,KC1,OAK,10,KCA,KC1
-1444,1964,AL,WS2,TEX,13,WSA,WS2
-1445,1964,NL,ML1,ATL,16,MLN,MLN
-1446,1964,NL,CHN,CHC,17,CHC,CHN
-1447,1964,NL,CIN,CIN,18,CIN,CIN
-1448,1964,NL,HOU,HOU,21,HOU,HOU
-1449,1964,NL,LAN,LAD,22,LAD,LAN
-1450,1964,NL,NYN,NYM,25,NYM,NYN
-1451,1964,NL,PHI,PHI,26,PHI,PHI
-1452,1964,NL,PIT,PIT,27,PIT,PIT
-1453,1964,NL,SLN,STL,28,STL,SLN
-1454,1964,NL,SFN,SFG,30,SFG,SFN
-1455,1965,AL,CAL,ANA,1,CAL,CAL
-1456,1965,AL,BAL,BAL,2,BAL,BAL
-1457,1965,AL,BOS,BOS,3,BOS,BOS
-1458,1965,AL,CHA,CHW,4,CHW,CHA
-1459,1965,AL,CLE,CLE,5,CLE,CLE
-1460,1965,AL,DET,DET,6,DET,DET
-1461,1965,AL,MIN,MIN,8,MIN,MIN
-1462,1965,AL,NYA,NYY,9,NYY,NYA
-1463,1965,AL,KC1,OAK,10,KCA,KC1
-1464,1965,AL,WS2,TEX,13,WSA,WS2
-1465,1965,NL,ML1,ATL,16,MLN,MLN
-1466,1965,NL,CHN,CHC,17,CHC,CHN
-1467,1965,NL,CIN,CIN,18,CIN,CIN
-1468,1965,NL,HOU,HOU,21,HOU,HOU
-1469,1965,NL,LAN,LAD,22,LAD,LAN
-1470,1965,NL,NYN,NYM,25,NYM,NYN
-1471,1965,NL,PHI,PHI,26,PHI,PHI
-1472,1965,NL,PIT,PIT,27,PIT,PIT
-1473,1965,NL,SLN,STL,28,STL,SLN
-1474,1965,NL,SFN,SFG,30,SFG,SFN
-1475,1966,AL,CAL,ANA,1,CAL,CAL
-1476,1966,AL,BAL,BAL,2,BAL,BAL
-1477,1966,AL,BOS,BOS,3,BOS,BOS
-1478,1966,AL,CHA,CHW,4,CHW,CHA
-1479,1966,AL,CLE,CLE,5,CLE,CLE
-1480,1966,AL,DET,DET,6,DET,DET
-1481,1966,AL,MIN,MIN,8,MIN,MIN
-1482,1966,AL,NYA,NYY,9,NYY,NYA
-1483,1966,AL,KC1,OAK,10,KCA,KC1
-1484,1966,AL,WS2,TEX,13,WSA,WS2
-1485,1966,NL,ATL,ATL,16,ATL,ATL
-1486,1966,NL,CHN,CHC,17,CHC,CHN
-1487,1966,NL,CIN,CIN,18,CIN,CIN
-1488,1966,NL,HOU,HOU,21,HOU,HOU
-1489,1966,NL,LAN,LAD,22,LAD,LAN
-1490,1966,NL,NYN,NYM,25,NYM,NYN
-1491,1966,NL,PHI,PHI,26,PHI,PHI
-1492,1966,NL,PIT,PIT,27,PIT,PIT
-1493,1966,NL,SLN,STL,28,STL,SLN
-1494,1966,NL,SFN,SFG,30,SFG,SFN
-1495,1967,AL,CAL,ANA,1,CAL,CAL
-1496,1967,AL,BAL,BAL,2,BAL,BAL
-1497,1967,AL,BOS,BOS,3,BOS,BOS
-1498,1967,AL,CHA,CHW,4,CHW,CHA
-1499,1967,AL,CLE,CLE,5,CLE,CLE
-1500,1967,AL,DET,DET,6,DET,DET
-1501,1967,AL,MIN,MIN,8,MIN,MIN
-1502,1967,AL,NYA,NYY,9,NYY,NYA
-1503,1967,AL,KC1,OAK,10,KCA,KC1
-1504,1967,AL,WS2,TEX,13,WSA,WS2
-1505,1967,NL,ATL,ATL,16,ATL,ATL
-1506,1967,NL,CHN,CHC,17,CHC,CHN
-1507,1967,NL,CIN,CIN,18,CIN,CIN
-1508,1967,NL,HOU,HOU,21,HOU,HOU
-1509,1967,NL,LAN,LAD,22,LAD,LAN
-1510,1967,NL,NYN,NYM,25,NYM,NYN
-1511,1967,NL,PHI,PHI,26,PHI,PHI
-1512,1967,NL,PIT,PIT,27,PIT,PIT
-1513,1967,NL,SLN,STL,28,STL,SLN
-1514,1967,NL,SFN,SFG,30,SFG,SFN
-1515,1968,AL,CAL,ANA,1,CAL,CAL
-1516,1968,AL,BAL,BAL,2,BAL,BAL
-1517,1968,AL,BOS,BOS,3,BOS,BOS
-1518,1968,AL,CHA,CHW,4,CHW,CHA
-1519,1968,AL,CLE,CLE,5,CLE,CLE
-1520,1968,AL,DET,DET,6,DET,DET
-1521,1968,AL,MIN,MIN,8,MIN,MIN
-1522,1968,AL,NYA,NYY,9,NYY,NYA
-1523,1968,AL,OAK,OAK,10,OAK,OAK
-1524,1968,AL,WS2,TEX,13,WSA,WS2
-1525,1968,NL,ATL,ATL,16,ATL,ATL
-1526,1968,NL,CHN,CHC,17,CHC,CHN
-1527,1968,NL,CIN,CIN,18,CIN,CIN
-1528,1968,NL,HOU,HOU,21,HOU,HOU
-1529,1968,NL,LAN,LAD,22,LAD,LAN
-1530,1968,NL,NYN,NYM,25,NYM,NYN
-1531,1968,NL,PHI,PHI,26,PHI,PHI
-1532,1968,NL,PIT,PIT,27,PIT,PIT
-1533,1968,NL,SLN,STL,28,STL,SLN
-1534,1968,NL,SFN,SFG,30,SFG,SFN
-1535,1969,AL,CAL,ANA,1,CAL,CAL
-1536,1969,AL,BAL,BAL,2,BAL,BAL
-1537,1969,AL,BOS,BOS,3,BOS,BOS
-1538,1969,AL,CHA,CHW,4,CHW,CHA
-1539,1969,AL,CLE,CLE,5,CLE,CLE
-1540,1969,AL,DET,DET,6,DET,DET
-1541,1969,AL,KCA,KCR,7,KCR,KCA
-1542,1969,AL,MIN,MIN,8,MIN,MIN
-1543,1969,AL,NYA,NYY,9,NYY,NYA
-1544,1969,AL,OAK,OAK,10,OAK,OAK
-1545,1969,AL,WS2,TEX,13,WSA,WS2
-1546,1969,NL,ATL,ATL,16,ATL,ATL
-1547,1969,NL,CHN,CHC,17,CHC,CHN
-1548,1969,NL,CIN,CIN,18,CIN,CIN
-1549,1969,NL,HOU,HOU,21,HOU,HOU
-1550,1969,NL,LAN,LAD,22,LAD,LAN
-1551,1969,AL,SE1,MIL,23,SEP,SE1
-1552,1969,NL,MON,WSN,24,MON,MON
-1553,1969,NL,NYN,NYM,25,NYM,NYN
-1554,1969,NL,PHI,PHI,26,PHI,PHI
-1555,1969,NL,PIT,PIT,27,PIT,PIT
-1556,1969,NL,SLN,STL,28,STL,SLN
-1557,1969,NL,SDN,SDP,29,SDP,SDN
-1558,1969,NL,SFN,SFG,30,SFG,SFN
-1559,1970,AL,CAL,ANA,1,CAL,CAL
-1560,1970,AL,BAL,BAL,2,BAL,BAL
-1561,1970,AL,BOS,BOS,3,BOS,BOS
-1562,1970,AL,CHA,CHW,4,CHW,CHA
-1563,1970,AL,CLE,CLE,5,CLE,CLE
-1564,1970,AL,DET,DET,6,DET,DET
-1565,1970,AL,KCA,KCR,7,KCR,KCA
-1566,1970,AL,MIN,MIN,8,MIN,MIN
-1567,1970,AL,NYA,NYY,9,NYY,NYA
-1568,1970,AL,OAK,OAK,10,OAK,OAK
-1569,1970,AL,WS2,TEX,13,WSA,WS2
-1570,1970,NL,ATL,ATL,16,ATL,ATL
-1571,1970,NL,CHN,CHC,17,CHC,CHN
-1572,1970,NL,CIN,CIN,18,CIN,CIN
-1573,1970,NL,HOU,HOU,21,HOU,HOU
-1574,1970,NL,LAN,LAD,22,LAD,LAN
-1575,1970,AL,ML4,MIL,23,MIL,MIL
-1576,1970,NL,MON,WSN,24,MON,MON
-1577,1970,NL,NYN,NYM,25,NYM,NYN
-1578,1970,NL,PHI,PHI,26,PHI,PHI
-1579,1970,NL,PIT,PIT,27,PIT,PIT
-1580,1970,NL,SLN,STL,28,STL,SLN
-1581,1970,NL,SDN,SDP,29,SDP,SDN
-1582,1970,NL,SFN,SFG,30,SFG,SFN
-1583,1971,AL,CAL,ANA,1,CAL,CAL
-1584,1971,AL,BAL,BAL,2,BAL,BAL
-1585,1971,AL,BOS,BOS,3,BOS,BOS
-1586,1971,AL,CHA,CHW,4,CHW,CHA
-1587,1971,AL,CLE,CLE,5,CLE,CLE
-1588,1971,AL,DET,DET,6,DET,DET
-1589,1971,AL,KCA,KCR,7,KCR,KCA
-1590,1971,AL,MIN,MIN,8,MIN,MIN
-1591,1971,AL,NYA,NYY,9,NYY,NYA
-1592,1971,AL,OAK,OAK,10,OAK,OAK
-1593,1971,AL,WS2,TEX,13,WSA,WS2
-1594,1971,NL,ATL,ATL,16,ATL,ATL
-1595,1971,NL,CHN,CHC,17,CHC,CHN
-1596,1971,NL,CIN,CIN,18,CIN,CIN
-1597,1971,NL,HOU,HOU,21,HOU,HOU
-1598,1971,NL,LAN,LAD,22,LAD,LAN
-1599,1971,AL,ML4,MIL,23,MIL,MIL
-1600,1971,NL,MON,WSN,24,MON,MON
-1601,1971,NL,NYN,NYM,25,NYM,NYN
-1602,1971,NL,PHI,PHI,26,PHI,PHI
-1603,1971,NL,PIT,PIT,27,PIT,PIT
-1604,1971,NL,SLN,STL,28,STL,SLN
-1605,1971,NL,SDN,SDP,29,SDP,SDN
-1606,1971,NL,SFN,SFG,30,SFG,SFN
-1607,1972,AL,CAL,ANA,1,CAL,CAL
-1608,1972,AL,BAL,BAL,2,BAL,BAL
-1609,1972,AL,BOS,BOS,3,BOS,BOS
-1610,1972,AL,CHA,CHW,4,CHW,CHA
-1611,1972,AL,CLE,CLE,5,CLE,CLE
-1612,1972,AL,DET,DET,6,DET,DET
-1613,1972,AL,KCA,KCR,7,KCR,KCA
-1614,1972,AL,MIN,MIN,8,MIN,MIN
-1615,1972,AL,NYA,NYY,9,NYY,NYA
-1616,1972,AL,OAK,OAK,10,OAK,OAK
-1617,1972,AL,TEX,TEX,13,TEX,TEX
-1618,1972,NL,ATL,ATL,16,ATL,ATL
-1619,1972,NL,CHN,CHC,17,CHC,CHN
-1620,1972,NL,CIN,CIN,18,CIN,CIN
-1621,1972,NL,HOU,HOU,21,HOU,HOU
-1622,1972,NL,LAN,LAD,22,LAD,LAN
-1623,1972,AL,ML4,MIL,23,MIL,MIL
-1624,1972,NL,MON,WSN,24,MON,MON
-1625,1972,NL,NYN,NYM,25,NYM,NYN
-1626,1972,NL,PHI,PHI,26,PHI,PHI
-1627,1972,NL,PIT,PIT,27,PIT,PIT
-1628,1972,NL,SLN,STL,28,STL,SLN
-1629,1972,NL,SDN,SDP,29,SDP,SDN
-1630,1972,NL,SFN,SFG,30,SFG,SFN
-1631,1973,AL,CAL,ANA,1,CAL,CAL
-1632,1973,AL,BAL,BAL,2,BAL,BAL
-1633,1973,AL,BOS,BOS,3,BOS,BOS
-1634,1973,AL,CHA,CHW,4,CHW,CHA
-1635,1973,AL,CLE,CLE,5,CLE,CLE
-1636,1973,AL,DET,DET,6,DET,DET
-1637,1973,AL,KCA,KCR,7,KCR,KCA
-1638,1973,AL,MIN,MIN,8,MIN,MIN
-1639,1973,AL,NYA,NYY,9,NYY,NYA
-1640,1973,AL,OAK,OAK,10,OAK,OAK
-1641,1973,AL,TEX,TEX,13,TEX,TEX
-1642,1973,NL,ATL,ATL,16,ATL,ATL
-1643,1973,NL,CHN,CHC,17,CHC,CHN
-1644,1973,NL,CIN,CIN,18,CIN,CIN
-1645,1973,NL,HOU,HOU,21,HOU,HOU
-1646,1973,NL,LAN,LAD,22,LAD,LAN
-1647,1973,AL,ML4,MIL,23,MIL,MIL
-1648,1973,NL,MON,WSN,24,MON,MON
-1649,1973,NL,NYN,NYM,25,NYM,NYN
-1650,1973,NL,PHI,PHI,26,PHI,PHI
-1651,1973,NL,PIT,PIT,27,PIT,PIT
-1652,1973,NL,SLN,STL,28,STL,SLN
-1653,1973,NL,SDN,SDP,29,SDP,SDN
-1654,1973,NL,SFN,SFG,30,SFG,SFN
-1655,1974,AL,CAL,ANA,1,CAL,CAL
-1656,1974,AL,BAL,BAL,2,BAL,BAL
-1657,1974,AL,BOS,BOS,3,BOS,BOS
-1658,1974,AL,CHA,CHW,4,CHW,CHA
-1659,1974,AL,CLE,CLE,5,CLE,CLE
-1660,1974,AL,DET,DET,6,DET,DET
-1661,1974,AL,KCA,KCR,7,KCR,KCA
-1662,1974,AL,MIN,MIN,8,MIN,MIN
-1663,1974,AL,NYA,NYY,9,NYY,NYA
-1664,1974,AL,OAK,OAK,10,OAK,OAK
-1665,1974,AL,TEX,TEX,13,TEX,TEX
-1666,1974,NL,ATL,ATL,16,ATL,ATL
-1667,1974,NL,CHN,CHC,17,CHC,CHN
-1668,1974,NL,CIN,CIN,18,CIN,CIN
-1669,1974,NL,HOU,HOU,21,HOU,HOU
-1670,1974,NL,LAN,LAD,22,LAD,LAN
-1671,1974,AL,ML4,MIL,23,MIL,MIL
-1672,1974,NL,MON,WSN,24,MON,MON
-1673,1974,NL,NYN,NYM,25,NYM,NYN
-1674,1974,NL,PHI,PHI,26,PHI,PHI
-1675,1974,NL,PIT,PIT,27,PIT,PIT
-1676,1974,NL,SLN,STL,28,STL,SLN
-1677,1974,NL,SDN,SDP,29,SDP,SDN
-1678,1974,NL,SFN,SFG,30,SFG,SFN
-1679,1975,AL,CAL,ANA,1,CAL,CAL
-1680,1975,AL,BAL,BAL,2,BAL,BAL
-1681,1975,AL,BOS,BOS,3,BOS,BOS
-1682,1975,AL,CHA,CHW,4,CHW,CHA
-1683,1975,AL,CLE,CLE,5,CLE,CLE
-1684,1975,AL,DET,DET,6,DET,DET
-1685,1975,AL,KCA,KCR,7,KCR,KCA
-1686,1975,AL,MIN,MIN,8,MIN,MIN
-1687,1975,AL,NYA,NYY,9,NYY,NYA
-1688,1975,AL,OAK,OAK,10,OAK,OAK
-1689,1975,AL,TEX,TEX,13,TEX,TEX
-1690,1975,NL,ATL,ATL,16,ATL,ATL
-1691,1975,NL,CHN,CHC,17,CHC,CHN
-1692,1975,NL,CIN,CIN,18,CIN,CIN
-1693,1975,NL,HOU,HOU,21,HOU,HOU
-1694,1975,NL,LAN,LAD,22,LAD,LAN
-1695,1975,AL,ML4,MIL,23,MIL,MIL
-1696,1975,NL,MON,WSN,24,MON,MON
-1697,1975,NL,NYN,NYM,25,NYM,NYN
-1698,1975,NL,PHI,PHI,26,PHI,PHI
-1699,1975,NL,PIT,PIT,27,PIT,PIT
-1700,1975,NL,SLN,STL,28,STL,SLN
-1701,1975,NL,SDN,SDP,29,SDP,SDN
-1702,1975,NL,SFN,SFG,30,SFG,SFN
-1703,1976,AL,CAL,ANA,1,CAL,CAL
-1704,1976,AL,BAL,BAL,2,BAL,BAL
-1705,1976,AL,BOS,BOS,3,BOS,BOS
-1706,1976,AL,CHA,CHW,4,CHW,CHA
-1707,1976,AL,CLE,CLE,5,CLE,CLE
-1708,1976,AL,DET,DET,6,DET,DET
-1709,1976,AL,KCA,KCR,7,KCR,KCA
-1710,1976,AL,MIN,MIN,8,MIN,MIN
-1711,1976,AL,NYA,NYY,9,NYY,NYA
-1712,1976,AL,OAK,OAK,10,OAK,OAK
-1713,1976,AL,TEX,TEX,13,TEX,TEX
-1714,1976,NL,ATL,ATL,16,ATL,ATL
-1715,1976,NL,CHN,CHC,17,CHC,CHN
-1716,1976,NL,CIN,CIN,18,CIN,CIN
-1717,1976,NL,HOU,HOU,21,HOU,HOU
-1718,1976,NL,LAN,LAD,22,LAD,LAN
-1719,1976,AL,ML4,MIL,23,MIL,MIL
-1720,1976,NL,MON,WSN,24,MON,MON
-1721,1976,NL,NYN,NYM,25,NYM,NYN
-1722,1976,NL,PHI,PHI,26,PHI,PHI
-1723,1976,NL,PIT,PIT,27,PIT,PIT
-1724,1976,NL,SLN,STL,28,STL,SLN
-1725,1976,NL,SDN,SDP,29,SDP,SDN
-1726,1976,NL,SFN,SFG,30,SFG,SFN
-1727,1977,AL,CAL,ANA,1,CAL,CAL
-1728,1977,AL,BAL,BAL,2,BAL,BAL
-1729,1977,AL,BOS,BOS,3,BOS,BOS
-1730,1977,AL,CHA,CHW,4,CHW,CHA
-1731,1977,AL,CLE,CLE,5,CLE,CLE
-1732,1977,AL,DET,DET,6,DET,DET
-1733,1977,AL,KCA,KCR,7,KCR,KCA
-1734,1977,AL,MIN,MIN,8,MIN,MIN
-1735,1977,AL,NYA,NYY,9,NYY,NYA
-1736,1977,AL,OAK,OAK,10,OAK,OAK
-1737,1977,AL,SEA,SEA,11,SEA,SEA
-1738,1977,AL,TEX,TEX,13,TEX,TEX
-1739,1977,AL,TOR,TOR,14,TOR,TOR
-1740,1977,NL,ATL,ATL,16,ATL,ATL
-1741,1977,NL,CHN,CHC,17,CHC,CHN
-1742,1977,NL,CIN,CIN,18,CIN,CIN
-1743,1977,NL,HOU,HOU,21,HOU,HOU
-1744,1977,NL,LAN,LAD,22,LAD,LAN
-1745,1977,AL,ML4,MIL,23,MIL,MIL
-1746,1977,NL,MON,WSN,24,MON,MON
-1747,1977,NL,NYN,NYM,25,NYM,NYN
-1748,1977,NL,PHI,PHI,26,PHI,PHI
-1749,1977,NL,PIT,PIT,27,PIT,PIT
-1750,1977,NL,SLN,STL,28,STL,SLN
-1751,1977,NL,SDN,SDP,29,SDP,SDN
-1752,1977,NL,SFN,SFG,30,SFG,SFN
-1753,1978,AL,CAL,ANA,1,CAL,CAL
