diff --git a/your-code/1.-Data-Cleaning.ipynb b/your-code/1.-Data-Cleaning.ipynb index d1c8eea..0c3d500 100644 --- a/your-code/1.-Data-Cleaning.ipynb +++ b/your-code/1.-Data-Cleaning.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -47,11 +47,282 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
0Aerial PowersDALF18371.021.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
1Alana BeardLAG/F18573.021.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
2Alex BentleyCONG17069.023.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
3Alex MontgomerySANG/F18584.024.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
4Alexis JonesMING17578.025.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
91Makayla EppsCHIG178NaNNaNUSJune 6, 199522KentuckyR145221414.3050.02540.02024104600
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" + ], + "text/plain": [ + " Name Team Pos Height Weight BMI Birth_Place Birthdate Age \\\n", + "91 Makayla Epps CHI G 178 NaN NaN US June 6, 1995 22 \n", + "\n", + " College Experience Games Played MIN FGM FGA FG% 3PM 3PA 3P% \\\n", + "91 Kentucky R 14 52 2 14 14.3 0 5 0.0 \n", + "\n", + " FTM FTA FT% OREB DREB REB AST STL BLK TO PTS DD2 TD3 \n", + "91 2 5 40.0 2 0 2 4 1 0 4 6 0 0 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba[wnba[\"BMI\"].isnull()]" ] }, { @@ -96,11 +645,24 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.006993006993006993" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba.shape\n", + "percentage = 1/143\n", + "percentage # if we remove the row, 0.006% of the values of the dataset will be removed. " ] }, { @@ -114,11 +676,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "wnba.dropna(inplace=True)" ] }, { @@ -134,7 +696,7 @@ "metadata": {}, "outputs": [], "source": [ - "#your answer here" + "#Yes, it was a good decision, because in this way our dataset in cleaner and that row would not make a huge difference in the final conclusions." ] }, { @@ -147,11 +709,54 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Name object\n", + "Team object\n", + "Pos object\n", + "Height int64\n", + "Weight float64\n", + "BMI float64\n", + "Birth_Place object\n", + "Birthdate object\n", + "Age int64\n", + "College object\n", + "Experience object\n", + "Games Played int64\n", + "MIN int64\n", + "FGM int64\n", + "FGA int64\n", + "FG% float64\n", + "3PM int64\n", + "3PA int64\n", + "3P% float64\n", + "FTM int64\n", + "FTA int64\n", + "FT% float64\n", + "OREB int64\n", + "DREB int64\n", + "REB int64\n", + "AST int64\n", + "STL int64\n", + "BLK int64\n", + "TO int64\n", + "PTS int64\n", + "DD2 int64\n", + "TD3 int64\n", + "dtype: object" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba.dtypes" ] }, { @@ -170,11 +775,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "wnba[\"Weight\"] = wnba[\"Weight\"].astype(int)" ] }, { @@ -186,11 +791,345 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightWeightBMIAgeGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
count142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000
mean184.61267678.97887323.09121427.11267624.429577500.10563474.401408168.70422543.10281714.83098643.69718324.97816939.53521149.42253575.82887322.06338061.59154983.65493044.51408517.7253529.78169032.288732203.1690141.1408450.007042
std8.69812810.9961102.0736913.6671807.075477289.37339355.980754117.1658099.85519917.37282946.15530218.45907536.74305344.24469718.53615121.51964849.66985468.20058541.49079013.41331212.53766921.447141153.0325592.9090020.083918
min165.00000055.00000018.39067521.0000002.00000012.0000001.0000003.00000016.7000000.0000000.0000000.0000000.0000000.0000000.0000000.0000002.0000002.0000000.0000000.0000000.0000002.0000002.0000000.0000000.000000
25%175.75000071.50000021.78587624.00000022.000000242.25000027.00000069.00000037.1250000.0000003.0000000.00000013.00000017.25000071.5750007.00000026.00000034.25000011.2500007.0000002.00000014.00000077.2500000.0000000.000000
50%185.00000079.00000022.87331427.00000027.500000506.00000069.000000152.50000042.05000010.50000032.00000030.55000029.00000035.50000080.00000013.00000050.00000062.50000034.00000015.0000005.00000028.000000181.0000000.0000000.000000
75%191.00000086.00000024.18071530.00000029.000000752.500000105.000000244.75000048.62500022.00000065.50000036.17500053.25000066.50000085.92500031.00000084.000000116.50000066.75000027.50000012.00000048.000000277.7500001.0000000.000000
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
0Aerial PowersDALF1837121.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
1Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
2Alex BentleyCONG1706923.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
3Alex MontgomerySANG/F1858424.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
4Alexis JonesMING1757825.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
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HeightWeightBMIAgeGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
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25%175.75000071.50000021.78587624.00000022.000000242.25000027.00000069.00000037.1250000.0000003.0000000.00000013.00000017.25000071.5750007.00000026.00000034.25000011.2500007.0000002.00000014.00000077.2500000.0000000.000000
50%185.00000079.00000022.87331427.00000027.500000506.00000069.000000152.50000042.05000010.50000032.00000030.55000029.00000035.50000080.00000013.00000050.00000062.50000034.00000015.0000005.00000028.000000181.0000000.0000000.000000
75%191.00000086.00000024.18071530.00000029.000000752.500000105.000000244.75000048.62500022.00000065.50000036.17500053.25000066.50000085.92500031.00000084.000000116.50000066.75000027.50000012.00000048.000000277.7500001.0000000.000000
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
126Sue BirdSEAG1756822.204082USOctober 16, 198036Connecticut152780610324442.25013437.3172470.8746531773135727310
1Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
67Jia PerkinsMING1737525.059307USFebruary 23, 198235Texas Tech143093217842042.44712338.211413485.124729610341118351700
105Plenette PiersonMINF/C1888824.898144USAugust 31, 198135Texas Tech15294025414238.0175133.3152075.0134962481243314000
109Rebekkah BrunsonMINF1888423.766410USNovember 12, 198135Georgetown14267199721844.5226036.7628374.746135181403194227820
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" + ], + "text/plain": [ + " Name Team Pos Height Weight BMI Birth_Place \\\n", + "126 Sue Bird SEA G 175 68 22.204082 US \n", + "1 Alana Beard LA G/F 185 73 21.329438 US \n", + "67 Jia Perkins MIN G 173 75 25.059307 US \n", + "105 Plenette Pierson MIN F/C 188 88 24.898144 US \n", + "109 Rebekkah Brunson MIN F 188 84 23.766410 US \n", + "\n", + " Birthdate Age College Experience Games Played MIN FGM \\\n", + "126 October 16, 1980 36 Connecticut 15 27 806 103 \n", + "1 May 14, 1982 35 Duke 12 30 947 90 \n", + "67 February 23, 1982 35 Texas Tech 14 30 932 178 \n", + "105 August 31, 1981 35 Texas Tech 15 29 402 54 \n", + "109 November 12, 1981 35 Georgetown 14 26 719 97 \n", + "\n", + " FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB AST STL \\\n", + "126 244 42.2 50 134 37.3 17 24 70.8 7 46 53 177 31 \n", + "1 177 50.8 5 18 27.8 32 41 78.0 19 82 101 72 63 \n", + "67 420 42.4 47 123 38.2 114 134 85.1 24 72 96 103 41 \n", + "105 142 38.0 17 51 33.3 15 20 75.0 13 49 62 48 12 \n", + "109 218 44.5 22 60 36.7 62 83 74.7 46 135 181 40 31 \n", + "\n", + " BLK TO PTS DD2 TD3 \n", + "126 3 57 273 1 0 \n", + "1 13 40 217 0 0 \n", + "67 11 83 517 0 0 \n", + "105 4 33 140 0 0 \n", + "109 9 42 278 2 0 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wnba.nlargest(5, \"Age\") # top 5 older players\n", + "\n", + "# still plays: Sue Bird\n", + "# ex-player: Alana Beard, Jia Perkins, Plenette Pierson, Rebekkah Brunson\n", + "\n", + "# As we can see from this top 5 older players, there are at least 4 ex-players, we can consider them outliers." + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
15Angel RobinsonPHOF/C1988822.446689USAugust 30, 199521Arizona State115237254456.811100.077100.01642588111165800
23Brionna JonesCONF19110428.507990USDecember 18, 199521MarylandR19112142653.8000.0161984.211142527174400
5Alexis PetersonSEAG1706321.799308USJune 20, 199522SyracuseR149093426.52922.266100.0313161150112600
19Breanna StewartSEAF/C1937720.671696USAugust 27, 199422Connecticut22995220141748.24612337.413617179.5432062497829476858480
38Courtney WilliamsCONG1736220.715694USNovember 5, 199422South Florida12975516833849.783026.7313686.13884122601563937510
