diff --git a/.ipynb_checkpoints/James Berent Flyer-checkpoint.pdf b/.ipynb_checkpoints/James Berent Flyer-checkpoint.pdf new file mode 100644 index 0000000..355ac34 Binary files /dev/null and b/.ipynb_checkpoints/James Berent Flyer-checkpoint.pdf differ diff --git a/.ipynb_checkpoints/argument-checkpoint.ipynb b/.ipynb_checkpoints/argument-checkpoint.ipynb index 72bd82b..3e95972 100644 --- a/.ipynb_checkpoints/argument-checkpoint.ipynb +++ b/.ipynb_checkpoints/argument-checkpoint.ipynb @@ -13,7 +13,7 @@ "id": "understanding-numbers", "metadata": {}, "source": [ - "I will research 3 different questions I had while looking at the NBA data. I will first find who the better player is between LeBron James and Kevin Durant and then find who the best players are on offense and defense. All of the data I will be using will come from the 2014-2022 NBA seasons." + "I will research 3 different questions I had while looking at the NBA data. I will first find who the better player is between LeBron James and Kevin Durant and then find who the best players are on offense and defense. All of the data I will be using will come from the 2014-2022 NBA seasons. This project will be able to give valuable insight to an NBA front office telling them who they should try to acquire in the offseason." ] }, { @@ -29,7 +29,7 @@ "id": "appreciated-testimony", "metadata": {}, "source": [ - "I want to look at this because I have played basketball most of my life and enjoy watching the NBA. I enjoy looking at NBA players' stats in my free time so this was very interesting to me. I want to see who some of the best players are on offense and defense." + "I want to look at this because I have played basketball most of my life and enjoy watching the NBA. I enjoy looking at NBA players' stats in my free time, so this was very interesting to me. I want to see who some of the best players are on offense and defense in the NBA." ] }, { @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 23, "id": "technical-evans", "metadata": {}, "outputs": [], @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 24, "id": "overhead-sigma", "metadata": { "scrolled": true @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 25, "id": "heated-blade", "metadata": {}, "outputs": [ @@ -281,7 +281,7 @@ "[5 rows x 21 columns]" ] }, - "execution_count": 3, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -297,7 +297,7 @@ "source": [ "**Data Overview**\n", "\n", - "This dataset comes from FiveThirtyEight and contains different data from NBA players from 2014-2022. It includes the players' names, players' ids, and the season year. It also includes number of possessions played and minutes played which both indicate how much time the player was on the court that season. Also, the data includes raptor stats for offense, defense, and total which shows how effective a player was on that side of the court. The data also shows predator stats for offense, defense, and total which is a prediction of how effective the player was. Also, WAR (wins above replacement) stats were shown in the data which show how many more wins that player got their team over the season compared to if a replacement level player was playing instead. Lastly, pace impact stats were shown which states the overall impact a player had on their team. The pace impact stat for each player was shown as if each player 48 minutes which the the length of the whole game." + "This dataset comes from FiveThirtyEight and contains different data from NBA players from 2014-2022. It includes the players' names, players' ids, and the season year. It also includes number of possessions played and minutes played which both indicate how much time the player was on the court that season. Also, the data includes raptor stats for offense, defense, and total which shows how effective a player was on that side of the court. The data also shows predator stats for offense, defense, and total which is a prediction of how effective the player was. Also, WAR (wins above replacement) stats were shown in the data which show how many more wins that player got their team over the season compared to if a replacement level player was playing instead. Lastly, pace impact stats were shown which state the overall impact a player had on their team. The pace impact stat for each player was shown as if each player played 48 minutes which is the length of the whole game." ] }, { @@ -354,7 +354,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 26, "id": "9e6b2020-4df8-4e45-8b00-fbc398964376", "metadata": {}, "outputs": [], @@ -364,7 +364,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 27, "id": "b57dfb5b-f725-43d7-bfaa-76e7d86ec69f", "metadata": { "scrolled": true @@ -376,7 +376,7 @@ "np.float64(5.958565647986926)" ] }, - "execution_count": 5, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -387,7 +387,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 28, "id": "496218f8-6823-4570-b97f-bdf2461e3e06", "metadata": { "scrolled": true @@ -399,7 +399,7 @@ "np.float64(0.3291714363332999)" ] }, - "execution_count": 6, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -410,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 29, "id": "247b1e93-df57-4b13-a22f-656e6740e715", "metadata": {}, "outputs": [ @@ -420,7 +420,7 @@ "np.float64(6.2877370843202245)" ] }, - "execution_count": 7, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -439,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 30, "id": "c0a35626-5356-4b7b-8e2b-eb83cc42b8f8", "metadata": {}, "outputs": [], @@ -449,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 31, "id": "08415fc5-0f02-4e13-906a-c9dee9f8118e", "metadata": {}, "outputs": [ @@ -459,7 +459,7 @@ "np.float64(5.66912563466798)" ] }, - "execution_count": 9, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -470,9 +470,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 32, "id": "f37fb84f-4861-4e14-b6dc-502c180dcb4b", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -480,7 +482,7 @@ "np.float64(0.20101735750424538)" ] }, - "execution_count": 10, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -491,7 +493,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 33, "id": "a60e7ec7-27fb-47df-ba2a-94a84c7926e5", "metadata": {}, "outputs": [ @@ -501,7 +503,7 @@ "np.float64(5.870142992047225)" ] }, - "execution_count": 11, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -516,7 +518,7 @@ "metadata": {}, "source": [ "#### Data Explained\n", - "LeBron James is better on average, offensively, defensively, and all around than Kevin Durant is. LeBron's raptor offense, defense, and total rating is higher than Durant's. This does not account for individual seasons but uses the data from all seasons from 2014-2022. I will graph LeBron James' and Kevin Durant's indivudal seaons below. " + "LeBron James is better on average, offensively, defensively, and all around than Kevin Durant is. LeBron's raptor offense, defense, and total rating is higher than Durant's. This does not account for individual seasons but uses the data from all seasons from 2014-2022. I will graph LeBron James' and Kevin Durant's individual seasons below. " ] }, { @@ -529,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 34, "id": "79e1f833-833a-4a75-b41d-eeab580bf713", "metadata": { "scrolled": true @@ -541,13 +543,13 @@ "Text(0.5, 1.0, 'LeBron James vs Kevin Durant')" ] }, - "execution_count": 12, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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" ] @@ -559,6 +561,7 @@ "source": [ "sns.scatterplot(data=Lebron_df,x=\"season\",y=\"raptor_total\")\n", "sns.scatterplot(data=Durant_df,x=\"season\",y=\"raptor_total\")\n", + "plt.ylim([0,9])\n", "plt.legend(labels=[\"LeBron James\",\"Kevin Durant\"])\n", "plt.title(\"LeBron James vs Kevin Durant\")" ] @@ -593,94 +596,363 @@ "id": "64e47d9c-d4af-4c02-87af-d0087597d629", "metadata": {}, "source": [ - "I will use the player_name, raptor_offense, predator_offense, and pace_impact data for each player. \n", + "I will use the player_name, raptor_offense, and predator_offense for each player. \n", " \n", "**Initial Problem:** \n", "I first completed this without excluding players who played less than 3000 minutes in a season and was finding that the data was showing me the best offensive NBA players were players who only played one game. This was happening because a few players played great offensively in one game and never played again. Obviously, those are not the best NBA players as they only played 1 or a few games in the NBA.