diff --git a/your-code/1.-Data-Cleaning.ipynb b/your-code/1.-Data-Cleaning.ipynb index d1c8eea..5422fbf 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 +47,284 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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2Alex BentleyCONG17069.023.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
3Alex MontgomerySANG/F18584.024.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
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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
...................................................................................................
138Tiffany HayesATLG1787022.093170USSeptember 20, 198927Connecticut62986114433143.54311238.413616184.52889117693785046700
139Tiffany JacksonLAF1918423.025685USApril 26, 198532Texas922127122548.0010.04666.75182331382800
140Tiffany MitchellINDG1756922.530612USSeptember 23, 198432South Carolina2276718323834.9176924.69410292.2167086393154027700
141Tina CharlesNYF/C1938422.550941USMay 12, 198829Connecticut82995222750944.6185632.111013581.55621226875212271582110
142Yvonne TurnerPHOG1755919.265306USOctober 13, 198729Nebraska2303565914042.1114723.4222878.6111324301813215100
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142 rows × 32 columns

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HeightWeightBMIAgeGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
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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
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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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Unnamed: 0NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
00Aerial PowersDALF18371.021.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
11Alana BeardLAG/F18573.021.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
22Alex BentleyCONG17069.023.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
33Alex MontgomerySANG/F18584.024.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
44Alexis JonesMING17578.025.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
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" + ], + "text/plain": [ + " Weight\n", + "Age \n", + "21 104.0\n", + "22 82.0\n", + "23 96.0\n", + "24 113.0\n", + "25 97.0\n", + "26 93.0\n", + "27 104.0\n", + "28 108.0\n", + "29 113.0\n", + "30 90.0\n", + "31 93.0\n", + "32 96.0\n", + "33 81.0\n", + "34 86.0\n", + "35 88.0\n", + "36 68.0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wnba.groupby('Age').agg({'Weight': 'max'})" ] }, { @@ -89,11 +846,34 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "#your code here\n", + "\n", + "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize= (10, 10))\n", + "ax1.set_title('Height')\n", + "ax2.set_title('Weight')\n", + "ax3.set_title('Age')\n", + "ax4.set_title('BMI')\n", + "ax1.hist(wnba['Height']);\n", + "ax2.hist(wnba['Weight']);\n", + "ax3.hist(wnba['Age'], bins= 16);\n", + "ax4.hist(wnba['Height']);" ] }, { @@ -109,7 +889,8 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "#your conclusions here\n", + "# BMI and Height have a similar distribution" ] }, { @@ -134,11 +915,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "#your code here\n", + "\n", + "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize= (10, 10))\n", + "ax1.set_title('REB')\n", + "ax2.set_title('AST')\n", + "ax3.set_title('STL')\n", + "ax4.set_title('BLK')\n", + "ax1.hist(wnba['REB']);\n", + "ax2.hist(wnba['AST']);\n", + "ax3.hist(wnba['STL']);\n", + "ax4.hist(wnba['BLK']);" ] }, { @@ -150,11 +954,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "#your conclusions here\n", + "#the characteristics seems to be normally distributed\n" ] }, { @@ -173,11 +978,36 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "#your code here\n", + "\n", + "fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(3, 2, figsize= (10, 12))\n", + "ax1.set_title('Rebounds')\n", + "ax1.hist(wnba['REB']/wnba['MIN']);\n", + "ax2.set_title('Assists')\n", + "ax2.hist(wnba['AST']/wnba['MIN']);\n", + "ax3.set_title('Steals')\n", + "ax3.hist(wnba['STL']/wnba['MIN']);\n", + "ax4.set_title('Points')\n", + "ax4.hist(wnba['PTS']/wnba['MIN']);\n", + "ax5.set_title('Blocks')\n", + "ax5.hist(wnba['BLK']/wnba['MIN']);" ] }, { @@ -189,11 +1019,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "#your conclusions here\n" ] }, { @@ -218,17 +1048,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "#your comments here" + "#your comments here\n", + "# informantion on sisters pyshical would be helpful" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -242,7 +1073,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/your-code/3.