-1754,1978,AL,BAL,BAL,2,BAL,BAL
-1755,1978,AL,BOS,BOS,3,BOS,BOS
-1756,1978,AL,CHA,CHW,4,CHW,CHA
-1757,1978,AL,CLE,CLE,5,CLE,CLE
-1758,1978,AL,DET,DET,6,DET,DET
-1759,1978,AL,KCA,KCR,7,KCR,KCA
-1760,1978,AL,MIN,MIN,8,MIN,MIN
-1761,1978,AL,NYA,NYY,9,NYY,NYA
-1762,1978,AL,OAK,OAK,10,OAK,OAK
-1763,1978,AL,SEA,SEA,11,SEA,SEA
-1764,1978,AL,TEX,TEX,13,TEX,TEX
-1765,1978,AL,TOR,TOR,14,TOR,TOR
-1766,1978,NL,ATL,ATL,16,ATL,ATL
-1767,1978,NL,CHN,CHC,17,CHC,CHN
-1768,1978,NL,CIN,CIN,18,CIN,CIN
-1769,1978,NL,HOU,HOU,21,HOU,HOU
-1770,1978,NL,LAN,LAD,22,LAD,LAN
-1771,1978,AL,ML4,MIL,23,MIL,MIL
-1772,1978,NL,MON,WSN,24,MON,MON
-1773,1978,NL,NYN,NYM,25,NYM,NYN
-1774,1978,NL,PHI,PHI,26,PHI,PHI
-1775,1978,NL,PIT,PIT,27,PIT,PIT
-1776,1978,NL,SLN,STL,28,STL,SLN
-1777,1978,NL,SDN,SDP,29,SDP,SDN
-1778,1978,NL,SFN,SFG,30,SFG,SFN
-1779,1979,AL,CAL,ANA,1,CAL,CAL
-1780,1979,AL,BAL,BAL,2,BAL,BAL
-1781,1979,AL,BOS,BOS,3,BOS,BOS
-1782,1979,AL,CHA,CHW,4,CHW,CHA
-1783,1979,AL,CLE,CLE,5,CLE,CLE
-1784,1979,AL,DET,DET,6,DET,DET
-1785,1979,AL,KCA,KCR,7,KCR,KCA
-1786,1979,AL,MIN,MIN,8,MIN,MIN
-1787,1979,AL,NYA,NYY,9,NYY,NYA
-1788,1979,AL,OAK,OAK,10,OAK,OAK
-1789,1979,AL,SEA,SEA,11,SEA,SEA
-1790,1979,AL,TEX,TEX,13,TEX,TEX
-1791,1979,AL,TOR,TOR,14,TOR,TOR
-1792,1979,NL,ATL,ATL,16,ATL,ATL
-1793,1979,NL,CHN,CHC,17,CHC,CHN
-1794,1979,NL,CIN,CIN,18,CIN,CIN
-1795,1979,NL,HOU,HOU,21,HOU,HOU
-1796,1979,NL,LAN,LAD,22,LAD,LAN
-1797,1979,AL,ML4,MIL,23,MIL,MIL
-1798,1979,NL,MON,WSN,24,MON,MON
-1799,1979,NL,NYN,NYM,25,NYM,NYN
-1800,1979,NL,PHI,PHI,26,PHI,PHI
-1801,1979,NL,PIT,PIT,27,PIT,PIT
-1802,1979,NL,SLN,STL,28,STL,SLN
-1803,1979,NL,SDN,SDP,29,SDP,SDN
-1804,1979,NL,SFN,SFG,30,SFG,SFN
-1805,1980,AL,CAL,ANA,1,CAL,CAL
-1806,1980,AL,BAL,BAL,2,BAL,BAL
-1807,1980,AL,BOS,BOS,3,BOS,BOS
-1808,1980,AL,CHA,CHW,4,CHW,CHA
-1809,1980,AL,CLE,CLE,5,CLE,CLE
-1810,1980,AL,DET,DET,6,DET,DET
-1811,1980,AL,KCA,KCR,7,KCR,KCA
-1812,1980,AL,MIN,MIN,8,MIN,MIN
-1813,1980,AL,NYA,NYY,9,NYY,NYA
-1814,1980,AL,OAK,OAK,10,OAK,OAK
-1815,1980,AL,SEA,SEA,11,SEA,SEA
-1816,1980,AL,TEX,TEX,13,TEX,TEX
-1817,1980,AL,TOR,TOR,14,TOR,TOR
-1818,1980,NL,ATL,ATL,16,ATL,ATL
-1819,1980,NL,CHN,CHC,17,CHC,CHN
-1820,1980,NL,CIN,CIN,18,CIN,CIN
-1821,1980,NL,HOU,HOU,21,HOU,HOU
-1822,1980,NL,LAN,LAD,22,LAD,LAN
-1823,1980,AL,ML4,MIL,23,MIL,MIL
-1824,1980,NL,MON,WSN,24,MON,MON
-1825,1980,NL,NYN,NYM,25,NYM,NYN
-1826,1980,NL,PHI,PHI,26,PHI,PHI
-1827,1980,NL,PIT,PIT,27,PIT,PIT
-1828,1980,NL,SLN,STL,28,STL,SLN
-1829,1980,NL,SDN,SDP,29,SDP,SDN
-1830,1980,NL,SFN,SFG,30,SFG,SFN
-1831,1981,AL,CAL,ANA,1,CAL,CAL
-1832,1981,AL,BAL,BAL,2,BAL,BAL
-1833,1981,AL,BOS,BOS,3,BOS,BOS
-1834,1981,AL,CHA,CHW,4,CHW,CHA
-1835,1981,AL,CLE,CLE,5,CLE,CLE
-1836,1981,AL,DET,DET,6,DET,DET
-1837,1981,AL,KCA,KCR,7,KCR,KCA
-1838,1981,AL,MIN,MIN,8,MIN,MIN
-1839,1981,AL,NYA,NYY,9,NYY,NYA
-1840,1981,AL,OAK,OAK,10,OAK,OAK
-1841,1981,AL,SEA,SEA,11,SEA,SEA
-1842,1981,AL,TEX,TEX,13,TEX,TEX
-1843,1981,AL,TOR,TOR,14,TOR,TOR
-1844,1981,NL,ATL,ATL,16,ATL,ATL
-1845,1981,NL,CHN,CHC,17,CHC,CHN
-1846,1981,NL,CIN,CIN,18,CIN,CIN
-1847,1981,NL,HOU,HOU,21,HOU,HOU
-1848,1981,NL,LAN,LAD,22,LAD,LAN
-1849,1981,AL,ML4,MIL,23,MIL,MIL
-1850,1981,NL,MON,WSN,24,MON,MON
-1851,1981,NL,NYN,NYM,25,NYM,NYN
-1852,1981,NL,PHI,PHI,26,PHI,PHI
-1853,1981,NL,PIT,PIT,27,PIT,PIT
-1854,1981,NL,SLN,STL,28,STL,SLN
-1855,1981,NL,SDN,SDP,29,SDP,SDN
-1856,1981,NL,SFN,SFG,30,SFG,SFN
-1857,1982,AL,CAL,ANA,1,CAL,CAL
-1858,1982,AL,BAL,BAL,2,BAL,BAL
-1859,1982,AL,BOS,BOS,3,BOS,BOS
-1860,1982,AL,CHA,CHW,4,CHW,CHA
-1861,1982,AL,CLE,CLE,5,CLE,CLE
-1862,1982,AL,DET,DET,6,DET,DET
-1863,1982,AL,KCA,KCR,7,KCR,KCA
-1864,1982,AL,MIN,MIN,8,MIN,MIN
-1865,1982,AL,NYA,NYY,9,NYY,NYA
-1866,1982,AL,OAK,OAK,10,OAK,OAK
-1867,1982,AL,SEA,SEA,11,SEA,SEA
-1868,1982,AL,TEX,TEX,13,TEX,TEX
-1869,1982,AL,TOR,TOR,14,TOR,TOR
-1870,1982,NL,ATL,ATL,16,ATL,ATL
-1871,1982,NL,CHN,CHC,17,CHC,CHN
-1872,1982,NL,CIN,CIN,18,CIN,CIN
-1873,1982,NL,HOU,HOU,21,HOU,HOU
-1874,1982,NL,LAN,LAD,22,LAD,LAN
-1875,1982,AL,ML4,MIL,23,MIL,MIL
-1876,1982,NL,MON,WSN,24,MON,MON
-1877,1982,NL,NYN,NYM,25,NYM,NYN
-1878,1982,NL,PHI,PHI,26,PHI,PHI
-1879,1982,NL,PIT,PIT,27,PIT,PIT
-1880,1982,NL,SLN,STL,28,STL,SLN
-1881,1982,NL,SDN,SDP,29,SDP,SDN
-1882,1982,NL,SFN,SFG,30,SFG,SFN
-1883,1983,AL,CAL,ANA,1,CAL,CAL
-1884,1983,AL,BAL,BAL,2,BAL,BAL
-1885,1983,AL,BOS,BOS,3,BOS,BOS
-1886,1983,AL,CHA,CHW,4,CHW,CHA
-1887,1983,AL,CLE,CLE,5,CLE,CLE
-1888,1983,AL,DET,DET,6,DET,DET
-1889,1983,AL,KCA,KCR,7,KCR,KCA
-1890,1983,AL,MIN,MIN,8,MIN,MIN
-1891,1983,AL,NYA,NYY,9,NYY,NYA
-1892,1983,AL,OAK,OAK,10,OAK,OAK
-1893,1983,AL,SEA,SEA,11,SEA,SEA
-1894,1983,AL,TEX,TEX,13,TEX,TEX
-1895,1983,AL,TOR,TOR,14,TOR,TOR
-1896,1983,NL,ATL,ATL,16,ATL,ATL
-1897,1983,NL,CHN,CHC,17,CHC,CHN
-1898,1983,NL,CIN,CIN,18,CIN,CIN
-1899,1983,NL,HOU,HOU,21,HOU,HOU
-1900,1983,NL,LAN,LAD,22,LAD,LAN
-1901,1983,AL,ML4,MIL,23,MIL,MIL
-1902,1983,NL,MON,WSN,24,MON,MON
-1903,1983,NL,NYN,NYM,25,NYM,NYN
-1904,1983,NL,PHI,PHI,26,PHI,PHI
-1905,1983,NL,PIT,PIT,27,PIT,PIT
-1906,1983,NL,SLN,STL,28,STL,SLN
-1907,1983,NL,SDN,SDP,29,SDP,SDN
-1908,1983,NL,SFN,SFG,30,SFG,SFN
-1909,1984,AL,CAL,ANA,1,CAL,CAL
-1910,1984,AL,BAL,BAL,2,BAL,BAL
-1911,1984,AL,BOS,BOS,3,BOS,BOS
-1912,1984,AL,CHA,CHW,4,CHW,CHA
-1913,1984,AL,CLE,CLE,5,CLE,CLE
-1914,1984,AL,DET,DET,6,DET,DET
-1915,1984,AL,KCA,KCR,7,KCR,KCA
-1916,1984,AL,MIN,MIN,8,MIN,MIN
-1917,1984,AL,NYA,NYY,9,NYY,NYA
-1918,1984,AL,OAK,OAK,10,OAK,OAK
-1919,1984,AL,SEA,SEA,11,SEA,SEA
-1920,1984,AL,TEX,TEX,13,TEX,TEX
-1921,1984,AL,TOR,TOR,14,TOR,TOR
-1922,1984,NL,ATL,ATL,16,ATL,ATL
-1923,1984,NL,CHN,CHC,17,CHC,CHN
-1924,1984,NL,CIN,CIN,18,CIN,CIN
-1925,1984,NL,HOU,HOU,21,HOU,HOU
-1926,1984,NL,LAN,LAD,22,LAD,LAN
-1927,1984,AL,ML4,MIL,23,MIL,MIL
-1928,1984,NL,MON,WSN,24,MON,MON
-1929,1984,NL,NYN,NYM,25,NYM,NYN
-1930,1984,NL,PHI,PHI,26,PHI,PHI
-1931,1984,NL,PIT,PIT,27,PIT,PIT
-1932,1984,NL,SLN,STL,28,STL,SLN
-1933,1984,NL,SDN,SDP,29,SDP,SDN
-1934,1984,NL,SFN,SFG,30,SFG,SFN
-1935,1985,AL,CAL,ANA,1,CAL,CAL
-1936,1985,AL,BAL,BAL,2,BAL,BAL
-1937,1985,AL,BOS,BOS,3,BOS,BOS
-1938,1985,AL,CHA,CHW,4,CHW,CHA
-1939,1985,AL,CLE,CLE,5,CLE,CLE
-1940,1985,AL,DET,DET,6,DET,DET
-1941,1985,AL,KCA,KCR,7,KCR,KCA
-1942,1985,AL,MIN,MIN,8,MIN,MIN
-1943,1985,AL,NYA,NYY,9,NYY,NYA
-1944,1985,AL,OAK,OAK,10,OAK,OAK
-1945,1985,AL,SEA,SEA,11,SEA,SEA
-1946,1985,AL,TEX,TEX,13,TEX,TEX
-1947,1985,AL,TOR,TOR,14,TOR,TOR
-1948,1985,NL,ATL,ATL,16,ATL,ATL
-1949,1985,NL,CHN,CHC,17,CHC,CHN
-1950,1985,NL,CIN,CIN,18,CIN,CIN
-1951,1985,NL,HOU,HOU,21,HOU,HOU
-1952,1985,NL,LAN,LAD,22,LAD,LAN
-1953,1985,AL,ML4,MIL,23,MIL,MIL
-1954,1985,NL,MON,WSN,24,MON,MON
-1955,1985,NL,NYN,NYM,25,NYM,NYN
-1956,1985,NL,PHI,PHI,26,PHI,PHI
-1957,1985,NL,PIT,PIT,27,PIT,PIT
-1958,1985,NL,SLN,STL,28,STL,SLN
-1959,1985,NL,SDN,SDP,29,SDP,SDN
-1960,1985,NL,SFN,SFG,30,SFG,SFN
-1961,1986,AL,CAL,ANA,1,CAL,CAL
-1962,1986,AL,BAL,BAL,2,BAL,BAL
-1963,1986,AL,BOS,BOS,3,BOS,BOS
-1964,1986,AL,CHA,CHW,4,CHW,CHA
-1965,1986,AL,CLE,CLE,5,CLE,CLE
-1966,1986,AL,DET,DET,6,DET,DET
-1967,1986,AL,KCA,KCR,7,KCR,KCA
-1968,1986,AL,MIN,MIN,8,MIN,MIN
-1969,1986,AL,NYA,NYY,9,NYY,NYA
-1970,1986,AL,OAK,OAK,10,OAK,OAK
-1971,1986,AL,SEA,SEA,11,SEA,SEA
-1972,1986,AL,TEX,TEX,13,TEX,TEX
-1973,1986,AL,TOR,TOR,14,TOR,TOR
-1974,1986,NL,ATL,ATL,16,ATL,ATL
-1975,1986,NL,CHN,CHC,17,CHC,CHN
-1976,1986,NL,CIN,CIN,18,CIN,CIN
-1977,1986,NL,HOU,HOU,21,HOU,HOU
-1978,1986,NL,LAN,LAD,22,LAD,LAN
-1979,1986,AL,ML4,MIL,23,MIL,MIL
-1980,1986,NL,MON,WSN,24,MON,MON
-1981,1986,NL,NYN,NYM,25,NYM,NYN
-1982,1986,NL,PHI,PHI,26,PHI,PHI
-1983,1986,NL,PIT,PIT,27,PIT,PIT
-1984,1986,NL,SLN,STL,28,STL,SLN
-1985,1986,NL,SDN,SDP,29,SDP,SDN
-1986,1986,NL,SFN,SFG,30,SFG,SFN
-1987,1987,AL,CAL,ANA,1,CAL,CAL
-1988,1987,AL,BAL,BAL,2,BAL,BAL
-1989,1987,AL,BOS,BOS,3,BOS,BOS
-1990,1987,AL,CHA,CHW,4,CHW,CHA
-1991,1987,AL,CLE,CLE,5,CLE,CLE
-1992,1987,AL,DET,DET,6,DET,DET
-1993,1987,AL,KCA,KCR,7,KCR,KCA
-1994,1987,AL,MIN,MIN,8,MIN,MIN
-1995,1987,AL,NYA,NYY,9,NYY,NYA
-1996,1987,AL,OAK,OAK,10,OAK,OAK
-1997,1987,AL,SEA,SEA,11,SEA,SEA
-1998,1987,AL,TEX,TEX,13,TEX,TEX
-1999,1987,AL,TOR,TOR,14,TOR,TOR
-2000,1987,NL,ATL,ATL,16,ATL,ATL
-2001,1987,NL,CHN,CHC,17,CHC,CHN
-2002,1987,NL,CIN,CIN,18,CIN,CIN
-2003,1987,NL,HOU,HOU,21,HOU,HOU
-2004,1987,NL,LAN,LAD,22,LAD,LAN
-2005,1987,AL,ML4,MIL,23,MIL,MIL
-2006,1987,NL,MON,WSN,24,MON,MON
-2007,1987,NL,NYN,NYM,25,NYM,NYN
-2008,1987,NL,PHI,PHI,26,PHI,PHI
-2009,1987,NL,PIT,PIT,27,PIT,PIT
-2010,1987,NL,SLN,STL,28,STL,SLN
-2011,1987,NL,SDN,SDP,29,SDP,SDN
-2012,1987,NL,SFN,SFG,30,SFG,SFN
-2013,1988,AL,CAL,ANA,1,CAL,CAL
-2014,1988,AL,BAL,BAL,2,BAL,BAL
-2015,1988,AL,BOS,BOS,3,BOS,BOS
-2016,1988,AL,CHA,CHW,4,CHW,CHA
-2017,1988,AL,CLE,CLE,5,CLE,CLE
-2018,1988,AL,DET,DET,6,DET,DET
-2019,1988,AL,KCA,KCR,7,KCR,KCA
-2020,1988,AL,MIN,MIN,8,MIN,MIN
-2021,1988,AL,NYA,NYY,9,NYY,NYA
-2022,1988,AL,OAK,OAK,10,OAK,OAK
-2023,1988,AL,SEA,SEA,11,SEA,SEA
-2024,1988,AL,TEX,TEX,13,TEX,TEX
-2025,1988,AL,TOR,TOR,14,TOR,TOR
-2026,1988,NL,ATL,ATL,16,ATL,ATL
-2027,1988,NL,CHN,CHC,17,CHC,CHN
-2028,1988,NL,CIN,CIN,18,CIN,CIN
-2029,1988,NL,HOU,HOU,21,HOU,HOU
-2030,1988,NL,LAN,LAD,22,LAD,LAN
-2031,1988,AL,ML4,MIL,23,MIL,MIL
-2032,1988,NL,MON,WSN,24,MON,MON
-2033,1988,NL,NYN,NYM,25,NYM,NYN
-2034,1988,NL,PHI,PHI,26,PHI,PHI
-2035,1988,NL,PIT,PIT,27,PIT,PIT
-2036,1988,NL,SLN,STL,28,STL,SLN
-2037,1988,NL,SDN,SDP,29,SDP,SDN
-2038,1988,NL,SFN,SFG,30,SFG,SFN
-2039,1989,AL,CAL,ANA,1,CAL,CAL
-2040,1989,AL,BAL,BAL,2,BAL,BAL
-2041,1989,AL,BOS,BOS,3,BOS,BOS
-2042,1989,AL,CHA,CHW,4,CHW,CHA
-2043,1989,AL,CLE,CLE,5,CLE,CLE
-2044,1989,AL,DET,DET,6,DET,DET
-2045,1989,AL,KCA,KCR,7,KCR,KCA
-2046,1989,AL,MIN,MIN,8,MIN,MIN
-2047,1989,AL,NYA,NYY,9,NYY,NYA
-2048,1989,AL,OAK,OAK,10,OAK,OAK
-2049,1989,AL,SEA,SEA,11,SEA,SEA
-2050,1989,AL,TEX,TEX,13,TEX,TEX
-2051,1989,AL,TOR,TOR,14,TOR,TOR
-2052,1989,NL,ATL,ATL,16,ATL,ATL
-2053,1989,NL,CHN,CHC,17,CHC,CHN
-2054,1989,NL,CIN,CIN,18,CIN,CIN
-2055,1989,NL,HOU,HOU,21,HOU,HOU
-2056,1989,NL,LAN,LAD,22,LAD,LAN
-2057,1989,AL,ML4,MIL,23,MIL,MIL
-2058,1989,NL,MON,WSN,24,MON,MON
-2059,1989,NL,NYN,NYM,25,NYM,NYN
-2060,1989,NL,PHI,PHI,26,PHI,PHI
-2061,1989,NL,PIT,PIT,27,PIT,PIT
-2062,1989,NL,SLN,STL,28,STL,SLN
-2063,1989,NL,SDN,SDP,29,SDP,SDN
-2064,1989,NL,SFN,SFG,30,SFG,SFN
