{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"

NFL Big Data Bowl analysis

\n\n","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n\nplt.style.use('fivethirtyeight')","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:11.365815Z","iopub.execute_input":"2022-01-03T03:21:11.366219Z","iopub.status.idle":"2022-01-03T03:21:12.296506Z","shell.execute_reply.started":"2022-01-03T03:21:11.366124Z","shell.execute_reply":"2022-01-03T03:21:12.295556Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n

1. Game Data

\n \n
\n ","metadata":{}},{"cell_type":"markdown","source":"### **Game data:** The games.csv contains the teams playing in each game. The key variable is gameId.\n\n* **gameId:** Game identifier, unique (numeric)\n\n* **gameDate:** Game Date (time, mm/dd/yyyy)\n\n* **gameTimeEastern:** Start time of game (time, HH:MM:SS, EST)\n\n* **homeTeamAbbr:** Home team three-letter code (text)\n\n* **visitorTeamAbbr:** Visiting team three-letter code (text)\n\n* **week:** Week of game (numeric)","metadata":{}},{"cell_type":"code","source":"games = pd.read_csv('../input/nfl-big-data-bowl-2022/games.csv')\ngames","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:12.298725Z","iopub.execute_input":"2022-01-03T03:21:12.299124Z","iopub.status.idle":"2022-01-03T03:21:12.340961Z","shell.execute_reply.started":"2022-01-03T03:21:12.299084Z","shell.execute_reply":"2022-01-03T03:21:12.340168Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Function for Downcast\nDowncast is a great skill to compress data size which helps to save memory.","metadata":{}},{"cell_type":"code","source":"def downcast(df, verbose=True):\n start_mem = df.memory_usage().sum() / 1024**2\n for col in df.columns:\n dtype_name = df[col].dtype.name\n if dtype_name == 'object':\n pass\n elif dtype_name == 'bool':\n df[col] = df[col].astype('int8')\n elif dtype_name.startswith('int') or (df[col].round() == df[col]).all():\n df[col] = pd.to_numeric(df[col], downcast='integer')\n else:\n df[col] = pd.to_numeric(df[col], downcast='float')\n end_mem = df.memory_usage().sum() / 1024**2\n if verbose:\n print('{:.1f}% Compressed'.format(100 * (start_mem - end_mem) / start_mem))\n \n return df","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:12.342275Z","iopub.execute_input":"2022-01-03T03:21:12.342702Z","iopub.status.idle":"2022-01-03T03:21:12.351647Z","shell.execute_reply.started":"2022-01-03T03:21:12.342674Z","shell.execute_reply":"2022-01-03T03:21:12.350574Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"games = downcast(games)","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:12.352925Z","iopub.execute_input":"2022-01-03T03:21:12.353268Z","iopub.status.idle":"2022-01-03T03:21:12.378660Z","shell.execute_reply.started":"2022-01-03T03:21:12.353237Z","shell.execute_reply":"2022-01-03T03:21:12.377549Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Function for making feature summary ","metadata":{}},{"cell_type":"code","source":"def resumetable(df):\n print(f'Shape : {df.shape}')\n summary = pd.DataFrame(df.dtypes, columns=['Data Type'])\n summary = summary.reset_index()\n summary = summary.rename(columns={'index': 'Feature'})\n summary['Num of null'] = df.isnull().sum().values\n summary['Num of unique'] = df.nunique().values\n summary['First value'] = df.loc[0].values\n summary['Second value'] = df.loc[1].values\n summary['Third value'] = df.loc[2].values\n return summary\n\nresumetable(games)","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:12.381611Z","iopub.execute_input":"2022-01-03T03:21:12.381908Z","iopub.status.idle":"2022-01-03T03:21:12.413011Z","shell.execute_reply.started":"2022-01-03T03:21:12.381879Z","shell.execute_reply":"2022-01-03T03:21:12.412191Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Function for writing percent at the top of the bar graph","metadata":{}},{"cell_type":"code","source":"def write_percent(ax, total_size):\n '''Traverse the figure object and display the ratio at the top of the bar graph.'''\n for patch in ax.patches:\n height = patch.get_height() # Figure height (number of data)\n width = patch.get_width() # Figure width\n left_coord = patch.get_x() # The x-axis position on the left edge of the figure\n percent = height/total_size*100 # percent\n \n # Type text in the (x, y) coordinates\n ax.text(x=left_coord + width/2.0, # x-axis position\n y=height + total_size*0.001, # y-axis position\n s=f'{percent:1.1f}%', # Text\n ha='center') # in the middle","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:12.414462Z","iopub.execute_input":"2022-01-03T03:21:12.414758Z","iopub.status.idle":"2022-01-03T03:21:12.421396Z","shell.execute_reply.started":"2022-01-03T03:21:12.414722Z","shell.execute_reply":"2022-01-03T03:21:12.420621Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Make derivative features (month, day, hour)","metadata":{}},{"cell_type":"code","source":"games['month'] = games['gameDate'].apply(lambda x: int(x.split('/')[0]))\ngames['day'] = games['gameDate'].apply(lambda x: int(x.split('/')[1]))\ngames['hour'] = games['gameTimeEastern'].apply(lambda x: int(x.split(':')[0]))","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:12.422965Z","iopub.execute_input":"2022-01-03T03:21:12.423642Z","iopub.status.idle":"2022-01-03T03:21:12.445519Z","shell.execute_reply.started":"2022-01-03T03:21:12.423599Z","shell.execute_reply":"2022-01-03T03:21:12.444375Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Data Visualization","metadata":{}},{"cell_type":"code","source":"mpl.rc('font', size=15) # Set font size\nplt.figure(figsize=(7, 6)) # Set figure size\n\nax = sns.countplot(x='season', data=games)\nwrite_percent(ax, len(games)) \nax.set_title('Number of games for season');","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:12.446524Z","iopub.execute_input":"2022-01-03T03:21:12.446834Z","iopub.status.idle":"2022-01-03T03:21:12.695201Z","shell.execute_reply.started":"2022-01-03T03:21:12.446795Z","shell.execute_reply":"2022-01-03T03:21:12.694573Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### As the years go by, the number of games increases","metadata":{}},{"cell_type":"code","source":"mpl.rc('font', size=15)\nplt.figure(figsize=(8, 6))\n\nax = sns.countplot(x='month', data=games)\nwrite_percent(ax, len(games))\nax.set_title('Number of games for month');","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:12.696139Z","iopub.execute_input":"2022-01-03T03:21:12.696759Z","iopub.status.idle":"2022-01-03T03:21:12.925769Z","shell.execute_reply.started":"2022-01-03T03:21:12.696728Z","shell.execute_reply":"2022-01-03T03:21:12.924577Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### The game was held from September to January. There are especially many games in December, and they are rarely held in January","metadata":{}},{"cell_type":"code","source":"mpl.rc('font', size=12) \nplt.figure(figsize=(15, 7))\n\nax = sns.countplot(x='day', data=games)\nwrite_percent(ax, len(games))\nax.set_title('Number of games for day');","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:12.927185Z","iopub.execute_input":"2022-01-03T03:21:12.927532Z","iopub.status.idle":"2022-01-03T03:21:13.530814Z","shell.execute_reply.started":"2022-01-03T03:21:12.927493Z","shell.execute_reply":"2022-01-03T03:21:13.529837Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mpl.rc('font', size=12) \nplt.figure(figsize=(15, 7))\n\nax = sns.countplot(x='gameTimeEastern', data=games)\nwrite_percent(ax, len(games))\nax.set_title('Number of games for gameTimeEastern');\nax.tick_params('x', labelrotation=30) # rotate 30 degree of x label","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:13.532219Z","iopub.execute_input":"2022-01-03T03:21:13.532520Z","iopub.status.idle":"2022-01-03T03:21:13.929297Z","shell.execute_reply.started":"2022-01-03T03:21:13.532483Z","shell.execute_reply":"2022-01-03T03:21:13.928410Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mpl.rc('font', size=12) \nplt.figure(figsize=(15, 7))\n\nax = sns.countplot(x='hour', data=games)\nwrite_percent(ax, len(games))\nax.set_title('Number of games for hour');","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:13.930798Z","iopub.execute_input":"2022-01-03T03:21:13.931206Z","iopub.status.idle":"2022-01-03T03:21:14.345957Z","shell.execute_reply.started":"2022-01-03T03:21:13.931165Z","shell.execute_reply":"2022-01-03T03:21:14.344995Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### The most games were held at 1, 4, and 8","metadata":{}},{"cell_type":"code","source":"mpl.rc('font', size=12) \nplt.figure(figsize=(15, 7))\n\nax = sns.countplot(x='week', data=games)\nwrite_percent(ax, len(games))\nax.set_title('Number of games for week');","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:14.347157Z","iopub.execute_input":"2022-01-03T03:21:14.347392Z","iopub.status.idle":"2022-01-03T03:21:14.708792Z","shell.execute_reply.started":"2022-01-03T03:21:14.347365Z","shell.execute_reply":"2022-01-03T03:21:14.708177Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Pairplot for the gamedata ","metadata":{}},{"cell_type":"code","source":"sns.pairplot(games, hue='homeTeamAbbr')","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.pairplot(games, hue='visitorTeamAbbr')","metadata":{"execution":{"iopub.status.busy":"2022-01-03T04:27:40.174387Z","iopub.execute_input":"2022-01-03T04:27:40.174746Z","iopub.status.idle":"2022-01-03T04:28:35.284047Z","shell.execute_reply.started":"2022-01-03T04:27:40.174704Z","shell.execute_reply":"2022-01-03T04:28:35.283457Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.pairplot(games, hue='season')","metadata":{"execution":{"iopub.status.busy":"2022-01-03T04:28:55.567943Z","iopub.execute_input":"2022-01-03T04:28:55.568819Z","iopub.status.idle":"2022-01-03T04:29:12.360150Z","shell.execute_reply.started":"2022-01-03T04:28:55.568761Z","shell.execute_reply":"2022-01-03T04:29:12.359190Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.pairplot(games, hue='week')","metadata":{"execution":{"iopub.status.busy":"2022-01-03T04:29:19.539089Z","iopub.execute_input":"2022-01-03T04:29:19.539633Z","iopub.status.idle":"2022-01-03T04:29:38.461544Z","shell.execute_reply.started":"2022-01-03T04:29:19.539596Z","shell.execute_reply":"2022-01-03T04:29:38.460672Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.pairplot(games, hue='gameTimeEastern')","metadata":{"execution":{"iopub.status.busy":"2022-01-03T04:29:38.463155Z","iopub.execute_input":"2022-01-03T04:29:38.463376Z","iopub.status.idle":"2022-01-03T04:30:17.485134Z","shell.execute_reply.started":"2022-01-03T04:29:38.463350Z","shell.execute_reply":"2022-01-03T04:30:17.484212Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n