-Inferential-Analysis.ipynb b/your-code/3.-Inferential-Analysis.ipynb index 366765b..ff25839 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": [ @@ -32,7 +32,8 @@ "from scipy import stats\n", "import matplotlib.pyplot as plt\n", "from scipy.stats import ttest_1samp\n", - "pd.set_option('display.max_columns', 50)" + "pd.set_option('display.max_columns', 50)\n", + "import scipy.stats as st" ] }, { @@ -46,11 +47,290 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], - "source": [ - "#your code here" + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
00Aerial PowersDALF18371.021.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
11Alana BeardLAG/F18573.021.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
22Alex BentleyCONG17069.023.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
33Alex MontgomerySANG/F18584.024.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
44Alexis JonesMING17578.025.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 Name Team Pos Height Weight BMI \\\n", + "0 0 Aerial Powers DAL F 183 71.0 21.200991 \n", + "1 1 Alana Beard LA G/F 185 73.0 21.329438 \n", + "2 2 Alex Bentley CON G 170 69.0 23.875433 \n", + "3 3 Alex Montgomery SAN G/F 185 84.0 24.543462 \n", + "4 4 Alexis Jones MIN G 175 78.0 25.469388 \n", + "\n", + " Birth_Place Birthdate Age College Experience \\\n", + "0 US January 17, 1994 23 Michigan State 2 \n", + "1 US May 14, 1982 35 Duke 12 \n", + "2 US October 27, 1990 26 Penn State 4 \n", + "3 US December 11, 1988 28 Georgia Tech 6 \n", + "4 US August 5, 1994 23 Baylor R \n", + "\n", + " Games Played MIN FGM FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB \\\n", + "0 8 173 30 85 35.3 12 32 37.5 21 26 80.8 6 \n", + "1 30 947 90 177 50.8 5 18 27.8 32 41 78.0 19 \n", + "2 26 617 82 218 37.6 19 64 29.7 35 42 83.3 4 \n", + "3 31 721 75 195 38.5 21 68 30.9 17 21 81.0 35 \n", + "4 24 137 16 50 32.0 7 20 35.0 11 12 91.7 3 \n", + "\n", + " DREB REB AST STL BLK TO PTS DD2 TD3 \n", + "0 22 28 12 3 6 12 93 0 0 \n", + "1 82 101 72 63 13 40 217 0 0 \n", + "2 36 40 78 22 3 24 218 0 0 \n", + "3 134 169 65 20 10 38 188 2 0 \n", + "4 9 12 12 7 0 14 50 0 0 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#your code here\n", + "\n", + "wnba = pd.read_csv('wnba_clean.csv')\n", + "wnba.head()" ] }, { @@ -70,11 +350,58 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# your answer here" + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean population 78.97887323943662\n", + "mean sample: 78.96211\n" + ] + } + ], + "source": [ + "# your answer here\n", + "weight_means = []\n", + "\n", + "for i in range(1000):\n", + " popl_sample = np.random.choice(wnba['Weight'],10) \n", + " mean_sample = np.mean(popl_sample)\n", + " weight_means.append(mean_sample)\n", + "\n", + "plt.hist(weight_means)\n", + "\n", + "weight_means = []\n", + "\n", + "for i in range(1000):\n", + " popl_sample = np.random.choice(wnba['Weight'],100) \n", + " mean_sample = np.mean(popl_sample)\n", + " weight_means.append(mean_sample)\n", + "\n", + "plt.hist(weight_means)\n", + "\n", + "plt.show()\n", + "\n", + "mu = np.mean(wnba['Weight'])\n", + "\n", + "print('mean population', mu)\n", + "\n", + "x_hat = np.mean(weight_means)\n", + "print('mean sample:', x_hat)\n" ] }, { @@ -86,11 +413,24 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(76.83127733557417, 81.12646914329908)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "sigma = wnba['Weight'].std(ddof=0)\n", + "st.norm.interval(0.95, loc=mu, scale=sigma/np.sqrt(100))" ] }, { @@ -106,7 +446,8 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "#the sister weight is not on the confidence interval " ] }, { @@ -122,7 +463,8 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "#grandmother seems to be right" ] }, { @@ -154,11 +496,68 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ - "# your answer here" + "# your code here\n", + "wnba_40 = wnba['FT%'] < 60.0" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean population 0.09859154929577464\n", + "mean sample: 0.09955000000000001\n" + ] + } + ], + "source": [ + "ft_means = []\n", + "\n", + "for i in range(1000):\n", + " pop_sample = np.random.choice(wnba_40,10) \n", + " mean_sample = np.mean(pop_sample)\n", + " ft_means.append(mean_sample)\n", + "\n", + "plt.hist(ft_means)\n", + "\n", + "ft_means = []\n", + "\n", + "for i in range(1000):\n", + " pop_sample = np.random.choice(wnba_40,100) \n", + " mean_sample = np.mean(pop_sample)\n", + " ft_means.append(mean_sample)\n", + "\n", + "plt.hist(ft_means)\n", + "\n", + "plt.show()\n", + "\n", + "mu = np.mean(wnba_40)\n", + "\n", + "print('mean population', mu)\n", + "\n", + "\n", + "x_hat = np.mean(ft_means)\n", + "print('mean sample:', x_hat)" ] }, { @@ -170,11 +569,24 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 24, "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.04895944132917377, 0.14822365726237552)\n" + ] + } + ], + "source": [ + "# your code here\n", + "n = len(wnba_40)\n", + "mean_ = wnba_40.mean()\n", + "s = wnba_40.std(ddof=1)\n", + "\n", + "print(st.t.interval(0.95,n-1,loc=mean_,scale= s/np.sqrt(n)))" ] }, { @@ -190,7 +602,8 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "# The sister is wrong, 9.8% fail more than 40% of the FT" ] }, { @@ -227,11 +640,58 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "#your-answer-here" + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean population param: 44.514084507042256\n", + "mean sample dist: 44.522380000000005\n" + ] + } + ], + "source": [ + "#your-answer-here\n", + "ast_means = []\n", + "\n", + "for i in range(1000):\n", + " pop_sample = np.random.choice(wnba['AST'],10) \n", + " mean_sample = np.mean(pop_sample)\n", + " ast_means.append(mean_sample)\n", + "\n", + "plt.hist(ast_means)\n", + "\n", + "ast_means = []\n", + "\n", + "for i in range(1000):\n", + " pop_sample = np.random.choice(wnba['AST'],100) \n", + " mean_sample = np.mean(pop_sample)\n", + " ast_means.append(mean_sample)\n", + "\n", + "plt.hist(ast_means)\n", + "\n", + "plt.show()\n", + "\n", + "mu = np.mean(wnba['AST'])\n", + "\n", + "print('mean population param: ', mu)\n", + "\n", + "x_hat = np.mean(ast_means)\n", + "print('mean sample dist:', x_hat)" ] }, { @@ -243,11 +703,25 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "#your code here" + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Ttest_1sampResult(statistic=-2.1499947192482898, pvalue=0.033261541354107166)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#your code here\n", + "alpha = 0.05\n", + "\n", + "st.ttest_1samp(wnba['AST'],52)" ] }, { @@ -256,7 +730,8 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "#we reject the hypothesis" ] }, { @@ -268,11 +743,23 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Ttest_1sampResult(statistic=-2.1499947192482898, pvalue=0.016630770677053583)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "st.ttest_1samp(wnba['AST'],52,alternative='less')" ] }, { @@ -343,7 +830,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -357,7 +844,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/your-code/wnba_clean.csv b/your-code/wnba_clean.csv new file mode 100644 index 0000000..6513af0 --- /dev/null +++ b/your-code/wnba_clean.csv @@ -0,0 +1,143 @@ +,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 +0,Aerial Powers,DAL,F,183,71.0,21.20099137,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 +1,Alana Beard,LA,G/F,185,73.0,21.32943755,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 +2,Alex Bentley,CON,G,170,69.0,23.87543253,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 +3,Alex Montgomery,SAN,G/F,185,84.0,24.54346238,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 +4,Alexis Jones,MIN,G,175,78.0,25.46938776,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 +5,Alexis Peterson,SEA,G,170,63.0,21.79930796,US,"June 20, 1995",22,Syracuse,R,14,90,9,34,26.5,2,9,22.2,6,6,100.0,3,13,16,11,5,0,11,26,0,0 +6,Alexis Prince,PHO,G,188,81.0,22.91760978,US,"February 5, 1994",23,Baylor,R,16,112,9,34,26.5,4,15,26.7,2,2,100.0,1,14,15,5,4,3,3,24,0,0 +7,Allie Quigley,CHI,G,178,64.0,20.19946976,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 +8,Allisha Gray,DAL,G,185,76.0,22.20598977,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 +9,Allison Hightower,WAS,G,178,77.0,24.30248706,US,"June 4, 1988",29,LSU,5,7,103,14,38,36.8,2,11,18.2,6,6,100.0,3,7,10,10,5,0,2,36,0,0 +10,Alysha Clark,SEA,F,180,76.0,23.45679012,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 +11,Alyssa