-2065,1990,AL,CAL,ANA,1,CAL,CAL
-2066,1990,AL,BAL,BAL,2,BAL,BAL
-2067,1990,AL,BOS,BOS,3,BOS,BOS
-2068,1990,AL,CHA,CHW,4,CHW,CHA
-2069,1990,AL,CLE,CLE,5,CLE,CLE
-2070,1990,AL,DET,DET,6,DET,DET
-2071,1990,AL,KCA,KCR,7,KCR,KCA
-2072,1990,AL,MIN,MIN,8,MIN,MIN
-2073,1990,AL,NYA,NYY,9,NYY,NYA
-2074,1990,AL,OAK,OAK,10,OAK,OAK
-2075,1990,AL,SEA,SEA,11,SEA,SEA
-2076,1990,AL,TEX,TEX,13,TEX,TEX
-2077,1990,AL,TOR,TOR,14,TOR,TOR
-2078,1990,NL,ATL,ATL,16,ATL,ATL
-2079,1990,NL,CHN,CHC,17,CHC,CHN
-2080,1990,NL,CIN,CIN,18,CIN,CIN
-2081,1990,NL,HOU,HOU,21,HOU,HOU
-2082,1990,NL,LAN,LAD,22,LAD,LAN
-2083,1990,AL,ML4,MIL,23,MIL,MIL
-2084,1990,NL,MON,WSN,24,MON,MON
-2085,1990,NL,NYN,NYM,25,NYM,NYN
-2086,1990,NL,PHI,PHI,26,PHI,PHI
-2087,1990,NL,PIT,PIT,27,PIT,PIT
-2088,1990,NL,SLN,STL,28,STL,SLN
-2089,1990,NL,SDN,SDP,29,SDP,SDN
-2090,1990,NL,SFN,SFG,30,SFG,SFN
-2091,1991,AL,CAL,ANA,1,CAL,CAL
-2092,1991,AL,BAL,BAL,2,BAL,BAL
-2093,1991,AL,BOS,BOS,3,BOS,BOS
-2094,1991,AL,CHA,CHW,4,CHW,CHA
-2095,1991,AL,CLE,CLE,5,CLE,CLE
-2096,1991,AL,DET,DET,6,DET,DET
-2097,1991,AL,KCA,KCR,7,KCR,KCA
-2098,1991,AL,MIN,MIN,8,MIN,MIN
-2099,1991,AL,NYA,NYY,9,NYY,NYA
-2100,1991,AL,OAK,OAK,10,OAK,OAK
-2101,1991,AL,SEA,SEA,11,SEA,SEA
-2102,1991,AL,TEX,TEX,13,TEX,TEX
-2103,1991,AL,TOR,TOR,14,TOR,TOR
-2104,1991,NL,ATL,ATL,16,ATL,ATL
-2105,1991,NL,CHN,CHC,17,CHC,CHN
-2106,1991,NL,CIN,CIN,18,CIN,CIN
-2107,1991,NL,HOU,HOU,21,HOU,HOU
-2108,1991,NL,LAN,LAD,22,LAD,LAN
-2109,1991,AL,ML4,MIL,23,MIL,MIL
-2110,1991,NL,MON,WSN,24,MON,MON
-2111,1991,NL,NYN,NYM,25,NYM,NYN
-2112,1991,NL,PHI,PHI,26,PHI,PHI
-2113,1991,NL,PIT,PIT,27,PIT,PIT
-2114,1991,NL,SLN,STL,28,STL,SLN
-2115,1991,NL,SDN,SDP,29,SDP,SDN
-2116,1991,NL,SFN,SFG,30,SFG,SFN
-2117,1992,AL,CAL,ANA,1,CAL,CAL
-2118,1992,AL,BAL,BAL,2,BAL,BAL
-2119,1992,AL,BOS,BOS,3,BOS,BOS
-2120,1992,AL,CHA,CHW,4,CHW,CHA
-2121,1992,AL,CLE,CLE,5,CLE,CLE
-2122,1992,AL,DET,DET,6,DET,DET
-2123,1992,AL,KCA,KCR,7,KCR,KCA
-2124,1992,AL,MIN,MIN,8,MIN,MIN
-2125,1992,AL,NYA,NYY,9,NYY,NYA
-2126,1992,AL,OAK,OAK,10,OAK,OAK
-2127,1992,AL,SEA,SEA,11,SEA,SEA
-2128,1992,AL,TEX,TEX,13,TEX,TEX
-2129,1992,AL,TOR,TOR,14,TOR,TOR
-2130,1992,NL,ATL,ATL,16,ATL,ATL
-2131,1992,NL,CHN,CHC,17,CHC,CHN
-2132,1992,NL,CIN,CIN,18,CIN,CIN
-2133,1992,NL,HOU,HOU,21,HOU,HOU
-2134,1992,NL,LAN,LAD,22,LAD,LAN
-2135,1992,AL,ML4,MIL,23,MIL,MIL
-2136,1992,NL,MON,WSN,24,MON,MON
-2137,1992,NL,NYN,NYM,25,NYM,NYN
-2138,1992,NL,PHI,PHI,26,PHI,PHI
-2139,1992,NL,PIT,PIT,27,PIT,PIT
-2140,1992,NL,SLN,STL,28,STL,SLN
-2141,1992,NL,SDN,SDP,29,SDP,SDN
-2142,1992,NL,SFN,SFG,30,SFG,SFN
-2143,1993,AL,CAL,ANA,1,CAL,CAL
-2144,1993,AL,BAL,BAL,2,BAL,BAL
-2145,1993,AL,BOS,BOS,3,BOS,BOS
-2146,1993,AL,CHA,CHW,4,CHW,CHA
-2147,1993,AL,CLE,CLE,5,CLE,CLE
-2148,1993,AL,DET,DET,6,DET,DET
-2149,1993,AL,KCA,KCR,7,KCR,KCA
-2150,1993,AL,MIN,MIN,8,MIN,MIN
-2151,1993,AL,NYA,NYY,9,NYY,NYA
-2152,1993,AL,OAK,OAK,10,OAK,OAK
-2153,1993,AL,SEA,SEA,11,SEA,SEA
-2154,1993,AL,TEX,TEX,13,TEX,TEX
-2155,1993,AL,TOR,TOR,14,TOR,TOR
-2156,1993,NL,ATL,ATL,16,ATL,ATL
-2157,1993,NL,CHN,CHC,17,CHC,CHN
-2158,1993,NL,CIN,CIN,18,CIN,CIN
-2159,1993,NL,COL,COL,19,COL,COL
-2160,1993,NL,FLO,FLA,20,FLA,FLO
-2161,1993,NL,HOU,HOU,21,HOU,HOU
-2162,1993,NL,LAN,LAD,22,LAD,LAN
-2163,1993,AL,ML4,MIL,23,MIL,MIL
-2164,1993,NL,MON,WSN,24,MON,MON
-2165,1993,NL,NYN,NYM,25,NYM,NYN
-2166,1993,NL,PHI,PHI,26,PHI,PHI
-2167,1993,NL,PIT,PIT,27,PIT,PIT
-2168,1993,NL,SLN,STL,28,STL,SLN
-2169,1993,NL,SDN,SDP,29,SDP,SDN
-2170,1993,NL,SFN,SFG,30,SFG,SFN
-2171,1994,AL,CAL,ANA,1,CAL,CAL
-2172,1994,AL,BAL,BAL,2,BAL,BAL
-2173,1994,AL,BOS,BOS,3,BOS,BOS
-2174,1994,AL,CHA,CHW,4,CHW,CHA
-2175,1994,AL,CLE,CLE,5,CLE,CLE
-2176,1994,AL,DET,DET,6,DET,DET
-2177,1994,AL,KCA,KCR,7,KCR,KCA
-2178,1994,AL,MIN,MIN,8,MIN,MIN
-2179,1994,AL,NYA,NYY,9,NYY,NYA
-2180,1994,AL,OAK,OAK,10,OAK,OAK
-2181,1994,AL,SEA,SEA,11,SEA,SEA
-2182,1994,AL,TEX,TEX,13,TEX,TEX
-2183,1994,AL,TOR,TOR,14,TOR,TOR
-2184,1994,NL,ATL,ATL,16,ATL,ATL
-2185,1994,NL,CHN,CHC,17,CHC,CHN
-2186,1994,NL,CIN,CIN,18,CIN,CIN
-2187,1994,NL,COL,COL,19,COL,COL
-2188,1994,NL,FLO,FLA,20,FLA,FLO
-2189,1994,NL,HOU,HOU,21,HOU,HOU
-2190,1994,NL,LAN,LAD,22,LAD,LAN
-2191,1994,AL,ML4,MIL,23,MIL,MIL
-2192,1994,NL,MON,WSN,24,MON,MON
-2193,1994,NL,NYN,NYM,25,NYM,NYN
-2194,1994,NL,PHI,PHI,26,PHI,PHI
-2195,1994,NL,PIT,PIT,27,PIT,PIT
-2196,1994,NL,SLN,STL,28,STL,SLN
-2197,1994,NL,SDN,SDP,29,SDP,SDN
-2198,1994,NL,SFN,SFG,30,SFG,SFN
-2199,1995,AL,CAL,ANA,1,CAL,CAL
-2200,1995,AL,BAL,BAL,2,BAL,BAL
-2201,1995,AL,BOS,BOS,3,BOS,BOS
-2202,1995,AL,CHA,CHW,4,CHW,CHA
-2203,1995,AL,CLE,CLE,5,CLE,CLE
-2204,1995,AL,DET,DET,6,DET,DET
-2205,1995,AL,KCA,KCR,7,KCR,KCA
-2206,1995,AL,MIN,MIN,8,MIN,MIN
-2207,1995,AL,NYA,NYY,9,NYY,NYA
-2208,1995,AL,OAK,OAK,10,OAK,OAK
-2209,1995,AL,SEA,SEA,11,SEA,SEA
-2210,1995,AL,TEX,TEX,13,TEX,TEX
-2211,1995,AL,TOR,TOR,14,TOR,TOR
-2212,1995,NL,ATL,ATL,16,ATL,ATL
-2213,1995,NL,CHN,CHC,17,CHC,CHN
-2214,1995,NL,CIN,CIN,18,CIN,CIN
-2215,1995,NL,COL,COL,19,COL,COL
-2216,1995,NL,FLO,FLA,20,FLA,FLO
-2217,1995,NL,HOU,HOU,21,HOU,HOU
-2218,1995,NL,LAN,LAD,22,LAD,LAN
-2219,1995,AL,ML4,MIL,23,MIL,MIL
-2220,1995,NL,MON,WSN,24,MON,MON
-2221,1995,NL,NYN,NYM,25,NYM,NYN
-2222,1995,NL,PHI,PHI,26,PHI,PHI
-2223,1995,NL,PIT,PIT,27,PIT,PIT
-2224,1995,NL,SLN,STL,28,STL,SLN
-2225,1995,NL,SDN,SDP,29,SDP,SDN
-2226,1995,NL,SFN,SFG,30,SFG,SFN
-2227,1996,AL,CAL,ANA,1,CAL,CAL
-2228,1996,AL,BAL,BAL,2,BAL,BAL
-2229,1996,AL,BOS,BOS,3,BOS,BOS
-2230,1996,AL,CHA,CHW,4,CHW,CHA
-2231,1996,AL,CLE,CLE,5,CLE,CLE
-2232,1996,AL,DET,DET,6,DET,DET
-2233,1996,AL,KCA,KCR,7,KCR,KCA
-2234,1996,AL,MIN,MIN,8,MIN,MIN
-2235,1996,AL,NYA,NYY,9,NYY,NYA
-2236,1996,AL,OAK,OAK,10,OAK,OAK
-2237,1996,AL,SEA,SEA,11,SEA,SEA
-2238,1996,AL,TEX,TEX,13,TEX,TEX
-2239,1996,AL,TOR,TOR,14,TOR,TOR
-2240,1996,NL,ATL,ATL,16,ATL,ATL
-2241,1996,NL,CHN,CHC,17,CHC,CHN
-2242,1996,NL,CIN,CIN,18,CIN,CIN
-2243,1996,NL,COL,COL,19,COL,COL
-2244,1996,NL,FLO,FLA,20,FLA,FLO
-2245,1996,NL,HOU,HOU,21,HOU,HOU
-2246,1996,NL,LAN,LAD,22,LAD,LAN
-2247,1996,AL,ML4,MIL,23,MIL,MIL
-2248,1996,NL,MON,WSN,24,MON,MON
-2249,1996,NL,NYN,NYM,25,NYM,NYN
-2250,1996,NL,PHI,PHI,26,PHI,PHI
-2251,1996,NL,PIT,PIT,27,PIT,PIT
-2252,1996,NL,SLN,STL,28,STL,SLN
-2253,1996,NL,SDN,SDP,29,SDP,SDN
-2254,1996,NL,SFN,SFG,30,SFG,SFN
-2255,1997,AL,ANA,ANA,1,ANA,ANA
-2256,1997,AL,BAL,BAL,2,BAL,BAL
-2257,1997,AL,BOS,BOS,3,BOS,BOS
-2258,1997,AL,CHA,CHW,4,CHW,CHA
-2259,1997,AL,CLE,CLE,5,CLE,CLE
-2260,1997,AL,DET,DET,6,DET,DET
-2261,1997,AL,KCA,KCR,7,KCR,KCA
-2262,1997,AL,MIN,MIN,8,MIN,MIN
-2263,1997,AL,NYA,NYY,9,NYY,NYA
-2264,1997,AL,OAK,OAK,10,OAK,OAK
-2265,1997,AL,SEA,SEA,11,SEA,SEA
-2266,1997,AL,TEX,TEX,13,TEX,TEX
-2267,1997,AL,TOR,TOR,14,TOR,TOR
-2268,1997,NL,ATL,ATL,16,ATL,ATL
-2269,1997,NL,CHN,CHC,17,CHC,CHN
-2270,1997,NL,CIN,CIN,18,CIN,CIN
-2271,1997,NL,COL,COL,19,COL,COL
-2272,1997,NL,FLO,FLA,20,FLA,FLO
-2273,1997,NL,HOU,HOU,21,HOU,HOU
-2274,1997,NL,LAN,LAD,22,LAD,LAN
-2275,1997,AL,ML4,MIL,23,MIL,MIL
-2276,1997,NL,MON,WSN,24,MON,MON
-2277,1997,NL,NYN,NYM,25,NYM,NYN
-2278,1997,NL,PHI,PHI,26,PHI,PHI
-2279,1997,NL,PIT,PIT,27,PIT,PIT
-2280,1997,NL,SLN,STL,28,STL,SLN
-2281,1997,NL,SDN,SDP,29,SDP,SDN
-2282,1997,NL,SFN,SFG,30,SFG,SFN
-2283,1998,AL,ANA,ANA,1,ANA,ANA
-2284,1998,AL,BAL,BAL,2,BAL,BAL
-2285,1998,AL,BOS,BOS,3,BOS,BOS
-2286,1998,AL,CHA,CHW,4,CHW,CHA
-2287,1998,AL,CLE,CLE,5,CLE,CLE
-2288,1998,AL,DET,DET,6,DET,DET
-2289,1998,AL,KCA,KCR,7,KCR,KCA
-2290,1998,AL,MIN,MIN,8,MIN,MIN
-2291,1998,AL,NYA,NYY,9,NYY,NYA
-2292,1998,AL,OAK,OAK,10,OAK,OAK
-2293,1998,AL,SEA,SEA,11,SEA,SEA
-2294,1998,AL,TBA,TBD,12,TBD,TBA
-2295,1998,AL,TEX,TEX,13,TEX,TEX
-2296,1998,AL,TOR,TOR,14,TOR,TOR
-2297,1998,NL,ARI,ARI,15,ARI,ARI
-2298,1998,NL,ATL,ATL,16,ATL,ATL
-2299,1998,NL,CHN,CHC,17,CHC,CHN
-2300,1998,NL,CIN,CIN,18,CIN,CIN
-2301,1998,NL,COL,COL,19,COL,COL
-2302,1998,NL,FLO,FLA,20,FLA,FLO
-2303,1998,NL,HOU,HOU,21,HOU,HOU
-2304,1998,NL,LAN,LAD,22,LAD,LAN
-2305,1998,NL,MIL,MIL,23,MIL,MIL
-2306,1998,NL,MON,WSN,24,MON,MON
-2307,1998,NL,NYN,NYM,25,NYM,NYN
-2308,1998,NL,PHI,PHI,26,PHI,PHI
-2309,1998,NL,PIT,PIT,27,PIT,PIT
-2310,1998,NL,SLN,STL,28,STL,SLN
-2311,1998,NL,SDN,SDP,29,SDP,SDN
-2312,1998,NL,SFN,SFG,30,SFG,SFN
-2313,1999,AL,ANA,ANA,1,ANA,ANA
-2314,1999,AL,BAL,BAL,2,BAL,BAL
-2315,1999,AL,BOS,BOS,3,BOS,BOS
-2316,1999,AL,CHA,CHW,4,CHW,CHA
-2317,1999,AL,CLE,CLE,5,CLE,CLE
-2318,1999,AL,DET,DET,6,DET,DET
-2319,1999,AL,KCA,KCR,7,KCR,KCA
-2320,1999,AL,MIN,MIN,8,MIN,MIN
-2321,1999,AL,NYA,NYY,9,NYY,NYA
-2322,1999,AL,OAK,OAK,10,OAK,OAK
-2323,1999,AL,SEA,SEA,11,SEA,SEA
-2324,1999,AL,TBA,TBD,12,TBD,TBA
-2325,1999,AL,TEX,TEX,13,TEX,TEX
-2326,1999,AL,TOR,TOR,14,TOR,TOR
-2327,1999,NL,ARI,ARI,15,ARI,ARI
-2328,1999,NL,ATL,ATL,16,ATL,ATL
-2329,1999,NL,CHN,CHC,17,CHC,CHN
-2330,1999,NL,CIN,CIN,18,CIN,CIN
-2331,1999,NL,COL,COL,19,COL,COL
-2332,1999,NL,FLO,FLA,20,FLA,FLO
-2333,1999,NL,HOU,HOU,21,HOU,HOU
-2334,1999,NL,LAN,LAD,22,LAD,LAN
-2335,1999,NL,MIL,MIL,23,MIL,MIL
-2336,1999,NL,MON,WSN,24,MON,MON
-2337,1999,NL,NYN,NYM,25,NYM,NYN
-2338,1999,NL,PHI,PHI,26,PHI,PHI
-2339,1999,NL,PIT,PIT,27,PIT,PIT
-2340,1999,NL,SLN,STL,28,STL,SLN
-2341,1999,NL,SDN,SDP,29,SDP,SDN
-2342,1999,NL,SFN,SFG,30,SFG,SFN
-2343,2000,AL,ANA,ANA,1,ANA,ANA
-2344,2000,AL,BAL,BAL,2,BAL,BAL
-2345,2000,AL,BOS,BOS,3,BOS,BOS
-2346,2000,AL,CHA,CHW,4,CHW,CHA
-2347,2000,AL,CLE,CLE,5,CLE,CLE
-2348,2000,AL,DET,DET,6,DET,DET
-2349,2000,AL,KCA,KCR,7,KCR,KCA
-2350,2000,AL,MIN,MIN,8,MIN,MIN
-2351,2000,AL,NYA,NYY,9,NYY,NYA
-2352,2000,AL,OAK,OAK,10,OAK,OAK
-2353,2000,AL,SEA,SEA,11,SEA,SEA
-2354,2000,AL,TBA,TBD,12,TBD,TBA
-2355,2000,AL,TEX,TEX,13,TEX,TEX
-2356,2000,AL,TOR,TOR,14,TOR,TOR
-2357,2000,NL,ARI,ARI,15,ARI,ARI
-2358,2000,NL,ATL,ATL,16,ATL,ATL
-2359,2000,NL,CHN,CHC,17,CHC,CHN
-2360,2000,NL,CIN,CIN,18,CIN,CIN
-2361,2000,NL,COL,COL,19,COL,COL
-2362,2000,NL,FLO,FLA,20,FLA,FLO
-2363,2000,NL,HOU,HOU,21,HOU,HOU
-2364,2000,NL,LAN,LAD,22,LAD,LAN
-2365,2000,NL,MIL,MIL,23,MIL,MIL
-2366,2000,NL,MON,WSN,24,MON,MON
-2367,2000,NL,NYN,NYM,25,NYM,NYN
-2368,2000,NL,PHI,PHI,26,PHI,PHI
-2369,2000,NL,PIT,PIT,27,PIT,PIT
-2370,2000,NL,SLN,STL,28,STL,SLN
-2371,2000,NL,SDN,SDP,29,SDP,SDN
-2372,2000,NL,SFN,SFG,30,SFG,SFN
-2373,2001,AL,ANA,ANA,1,ANA,ANA
-2374,2001,AL,BAL,BAL,2,BAL,BAL
-2375,2001,AL,BOS,BOS,3,BOS,BOS