2. Player Data

\n \n
","metadata":{}},{"cell_type":"markdown","source":"### **Player data:** The players.csv file contains player-level information from players that participated in any of the tracking data files. The key variable is nflId\n\n* **nflId:** Player identification number, unique across players (numeric)\n\n* **height:** Player height (text)\n\n* **weight:** Player weight (numeric)\n\n* **birthDate:** Date of birth (YYYY-MM-DD)\n\n* **collegeName:** Player college (text)\n\n* **position:** Player position (text)\n\n* **displayName:** Player name (text)","metadata":{}},{"cell_type":"code","source":"players = pd.read_csv('../input/nfl-big-data-bowl-2022/players.csv')\nplayers","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:14.711699Z","iopub.execute_input":"2022-01-03T03:21:14.712249Z","iopub.status.idle":"2022-01-03T03:21:14.749944Z","shell.execute_reply.started":"2022-01-03T03:21:14.712212Z","shell.execute_reply":"2022-01-03T03:21:14.749362Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"players = downcast(players)","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:14.751010Z","iopub.execute_input":"2022-01-03T03:21:14.751369Z","iopub.status.idle":"2022-01-03T03:21:14.761566Z","shell.execute_reply.started":"2022-01-03T03:21:14.751341Z","shell.execute_reply":"2022-01-03T03:21:14.760597Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"resumetable(players)","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:14.763018Z","iopub.execute_input":"2022-01-03T03:21:14.763635Z","iopub.status.idle":"2022-01-03T03:21:14.794804Z","shell.execute_reply.started":"2022-01-03T03:21:14.763591Z","shell.execute_reply":"2022-01-03T03:21:14.793946Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"check = players['height'].str.split('-', expand=True)\n\ncheck.columns = ['first', 'second']\n\ncheck.loc[(check['second'].notnull()), 'first'] = check[check['second'].notnull()]['first'].astype(np.int16) * 12 + check[check['second'].notnull()]['second'].astype(np.int16)","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:14.795937Z","iopub.execute_input":"2022-01-03T03:21:14.796178Z","iopub.status.idle":"2022-01-03T03:21:14.812995Z","shell.execute_reply.started":"2022-01-03T03:21:14.796151Z","shell.execute_reply":"2022-01-03T03:21:14.812379Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"players[\"birthYear\"] = 0\nplayers[\"birthMonth\"] = 0\n#There are NA values in birthDate so that we should drop them\nplayers.dropna(subset=[\"birthDate\"], inplace=True)\nfor idx, row in players.iterrows():\n if len(row['birthDate'].split('/')) == 3: # 05/17/1994 \n players.loc[idx, 'birthYear'] = row['birthDate'].split('/')[2]\n players.loc[idx, 'birthMonth'] = row['birthDate'].split('/')[0]\n \n elif len(row['birthDate'].split('-')) == 3: # 1995-05-05\n players.loc[idx, 'birthYear'] = row['birthDate'].split('-')[0]\n players.loc[idx, 'birthMonth'] = row['birthDate'].split('-')[1]","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:14.814002Z","iopub.execute_input":"2022-01-03T03:21:14.815066Z","iopub.status.idle":"2022-01-03T03:21:16.411660Z","shell.execute_reply.started":"2022-01-03T03:21:14.815027Z","shell.execute_reply":"2022-01-03T03:21:16.410836Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Converting heights to CM and weights to Kg**","metadata":{}},{"cell_type":"code","source":"players_heights = players[\"height\"] # Get the Height data from DataFrame\nplayers_heights = players_heights.apply(lambda x: x.split(\"-\")) # Split the heights by hyphen (\"-\")\n\n# Convert Heights to Centimeters and add them to DataFrame\nplayers[\"height\"] = players_heights.apply(lambda x: int(x[0]) * 12 + int(x[1]) if len(x) == 2 else int(x[0])) * 2.54\n\n# Convert Weights to Kilograms and them to DataFrame\nplayers[\"weight\"] = round(players.weight * 0.453592, 2)\n\nplayers","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:16.413183Z","iopub.execute_input":"2022-01-03T03:21:16.413490Z","iopub.status.idle":"2022-01-03T03:21:16.445088Z","shell.execute_reply.started":"2022-01-03T03:21:16.413453Z","shell.execute_reply":"2022-01-03T03:21:16.444270Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Time For Some EDA on Players DataSet","metadata":{}},{"cell_type":"code","source":"import seaborn as sns\ncollege_df = players['collegeName'].value_counts()\nsns.set_style('darkgrid')\nfig, axes = plt.subplots(1,2,figsize=(12,6))\naxes[0] = sns.barplot(x=college_df[:10].values, y=college_df[:10].index, edgecolor=\"black\", ax=axes[0] )\naxes[0].set_title(\"Top 10 College player counts\", fontsize=20)\naxes[1].pie(x= college_df[:10], labels = college_df[:10].index, autopct='%.0f%%',\n explode=[0.03 for i in college_df[:10].index])\naxes[1].add_artist(plt.Circle((0,0),0.4,fc='white'))\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:16.446490Z","iopub.execute_input":"2022-01-03T03:21:16.447032Z","iopub.status.idle":"2022-01-03T03:21:16.944717Z","shell.execute_reply.started":"2022-01-03T03:21:16.446994Z","shell.execute_reply":"2022-01-03T03:21:16.944063Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**From the above graph, we can see that, Alabama is at the top with approximately 68 players.