Thomas,CON,F,188,84.0,23.76641014,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 +12,Amanda Zahui B.,NY,C,196,113.0,29.41482716,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 +13,Amber Harris,CHI,F,196,88.0,22.90712203,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 +14,Aneika Henry,ATL,F/C,193,87.0,23.35633171,JM,"February 13, 1986",31,Florida,6,4,22,4,4,100.0,0,0,0.0,0,0,0.0,0,4,4,1,2,0,3,8,0,0 +15,Angel Robinson,PHO,F/C,198,88.0,22.44668911,US,"August 30, 1995",21,Arizona State,1,15,237,25,44,56.8,1,1,100.0,7,7,100.0,16,42,58,8,1,11,16,58,0,0 +16,Asia Taylor,WAS,F,185,76.0,22.20598977,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 +17,Bashaara Graves,CHI,F,188,91.0,25.74694432,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 +18,Breanna Lewis,DAL,C,196,93.0,24.20866306,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 +19,Breanna Stewart,SEA,F/C,193,77.0,20.67169588,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 +20,Bria Hartley,NY,G,173,66.0,22.05219018,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 +21,Bria Holmes,ATL,G,185,77.0,22.49817385,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 +22,Briann January,IND,G,173,65.0,21.71806609,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 +23,Brionna Jones,CON,F,191,104.0,28.50799046,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 +24,Brittany Boyd,NY,G,175,71.0,23.18367347,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 +25,Brittney Griner,PHO,C,206,93.0,21.91535489,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 +26,Brittney Sykes,ATL,G,175,66.0,21.55102041,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 +27,Camille Little,PHO,F,188,82.0,23.20054323,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 +28,Candace Parker,LA,F/C,193,79.0,21.20862305,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 +29,Candice Dupree,IND,F,188,81.0,22.91760978,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 +30,Cappie Pondexter,CHI,G,175,73.0,23.83673469,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 +31,Carolyn Swords,SEA,C,198,95.0,24.2322212,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 +32,Cayla George,PHO,C,193,87.0,23.35633171,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 +33,Chelsea Gray,LA,G,180,77.0,23.7654321,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 +34,Cheyenne Parker,CHI,F,193,86.0,23.08786813,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 +35,Clarissa dos Santos,SAN,C,185,89.0,26.00438276,BR,"October 3, 1988",28,Brazil,4,7,52,8,14,57.1,1,1,100.0,0,0,0.0,3,7,10,7,1,1,5,17,0,0 +36,Courtney Paris,DAL,C,193,113.0,30.33638487,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 +37,Courtney Vandersloot,CHI,G,173,66.0,22.05219018,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 +38,Courtney Williams,CON,G,173,62.0,20.71569381,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 +39,Crystal Langhorne,SEA,F/C,188,84.0,23.76641014,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 +40,Damiris Dantas,ATL,C,191,89.0,24.39626107,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 +41,Danielle Adams,CON,F/C,185,108.0,31.5558802,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 +42,Danielle Robinson,PHO,G,175,57.0,18.6122449,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 +43,Dearica Hamby,SAN,F,191,86.0,23.57391519,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 +44,Devereaux Peters,IND,F,188,79.0,22.35174287,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 +45,Diana Taurasi,PHO,G,183,74.0,22.09680791,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 +46,Elena Delle Donne,WAS,G/F,196,85.0,22.12619742,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 +47,Elizabeth Williams,ATL,F/C,191,87.0,23.84803048,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 +48,Emma Cannon,PHO,F,188,86.0,24.33227705,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 +49,Emma Meesseman,WAS,C,193,83.0,22.28247738,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 +50,Epiphanny Prince,NY,G,175,81.0,26.44897959,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 +51,Erica Wheeler,IND,G,170,65.0,22.49134948,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 +52,Érika de Souza,SAN,C,196,86.0,22.38650562,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 +53,Erlana Larkins,IND,F,185,93.0,27.17311907,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 +54,Essence