-2376,2001,AL,CHA,CHW,4,CHW,CHA
-2377,2001,AL,CLE,CLE,5,CLE,CLE
-2378,2001,AL,DET,DET,6,DET,DET
-2379,2001,AL,KCA,KCR,7,KCR,KCA
-2380,2001,AL,MIN,MIN,8,MIN,MIN
-2381,2001,AL,NYA,NYY,9,NYY,NYA
-2382,2001,AL,OAK,OAK,10,OAK,OAK
-2383,2001,AL,SEA,SEA,11,SEA,SEA
-2384,2001,AL,TBA,TBD,12,TBD,TBA
-2385,2001,AL,TEX,TEX,13,TEX,TEX
-2386,2001,AL,TOR,TOR,14,TOR,TOR
-2387,2001,NL,ARI,ARI,15,ARI,ARI
-2388,2001,NL,ATL,ATL,16,ATL,ATL
-2389,2001,NL,CHN,CHC,17,CHC,CHN
-2390,2001,NL,CIN,CIN,18,CIN,CIN
-2391,2001,NL,COL,COL,19,COL,COL
-2392,2001,NL,FLO,FLA,20,FLA,FLO
-2393,2001,NL,HOU,HOU,21,HOU,HOU
-2394,2001,NL,LAN,LAD,22,LAD,LAN
-2395,2001,NL,MIL,MIL,23,MIL,MIL
-2396,2001,NL,MON,WSN,24,MON,MON
-2397,2001,NL,NYN,NYM,25,NYM,NYN
-2398,2001,NL,PHI,PHI,26,PHI,PHI
-2399,2001,NL,PIT,PIT,27,PIT,PIT
-2400,2001,NL,SLN,STL,28,STL,SLN
-2401,2001,NL,SDN,SDP,29,SDP,SDN
-2402,2001,NL,SFN,SFG,30,SFG,SFN
-2403,2002,AL,ANA,ANA,1,ANA,ANA
-2404,2002,AL,BAL,BAL,2,BAL,BAL
-2405,2002,AL,BOS,BOS,3,BOS,BOS
-2406,2002,AL,CHA,CHW,4,CHW,CHA
-2407,2002,AL,CLE,CLE,5,CLE,CLE
-2408,2002,AL,DET,DET,6,DET,DET
-2409,2002,AL,KCA,KCR,7,KCR,KCA
-2410,2002,AL,MIN,MIN,8,MIN,MIN
-2411,2002,AL,NYA,NYY,9,NYY,NYA
-2412,2002,AL,OAK,OAK,10,OAK,OAK
-2413,2002,AL,SEA,SEA,11,SEA,SEA
-2414,2002,AL,TBA,TBD,12,TBD,TBA
-2415,2002,AL,TEX,TEX,13,TEX,TEX
-2416,2002,AL,TOR,TOR,14,TOR,TOR
-2417,2002,NL,ARI,ARI,15,ARI,ARI
-2418,2002,NL,ATL,ATL,16,ATL,ATL
-2419,2002,NL,CHN,CHC,17,CHC,CHN
-2420,2002,NL,CIN,CIN,18,CIN,CIN
-2421,2002,NL,COL,COL,19,COL,COL
-2422,2002,NL,FLO,FLA,20,FLA,FLO
-2423,2002,NL,HOU,HOU,21,HOU,HOU
-2424,2002,NL,LAN,LAD,22,LAD,LAN
-2425,2002,NL,MIL,MIL,23,MIL,MIL
-2426,2002,NL,MON,WSN,24,MON,MON
-2427,2002,NL,NYN,NYM,25,NYM,NYN
-2428,2002,NL,PHI,PHI,26,PHI,PHI
-2429,2002,NL,PIT,PIT,27,PIT,PIT
-2430,2002,NL,SLN,STL,28,STL,SLN
-2431,2002,NL,SDN,SDP,29,SDP,SDN
-2432,2002,NL,SFN,SFG,30,SFG,SFN
-2433,2003,AL,ANA,ANA,1,ANA,ANA
-2434,2003,AL,BAL,BAL,2,BAL,BAL
-2435,2003,AL,BOS,BOS,3,BOS,BOS
-2436,2003,AL,CHA,CHW,4,CHW,CHA
-2437,2003,AL,CLE,CLE,5,CLE,CLE
-2438,2003,AL,DET,DET,6,DET,DET
-2439,2003,AL,KCA,KCR,7,KCR,KCA
-2440,2003,AL,MIN,MIN,8,MIN,MIN
-2441,2003,AL,NYA,NYY,9,NYY,NYA
-2442,2003,AL,OAK,OAK,10,OAK,OAK
-2443,2003,AL,SEA,SEA,11,SEA,SEA
-2444,2003,AL,TBA,TBD,12,TBD,TBA
-2445,2003,AL,TEX,TEX,13,TEX,TEX
-2446,2003,AL,TOR,TOR,14,TOR,TOR
-2447,2003,NL,ARI,ARI,15,ARI,ARI
-2448,2003,NL,ATL,ATL,16,ATL,ATL
-2449,2003,NL,CHN,CHC,17,CHC,CHN
-2450,2003,NL,CIN,CIN,18,CIN,CIN
-2451,2003,NL,COL,COL,19,COL,COL
-2452,2003,NL,FLO,FLA,20,FLA,FLO
-2453,2003,NL,HOU,HOU,21,HOU,HOU
-2454,2003,NL,LAN,LAD,22,LAD,LAN
-2455,2003,NL,MIL,MIL,23,MIL,MIL
-2456,2003,NL,MON,WSN,24,MON,MON
-2457,2003,NL,NYN,NYM,25,NYM,NYN
-2458,2003,NL,PHI,PHI,26,PHI,PHI
-2459,2003,NL,PIT,PIT,27,PIT,PIT
-2460,2003,NL,SLN,STL,28,STL,SLN
-2461,2003,NL,SDN,SDP,29,SDP,SDN
-2462,2003,NL,SFN,SFG,30,SFG,SFN
-2463,2004,AL,ANA,ANA,1,ANA,ANA
-2464,2004,AL,BAL,BAL,2,BAL,BAL
-2465,2004,AL,BOS,BOS,3,BOS,BOS
-2466,2004,AL,CHA,CHW,4,CHW,CHA
-2467,2004,AL,CLE,CLE,5,CLE,CLE
-2468,2004,AL,DET,DET,6,DET,DET
-2469,2004,AL,KCA,KCR,7,KCR,KCA
-2470,2004,AL,MIN,MIN,8,MIN,MIN
-2471,2004,AL,NYA,NYY,9,NYY,NYA
-2472,2004,AL,OAK,OAK,10,OAK,OAK
-2473,2004,AL,SEA,SEA,11,SEA,SEA
-2474,2004,AL,TBA,TBD,12,TBD,TBA
-2475,2004,AL,TEX,TEX,13,TEX,TEX
-2476,2004,AL,TOR,TOR,14,TOR,TOR
-2477,2004,NL,ARI,ARI,15,ARI,ARI
-2478,2004,NL,ATL,ATL,16,ATL,ATL
-2479,2004,NL,CHN,CHC,17,CHC,CHN
-2480,2004,NL,CIN,CIN,18,CIN,CIN
-2481,2004,NL,COL,COL,19,COL,COL
-2482,2004,NL,FLO,FLA,20,FLA,FLO
-2483,2004,NL,HOU,HOU,21,HOU,HOU
-2484,2004,NL,LAN,LAD,22,LAD,LAN
-2485,2004,NL,MIL,MIL,23,MIL,MIL
-2486,2004,NL,MON,WSN,24,MON,MON
-2487,2004,NL,NYN,NYM,25,NYM,NYN
-2488,2004,NL,PHI,PHI,26,PHI,PHI
-2489,2004,NL,PIT,PIT,27,PIT,PIT
-2490,2004,NL,SLN,STL,28,STL,SLN
-2491,2004,NL,SDN,SDP,29,SDP,SDN
-2492,2004,NL,SFN,SFG,30,SFG,SFN
-2493,2005,AL,LAA,ANA,1,LAA,ANA
-2494,2005,AL,BAL,BAL,2,BAL,BAL
-2495,2005,AL,BOS,BOS,3,BOS,BOS
-2496,2005,AL,CHA,CHW,4,CHW,CHA
-2497,2005,AL,CLE,CLE,5,CLE,CLE
-2498,2005,AL,DET,DET,6,DET,DET
-2499,2005,AL,KCA,KCR,7,KCR,KCA
-2500,2005,AL,MIN,MIN,8,MIN,MIN
-2501,2005,AL,NYA,NYY,9,NYY,NYA
-2502,2005,AL,OAK,OAK,10,OAK,OAK
-2503,2005,AL,SEA,SEA,11,SEA,SEA
-2504,2005,AL,TBA,TBD,12,TBD,TBA
-2505,2005,AL,TEX,TEX,13,TEX,TEX
-2506,2005,AL,TOR,TOR,14,TOR,TOR
-2507,2005,NL,ARI,ARI,15,ARI,ARI
-2508,2005,NL,ATL,ATL,16,ATL,ATL
-2509,2005,NL,CHN,CHC,17,CHC,CHN
-2510,2005,NL,CIN,CIN,18,CIN,CIN
-2511,2005,NL,COL,COL,19,COL,COL
-2512,2005,NL,FLO,FLA,20,FLA,FLO
-2513,2005,NL,HOU,HOU,21,HOU,HOU
-2514,2005,NL,LAN,LAD,22,LAD,LAN
-2515,2005,NL,MIL,MIL,23,MIL,MIL
-2516,2005,NL,WAS,WSN,24,WSN,WAS
-2517,2005,NL,NYN,NYM,25,NYM,NYN
-2518,2005,NL,PHI,PHI,26,PHI,PHI
-2519,2005,NL,PIT,PIT,27,PIT,PIT
-2520,2005,NL,SLN,STL,28,STL,SLN
-2521,2005,NL,SDN,SDP,29,SDP,SDN
-2522,2005,NL,SFN,SFG,30,SFG,SFN
-2523,2006,AL,LAA,ANA,1,LAA,ANA
-2524,2006,AL,BAL,BAL,2,BAL,BAL
-2525,2006,AL,BOS,BOS,3,BOS,BOS
-2526,2006,AL,CHA,CHW,4,CHW,CHA
-2527,2006,AL,CLE,CLE,5,CLE,CLE
-2528,2006,AL,DET,DET,6,DET,DET
-2529,2006,AL,KCA,KCR,7,KCR,KCA
-2530,2006,AL,MIN,MIN,8,MIN,MIN
-2531,2006,AL,NYA,NYY,9,NYY,NYA
-2532,2006,AL,OAK,OAK,10,OAK,OAK
-2533,2006,AL,SEA,SEA,11,SEA,SEA
-2534,2006,AL,TBA,TBD,12,TBD,TBA
-2535,2006,AL,TEX,TEX,13,TEX,TEX
-2536,2006,AL,TOR,TOR,14,TOR,TOR
-2537,2006,NL,ARI,ARI,15,ARI,ARI
-2538,2006,NL,ATL,ATL,16,ATL,ATL
-2539,2006,NL,CHN,CHC,17,CHC,CHN
-2540,2006,NL,CIN,CIN,18,CIN,CIN
-2541,2006,NL,COL,COL,19,COL,COL
-2542,2006,NL,FLO,FLA,20,FLA,FLO
-2543,2006,NL,HOU,HOU,21,HOU,HOU
-2544,2006,NL,LAN,LAD,22,LAD,LAN
-2545,2006,NL,MIL,MIL,23,MIL,MIL
-2546,2006,NL,WAS,WSN,24,WSN,WAS
-2547,2006,NL,NYN,NYM,25,NYM,NYN
-2548,2006,NL,PHI,PHI,26,PHI,PHI
-2549,2006,NL,PIT,PIT,27,PIT,PIT
-2550,2006,NL,SLN,STL,28,STL,SLN
-2551,2006,NL,SDN,SDP,29,SDP,SDN
-2552,2006,NL,SFN,SFG,30,SFG,SFN
-2553,2007,AL,LAA,ANA,1,LAA,ANA
-2554,2007,AL,BAL,BAL,2,BAL,BAL
-2555,2007,AL,BOS,BOS,3,BOS,BOS
-2556,2007,AL,CHA,CHW,4,CHW,CHA
-2557,2007,AL,CLE,CLE,5,CLE,CLE
-2558,2007,AL,DET,DET,6,DET,DET
-2559,2007,AL,KCA,KCR,7,KCR,KCA
-2560,2007,AL,MIN,MIN,8,MIN,MIN
-2561,2007,AL,NYA,NYY,9,NYY,NYA
-2562,2007,AL,OAK,OAK,10,OAK,OAK
-2563,2007,AL,SEA,SEA,11,SEA,SEA
-2564,2007,AL,TBA,TBD,12,TBD,TBA
-2565,2007,AL,TEX,TEX,13,TEX,TEX
-2566,2007,AL,TOR,TOR,14,TOR,TOR
-2567,2007,NL,ARI,ARI,15,ARI,ARI
-2568,2007,NL,ATL,ATL,16,ATL,ATL
-2569,2007,NL,CHN,CHC,17,CHC,CHN
-2570,2007,NL,CIN,CIN,18,CIN,CIN
-2571,2007,NL,COL,COL,19,COL,COL
-2572,2007,NL,FLO,FLA,20,FLA,FLO
-2573,2007,NL,HOU,HOU,21,HOU,HOU
-2574,2007,NL,LAN,LAD,22,LAD,LAN
-2575,2007,NL,MIL,MIL,23,MIL,MIL
-2576,2007,NL,WAS,WSN,24,WSN,WAS
-2577,2007,NL,NYN,NYM,25,NYM,NYN
-2578,2007,NL,PHI,PHI,26,PHI,PHI
-2579,2007,NL,PIT,PIT,27,PIT,PIT
-2580,2007,NL,SLN,STL,28,STL,SLN
-2581,2007,NL,SDN,SDP,29,SDP,SDN
-2582,2007,NL,SFN,SFG,30,SFG,SFN
-2583,2008,AL,LAA,ANA,1,LAA,ANA
-2584,2008,AL,BAL,BAL,2,BAL,BAL
-2585,2008,AL,BOS,BOS,3,BOS,BOS
-2586,2008,AL,CHA,CHW,4,CHW,CHA
-2587,2008,AL,CLE,CLE,5,CLE,CLE
-2588,2008,AL,DET,DET,6,DET,DET
-2589,2008,AL,KCA,KCR,7,KCR,KCA
-2590,2008,AL,MIN,MIN,8,MIN,MIN
-2591,2008,AL,NYA,NYY,9,NYY,NYA
-2592,2008,AL,OAK,OAK,10,OAK,OAK
-2593,2008,AL,SEA,SEA,11,SEA,SEA
-2594,2008,AL,TBA,TBD,12,TBR,TBA
-2595,2008,AL,TEX,TEX,13,TEX,TEX
-2596,2008,AL,TOR,TOR,14,TOR,TOR
-2597,2008,NL,ARI,ARI,15,ARI,ARI
-2598,2008,NL,ATL,ATL,16,ATL,ATL
-2599,2008,NL,CHN,CHC,17,CHC,CHN
-2600,2008,NL,CIN,CIN,18,CIN,CIN
-2601,2008,NL,COL,COL,19,COL,COL
-2602,2008,NL,FLO,FLA,20,FLA,FLO
-2603,2008,NL,HOU,HOU,21,HOU,HOU
-2604,2008,NL,LAN,LAD,22,LAD,LAN
-2605,2008,NL,MIL,MIL,23,MIL,MIL
-2606,2008,NL,WAS,WSN,24,WSN,WAS
-2607,2008,NL,NYN,NYM,25,NYM,NYN
-2608,2008,NL,PHI,PHI,26,PHI,PHI
-2609,2008,NL,PIT,PIT,27,PIT,PIT
-2610,2008,NL,SLN,STL,28,STL,SLN
-2611,2008,NL,SDN,SDP,29,SDP,SDN
-2612,2008,NL,SFN,SFG,30,SFG,SFN
-2613,2009,AL,LAA,ANA,1,LAA,ANA
-2614,2009,AL,BAL,BAL,2,BAL,BAL
-2615,2009,AL,BOS,BOS,3,BOS,BOS
-2616,2009,AL,CHA,CHW,4,CHW,CHA
-2617,2009,AL,CLE,CLE,5,CLE,CLE
-2618,2009,AL,DET,DET,6,DET,DET
-2619,2009,AL,KCA,KCR,7,KCR,KCA
-2620,2009,AL,MIN,MIN,8,MIN,MIN
-2621,2009,AL,NYA,NYY,9,NYY,NYA
-2622,2009,AL,OAK,OAK,10,OAK,OAK
-2623,2009,AL,SEA,SEA,11,SEA,SEA
-2624,2009,AL,TBA,TBD,12,TBR,TBA
-2625,2009,AL,TEX,TEX,13,TEX,TEX
-2626,2009,AL,TOR,TOR,14,TOR,TOR
-2627,2009,NL,ARI,ARI,15,ARI,ARI
-2628,2009,NL,ATL,ATL,16,ATL,ATL
-2629,2009,NL,CHN,CHC,17,CHC,CHN
-2630,2009,NL,CIN,CIN,18,CIN,CIN
-2631,2009,NL,COL,COL,19,COL,COL
-2632,2009,NL,FLO,FLA,20,FLA,FLO
-2633,2009,NL,HOU,HOU,21,HOU,HOU
-2634,2009,NL,LAN,LAD,22,LAD,LAN
-2635,2009,NL,MIL,MIL,23,MIL,MIL
-2636,2009,NL,WAS,WSN,24,WSN,WAS
-2637,2009,NL,NYN,NYM,25,NYM,NYN
-2638,2009,NL,PHI,PHI,26,PHI,PHI
-2639,2009,NL,PIT,PIT,27,PIT,PIT
-2640,2009,NL,SLN,STL,28,STL,SLN
-2641,2009,NL,SDN,SDP,29,SDP,SDN
-2642,2009,NL,SFN,SFG,30,SFG,SFN
-2643,2010,AL,LAA,ANA,1,LAA,ANA
-2644,2010,AL,BAL,BAL,2,BAL,BAL
-2645,2010,AL,BOS,BOS,3,BOS,BOS
-2646,2010,AL,CHA,CHW,4,CHW,CHA
-2647,2010,AL,CLE,CLE,5,CLE,CLE
-2648,2010,AL,DET,DET,6,DET,DET
-2649,2010,AL,KCA,KCR,7,KCR,KCA
-2650,2010,AL,MIN,MIN,8,MIN,MIN
-2651,2010,AL,NYA,NYY,9,NYY,NYA
-2652,2010,AL,OAK,OAK,10,OAK,OAK
-2653,2010,AL,SEA,SEA,11,SEA,SEA
-2654,2010,AL,TBA,TBD,12,TBR,TBA
-2655,2010,AL,TEX,TEX,13,TEX,TEX
-2656,2010,AL,TOR,TOR,14,TOR,TOR
-2657,2010,NL,ARI,ARI,15,ARI,ARI
-2658,2010,NL,ATL,ATL,16,ATL,ATL
-2659,2010,NL,CHN,CHC,17,CHC,CHN
-2660,2010,NL,CIN,CIN,18,CIN,CIN
-2661,2010,NL,COL,COL,19,COL,COL
-2662,2010,NL,FLO,FLA,20,FLA,FLO
-2663,2010,NL,HOU,HOU,21,HOU,HOU
-2664,2010,NL,LAN,LAD,22,LAD,LAN
-2665,2010,NL,MIL,MIL,23,MIL,MIL
-2666,2010,NL,WAS,WSN,24,WSN,WAS
-2667,2010,NL,NYN,NYM,25,NYM,NYN
-2668,2010,NL,PHI,PHI,26,PHI,PHI
-2669,2010,NL,PIT,PIT,27,PIT,PIT
-2670,2010,NL,SLN,STL,28,STL,SLN
-2671,2010,NL,SDN,SDP,29,SDP,SDN
-2672,2010,NL,SFN,SFG,30,SFG,SFN
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diff --git a/pybaseball/datahelpers/postprocessing.py b/pybaseball/datahelpers/postprocessing.py
index db377b62..bf297bb2 100644
--- a/pybaseball/datahelpers/postprocessing.py
+++ b/pybaseball/datahelpers/postprocessing.py
@@ -1,7 +1,8 @@
 import re
 from datetime import datetime
-from typing import List, Optional, Union
+from typing import Any, List, Union, Optional
 