**","metadata":{}},{"cell_type":"markdown","source":"**Postions played by players**","metadata":{}},{"cell_type":"code","source":"pos_df = players['Position'].value_counts()\nsns.set_style('darkgrid')\nfig, axes = plt.subplots(1,2,figsize=(12,6))\naxes[0] = sns.barplot(x=pos_df[:10].values, y=pos_df[:10].index, edgecolor=\"black\", ax=axes[0])\naxes[0].set_title(\"Top 10 Postions played by player (By Count)\", fontsize=20)\naxes[1].pie(x= pos_df[:10], labels = pos_df[:10].index, autopct='%.0f%%',\n explode=[0.03 for i in pos_df[:10].index])\naxes[1].add_artist(plt.Circle((0,0),0.4,fc='white'))\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:16.945822Z","iopub.execute_input":"2022-01-03T03:21:16.946181Z","iopub.status.idle":"2022-01-03T03:21:17.385252Z","shell.execute_reply.started":"2022-01-03T03:21:16.946152Z","shell.execute_reply":"2022-01-03T03:21:17.384579Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**The postion 'WR' is played the most by the players. It is approximately 320 i.e is 16%**","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(10, 6), dpi=100)\nsns.regplot(x=players.weight, y=players.height, line_kws={\"color\": \"red\"})\nplt.title(\"Player Weight(Kg) vs Player Height(cm)\");","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:17.386321Z","iopub.execute_input":"2022-01-03T03:21:17.386711Z","iopub.status.idle":"2022-01-03T03:21:17.978453Z","shell.execute_reply.started":"2022-01-03T03:21:17.386681Z","shell.execute_reply":"2022-01-03T03:21:17.977564Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**It looks like the taller the player is the heavier he is.**","metadata":{}},{"cell_type":"markdown","source":"**Weight and Height Distribution.**","metadata":{}},{"cell_type":"code","source":"fig = plt.figure(figsize=(20, 15), dpi=80)\n\nax1 = fig.add_subplot(223)\nsns.histplot(players.weight, ax=ax1)\nax1.set_title(\"Weight(Kg) Distribution\")\n\nax2 = fig.add_subplot(224)\nsns.histplot(players.height, ax=ax2, bins=10)\nax2.set_title(\"Height(cm) Distribution\");","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:17.979895Z","iopub.execute_input":"2022-01-03T03:21:17.980384Z","iopub.status.idle":"2022-01-03T03:21:18.655600Z","shell.execute_reply.started":"2022-01-03T03:21:17.980341Z","shell.execute_reply":"2022-01-03T03:21:18.654713Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**From the above distribution, we can see that most of the players are between 190cm - 195cm height(390+310 = 700 approximately). Players who are on the shorter side i.e < 170cm are very less roughly 30 in count. Same is with the taller side i.e 200cm - 205cm, And most players are seen to be in between 80kg to 100. Very less people on both the extremes.**","metadata":{}},{"cell_type":"markdown","source":"**Player birthyear and birthmonth Distribution**","metadata":{}},{"cell_type":"code","source":"fig = plt.figure(figsize=(20, 15), dpi=80)\n\nbirthyear = players['birthYear'].value_counts()\nax1 = fig.add_subplot(223)\nsns.barplot(x=birthyear.index, y=birthyear.values, ci=None, ax=ax1)\nax1.tick_params(axis='x', rotation=45)\nax1.set_title(\"BirthYear Distribution\",size=20)\nplt.xlabel(\"Year\", size=15)\n\nbirthmonth = players['birthMonth'].value_counts()\nax2 = fig.add_subplot(224)\nsns.barplot(x=birthmonth.index, y=birthmonth.values, ci=None, ax=ax2)\nax2.set_title(\"BirthMonth Distribution\",size=20)\nplt.xlabel(\"Month\",size=15);","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:18.656958Z","iopub.execute_input":"2022-01-03T03:21:18.657228Z","iopub.status.idle":"2022-01-03T03:21:19.449857Z","shell.execute_reply.started":"2022-01-03T03:21:18.657198Z","shell.execute_reply":"2022-01-03T03:21:19.448949Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**From the above charts, it can be determined that most players are born in the year 1995. The most frequent birth month is September.**","metadata":{}},{"cell_type":"markdown","source":"### Convert all heights to feet","metadata":{}},{"cell_type":"code","source":"players['height'] = check['first']\nplayers['height'] = players['height'].astype(np.float32)\nplayers['height'] /= 12\n\nplayers","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:19.451233Z","iopub.execute_input":"2022-01-03T03:21:19.451527Z","iopub.status.idle":"2022-01-03T03:21:19.477817Z","shell.execute_reply.started":"2022-01-03T03:21:19.451491Z","shell.execute_reply":"2022-01-03T03:21:19.476774Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mpl.rc('font', size=15) \nplt.figure(figsize=(10, 6))\n\nax = sns.distplot(players['height'], bins=12)\nax.set_title('Height Distribution');","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:19.479346Z","iopub.execute_input":"2022-01-03T03:21:19.479589Z","iopub.status.idle":"2022-01-03T03:21:19.850548Z","shell.execute_reply.started":"2022-01-03T03:21:19.479561Z","shell.execute_reply":"2022-01-03T03:21:19.849558Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mpl.rc('font', size=15) \nplt.figure(figsize=(10, 