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" + ], + "text/plain": [ + " Name Team Pos Height Weight BMI Birth_Place \\\n", + "15 Angel Robinson PHO F/C 198 88 22.446689 US \n", + "23 Brionna Jones CON F 191 104 28.507990 US \n", + "5 Alexis Peterson SEA G 170 63 21.799308 US \n", + "19 Breanna Stewart SEA F/C 193 77 20.671696 US \n", + "38 Courtney Williams CON G 173 62 20.715694 US \n", + "\n", + " Birthdate Age College Experience Games Played MIN FGM \\\n", + "15 August 30, 1995 21 Arizona State 1 15 237 25 \n", + "23 December 18, 1995 21 Maryland R 19 112 14 \n", + "5 June 20, 1995 22 Syracuse R 14 90 9 \n", + "19 August 27, 1994 22 Connecticut 2 29 952 201 \n", + "38 November 5, 1994 22 South Florida 1 29 755 168 \n", + "\n", + " FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB AST STL \\\n", + "15 44 56.8 1 1 100.0 7 7 100.0 16 42 58 8 1 \n", + "23 26 53.8 0 0 0.0 16 19 84.2 11 14 25 2 7 \n", + "5 34 26.5 2 9 22.2 6 6 100.0 3 13 16 11 5 \n", + "19 417 48.2 46 123 37.4 136 171 79.5 43 206 249 78 29 \n", + "38 338 49.7 8 30 26.7 31 36 86.1 38 84 122 60 15 \n", + "\n", + " BLK TO PTS DD2 TD3 \n", + "15 11 16 58 0 0 \n", + "23 1 7 44 0 0 \n", + "5 0 11 26 0 0 \n", + "19 47 68 584 8 0 \n", + "38 6 39 375 1 0 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wnba.nsmallest(5, \"Age\") # top 5 younger players" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
12Amanda Zahui B.NYC19611329.414827SEAugust 9, 199324Minnesota325133205337.72825.091275.051823745125100
36Courtney ParisDALC19311330.336385USSeptember 21, 198729Oklahoma716217325756.1000.061250.0283462568187000
41Danielle AdamsCONF/C18510831.555880USFebruary 19, 198928Texas A&M51881164337.2123040.055100.0641044474900
23Brionna JonesCONF19110428.507990USDecember 18, 199521MarylandR19112142653.8000.0161984.211142527174400
89Lynetta KizerCONC19310427.920213USApril 4, 199027Maryland5202384810048.0010.0233076.722355761171011900
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" + ], + "text/plain": [ + " Name Team Pos Height Weight BMI Birth_Place \\\n", + "12 Amanda Zahui B. NY C 196 113 29.414827 SE \n", + "36 Courtney Paris DAL C 193 113 30.336385 US \n", + "41 Danielle Adams CON F/C 185 108 31.555880 US \n", + "23 Brionna Jones CON F 191 104 28.507990 US \n", + "89 Lynetta Kizer CON C 193 104 27.920213 US \n", + "\n", + " Birthdate Age College Experience Games Played MIN FGM \\\n", + "12 August 9, 1993 24 Minnesota 3 25 133 20 \n", + "36 September 21, 1987 29 Oklahoma 7 16 217 32 \n", + "41 February 19, 1989 28 Texas A&M 5 18 81 16 \n", + "23 December 18, 1995 21 Maryland R 19 112 14 \n", + "89 April 4, 1990 27 Maryland 5 20 238 48 \n", + "\n", + " FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB AST STL \\\n", + "12 53 37.7 2 8 25.0 9 12 75.0 5 18 23 7 4 \n", + "36 57 56.1 0 0 0.0 6 12 50.0 28 34 62 5 6 \n", + "41 43 37.2 12 30 40.0 5 5 100.0 6 4 10 4 4 \n", + "23 26 53.8 0 0 0.0 16 19 84.2 11 14 25 2 7 \n", + "89 100 48.0 0 1 0.0 23 30 76.7 22 35 57 6 11 \n", + "\n", + " BLK TO PTS DD2 TD3 \n", + "12 5 12 51 0 0 \n", + "36 8 18 70 0 0 \n", + "41 4 7 49 0 0 \n", + "23 1 7 44 0 0 \n", + "89 7 10 119 0 0 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wnba.nlargest(5, \"Weight\") " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
96Moriah JeffersonSANG1685519.486961USAugust 3, 199423Connecticut1215148115552.392045.0202774.163137923324319100
42Danielle RobinsonPHOG1755718.612245USOctober 5, 198927Oklahoma7286807917844.4050.0516183.61373861063345820900
86Leilani MitchellPHOG1655821.303949USJune 15, 198532Utah9306237018238.5319233.7627582.71257691082695023300
82Kristi ToliverWASG1705920.415225USJanuary 27, 198730Maryland92984511928441.96719434.5444989.895059912084834900
141Yvonne TurnerPHOG1755919.265306USOctober 13, 198729Nebraska2303565914042.1114723.4222878.6111324301813215100
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" + ], + "text/plain": [ + " Name Team Pos Height Weight BMI Birth_Place \\\n", + "96 Moriah Jefferson SAN G 168 55 19.486961 US \n", + "42 Danielle Robinson PHO G 175 57 18.612245 US \n", + "86 Leilani Mitchell PHO G 165 58 21.303949 US \n", + "82 Kristi Toliver WAS G 170 59 20.415225 US \n", + "141 Yvonne Turner PHO G 175 59 19.265306 US \n", + "\n", + " Birthdate Age College Experience Games Played MIN FGM \\\n", + "96 August 3, 1994 23 Connecticut 1 21 514 81 \n", + "42 October 5, 1989 27 Oklahoma 7 28 680 79 \n", + "86 June 15, 1985 32 Utah 9 30 623 70 \n", + "82 January 27, 1987 30 Maryland 9 29 845 119 \n", + "141 October 13, 1987 29 Nebraska 2 30 356 59 \n", + "\n", + " FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB AST STL \\\n", + "96 155 52.3 9 20 45.0 20 27 74.1 6 31 37 92 33 \n", + "42 178 44.4 0 5 0.0 51 61 83.6 13 73 86 106 33 \n", + "86 182 38.5 31 92 33.7 62 75 82.7 12 57 69 108 26 \n", + "82 284 41.9 67 194 34.5 44 49 89.8 9 50 59 91 20 \n", + "141 140 42.1 11 47 23.4 22 28 78.6 11 13 24 30 18 \n", + "\n", + " BLK TO PTS DD2 TD3 \n", + "96 2 43 191 0 0 \n", + "42 4 58 209 0 0 \n", + "86 9 50 233 0 0 \n", + "82 8 48 349 0 0 \n", + "141 1 32 151 0 0 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba.nsmallest(5, \"Weight\") " ] }, { @@ -89,11 +1808,37 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "plot_options, ((plot_1, plot_2), (plot_3, plot_4)) = plt.subplots(nrows = 2, ncols = 2)\n", + "\n", + "plot_1.hist(wnba[\"Height\"], color = \"red\")\n", + "plot_1.set_xlabel(\"Height\")\n", + "\n", + "plot_2.hist(wnba[\"Weight\"], color = \"green\")\n", + "plot_2.set_xlabel(\"Weight\")\n", + "\n", + "plot_3.hist(wnba[\"Age\"], color = \"purple\")\n", + "plot_3.set_xlabel(\"Age\")\n", + "\n", + "plot_4.hist(wnba[\"BMI\"], color = \"pink\")\n", + "plot_4.set_xlabel(\"BMI\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { @@ -109,7 +1854,15 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "\"\"\"\n", + "1. The height and weight distributions seem to have different shapes, \n", + "suggesting that there might not be a strong linear relationship between height and weight among WNBA players.\n", + "\n", + "2. There could be some outliers in the weight and BMI distributions, possibly representing players with extreme values.\n", + "\n", + "3. The age distribution indicates a fairly even distribution of players across different age groups, \n", + "but further analysis might be needed to explore age-related trends or patterns.\n", + "\"\"\"" ] }, { @@ -134,11 +1887,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "fig, ((plot_1, plot_2, plot_3), (plot_4, plot_5, _)) = plt.subplots(nrows = 2, ncols = 3, figsize = (15, 8))\n", + "\n", + "plot_1.hist(wnba[\"REB\"], color = \"brown\")\n", + "plot_1.set_xlabel(\"REB\")\n", + "\n", + "plot_2.hist(wnba[\"AST\"], color = \"grey\")\n", + "plot_2.set_xlabel(\"AST\")\n", + "\n", + "plot_3.hist(wnba[\"STL\"], color = \"orange\")\n", + "plot_3.set_xlabel(\"STL\")\n", + "\n", + "plot_4.hist(wnba[\"PTS\"], color = \"blue\")\n", + "plot_4.set_xlabel(\"PTS\")\n", + "\n", + "plot_5.hist(wnba[\"BLK\"], color = \"yellow\")\n", + "plot_5.set_xlabel(\"BLK\")\n", + "\n", + "# Hide the last subplot\n", + "_.axis(\"off\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { @@ -154,7 +1939,10 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "\"\"\"\n", + "The distributions seem skewed, that might suggest certain trends or characteristics of players in that category. \n", + "For example, a right-skewed distribution for points might indicate that most players score a relatively low number of points.\n", + "\"\"\"" ] }, { @@ -173,11 +1961,43 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "fig, ((plot_1, plot_2, plot_3), (plot_4, plot_5, _)) = plt.subplots(nrows = 2, ncols = 3, figsize = (15, 8))\n", + "\n", + "plot_1.hist(wnba[\"REB\"] / wnba[\"MIN\"], color = \"brown\")\n", + "plot_1.set_xlabel(\"REB\")\n", + "\n", + "plot_2.hist(wnba[\"AST\"] / wnba[\"MIN\"], color = \"grey\")\n", + "plot_2.set_xlabel(\"AST\")\n", + "\n", + "plot_3.hist(wnba[\"STL\"] / wnba[\"MIN\"], color = \"orange\")\n", + "plot_3.set_xlabel(\"STL\")\n", + "\n", + "plot_4.hist(wnba[\"PTS\"] / wnba[\"MIN\"], color = \"blue\")\n", + "plot_4.set_xlabel(\"PTS\")\n", + "\n", + "plot_5.hist(wnba[\"BLK\"] / wnba[\"MIN\"], color = \"yellow\")\n", + "plot_5.set_xlabel(\"BLK\")\n", + "\n", + "# Hide the last subplot\n", + "_.axis(\"off\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { @@ -193,7 +2013,7 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "\"\"\"You can observe the distribution of normalized statistics for each category.\"\"\"" ] }, { @@ -222,13 +2042,17 @@ "metadata": {}, "outputs": [], "source": [ - "#your comments here" + "\"\"\"\n", + "In summary, while your exploratory analysis provides valuable insights about the WNBA dataset, \n", + "some of the specific questions discussed require additional data or context to draw definitive conclusions. \n", + "To address these discussions, you may need to gather more relevant data or information from reliable sources.\n", + "\"\"\"" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -242,7 +2066,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.10.9" } }, "nbformat": 4, diff --git a/your-code/3.-Inferential-Analysis.ipynb b/your-code/3.-Inferential-Analysis.ipynb index 366765b..7628aec 100644 --- a/your-code/3.-Inferential-Analysis.ipynb +++ b/your-code/3.-Inferential-Analysis.ipynb @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -29,7 +29,7 @@ "import math\n", "import pandas as pd\n", "import numpy as np\n", - "from scipy import stats\n", + "import scipy.stats as st\n", "import matplotlib.pyplot as plt\n", "from scipy.stats import ttest_1samp\n", "pd.set_option('display.max_columns', 50)" @@ -46,11 +46,204 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#your code here" + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