+import attr
 import numpy as np
 import pandas as pd
 
@@ -12,7 +13,7 @@
 
 date_formats = [
     # Standard statcast format
-    (re.compile(r'^\d{4}-\d{1,2}-\d{1,2}$'),                            '%Y-%m-%d'),
+    (re.compile(r'^\d{4}-\d{1,2}-\d{1,2}$'), '%Y-%m-%d'),
 
     # Just in case (https://github.com/jldbc/pybaseball/issues/104)
     (re.compile(r'^\d{4}-\d{1,2}-\d{1,2}T\d{2}:\d{2}:\d{2}.\d{1,6}Z$'), '%Y-%m-%dT%H:%M:%S.%fZ'),
@@ -23,15 +24,21 @@ def try_parse_dataframe(
     data: pd.DataFrame,
     parse_numerics: bool = True,
     null_replacement: Union[str, int, float, datetime] = np.nan,
-    known_percentages: List[str] = []
+    known_percentages: Optional[List[str]] = None
 ) -> pd.DataFrame:
     data_copy = data.copy()
 
     if parse_numerics:
         data_copy = coalesce_nulls(data_copy, null_replacement)
-        data_copy = data_copy.apply(pd.to_numeric, errors='ignore', downcast='signed').convert_dtypes(convert_string=False)
-    
-    string_columns = [dtype_tuple[0] for dtype_tuple in data_copy.dtypes.items() if str(dtype_tuple[1]) in ["object", "string"]]
+        data_copy = data_copy.apply(
+            pd.to_numeric,
+            errors='ignore',
+            downcast='signed'
+        ).convert_dtypes(convert_string=False)
+
+    string_columns = [
+        dtype_tuple[0] for dtype_tuple in data_copy.dtypes.items() if str(dtype_tuple[1]) in ["object", "string"]
+    ]
     for column in string_columns:
         # Only check the first value of the column and test that;
         # this is faster than blindly trying to convert entire columns
@@ -41,11 +48,12 @@ def try_parse_dataframe(
             continue
         first_value = data_copy[column].loc[first_value_index]
 
-        if str(first_value).endswith('%') or column.endswith('%') or column in known_percentages:
+        if str(first_value).endswith('%') or column.endswith('%') or \
+                (known_percentages is not None and column in known_percentages):
             data_copy[column] = data_copy[column].astype(str).str.replace("%", "").astype(float) / 100.0
         else:
             # Doing it this way as just applying pd.to_datetime on
-            #the whole dataframe just tries to gobble up ints/floats as timestamps
+            # the whole dataframe just tries to gobble up ints/floats as timestamps
             for date_regex, date_format in date_formats:
                 if isinstance(first_value, str) and date_regex.match(first_value):
                     data_copy[column] = data_copy[column].apply(pd.to_datetime, errors='ignore', format=date_format)
@@ -55,11 +63,12 @@ def try_parse_dataframe(
     return data_copy
 
 
+# pylint: disable=too-many-return-statements
 def try_parse(
     value: Union[None, str, int, datetime, float],
     column_name: str,
     null_replacement: Union[str, int, float, datetime] = np.nan,
-    known_percentages: List[str] = []
+    known_percentages: Optional[List[str]] = None
 ) -> Union[str, int, float, datetime]:
     if value is None:
         return null_replacement
@@ -76,19 +85,23 @@ def try_parse(
         if date_regex.match(value):
             try:
                 return datetime.strptime(value, date_format)
-            except:
+            except:  # pylint: disable=bare-except
                 pass
 
     # Is it an float or an int (including percetages)?
     try:
-        percentage = (value.endswith('%') or column_name.endswith('%') or column_name in known_percentages)
+        percentage = (
+            value.endswith('%') or column_name.endswith('%') or \
+            (known_percentages is not None and  column_name in known_percentages)
+        )
         if percentage:
             return try_parse_percentage(value)
+
         if '.' in value:
             return float(value)
-        else:
-            return int(value)
-    except:
+
+        return int(value)
+    except:  # pylint: disable=bare-except
         pass
 
     return value
@@ -127,7 +140,7 @@ def convert_percentages(data: pd.DataFrame, columns: List[str]) -> pd.DataFrame:
         # Skip if column is all NA (happens for some of the more obscure stats + in older seasons)
         if col in data.columns and data[col].count() > 0:
             data[col] = data[col].str.strip(' %')
-            data[col] = data[col].astype(float)/100.0
+            data[col] = data[col].astype(float) / 100.0
         else:
             # print(col)
             pass
@@ -143,10 +156,10 @@ def compute_pa(bat_df: pd.DataFrame) -> pd.Series:
     :param bat_df:
     :return:
     """
-    PA = bat_df.loc[:, "AB"].fillna(0)
+    plate_appearances = bat_df.loc[:, "AB"].fillna(0)
     for stat in ["BB", "HBP", "SH", "SF"]:
-        PA += bat_df.loc[:, stat].fillna(0)
-    return PA.astype(int)
+        plate_appearances += bat_df.loc[:, stat].fillna(0)
+    return plate_appearances.astype(int)
 
 
 def augment_lahman_batting(bat_df: pd.DataFrame) -> pd.DataFrame:
@@ -156,21 +169,23 @@ def augment_lahman_batting(bat_df: pd.DataFrame) -> pd.DataFrame:
     :param bat_df:
     :return:
     """
-    PA = compute_pa(bat_df)
-    X1B = (
+    plate_appearances = compute_pa(bat_df)
+    singles = (
         bat_df.loc[:, "H"]
         - bat_df.loc[:, "2B"]
         - bat_df.loc[:, "3B"]
         - bat_df.loc[:, "HR"]
     )
-    TB = (
+    total_bases = (
         bat_df.loc[:, "HR"] * 4
         + bat_df.loc[:, "3B"] * 3
         + bat_df.loc[:, "2B"] * 2
-        + X1B
+        + singles
     )
     return bat_df.assign(
-        PA=PA.astype(int), X1B=X1B.astype(int), TB=TB.astype(int)
+        PA=plate_appearances.astype(int),
+        X1B=singles.astype(int),
+        TB=total_bases.astype(int)
     ).rename({"X1B": "1B"}, axis=1)
 
 
@@ -187,20 +202,20 @@ def augment_lahman_pitching(stats_df: pd.DataFrame) -> pd.DataFrame:
 def aggregate_by_season(stats_df: pd.DataFrame) -> pd.DataFrame:
     return stats_df.groupby(["playerID", "yearID"]).sum().reset_index()
 
-
-def check_is_zero_one(instance, attribute, value):
+# pylint: disable=unused-argument
+def check_is_zero_one(instance: Any, attribute: attr.Attribute, value: Union[int, float]) -> None:
     if value not in [0, 1]:
         raise ValueError(f"{attribute} must be either 0 or 1, not {value}")
 
-
-def check_greater_zero(instance, attribute, value):
+# pylint: disable=unused-argument
+def check_greater_zero(instance: Any, attribute: attr.Attribute, value: Union[int, float]) -> None:
     if value <= 0:
         raise ValueError(
             f"{attribute} must be greater than zero, not {value}"
         )
 
