From af0db071e657e464ff9a551acc8f29f85b37fc44 Mon Sep 17 00:00:00 2001 From: Sergio Soutinho Date: Sun, 13 Aug 2023 22:41:57 +0100 Subject: [PATCH 1/2] lab done --- your-code/1.-Data-Cleaning.ipynb | 1183 +++++++++++++++++- your-code/2.-Exploratory-Data-Analysis.ipynb | 959 +++++++++++++- your-code/3.-Inferential-Analysis.ipynb | 481 ++++++- {data => your-code/data}/codebook.md | 0 {data => your-code/data}/wnba.csv | 286 ++--- {data => your-code/data}/wnba_clean.csv | 6 +- 6 files changed, 2670 insertions(+), 245 deletions(-) rename {data => your-code/data}/codebook.md (100%) rename {data => your-code/data}/wnba.csv (99%) rename {data => your-code/data}/wnba_clean.csv (97%) diff --git a/your-code/1.-Data-Cleaning.ipynb b/your-code/1.-Data-Cleaning.ipynb index d1c8eea..85920fe 100644 --- a/your-code/1.-Data-Cleaning.ipynb +++ b/your-code/1.-Data-Cleaning.ipynb @@ -49,9 +49,518 @@ "cell_type": "code", "execution_count": 3, "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
...................................................................................................
138Tiffany HayesATLG17870.022.093170USSeptember 20, 198927Connecticut62986114433143.54311238.413616184.52889117693785046700
139Tiffany JacksonLAF19184.023.025685USApril 26, 198532Texas922127122548.0010.04666.75182331382800
140Tiffany MitchellINDG17569.022.530612USSeptember 23, 198432South Carolina2276718323834.9176924.69410292.2167086393154027700
141Tina CharlesNYF/C19384.022.550941USMay 12, 198829Connecticut82995222750944.6185632.111013581.55621226875212271582110
142Yvonne TurnerPHOG17559.019.265306USOctober 13, 198729Nebraska2303565914042.1114723.4222878.6111324301813215100
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143 rows × 32 columns

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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": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wnba[wnba[\"BMI\"].isna()]" ] }, { @@ -96,11 +761,23 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.006993006993006993" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "1/143" ] }, { @@ -114,11 +791,56 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Name 0\n", + "Team 0\n", + "Pos 0\n", + "Height 0\n", + "Weight 0\n", + "BMI 0\n", + "Birth_Place 0\n", + "Birthdate 0\n", + "Age 0\n", + "College 0\n", + "Experience 0\n", + "Games Played 0\n", + "MIN 0\n", + "FGM 0\n", + "FGA 0\n", + "FG% 0\n", + "3PM 0\n", + "3PA 0\n", + "3P% 0\n", + "FTM 0\n", + "FTA 0\n", + "FT% 0\n", + "OREB 0\n", + "DREB 0\n", + "REB 0\n", + "AST 0\n", + "STL 0\n", + "BLK 0\n", + "TO 0\n", + "PTS 0\n", + "DD2 0\n", + "TD3 0\n", + "dtype: int64" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wnba = wnba.dropna()\n", + "wnba.isna().sum()" ] }, { @@ -130,7 +852,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -147,11 +869,55 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "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": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wnba.dtypes" ] }, { @@ -170,11 +936,36 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\sfsou\\AppData\\Local\\Temp\\ipykernel_22216\\3383670552.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " wnba[\"Weight\"] = wnba[\"Weight\"].astype(\"int64\")\n" + ] + }, + { + "data": { + "text/plain": [ + "dtype('int64')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wnba[\"Weight\"] = wnba[\"Weight\"].astype(\"int64\")\n", + "wnba[\"Weight\"].dtype" ] }, { @@ -186,11 +977,346 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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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
max206.000000113.00000031.55588036.00000032.0000001018.000000227.000000509.000000100.00000088.000000225.000000100.000000168.000000186.000000100.000000113.000000226.000000334.000000206.00000063.00000064.00000087.000000584.00000017.0000001.000000
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" + ], + "text/plain": [ + " Height Weight BMI Age Games Played \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 184.612676 78.978873 23.091214 27.112676 24.429577 \n", + "std 8.698128 10.996110 2.073691 3.667180 7.075477 \n", + "min 165.000000 55.000000 18.390675 21.000000 2.000000 \n", + "25% 175.750000 71.500000 21.785876 24.000000 22.000000 \n", + "50% 185.000000 79.000000 22.873314 27.000000 27.500000 \n", + "75% 191.000000 86.000000 24.180715 30.000000 29.000000 \n", + "max 206.000000 113.000000 31.555880 36.000000 32.000000 \n", + "\n", + " MIN FGM FGA FG% 3PM \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 500.105634 