0Aerial PowersDALF1837121.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
1Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
2Alex BentleyCONG1706923.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
\n", + "
" + ], + "text/plain": [ + " Name Team Pos Height Weight BMI Birth_Place \\\n", + "0 Aerial Powers DAL F 183 71 21.200991 US \n", + "1 Alana Beard LA G/F 185 73 21.329438 US \n", + "2 Alex Bentley CON G 170 69 23.875433 US \n", + "\n", + " Birthdate Age College Experience Games Played MIN FGM \\\n", + "0 January 17, 1994 23 Michigan State 2 8 173 30 \n", + "1 May 14, 1982 35 Duke 12 30 947 90 \n", + "2 October 27, 1990 26 Penn State 4 26 617 82 \n", + "\n", + " FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB AST STL BLK \\\n", + "0 85 35.3 12 32 37.5 21 26 80.8 6 22 28 12 3 6 \n", + "1 177 50.8 5 18 27.8 32 41 78.0 19 82 101 72 63 13 \n", + "2 218 37.6 19 64 29.7 35 42 83.3 4 36 40 78 22 3 \n", + "\n", + " TO PTS DD2 TD3 \n", + "0 12 93 0 0 \n", + "1 40 217 0 0 \n", + "2 24 218 0 0 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wnba = pd.read_csv(\"wnba_clean.csv\")\n", + "wnba.head(3)" ] }, { @@ -74,7 +267,20 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here" + "\"\"\"\n", + "In order to infer the average weight of professional wnba players, \n", + "we need to consider the requirements and assumptions for using the sample to make an inference. \n", + "Here are some important considerations:\n", + "\n", + "Random Sample: The sample of players from the wnba dataset should ideally be a random sample of all professional \n", + "female basketball players. If the sample is not representative, the inference might not be accurate.\n", + "\n", + "Sample Size: The sample size should be large enough to satisfy the assumptions of the Central Limit Theorem. \n", + "A larger sample size tends to result in a more normal distribution of sample means.\n", + "\n", + "Independence: The weights of the players in the sample should be independent of each other. \n", + "This assumption is usually satisfied if the sample is drawn randomly and without replacement.\n", + "\"\"\"" ] }, { @@ -86,11 +292,26 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "95% Confidence Interval for Average Weight: (77.17, 80.79)\n" + ] + } + ], + "source": [ + "mean = wnba[\"Weight\"].mean()\n", + "std = wnba[\"Weight\"].std(ddof = 1)\n", + "n = len(wnba[\"Weight\"])\n", + "cl = 0.95\n", + "\n", + "ci = st.norm.interval(confidence = cl, loc = mean, scale = std / np.sqrt(n))\n", + "\n", + "print(f\"95% Confidence Interval for Average Weight: ({ci[0]:.2f}, {ci[1]:.2f})\")" ] }, { @@ -106,7 +327,10 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "\"\"\"\n", + "It means that we are 95% confident that the true average weight of professional female basketball \n", + "players lies between 77.17 kg and 80.79 kg.\n", + "\"\"\"" ] }, { @@ -122,7 +346,11 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "\"\"\"\n", + "It appears that the average weight of professional female basketball players is between 77.1 and 80.7 kg.\n", + "Since your sister's weight of 67 kg falls below this range,\n", + "it is possible that her weight could be a disadvantage in playing professionally.\n", + "\"\"\"" ] }, { @@ -154,11 +382,23 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 22, "metadata": {}, - "outputs": [], - "source": [ - "# your answer here" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Proportion of players who miss more than 40% of their free throws: 0.105\n" + ] + } + ], + "source": [ + "missed_ft = wnba[wnba[\"FT%\"] < 60]\n", + "\n", + "prop_missed_ft = len(missed_ft) / len(wnba)\n", + "\n", + "print(f\"Proportion of players who miss more than 40% of their free throws: {prop_missed_ft:.3f}\")" ] }, { @@ -170,11 +410,29 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Proportion of players who miss more than 40% of their free throws: 0.004857958873411759, 0.06507211105665818\n" + ] + } + ], + "source": [ + "sample_size = len(wnba)\n", + "sample_proportion = players_missed / sample_size\n", + "\n", + "z_value = st.norm.ppf(1 - (1 - 0.95)/ 2)\n", + "\n", + "margin_of_error = z_value * np.sqrt((sample_proportion * (1 - sample_proportion))/n)\n", + "\n", + "lower_bound = sample_proportion - margin_of_error\n", + "upper_bound = sample_proportion + margin_of_error\n", + "\n", + "print(f\"Proportion of players who miss more than 40% of their free throws: {lower_bound}, {upper_bound}\")" ] }, { @@ -190,14 +448,19 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "\"\"\"\n", + "Based on the calculated confidence interval, you can inform your sister that the estimated proportion \n", + "of players who miss more than 40% of their free throws is relatively low, and it's unlikely that the majority of \n", + "players fall into this category. However, you should also emphasize the uncertainty inherent in any statistical \n", + "estimate and the need to interpret the results cautiously.\n", + "\"\"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Bonus: Can you plot the probability distribution of the proportion of missed free throws, indicating where the critical region is?" + "### Bonus: Can you plot the probability distribution of the proportion of missed free throws, indicating where the critical region is?" ] }, { @@ -225,15 +488,6 @@ "**How would you do it? Try and think about the requirements that your sample must satisfy in order to do that. Do you feel it actually fulfills those requirements? Do you need to make any assumptions?**" ] }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "#your-answer-here" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -243,20 +497,38 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "#your code here" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "#your-answer-here" + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We CAN reject the null hypothesis\n" + ] + } + ], + "source": [ + "# 1. Set the hypothesis\n", + "# H0: mu = 52\n", + "# H1: mu != 52\n", + "mu = 52\n", + "\n", + "# 2. Significance level\n", + "alpha = 0.05\n", + "\n", + "# 3. Sample\n", + "n = len(wnba)\n", + "sample = wnba[\"AST\"]\n", + "\n", + "# 4. Compute the statistic / 5. Get p-value\n", + "t_test_result = st.ttest_1samp(sample, mu)\n", + "\n", + "# 6. Decide\n", + "if t_test_result.pvalue < alpha: \n", + " print(\"We CAN reject the null hypothesis\")\n", + "else:\n", + " print(\"We CAN NOT reject the null hypothesis\") " ] }, { @@ -268,11 +540,38 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "#your-answer-here" + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We CAN NOT reject the null hypothesis\n" + ] + } + ], + "source": [ + "# 1. Set the hypothesis\n", + "# H0: mu <= 52\n", + "# H1: mu > 52\n", + "mu = 52\n", + "\n", + "# 2. Significance level\n", + "alpha = 0.05\n", + "\n", + "# 3. Sample\n", + "n = len(wnba)\n", + "sample = wnba[\"AST\"]\n", + "\n", + "# 4. Compute the statistic / 5. Get p-value\n", + "t_test_result = st.ttest_1samp(sample, mu, alternative=\"greater\")\n", + "\n", + "# 6. Decide\n", + "if t_test_result.pvalue < alpha: \n", + " print(\"We CAN reject the null hypothesis\")\n", + "else:\n", + " print(\"We CAN NOT reject the null hypothesis\") " ] }, { @@ -343,7 +642,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -357,7 +656,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.10.9" } }, "nbformat": 4, diff --git a/data/codebook.md b/your-code/codebook.md similarity index 100% rename from data/codebook.md rename to your-code/codebook.md diff --git a/data/wnba.csv b/your-code/wnba.csv similarity index 99% rename from data/wnba.csv rename to your-code/wnba.csv index 0e35127..bb13374 100644 --- a/data/wnba.csv +++ b/your-code/wnba.csv @@ -1,144 +1,144 @@ -Name,Team,Pos,Height,Weight,BMI,Birth_Place,Birthdate,Age,College,Experience,Games Played,MIN,FGM,FGA,FG%,3PM,3PA,3P%,FTM,FTA,FT%,OREB,DREB,REB,AST,STL,BLK,TO,PTS,DD2,TD3 -Aerial Powers,DAL,F,183,71,21.20099137,US,"January 17, 1994",23,Michigan State,2,8,173,30,85,35.3,12,32,37.5,21,26,80.8,6,22,28,12,3,6,12,93,0,0 -Alana Beard,LA,G/F,185,73,21.32943755,US,"May 14, 1982",35,Duke,12,30,947,90,177,50.8,5,18,27.8,32,41,78.0,19,82,101,72,63,13,40,217,0,0 -Alex Bentley,CON,G,170,69,23.87543253,US,"October 27, 1990",26,Penn State,4,26,617,82,218,37.6,19,64,29.7,35,42,83.3,4,36,40,78,22,3,24,218,0,0 -Alex Montgomery,SAN,G/F,185,84,24.54346238,US,"December 11, 1988",28,Georgia Tech,6,31,721,75,195,38.5,21,68,30.9,17,21,81.0,35,134,169,65,20,10,38,188,2,0 -Alexis Jones,MIN,G,175,78,25.46938776,US,"August 5, 1994",23,Baylor,R,24,137,16,50,32.0,7,20,35.0,11,12,91.7,3,9,12,12,7,0,14,50,0,0 -Alexis Peterson,SEA,G,170,63,21.79930796,US,"June 20, 1995",22,Syracuse,R,14,90,9,34,26.5,2,9,22.2,6,6,100,3,13,16,11,5,0,11,26,0,0 -Alexis Prince,PHO,G,188,81,22.91760978,US,"February 5, 1994",23,Baylor,R,16,112,9,34,26.5,4,15,26.7,2,2,100,1,14,15,5,4,3,3,24,0,0 -Allie Quigley,CHI,G,178,64,20.19946976,US,"June 20, 1986",31,DePaul,8,26,847,166,319,52.0,70,150,46.7,40,46,87.0,9,83,92,95,20,13,59,442,0,0 -Allisha Gray,DAL,G,185,76,22.20598977,US,"October 20, 1992",24,South Carolina,2,30,834,131,346,37.9,29,103,28.2,104,129,80.6,52,75,127,40,47,19,37,395,0,0 -Allison Hightower,WAS,G,178,77,24.30248706,US,"June 4, 1988",29,LSU,5,7,103,14,38,36.8,2,11,18.2,6,6,100,3,7,10,10,5,0,2,36,0,0 -Alysha Clark,SEA,F,180,76,23.45679012,US,"July 7, 1987",30,Middle Tennessee,6,30,843,93,183,50.8,20,62,32.3,38,51,74.5,29,97,126,50,22,4,32,244,0,0 -Alyssa Thomas,CON,F,188,84,23.76641014,US,"December 4, 1992",24,Maryland,3,28,833,154,303,50.8,0,3,0.0,91,158,57.6,34,158,192,136,48,11,87,399,4,0 -Amanda Zahui B.,NY,C,196,113,29.41482716,SE,"August 9, 1993",24,Minnesota,3,25,133,20,53,37.7,2,8,25.0,9,12,75.0,5,18,23,7,4,5,12,51,0,0 -Amber Harris,CHI,F,196,88,22.90712203,US,"January 16, 1988",29,Xavier,3,22,146,18,44,40.9,0,10,0.0,5,8,62.5,12,28,40,5,3,9,6,41,0,0 -Aneika Henry,ATL,F/C,193,87,23.35633171,JM,"February 13, 1986",31,Florida,6,4,22,4,4,100,0,0,0.0,0,0,0.0,0,4,4,1,2,0,3,8,0,0 -Angel Robinson,PHO,F/C,198,88,22.44668911,US,"August 30, 1995",21,Arizona State,1,15,237,25,44,56.8,1,1,100,7,7,100,16,42,58,8,1,11,16,58,0,0 -Asia Taylor,WAS,F,185,76,22.20598977,US,"August 22, 1991",26,Louisville,3,20,128,10,31,32.3,0,0,0.0,11,18,61.1,16,21,37,9,5,2,10,31,0,0 -Bashaara Graves,CHI,F,188,91,25.74694432,US,"March 17, 1994",23,Tennessee,1,5,59,8,14,57.1,0,0,0.0,3,4,75.0,4,13,17,3,0,1,3,19,0,0 -Breanna Lewis,DAL,C,196,93,24.20866306,US,"June 22, 1994",23,Kansas State,R,12,50,2,12,16.7,0,0,0.0,3,4,75.0,2,7,9,2,0,0,7,7,0,0 -Breanna Stewart,SEA,F/C,193,77,20.67169588,US,"August 27, 1994",22,Connecticut,2,29,952,201,417,48.2,46,123,37.4,136,171,79.5,43,206,249,78,29,47,68,584,8,0 -Bria Hartley,NY,G,173,66,22.05219018,US,"September 30, 1992",24,Connecticut,4,29,598,80,192,41.7,32,93,34.4,25,33,75.8,7,50,57,58,15,5,44,217,0,0 -Bria Holmes,ATL,G,185,77,22.49817385,US,"April 19, 1994",23,West Virginia,R,28,655,85,231,36.8,9,50,18.0,56,84,66.7,29,56,85,52,23,7,31,235,0,0 -Briann January,IND,G,173,65,21.71806609,US,"November 1, 1987",29,Arizona State,9,25,657,81,205,39.5,18,57,31.6,58,71,81.7,12,25,37,98,23,4,53,238,0,0 -Brionna Jones,CON,F,191,104,28.50799046,US,"December 18, 1995",21,Maryland,R,19,112,14,26,53.8,0,0,0.0,16,19,84.2,11,14,25,2,7,1,7,44,0,0 -Brittany Boyd,NY,G,175,71,23.18367347,US,"November 6, 1993",23,UC Berkeley,3,2,32,9,15,60.0,0,1,0.0,8,11,72.7,3,5,8,5,3,0,2,26,0,0 -Brittney Griner,PHO,C,206,93,21.91535489,US,"October 18, 1990",26,Baylor,5,22,682,167,293,57.0,0,0,0.0,127,154,82.5,43,129,172,39,13,54,52,461,6,0 -Brittney Sykes,ATL,G,175,66,21.55102041,US,"July 2, 1994",23,Rutgers,10,30,734,146,362,40.3,29,87,33.3,76,102,74.5,25,94,119,59,18,17,49,397,1,0 -Camille Little,PHO,F,188,82,23.20054323,US,"January 18, 1985",32,North Carolina,11,30,759,93,219,42.5,9,52,17.3,33,52,63.5,42,71,113,42,28,13,50,228,0,0 -Candace Parker,LA,F/C,193,79,21.20862305,US,"April 19, 1986",31,Tennessee,10,29,889,183,383,47.8,40,114,35.1,88,115,76.5,37,205,242,127,43,53,80,494,10,1 -Candice Dupree,IND,F,188,81,22.91760978,US,"February 25, 1984",33,Temple,12,29,911,189,370,51.1,0,2,0.0,57,65,87.7,31,124,155,47,28,12,42,435,2,0 -Cappie Pondexter,CHI,G,175,73,23.83673469,US,"July 1, 1983",34,Rutgers,11,24,676,94,258,36.4,8,32,25.0,54,67,80.6,10,59,69,104,17,5,56,250,2,0 -Carolyn Swords,SEA,C,198,95,24.2322212,US,"July 19, 1989",28,Boston College,6,26,218,19,39,48.7,0,0,0.0,16,20,80.0,10,29,39,9,5,4,22,54,0,0 -Cayla George,PHO,C,193,87,23.35633171,AU,"April 20, 1987",30,Georgia,1,28,365,40,105,38.1,13,45,28.9,7,12,58.3,10,71,81,15,9,11,13,100,1,0 -Chelsea Gray,LA,G,180,77,23.7654321,US,"August 10, 1992",25,Duke,3,30,996,165,326,50.6,48,100,48.0,78,94,83.0,19,80,99,132,29,7,61,456,1,0 -Cheyenne Parker,CHI,F,193,86,23.08786813,US,"August 22, 1992",25,Middle Tennessee,2,23,286,32,69,46.4,0,3,0.0,23,36,63.9,31,47,78,13,8,15,21,87,0,0 -Clarissa dos Santos,SAN,C,185,89,26.00438276,BR,"October 3, 1988",28,Brazil,4,7,52,8,14,57.1,1,1,100,0,0,0.0,3,7,10,7,1,1,5,17,0,0 -Courtney Paris,DAL,C,193,113,30.33638487,US,"September 21, 1987",29,Oklahoma,7,16,217,32,57,56.1,0,0,0.0,6,12,50.0,28,34,62,5,6,8,18,70,0,0 -Courtney Vandersloot,CHI,G,173,66,22.05219018,US,"August 2, 1989",28,Gonzaga,6,22,673,104,199,52.3,23,60,38.3,24,29,82.8,13,75,88,175,22,5,64,255,10,0 -Courtney Williams,CON,G,173,62,20.71569381,US,"November 5, 1994",22,South Florida,1,29,755,168,338,49.7,8,30,26.7,31,36,86.1,38,84,122,60,15,6,39,375,1,0 -Crystal Langhorne,SEA,F/C,188,84,23.76641014,US,"October 27, 1986",30,Maryland,10,30,848,160,240,66.7,1,2,50.0,49,68,72.1,35,140,175,46,16,11,50,370,2,0 -Damiris Dantas,ATL,C,191,89,24.39626107,BR,"November 17, 1992",24,Brazil,4,30,569,98,243,40.3,25,91,27.5,33,43,76.7,29,84,113,19,17,18,26,254,0,0 -Danielle Adams,CON,F/C,185,108,31.5558802,US,"February 19, 1989",28,Texas A&M,5,18,81,16,43,37.2,12,30,40.0,5,5,100,6,4,10,4,4,4,7,49,0,0 -Danielle Robinson,PHO,G,175,57,18.6122449,US,"October 5, 1989",27,Oklahoma,7,28,680,79,178,44.4,0,5,0.0,51,61,83.6,13,73,86,106,33,4,58,209,0,0 -Dearica Hamby,SAN,F,191,86,23.57391519,US,"June 11, 1993",24,Wake Forest,2,31,650,96,207,46.4,3,8,37.5,58,95,61.1,48,91,139,32,29,8,43,253,1,0 -Devereaux Peters,IND,F,188,79,22.35174287,US,"August 10, 1989",28,Notre Dame,6,28,796,154,380,40.5,88,225,39.1,118,130,90.8,8,69,77,76,16,9,56,514,0,0 -Diana Taurasi,PHO,G,183,74,22.09680791,US,"November 6, 1982",34,Connecticut,13,20,591,121,255,47.5,22,66,33.3,112,118,94.9,31,98,129,32,20,31,28,376,3,0 -Elena Delle Donne,WAS,G/F,196,85,22.12619742,US,"May 9, 1989",28,Delaware,5,30,939,133,272,48.9,0,1,0.0,51,78,65.4,99,116,215,43,32,64,36,317,4,0 -Elizabeth Williams,ATL,F/C,191,87,23.84803048,US,"June 23, 1993",24,Duke,3,30,377,48,96,50.0,0,1,0.0,32,55,58.2,35,61,96,5,5,4,21,128,0,0 -Emma Cannon,PHO,F,188,86,24.33227705,US,"January 6, 1989",28,Central Florida,R,18,508,105,220,47.7,11,33,33.3,31,34,91.2,33,72,105,52,21,27,30,252,1,0 -Emma Meesseman,WAS,C,193,83,22.28247738,BE,"May 13, 1993",24,Belgium,5,23,617,89,233,38.2,25,79,31.6,56,65,86.2,23,58,81,70,34,5,30,259,0,0 -Epiphanny Prince,NY,G,175,81,26.44897959,US,"November 1, 1988",28,Rutgers,8,26,282,36,86,41.9,1,3,33.3,15,22,68.2,17,44,61,5,4,8,17,88,0,0 -Erica Wheeler,IND,G,170,65,22.49134948,US,"February 5, 1991",26,Rutgers,3,30,767,130,321,40.5,42,129,32.6,34,40,85.0,11,57,68,117,38,1,68,336,0,0 -Érika de Souza,SAN,C,196,86,22.38650562,BR,"September 3, 1982",34,Brazil,13,30,579,65,112,58.0,0,0,0.0,29,32,90.6,58,74,132,35,18,7,37,159,0,0 -Erlana Larkins,IND,F,185,93,27.17311907,US,"February 4, 1986",31,North Carolina,9,20,386,36,92,39.1,9,35,25.7,21,24,87.5,9,26,35,24,11,8,13,102,0,0 -Essence Carson,LA,G/F,183,74,22.09680791,US,"July 28, 1986",31,Rutgers,10,15,61,4,16,25.0,0,0,0.0,5,6,83.3,7,2,9,0,1,3,5,13,0,0 -Evelyn Akhator,DAL,F,191,82,22.47745402,NG,"March 2, 1995",22,Kentucky,R,30,926,165,365,45.2,20,60,33.3,92,117,78.6,73,199,272,50,37,13,67,442,13,0 -Glory Johnson,DAL,F,191,77,21.10687755,US,"July 27, 1990",27,Tennessee,4,4,42,3,9,33.3,3,6,50.0,0,0,0.0,0,3,3,1,0,0,4,9,0,0 -Imani Boyette,ATL,C,201,88,21.78163907,US,"November 10, 1992",24,Texas,1,29,410,56,119,47.1,1,3,33.3,14,20,70.0,43,75,118,14,9,23,22,127,1,0 -Isabelle Harrison,SAN,C,191,83,22.75156931,US,"September 27, 1993",23,Kentucky,3,31,832,154,300,51.3,1,2,50.0,55,85,64.7,66,134,200,46,26,24,63,364,5,0 -Ivory Latta,WAS,G,168,63,22.32142857,US,"September 25, 1984",32,North Carolina,12,29,499,79,218,36.2,40,114,35.1,47,55,85.5,7,20,27,49,12,1,22,245,0,0 -Jantel Lavender,LA,C,193,84,22.55094096,US,"December 11, 1988",28,Ohio State,7,28,481,89,184,48.4,4,13,30.8,18,22,81.8,31,56,87,28,8,5,35,200,0,0 -Jasmine Thomas,CON,G,175,66,21.55102041,US,"September 30, 1989",27,Duke,6,27,762,151,341,44.3,50,116,43.1,39,55,70.9,9,55,64,118,45,4,58,391,1,0 -Jazmon Gwathmey,IND,G,188,65,18.39067451,PR,"January 24, 1993",24,James Madison,2,24,371,50,140,35.7,12,49,24.5,30,39,76.9,15,34,49,17,13,19,32,142,0,0 -Jeanette Pohlen,IND,G,183,78,23.29122996,US,"February 5, 1989",28,Stanford,6,25,278,20,52,38.5,13,29,44.8,17,20,85.0,3,19,22,13,5,0,15,70,0,0 -Jennifer Hamson,IND,C,201,95,23.51426945,US,"January 23, 1992",25,Brigham Young,1,10,50,2,12,16.7,0,3,0.0,8,10,80.0,5,6,11,6,2,2,3,12,0,0 -Jessica Breland,CHI,F,191,77,21.10687755,US,"February 23, 1988",29,North Carolina,5,10,78,9,16,56.3,0,0,0.0,4,5,80.0,5,13,18,2,1,9,3,22,0,0 -Jewell Loyd,SEA,G,178,67,21.14631991,US,"May 10, 