6))\n\nax = sns.distplot(players['weight'])\nax.set_title('Weight Distribution');","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:19.852082Z","iopub.execute_input":"2022-01-03T03:21:19.852318Z","iopub.status.idle":"2022-01-03T03:21:20.263927Z","shell.execute_reply.started":"2022-01-03T03:21:19.852291Z","shell.execute_reply":"2022-01-03T03:21:20.263143Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"top_players_colleage = players['collegeName'].value_counts()[:20].reset_index()\ntop_players_colleage.columns = ['collageName', 'numberOfPlayers']","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:20.265341Z","iopub.execute_input":"2022-01-03T03:21:20.265552Z","iopub.status.idle":"2022-01-03T03:21:20.273190Z","shell.execute_reply.started":"2022-01-03T03:21:20.265527Z","shell.execute_reply":"2022-01-03T03:21:20.272458Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mpl.rc('font', size=10) \nplt.figure(figsize=(15, 12))\n\nax = sns.barplot(x='numberOfPlayers', y='collageName', data=top_players_colleage)\nax.set_title('Number of players for collegeName');","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:20.274325Z","iopub.execute_input":"2022-01-03T03:21:20.274543Z","iopub.status.idle":"2022-01-03T03:21:20.915330Z","shell.execute_reply.started":"2022-01-03T03:21:20.274518Z","shell.execute_reply":"2022-01-03T03:21:20.914431Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Create birth year feature","metadata":{}},{"cell_type":"code","source":"players['birthYear'] = 0","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:20.916468Z","iopub.execute_input":"2022-01-03T03:21:20.916680Z","iopub.status.idle":"2022-01-03T03:21:20.922169Z","shell.execute_reply.started":"2022-01-03T03:21:20.916656Z","shell.execute_reply":"2022-01-03T03:21:20.921355Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"players.dropna(subset=['birthDate'], inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:20.923257Z","iopub.execute_input":"2022-01-03T03:21:20.923458Z","iopub.status.idle":"2022-01-03T03:21:20.938726Z","shell.execute_reply.started":"2022-01-03T03:21:20.923435Z","shell.execute_reply":"2022-01-03T03:21:20.937819Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Extract birth year","metadata":{}},{"cell_type":"code","source":"for idx, row in players.iterrows():\n if len(row['birthDate'].split('/')) == 3: # ex) 05/17/1994 \n players.loc[idx, 'birthYear'] = row['birthDate'].split('/')[2]\n \n elif len(row['birthDate'].split('-')) == 3: # ex) 1995-05-05\n players.loc[idx, 'birthYear'] = row['birthDate'].split('-')[0]","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:20.939924Z","iopub.execute_input":"2022-01-03T03:21:20.940183Z","iopub.status.idle":"2022-01-03T03:21:21.852436Z","shell.execute_reply.started":"2022-01-03T03:21:20.940156Z","shell.execute_reply":"2022-01-03T03:21:21.851700Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mpl.rc('font', size=15) \nplt.figure(figsize=(10, 5))\n\nax = sns.distplot(players['birthYear'], bins=25)\nax.set_title('Players birth year Distribution');","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:21.853732Z","iopub.execute_input":"2022-01-03T03:21:21.854178Z","iopub.status.idle":"2022-01-03T03:21:22.211030Z","shell.execute_reply.started":"2022-01-03T03:21:21.854148Z","shell.execute_reply":"2022-01-03T03:21:22.210199Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Those born in 1995 are the most common","metadata":{}},{"cell_type":"code","source":"players['birthYear'].min(), players['birthYear'].max()","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:22.212445Z","iopub.execute_input":"2022-01-03T03:21:22.212842Z","iopub.status.idle":"2022-01-03T03:21:22.220937Z","shell.execute_reply.started":"2022-01-03T03:21:22.212805Z","shell.execute_reply":"2022-01-03T03:21:22.220121Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### The oldest player was born in 1972, and the youngest player was born in 1999","metadata":{}},{"cell_type":"markdown","source":"### Pairplot of the players W.R.T position and height","metadata":{}},{"cell_type":"code","source":"sns.pairplot(players, hue='Position')","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:38:15.742808Z","iopub.execute_input":"2022-01-03T03:38:15.743189Z","iopub.status.idle":"2022-01-03T03:38:25.429580Z","shell.execute_reply.started":"2022-01-03T03:38:15.743155Z","shell.execute_reply":"2022-01-03T03:38:25.428615Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.pairplot(players, hue='height')","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:38:41.231546Z","iopub.execute_input":"2022-01-03T03:38:41.231865Z","iopub.status.idle":"2022-01-03T03:38:44.031563Z","shell.execute_reply.started":"2022-01-03T03:38:41.231829Z","shell.execute_reply":"2022-01-03T03:38:44.030422Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n

3. Play Data

\n \n