\n", "\n", "**How I fixed this:** \n", - "I fixed this by removing all NBA players who played less than 3000 minutes throughout the 82 game season. This only allowed the players who were good enough to play many minutes for their teams throughout the season. This means that these were some of the best NBA players.\n", + "I fixed this by removing all NBA players who played less than 3000 minutes throughout the 82 game season. This only allowed the players who were good enough to play many minutes for their teams throughout the season. This means that these were some of the best NBA players. There are 48 minutes in an NBA game so I chose 3000 minutes since 3000/48 = 62.5 and that is about three quarters of the NBA season.\n", "\n", "**What I did:** \n", - "I first removed all players who played less than 3000 minutes. I then grouped the data by the players' names as I called these the eligible players. I then took all of the offensive stats in this dataset which were the raptor offense, predator offense, and pace impact data and took the averages of all of them which I called the offense stats. I then added all of the offense stats together and sorted them by highest to lowest number. The higher the number correlates with the better the offensive player which gives us a list ranking the best to worst offensive players that have all played over 3000 minutes." + "I first removed all players who played less than 3000 minutes. I then grouped the data by the players' names as I called these the eligible players. I then took all of the offensive stats in this dataset which were the raptor offense and predator offense data and took the averages of them which I called the offense stats. I then added all of the offense stats together and sorted them by highest to lowest number. The higher the number correlates with the better the offensive player which gives us a list ranking the best to worst offensive players that have all played over 3000 minutes. I then turned the data back into a data frame, renamed the number column to be offense_stat and showed the new data frame containing a player’s name and the offensive value associated." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 35, "id": "e40a1730-6c77-425c-81b2-55f2bcd7d5d2", - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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offense_stat
player_name
Stephen Curry17.720458
Chris Paul17.330227
James Harden15.526875
Isaiah Thomas14.996093
LeBron James12.947326
Kevin Durant12.235575
Russell Westbrook11.888737
Kyrie Irving11.735169
Nikola Jokic10.950040
Damian Lillard10.012836
Kyle Lowry9.180744
Karl-Anthony Towns8.111974
Bradley Beal7.866275
Jimmy Butler7.821279
Paul George6.961966
Devin Booker6.766783
Kemba Walker6.752003
Kevin Love6.741710
JR Smith6.593562
Blake Griffin6.389779
Giannis Antetokounmpo6.112044
John Wall5.753245
Khris Middleton5.306315
CJ McCollum5.276419
Jalen Brunson5.237223
Klay Thompson4.973756
Jrue Holiday4.947378
Draymond Green4.784848
Wesley Matthews4.769245
Chandler Parsons4.669383
DeAndre Jordan4.652438
DeMar DeRozan4.455351
Nicolas Batum4.372284
Jayson Tatum4.184372
Otto Porter Jr.4.088359
Anthony Davis4.076098
Jaylen Brown4.026351
Joe Johnson3.184046
Mikal Bridges3.180401
Donovan Mitchell2.917105
Marcus Smart2.869965
Pascal Siakam2.734799
Monta Ellis2.503306
Tyrese Maxey2.304135
Ben Simmons2.176836
Joakim Noah2.092434
Dorian Finney-Smith2.053527
Paul Millsap1.773395
Trevor Ariza1.711537
Chris Bosh1.506055
Tobias Harris1.468812
David West0.994692
Andrew Wiggins0.938364
Bam Adebayo0.618696
Serge Ibaka0.557475
Marc Gasol0.541082
Lance Stephenson-0.108353
Marcin Gortat-0.148795
George Hill-0.353735
PJ Tucker-1.726914
\n", + "
" + ], "text/plain": [ - "player_name\n", - "Stephen Curry 20.604269\n", - "Chris Paul 16.936584\n", - "James Harden 16.727092\n", - "Isaiah Thomas 15.818621\n", - "Russell Westbrook 14.313888\n", - "Kevin Durant 13.773243\n", - "LeBron James 13.155568\n", - "Kyrie Irving 12.028666\n", - "Nikola Jokic 10.837165\n", - "Damian Lillard 10.753352\n", - "Kyle Lowry 9.822552\n", - "Bradley Beal 8.568266\n", - "Paul George 8.376341\n", - "Karl-Anthony Towns 7.843553\n", - "Devin Booker 7.375421\n", - "Jimmy Butler 7.328433\n", - "John Wall 7.261891\n", - "Kevin Love 6.794852\n", - "Blake Griffin 6.696963\n", - "Draymond Green 6.510247\n", - "JR Smith 6.351795\n", - "Giannis Antetokounmpo 5.934426\n", - "Khris Middleton 5.881128\n", - "Jrue Holiday 5.854937\n", - "Kemba Walker 5.684619\n", - "Nicolas Batum 5.481139\n", - "Klay Thompson 5.373458\n", - "CJ McCollum 5.305970\n", - "Anthony Davis 5.293702\n", - "Wesley Matthews 5.104814\n", - "Chandler Parsons 4.708605\n", - "Jaylen Brown 4.623347\n", - "Otto Porter Jr. 4.309499\n", - "Jayson Tatum 4.129525\n", - "DeMar DeRozan 4.126880\n", - "DeAndre Jordan 4.064953\n", - "Marcus Smart 4.058944\n", - "Jalen Brunson 3.748525\n", - "Donovan Mitchell 3.530766\n", - "Monta Ellis 3.105204\n", - "Ben Simmons 3.067391\n", - "Mikal Bridges 2.747403\n", - "Paul Millsap 2.468656\n", - "Pascal Siakam 2.379094\n", - "Trevor Ariza 2.159732\n", - "Joe Johnson 2.158377\n", - "Joakim Noah 1.756960\n", - "Chris Bosh 1.453419\n", - "Andrew Wiggins 1.195676\n", - "Tyrese Maxey 1.086156\n", - "David West 0.962411\n", - "Dorian Finney-Smith 0.762526\n", - "Tobias Harris 0.491411\n", - "Serge Ibaka 0.305851\n", - "Lance Stephenson 0.142647\n", - "Marc Gasol 0.107053\n", - "Bam Adebayo -0.030398\n", - "Marcin Gortat -0.718653\n", - "George Hill -1.871933\n", - "PJ Tucker -2.363312\n", - "dtype: float64" + " offense_stat\n", + "player_name \n", + "Stephen Curry 17.720458\n", + "Chris Paul 17.330227\n", + "James Harden 15.526875\n", + "Isaiah Thomas 14.996093\n", + "LeBron James 12.947326\n", + "Kevin Durant 12.235575\n", + "Russell Westbrook 11.888737\n", + "Kyrie Irving 11.735169\n", + "Nikola Jokic 10.950040\n", + "Damian Lillard 10.012836\n", + "Kyle Lowry 9.180744\n", + "Karl-Anthony Towns 8.111974\n", + "Bradley Beal 7.866275\n", + "Jimmy Butler 7.821279\n", + "Paul George 6.961966\n", + "Devin Booker 6.766783\n", + "Kemba Walker 6.752003\n", + "Kevin Love 6.741710\n", + "JR Smith 6.593562\n", + "Blake Griffin 6.389779\n", + "Giannis Antetokounmpo 6.112044\n", + "John Wall 5.753245\n", + "Khris Middleton 5.306315\n", + "CJ McCollum 5.276419\n", + "Jalen Brunson 5.237223\n", + "Klay Thompson 4.973756\n", + "Jrue Holiday 4.947378\n", + "Draymond Green 4.784848\n", + "Wesley Matthews 4.769245\n", + "Chandler Parsons 4.669383\n", + "DeAndre Jordan 4.652438\n", + "DeMar DeRozan 4.455351\n", + "Nicolas Batum 4.372284\n", + "Jayson Tatum 4.184372\n", + "Otto Porter Jr. 4.088359\n", + "Anthony Davis 4.076098\n", + "Jaylen Brown 4.026351\n", + "Joe Johnson 3.184046\n", + "Mikal Bridges 3.180401\n", + "Donovan Mitchell 2.917105\n", + "Marcus Smart 2.869965\n", + "Pascal Siakam 2.734799\n", + "Monta Ellis 2.503306\n", + "Tyrese Maxey 2.304135\n", + "Ben Simmons 2.176836\n", + "Joakim Noah 2.092434\n", + "Dorian Finney-Smith 2.053527\n", + "Paul Millsap 1.773395\n", + "Trevor Ariza 1.711537\n", + "Chris Bosh 1.506055\n", + "Tobias Harris 1.468812\n", + "David West 0.994692\n", + "Andrew Wiggins 0.938364\n", + "Bam Adebayo 0.618696\n", + "Serge Ibaka 0.557475\n", + "Marc Gasol 0.541082\n", + "Lance Stephenson -0.108353\n", + "Marcin Gortat -0.148795\n", + "George Hill -0.353735\n", + "PJ Tucker -1.726914" ] }, - "execution_count": 13, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -688,8 +960,35 @@ "source": [ "players_over_3000 = df[df.mp>3000]\n", "eligible_players = players_over_3000.groupby(\"player_name\")\n", - "offense_stats= eligible_players[[\"raptor_offense\",\"predator_offense\",\"pace_impact\"]].mean()\n", - "offense_stats.sum(axis=1).sort_values(ascending=False)" + "offense_stats= eligible_players[[\"raptor_offense\",\"predator_offense\"]].mean()\n", + "off_stat = offense_stats.sum(axis=1).sort_values(ascending=False)\n", + "df_offense = pd.DataFrame(off_stat)\n", + "df_offense.columns = [\"offense_stat\"]\n", + "df_offense" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "8ea17bc0-35b9-4378-a35d-90f9cfd71064", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Top 5 Offensive Players and their Offensive Stat\")\n", + "off_plot = sns.barplot(data=df_offense.head(), x=\"player_name\",y=\"offense_stat\",errorbar=None)\n", + "plt.xticks(df_offense.head().index,rotation=45)\n", + "plt.show()" ] }, { @@ -698,7 +997,16 @@ "metadata": {}, "source": [ "### Results\n", - "I found that the best offensive player is Stephen Curry and it is him by a far margin. This makes sense as he is known as one of the best offensive players in the NBA and has been for years. The next best offensive players are Chris Paul, James Harden, Isaiah Thomas, and Russell Westbrook. These are all guards that are best on offense so this also makes sense. I think that using the data points of raptor offense, predator offense, and pace impact were useful as it outputted a list of some of the best offensive players from 2014-2022." + "I found that the best offensive player is Stephen Curry. This makes sense as he is known as one of the best offensive players in the NBA and has been for years. The next best offensive players are Chris Paul, James Harden, Isaiah Thomas, and LeBron James. These are all guards, and LeBron James who has pretty much played every position in his career that are best on offense so this also makes sense. I think that using the data points of raptor offense and predator offense were useful as it outputted a list of some of the best offensive players from 2014-2022." + ] + }, + { + "cell_type": "markdown", + "id": "9abb60c3-4aa0-45cf-bf6f-dbc2302ed7ea", + "metadata": {}, + "source": [ + "#### Results from Graph\n", + "This graph shows the difference between Stephen Curry, Chris Paul, and everyone else. This shows the top 5 offensive players in the NBA from 2014-2022. The offense_stat is the value of raptor_offense and predator_offense stats added together. It is interesting to note that the majority of the best offensive players play point guard or shooting guard." ] }, { @@ -723,83 +1031,354 @@ "metadata": {}, "source": [ "**What I did:** \n", - "This is very similar to the best offensive stats. I only allowed players with 3000 minutes played which is the same pool of players that I included in seeing who the best offensive player was by making the players_over_3000 dataframe. This time I looked at the data for raptor defense, predator defense, and pace impact. I took the average of all three of these data points and added the number together. This gives a good represenation of how effective a player is on defense. These are good data values to see who the best defensive players are." + "This is very similar to the best offensive stats. I only allowed players with 3000 minutes played which is the same pool of players that I included in seeing who the best offensive player was by making the players_over_3000 dataframe. This time I looked at the data for raptor defense and predator defense. I took the average of these two data points and added the number together. This gives a good representation of how effective a player is on defense. These are good data values to see who the best defensive players are." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 37, "id": "5aca0be4-035f-4de2-a955-b9584221eccd", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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defense_stat
player_name
Draymond Green10.787943
Joakim Noah9.358453
Anthony Davis7.895150
Marc Gasol6.534351
Paul George6.408697
Paul Millsap5.699113
George Hill5.241234
Giannis Antetokounmpo4.803693
PJ Tucker4.780241
Serge Ibaka4.703840
Marcin Gortat4.511971
Jrue Holiday4.421153
Chris Paul4.249789
Nikola Jokic4.021482
Marcus Smart3.850867
Bam Adebayo3.707044
Jimmy Butler3.183364
DeAndre Jordan3.154674
Kyle Lowry3.141026
Otto Porter Jr.2.968337
Pascal Siakam2.750881
Mikal Bridges2.670235
David West2.666690
Stephen Curry2.582205
Donovan Mitchell2.433277
Kevin Love2.424493
Jaylen Brown2.380194
Jayson Tatum2.100925
Dorian Finney-Smith2.022662
Chris Bosh1.673919
Blake Griffin1.627615
Trevor Ariza1.546710
Kemba Walker1.342204
Andrew Wiggins1.244490
Nicolas Batum0.968720
James Harden0.769140
Klay Thompson0.543766
Russell Westbrook0.523426
Ben Simmons0.456007
LeBron James0.354197
Wesley Matthews0.299899
CJ McCollum-0.060706
Bradley Beal-0.200945
Khris Middleton-0.477710
Jalen Brunson-0.508995
John Wall-0.695989
Chandler Parsons-0.873292
Kevin Durant-0.913675
Devin Booker-0.916546
Lance Stephenson-1.036529
Monta Ellis-1.169090
Tobias Harris-1.576114
Karl-Anthony Towns-1.776151
Damian Lillard-2.258608
Tyrese Maxey-2.419741
JR Smith-2.516657
Kyrie Irving-3.997132
DeMar DeRozan-4.334230
Joe Johnson-4.882167
Isaiah Thomas-6.412895
\n", + "
" + ], "text/plain": [ - "player_name\n", - "Draymond Green 12.513342\n", - "Anthony Davis 9.112754\n", - "Joakim Noah 9.022979\n", - "Paul George 7.823072\n", - "Paul Millsap 6.394374\n", - "Marc Gasol 6.100323\n", - "Stephen Curry 5.466016\n", - "Jrue Holiday 5.328712\n", - "Marcus Smart 5.039845\n", - "Giannis Antetokounmpo 4.626074\n", - "Serge Ibaka 4.452216\n", - "PJ Tucker 4.143843\n", - "Marcin Gortat 3.942113\n", - "Nikola Jokic 3.908606\n", - "Chris Paul 3.856146\n", - "Kyle Lowry 3.782834\n", - "George Hill 3.723036\n", - "Otto Porter Jr. 3.189476\n", - "Bam Adebayo 3.057949\n", - "Donovan Mitchell 3.046938\n", - "Jaylen Brown 2.977191\n", - "Russell Westbrook 2.948577\n", - "Jimmy Butler 2.690517\n", - "David West 2.634409\n", - "DeAndre Jordan 2.567188\n", - "Kevin Love 2.477635\n", - "Pascal Siakam 2.395175\n", - "Mikal Bridges 2.237237\n", - "Nicolas Batum 2.077575\n", - "Jayson Tatum 2.046078\n", - "Trevor Ariza 1.994906\n", - "James Harden 1.969358\n", - "Blake Griffin 1.934800\n", - "Chris Bosh 1.621284\n", - "Andrew Wiggins 1.501802\n", - "Ben Simmons 1.346561\n", - "Klay Thompson 0.943468\n", - "John Wall 0.812657\n", - "Dorian Finney-Smith 0.731661\n", - "Wesley Matthews 0.635469\n", - "Kevin Durant 0.623994\n", - "LeBron James 0.562439\n", - "Bradley Beal 0.501046\n", - "Kemba Walker 0.274820\n", - "Khris Middleton 0.097103\n", - "CJ McCollum -0.031155\n", - "Devin Booker -0.307908\n", - "Monta Ellis -0.567192\n", - "Lance Stephenson -0.785529\n", - "Chandler Parsons -0.834070\n", - "Damian Lillard -1.518093\n", - "Jalen Brunson -1.997692\n", - "Karl-Anthony Towns -2.044572\n", - "Tobias Harris -2.553516\n", - "JR Smith -2.758424\n", - "Tyrese Maxey -3.637720\n", - "Kyrie Irving -3.703635\n", - "DeMar DeRozan -4.662701\n", - "Isaiah Thomas -5.590367\n", - "Joe Johnson -5.907836\n", - "dtype: float64" + " defense_stat\n", + "player_name \n", + "Draymond Green 10.787943\n", + "Joakim Noah 9.358453\n", + "Anthony Davis 7.895150\n", + "Marc Gasol 6.534351\n", + "Paul George 6.408697\n", + "Paul Millsap 5.699113\n", + "George Hill 5.241234\n", + "Giannis Antetokounmpo 4.803693\n", + "PJ Tucker 4.780241\n", + "Serge Ibaka 4.703840\n", + "Marcin Gortat 4.511971\n", + "Jrue Holiday 4.421153\n", + "Chris Paul 4.249789\n", + "Nikola Jokic 4.021482\n", + "Marcus Smart 3.850867\n", + "Bam Adebayo 3.707044\n", + "Jimmy Butler 3.183364\n", + "DeAndre Jordan 3.154674\n", + "Kyle Lowry 3.141026\n", + "Otto Porter Jr. 2.968337\n", + "Pascal Siakam 2.750881\n", + "Mikal Bridges 2.670235\n", + "David West 2.666690\n", + "Stephen Curry 2.582205\n", + "Donovan Mitchell 2.433277\n", + "Kevin Love 2.424493\n", + "Jaylen Brown 2.380194\n", + "Jayson Tatum 2.100925\n", + "Dorian Finney-Smith 2.022662\n", + "Chris Bosh 1.673919\n", + "Blake Griffin 1.627615\n", + "Trevor Ariza 1.546710\n", + "Kemba Walker 1.342204\n", + "Andrew Wiggins 1.244490\n", + "Nicolas Batum 0.968720\n", + "James Harden 0.769140\n", + "Klay Thompson 0.543766\n", + "Russell Westbrook 0.523426\n", + "Ben Simmons 0.456007\n", + "LeBron James 0.354197\n", + "Wesley Matthews 0.299899\n", + "CJ McCollum -0.060706\n", + "Bradley Beal -0.200945\n", + "Khris Middleton -0.477710\n", + "Jalen Brunson -0.508995\n", + "John Wall -0.695989\n", + "Chandler Parsons -0.873292\n", + "Kevin Durant -0.913675\n", + "Devin Booker -0.916546\n", + "Lance Stephenson -1.036529\n", + "Monta Ellis -1.169090\n", + "Tobias Harris -1.576114\n", + "Karl-Anthony Towns -1.776151\n", + "Damian Lillard -2.258608\n", + "Tyrese Maxey -2.419741\n", + "JR Smith -2.516657\n", + "Kyrie Irving -3.997132\n", + "DeMar DeRozan -4.334230\n", + "Joe Johnson -4.882167\n", + "Isaiah Thomas -6.412895" ] }, - "execution_count": 14, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -807,8 +1386,35 @@ "source": [ "players_over_3000 = df[df.mp>3000]\n", "eligible_players = players_over_3000.groupby(\"player_name\")\n", - "defense_stats= eligible_players[[\"raptor_defense\",\"predator_defense\",\"pace_impact\"]].mean()\n", - "defense_stats.sum(axis=1).sort_values(ascending=False)" + "defense_stats= eligible_players[[\"raptor_defense\",\"predator_defense\"]].mean()\n", + "def_stat = defense_stats.sum(axis=1).sort_values(ascending=False)\n", + "df_defense = pd.DataFrame(def_stat)\n", + "df_defense.columns = [\"defense_stat\"]\n", + "df_defense" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "afe7a597-ce41-45f5-89f1-b82064088890", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Top 5 Defensive Players and their Defensive Stat\")\n", + "def_plot = sns.barplot(data=df_defense.head(), x=\"player_name\",y=\"defense_stat\",errorbar=None)\n", + "plt.xticks(df_defense.head().index,rotation=45)\n", + "plt.show()" ] }, { @@ -817,7 +1423,16 @@ "metadata": {}, "source": [ "### Results\n", - "I found that the best defensive player from 2014-2022 was Draymond Green. This makes sense as he has won the Defensive Player Of The Year award and is known primarily for his defense. He is known as one of the best defensive plaeyrs in the league. I am not surprised to see Draymond Green be the number one ranked defensive player. The three next best defensive players are Anthony Davis, Joakim Noah, and Paul George. These players are also known for their defense so it makes sense that they are rated so highly. It is interesting to note that 3 of the top 4 players are Power Forwards or Centers which also shows that this could lead to research on if Power Forwards and Centers generally play the best defense." + "I found that the best defensive player from 2014-2022 was Draymond Green. This makes sense as he has won the Defensive Player Of The Year award and is known primarily for his defense. He is known as one of the best defensive players in the league. I am not surprised to see Draymond Green be the number one ranked defensive player. The three next best defensive players are Anthony Davis, Joakim Noah, and Paul George. These players are also known for their defense, so it makes sense that they are rated so highly. It is interesting to note that 4 of the top 5 players are Power Forwards or Centers which also shows that this could lead to research on whether Power Forwards and Centers generally play the best defense." + ] + }, + { + "cell_type": "markdown", + "id": "90e25901-eb61-42cc-9d11-742ba515bfd9", + "metadata": {}, + "source": [ + "#### Results from Graph\n", + "This graph shows the difference between Draymond Green and everyone else. This shows the top 5 defensive players in the NBA from 2014-2022. The defense_stat is the value of raptor_defense and predator_defense stats added together." ] }, { @@ -843,7 +1458,7 @@ "id": "27b18f33-f5fc-4eda-a0d2-1be37c5b6d76", "metadata": {}, "source": [ - "My results give an accurate depiction of showing who is better between Kevin Durant and LeBron James and who the best offensive and defensive players in the NBA are. Determining who is better between Kevin Durant and LeBron James is accurate as I used offensive, defensive, and overall impact data for both players and compared them. I decided to add all of the values together as I thought that would accurately determine who has been better. A person could use other data not given in the data set as there could be other metrics that better capture who the better player is. In regards to who the best offensive and defensive players are in the NBA, the results are accurate as I used 3 different data points for offense and 3 data points for defense. This makes the data give the overall best offensive and defensive player. Also, the the players given at the top of the best offensive and defensive players lists are known for their skill on that side of the court which further suggests that the data is accurate.\n", + "My results give an accurate depiction of showing who is better between Kevin Durant and LeBron James and who the best offensive and defensive players in the NBA are. Determining who is better between Kevin Durant and LeBron James is accurate as I used offensive, defensive, and overall impact data for both players and compared them. I decided to add all of the values together as I thought that would accurately determine who has been better. A person could use other data not given in the data set as there could be other metrics that better capture who the better player is. In regard to who the best offensive and defensive players are in the NBA, the results are accurate as I used 2 different data points for offense and 2 data points for defense. This makes the data give the overall best offensive and defensive player. Also, the players given at the top of the best offensive and defensive players lists are known for their skill on that side of the court which further suggests that the data is accurate.\n", "\n", "Some limitations of the data set are that there are other statistics that were not factored in the data. Also, this compares players only by specific data points and does not factor in how they effect their teammates which is very important in determining how good a player is. This includes the leadership a player brings to their team and how that elevates their teammates. Another limitation is that I used 3000 or more minutes played as a qualification to be considered. There could be good players that played 2999 minutes and were not counted. The other limitation is that the data goes from 2014-2022. This is not recent enough as we are in 2025 and does not include data from the previous 2-3 seasons. Also, it is necessary to note that it does not include data from before 2014. This is a problem when discussing LeBron James and Kevin Durant as they both played prior to 2014.\n", "\n", @@ -865,7 +1480,9 @@ "source": [ "I learned that my media consumption has led me to decide to use this data set. I see a lot of NBA media so I was interested in using NBA data for this project.\n", "\n", - "I learned that LeBron James is better than Kevin Durant both offensively and defensively. This can make sense although it is pretty surprising that LeBron James was better on offense. Stephen Curry, Chris Paul, and James Harden were found to be the best players offensively which completely makes sense as they were been some of the best offesnive players from 2014-2022. Draymond Green, Anthony Davis, and Joakim Noah were found to be the best players defensively which completely makes sense as Draymond Green and Joakim Noah were some of the best defenders from 2014-2022. Overall, I would say that the results make sense to me showing that the data was accurate.\n", + "When reflecting on this project, this data could definitely be used by NBA teams to decide which players they should try to acquire. In the off-season teams are allowed to sign free agents and trade for players. This data could allow teams to realize who the better player is and acquire that player.\n", + "\n", + "I learned that LeBron James is better than Kevin Durant both offensively and defensively. This can make sense although it is pretty surprising that LeBron James was better on offense. Stephen Curry, Chris Paul, and James Harden were found to be the best players offensively which completely makes sense as they have been some of the best offensive players from 2014-2022. Draymond Green, Joakim Noah, and Anthony Davis were found to be the best players defensively which completely makes sense as Draymond Green and Joakim Noah were some of the best defenders from 2014-2022. Overall, I would say that the results make sense to me showing that the data was accurate.\n", "\n", "The most surprising thing was that LeBron James was better offensively than Kevin Durant. I assumed he would be better defensively but I was not expecting him to be better on offense. Kevin Durant is known as one of the best overall scorers and shooters so I definitely did not expect LeBron James to be better on offense. I was also surprised that Stephen Curry was rated so highly defensively. He is not thought of as one of the best defenders so this was interesting to see.