1993",24,Notre Dame,3,29,715,116,245,47.3,8,21,38.1,28,37,75.7,50,139,189,46,18,50,57,268,4,0 -Jia Perkins,MIN,G,173,75,25.05930703,US,"February 23, 1982",35,Texas Tech,14,30,932,178,420,42.4,47,123,38.2,114,134,85.1,24,72,96,103,41,11,83,517,0,0 -Jonquel Jones,CON,F/C,198,86,21.93653709,BS,"May 1, 1994",23,George Washington,1,29,463,47,124,37.9,11,32,34.4,11,15,73.3,11,46,57,39,30,1,24,116,0,0 -Jordan Hooper,CHI,F,188,84,23.76641014,US,"February 20, 1992",25,Nebraska,3,29,833,164,299,54.8,22,49,44.9,117,142,82.4,108,226,334,40,29,46,46,467,17,0 -Kaela Davis,DAL,G,188,77,21.78587596,US,"March 15, 1995",22,South Carolina,R,23,208,27,75,36.0,20,55,36.4,3,4,75.0,2,20,22,5,7,1,6,77,0,0 -Kahleah Copper,CHI,G/F,185,70,20.45288532,US,"August 28, 1994",22,Rutgers,1,29,475,62,163,38.0,12,32,37.5,49,65,75.4,10,33,43,32,13,3,48,185,0,0 -Kaleena Mosqueda-Lewis,SEA,F,180,82,25.30864198,US,"March 11, 1993",24,Connecticut,3,29,369,60,140,42.9,5,23,21.7,36,45,80.0,11,43,54,11,9,2,22,161,0,0 -Karima Christmas-Kelly,DAL,G/F,183,82,24.48565201,US,"November 9, 1989",27,Duke,6,14,142,23,43,53.5,9,21,42.9,10,10,100,4,10,14,6,1,1,13,65,0,0 -Kayla Alexander,SAN,C,193,88,23.6247953,CA,"May 1, 1991",26,Arizona State,4,30,889,91,239,38.1,25,83,30.1,111,129,86.0,45,75,120,65,39,5,50,318,0,0 -Kayla McBride,SAN,G/F,180,79,24.38271605,US,"June 25, 1992",25,Notre Dame,3,31,433,78,141,55.3,0,0,0.0,15,16,93.8,40,47,87,17,13,15,30,171,0,0 -Kayla Pedersen,CON,F,193,86,23.08786813,US,"April 14, 1989",28,Stanford,5,27,882,128,337,38.0,47,147,32.0,108,118,91.5,12,93,105,59,32,5,54,411,0,0 -Kayla Thornton,DAL,F,185,86,25.12783053,US,"October 20, 1992",24,Texas–El Paso,2,21,224,11,30,36.7,0,1,0.0,10,14,71.4,19,26,45,13,6,2,9,32,0,0 -Keisha Hampton,CHI,F,185,78,22.79035793,US,"February 22, 1990",27,DePaul,1,30,504,64,157,40.8,14,52,26.9,65,81,80.2,36,59,95,24,20,7,21,207,0,0 -Kelsey Plum,SAN,G,173,66,22.05219018,US,"August 24, 1994",23,Washington,R,28,610,73,210,34.8,29,78,37.2,50,58,86.2,11,42,53,91,13,4,72,225,0,0 -Kia Vaughn,NY,C,193,90,24.16172246,US,"January 24, 1987",30,Rutgers,9,23,455,62,116,53.4,0,0,0.0,10,19,52.6,39,71,110,16,8,9,21,134,1,0 -Kiah Stokes,NY,C,191,87,23.84803048,US,"March 30, 1993",24,Connecticut,3,29,576,50,98,51.0,0,1,0.0,41,52,78.8,63,122,185,21,8,32,33,141,3,0 -Kristi Toliver,WAS,G,170,59,20.41522491,US,"January 27, 1987",30,Maryland,9,29,845,119,284,41.9,67,194,34.5,44,49,89.8,9,50,59,91,20,8,48,349,0,0 -Krystal Thomas,WAS,C,196,88,22.90712203,US,"October 6, 1989",27,Duke,6,29,737,81,149,54.4,0,0,0.0,37,61,60.7,97,172,269,30,15,31,45,199,2,0 -Lanay Montgomery,SEA,C,196,96,24.98958767,US,"September 17, 1993",23,West Virginia,R,7,28,3,7,42.9,0,0,0.0,0,0,0.0,0,5,5,0,1,4,2,6,0,0 -Layshia Clarendon,ATL,G,175,64,20.89795918,US,"February 5, 1991",26,UC Berkeley,5,30,900,124,320,38.8,8,53,15.1,73,81,90.1,27,88,115,206,29,1,82,329,3,0 -Leilani Mitchell,PHO,G,165,58,21.30394858,US,"June 15, 1985",32,Utah,9,30,623,70,182,38.5,31,92,33.7,62,75,82.7,12,57,69,108,26,9,50,233,0,0 -Lindsay Allen,NY,G,173,65,21.71806609,US,"March 20, 1995",22,Notre Dame,R,23,314,21,50,42.0,0,11,0.0,6,9,66.7,8,28,36,47,13,1,18,48,0,0 -Lindsay Whalen,MIN,G,175,78,25.46938776,US,"September 5, 1982",34,Minnesota,14,22,520,69,153,45.1,12,34,35.3,27,36,75.0,8,46,54,90,11,2,44,177,0,0 -Lynetta Kizer,CON,C,193,104,27.92021262,US,"April 4, 1990",27,Maryland,5,20,238,48,100,48.0,0,1,0.0,23,30,76.7,22,35,57,6,11,7,10,119,0,0 -Maimouna Diarra,LA,C,198,90,22.95684114,SN,"January 30, 1991",26,Sengal,R,9,16,1,3,33.3,0,0,0.0,1,2,50.0,3,4,7,1,1,0,3,3,0,0 -Makayla Epps,CHI,G,178,,,US,"June 6, 1995",22,Kentucky,R,14,52,2,14,14.3,0,5,0.0,2,5,40.0,2,0,2,4,1,0,4,6,0,0 -Marissa Coleman,IND,G/F,185,73,21.32943755,US,"April 1, 1987",30,Maryland,9,30,539,50,152,32.9,27,79,34.2,27,33,81.8,7,53,60,25,8,4,34,154,0,0 -Matee Ajavon,ATL,G,173,73,24.39105884,US,"July 5, 1986",31,Syracruse,R,27,218,22,69,31.9,0,3,0.0,29,35,82.9,8,26,34,27,10,0,26,73,0,0 -Maya Moore,MIN,F,183,80,23.88844098,US,"November 6, 1989",27,Connecticut,7,29,904,170,398,42.7,52,132,39.4,98,114,86.0,50,106,156,99,53,13,56,490,3,0 -Monique Currie,PHO,G/F,183,80,23.88844098,US,"February 25, 1983",34,Duke,11,32,717,121,284,42.6,37,93,39.8,85,103,82.5,19,103,122,67,22,11,48,364,0,0 -Morgan Tuck,CON,F,188,91,25.74694432,US,"April 30, 1994",23,Connecticut,1,17,294,35,101,34.7,8,28,28.6,13,16,81.3,9,34,43,19,7,0,15,91,1,0 -Moriah Jefferson,SAN,G,168,55,19.48696145,US,"August 3, 1994",23,Connecticut,1,21,514,81,155,52.3,9,20,45.0,20,27,74.1,6,31,37,92,33,2,43,191,0,0 -Natalie Achonwa,IND,C,193,83,22.28247738,CA,"November 22, 1992",24,Notre Dame,3,30,529,82,151,54.3,0,0,0.0,43,55,78.2,31,70,101,21,11,16,25,207,0,0 -Natasha Cloud,WAS,G,183,73,21.79820239,US,"February 22, 1992",25,Saint Joseph's,3,24,448,37,118,31.4,12,51,23.5,20,27,74.1,7,52,59,69,17,3,23,106,0,0 -Natasha Howard,MIN,F,188,75,21.22000905,US,"February 9, 1991",26,Florida State,4,29,315,48,104,46.2,3,13,23.1,17,23,73.9,25,38,63,16,11,19,20,116,0,0 -Nayo Raincock-Ekunwe,NY,F/C,188,79,22.35174287,CA,"August 29, 1991",25,Simon Fraser,R,27,243,33,63,52.4,0,4,0.0,30,49,61.2,24,22,46,8,2,1,13,96,0,0 -Nia Coffey,SAN,F,185,77,22.49817385,US,"May 21, 1995",22,Northwestern,R,25,203,16,59,27.1,0,4,0.0,16,22,72.7,16,30,46,6,5,6,14,48,0,0 -Nneka Ogwumike,LA,F,188,79,22.35174287,US,"February 7, 1990",27,Stanford,6,30,948,215,386,55.7,18,49,36.7,129,148,87.2,57,179,236,63,53,14,47,577,9,0 -Noelle Quinn,SEA,G,183,81,24.18704649,US,"March 1, 1985",32,UCLA,11,29,459,24,58,41.4,14,35,40.0,17,18,94.4,1,48,49,78,12,5,27,79,0,0 -Odyssey Sims,LA,G,173,73,24.39105884,US,"July 13, 1992",25,Baylor,4,27,626,86,198,43.4,11,49,22.4,47,55,85.5,10,34,44,87,38,5,39,230,1,0 -Plenette Pierson,MIN,F/C,188,88,24.89814396,US,"August 31, 1981",35,Texas Tech,15,29,402,54,142,38.0,17,51,33.3,15,20,75.0,13,49,62,48,12,4,33,140,0,0 -Rachel Banham,CON,G,175,76,24.81632653,US,"July 15, 1993",24,Minnesota,2,26,238,32,87,36.8,16,48,33.3,16,20,80.0,2,27,29,20,4,0,12,96,0,0 -Ramu Tokashiki,SEA,F,193,80,21.47708663,JP,"November 6, 1991",25,Japan,1,29,378,42,92,45.7,0,3,0.0,22,27,81.5,19,29,48,16,8,8,25,106,0,0 -Rebecca Allen,NY,G/F,188,74,20.9370756,AU,"June 11, 1992",25,Australia,3,28,254,31,86,36.0,14,40,35.0,2,6,33.3,13,51,64,15,9,12,17,78,0,0 -Rebekkah Brunson,MIN,F,188,84,23.76641014,US,"November 12, 1981",35,Georgetown,14,26,719,97,218,44.5,22,60,36.7,62,83,74.7,46,135,181,40,31,9,42,278,2,0 -Renee Montgomery,MIN,G,170,63,21.79930796,US,"February 12, 1986",31,Connecticut,9,29,614,71,181,39.2,30,89,33.7,44,51,86.3,12,34,46,96,24,1,43,216,0,0 -Riquna Williams,LA,G,170,75,25.95155709,US,"May 28, 1990",27,Miami (FL),5,23,408,45,140,32.1,20,74,27.0,38,44,86.4,6,26,32,16,19,3,26,148,0,0 -Sami Whitcomb,SEA,G,178,66,20.83070319,US,"July 20, 1988",29,Washington,R,29,354,46,120,38.3,33,94,35.1,14,17,82.4,12,40,52,24,22,0,24,139,0,0 -Sancho Lyttle,ATL,F,193,79,21.20862305,ES,"September 20, 1983",33,Houston,13,25,703,71,163,43.6,1,7,14.3,13,19,68.4,42,138,180,41,40,17,34,156,0,0 -Sandrine Gruda,LA,F/C,193,84,22.55094096,FR,"June 25, 1987",30,France,5,4,12,1,3,33.3,0,0,0.0,0,0,0.0,0,2,2,0,0,0,2,2,0,0 -Saniya Chong,DAL,G,173,64,21.383942,US,"June 27, 1994",23,Connecticut,R,29,348,27,74,36.5,8,35,22.9,25,29,86.2,9,19,28,33,21,3,23,87,0,0 -Seimone Augustus,MIN,G/F,183,77,22.99262444,US,"April 30, 1984",33,LSU,12,27,756,125,251,49.8,18,41,43.9,30,35,85.7,12,70,82,108,17,1,39,298,1,0 -Sequoia Holmes,SAN,G,185,70,20.45288532,US,"June 13, 1986",31,UNLV,2,24,280,31,89,34.8,13,46,28.3,6,11,54.5,12,12,24,23,13,5,11,81,0,0 -Shatori Walker-Kimbrough,WAS,G,180,64,19.75308642,US,"May 18, 1995",22,Maryland,R,22,260,29,78,37.2,9,26,34.6,29,32,90.6,4,13,17,10,11,1,12,96,0,0 -Shavonte Zellous,NY,G,178,85,26.82742078,US,"August 28, 1986",30,Pittsburgh,9,29,865,107,249,43.0,14,41,34.1,118,144,81.9,30,92,122,87,23,8,62,346,1,0 -Shay Murphy,SAN,G,180,74,22.83950617,US,"April 15, 1985",32,Southern California,9,23,242,23,62,37.1,12,35,34.3,8,12,66.7,12,26,38,17,10,1,12,66,0,0 -Shekinna Stricklen,CON,G/F,188,81,22.91760978,US,"July 30, 1990",27,Tennessee,5,29,795,80,202,39.6,59,149,39.6,26,31,83.9,15,71,86,30,36,2,23,245,0,0 -Shenise Johnson,IND,G,180,78,24.07407407,US,"September 12, 1990",26,Miami (FL),6,14,348,55,127,43.3,10,30,33.3,38,40,95.0,13,35,48,35,21,4,18,158,0,0 -Skylar Diggins-Smith,DAL,G,175,66,21.55102041,US,"February 8, 1990",27,Notre Dame,4,30,1018,167,394,42.4,43,119,36.1,168,186,90.3,21,86,107,173,38,24,83,545,1,0 -Stefanie Dolson,CHI,C,196,97,25.24989588,US,"August 1, 1992",25,Connecticut,3,28,823,162,293,55.3,24,60,40.0,50,58,86.2,35,121,156,65,14,37,65,398,3,0 -Stephanie Talbot,PHO,G,185,87,25.42001461,AU,"December 20, 1990",26,Australia,R,30,555,47,114,41.2,15,38,39.5,29,44,65.9,28,58,86,50,22,8,28,138,0,0 -Sue Bird,SEA,G,175,68,22.20408163,US,"October 16, 1980",36,Connecticut,15,27,806,103,244,42.2,50,134,37.3,17,24,70.8,7,46,53,177,31,3,57,273,1,0 -Sugar Rodgers,NY,G,175,75,24.48979592,US,"August 