-
-def check_between_zero_one(instance, attribute, value):
+# pylint: disable=unused-argument
+def check_between_zero_one(instance: Any, attribute: attr.Attribute, value: Union[int, float]) -> None:
     if not 0 <= value <= 1:
         raise ValueError(
             f"{attribute} must be between zero and one, not {value}"
diff --git a/pybaseball/datahelpers/statcast_utils.py b/pybaseball/datahelpers/statcast_utils.py
index 3d0c244e..12dcc8c2 100644
--- a/pybaseball/datahelpers/statcast_utils.py
+++ b/pybaseball/datahelpers/statcast_utils.py
@@ -1,8 +1,8 @@
 import numpy as np
 import pandas as pd
-from typing import Optional
 
-def add_spray_angle(df: pd.DataFrame, adjusted: bool = False) -> pd.DataFrame:
+
+def add_spray_angle(raw_df: pd.DataFrame, adjusted: bool = False) -> pd.DataFrame:
     """Adds spray angle and adjusted spray angle to StatCast DataFrames
     - Spray angle is the raw left-right angle of the hit
     - Adjusted spray angle flips the sign for left handed batters, making it a push/pull angle
@@ -12,9 +12,14 @@ def add_spray_angle(df: pd.DataFrame, adjusted: bool = False) -> pd.DataFrame:
     Returns:
         pd.DataFrame: Input dataframe with spray angle columns appended
     """
-    
-    df["spray_angle"] = np.arctan((df["hc_x"]-125.42)/(198.27-df["hc_y"]))*180/np.pi*.75
+
+    df = raw_df.copy()
+
+    df["spray_angle"] = np.arctan((df["hc_x"] - 125.42) / (198.27 - df["hc_y"])) * 180 / np.pi * .75
     if adjusted:
-        df["adj_spray_angle"] = df.apply(lambda row: -row["spray_angle"] if row["stand"] == "L" else row["spray_angle"], axis=1)
+        df["adj_spray_angle"] = df.apply(
+            lambda row: -row["spray_angle"] if row["stand"] == "L" else row["spray_angle"],
+            axis=1
+        )
         df = df.drop(["spray_angle"], axis=1)
     return df
diff --git a/pybaseball/datahelpers/transform.py b/pybaseball/datahelpers/transform.py
index 2bf6cf2d..f341fbb2 100644
--- a/pybaseball/datahelpers/transform.py
+++ b/pybaseball/datahelpers/transform.py
@@ -1,21 +1,21 @@
 
 import pandas as pd
 
-from pybaseball.utils import NULLABLE_INT
 from pybaseball.lahman import people
 
 
-def get_age(stats_df, people_df=None):
+def get_age(stats_df: pd.DataFrame, people_df: pd.DataFrame = None) -> pd.DataFrame:
     if people_df is None:
         people_df = people()
     return (
         stats_df.merge(
             people_df.filter(["playerID", "birthYear"], axis=1), on="playerID"
         )
-        .assign(age=lambda row: (row.yearID - row.birthYear).astype(NULLABLE_INT))
+        .assign(age=lambda row: (row.yearID - row.birthYear).astype(pd.Int32Dtype()))
         .filter(["playerID", "yearID", "age"], axis=1)
     )
 
+
 def get_primary_position(fielding_df: pd.DataFrame) -> pd.DataFrame:
     """
     Determines the primary position of a player during a season. `fielding_df` is
diff --git a/pybaseball/datasources/html_table_processor.py b/pybaseball/datasources/html_table_processor.py
index cee7d275..0884a072 100644
--- a/pybaseball/datasources/html_table_processor.py
+++ b/pybaseball/datasources/html_table_processor.py
@@ -1,4 +1,5 @@
-from typing import Callable, Dict, Iterator, List, Union, Optional, Any
+from datetime import datetime
+from typing import Any, Callable, Dict, List, Optional, Union
 
 import lxml.etree
 import pandas as pd
@@ -6,7 +7,6 @@
 
 from ..datahelpers import postprocessing
 from ..datahelpers.column_mapper import ColumnListMapperFunction
-from datetime import datetime
 
 RowIdFunction = Optional[Callable[[Any, lxml.etree.Element], Optional[Union[int, str]]]]
 
@@ -26,7 +26,7 @@ def table_xpath(self) -> str:
         return '//table'
 
     def get_tabular_data_from_element(self, element: lxml.etree.Element, column_name_mapper: ColumnListMapperFunction = None,
-                                      known_percentages: List[str] = [], row_id_func: RowIdFunction = None,
+                                      known_percentages: Optional[List[str]] = None, row_id_func: RowIdFunction = None,
                                       row_id_name: Optional[str] = None) -> pd.DataFrame:
         headings = element.xpath(self.headings_xpath)
 
@@ -52,7 +52,7 @@ def get_tabular_data_from_element(self, element: lxml.etree.Element, column_name
         return fg_data
 
     def get_tabular_data_from_html(self, html: Union[str, bytes], column_name_mapper: ColumnListMapperFunction = None,
-                                   known_percentages: List[str] = [], row_id_func: RowIdFunction = None,
+                                   known_percentages: Optional[List[str]] = None, row_id_func: RowIdFunction = None,
                                       row_id_name: Optional[str] = None) -> pd.DataFrame:
         html_dom = lxml.etree.HTML(html)
 
@@ -66,7 +66,7 @@ def get_tabular_data_from_html(self, html: Union[str, bytes], column_name_mapper
 
     def get_tabular_data_from_url(self, url: str, query_params: Dict[str, Union[str, int]] = None,
                                   column_name_mapper: ColumnListMapperFunction = None,
-                                  known_percentages: List[str] = [], row_id_func: RowIdFunction = None,
+                                  known_percentages: Optional[List[str]] = None, row_id_func: RowIdFunction = None,
                                       row_id_name: Optional[str] = None) -> pd.DataFrame:
         response = requests.get(self.root_url + url, params=query_params)
 
@@ -85,7 +85,7 @@ def get_tabular_data_from_url(self, url: str, query_params: Dict[str, Union[str,
 
     def get_tabular_data_from_options(self, base_url: str, query_params: Dict[str, Union[str, int]],
                                       column_name_mapper: ColumnListMapperFunction = None,
-                                      known_percentages: List[str] = [], row_id_func: RowIdFunction = None,
+                                      known_percentages: Optional[List[str]] = None, row_id_func: RowIdFunction = None,
                                       row_id_name: Optional[str] = None) -> pd.DataFrame:
         return self.get_tabular_data_from_url(
             base_url,
diff --git a/pybaseball/teamid_lookup.py b/pybaseball/teamid_lookup.py
index 367aa0dc..28c41f02 100644
--- a/pybaseball/teamid_lookup.py
+++ b/pybaseball/teamid_lookup.py
@@ -1,8 +1,10 @@
 import logging
 import os
-from datetime import date
-from typing import Optional
+import re
+from difflib import SequenceMatcher
+from typing import Dict, List, Optional, Set
 
+import numpy as np
 import pandas as pd
 
 from . import lahman
@@ -10,7 +12,8 @@
 from .utils import most_recent_season
 
 _DATA_FILENAME = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data', 'fangraphs_teams.csv')
-
+LOG_LEVEL = os.environ.get('LOG_LEVEL', 'WARNING').upper()
+logging.basicConfig(level=LOG_LEVEL)
 
 def team_ids(season: Optional[int] = None, league: str = 'ALL') -> pd.DataFrame:
     if not os.path.exists(_DATA_FILENAME):
@@ -27,24 +30,67 @@ def team_ids(season: Optional[int] = None, league: str = 'ALL') -> pd.DataFrame:
     return fg_team_data
 
 
-_known_cities = ['Altoona', 'Anaheim', 'Arizona', 'Atlanta', 'Baltimore', 'Boston', 'Brooklyn', 'Buffalo',
-                 'California', 'Chicago', 'Cincinnati', 'Cleveland', 'Colorado', 'Detroit', 'Elizabeth', 'Florida',
-                 'Fort Wayne', 'Hartford', 'Houston', 'Indianapolis', 'Kansas City', 'Los Angeles', 'Milwaukee',
-                 'Minnesota', 'Montreal', 'New York', 'Newark', 'Oakland', 'Philadelphia', 'Pittsburg',
-                 'Pittsburgh', 'Richmond', 'San Diego', 'San Francisco', 'Seattle', 'St. Louis', 'St. Paul',
-                 'Syracuse', 'Tampa Bay', 'Texas', 'Toronto', 'Troy', 'Washington', 'Washington', 'Wilmington']
+# franchID: teamIDfg
+_manual_matches: Dict[str, int] = {
+    'BLT': 1007,
+    'BLU': 1008,
+    'BRG': 1012,
+    'BTT': 1014,
+    'CEN': 1019,
+    'CHP': 1022,
+    'CPI': 1030,
+    'ECK': 1032,
+    'MAR': 1046,
+    'NYY': 9,
+    'SLM': 1072,
+}
+
+
+def _front_loaded_ratio(str_1: str, str_2: str) -> float:
+    '''
+        A difflib ration based on difflint's SequenceMatcher ration.
+
+        It gives higher weight to a name that starts the same.
+
+        For example:
+
+        In the default ratio, 'LSA' and 'BSN' both match to 'BSA' with a score of 67.
+        However, for team names, the first letter or two are almost always the city, which is likely to be the same.
+        So, in this scorer 'LSA' would match to 'BSA' with a score of 83, while 'BSN' would match at 58.
+    '''
+
+    if len(str_1) != 3 or len(str_2) != 3:
+        logging.warn(
+            "This ratio is intended for 3 length string comparison (such as a lahman teamID, franchID, or teamIDBR."
+            "Returning 0 for non-compliant string(s)."
+        )
+        return 0.0
+
+    full_score = SequenceMatcher(a=str_1, b=str_2).ratio()
+    front_score = SequenceMatcher(a=str_1[:-1], b=str_2[:-1]).ratio()
+
+    return (full_score + front_score) / 2
 
-_manual_matches = {'CPI': 'Browns/Stogies', 'ANA': 'Angels'}
 
+def _get_close_team_matches(lahman_row: pd.Series, fg_data: pd.DataFrame, min_score: int = 50) -> Optional[str]:
+    columns_to_check = ['franchID', 'teamID', 'teamIDBR', 'initials', 'city_start']
+    best_of = 3
 
-def _estimate_name(team_row: pd.DataFrame, column: str) -> str:
-    if team_row['franchID'] in _manual_matches:
-        return _manual_matches[team_row['franchID']]
-    estimate = str(team_row[column])
-    for city in _known_cities + [str(team_row['city'])]:
-        estimate = estimate.replace(f'{city} ', '') if estimate.startswith(city) else estimate
+    choices: Set[str] = set(fg_data[fg_data['Season'] == lahman_row.yearID]['Team'].values)
 
-    return estimate
+    if len(choices) == 0:
+        return None
+
+    scores: Dict[str, List[float]] = {choice: [] for choice in choices}
+    for join_column in columns_to_check:
+        for choice in choices:
+            scores[choice].append(
+                _front_loaded_ratio(lahman_row[join_column], choice) * 100 if len(lahman_row[join_column]) == 3 else 0.0
+            )
+    scores = {key: sorted(value, reverse=True)[:best_of] for key, value in scores.items()}
+    scores_list = [(key, round(np.mean(value))) for key, value in scores.items()]
+    choice, score = sorted(scores_list, key=lambda x: x[1], reverse=True)[0]
+    return choice if score >= min_score else None
 
 
 def _generate_teams() -> pd.DataFrame:
@@ -57,56 +103,107 @@ def _generate_teams() -> pd.DataFrame:
     start_season = 1871
     end_season = most_recent_season()
 
+    lahman_columns = ['yearID', 'lgID', 'teamID', 'franchID', 'divID', 'name', 'teamIDBR', 'teamIDlahman45',
+                      'teamIDretro']
+
+    lahman_teams = lahman.teams()[lahman_columns]
+
     # Only getting AB to make payload small, and you have to specify at least one column
-    team_data = fangraphs.fg_team_batting_data(start_season, end_season, "ALL", stat_columns=['AB'])
-
-    # Join the lahman data
-    teams_franchises = lahman.teams().merge(lahman.teams_franchises(), how='left', on='franchID', suffixes=['', '.fr'])
-    teams_franchises = teams_franchises.merge(lahman.parks(), how='left', left_on='park', right_on='park.name',
-                                              suffixes=['', '.p'])
-
-    # Drop lahman data down to just what we need
-    teams_franchises = teams_franchises[
-        ['yearID', 'lgID', 'teamID', 'franchID', 'divID', 'name', 'park', 'teamIDBR', 'teamIDlahman45', 'teamIDretro',
-         'franchName', 'city', 'state']
-    ]
-
-    # Try to guess the name Fangraphs would use
-    teams_franchises['possibleName'] = teams_franchises.apply(lambda row: _estimate_name(row, 'name'), axis=1)
-    teams_franchises['possibleFranchName'] = teams_franchises.apply(lambda row: _estimate_name(row, 'franchName'),
-                                                                    axis=1)
-
-    # Join up the data by team name, and look for what is still without a match
-    outer_joined = teams_franchises.merge(team_data, how='outer', left_on=['yearID', 'possibleName'],
-                                          right_on=['Season', 'Team'])
-    unjoined_teams_franchises = outer_joined.query('Season.isnull()').drop(team_data.columns, axis=1)
-    unjoined_team_data = outer_joined.query('yearID.isnull()').drop(teams_franchises.columns, axis=1)
-
-    # Take all the unmatched data and try to join off franchise name, instead of team name
-    inner_joined = teams_franchises.merge(team_data, how='inner', left_on=['yearID', 'possibleName'],
-                                          right_on=['Season', 'Team'])
-    franch_inner_joined = unjoined_teams_franchises.merge(unjoined_team_data, how='inner',
-                                                          left_on=['yearID', 'possibleFranchName'],
-                                                          right_on=['Season', 'Team'])
+    fg_team_data = fangraphs.fg_team_batting_data(start_season, end_season, "ALL", stat_columns=['AB'])
+
+    fg_columns = list(fg_team_data.columns.values)
+
+    unjoined_fangraphs_teams = fg_team_data.copy(deep=True)
+
+    unjoined_lahman_teams = lahman_teams.copy(deep=True)
+
+    unjoined_lahman_teams['manual_teamid'] = unjoined_lahman_teams.apply(
+        lambda row: _manual_matches.get(row.franchID, -1),
+        axis=1
+    )
+
+    lahman_columns += ['manual_teamid']
+
+    unjoined_lahman_teams['initials'] = unjoined_lahman_teams.apply(
+        lambda row: re.sub(r'[^A-Z]', '', row['name']),
+        axis=1
+    )
+
+    lahman_columns += ['initials']
+
+    unjoined_lahman_teams['city_start'] = unjoined_lahman_teams.apply(
+        lambda row: row['name'][:3].upper(),
+        axis=1
+    )
+
+    lahman_columns += ['city_start']
+
+    joined: pd.DataFrame = None
+
+    for join_column in ['manual_teamid', 'teamID', 'franchID', 'teamIDBR', 'initials', 'city_start']:
+        joined_count = len(joined.index) if (joined is not None) else 0
+        if join_column == 'manual_teamid':
+            outer_joined = unjoined_lahman_teams.merge(unjoined_fangraphs_teams, how='outer',
+                                                       left_on=['yearID', join_column],
+                                                       right_on=['Season', 'teamIDfg'])
+        else:
+            outer_joined = unjoined_lahman_teams.merge(unjoined_fangraphs_teams, how='outer',
+                                                       left_on=['yearID', join_column],
+                                                       right_on=['Season', 'Team'])
+
+        # Clean up the data
+        found = outer_joined.query("not Season.isnull() and not yearID.isnull()")
+        joined = pd.concat([joined, found]) if (joined is not None) else found
+
+        # My kingdom for an xor function
+        unjoined = outer_joined.query('yearID.isnull() or Season.isnull()')
+
+        unjoined_lahman_teams = unjoined.query('Season.isnull()').drop(labels=fg_columns, axis=1)
+        unjoined_fangraphs_teams = unjoined.query('yearID.isnull()').drop(labels=lahman_columns, axis=1)
+
+        logging.info("Matched %s teams off of %s. %s teams remaining to match.", len(joined.index) - joined_count, join_column, len(unjoined_lahman_teams.index))
+
+    joined_count = len(joined.index) if (joined is not None) else 0
+
+    # Try to close match the rest
+    unjoined_lahman_teams['close_match'] = unjoined_lahman_teams.apply(
+        lambda row: _get_close_team_matches(row, unjoined_fangraphs_teams),
+        axis=1
+    )
+
+    outer_joined = unjoined_lahman_teams.merge(unjoined_fangraphs_teams, how='outer', left_on=['yearID', 'close_match'],
+                                               right_on=['Season', 'Team'])
 
     # Clean up the data
-    joined = pd.concat([inner_joined, franch_inner_joined])
+    joined = pd.concat([joined, outer_joined.query("not Season.isnull() and not yearID.isnull()")])
+
+    unjoined = outer_joined.query('(yearID.isnull() or Season.isnull()) and not (yearID.isnull() and Season.isnull())')
 
-    outer_joined = joined.merge(team_data, how='outer', left_on=['yearID', 'teamIDfg'],
-                                right_on=['Season', 'teamIDfg'], suffixes=['', '_y'])
+    unjoined_lahman_teams = unjoined.query('Season.isnull()').drop(unjoined_fangraphs_teams.columns.values, axis=1)
+    unjoined_fangraphs_teams = unjoined.query('yearID.isnull()').drop(unjoined_lahman_teams.columns, axis=1)
 
-    unjoined_teams_franchises = outer_joined.query('Season_y.isnull()').drop(team_data.columns, axis=1,
-                                                                             errors='ignore')
+    logging.info("Matched %s teams off of close match. %s teams remaining to match.", len(joined.index) - joined_count, len(unjoined_lahman_teams.index))
 
-    if not unjoined_teams_franchises.empty:
-        logging.warning('When trying to join FG data to lahman, found the following extraneous lahman data',
-                        extra=unjoined_teams_franchises)
+    error_state = False
 
-    unjoined_team_data = outer_joined.query('yearID.isnull()').drop(teams_franchises.columns, axis=1, errors='ignore')
+    if not unjoined_lahman_teams.empty:
+        logging.warning(
+            'When trying to join lahman data to Fangraphs, found %s rows of extraneous lahman data: %s',
+            len(unjoined_lahman_teams.index),
+            unjoined_lahman_teams.sort_values(['yearID', 'lgID', 'teamID', 'franchID'])
+        )
+        error_state = True
 