74.401408 168.704225 43.102817 14.830986 \n", + "std 289.373393 55.980754 117.165809 9.855199 17.372829 \n", + "min 12.000000 1.000000 3.000000 16.700000 0.000000 \n", + "25% 242.250000 27.000000 69.000000 37.125000 0.000000 \n", + "50% 506.000000 69.000000 152.500000 42.050000 10.500000 \n", + "75% 752.500000 105.000000 244.750000 48.625000 22.000000 \n", + "max 1018.000000 227.000000 509.000000 100.000000 88.000000 \n", + "\n", + " 3PA 3P% FTM FTA FT% OREB \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 43.697183 24.978169 39.535211 49.422535 75.828873 22.063380 \n", + "std 46.155302 18.459075 36.743053 44.244697 18.536151 21.519648 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 3.000000 0.000000 13.000000 17.250000 71.575000 7.000000 \n", + "50% 32.000000 30.550000 29.000000 35.500000 80.000000 13.000000 \n", + "75% 65.500000 36.175000 53.250000 66.500000 85.925000 31.000000 \n", + "max 225.000000 100.000000 168.000000 186.000000 100.000000 113.000000 \n", + "\n", + " DREB REB AST STL BLK TO \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 61.591549 83.654930 44.514085 17.725352 9.781690 32.288732 \n", + "std 49.669854 68.200585 41.490790 13.413312 12.537669 21.447141 \n", + "min 2.000000 2.000000 0.000000 0.000000 0.000000 2.000000 \n", + "25% 26.000000 34.250000 11.250000 7.000000 2.000000 14.000000 \n", + "50% 50.000000 62.500000 34.000000 15.000000 5.000000 28.000000 \n", + "75% 84.000000 116.500000 66.750000 27.500000 12.000000 48.000000 \n", + "max 226.000000 334.000000 206.000000 63.000000 64.000000 87.000000 \n", + "\n", + " PTS DD2 TD3 \n", + "count 142.000000 142.000000 142.000000 \n", + "mean 203.169014 1.140845 0.007042 \n", + "std 153.032559 2.909002 0.083918 \n", + "min 2.000000 0.000000 0.000000 \n", + "25% 77.250000 0.000000 0.000000 \n", + "50% 181.000000 0.000000 0.000000 \n", + "75% 277.750000 1.000000 0.000000 \n", + "max 584.000000 17.000000 1.000000 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wnba.describe()" ] }, { @@ -202,7 +1328,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -218,11 +1344,12 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "#your code here\n", + "wnba.to_csv(\"data/wnba_clean.csv\", index=False)" ] } ], @@ -242,7 +1369,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.10.9" } }, "nbformat": 4, diff --git a/your-code/2.-Exploratory-Data-Analysis.ipynb b/your-code/2.-Exploratory-Data-Analysis.ipynb index 48b485c..6aa6b32 100644 --- a/your-code/2.-Exploratory-Data-Analysis.ipynb +++ b/your-code/2.-Exploratory-Data-Analysis.ipynb @@ -36,11 +36,283 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, - "outputs": [], + "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
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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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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" + ], + "text/plain": [ + " Height Weight BMI Age Games Played \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 184.612676 78.978873 23.091214 27.112676 24.429577 \n", + "std 8.698128 10.996110 2.073691 3.667180 7.075477 \n", + "min 165.000000 55.000000 18.390675 21.000000 2.000000 \n", + "25% 175.750000 71.500000 21.785876 24.000000 22.000000 \n", + "50% 185.000000 79.000000 22.873314 27.000000 27.500000 \n", + "75% 191.000000 86.000000 24.180715 30.000000 29.000000 \n", + "max 206.000000 113.000000 31.555880 36.000000 32.000000 \n", + "\n", + " MIN FGM FGA FG% 3PM \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 500.105634 74.401408 168.704225 43.102817 14.830986 \n", + "std 289.373393 55.980754 117.165809 9.855199 17.372829 \n", + "min 12.000000 1.000000 3.000000 16.700000 0.000000 \n", + "25% 242.250000 27.000000 69.000000 37.125000 0.000000 \n", + "50% 506.000000 69.000000 152.500000 42.050000 10.500000 \n", + "75% 752.500000 105.000000 244.750000 48.625000 22.000000 \n", + "max 1018.000000 227.000000 509.000000 100.000000 88.000000 \n", + "\n", + " 3PA 3P% FTM FTA FT% OREB \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 43.697183 24.978169 39.535211 49.422535 75.828873 22.063380 \n", + "std 46.155302 18.459075 36.743053 44.244697 18.536151 21.519648 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 3.000000 0.000000 13.000000 17.250000 71.575000 7.000000 \n", + "50% 32.000000 30.550000 29.000000 35.500000 80.000000 13.000000 \n", + "75% 65.500000 36.175000 53.250000 66.500000 85.925000 31.000000 \n", + "max 225.000000 100.000000 168.000000 186.000000 100.000000 113.000000 \n", + "\n", + " DREB REB AST STL BLK TO \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 61.591549 83.654930 44.514085 17.725352 9.781690 32.288732 \n", + "std 49.669854 68.200585 41.490790 13.413312 12.537669 21.447141 \n", + "min 2.000000 2.000000 0.000000 0.000000 0.000000 2.000000 \n", + "25% 26.000000 34.250000 11.250000 7.000000 2.000000 14.000000 \n", + "50% 50.000000 62.500000 34.000000 15.000000 5.000000 28.000000 \n", + "75% 84.000000 116.500000 66.750000 27.500000 12.000000 48.000000 \n", + "max 226.000000 334.000000 206.000000 63.000000 64.000000 87.000000 \n", + "\n", + " PTS DD2 TD3 \n", + "count 142.000000 142.000000 142.000000 \n", + "mean 203.169014 1.140845 0.007042 \n", + "std 153.032559 2.909002 0.083918 \n", + "min 2.000000 0.000000 0.000000 \n", + "25% 77.250000 0.000000 0.000000 \n", + "50% 181.000000 0.000000 0.000000 \n", + "75% 277.750000 1.000000 0.000000 \n", + "max 584.000000 17.000000 1.000000 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wnba.describe()" ] }, { @@ -70,11 +677,224 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "113\n", + "55\n", + "36\n", + "21\n" + ] + }, + { + "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
96Moriah JeffersonSANG1685519.486961USAugust 3, 199423Connecticut1215148115552.392045.0202774.163137923324319100
\n", + "
" + ], + "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", + "96 Moriah Jefferson SAN G 168 55 19.486961 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", + "96 August 3, 1994 23 Connecticut 1 21 514 81 \n", + "\n", + " FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB AST STL BLK \\\n", + "12 53 37.7 2 8 25.0 9 12 75.0 5 18 23 7 4 5 \n", + "36 57 56.1 0 0 0.0 6 12 50.0 28 34 62 5 6 8 \n", + "96 155 52.3 9 20 45.0 20 27 74.1 6 31 37 92 33 2 \n", + "\n", + " TO PTS DD2 TD3 \n", + "12 12 51 0 0 \n", + "36 18 70 0 0 \n", + "96 43 191 0 0 " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "\n", + "# wnba.nlargest(5, \"Age\") \n", + "# wnba.nsmallest(5, \"Age\")\n", + "\n", + "print(wnba[\"Weight\"].max())\n", + "print(wnba[\"Weight\"].min())\n", + "\n", + "print(wnba[\"Age\"].max())\n", + "print(wnba[\"Age\"].min())\n", + "\n", + "wnba[(wnba[\"Weight\"]==wnba[\"Weight\"].max()) | (wnba[\"Weight\"]==wnba[\"Weight\"].min())]" ] }, { @@ -89,11 +909,39 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "#your code here\n", + "\n", + "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 = \"blue\")\n", + "plot_3.set_xlabel(\"Age\")\n", + "\n", + "plot_4.hist(wnba[\"BMI\"], color = \"orange\")\n", + "plot_4.set_xlabel(\"BMI\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { @@ -134,11 +982,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "#your code here\n", + "\n", + "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()" ] }, { @@ -150,7 +1032,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -173,11 +1055,44 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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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
\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", + "3 Alex Montgomery SAN G/F 185 84 24.543462 US \n", + "4 Alexis Jones MIN G 175 78 25.469388 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", + "3 December 11, 1988 28 Georgia Tech 6 31 721 75 \n", + "4 August 5, 1994 23 Baylor R 24 137 16 \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", + "3 195 38.5 21 68 30.9 17 21 81.0 35 134 169 65 20 10 \n", + "4 50 32.0 7 20 35.0 11 12 91.7 3 9 12 12 7 0 \n", + "\n", + " TO PTS DD2 TD3 \n", + "0 12 93 0 0 \n", + "1 40 217 0 0 \n", + "2 24 218 0 0 \n", + "3 38 188 2 0 \n", + "4 14 50 0 0 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#your code here\n", + "wnba = pd.read_csv(r\"data/wnba_clean.csv\")\n", + "wnba.head()" ] }, { @@ -74,7 +346,20 @@ "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", + "\"\"\"" ] }, { @@ -88,9 +373,25 @@ "cell_type": "code", "execution_count": 6, "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "95% Confidence Interval for Average Weight: (77.17, 80.79)\n" + ] + } + ], + "source": [ + "# your code here\n", + "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 +407,10 @@ "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 +426,11 @@ "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 +462,23 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# your answer here" + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Proportion of players who miss more than 40% of their free throws: 0.099\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 +490,33 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'players_missed' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[10], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m sample_size \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(wnba)\n\u001b[1;32m----> 2\u001b[0m sample_proportion \u001b[39m=\u001b[39m players_missed \u001b[39m/\u001b[39m sample_size\n\u001b[0;32m 4\u001b[0m z_value \u001b[39m=\u001b[39m st\u001b[39m.