12, 1989",28,Georgetown,6,28,745,108,310,34.8,59,163,36.2,42,52,80.8,21,85,106,68,28,17,43,317,0,0 -Sydney Colson,SAN,G,173,64,21.383942,US,"June 8, 1989",28,Texas A&M,3,25,296,25,78,32.1,2,10,20.0,20,30,66.7,3,11,14,51,13,2,25,72,0,0 -Sydney Wiese,LA,G,183,68,20.30517483,US,"July 13, 1992",25,Oregon State,R,25,189,19,50,38.0,13,32,40.6,4,8,50.0,3,18,21,6,4,3,2,55,0,0 -Sylvia Fowles,MIN,C,198,96,24.48729721,US,"June 10, 1985",32,LSU,10,29,895,222,336,66.1,0,0,0.0,128,162,79.0,113,184,297,39,39,61,71,572,16,0 -Tamera Young,ATL,G/F,188,77,21.78587596,US,"October 30, 1986",30,Tennessee,9,31,820,105,297,35.4,23,70,32.9,44,65,67.7,23,87,110,66,36,14,61,277,0,0 -Tayler Hill,WAS,G,175,66,21.55102041,US,"October 23, 1990",26,Ohio State,5,18,462,69,191,36.1,27,89,30.3,75,80,93.8,5,29,34,47,16,1,26,240,0,0 -Temi Fagbenle,MIN,C,193,89,23.89325888,UK,"August 9, 1992",25,Southern California,R,17,74,6,14,42.9,0,0,0.0,5,6,83.3,3,13,16,1,3,3,8,17,0,0 -Theresa Plaisance,DAL,F,196,91,23.68804665,US,"May 18, 1992",25,LSU,4,30,604,80,213,37.6,35,101,34.7,22,24,91.7,38,89,127,24,23,22,24,217,1,0 -Tianna Hawkins,WAS,F,191,87,23.84803048,US,"February 3, 1991",26,Maryland,4,29,483,79,165,47.9,11,41,26.8,41,43,95.3,42,82,124,9,15,7,23,210,0,0 -Tierra Ruffin-Pratt,WAS,G,178,83,26.19618735,US,"November 4, 1991",25,North Carolina,5,29,703,77,217,35.5,0,4,0.0,71,96,74.0,45,120,165,68,30,16,47,225,2,0 -Tiffany Hayes,ATL,G,178,70,22.09317005,US,"September 20, 1989",27,Connecticut,6,29,861,144,331,43.5,43,112,38.4,136,161,84.5,28,89,117,69,37,8,50,467,0,0 -Tiffany Jackson,LA,F,191,84,23.0256846,US,"April 26, 1985",32,Texas,9,22,127,12,25,48.0,0,1,0.0,4,6,66.7,5,18,23,3,1,3,8,28,0,0 -Tiffany Mitchell,IND,G,175,69,22.53061224,US,"September 23, 1984",32,South Carolina,2,27,671,83,238,34.9,17,69,24.6,94,102,92.2,16,70,86,39,31,5,40,277,0,0 -Tina Charles,NY,F/C,193,84,22.55094096,US,"May 12, 1988",29,Connecticut,8,29,952,227,509,44.6,18,56,32.1,110,135,81.5,56,212,268,75,21,22,71,582,11,0 +Name,Team,Pos,Height,Weight,BMI,Birth_Place,Birthdate,Age,College,Experience,Games Played,MIN,FGM,FGA,FG%,3PM,3PA,3P%,FTM,FTA,FT%,OREB,DREB,REB,AST,STL,BLK,TO,PTS,DD2,TD3 +Aerial Powers,DAL,F,183,71,21.20099137,US,"January 17, 1994",23,Michigan State,2,8,173,30,85,35.3,12,32,37.5,21,26,80.8,6,22,28,12,3,6,12,93,0,0 +Alana Beard,LA,G/F,185,73,21.32943755,US,"May 14, 1982",35,Duke,12,30,947,90,177,50.8,5,18,27.8,32,41,78.0,19,82,101,72,63,13,40,217,0,0 +Alex Bentley,CON,G,170,69,23.87543253,US,"October 27, 1990",26,Penn State,4,26,617,82,218,37.6,19,64,29.7,35,42,83.3,4,36,40,78,22,3,24,218,0,0 +Alex Montgomery,SAN,G/F,185,84,24.54346238,US,"December 11, 1988",28,Georgia Tech,6,31,721,75,195,38.5,21,68,30.9,17,21,81.0,35,134,169,65,20,10,38,188,2,0 +Alexis Jones,MIN,G,175,78,25.46938776,US,"August 5, 1994",23,Baylor,R,24,137,16,50,32.0,7,20,35.0,11,12,91.7,3,9,12,12,7,0,14,50,0,0 +Alexis Peterson,SEA,G,170,63,21.79930796,US,"June 20, 1995",22,Syracuse,R,14,90,9,34,26.5,2,9,22.2,6,6,100,3,13,16,11,5,0,11,26,0,0 +Alexis Prince,PHO,G,188,81,22.91760978,US,"February 5, 1994",23,Baylor,R,16,112,9,34,26.5,4,15,26.7,2,2,100,1,14,15,5,4,3,3,24,0,0 +Allie Quigley,CHI,G,178,64,20.19946976,US,"June 20, 1986",31,DePaul,8,26,847,166,319,52.0,70,150,46.7,40,46,87.0,9,83,92,95,20,13,59,442,0,0 +Allisha Gray,DAL,G,185,76,22.20598977,US,"October 20, 1992",24,South Carolina,2,30,834,131,346,37.9,29,103,28.2,104,129,80.6,52,75,127,40,47,19,37,395,0,0 +Allison Hightower,WAS,G,178,77,24.30248706,US,"June 4, 1988",29,LSU,5,7,103,14,38,36.8,2,11,18.2,6,6,100,3,7,10,10,5,0,2,36,0,0 +Alysha Clark,SEA,F,180,76,23.45679012,US,"July 7, 1987",30,Middle Tennessee,6,30,843,93,183,50.8,20,62,32.3,38,51,74.5,29,97,126,50,22,4,32,244,0,0 +Alyssa Thomas,CON,F,188,84,23.76641014,US,"December 4, 1992",24,Maryland,3,28,833,154,303,50.8,0,3,0.0,91,158,57.6,34,158,192,136,48,11,87,399,4,0 +Amanda Zahui B.,NY,C,196,113,29.41482716,SE,"August 9, 1993",24,Minnesota,3,25,133,20,53,37.7,2,8,25.0,9,12,75.0,5,18,23,7,4,5,12,51,0,0 +Amber Harris,CHI,F,196,88,22.90712203,US,"January 16, 1988",29,Xavier,3,22,146,18,44,40.9,0,10,0.0,5,8,62.5,12,28,40,5,3,9,6,41,0,0 +Aneika Henry,ATL,F/C,193,87,23.35633171,JM,"February 13, 1986",31,Florida,6,4,22,4,4,100,0,0,0.0,0,0,0.0,0,4,4,1,2,0,3,8,0,0 +Angel Robinson,PHO,F/C,198,88,22.44668911,US,"August 30, 1995",21,Arizona State,1,15,237,25,44,56.8,1,1,100,7,7,100,16,42,58,8,1,11,16,58,0,0 +Asia Taylor,WAS,F,185,76,22.20598977,US,"August 22, 1991",26,Louisville,3,20,128,10,31,32.3,0,0,0.0,11,18,61.1,16,21,37,9,5,2,10,31,0,0 +Bashaara Graves,CHI,F,188,91,25.74694432,US,"March 17, 1994",23,Tennessee,1,5,59,8,14,57.1,0,0,0.0,3,4,75.0,4,13,17,3,0,1,3,19,0,0 +Breanna Lewis,DAL,C,196,93,24.20866306,US,"June 22, 1994",23,Kansas State,R,12,50,2,12,16.7,0,0,0.0,3,4,75.0,2,7,9,2,0,0,7,7,0,0 +Breanna Stewart,SEA,F/C,193,77,20.67169588,US,"August 27, 1994",22,Connecticut,2,29,952,201,417,48.2,46,123,37.4,136,171,79.5,43,206,249,78,29,47,68,584,8,0 +Bria Hartley,NY,G,173,66,22.05219018,US,"September 30, 1992",24,Connecticut,4,29,598,80,192,41.7,32,93,34.4,25,33,75.8,7,50,57,58,15,5,44,217,0,0 +Bria Holmes,ATL,G,185,77,22.49817385,US,"April 19, 1994",23,West Virginia,R,28,655,85,231,36.8,9,50,18.0,56,84,66.7,29,56,85,52,23,7,31,235,0,0 +Briann January,IND,G,173,65,21.71806609,US,"November 1, 1987",29,Arizona State,9,25,657,81,205,39.5,18,57,31.6,58,71,81.7,12,25,37,98,23,4,53,238,0,0 +Brionna Jones,CON,F,191,104,28.50799046,US,"December 18, 1995",21,Maryland,R,19,112,14,26,53.8,0,0,0.0,16,19,84.2,11,14,25,2,7,1,7,44,0,0 +Brittany Boyd,NY,G,175,71,23.18367347,US,"November 6, 1993",23,UC Berkeley,3,2,32,9,15,60.0,0,1,0.0,8,11,72.7,3,5,8,5,3,0,2,26,0,0 +Brittney Griner,PHO,C,206,93,21.91535489,US,"October 18, 1990",26,Baylor,5,22,682,167,293,57.0,0,0,0.0,127,154,82.5,43,129,172,39,13,54,52,461,6,0 +Brittney Sykes,ATL,G,175,66,21.55102041,US,"July 2, 1994",23,Rutgers,10,30,734,146,362,40.3,29,87,33.3,76,102,74.5,25,94,119,59,18,17,49,397,1,0 +Camille Little,PHO,F,188,82,23.20054323,US,"January 18, 1985",32,North Carolina,11,30,759,93,219,42.5,9,52,17.3,33,52,63.5,42,71,113,42,28,13,50,228,0,0 +Candace Parker,LA,F/C,193,79,21.20862305,US,"April 19, 1986",31,Tennessee,10,29,889,183,383,47.8,40,114,35.1,88,115,76.5,37,205,242,127,43,53,80,494,10,1 +Candice Dupree,IND,F,188,81,22.91760978,US,"February 25, 1984",33,Temple,12,29,911,189,370,51.1,0,2,0.0,57,65,87.7,31,124,155,47,28,12,42,435,2,0 +Cappie Pondexter,CHI,G,175,73,23.83673469,US,"July 1, 1983",34,Rutgers,11,24,676,94,258,36.4,8,32,25.0,54,67,80.6,10,59,69,104,17,5,56,250,2,0 +Carolyn Swords,SEA,C,198,95,24.2322212,US,"July 19, 1989",28,Boston College,6,26,218,19,39,48.7,0,0,0.0,16,20,80.0,10,29,39,9,5,4,22,54,0,0 +Cayla George,PHO,C,193,87,23.35633171,AU,"April 20, 1987",30,Georgia,1,28,365,40,105,38.1,13,45,28.9,7,12,58.3,10,71,81,15,9,11,13,100,1,0 +Chelsea Gray,LA,G,180,77,23.7654321,US,"August 10, 1992",25,Duke,3,30,996,165,326,50.6,48,100,48.0,78,94,83.0,19,80,99,132,29,7,61,456,1,0 +Cheyenne Parker,CHI,F,193,86,23.08786813,US,"August 22, 1992",25,Middle Tennessee,2,23,286,32,69,46.4,0,3,0.0,23,36,63.9,31,47,78,13,8,15,21,87,0,0 +Clarissa dos Santos,SAN,C,185,89,26.00438276,BR,"October 3, 1988",28,Brazil,4,7,52,8,14,57.1,1,1,100,0,0,0.0,3,7,10,7,1,1,5,17,0,0 +Courtney Paris,DAL,C,193,113,30.33638487,US,"September 21, 1987",29,Oklahoma,7,16,217,32,57,56.1,0,0,0.0,6,12,50.0,28,34,62,5,6,8,18,70,0,0 +Courtney Vandersloot,CHI,G,173,66,22.05219018,US,"August 2, 1989",28,Gonzaga,6,22,673,104,199,52.3,23,60,38.3,24,29,82.8,13,75,88,175,22,5,64,255,10,0 +Courtney Williams,CON,G,173,62,20.71569381,US,"November 5, 1994",22,South Florida,1,29,755,168,338,49.7,8,30,26.7,31,36,86.1,38,84,122,60,15,6,39,375,1,0 +Crystal Langhorne,SEA,F/C,188,84,23.76641014,US,"October 27, 1986",30,Maryland,10,30,848,160,240,66.7,1,2,50.0,49,68,72.1,35,140,175,46,16,11,50,370,2,0 +Damiris Dantas,ATL,C,191,89,24.39626107,BR,"November 17, 1992",24,Brazil,4,30,569,98,243,40.3,25,91,27.5,33,43,76.7,29,84,113,19,17,18,26,254,0,0 +Danielle Adams,CON,F/C,185,108,31.5558802,US,"February 19, 1989",28,Texas A&M,5,18,81,16,43,37.2,12,30,40.0,5,5,100,6,4,10,4,4,4,7,49,0,0 +Danielle Robinson,PHO,G,175,57,18.6122449,US,"October 5, 1989",27,Oklahoma,7,28,680,79,178,44.4,0,5,0.0,51,61,83.6,13,73,86,106,33,4,58,209,0,0 +Dearica Hamby,SAN,F,191,86,23.57391519,US,"June 11, 1993",24,Wake Forest,2,31,650,96,207,46.4,3,8,37.5,58,95,61.1,48,91,139,32,29,8,43,253,1,0 +Devereaux Peters,IND,F,188,79,22.35174287,US,"August 10, 1989",28,Notre Dame,6,28,796,154,380,40.5,88,225,39.1,118,130,90.8,8,69,77,76,16,9,56,514,0,0 +Diana Taurasi,PHO,G,183,74,22.09680791,US,"November 6, 1982",34,Connecticut,13,20,591,121,255,47.5,22,66,33.3,112,118,94.9,31,98,129,32,20,31,28,376,3,0 +Elena Delle Donne,WAS,G/F,196,85,22.12619742,US,"May 9, 1989",28,Delaware,5,30,939,133,272,48.9,0,1,0.0,51,78,65.4,99,116,215,43,32,64,36,317,4,0 +Elizabeth Williams,ATL,F/C,191,87,23.84803048,US,"June 