-    if not unjoined_team_data.empty:
-        logging.warning('When trying to join Fangraphs data to lahman, found the following extraneous Fangraphs data',
-                        extra=unjoined_team_data)
+    if not unjoined_fangraphs_teams.empty:
+        logging.warning(
+            'When trying to join Fangraphs data to lahman, found %s rows of extraneous Fangraphs data: %s',
+            len(unjoined_fangraphs_teams.index),
+            unjoined_fangraphs_teams.sort_values(['Season', 'Team'])
+        )
+        error_state = True
+
+    if error_state:
+        raise Exception("Extraneous data was not matched. Aborting.")
 
     joined = joined[['yearID', 'lgID', 'teamID', 'franchID', 'teamIDfg', 'teamIDBR', 'teamIDretro']]
 
@@ -120,5 +217,6 @@ def _generate_teams() -> pd.DataFrame:
 
     return joined
 
+
 # For backwards API compatibility
 fangraphs_teams = team_ids
diff --git a/pybaseball/utils.py b/pybaseball/utils.py
index 658fe3a1..2ae7f1f0 100644
--- a/pybaseball/utils.py
+++ b/pybaseball/utils.py
@@ -10,7 +10,6 @@
 
 from . import cache
 
-NULLABLE_INT = pd.Int32Dtype()
 DATE_FORMAT = "%Y-%m-%d"
 
 # dictionary containing team abbreviations and their first year in existance
@@ -62,8 +61,8 @@ def validate_datestring(date_text: Optional[str]) -> date:
     try:
         assert date_text
         return datetime.strptime(date_text, DATE_FORMAT).date()
-    except (AssertionError, ValueError):
-        raise ValueError("Incorrect data format, should be YYYY-MM-DD")
+    except (AssertionError, ValueError) as ex:
+        raise ValueError("Incorrect data format, should be YYYY-MM-DD") from ex
 
 
 @functools.lru_cache()
@@ -107,8 +106,8 @@ def date_range(start: date, stop: date, step: int = 1, verbose: bool = True) ->
 
         if low > stop:
             return
-        hi = min(low + timedelta(step - 1), stop)
-        yield low, hi
+        high = min(low + timedelta(step - 1), stop)
+        yield low, high
         low += timedelta(days=step)
 
 
@@ -122,22 +121,21 @@ def statcast_date_range(start: date, stop: date, step: int, verbose: bool = True
     low = start
 
     while low <= stop:
-        season_start, season_end = STATCAST_VALID_DATES.get(low.year,
-                                                     (low.replace(month=3, day=15), low.replace(month=11, day=15))
-                                                     )
+        date_span = low.replace(month=3, day=15), low.replace(month=11, day=15)
+        season_start, season_end = STATCAST_VALID_DATES.get(low.year, date_span)
         if low < season_start:
             low = season_start
             if verbose:
                 print('Skipping offseason dates')
         elif low > season_end:
-            low, _ = STATCAST_VALID_DATES.get(low.year+1, (date(month=3, day=15, year=low.year + 1), None))
+            low, _ = STATCAST_VALID_DATES.get(low.year + 1, (date(month=3, day=15, year=low.year + 1), None))
             if verbose:
                 print('Skipping offseason dates')
 
         if low > stop:
             return
-        hi = min(low + timedelta(step - 1), stop)
-        yield low, hi
+        high = min(low + timedelta(step - 1), stop)
+        yield low, high
         low += timedelta(days=step)
 
 
@@ -153,7 +151,8 @@ def sanitize_date_range(start_dt: Optional[str], end_dt: Optional[str]) -> Tuple
         print('end_dt', end_dt)
 
         print(
-            "Warning: no date range supplied. Returning yesterday's Statcast data. For a different date range, try get_statcast(start_dt, end_dt)."
+            "Warning: no date range supplied. Returning yesterday's Statcast data. For a different date range, "
+            "try get_statcast(start_dt, end_dt)."
         )
 
     # If only one date is supplied, assume they only want that day's stats
@@ -174,14 +173,18 @@ def sanitize_date_range(start_dt: Optional[str], end_dt: Optional[str]) -> Tuple
     return start_dt_date, end_dt_date
 
 
-def sanitize_input(start_dt, end_dt, player_id):
+def sanitize_input(start_dt: Optional[str], end_dt: Optional[str], player_id: Optional[int]) -> Tuple[str, str, str]:
     # error if no player ID provided
     if player_id is None:
-        raise ValueError("Player ID is required. If you need to find a player's id, try pybaseball.playerid_lookup(last_name, first_name) and use their key_mlbam. If you want statcast data for all players, try the statcast() function.")
+        raise ValueError(
+            "Player ID is required. If you need to find a player's id, try "
+            "pybaseball.playerid_lookup(last_name, first_name) and use their key_mlbam. "
+            "If you want statcast data for all players, try the statcast() function."
+        )
     # this id should be a string to place inside a url
-    player_id = str(player_id)
+    player_id_str = str(player_id)
     start_dt_date, end_dt_date = sanitize_date_range(start_dt, end_dt)
-    return str(start_dt_date), str(end_dt_date), player_id
+    return str(start_dt_date), str(end_dt_date), player_id_str
 
 
 @cache.df_cache()
@@ -211,24 +214,24 @@ def split_request(start_dt: str, end_dt: str, player_id: int, url: str) -> pd.Da
     return pd.concat(results)
 
 
-def get_zip_file(url):
+def get_zip_file(url: str) -> zipfile.ZipFile:
     """
     Get zip file from provided URL
     """
-    with requests.get(url, stream=True) as f:
-        z = zipfile.ZipFile(io.BytesIO(f.content))
-    return z
+    with requests.get(url, stream=True) as file_stream:
+        zip_file = zipfile.ZipFile(io.BytesIO(file_stream.content))
+    return zip_file
 
 
-def get_text_file(url):
+def get_text_file(url: str) -> str:
     """
     Get raw github file from provided URL
     """
 
-    with requests.get(url, stream=True) as f:
-        s = f.text
+    with requests.get(url, stream=True) as file_stream:
+        text = file_stream.text
 
-    return s
+    return text
 
 
 def flag_imputed_data(statcast_df: pd.DataFrame) -> pd.DataFrame:
diff --git a/pyproject.toml b/pyproject.toml
index 976ed8df..11a93236 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,8 +1,19 @@
+[tool.autopep8]
+max_line_length = 120
+ignore=["E265"]
+
 [tool.pylint.pep8]
 'max-line-length' = 120
 
+[tool.pylint.BASIC]
+# Good variable names which should always be accepted, separated by a comma.
+good-names=["i", "j", "k", "ex", "_", "df"]
+
 [tool.pylint.'MESSAGES CONTROL']
 disable = [
     "logging-fstring-interpolation",
+    "missing-class-docstring",
+    "missing-function-docstring",
     "missing-module-docstring"
 ]
+


Test patch:
diff --git a/tests/integration/pybaseball/test_statcast.py b/tests/integration/pybaseball/test_statcast.py
index 5eee7094..da4bf5ed 100644
--- a/tests/integration/pybaseball/test_statcast.py
+++ b/tests/integration/pybaseball/test_statcast.py
@@ -1,11 +1,9 @@
 from datetime import date, timedelta
-from typing import cast
-from unittest.mock import MagicMock, patch
 
 import pandas as pd
+import pytest
 
-import pybaseball
-from pybaseball.statcast import _small_request, _handle_request, statcast, statcast_single_game
+from pybaseball.statcast import _handle_request, _small_request, statcast, statcast_single_game
 from pybaseball.utils import sanitize_date_range
 
 
@@ -65,13 +63,14 @@ def test_handle_request_pre_season() -> None:
 
 def test_handle_request_post_season() -> None:
     start_dt, end_dt = sanitize_date_range('2018-11-14', '2019-03-22')
-    result = _handle_request(start_dt, end_dt, step=1, verbose=False)
+    with pytest.warns(UserWarning):
+        result = _handle_request(start_dt, end_dt, step=1, verbose=False)
 
-    assert result is not None
-    assert not result.empty
+        assert result is not None
+        assert not result.empty
 
-    assert len(result.columns) == 89
-    assert len(result) == 689
+        assert len(result.columns) == 89
+        assert len(result) == 689
 
 
 def test_handle_request_post_season_same_year() -> None:
diff --git a/tests/integration/pybaseball/test_teamid_lookup.py b/tests/integration/pybaseball/test_teamid_lookup.py
index 4c8f5789..40037bd2 100644
--- a/tests/integration/pybaseball/test_teamid_lookup.py
+++ b/tests/integration/pybaseball/test_teamid_lookup.py
@@ -1,14 +1,32 @@
-from pybaseball.teamid_lookup import _generate_teams
+from unittest.mock import MagicMock, patch
+
+import pandas as pd
+
+from pybaseball.teamid_lookup import _DATA_FILENAME, _generate_teams
 
 
 def test_generate_fangraphs_teams() -> None:
-    result = _generate_teams()
+    with patch.object(pd.DataFrame, 'to_csv', MagicMock()) as to_csv_mock:
+        result = _generate_teams()
+
+        assert result is not None
+        assert not result.empty
+
+        # Remove newer results to ensure we get the count right
+        result = result.query('yearID <= 2019')
 
-    assert result is not None
-    assert not result.empty
+        assert len(result.columns) == 7
 
-    # Remove newer results to ensure we get the count right
-    result = result.query('yearID <= 2019')
+        to_csv_mock.assert_called_once_with(_DATA_FILENAME)
 
-    assert len(result.columns) == 7
-    assert len(result) == 2943
+        # Each franchID should match one and only one teamIDfg
+        for franch_id in set(result['franchID'].values):
+            teams = result.query(f"franchID == '{franch_id}'")
+            franchises = teams.groupby("teamIDfg").size()
+            if len(franchises.index) > 1:
+                # Print it out so we know who the offender is
+                print('franch_id', franch_id)
+                print('franchises', franchises)
+                print(teams)
+            
+            assert len(franchises.index) == 1
diff --git a/tests/pybaseball/datahelpers/test_postprocessing.py b/tests/pybaseball/datahelpers/test_postprocessing.py
index c4c0ccd1..ed8c9797 100644
--- a/tests/pybaseball/datahelpers/test_postprocessing.py
+++ b/tests/pybaseball/datahelpers/test_postprocessing.py
@@ -8,121 +8,135 @@
 
 
 @pytest.fixture(name='sample_unprocessed_result')
-def _sample_unprocessed_result():
+def _sample_unprocessed_result() -> pd.DataFrame:
     return pd.DataFrame(
         [
             ['TBR', '1', '2', '50 %', '8'],
-            ['555', '3', '4', '45%', 'null']
+            ['555', '3', '4', '45%', 'null'],
         ],
         columns=['Team', 'Runs', 'Hits', 'CS%', 'HR']
     ).reset_index(drop=True)
 
 
-class TestPostProcessing:
-    def test_try_parse_short_date(self):
-        assert postprocessing.try_parse('2020-09-04', 'game_dt') == datetime(year=2020, month=9, day=4)
+def test_try_parse_short_date() -> None:
+    assert postprocessing.try_parse('2020-09-04', 'game_dt') == datetime(year=2020, month=9, day=4)
 
-    def test_try_parse_long_date(self):
-        expected_datetime = datetime(year=2020, month=9, day=3, hour=5, minute=40, second=30, microsecond=210000)
-        assert postprocessing.try_parse('2020-09-03T05:40:30.210Z', 'game_dt') == expected_datetime
 
-    def test_try_parse_date_nonstr(self):
-        dt = datetime(year=2020, month=9, day=4)
-        assert postprocessing.try_parse(dt, 'game_dt') == dt
+def test_try_parse_long_date() -> None:
+    expected_datetime = datetime(year=2020, month=9, day=3, hour=5, minute=40, second=30, microsecond=210000)
+    assert postprocessing.try_parse('2020-09-03T05:40:30.210Z', 'game_dt') == expected_datetime
 
-    def test_try_parse_int(self):
-        assert postprocessing.try_parse('1', 'runs') == 1
 
-    def test_try_parse_int_nonstr(self):
-        assert postprocessing.try_parse(1, 'runs') == 1
+def test_try_parse_date_nonstr() -> None:
+    expected = datetime(year=2020, month=9, day=4)
+    assert postprocessing.try_parse(expected, 'game_dt') == expected
 
-    def test_try_parse_float(self):
-        assert postprocessing.try_parse('1.0', 'runs') == 1.0
 
-    def test_try_parse_float_nonstr(self):
-        assert postprocessing.try_parse(1.0, 'runs') == 1.0
+def test_try_parse_int() -> None:
+    assert postprocessing.try_parse('1', 'runs') == 1
 
-    def test_try_parse_percentage_value(self):
-        assert postprocessing.try_parse('10%', 'avg') == 0.1
 
-    def test_try_parse_percentage_column(self):
-        assert postprocessing.try_parse('50', 'CS%') == 0.5
+def test_try_parse_int_nonstr() -> None:
+    assert postprocessing.try_parse(1, 'runs') == 1
 
-    def test_try_parse_percentage_column_known(self):
-        assert postprocessing.try_parse('50', 'CS', known_percentages=['CS']) == 0.5
 
-    def test_try_parse_null(self):
-        assert pd.isna(postprocessing.try_parse(None, 'runs'))
+def test_try_parse_float() -> None:
+    assert postprocessing.try_parse('1.0', 'runs') == 1.0
 
-    def test_try_parse_dataframe(self):
-        raw_data = pd.DataFrame(
-            [
-                ['1', 'TBR', '2019-01-01', '0.5', '40%', 8, '1048576'],
-                ['2', 'NYY', '2019-02-01', '0.6', '20%', 'null', '4294967296']
-            ],
-            columns=['id', 'team', 'dt', 'rate', 'chance', 'wins', 'long_number']
-        )
 
-        processed_data = postprocessing.try_parse_dataframe(raw_data)
+def test_try_parse_float_nonstr() -> None:
+    assert postprocessing.try_parse(1.0, 'runs') == 1.0
 
-        expected_data = pd.DataFrame(
-            [
-                [1, 'TBR', np.datetime64('2019-01-01'), 0.5, 0.4, 8,      1048576   ],
-                [2, 'NYY', np.datetime64('2019-02-01'), 0.6, 0.2, np.nan, 4294967296],
-            ],
-            columns=['id', 'team', 'dt', 'rate', 'chance', 'wins', 'long_number']
-        ).convert_dtypes(convert_string=False)
 
-        pd.testing.assert_frame_equal(processed_data, expected_data, check_dtype=False)
+def test_try_parse_percentage_value() -> None:
+    assert postprocessing.try_parse('10%', 'avg') == 0.1
 
-    def test_coalesce_nulls(self, sample_unprocessed_result):
-        expected_result = pd.DataFrame(
-            [
-                ['TBR', '1', '2', '50 %', '8'],
-                ['555', '3', '4', '45%', np.nan]
-            ],
-            columns=['Team', 'Runs', 'Hits', 'CS%', 'HR']
-        ).reset_index(drop=True)
 
-        actual_result = postprocessing.coalesce_nulls(sample_unprocessed_result).reset_index(drop=True)
+def test_try_parse_percentage_column() -> None:
+    assert postprocessing.try_parse('50', 'CS%') == 0.5
 
-        pd.testing.assert_frame_equal(actual_result, expected_result)
 
-    def test_columns_except(self, sample_unprocessed_result):
-        expected_columns = ['Runs', 'Hits', 'CS%', 'HR']
+def test_try_parse_percentage_column_known() -> None:
+    assert postprocessing.try_parse('50', 'CS', known_percentages=['CS']) == 0.5
 
-        actual_columns = postprocessing.columns_except(sample_unprocessed_result, ['Team'])
 
-        assert set(expected_columns) == set(actual_columns)
+def test_try_parse_null() -> None:
+    assert pd.isna(postprocessing.try_parse(None, 'runs'))
 
-    def test_convert_numeric(self, sample_unprocessed_result):
-        expected_result = pd.DataFrame(
-            [
-                ['TBR', 1.0, 2.0, '50 %', 8],
-                ['555', 3.0, 4.0, '45%', np.nan]
-            ],
-            columns=['Team', 'Runs', 'Hits', 'CS%', 'HR']
-        ).reset_index(drop=True)
 
-        actual_result = postprocessing.convert_numeric(
-            postprocessing.coalesce_nulls(sample_unprocessed_result),
-            ['Runs', 'Hits', 'HR']
-        ).reset_index(drop=True)
+def test_try_parse_dataframe() -> None:
+    raw_data = pd.DataFrame(
+        [
+            ['1', 'TBR', '2019-01-01', '0.5', '40%', 8, '1048576'],
+            ['2', 'NYY', '2019-02-01', '0.6', '20%', 'null', '4294967296'],
+        ],
+        columns=['id', 'team', 'dt', 'rate', 'chance', 'wins', 'long_number']
+    )
+
+    processed_data = postprocessing.try_parse_dataframe(raw_data)
+
+    expected_data = pd.DataFrame(
+        [
+            [1, 'TBR', np.datetime64('2019-01-01'), 0.5, 0.4, 8, 1048576],
+            [2, 'NYY', np.datetime64('2019-02-01'), 0.6, 0.2, np.nan, 4294967296],
+        ],
+        columns=['id', 'team', 'dt', 'rate', 'chance', 'wins', 'long_number']
+    ).convert_dtypes(convert_string=False)
+
+    pd.testing.assert_frame_equal(processed_data, expected_data, check_dtype=False)
+
+
+def test_coalesce_nulls(sample_unprocessed_result: pd.DataFrame) -> None:
+    expected_result = pd.DataFrame(
+        [
+            ['TBR', '1', '2', '50 %', '8'],
+            ['555', '3', '4', '45%', np.nan],
+        ],
+        columns=['Team', 'Runs', 'Hits', 'CS%', 'HR']
+    ).reset_index(drop=True)
+
+    actual_result = postprocessing.coalesce_nulls(sample_unprocessed_result).reset_index(drop=True)
+
+    pd.testing.assert_frame_equal(actual_result, expected_result)
+
 