\u001b[39mnorm\u001b[39m.\u001b[39mppf(\u001b[39m1\u001b[39m \u001b[39m-\u001b[39m (\u001b[39m1\u001b[39m \u001b[39m-\u001b[39m \u001b[39m0.95\u001b[39m)\u001b[39m/\u001b[39m \u001b[39m2\u001b[39m)\n\u001b[0;32m 6\u001b[0m margin_of_error \u001b[39m=\u001b[39m z_value \u001b[39m*\u001b[39m np\u001b[39m.\u001b[39msqrt((sample_proportion \u001b[39m*\u001b[39m (\u001b[39m1\u001b[39m \u001b[39m-\u001b[39m sample_proportion))\u001b[39m/\u001b[39mn)\n", + "\u001b[1;31mNameError\u001b[0m: name 'players_missed' is not defined" + ] + } + ], + "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}\")" ] }, { @@ -190,7 +532,12 @@ "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", + "\"\"\"" ] }, { @@ -225,15 +572,6 @@ "**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": {}, @@ -245,18 +583,36 @@ "cell_type": "code", "execution_count": 15, "metadata": {}, - "outputs": [], - "source": [ - "#your code here" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "#your-answer-here" + "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 +624,38 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "#your-answer-here" + "execution_count": 14, + "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\") " ] }, { @@ -357,7 +740,7 @@ "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/data/codebook.md similarity index 100% rename from data/codebook.md rename to your-code/data/codebook.md diff --git a/data/wnba.csv b/your-code/data/wnba.csv similarity index 99% rename from data/wnba.csv rename to your-code/data/wnba.csv index 0e35127..bb13374 100644 --- a/data/wnba.csv +++ b/your-code/data/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/data/wnba_clean.csv similarity index 97% rename from data/wnba_clean.csv rename to your-code/data/wnba_clean.csv index 3d702f3..3bf5421 100644 --- a/data/wnba_clean.csv +++ b/your-code/data/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 From 6bf007b873fe3c343bfdbd24d07e2d4dd1c70744 Mon Sep 17 00:00:00 2001 From: Sergio Soutinho Date: Fri, 18 Aug 2023 09:17:34 +0100 Subject: [PATCH 2/2] lab done --- your-code/3.-Inferential-Analysis.ipynb | 30 +++++++++++-------------- 1 file changed, 13 insertions(+), 17 deletions(-) diff --git a/your-code/3.-Inferential-Analysis.ipynb b/your-code/3.-Inferential-Analysis.ipynb index 0828578..66a18d8 100644 --- a/your-code/3.-Inferential-Analysis.ipynb +++ b/your-code/3.-Inferential-Analysis.ipynb @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -314,7 +314,7 @@ "4 14 50 0 0 " ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -371,7 +371,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -462,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -490,24 +490,20 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'players_missed' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[10], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m sample_size \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(wnba)\n\u001b[1;32m----> 2\u001b[0m sample_proportion \u001b[39m=\u001b[39m players_missed \u001b[39m/\u001b[39m sample_size\n\u001b[0;32m 4\u001b[0m z_value \u001b[39m=\u001b[39m st\u001b[39m.\u001b[39mnorm\u001b[39m.\u001b[39mppf(\u001b[39m1\u001b[39m \u001b[39m-\u001b[39m (\u001b[39m1\u001b[39m \u001b[39m-\u001b[39m \u001b[39m0.95\u001b[39m)\u001b[39m/\u001b[39m \u001b[39m2\u001b[39m)\n\u001b[0;32m 6\u001b[0m margin_of_error \u001b[39m=\u001b[39m z_value \u001b[39m*\u001b[39m np\u001b[39m.\u001b[39msqrt((sample_proportion \u001b[39m*\u001b[39m (\u001b[39m1\u001b[39m \u001b[39m-\u001b[39m sample_proportion))\u001b[39m/\u001b[39mn)\n", - "\u001b[1;31mNameError\u001b[0m: name 'players_missed' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "Proportion of players who miss more than 40% of their free throws: -0.0036380955691120032, 0.005026708939475027\n" ] } ], "source": [ "sample_size = len(wnba)\n", - "sample_proportion = players_missed / sample_size\n", + "sample_proportion = prop_missed_ft / sample_size\n", "\n", "z_value = st.norm.ppf(1 - (1 - 0.95)/ 2)\n", "\n", @@ -581,7 +577,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -624,7 +620,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "metadata": {}, "outputs": [ {