23, 1993",24,Duke,3,30,377,48,96,50.0,0,1,0.0,32,55,58.2,35,61,96,5,5,4,21,128,0,0 +Emma Cannon,PHO,F,188,86,24.33227705,US,"January 6, 1989",28,Central Florida,R,18,508,105,220,47.7,11,33,33.3,31,34,91.2,33,72,105,52,21,27,30,252,1,0 +Emma Meesseman,WAS,C,193,83,22.28247738,BE,"May 13, 1993",24,Belgium,5,23,617,89,233,38.2,25,79,31.6,56,65,86.2,23,58,81,70,34,5,30,259,0,0 +Epiphanny Prince,NY,G,175,81,26.44897959,US,"November 1, 1988",28,Rutgers,8,26,282,36,86,41.9,1,3,33.3,15,22,68.2,17,44,61,5,4,8,17,88,0,0 +Erica Wheeler,IND,G,170,65,22.49134948,US,"February 5, 1991",26,Rutgers,3,30,767,130,321,40.5,42,129,32.6,34,40,85.0,11,57,68,117,38,1,68,336,0,0 +Érika de Souza,SAN,C,196,86,22.38650562,BR,"September 3, 1982",34,Brazil,13,30,579,65,112,58.0,0,0,0.0,29,32,90.6,58,74,132,35,18,7,37,159,0,0 +Erlana Larkins,IND,F,185,93,27.17311907,US,"February 4, 1986",31,North Carolina,9,20,386,36,92,39.1,9,35,25.7,21,24,87.5,9,26,35,24,11,8,13,102,0,0 +Essence Carson,LA,G/F,183,74,22.09680791,US,"July 28, 1986",31,Rutgers,10,15,61,4,16,25.0,0,0,0.0,5,6,83.3,7,2,9,0,1,3,5,13,0,0 +Evelyn Akhator,DAL,F,191,82,22.47745402,NG,"March 2, 1995",22,Kentucky,R,30,926,165,365,45.2,20,60,33.3,92,117,78.6,73,199,272,50,37,13,67,442,13,0 +Glory Johnson,DAL,F,191,77,21.10687755,US,"July 27, 1990",27,Tennessee,4,4,42,3,9,33.3,3,6,50.0,0,0,0.0,0,3,3,1,0,0,4,9,0,0 +Imani Boyette,ATL,C,201,88,21.78163907,US,"November 10, 1992",24,Texas,1,29,410,56,119,47.1,1,3,33.3,14,20,70.0,43,75,118,14,9,23,22,127,1,0 +Isabelle Harrison,SAN,C,191,83,22.75156931,US,"September 27, 1993",23,Kentucky,3,31,832,154,300,51.3,1,2,50.0,55,85,64.7,66,134,200,46,26,24,63,364,5,0 +Ivory Latta,WAS,G,168,63,22.32142857,US,"September 25, 1984",32,North Carolina,12,29,499,79,218,36.2,40,114,35.1,47,55,85.5,7,20,27,49,12,1,22,245,0,0 +Jantel Lavender,LA,C,193,84,22.55094096,US,"December 11, 1988",28,Ohio State,7,28,481,89,184,48.4,4,13,30.8,18,22,81.8,31,56,87,28,8,5,35,200,0,0 +Jasmine Thomas,CON,G,175,66,21.55102041,US,"September 30, 1989",27,Duke,6,27,762,151,341,44.3,50,116,43.1,39,55,70.9,9,55,64,118,45,4,58,391,1,0 +Jazmon Gwathmey,IND,G,188,65,18.39067451,PR,"January 24, 1993",24,James Madison,2,24,371,50,140,35.7,12,49,24.5,30,39,76.9,15,34,49,17,13,19,32,142,0,0 +Jeanette Pohlen,IND,G,183,78,23.29122996,US,"February 5, 1989",28,Stanford,6,25,278,20,52,38.5,13,29,44.8,17,20,85.0,3,19,22,13,5,0,15,70,0,0 +Jennifer Hamson,IND,C,201,95,23.51426945,US,"January 23, 1992",25,Brigham Young,1,10,50,2,12,16.7,0,3,0.0,8,10,80.0,5,6,11,6,2,2,3,12,0,0 +Jessica Breland,CHI,F,191,77,21.10687755,US,"February 23, 1988",29,North Carolina,5,10,78,9,16,56.3,0,0,0.0,4,5,80.0,5,13,18,2,1,9,3,22,0,0 +Jewell Loyd,SEA,G,178,67,21.14631991,US,"May 10, 1993",24,Notre Dame,3,29,715,116,245,47.3,8,21,38.1,28,37,75.7,50,139,189,46,18,50,57,268,4,0 +Jia Perkins,MIN,G,173,75,25.05930703,US,"February 23, 1982",35,Texas Tech,14,30,932,178,420,42.4,47,123,38.2,114,134,85.1,24,72,96,103,41,11,83,517,0,0 +Jonquel Jones,CON,F/C,198,86,21.93653709,BS,"May 1, 1994",23,George Washington,1,29,463,47,124,37.9,11,32,34.4,11,15,73.3,11,46,57,39,30,1,24,116,0,0 +Jordan Hooper,CHI,F,188,84,23.76641014,US,"February 20, 1992",25,Nebraska,3,29,833,164,299,54.8,22,49,44.9,117,142,82.4,108,226,334,40,29,46,46,467,17,0 +Kaela Davis,DAL,G,188,77,21.78587596,US,"March 15, 1995",22,South Carolina,R,23,208,27,75,36.0,20,55,36.4,3,4,75.0,2,20,22,5,7,1,6,77,0,0 +Kahleah Copper,CHI,G/F,185,70,20.45288532,US,"August 28, 1994",22,Rutgers,1,29,475,62,163,38.0,12,32,37.5,49,65,75.4,10,33,43,32,13,3,48,185,0,0 +Kaleena Mosqueda-Lewis,SEA,F,180,82,25.30864198,US,"March 11, 1993",24,Connecticut,3,29,369,60,140,42.9,5,23,21.7,36,45,80.0,11,43,54,11,9,2,22,161,0,0 +Karima Christmas-Kelly,DAL,G/F,183,82,24.48565201,US,"November 9, 1989",27,Duke,6,14,142,23,43,53.5,9,21,42.9,10,10,100,4,10,14,6,1,1,13,65,0,0 +Kayla Alexander,SAN,C,193,88,23.6247953,CA,"May 1, 1991",26,Arizona State,4,30,889,91,239,38.1,25,83,30.1,111,129,86.0,45,75,120,65,39,5,50,318,0,0 +Kayla McBride,SAN,G/F,180,79,24.38271605,US,"June 25, 1992",25,Notre Dame,3,31,433,78,141,55.3,0,0,0.0,15,16,93.8,40,47,87,17,13,15,30,171,0,0 +Kayla Pedersen,CON,F,193,86,23.08786813,US,"April 14, 1989",28,Stanford,5,27,882,128,337,38.0,47,147,32.0,108,118,91.5,12,93,105,59,32,5,54,411,0,0 +Kayla Thornton,DAL,F,185,86,25.12783053,US,"October 20, 1992",24,Texas–El Paso,2,21,224,11,30,36.7,0,1,0.0,10,14,71.4,19,26,45,13,6,2,9,32,0,0 +Keisha Hampton,CHI,F,185,78,22.79035793,US,"February 22, 1990",27,DePaul,1,30,504,64,157,40.8,14,52,26.9,65,81,80.2,36,59,95,24,20,7,21,207,0,0 +Kelsey Plum,SAN,G,173,66,22.05219018,US,"August 24, 1994",23,Washington,R,28,610,73,210,34.8,29,78,37.2,50,58,86.2,11,42,53,91,13,4,72,225,0,0 +Kia Vaughn,NY,C,193,90,24.16172246,US,"January 24, 1987",30,Rutgers,9,23,455,62,116,53.4,0,0,0.0,10,19,52.6,39,71,110,16,8,9,21,134,1,0 +Kiah Stokes,NY,C,191,87,23.84803048,US,"March 30, 1993",24,Connecticut,3,29,576,50,98,51.0,0,1,0.0,41,52,78.8,63,122,185,21,8,32,33,141,3,0 +Kristi Toliver,WAS,G,170,59,20.41522491,US,"January 27, 1987",30,Maryland,9,29,845,119,284,41.9,67,194,34.5,44,49,89.8,9,50,59,91,20,8,48,349,0,0 +Krystal Thomas,WAS,C,196,88,22.90712203,US,"October 6, 1989",27,Duke,6,29,737,81,149,54.4,0,0,0.0,37,61,60.7,97,172,269,30,15,31,45,199,2,0 +Lanay Montgomery,SEA,C,196,96,24.98958767,US,"September 17, 1993",23,West Virginia,R,7,28,3,7,42.9,0,0,0.0,0,0,0.0,0,5,5,0,1,4,2,6,0,0 +Layshia Clarendon,ATL,G,175,64,20.89795918,US,"February 5, 1991",26,UC Berkeley,5,30,900,124,320,38.8,8,53,15.1,73,81,90.1,27,88,115,206,29,1,82,329,3,0 +Leilani Mitchell,PHO,G,165,58,21.30394858,US,"June 15, 1985",32,Utah,9,30,623,70,182,38.5,31,92,33.7,62,75,82.7,12,57,69,108,26,9,50,233,0,0 +Lindsay Allen,NY,G,173,65,21.71806609,US,"March 20, 1995",22,Notre Dame,R,23,314,21,50,42.0,0,11,0.0,6,9,66.7,8,28,36,47,13,1,18,48,0,0 +Lindsay Whalen,MIN,G,175,78,25.46938776,US,"September 5, 1982",34,Minnesota,14,22,520,69,153,45.1,12,34,35.3,27,36,75.0,8,46,54,90,11,2,44,177,0,0 +Lynetta Kizer,CON,C,193,104,27.92021262,US,"April 4, 1990",27,Maryland,5,20,238,48,100,48.0,0,1,0.0,23,30,76.7,22,35,57,6,11,7,10,119,0,0 +Maimouna Diarra,LA,C,198,90,22.95684114,SN,"January 30, 1991",26,Sengal,R,9,16,1,3,33.3,0,0,0.0,1,2,50.0,3,4,7,1,1,0,3,3,0,0 +Makayla Epps,CHI,G,178,,,US,"June 6, 1995",22,Kentucky,R,14,52,2,14,14.3,0,5,0.0,2,5,40.0,2,0,2,4,1,0,4,6,0,0 +Marissa Coleman,IND,G/F,185,73,21.32943755,US,"April 1, 1987",30,Maryland,9,30,539,50,152,32.9,27,79,34.2,27,33,81.8,7,53,60,25,8,4,34,154,0,0 +Matee Ajavon,ATL,G,173,73,24.39105884,US,"July 5, 1986",31,Syracruse,R,27,218,22,69,31.9,0,3,0.0,29,35,82.9,8,26,34,27,10,0,26,73,0,0 +Maya Moore,MIN,F,183,80,23.88844098,US,"November 6, 1989",27,Connecticut,7,29,904,170,398,42.7,52,132,39.4,98,114,86.0,50,106,156,99,53,13,56,490,3,0 +Monique Currie,PHO,G/F,183,80,23.88844098,US,"February 25, 1983",34,Duke,11,32,717,121,284,42.6,37,93,39.8,85,103,82.5,19,103,122,67,22,11,48,364,0,0 +Morgan Tuck,CON,F,188,91,25.74694432,US,"April 30, 1994",23,Connecticut,1,17,294,35,101,34.7,8,28,28.6,13,16,81.3,9,34,43,19,7,0,15,91,1,0 +Moriah Jefferson,SAN,G,168,55,19.48696145,US,"August 3, 1994",23,Connecticut,1,21,514,81,155,52.3,9,20,45.0,20,27,74.1,6,31,37,92,33,2,43,191,0,0 +Natalie Achonwa,IND,C,193,83,22.28247738,CA,"November 22, 1992",24,Notre Dame,3,30,529,82,151,54.3,0,0,0.0,43,55,78.2,31,70,101,21,11,16,25,207,0,0 +Natasha Cloud,WAS,G,183,73,21.79820239,US,"February 22, 1992",25,Saint Joseph's,3,24,448,37,118,31.4,12,51,23.5,20,27,74.1,7,52,59,69,17,3,23,106,0,0 +Natasha Howard,MIN,F,188,75,21.22000905,US,"February 9, 1991",26,Florida State,4,29,315,48,104,46.2,3,13,23.1,17,23,73.9,25,38,63,16,11,19,20,116,0,0 +Nayo Raincock-Ekunwe,NY,F/C,188,79,22.35174287,CA,"August 29, 1991",25,Simon Fraser,R,27,243,33,63,52.4,0,4,0.0,30,49,61.2,24,22,46,8,2,1,13,96,0,0 +Nia Coffey,SAN,F,185,77,22.49817385,US,"May 21, 1995",22,Northwestern,R,25,203,16,59,27.1,0,4,0.0,16,22,72.7,16,30,46,6,5,6,14,48,0,0 +Nneka Ogwumike,LA,F,188,79,22.35174287,US,"February 7, 1990",27,Stanford,6,30,948,215,386,55.7,18,49,36.7,129,148,87.2,57,179,236,63,53,14,47,577,9,0 +Noelle Quinn,SEA,G,183,81,24.18704649,US,"March 1, 1985",32,UCLA,11,29,459,24,58,41.4,14,35,40.0,17,18,94.4,1,48,49,78,12,5,27,79,0,0 +Odyssey Sims,LA,G,173,73,24.39105884,US,"July 13, 1992",25,Baylor,4,27,626,86,198,43.4,11,49,22.4,47,55,85.5,10,34,44,87,38,5,39,230,1,0 +Plenette Pierson,MIN,F/C,188,88,24.89814396,US,"August 31, 1981",35,Texas Tech,15,29,402,54,142,38.0,17,51,33.3,15,20,75.0,13,49,62,48,12,4,33,140,0,0 +Rachel Banham,CON,G,175,76,24.81632653,US,"July 15, 1993",24,Minnesota,2,26,238,32,87,36.8,16,48,33.3,16,20,80.0,2,27,29,20,4,0,12,96,0,0 +Ramu Tokashiki,SEA,F,193,80,21.47708663,JP,"November 6, 1991",25,Japan,1,29,378,42,92,45.7,0,3,0.0,22,27,81.5,19,29,48,16,8,8,25,106,0,0 +Rebecca Allen,NY,G/F,188,74,20.9370756,AU,"June 11, 