Carson,LA,G/F,183,74.0,22.09680791,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 +55,Evelyn Akhator,DAL,F,191,82.0,22.47745402,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 +56,Glory Johnson,DAL,F,191,77.0,21.10687755,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 +57,Imani Boyette,ATL,C,201,88.0,21.78163907,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 +58,Isabelle Harrison,SAN,C,191,83.0,22.75156931,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 +59,Ivory Latta,WAS,G,168,63.0,22.32142857,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 +60,Jantel Lavender,LA,C,193,84.0,22.55094096,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 +61,Jasmine Thomas,CON,G,175,66.0,21.55102041,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 +62,Jazmon Gwathmey,IND,G,188,65.0,18.39067451,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 +63,Jeanette Pohlen,IND,G,183,78.0,23.29122996,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 +64,Jennifer Hamson,IND,C,201,95.0,23.51426945,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 +65,Jessica Breland,CHI,F,191,77.0,21.10687755,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 +66,Jewell Loyd,SEA,G,178,67.0,21.14631991,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 +67,Jia Perkins,MIN,G,173,75.0,25.05930703,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 +68,Jonquel Jones,CON,F/C,198,86.0,21.93653709,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 +69,Jordan Hooper,CHI,F,188,84.0,23.76641014,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 +70,Kaela Davis,DAL,G,188,77.0,21.78587596,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 +71,Kahleah Copper,CHI,G/F,185,70.0,20.45288532,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 +72,Kaleena Mosqueda-Lewis,SEA,F,180,82.0,25.30864198,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 +73,Karima Christmas-Kelly,DAL,G/F,183,82.0,24.48565201,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 +74,Kayla Alexander,SAN,C,193,88.0,23.6247953,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 +75,Kayla McBride,SAN,G/F,180,79.0,24.38271605,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 +76,Kayla Pedersen,CON,F,193,86.0,23.08786813,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 +77,Kayla Thornton,DAL,F,185,86.0,25.12783053,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 +78,Keisha Hampton,CHI,F,185,78.0,22.79035793,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 +79,Kelsey Plum,SAN,G,173,66.0,22.05219018,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 +80,Kia Vaughn,NY,C,193,90.0,24.16172246,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 +81,Kiah Stokes,NY,C,191,87.0,23.84803048,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 +82,Kristi Toliver,WAS,G,170,59.0,20.41522491,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 +83,Krystal Thomas,WAS,C,196,88.0,22.90712203,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 +84,Lanay Montgomery,SEA,C,196,96.0,24.98958767,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 +85,Layshia Clarendon,ATL,G,175,64.0,20.89795918,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 +86,Leilani Mitchell,PHO,G,165,58.0,21.30394858,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 +87,Lindsay Allen,NY,G,173,65.0,21.71806609,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 +88,Lindsay Whalen,MIN,G,175,78.0,25.46938776,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 +89,Lynetta Kizer,CON,C,193,104.0,27.92021262,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 +90,Maimouna Diarra,LA,C,198,90.0,22.95684114,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 +92,Marissa Coleman,IND,G/F,185,73.0,21.32943755,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 +93,Matee Ajavon,ATL,G,173,73.0,24.39105884,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 +94,Maya Moore,MIN,F,183,80.0,23.88844098,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 +95,Monique Currie,PHO,G/F,183,80.0,23.88844098,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 +96,Morgan Tuck,CON,F,188,91.0,25.74694432,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 +97,Moriah Jefferson,SAN,G,168,55.0,19.48696145,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 +98,Natalie Achonwa,IND,C,193,83.0,22.28247738,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 +99,Natasha