\n", "\n", @@ -880,10 +1497,26 @@ "## Final Poster" ] }, + { + "cell_type": "markdown", + "id": "c7b9c078-dd64-40ae-b1e5-496afa39fd45", + "metadata": {}, + "source": [ + "The final poster is attached as the James Berent Flyer.pdf" + ] + }, + { + "cell_type": "markdown", + "id": "44248208-44a9-47f5-85e9-c61d4b06c9a0", + "metadata": {}, + "source": [ + "# Add Data Structures" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "cfe489ca-77ea-4071-9622-397638f82c90", + "id": "398f3b13-0813-4052-a43f-68903048ed39", "metadata": {}, "outputs": [], "source": [] diff --git a/James Berent Flyer.pdf b/James Berent Flyer.pdf new file mode 100644 index 0000000..355ac34 Binary files /dev/null and b/James Berent Flyer.pdf differ diff --git a/argument.ipynb b/argument.ipynb index 72bd82b..3e95972 100644 --- a/argument.ipynb +++ b/argument.ipynb @@ -13,7 +13,7 @@ "id": "understanding-numbers", "metadata": {}, "source": [ - "I will research 3 different questions I had while looking at the NBA data. I will first find who the better player is between LeBron James and Kevin Durant and then find who the best players are on offense and defense. All of the data I will be using will come from the 2014-2022 NBA seasons." + "I will research 3 different questions I had while looking at the NBA data. I will first find who the better player is between LeBron James and Kevin Durant and then find who the best players are on offense and defense. All of the data I will be using will come from the 2014-2022 NBA seasons. This project will be able to give valuable insight to an NBA front office telling them who they should try to acquire in the offseason." ] }, { @@ -29,7 +29,7 @@ "id": "appreciated-testimony", "metadata": {}, "source": [ - "I want to look at this because I have played basketball most of my life and enjoy watching the NBA. I enjoy looking at NBA players' stats in my free time so this was very interesting to me. I want to see who some of the best players are on offense and defense." + "I want to look at this because I have played basketball most of my life and enjoy watching the NBA. I enjoy looking at NBA players' stats in my free time, so this was very interesting to me. I want to see who some of the best players are on offense and defense in the NBA." ] }, { @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 23, "id": "technical-evans", "metadata": {}, "outputs": [], @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 24, "id": "overhead-sigma", "metadata": { "scrolled": true @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 25, "id": "heated-blade", "metadata": {}, "outputs": [ @@ -281,7 +281,7 @@ "[5 rows x 21 columns]" ] }, - "execution_count": 3, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -297,7 +297,7 @@ "source": [ "**Data Overview**\n", "\n", - "This dataset comes from FiveThirtyEight and contains different data from NBA players from 2014-2022. It includes the players' names, players' ids, and the season year. It also includes number of possessions played and minutes played which both indicate how much time the player was on the court that season. Also, the data includes raptor stats for offense, defense, and total which shows how effective a player was on that side of the court. The data also shows predator stats for offense, defense, and total which is a prediction of how effective the player was. Also, WAR (wins above replacement) stats were shown in the data which show how many more wins that player got their team over the season compared to if a replacement level player was playing instead. Lastly, pace impact stats were shown which states the overall impact a player had on their team. The pace impact stat for each player was shown as if each player 48 minutes which the the length of the whole game." + "This dataset comes from FiveThirtyEight and contains different data from NBA players from 2014-2022. It includes the players' names, players' ids, and the season year. It also includes number of possessions played and minutes played which both indicate how much time the player was on the court that season. Also, the data includes raptor stats for offense, defense, and total which shows how effective a player was on that side of the court. The data also shows predator stats for offense, defense, and total which is a prediction of how effective the player was. Also, WAR (wins above replacement) stats were shown in the data which show how many more wins that player got their team over the season compared to if a replacement level player was playing instead. Lastly, pace impact stats were shown which state the overall impact a player had on their team. The pace impact stat for each player was shown as if each player played 48 minutes which is the length of the whole game." ] }, { @@ -354,7 +354,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 26, "id": "9e6b2020-4df8-4e45-8b00-fbc398964376", "metadata": {}, "outputs": [], @@ -364,7 +364,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 27, "id": "b57dfb5b-f725-43d7-bfaa-76e7d86ec69f", "metadata": { "scrolled": true @@ -376,7 +376,7 @@ "np.float64(5.958565647986926)" ] }, - "execution_count": 5, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -387,7 +387,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 28, "id": "496218f8-6823-4570-b97f-bdf2461e3e06", "metadata": { "scrolled": true @@ -399,7 +399,7 @@ "np.float64(0.3291714363332999)" ] }, - "execution_count": 6, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -410,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 29, "id": "247b1e93-df57-4b13-a22f-656e6740e715", "metadata": {}, "outputs": [ @@ -420,7 +420,7 @@ "np.float64(6.2877370843202245)" ] }, - "execution_count": 7, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -439,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 30, "id": "c0a35626-5356-4b7b-8e2b-eb83cc42b8f8", "metadata": {}, "outputs": [], @@ -449,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 31, "id": "08415fc5-0f02-4e13-906a-c9dee9f8118e", "metadata": {}, "outputs": [ @@ -459,7 +459,7 @@ "np.float64(5.66912563466798)" ] }, - "execution_count": 9, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -470,9 +470,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 32, "id": "f37fb84f-4861-4e14-b6dc-502c180dcb4b", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -480,7 +482,7 @@ "np.float64(0.20101735750424538)" ] }, - "execution_count": 10, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -491,7 +493,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 33, "id": "a60e7ec7-27fb-47df-ba2a-94a84c7926e5", "metadata": {}, "outputs": [ @@ -501,7 +503,7 @@ "np.float64(5.870142992047225)" ] }, - "execution_count": 11, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -516,7 +518,7 @@ "metadata": {}, "source": [ "#### Data Explained\n", - "LeBron James is better on average, offensively, defensively, and all around than Kevin Durant is. LeBron's raptor offense, defense, and total rating is higher than Durant's. This does not account for individual seasons but uses the data from all seasons from 2014-2022. I will graph LeBron James' and Kevin Durant's indivudal seaons below. " + "LeBron James is better on average, offensively, defensively, and all around than Kevin Durant is. LeBron's raptor offense, defense, and total rating is higher than Durant's. This does not account for individual seasons but uses the data from all seasons from 2014-2022. I will graph LeBron James' and Kevin Durant's individual seasons below. " ] }, { @@ -529,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 34, "id": "79e1f833-833a-4a75-b41d-eeab580bf713", "metadata": { "scrolled": true @@ -541,13 +543,13 @@ "Text(0.5, 1.0, 'LeBron James vs Kevin Durant')" ] }, - "execution_count": 12, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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" ] @@ -559,6 +561,7 @@ "source": [ "sns.scatterplot(data=Lebron_df,x=\"season\",y=\"raptor_total\")\n", "sns.scatterplot(data=Durant_df,x=\"season\",y=\"raptor_total\")\n", + "plt.ylim([0,9])\n", "plt.legend(labels=[\"LeBron James\",\"Kevin Durant\"])\n", "plt.title(\"LeBron James vs Kevin Durant\")" ] @@ -593,94 +596,363 @@ "id": "64e47d9c-d4af-4c02-87af-d0087597d629", "metadata": {}, "source": [ - "I will use the player_name, raptor_offense, predator_offense, and pace_impact data for each player. \n", + "I will use the player_name, raptor_offense, and predator_offense for each player. \n", " \n", "**Initial Problem:** \n", "I first completed this without excluding players who played less than 3000 minutes in a season and was finding that the data was showing me the best offensive NBA players were players who only played one game. This was happening because a few players played great offensively in one game and never played again. Obviously, those are not the best NBA players as they only played 1 or a few games in the NBA.