1992",25,Australia,3,28,254,31,86,36.0,14,40,35.0,2,6,33.3,13,51,64,15,9,12,17,78,0,0 +Rebekkah Brunson,MIN,F,188,84,23.76641014,US,"November 12, 1981",35,Georgetown,14,26,719,97,218,44.5,22,60,36.7,62,83,74.7,46,135,181,40,31,9,42,278,2,0 +Renee Montgomery,MIN,G,170,63,21.79930796,US,"February 12, 1986",31,Connecticut,9,29,614,71,181,39.2,30,89,33.7,44,51,86.3,12,34,46,96,24,1,43,216,0,0 +Riquna Williams,LA,G,170,75,25.95155709,US,"May 28, 1990",27,Miami (FL),5,23,408,45,140,32.1,20,74,27.0,38,44,86.4,6,26,32,16,19,3,26,148,0,0 +Sami Whitcomb,SEA,G,178,66,20.83070319,US,"July 20, 1988",29,Washington,R,29,354,46,120,38.3,33,94,35.1,14,17,82.4,12,40,52,24,22,0,24,139,0,0 +Sancho Lyttle,ATL,F,193,79,21.20862305,ES,"September 20, 1983",33,Houston,13,25,703,71,163,43.6,1,7,14.3,13,19,68.4,42,138,180,41,40,17,34,156,0,0 +Sandrine Gruda,LA,F/C,193,84,22.55094096,FR,"June 25, 1987",30,France,5,4,12,1,3,33.3,0,0,0.0,0,0,0.0,0,2,2,0,0,0,2,2,0,0 +Saniya Chong,DAL,G,173,64,21.383942,US,"June 27, 1994",23,Connecticut,R,29,348,27,74,36.5,8,35,22.9,25,29,86.2,9,19,28,33,21,3,23,87,0,0 +Seimone Augustus,MIN,G/F,183,77,22.99262444,US,"April 30, 1984",33,LSU,12,27,756,125,251,49.8,18,41,43.9,30,35,85.7,12,70,82,108,17,1,39,298,1,0 +Sequoia Holmes,SAN,G,185,70,20.45288532,US,"June 13, 1986",31,UNLV,2,24,280,31,89,34.8,13,46,28.3,6,11,54.5,12,12,24,23,13,5,11,81,0,0 +Shatori Walker-Kimbrough,WAS,G,180,64,19.75308642,US,"May 18, 1995",22,Maryland,R,22,260,29,78,37.2,9,26,34.6,29,32,90.6,4,13,17,10,11,1,12,96,0,0 +Shavonte Zellous,NY,G,178,85,26.82742078,US,"August 28, 1986",30,Pittsburgh,9,29,865,107,249,43.0,14,41,34.1,118,144,81.9,30,92,122,87,23,8,62,346,1,0 +Shay Murphy,SAN,G,180,74,22.83950617,US,"April 15, 1985",32,Southern California,9,23,242,23,62,37.1,12,35,34.3,8,12,66.7,12,26,38,17,10,1,12,66,0,0 +Shekinna Stricklen,CON,G/F,188,81,22.91760978,US,"July 30, 1990",27,Tennessee,5,29,795,80,202,39.6,59,149,39.6,26,31,83.9,15,71,86,30,36,2,23,245,0,0 +Shenise Johnson,IND,G,180,78,24.07407407,US,"September 12, 1990",26,Miami (FL),6,14,348,55,127,43.3,10,30,33.3,38,40,95.0,13,35,48,35,21,4,18,158,0,0 +Skylar Diggins-Smith,DAL,G,175,66,21.55102041,US,"February 8, 1990",27,Notre Dame,4,30,1018,167,394,42.4,43,119,36.1,168,186,90.3,21,86,107,173,38,24,83,545,1,0 +Stefanie Dolson,CHI,C,196,97,25.24989588,US,"August 1, 1992",25,Connecticut,3,28,823,162,293,55.3,24,60,40.0,50,58,86.2,35,121,156,65,14,37,65,398,3,0 +Stephanie Talbot,PHO,G,185,87,25.42001461,AU,"December 20, 1990",26,Australia,R,30,555,47,114,41.2,15,38,39.5,29,44,65.9,28,58,86,50,22,8,28,138,0,0 +Sue Bird,SEA,G,175,68,22.20408163,US,"October 16, 1980",36,Connecticut,15,27,806,103,244,42.2,50,134,37.3,17,24,70.8,7,46,53,177,31,3,57,273,1,0 +Sugar Rodgers,NY,G,175,75,24.48979592,US,"August 12, 1989",28,Georgetown,6,28,745,108,310,34.8,59,163,36.2,42,52,80.8,21,85,106,68,28,17,43,317,0,0 +Sydney Colson,SAN,G,173,64,21.383942,US,"June 8, 1989",28,Texas A&M,3,25,296,25,78,32.1,2,10,20.0,20,30,66.7,3,11,14,51,13,2,25,72,0,0 +Sydney Wiese,LA,G,183,68,20.30517483,US,"July 13, 1992",25,Oregon State,R,25,189,19,50,38.0,13,32,40.6,4,8,50.0,3,18,21,6,4,3,2,55,0,0 +Sylvia Fowles,MIN,C,198,96,24.48729721,US,"June 10, 1985",32,LSU,10,29,895,222,336,66.1,0,0,0.0,128,162,79.0,113,184,297,39,39,61,71,572,16,0 +Tamera Young,ATL,G/F,188,77,21.78587596,US,"October 30, 1986",30,Tennessee,9,31,820,105,297,35.4,23,70,32.9,44,65,67.7,23,87,110,66,36,14,61,277,0,0 +Tayler Hill,WAS,G,175,66,21.55102041,US,"October 23, 1990",26,Ohio State,5,18,462,69,191,36.1,27,89,30.3,75,80,93.8,5,29,34,47,16,1,26,240,0,0 +Temi Fagbenle,MIN,C,193,89,23.89325888,UK,"August 9, 1992",25,Southern California,R,17,74,6,14,42.9,0,0,0.0,5,6,83.3,3,13,16,1,3,3,8,17,0,0 +Theresa Plaisance,DAL,F,196,91,23.68804665,US,"May 18, 1992",25,LSU,4,30,604,80,213,37.6,35,101,34.7,22,24,91.7,38,89,127,24,23,22,24,217,1,0 +Tianna Hawkins,WAS,F,191,87,23.84803048,US,"February 3, 1991",26,Maryland,4,29,483,79,165,47.9,11,41,26.8,41,43,95.3,42,82,124,9,15,7,23,210,0,0 +Tierra Ruffin-Pratt,WAS,G,178,83,26.19618735,US,"November 4, 1991",25,North Carolina,5,29,703,77,217,35.5,0,4,0.0,71,96,74.0,45,120,165,68,30,16,47,225,2,0 +Tiffany Hayes,ATL,G,178,70,22.09317005,US,"September 20, 1989",27,Connecticut,6,29,861,144,331,43.5,43,112,38.4,136,161,84.5,28,89,117,69,37,8,50,467,0,0 +Tiffany Jackson,LA,F,191,84,23.0256846,US,"April 26, 1985",32,Texas,9,22,127,12,25,48.0,0,1,0.0,4,6,66.7,5,18,23,3,1,3,8,28,0,0 +Tiffany Mitchell,IND,G,175,69,22.53061224,US,"September 23, 1984",32,South Carolina,2,27,671,83,238,34.9,17,69,24.6,94,102,92.2,16,70,86,39,31,5,40,277,0,0 +Tina Charles,NY,F/C,193,84,22.55094096,US,"May 12, 1988",29,Connecticut,8,29,952,227,509,44.6,18,56,32.1,110,135,81.5,56,212,268,75,21,22,71,582,11,0 Yvonne Turner,PHO,G,175,59,19.26530612,US,"October 13, 1987",29,Nebraska,2,30,356,59,140,42.1,11,47,23.4,22,28,78.6,11,13,24,30,18,1,32,151,0,0 \ No newline at end of file diff --git a/data/wnba_clean.csv b/your-code/wnba_clean.csv similarity index 97% rename from data/wnba_clean.csv rename to your-code/wnba_clean.csv index 3d702f3..3bf5421 100644 --- a/data/wnba_clean.csv +++ b/your-code/wnba_clean.csv @@ -40,7 +40,7 @@ Courtney Vandersloot,CHI,G,173,66,22.05219018,US,"August 2, 1989",28,Gonzaga,6,2 Courtney Williams,CON,G,173,62,20.71569381,US,"November 5, 1994",22,South Florida,1,29,755,168,338,49.7,8,30,26.7,31,36,86.1,38,84,122,60,15,6,39,375,1,0 Crystal Langhorne,SEA,F/C,188,84,23.76641014,US,"October 27, 1986",30,Maryland,10,30,848,160,240,66.7,1,2,50.0,49,68,72.1,35,140,175,46,16,11,50,370,2,0 Damiris Dantas,ATL,C,191,89,24.39626107,BR,"November 17, 1992",24,Brazil,4,30,569,98,243,40.3,25,91,27.5,33,43,76.7,29,84,113,19,17,18,26,254,0,0 -Danielle Adams,CON,F/C,185,108,31.555880199999997,US,"February 19, 1989",28,Texas A&M,5,18,81,16,43,37.2,12,30,40.0,5,5,100.0,6,4,10,4,4,4,7,49,0,0 +Danielle Adams,CON,F/C,185,108,31.5558802,US,"February 19, 1989",28,Texas A&M,5,18,81,16,43,37.2,12,30,40.0,5,5,100.0,6,4,10,4,4,4,7,49,0,0 Danielle Robinson,PHO,G,175,57,18.6122449,US,"October 5, 1989",27,Oklahoma,7,28,680,79,178,44.4,0,5,0.0,51,61,83.6,13,73,86,106,33,4,58,209,0,0 Dearica Hamby,SAN,F,191,86,23.57391519,US,"June 11, 1993",24,Wake Forest,2,31,650,96,207,46.4,3,8,37.5,58,95,61.1,48,91,139,32,29,8,43,253,1,0 Devereaux Peters,IND,F,188,79,22.35174287,US,"August 10, 1989",28,Notre Dame,6,28,796,154,380,40.5,88,225,39.1,118,130,90.8,8,69,77,76,16,9,56,514,0,0 @@ -73,7 +73,7 @@ Kaela Davis,DAL,G,188,77,21.78587596,US,"March 15, 1995",22,South Carolina,R,23, Kahleah Copper,CHI,G/F,185,70,20.45288532,US,"August 28, 1994",22,Rutgers,1,29,475,62,163,38.0,12,32,37.5,49,65,75.4,10,33,43,32,13,3,48,185,0,0 Kaleena Mosqueda-Lewis,SEA,F,180,82,25.30864198,US,"March 11, 1993",24,Connecticut,3,29,369,60,140,42.9,5,23,21.7,36,45,80.0,11,43,54,11,9,2,22,161,0,0 Karima Christmas-Kelly,DAL,G/F,183,82,24.48565201,US,"November 9, 1989",27,Duke,6,14,142,23,43,53.5,9,21,42.9,10,10,100.0,4,10,14,6,1,1,13,65,0,0 -Kayla Alexander,SAN,C,193,88,23.624795300000002,CA,"May 1, 1991",26,Arizona State,4,30,889,91,239,38.1,25,83,30.1,111,129,86.0,45,75,120,65,39,5,50,318,0,0 +Kayla Alexander,SAN,C,193,88,23.6247953,CA,"May 1, 1991",26,Arizona State,4,30,889,91,239,38.1,25,83,30.1,111,129,86.0,45,75,120,65,39,5,50,318,0,0 Kayla McBride,SAN,G/F,180,79,24.38271605,US,"June 25, 1992",25,Notre Dame,3,31,433,78,141,55.3,0,0,0.0,15,16,93.8,40,47,87,17,13,15,30,171,0,0 Kayla Pedersen,CON,F,193,86,23.08786813,US,"April 14, 1989",28,Stanford,5,27,882,128,337,38.0,47,147,32.0,108,118,91.5,12,93,105,59,32,5,54,411,0,0 Kayla Thornton,DAL,F,185,86,25.12783053,US,"October 20, 1992",24,Texas–El Paso,2,21,224,11,30,36.7,0,1,0.0,10,14,71.4,19,26,45,13,6,2,9,32,0,0 @@ -107,7 +107,7 @@ Odyssey Sims,LA,G,173,73,24.39105884,US,"July 13, 1992",25,Baylor,4,27,626,86,19 Plenette Pierson,MIN,F/C,188,88,24.89814396,US,"August 31, 1981",35,Texas Tech,15,29,402,54,142,38.0,17,51,33.3,15,20,75.0,13,49,62,48,12,4,33,140,0,0 Rachel Banham,CON,G,175,76,24.81632653,US,"July 15, 1993",24,Minnesota,2,26,238,32,87,36.8,16,48,33.3,16,20,80.0,2,27,29,20,4,0,12,96,0,0 Ramu Tokashiki,SEA,F,193,80,21.47708663,JP,"November 6, 1991",25,Japan,1,29,378,42,92,45.7,0,3,0.0,22,27,81.5,19,29,48,16,8,8,25,106,0,0 -Rebecca Allen,NY,G/F,188,74,20.937075600000004,AU,"June 11, 1992",25,Australia,3,28,254,31,86,36.0,14,40,35.0,2,6,33.3,13,51,64,15,9,12,17,78,0,0 +Rebecca Allen,NY,G/F,188,74,20.9370756,AU,"June 11, 1992",25,Australia,3,28,254,31,86,36.0,14,40,35.0,2,6,33.3,13,51,64,15,9,12,17,78,0,0 Rebekkah Brunson,MIN,F,188,84,23.76641014,US,"November 12, 1981",35,Georgetown,14,26,719,97,218,44.5,22,60,36.7,62,83,74.7,46,135,181,40,31,9,42,278,2,0 Renee Montgomery,MIN,G,170,63,21.79930796,US,"February 12, 1986",31,Connecticut,9,29,614,71,181,39.2,30,89,33.7,44,51,86.3,12,34,46,96,24,1,43,216,0,0 Riquna Williams,LA,G,170,75,25.95155709,US,"May 28, 1990",27,Miami (FL),5,23,408,45,140,32.1,20,74,27.0,38,44,86.4,6,26,32,16,19,3,26,148,0,0