-        pd.testing.assert_frame_equal(actual_result, expected_result)
+def test_columns_except(sample_unprocessed_result: pd.DataFrame) -> None:
+    expected_columns = ['Runs', 'Hits', 'CS%', 'HR']
 
-    def test_convert_percentages(self, sample_unprocessed_result):
-        expected_result = pd.DataFrame(
-            [
-                ['TBR', '1', '2', 0.50, '8'],
-                ['555', '3', '4', 0.45, 'null']
-            ],
-            columns=['Team', 'Runs', 'Hits', 'CS%', 'HR']
-        ).reset_index(drop=True)
+    actual_columns = postprocessing.columns_except(sample_unprocessed_result, ['Team'])
 
-        actual_result = postprocessing.convert_percentages(
-            sample_unprocessed_result,
-            ['CS%']
-        ).reset_index(drop=True)
+    assert set(expected_columns) == set(actual_columns)
+
+
+def test_convert_numeric(sample_unprocessed_result: pd.DataFrame) -> None:
+    expected_result = pd.DataFrame(
+        [
+            ['TBR', 1.0, 2.0, '50 %', 8],
+            ['555', 3.0, 4.0, '45%', np.nan],
+        ],
+        columns=['Team', 'Runs', 'Hits', 'CS%', 'HR']
+    ).reset_index(drop=True)
+
+    actual_result = postprocessing.convert_numeric(
+        postprocessing.coalesce_nulls(sample_unprocessed_result),
+        ['Runs', 'Hits', 'HR']
+    ).reset_index(drop=True)
+
+    pd.testing.assert_frame_equal(actual_result, expected_result)
+
+
+def test_convert_percentages(sample_unprocessed_result: pd.DataFrame) -> None:
+    expected_result = pd.DataFrame(
+        [
+            ['TBR', '1', '2', 0.50, '8'],
+            ['555', '3', '4', 0.45, 'null'],
+        ],
+        columns=['Team', 'Runs', 'Hits', 'CS%', 'HR']
+    ).reset_index(drop=True)
+
+    actual_result = postprocessing.convert_percentages(
+        sample_unprocessed_result,
+        ['CS%']
+    ).reset_index(drop=True)
 
-        pd.testing.assert_frame_equal(actual_result, expected_result)
+    pd.testing.assert_frame_equal(actual_result, expected_result)
diff --git a/tests/pybaseball/datahelpers/test_statcast_utils.py b/tests/pybaseball/datahelpers/test_statcast_utils.py
new file mode 100644
index 00000000..fc6a1bca
--- /dev/null
+++ b/tests/pybaseball/datahelpers/test_statcast_utils.py
@@ -0,0 +1,34 @@
+from pybaseball.datahelpers import statcast_utils
+import pandas as pd
+import pytest
+
+@pytest.fixture(name='unprocessed_data')
+def _unprocessed_data() -> pd.DataFrame:
+    return pd.DataFrame([
+        ['L', 139.98, 150.23],
+        ['R', 23.86, 96.65]
+    ],
+    columns=['stand', 'hc_x', 'hc_y'])
+
+def test_add_spray_angle(unprocessed_data: pd.DataFrame) -> None:
+    spray_angle_df = statcast_utils.add_spray_angle(unprocessed_data)
+
+    expected = pd.DataFrame([
+        ['L', 139.98, 150.23, 12.6457],
+        ['R', 23.86, 96.65, -33.7373]
+    ],
+    columns=['stand', 'hc_x', 'hc_y', 'spray_angle'])
+
+    pd.testing.assert_frame_equal(spray_angle_df, expected)
+
+def test_add_spray_angle_adjusted(unprocessed_data: pd.DataFrame) -> None:
+    spray_angle_df = statcast_utils.add_spray_angle(unprocessed_data, adjusted=True)
+
+    expected = pd.DataFrame([
+        ['L', 139.98, 150.23, -12.6457],
+        ['R', 23.86, 96.65, -33.7373]
+    ],
+    columns=['stand', 'hc_x', 'hc_y', 'adj_spray_angle'])
+
+    pd.testing.assert_frame_equal(spray_angle_df, expected)
+
diff --git a/tests/pybaseball/datahelpers/test_transform.py b/tests/pybaseball/datahelpers/test_transform.py
new file mode 100644
index 00000000..9ce518a5
--- /dev/null
+++ b/tests/pybaseball/datahelpers/test_transform.py
@@ -0,0 +1,79 @@
+import pandas as pd
+import pytest
+from pybaseball.datahelpers import transform
+from unittest.mock import patch
+
+
+@pytest.fixture(name='fielding')
+def _fielding() -> pd.DataFrame:
+    return pd.DataFrame([
+        ['1', 2015, 'P',  157],
+        ['1', 2015, 'CF', 5],
+        ['1', 2016, 'CF', 162],
+        ['2', 2015, 'C',  1],
+    ],
+    columns=['playerID', 'yearID', 'POS', 'G'])
+
+@pytest.fixture(name='people')
+def _people() -> pd.DataFrame:
+    return pd.DataFrame([
+        ['1', 1990],
+        ['2', 1985],
+    ],
+    columns=['playerID', 'birthYear'])
+
+
+@pytest.fixture(name='stats')
+def _stats() -> pd.DataFrame:
+    return pd.DataFrame([
+        ['1', 2015],
+        ['1', 2016],
+        ['2', 2015],
+    ],
+    columns=['playerID', 'yearID'])
+
+def test_get_age(stats: pd.DataFrame, people: pd.DataFrame) -> None:
+    expected = pd.DataFrame([
+            ['1', 2015, 25],
+            ['1', 2016, 26],
+            ['2', 2015, 30],
+        ],
+        columns=['playerID', 'yearID', 'age']
+    )
+    
+    result = transform.get_age(stats, people)
+
+    pd.testing.assert_frame_equal(expected, result, check_dtype=False)
+
+def test_get_age_default_people(stats: pd.DataFrame, people: pd.DataFrame) -> None:
+    expected = pd.DataFrame([
+            ['1', 2015, 25],
+            ['1', 2016, 26],
+            ['2', 2015, 30],
+        ],
+        columns=['playerID', 'yearID', 'age']
+    )
+    
+    with patch('pybaseball.datahelpers.transform.people', return_value=people) as people_mock:
+        result = transform.get_age(stats)
+
+        pd.testing.assert_frame_equal(expected, result, check_dtype=False)
+
+        people_mock.assert_called_once()
+
+def test_get_primary_position(fielding: pd.DataFrame) -> None:
+    expected = pd.DataFrame([
+            ['1', 2015, 'P'],
+            ['1', 2016, 'CF'],
+            ['2', 2015, 'C'],
+        ],
+        columns=['playerID', 'yearID', 'primaryPos'],
+        index=pd.RangeIndex(1, 4),
+    )
+    
+    result = transform.get_primary_position(fielding)
+
+    print(expected)
+    print(result)
+
+    pd.testing.assert_frame_equal(expected, result)
diff --git a/tests/pybaseball/test_teamid_lookup.py b/tests/pybaseball/test_teamid_lookup.py
index f489298d..560a7944 100644
--- a/tests/pybaseball/test_teamid_lookup.py
+++ b/tests/pybaseball/test_teamid_lookup.py
@@ -1,4 +1,7 @@
-from pybaseball.teamid_lookup import team_ids
+import pandas as pd
+import pytest
+
+from pybaseball.teamid_lookup import _front_loaded_ratio, _get_close_team_matches, team_ids
 
 
 def test_team_id_lookup_all() -> None:
@@ -11,7 +14,7 @@ def test_team_id_lookup_all() -> None:
     result = result.query('yearID <= 2019')
 
     assert len(result.columns) == 7
-    assert len(result) == 2943
+    assert len(result) == 2925
 
 
 def test_team_id_lookup_season() -> None:
@@ -45,3 +48,95 @@ def test_team_id_lookup_season_league() -> None:
 
     assert len(result.columns) == 7
     assert len(result) == 15
+
+@pytest.mark.parametrize(
+    'team1, team2, expected_ratio',
+    [
+        ['LSA', 'BSA', .58],
+        ['BSN', 'BSA', .83],
+        ['ABC', 'XYZ', 0],
+        ['AA', 'ABC', 0],
+        ['A', 'ABC', 0],
+        ['AAA', 'AB', 0],
+        ['AAA', 'A', 0],
+    ]
+)
+
+def test_front_loaded_ratio(team1: str, team2: str, expected_ratio: int) -> None:
+    if len(team1) == 3 and len(team2) == 3:
+        assert round(_front_loaded_ratio(team1, team2), 2) == expected_ratio
+    else:
+        with pytest.warns(None):
+            assert _front_loaded_ratio(team1, team2) == 0
+
+def test_get_close_team_matches():
+    lahman_teams = pd.DataFrame(
+        [
+            {'yearID': 2020, 'franchID': 'ARI', 'teamID': 'ARI', 'teamIDBR': 'ARI', 'name': 'Arizona Diamondbacks', 'initials': 'AD', 'city_start': 'ARI', 'expected': 'ARI'},
+            {'yearID': 2020, 'franchID': 'ATL', 'teamID': 'ATL', 'teamIDBR': 'ATL', 'name': 'Atlanta Braves', 'initials': 'AB', 'city_start': 'ATL', 'expected': 'ATL'},
+            {'yearID': 2020, 'franchID': 'BAL', 'teamID': 'BAL', 'teamIDBR': 'BAL', 'name': 'Baltimore Orioles', 'initials': 'BO', 'city_start': 'BAL', 'expected': 'BAL'},
+            {'yearID': 2020, 'franchID': 'BOS', 'teamID': 'BOS', 'teamIDBR': 'BOS', 'name': 'Boston Red Sox', 'initials': 'BRS', 'city_start': 'BOS', 'expected': 'BOS'},
+            {'yearID': 2020, 'franchID': 'CHW', 'teamID': 'CHA', 'teamIDBR': 'CHW', 'name': 'Chicago White Sox', 'initials': 'CWS', 'city_start': 'CHI', 'expected': 'CHW'},
+            {'yearID': 2020, 'franchID': 'CHC', 'teamID': 'CHN', 'teamIDBR': 'CHC', 'name': 'Chicago Cubs', 'initials': 'CC', 'city_start': 'CHI', 'expected': 'CHC'},
+            {'yearID': 2020, 'franchID': 'CIN', 'teamID': 'CIN', 'teamIDBR': 'CIN', 'name': 'Cincinnati Reds', 'initials': 'CR', 'city_start': 'CIN', 'expected': 'CIN'},
+            {'yearID': 2020, 'franchID': 'CLE', 'teamID': 'CLE', 'teamIDBR': 'CLE', 'name': 'Cleveland Indians', 'initials': 'CI', 'city_start': 'CLE', 'expected': 'CLE'},
+            {'yearID': 2020, 'franchID': 'COL', 'teamID': 'COL', 'teamIDBR': 'COL', 'name': 'Colorado Rockies', 'initials': 'CR', 'city_start': 'COL', 'expected': 'COL'},
+            {'yearID': 2020, 'franchID': 'DET', 'teamID': 'DET', 'teamIDBR': 'DET', 'name': 'Detroit Tigers', 'initials': 'DT', 'city_start': 'DET', 'expected': 'DET'},
+            {'yearID': 2020, 'franchID': 'HOU', 'teamID': 'HOU', 'teamIDBR': 'HOU', 'name': 'Houston Astros', 'initials': 'HA', 'city_start': 'HOU', 'expected': 'HOU'},
+            {'yearID': 2020, 'franchID': 'KCR', 'teamID': 'KCA', 'teamIDBR': 'KCR', 'name': 'Kansas City Royals', 'initials': 'KCR', 'city_start': 'KAN', 'expected': 'KCR'},
+            {'yearID': 2020, 'franchID': 'ANA', 'teamID': 'LAA', 'teamIDBR': 'LAA', 'name': 'Los Angeles Angels of Anaheim', 'initials': 'LAA', 'city_start': 'LOS', 'expected': 'LAA'},
+            {'yearID': 2020, 'franchID': 'LAD', 'teamID': 'LAN', 'teamIDBR': 'LAD', 'name': 'Los Angeles Dodgers', 'initials': 'LAD', 'city_start': 'LOS', 'expected': 'LAD'},
+            {'yearID': 2020, 'franchID': 'FLA', 'teamID': 'MIA', 'teamIDBR': 'MIA', 'name': 'Miami Marlins', 'initials': 'MM', 'city_start': 'MIA', 'expected': 'MIA'},
+            {'yearID': 2020, 'franchID': 'MIL', 'teamID': 'MIL', 'teamIDBR': 'MIL', 'name': 'Milwaukee Brewers', 'initials': 'MB', 'city_start': 'MIL', 'expected': 'MIL'},
+            {'yearID': 2020, 'franchID': 'MIN', 'teamID': 'MIN', 'teamIDBR': 'MIN', 'name': 'Minnesota Twins', 'initials': 'MT', 'city_start': 'MIN', 'expected': 'MIN'},
+            {'yearID': 2020, 'franchID': 'NYY', 'teamID': 'NYA', 'teamIDBR': 'NYY', 'name': 'New York Yankees', 'initials': 'NYY', 'city_start': 'NEW', 'expected': 'NYY'},
+            {'yearID': 2020, 'franchID': 'NYM', 'teamID': 'NYN', 'teamIDBR': 'NYM', 'name': 'New York Mets', 'initials': 'NYM', 'city_start': 'NEW', 'expected': 'NYM'},
+            {'yearID': 2020, 'franchID': 'OAK', 'teamID': 'OAK', 'teamIDBR': 'OAK', 'name': 'Oakland Athletics', 'initials': 'OA', 'city_start': 'OAK', 'expected': 'OAK'},
+            {'yearID': 2020, 'franchID': 'PHI', 'teamID': 'PHI', 'teamIDBR': 'PHI', 'name': 'Philadelphia Phillies', 'initials': 'PP', 'city_start': 'PHI', 'expected': 'PHI'},
+            {'yearID': 2020, 'franchID': 'PIT', 'teamID': 'PIT', 'teamIDBR': 'PIT', 'name': 'Pittsburgh Pirates', 'initials': 'PP', 'city_start': 'PIT', 'expected': 'PIT'},
+            {'yearID': 2020, 'franchID': 'SDP', 'teamID': 'SDN', 'teamIDBR': 'SDP', 'name': 'San Diego Padres', 'initials': 'SDP', 'city_start': 'SAN', 'expected': 'SDP'},
+            {'yearID': 2020, 'franchID': 'SEA', 'teamID': 'SEA', 'teamIDBR': 'SEA', 'name': 'Seattle Mariners', 'initials': 'SM', 'city_start': 'SEA', 'expected': 'SEA'},
+            {'yearID': 2020, 'franchID': 'SFG', 'teamID': 'SFN', 'teamIDBR': 'SFG', 'name': 'San Francisco Giants', 'initials': 'SFG', 'city_start': 'SAN', 'expected': 'SFG'},
+            {'yearID': 2020, 'franchID': 'STL', 'teamID': 'SLN', 'teamIDBR': 'STL', 'name': 'St. Louis Cardinals', 'initials': 'SLC', 'city_start': 'ST.', 'expected': 'STL'},
+            {'yearID': 2020, 'franchID': 'TBD', 'teamID': 'TBA', 'teamIDBR': 'TBR', 'name': 'Tampa Bay Rays', 'initials': 'TBR', 'city_start': 'TAM', 'expected': 'TBR'},
+            {'yearID': 2020, 'franchID': 'TEX', 'teamID': 'TEX', 'teamIDBR': 'TEX', 'name': 'Texas Rangers', 'initials': 'TR', 'city_start': 'TEX', 'expected': 'TEX'},
+            {'yearID': 2020, 'franchID': 'TOR', 'teamID': 'TOR', 'teamIDBR': 'TOR', 'name': 'Toronto Blue Jays', 'initials': 'TBJ', 'city_start': 'TOR', 'expected': 'TOR'},
+            {'yearID': 2020, 'franchID': 'WSN', 'teamID': 'WAS', 'teamIDBR': 'WSN', 'name': 'Washington Nationals', 'initials': 'WN', 'city_start': 'WAS', 'expected': 'WSN'},
+        ]
+    )
+
+    fg_teams = pd.DataFrame([
+        {'Season': 2020, 'Team': 'CHW'},
+        {'Season': 2020, 'Team': 'SDP'},
+        {'Season': 2020, 'Team': 'LAD'},
+        {'Season': 2020, 'Team': 'ATL'},
+        {'Season': 2020, 'Team': 'NYM'},
+        {'Season': 2020, 'Team': 'NYY'},
+        {'Season': 2020, 'Team': 'OAK'},
+        {'Season': 2020, 'Team': 'TBR'},
+        {'Season': 2020, 'Team': 'MIN'},
+        {'Season': 2020, 'Team': 'SFG'},
+        {'Season': 2020, 'Team': 'CHC'},
+        {'Season': 2020, 'Team': 'LAA'},
+        {'Season': 2020, 'Team': 'STL'},
+        {'Season': 2020, 'Team': 'BOS'},
+        {'Season': 2020, 'Team': 'KCR'},
+        {'Season': 2020, 'Team': 'PHI'},
+        {'Season': 2020, 'Team': 'CLE'},
+        {'Season': 2020, 'Team': 'HOU'},
+        {'Season': 2020, 'Team': 'TOR'},
+        {'Season': 2020, 'Team': 'DET'},
+        {'Season': 2020, 'Team': 'SEA'},
+        {'Season': 2020, 'Team': 'CIN'},
+        {'Season': 2020, 'Team': 'ARI'},
+        {'Season': 2020, 'Team': 'BAL'},
+        {'Season': 2020, 'Team': 'MIL'},
+        {'Season': 2020, 'Team': 'MIA'},
+        {'Season': 2020, 'Team': 'WSN'},
+        {'Season': 2020, 'Team': 'COL'},
+        {'Season': 2020, 'Team': 'PIT'},
+        {'Season': 2020, 'Team': 'TEX'},
+    ])
+
+    for _, row in lahman_teams.iterrows():
+        assert _get_close_team_matches(row, fg_teams) == row.expected