\n", "\n", "**How I fixed this:** \n", - "I fixed this by removing all NBA players who played less than 3000 minutes throughout the 82 game season. This only allowed the players who were good enough to play many minutes for their teams throughout the season. This means that these were some of the best NBA players.\n", + "I fixed this by removing all NBA players who played less than 3000 minutes throughout the 82 game season. This only allowed the players who were good enough to play many minutes for their teams throughout the season. This means that these were some of the best NBA players. There are 48 minutes in an NBA game so I chose 3000 minutes since 3000/48 = 62.5 and that is about three quarters of the NBA season.\n", "\n", "**What I did:** \n", - "I first removed all players who played less than 3000 minutes. I then grouped the data by the players' names as I called these the eligible players. I then took all of the offensive stats in this dataset which were the raptor offense, predator offense, and pace impact data and took the averages of all of them which I called the offense stats. I then added all of the offense stats together and sorted them by highest to lowest number. The higher the number correlates with the better the offensive player which gives us a list ranking the best to worst offensive players that have all played over 3000 minutes." + "I first removed all players who played less than 3000 minutes. I then grouped the data by the players' names as I called these the eligible players. I then took all of the offensive stats in this dataset which were the raptor offense and predator offense data and took the averages of them which I called the offense stats. I then added all of the offense stats together and sorted them by highest to lowest number. The higher the number correlates with the better the offensive player which gives us a list ranking the best to worst offensive players that have all played over 3000 minutes. I then turned the data back into a data frame, renamed the number column to be offense_stat and showed the new data frame containing a player’s name and the offensive value associated." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 35, "id": "e40a1730-6c77-425c-81b2-55f2bcd7d5d2", - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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offense_stat
player_name
Stephen Curry17.720458
Chris Paul17.330227
James Harden15.526875
Isaiah Thomas14.996093
LeBron James12.947326
Kevin Durant12.235575
Russell Westbrook11.888737
Kyrie Irving11.735169
Nikola Jokic10.950040
Damian Lillard10.012836
Kyle Lowry9.180744
Karl-Anthony Towns8.111974
Bradley Beal7.866275
Jimmy Butler7.821279
Paul George6.961966
Devin Booker6.766783
Kemba Walker6.752003
Kevin Love6.741710
JR Smith6.593562
Blake Griffin6.389779
Giannis Antetokounmpo6.112044
John Wall5.753245
Khris Middleton5.306315
CJ McCollum5.276419
Jalen Brunson5.237223
Klay Thompson4.973756
Jrue Holiday4.947378
Draymond Green4.784848
Wesley Matthews4.769245
Chandler Parsons4.669383
DeAndre Jordan4.652438
DeMar DeRozan4.455351
Nicolas Batum4.372284
Jayson Tatum4.184372
Otto Porter Jr.4.088359
Anthony Davis4.076098
Jaylen Brown4.026351
Joe Johnson3.184046
Mikal Bridges3.180401
Donovan Mitchell2.917105
Marcus Smart2.869965
Pascal Siakam2.734799
Monta Ellis2.503306
Tyrese Maxey2.304135
Ben Simmons2.176836
Joakim Noah2.092434
Dorian Finney-Smith2.053527
Paul Millsap1.773395
Trevor Ariza1.711537
Chris Bosh1.506055
Tobias Harris1.468812
David West0.994692
Andrew Wiggins0.938364
Bam Adebayo0.618696
Serge Ibaka0.557475
Marc Gasol0.541082
Lance Stephenson-0.108353
Marcin Gortat-0.148795
George Hill-0.353735
PJ Tucker-1.726914
\n", + "
" + ], "text/plain": [ - "player_name\n", - "Stephen Curry 20.604269\n", - "Chris Paul 16.936584\n", - "James Harden 16.727092\n", - "Isaiah Thomas 15.818621\n", - "Russell Westbrook 14.313888\n", - "Kevin Durant 13.773243\n", - "LeBron James 13.155568\n", - "Kyrie Irving 12.028666\n", - "Nikola Jokic 10.837165\n", - "Damian Lillard 10.753352\n", - "Kyle Lowry 9.822552\n", - "Bradley Beal 8.568266\n", - "Paul George 8.376341\n", - "Karl-Anthony Towns 7.843553\n", - "Devin Booker 7.375421\n", - "Jimmy Butler 7.328433\n", - "John Wall 7.261891\n", - "Kevin Love 6.794852\n", - "Blake Griffin 6.696963\n", - "Draymond Green 6.510247\n", - "JR Smith 6.351795\n", - "Giannis Antetokounmpo 5.934426\n", - "Khris Middleton 5.881128\n", - "Jrue Holiday 5.854937\n", - "Kemba Walker 5.684619\n", - "Nicolas Batum 5.481139\n", - "Klay Thompson 5.373458\n", - "CJ McCollum 5.305970\n", - "Anthony Davis 5.293702\n", - "Wesley Matthews 5.104814\n", - "Chandler Parsons 4.708605\n", - "Jaylen Brown 4.623347\n", - "Otto Porter Jr. 4.309499\n", - "Jayson Tatum 4.129525\n", - "DeMar DeRozan 4.126880\n", - "DeAndre Jordan 4.064953\n", - "Marcus Smart 4.058944\n", - "Jalen Brunson 3.748525\n", - "Donovan Mitchell 3.530766\n", - "Monta Ellis 3.105204\n", - "Ben Simmons 3.067391\n", - "Mikal Bridges 2.747403\n", - "Paul Millsap 2.468656\n", - "Pascal Siakam 2.379094\n", - "Trevor Ariza 2.159732\n", - "Joe Johnson 2.158377\n", - "Joakim Noah 1.756960\n", - "Chris Bosh 1.453419\n", - "Andrew Wiggins 1.195676\n", - "Tyrese Maxey 1.086156\n", - "David West 0.962411\n", - "Dorian Finney-Smith 0.762526\n", - "Tobias Harris 0.491411\n", - "Serge Ibaka 0.305851\n", - "Lance Stephenson 0.142647\n", - "Marc Gasol 0.107053\n", - "Bam Adebayo -0.030398\n", - "Marcin Gortat -0.718653\n", - "George Hill -1.871933\n", - "PJ Tucker -2.363312\n", - "dtype: float64" + " offense_stat\n", + "player_name \n", + "Stephen Curry 17.720458\n", + "Chris Paul 17.330227\n", + "James Harden 15.526875\n", + "Isaiah Thomas 14.996093\n", + "LeBron James 12.947326\n", + "Kevin Durant 12.235575\n", + "Russell Westbrook 11.888737\n", + "Kyrie Irving 11.735169\n", + "Nikola Jokic 10.950040\n", + "Damian Lillard 10.012836\n", + "Kyle Lowry 9.180744\n", + "Karl-Anthony Towns 8.111974\n", + "Bradley Beal 7.866275\n", + "Jimmy Butler 7.821279\n", + "Paul George 6.961966\n", + "Devin Booker 6.766783\n", + "Kemba Walker 6.752003\n", + "Kevin Love 6.741710\n", + "JR Smith 6.593562\n", + "Blake Griffin 6.389779\n", + "Giannis Antetokounmpo 6.112044\n", + "John Wall 5.753245\n", + "Khris Middleton 5.306315\n", + "CJ McCollum 5.276419\n", + "Jalen Brunson 5.237223\n", + "Klay Thompson 4.973756\n", + "Jrue Holiday 4.947378\n", + "Draymond Green 4.784848\n", + "Wesley Matthews 4.769245\n", + "Chandler Parsons 4.669383\n", + "DeAndre Jordan 4.652438\n", + "DeMar DeRozan 4.455351\n", + "Nicolas Batum 4.372284\n", + "Jayson Tatum 4.184372\n", + "Otto Porter Jr. 4.088359\n", + "Anthony Davis 4.076098\n", + "Jaylen Brown 4.026351\n", + "Joe Johnson 3.184046\n", + "Mikal Bridges 3.180401\n", + "Donovan Mitchell 2.917105\n", + "Marcus Smart 2.869965\n", + "Pascal Siakam 2.734799\n", + "Monta Ellis 2.503306\n", + "Tyrese Maxey 2.304135\n", + "Ben Simmons 2.176836\n", + "Joakim Noah 2.092434\n", + "Dorian Finney-Smith 2.053527\n", + "Paul Millsap 1.773395\n", + "Trevor Ariza 1.711537\n", + "Chris Bosh 1.506055\n", + "Tobias Harris 1.468812\n", + "David West 0.994692\n", + "Andrew Wiggins 0.938364\n", + "Bam Adebayo 0.618696\n", + "Serge Ibaka 0.557475\n", + "Marc Gasol 0.541082\n", + "Lance Stephenson -0.108353\n", + "Marcin Gortat -0.148795\n", + "George Hill -0.353735\n", + "PJ Tucker -1.726914" ] }, - "execution_count": 13, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -688,8 +960,35 @@ "source": [ "players_over_3000 = df[df.mp>3000]\n", "eligible_players = players_over_3000.groupby(\"player_name\")\n", - "offense_stats= eligible_players[[\"raptor_offense\",\"predator_offense\",\"pace_impact\"]].mean()\n", - "offense_stats.sum(axis=1).sort_values(ascending=False)" + "offense_stats= eligible_players[[\"raptor_offense\",\"predator_offense\"]].mean()\n", + "off_stat = offense_stats.sum(axis=1).sort_values(ascending=False)\n", + "df_offense = pd.DataFrame(off_stat)\n", + "df_offense.columns = [\"offense_stat\"]\n", + "df_offense" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "8ea17bc0-35b9-4378-a35d-90f9cfd71064", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Top 5 Offensive Players and their Offensive Stat\")\n", + "off_plot = sns.barplot(data=df_offense.head(), x=\"player_name\",y=\"offense_stat\",errorbar=None)\n", + "plt.xticks(df_offense.head().index,rotation=45)\n", + "plt.show()" ] }, { @@ -698,7 +997,16 @@ "metadata": {}, "source": [ "### Results\n", - "I found that the best offensive player is Stephen Curry and it is him by a far margin. This makes sense as he is known as one of the best offensive players in the NBA and has been for years. The next best offensive players are Chris Paul, James Harden, Isaiah Thomas, and Russell Westbrook. These are all guards that are best on offense so this also makes sense. I think that using the data points of raptor offense, predator offense, and pace impact were useful as it outputted a list of some of the best offensive players from 2014-2022." + "I found that the best offensive player is Stephen Curry. This makes sense as he is known as one of the best offensive players in the NBA and has been for years. The next best offensive players are Chris Paul, James Harden, Isaiah Thomas, and LeBron James. These are all guards, and LeBron James who has pretty much played every position in his career that are best on offense so this also makes sense. I think that using the data points of raptor offense and predator offense were useful as it outputted a list of some of the best offensive players from 2014-2022." + ] + }, + { + "cell_type": "markdown", + "id": "9abb60c3-4aa0-45cf-bf6f-dbc2302ed7ea", + "metadata": {}, + "source": [ + "#### Results from Graph\n", + "This graph shows the difference between Stephen Curry, Chris Paul, and everyone else. This shows the top 5 offensive players in the NBA from 2014-2022. The offense_stat is the value of raptor_offense and predator_offense stats added together. It is interesting to note that the majority of the best offensive players play point guard or shooting guard." ] }, { @@ -723,83 +1031,354 @@ "metadata": {}, "source": [ "**What I did:** \n", - "This is very similar to the best offensive stats. I only allowed players with 3000 minutes played which is the same pool of players that I included in seeing who the best offensive player was by making the players_over_3000 dataframe. This time I looked at the data for raptor defense, predator defense, and pace impact. I took the average of all three of these data points and added the number together. This gives a good represenation of how effective a player is on defense. These are good data values to see who the best defensive players are." + "This is very similar to the best offensive stats. I only allowed players with 3000 minutes played which is the same pool of players that I included in seeing who the best offensive player was by making the players_over_3000 dataframe. This time I looked at the data for raptor defense and predator defense. I took the average of these two data points and added the number together. This gives a good representation of how effective a player is on defense. These are good data values to see who the best defensive players are." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 37, "id": "5aca0be4-035f-4de2-a955-b9584221eccd", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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defense_stat
player_name
Draymond Green10.787943
Joakim Noah9.358453
Anthony Davis7.895150
Marc Gasol6.534351
Paul George6.408697
Paul Millsap5.699113
George Hill5.241234
Giannis Antetokounmpo4.803693
PJ Tucker4.780241
Serge Ibaka4.703840
Marcin Gortat4.511971
Jrue Holiday4.421153
Chris Paul4.249789
Nikola Jokic4.021482
Marcus Smart3.850867
Bam Adebayo3.707044
Jimmy Butler3.183364
DeAndre Jordan3.154674
Kyle Lowry3.141026
Otto Porter Jr.2.968337
Pascal Siakam2.750881
Mikal Bridges2.670235
David West2.666690
Stephen Curry2.582205
Donovan Mitchell2.433277
Kevin Love2.424493
Jaylen Brown2.380194
Jayson Tatum2.100925
Dorian Finney-Smith2.022662
Chris Bosh1.673919
Blake Griffin1.627615
Trevor Ariza1.546710
Kemba Walker1.342204
Andrew Wiggins1.244490
Nicolas Batum0.968720
James Harden0.769140
Klay Thompson0.543766
Russell Westbrook0.523426
Ben Simmons0.456007
LeBron James0.354197
Wesley Matthews0.299899
CJ McCollum-0.060706
Bradley Beal-0.200945
Khris Middleton-0.477710
Jalen Brunson-0.508995
John Wall-0.695989
Chandler Parsons-0.873292
Kevin Durant-0.913675
Devin Booker-0.916546
Lance Stephenson-1.036529
Monta Ellis-1.169090
Tobias Harris-1.576114
Karl-Anthony Towns-1.776151
Damian Lillard-2.258608
Tyrese Maxey-2.419741
JR Smith-2.516657
Kyrie Irving-3.997132
DeMar DeRozan-4.334230
Joe Johnson-4.882167
Isaiah Thomas-6.412895
\n", + "
" + ], "text/plain": [ - "player_name\n", - "Draymond Green 12.513342\n", - "Anthony Davis 9.112754\n", - "Joakim Noah 9.022979\n", - "Paul George 7.823072\n", - "Paul Millsap 6.394374\n", - "Marc Gasol 6.100323\n", - "Stephen Curry 5.466016\n", - "Jrue Holiday 5.328712\n", - "Marcus Smart 5.039845\n", - "Giannis Antetokounmpo 4.626074\n", - "Serge Ibaka 4.452216\n", - "PJ Tucker 4.143843\n", - "Marcin Gortat 3.942113\n", - "Nikola Jokic 3.908606\n", - "Chris Paul 3.856146\n", - "Kyle Lowry 3.782834\n", - "George Hill 3.723036\n", - "Otto Porter Jr. 3.189476\n", - "Bam Adebayo 3.057949\n", - "Donovan Mitchell 3.046938\n", - "Jaylen Brown 2.977191\n", - "Russell Westbrook 2.948577\n", - "Jimmy Butler 2.690517\n", - "David West 2.634409\n", - "DeAndre Jordan 2.567188\n", - "Kevin Love 2.477635\n", - "Pascal Siakam 2.395175\n", - "Mikal Bridges 2.237237\n", - "Nicolas Batum 2.077575\n", - "Jayson Tatum 2.046078\n", - "Trevor Ariza 1.994906\n", - "James Harden 1.969358\n", - "Blake Griffin 1.934800\n", - "Chris Bosh 1.621284\n", - "Andrew Wiggins 1.501802\n", - "Ben Simmons 1.346561\n", - "Klay Thompson 0.943468\n", - "John Wall 0.812657\n", - "Dorian Finney-Smith 0.731661\n", - "Wesley Matthews 0.635469\n", - "Kevin Durant 0.623994\n", - "LeBron James 0.562439\n", - "Bradley Beal 0.501046\n", - "Kemba Walker 0.274820\n", - "Khris Middleton 0.097103\n", - "CJ McCollum -0.031155\n", - "Devin Booker -0.307908\n", - "Monta Ellis -0.567192\n", - "Lance Stephenson -0.785529\n", - "Chandler Parsons -0.834070\n", - "Damian Lillard -1.518093\n", - "Jalen Brunson -1.997692\n", - "Karl-Anthony Towns -2.044572\n", - "Tobias Harris -2.553516\n", - "JR Smith -2.758424\n", - "Tyrese Maxey -3.637720\n", - "Kyrie Irving -3.703635\n", - "DeMar DeRozan -4.662701\n", - "Isaiah Thomas -5.590367\n", - "Joe Johnson -5.907836\n", - "dtype: float64" + " defense_stat\n", + "player_name \n", + "Draymond Green 10.787943\n", + "Joakim Noah 9.358453\n", + "Anthony Davis 7.895150\n", + "Marc Gasol 6.534351\n", + "Paul George 6.408697\n", + "Paul Millsap 5.699113\n", + "George Hill 5.241234\n", + "Giannis Antetokounmpo 4.803693\n", + "PJ Tucker 4.780241\n", + "Serge Ibaka 4.703840\n", + "Marcin Gortat 4.511971\n", + "Jrue Holiday 4.421153\n", + "Chris Paul 4.249789\n", + "Nikola Jokic 4.021482\n", + "Marcus Smart 3.850867\n", + "Bam Adebayo 3.707044\n", + "Jimmy Butler 3.183364\n", + "DeAndre Jordan 3.154674\n", + "Kyle Lowry 3.141026\n", + "Otto Porter Jr. 2.968337\n", + "Pascal Siakam 2.750881\n", + "Mikal Bridges 2.670235\n", + "David West 2.666690\n", + "Stephen Curry 2.582205\n", + "Donovan Mitchell 2.433277\n", + "Kevin Love 2.424493\n", + "Jaylen Brown 2.380194\n", + "Jayson Tatum 2.100925\n", + "Dorian Finney-Smith 2.022662\n", + "Chris Bosh 1.673919\n", + "Blake Griffin 1.627615\n", + "Trevor Ariza 1.546710\n", + "Kemba Walker 1.342204\n", + "Andrew Wiggins 1.244490\n", + "Nicolas Batum 0.968720\n", + "James Harden 0.769140\n", + "Klay Thompson 0.543766\n", + "Russell Westbrook 0.523426\n", + "Ben Simmons 0.456007\n", + "LeBron James 0.354197\n", + "Wesley Matthews 0.299899\n", + "CJ McCollum -0.060706\n", + "Bradley Beal -0.200945\n", + "Khris Middleton -0.477710\n", + "Jalen Brunson -0.508995\n", + "John Wall -0.695989\n", + "Chandler Parsons -0.873292\n", + "Kevin Durant -0.913675\n", + "Devin Booker -0.916546\n", + "Lance Stephenson -1.036529\n", + "Monta Ellis -1.169090\n", + "Tobias Harris -1.576114\n", + "Karl-Anthony Towns -1.776151\n", + "Damian Lillard -2.258608\n", + "Tyrese Maxey -2.419741\n", + "JR Smith -2.516657\n", + "Kyrie Irving -3.997132\n", + "DeMar DeRozan -4.334230\n", + "Joe Johnson -4.882167\n", + "Isaiah Thomas -6.412895" ] }, - "execution_count": 14, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -807,8 +1386,35 @@ "source": [ "players_over_3000 = df[df.mp>3000]\n", "eligible_players = players_over_3000.groupby(\"player_name\")\n", - "defense_stats= eligible_players[[\"raptor_defense\",\"predator_defense\",\"pace_impact\"]].mean()\n", - "defense_stats.sum(axis=1).sort_values(ascending=False)" + "defense_stats= eligible_players[[\"raptor_defense\",\"predator_defense\"]].mean()\n", + "def_stat = defense_stats.sum(axis=1).sort_values(ascending=False)\n", + "df_defense = pd.DataFrame(def_stat)\n", + "df_defense.columns = [\"defense_stat\"]\n", + "df_defense" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "afe7a597-ce41-45f5-89f1-b82064088890", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Top 5 Defensive Players and their Defensive Stat\")\n", + "def_plot = sns.barplot(data=df_defense.head(), x=\"player_name\",y=\"defense_stat\",errorbar=None)\n", + "plt.xticks(df_defense.head().index,rotation=45)\n", + "plt.show()" ] }, { @@ -817,7 +1423,16 @@ "metadata": {}, "source": [ "### Results\n", - "I found that the best defensive player from 2014-2022 was Draymond Green. This makes sense as he has won the Defensive Player Of The Year award and is known primarily for his defense. He is known as one of the best defensive plaeyrs in the league. I am not surprised to see Draymond Green be the number one ranked defensive player. The three next best defensive players are Anthony Davis, Joakim Noah, and Paul George. These players are also known for their defense so it makes sense that they are rated so highly. It is interesting to note that 3 of the top 4 players are Power Forwards or Centers which also shows that this could lead to research on if Power Forwards and Centers generally play the best defense." + "I found that the best defensive player from 2014-2022 was Draymond Green. This makes sense as he has won the Defensive Player Of The Year award and is known primarily for his defense. He is known as one of the best defensive players in the league. I am not surprised to see Draymond Green be the number one ranked defensive player. The three next best defensive players are Anthony Davis, Joakim Noah, and Paul George. These players are also known for their defense, so it makes sense that they are rated so highly. It is interesting to note that 4 of the top 5 players are Power Forwards or Centers which also shows that this could lead to research on whether Power Forwards and Centers generally play the best defense." + ] + }, + { + "cell_type": "markdown", + "id": "90e25901-eb61-42cc-9d11-742ba515bfd9", + "metadata": {}, + "source": [ + "#### Results from Graph\n", + "This graph shows the difference between Draymond Green and everyone else. This shows the top 5 defensive players in the NBA from 2014-2022. The defense_stat is the value of raptor_defense and predator_defense stats added together." ] }, { @@ -843,7 +1458,7 @@ "id": "27b18f33-f5fc-4eda-a0d2-1be37c5b6d76", "metadata": {}, "source": [ - "My results give an accurate depiction of showing who is better between Kevin Durant and LeBron James and who the best offensive and defensive players in the NBA are. Determining who is better between Kevin Durant and LeBron James is accurate as I used offensive, defensive, and overall impact data for both players and compared them. I decided to add all of the values together as I thought that would accurately determine who has been better. A person could use other data not given in the data set as there could be other metrics that better capture who the better player is. In regards to who the best offensive and defensive players are in the NBA, the results are accurate as I used 3 different data points for offense and 3 data points for defense. This makes the data give the overall best offensive and defensive player. Also, the the players given at the top of the best offensive and defensive players lists are known for their skill on that side of the court which further suggests that the data is accurate.\n", + "My results give an accurate depiction of showing who is better between Kevin Durant and LeBron James and who the best offensive and defensive players in the NBA are. Determining who is better between Kevin Durant and LeBron James is accurate as I used offensive, defensive, and overall impact data for both players and compared them. I decided to add all of the values together as I thought that would accurately determine who has been better. A person could use other data not given in the data set as there could be other metrics that better capture who the better player is. In regard to who the best offensive and defensive players are in the NBA, the results are accurate as I used 2 different data points for offense and 2 data points for defense. This makes the data give the overall best offensive and defensive player. Also, the players given at the top of the best offensive and defensive players lists are known for their skill on that side of the court which further suggests that the data is accurate.\n", "\n", "Some limitations of the data set are that there are other statistics that were not factored in the data. Also, this compares players only by specific data points and does not factor in how they effect their teammates which is very important in determining how good a player is. This includes the leadership a player brings to their team and how that elevates their teammates. Another limitation is that I used 3000 or more minutes played as a qualification to be considered. There could be good players that played 2999 minutes and were not counted. The other limitation is that the data goes from 2014-2022. This is not recent enough as we are in 2025 and does not include data from the previous 2-3 seasons. Also, it is necessary to note that it does not include data from before 2014. This is a problem when discussing LeBron James and Kevin Durant as they both played prior to 2014.\n", "\n", @@ -865,7 +1480,9 @@ "source": [ "I learned that my media consumption has led me to decide to use this data set. I see a lot of NBA media so I was interested in using NBA data for this project.\n", "\n", - "I learned that LeBron James is better than Kevin Durant both offensively and defensively. This can make sense although it is pretty surprising that LeBron James was better on offense. Stephen Curry, Chris Paul, and James Harden were found to be the best players offensively which completely makes sense as they were been some of the best offesnive players from 2014-2022. Draymond Green, Anthony Davis, and Joakim Noah were found to be the best players defensively which completely makes sense as Draymond Green and Joakim Noah were some of the best defenders from 2014-2022. Overall, I would say that the results make sense to me showing that the data was accurate.\n", + "When reflecting on this project, this data could definitely be used by NBA teams to decide which players they should try to acquire. In the off-season teams are allowed to sign free agents and trade for players. This data could allow teams to realize who the better player is and acquire that player.\n", + "\n", + "I learned that LeBron James is better than Kevin Durant both offensively and defensively. This can make sense although it is pretty surprising that LeBron James was better on offense. Stephen Curry, Chris Paul, and James Harden were found to be the best players offensively which completely makes sense as they have been some of the best offensive players from 2014-2022. Draymond Green, Joakim Noah, and Anthony Davis were found to be the best players defensively which completely makes sense as Draymond Green and Joakim Noah were some of the best defenders from 2014-2022. Overall, I would say that the results make sense to me showing that the data was accurate.\n", "\n", "The most surprising thing was that LeBron James was better offensively than Kevin Durant. I assumed he would be better defensively but I was not expecting him to be better on offense. Kevin Durant is known as one of the best overall scorers and shooters so I definitely did not expect LeBron James to be better on offense. I was also surprised that Stephen Curry was rated so highly defensively. He is not thought of as one of the best defenders so this was interesting to see.\n", "\n", @@ -880,10 +1497,26 @@ "## Final Poster" ] }, + { + "cell_type": "markdown", + "id": "c7b9c078-dd64-40ae-b1e5-496afa39fd45", + "metadata": {}, + "source": [ + "The final poster is attached as the James Berent Flyer.pdf" + ] + }, + { + "cell_type": "markdown", + "id": "44248208-44a9-47f5-85e9-c61d4b06c9a0", + "metadata": {}, + "source": [ + "# Add Data Structures" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "cfe489ca-77ea-4071-9622-397638f82c90", + "id": "398f3b13-0813-4052-a43f-68903048ed39", "metadata": {}, "outputs": [], "source": []