Cloud,WAS,G,183,73.0,21.79820239,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 +100,Natasha Howard,MIN,F,188,75.0,21.22000905,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 +101,Nayo Raincock-Ekunwe,NY,F/C,188,79.0,22.35174287,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 +102,Nia Coffey,SAN,F,185,77.0,22.49817385,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 +103,Nneka Ogwumike,LA,F,188,79.0,22.35174287,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 +104,Noelle Quinn,SEA,G,183,81.0,24.18704649,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 +105,Odyssey Sims,LA,G,173,73.0,24.39105884,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 +106,Plenette Pierson,MIN,F/C,188,88.0,24.89814396,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 +107,Rachel Banham,CON,G,175,76.0,24.81632653,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 +108,Ramu Tokashiki,SEA,F,193,80.0,21.47708663,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 +109,Rebecca Allen,NY,G/F,188,74.0,20.9370756,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 +110,Rebekkah Brunson,MIN,F,188,84.0,23.76641014,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 +111,Renee Montgomery,MIN,G,170,63.0,21.79930796,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 +112,Riquna Williams,LA,G,170,75.0,25.95155709,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 +113,Sami Whitcomb,SEA,G,178,66.0,20.83070319,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 +114,Sancho Lyttle,ATL,F,193,79.0,21.20862305,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 +115,Sandrine Gruda,LA,F/C,193,84.0,22.55094096,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 +116,Saniya Chong,DAL,G,173,64.0,21.383942,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 +117,Seimone Augustus,MIN,G/F,183,77.0,22.99262444,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 +118,Sequoia Holmes,SAN,G,185,70.0,20.45288532,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 +119,Shatori Walker-Kimbrough,WAS,G,180,64.0,19.75308642,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 +120,Shavonte Zellous,NY,G,178,85.0,26.82742078,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 +121,Shay Murphy,SAN,G,180,74.0,22.83950617,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 +122,Shekinna Stricklen,CON,G/F,188,81.0,22.91760978,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 +123,Shenise Johnson,IND,G,180,78.0,24.07407407,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 +124,Skylar Diggins-Smith,DAL,G,175,66.0,21.55102041,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 +125,Stefanie Dolson,CHI,C,196,97.0,25.24989588,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 +126,Stephanie Talbot,PHO,G,185,87.0,25.42001461,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 +127,Sue Bird,SEA,G,175,68.0,22.20408163,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 +128,Sugar Rodgers,NY,G,175,75.0,24.48979592,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 +129,Sydney Colson,SAN,G,173,64.0,21.383942,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 +130,Sydney Wiese,LA,G,183,68.0,20.30517483,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 +131,Sylvia Fowles,MIN,C,198,96.0,24.48729721,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 +132,Tamera Young,ATL,G/F,188,77.0,21.78587596,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 +133,Tayler Hill,WAS,G,175,66.0,21.55102041,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 +134,Temi Fagbenle,MIN,C,193,89.0,23.89325888,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 +135,Theresa Plaisance,DAL,F,196,91.0,23.68804665,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 +136,Tianna Hawkins,WAS,F,191,87.0,23.84803048,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 +137,Tierra Ruffin-Pratt,WAS,G,178,83.0,26.19618735,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 +138,Tiffany Hayes,ATL,G,178,70.0,22.09317005,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 +139,Tiffany Jackson,LA,F,191,84.0,23.0256846,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 +140,Tiffany Mitchell,IND,G,175,69.0,22.53061224,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 +141,Tina Charles,NY,F/C,193,84.0,22.55094096,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 +142,Yvonne Turner,PHO,G,175,59.0,19.26530612,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