","metadata":{}},{"cell_type":"markdown","source":"### **Play data:** The plays.csv file contains play-level information from each game. The key variables are gameId and playId\n- gameId: Game identifier, unique (numeric)\n- playId: Play identifier, not unique across games (numeric)\n- playDescription: Description of play (text)\n- quarter: Game quarter (numeric)\n- down: Down (numeric)\n- yardsToGo: Distance needed for a first down (numeric)\n- possessionTeam: Team punting, placekicking or kicking off the ball (text)\n- specialTeamsPlayType: Formation of play: Extra Point, Field Goal, Kickoff or Punt (text)\n- specialTeamsPlayResult: Special Teams outcome of play dependent on play type: Blocked Kick Attempt, Blocked Punt, Downed, Fair Catch, Kick Attempt Good, Kick Attempt No Good, Kickoff Team Recovery, Muffed, Non-Special Teams Result, Out of Bounds, Return or Touchback (text)\n- kickerId: nflId of placekicker, punter or kickoff specialist on play (numeric)\n- returnerId: nflId(s) of returner(s) on play if there was a special teams return. Multiple returners on a play are separated by a ; (text)\n- kickBlockerId: nflId of blocker of kick on play if there was a blocked field goal or blocked punt (numeric)\n- yardlineSide: 3-letter team code corresponding to line-of-scrimmage (text)\n- yardlineNumber: Yard line at line-of-scrimmage (numeric) \n- gameClock: Time on clock of play (MM:SS)\n- penaltyCodes: NFL categorization of the penalties that occurred on the play. Multiple penalties on a play are separated by a ; (text)\n- penaltyJerseyNumber: Jersey number and team code of the player committing each penalty. Multiple penalties on a play are separated by a ; (text)\n- penaltyYards: yards gained by possessionTeam by penalty (numeric)\n- preSnapHomeScore: Home score prior to the play (numeric)\n- preSnapVisitorScore: Visiting team score prior to the play (numeric)\n- passResult: Scrimmage outcome of the play if specialTeamsPlayResult is \"Non-Special Teams Result\" (C: Complete pass, I: Incomplete pass, S: Quarterback sack, IN: Intercepted pass, R: Scramble, ' ': Designed Rush, text)\n- kickLength: Kick length in air of kickoff, field goal or punt (numeric)\n- kickReturnYardage: Yards gained by return team if there was a return on a kickoff or punt (numeric)\n- playResult: Net yards gained by the kicking team, including penalty yardage (numeric)\n- absoluteYardlineNumber: Location of ball downfield in tracking data coordinates (numeric)","metadata":{}},{"cell_type":"code","source":"plays = pd.read_csv('../input/nfl-big-data-bowl-2022/plays.csv')\n\nplays","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:22.222146Z","iopub.execute_input":"2022-01-03T03:21:22.222526Z","iopub.status.idle":"2022-01-03T03:21:22.406094Z","shell.execute_reply.started":"2022-01-03T03:21:22.222498Z","shell.execute_reply":"2022-01-03T03:21:22.405352Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plays = downcast(plays)","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:22.407313Z","iopub.execute_input":"2022-01-03T03:21:22.407522Z","iopub.status.idle":"2022-01-03T03:21:22.433839Z","shell.execute_reply.started":"2022-01-03T03:21:22.407498Z","shell.execute_reply":"2022-01-03T03:21:22.432880Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"resumetable(plays)","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:22.435571Z","iopub.execute_input":"2022-01-03T03:21:22.435849Z","iopub.status.idle":"2022-01-03T03:21:22.512794Z","shell.execute_reply.started":"2022-01-03T03:21:22.435823Z","shell.execute_reply":"2022-01-03T03:21:22.512172Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig = plt.figure(figsize=(12,6))\nsns.scatterplot(x='quarter', y='down', data=plays)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:22.514149Z","iopub.execute_input":"2022-01-03T03:21:22.514380Z","iopub.status.idle":"2022-01-03T03:21:22.818878Z","shell.execute_reply.started":"2022-01-03T03:21:22.514353Z","shell.execute_reply":"2022-01-03T03:21:22.818261Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig = plt.figure(figsize=(12,6))\ng = sns.barplot(x='quarter', y='yardsToGo', data=plays, ci=None)\ng.bar_label(g.containers[0])\nplt.title('Yards to Go in Each Quarter', size=15)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:22.819917Z","iopub.execute_input":"2022-01-03T03:21:22.820250Z","iopub.status.idle":"2022-01-03T03:21:23.025809Z","shell.execute_reply.started":"2022-01-03T03:21:22.820223Z","shell.execute_reply":"2022-01-03T03:21:23.024907Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig = plt.figure(figsize=(12,6))\ng = sns.barplot(x='quarter', y='playResult', data=plays, ci=None)\ng.bar_label(g.containers[0])\nplt.title(\"Play result for every quarter\", size=15)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:23.027145Z","iopub.execute_input":"2022-01-03T03:21:23.027379Z","iopub.status.idle":"2022-01-03T03:21:23.238844Z","shell.execute_reply.started":"2022-01-03T03:21:23.027353Z","shell.execute_reply":"2022-01-03T03:21:23.238078Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig = plt.figure(figsize=(12,6))\nsns.distplot(plays['kickLength'])\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:23.244992Z","iopub.execute_input":"2022-01-03T03:21:23.245256Z","iopub.status.idle":"2022-01-03T03:21:23.747581Z","shell.execute_reply.started":"2022-01-03T03:21:23.245231Z","shell.execute_reply":"2022-01-03T03:21:23.746845Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plays['kickLength'].describe()","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:23.751018Z","iopub.execute_input":"2022-01-03T03:21:23.751751Z","iopub.status.idle":"2022-01-03T03:21:23.764260Z","shell.execute_reply.started":"2022-01-03T03:21:23.751712Z","shell.execute_reply":"2022-01-03T03:21:23.763302Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## The mean kick length is 54.744166. The minimum is 2.000 and the max is 90.000.","metadata":{}},{"cell_type":"code","source":"fig = plt.figure(figsize=(12,6))\nsns.histplot(plays['passResult'])\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:23.765507Z","iopub.execute_input":"2022-01-03T03:21:23.765854Z","iopub.status.idle":"2022-01-03T03:21:24.011816Z","shell.execute_reply.started":"2022-01-03T03:21:23.765820Z","shell.execute_reply":"2022-01-03T03:21:24.010892Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plays['passResult'].describe()","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:24.013579Z","iopub.execute_input":"2022-01-03T03:21:24.013924Z","iopub.status.idle":"2022-01-03T03:21:24.024602Z","shell.execute_reply.started":"2022-01-03T03:21:24.013882Z","shell.execute_reply":"2022-01-03T03:21:24.023737Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig = plt.figure(figsize=(12,6))\nsns.histplot(plays['possessionTeam'])\nplt.xticks(rotation=90)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:24.026528Z","iopub.execute_input":"2022-01-03T03:21:24.026849Z","iopub.status.idle":"2022-01-03T03:21:24.583058Z","shell.execute_reply.started":"2022-01-03T03:21:24.026811Z","shell.execute_reply":"2022-01-03T03:21:24.582455Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import plotly.express as px\nimport plotly.graph_objects as pg\nfrom plotly import tools as tl","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:24.583941Z","iopub.execute_input":"2022-01-03T03:21:24.584506Z","iopub.status.idle":"2022-01-03T03:21:26.162078Z","shell.execute_reply.started":"2022-01-03T03:21:24.584475Z","shell.execute_reply":"2022-01-03T03:21:26.161199Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tr18 = pd.read_csv(\"../input/nfl-big-data-bowl-2022/tracking2018.csv\")\ntr18.head()","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:21:26.163421Z","iopub.execute_input":"2022-01-03T03:21:26.163944Z","iopub.status.idle":"2022-01-03T03:22:11.751568Z","shell.execute_reply.started":"2022-01-03T03:21:26.163906Z","shell.execute_reply":"2022-01-03T03:22:11.750716Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = tr18.query('playId == 36 and gameId == 2018123000')\nprint(data[[\"x\", \"y\", \"team\"]])","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:22:11.752903Z","iopub.execute_input":"2022-01-03T03:22:11.753461Z","iopub.status.idle":"2022-01-03T03:22:11.869004Z","shell.execute_reply.started":"2022-01-03T03:22:11.753417Z","shell.execute_reply":"2022-01-03T03:22:11.868154Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig = px.scatter(data, x='x', y='y', color='team')\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:22:11.870225Z","iopub.execute_input":"2022-01-03T03:22:11.870625Z","iopub.status.idle":"2022-01-03T03:22:12.890850Z","shell.execute_reply.started":"2022-01-03T03:22:11.870596Z","shell.execute_reply":"2022-01-03T03:22:12.889945Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = tr18.query('playId == 36 and gameId == 2018102107')\nprint(data[[\"x\", \"y\", \"team\"]])","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:22:12.892006Z","iopub.execute_input":"2022-01-03T03:22:12.892290Z","iopub.status.idle":"2022-01-03T03:22:12.990185Z","shell.execute_reply.started":"2022-01-03T03:22:12.892260Z","shell.execute_reply":"2022-01-03T03:22:12.989256Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig = px.scatter(data, x=\"x\", y=\"y\", color=\"team\")\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:22:12.991670Z","iopub.execute_input":"2022-01-03T03:22:12.991894Z","iopub.status.idle":"2022-01-03T03:22:13.077492Z","shell.execute_reply.started":"2022-01-03T03:22:12.991866Z","shell.execute_reply":"2022-01-03T03:22:13.076485Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = tr18.query('position == \"CB\" and gameId == 2018111900')\nprint(data[[\"x\", \"y\", \"team\"]])","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:22:13.078804Z","iopub.execute_input":"2022-01-03T03:22:13.079053Z","iopub.status.idle":"2022-01-03T03:22:13.617568Z","shell.execute_reply.started":"2022-01-03T03:22:13.079022Z","shell.execute_reply":"2022-01-03T03:22:13.616666Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig = px.scatter(data, x=\"x\", y=\"y\", color=\"team\")\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:22:13.618784Z","iopub.execute_input":"2022-01-03T03:22:13.619024Z","iopub.status.idle":"2022-01-03T03:22:13.721769Z","shell.execute_reply.started":"2022-01-03T03:22:13.618997Z","shell.execute_reply":"2022-01-03T03:22:13.720853Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### There are lots of null values in `returnerld`, `kickBlockerId`, `penaltyCodes`, `penaltyJerseyNumbers`, `penaltyYards`, `passResult`, `kickReturnYardage` features","metadata":{}},{"cell_type":"code","source":"mpl.rc('font', size=12) \nplt.figure(figsize=(12, 6))\n\nax = sns.countplot(x='quarter', data=plays)\nwrite_percent(ax, len(plays))\nax.set_title('Number of plays of every quarter');","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:22:13.723208Z","iopub.execute_input":"2022-01-03T03:22:13.723662Z","iopub.status.idle":"2022-01-03T03:22:13.991092Z","shell.execute_reply.started":"2022-01-03T03:22:13.723628Z","shell.execute_reply":"2022-01-03T03:22:13.990526Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mpl.rc('font', size=12) \nplt.figure(figsize=(12, 6))\n\nax = sns.countplot(x='down', data=plays)\nwrite_percent(ax, len(plays))\nax.set_title('Number of plays of every down');","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:22:13.992227Z","iopub.execute_input":"2022-01-03T03:22:13.992672Z","iopub.status.idle":"2022-01-03T03:22:14.268603Z","shell.execute_reply.started":"2022-01-03T03:22:13.992624Z","shell.execute_reply":"2022-01-03T03:22:14.268024Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mpl.rc('font', size=12) \nplt.figure(figsize=(12, 6))\n\nax = sns.countplot(x='yardsToGo', data=plays)\nax.set_title('Number of plays for every yards to go category');","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:22:14.269814Z","iopub.execute_input":"2022-01-03T03:22:14.270578Z","iopub.status.idle":"2022-01-03T03:22:14.908854Z","shell.execute_reply.started":"2022-01-03T03:22:14.270543Z","shell.execute_reply":"2022-01-03T03:22:14.907936Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mpl.rc('font', size=15) \nplt.figure(figsize=(10, 5))\n\nax = sns.distplot(plays['playResult'], bins=25);\nax.set_title('playResult Distribution'); ","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:22:14.910048Z","iopub.execute_input":"2022-01-03T03:22:14.910299Z","iopub.status.idle":"2022-01-03T03:22:15.465338Z","shell.execute_reply.started":"2022-01-03T03:22:14.910271Z","shell.execute_reply":"2022-01-03T03:22:15.464465Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## playResult: Net yards gained by the offense, including penalty yardage (numeric)","metadata":{}},{"cell_type":"code","source":"mpl.rc('font', size=15) \nplt.figure(figsize=(10, 5))\n\nax = sns.distplot(plays['preSnapHomeScore'], bins=12);\nax.set_title('preSnapHomeScore Distribution'); ","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:22:15.466809Z","iopub.execute_input":"2022-01-03T03:22:15.467022Z","iopub.status.idle":"2022-01-03T03:22:15.980111Z","shell.execute_reply.started":"2022-01-03T03:22:15.466997Z","shell.execute_reply":"2022-01-03T03:22:15.979252Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"> preSnapHomeScore: Home score prior to the play (numeric)","metadata":{}},{"cell_type":"code","source":"mpl.rc('font', size=15) \nplt.figure(figsize=(10, 5))\n\nax = sns.distplot(plays['preSnapVisitorScore'], bins=12);\nax.set_title('preSnapVisitorScore Distribution'); ","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:22:15.981205Z","iopub.execute_input":"2022-01-03T03:22:15.981499Z","iopub.status.idle":"2022-01-03T03:22:16.495182Z","shell.execute_reply.started":"2022-01-03T03:22:15.981470Z","shell.execute_reply":"2022-01-03T03:22:16.494290Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### A pairplot for the gameplay information file with the hue ","metadata":{}},{"cell_type":"code","source":"sns.pairplot(plays,hue='quarter')","metadata":{"execution":{"iopub.status.busy":"2022-01-03T04:01:01.244749Z","iopub.execute_input":"2022-01-03T04:01:01.245047Z","iopub.status.idle":"2022-01-03T04:05:18.240470Z","shell.execute_reply.started":"2022-01-03T04:01:01.245019Z","shell.execute_reply":"2022-01-03T04:05:18.239808Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.pairplot(plays,hue='possessionTeam')","metadata":{"execution":{"iopub.status.busy":"2022-01-03T04:05:18.241757Z","iopub.execute_input":"2022-01-03T04:05:18.242105Z","iopub.status.idle":"2022-01-03T04:14:25.681824Z","shell.execute_reply.started":"2022-01-03T04:05:18.242055Z","shell.execute_reply":"2022-01-03T04:14:25.680215Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.pairplot(plays,hue='specialTeamsPlayType')","metadata":{"execution":{"iopub.status.busy":"2022-01-03T04:14:25.682986Z","iopub.execute_input":"2022-01-03T04:14:25.683216Z","iopub.status.idle":"2022-01-03T04:19:49.430015Z","shell.execute_reply.started":"2022-01-03T04:14:25.683190Z","shell.execute_reply":"2022-01-03T04:19:49.428934Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.pairplot(plays,hue='specialTeamsResult')","metadata":{"execution":{"iopub.status.busy":"2022-01-03T04:19:49.432892Z","iopub.execute_input":"2022-01-03T04:19:49.433671Z","iopub.status.idle":"2022-01-03T04:26:42.938970Z","shell.execute_reply.started":"2022-01-03T04:19:49.433620Z","shell.execute_reply":"2022-01-03T04:26:42.937784Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n

4. Tracking Data

\n \n
","metadata":{}},{"cell_type":"markdown","source":"# Tracking data visualization\n\n**Hello everyone!**\n\nAs a great sports fan, I'm always more than happy to participate in sport's competitions on Kaggle! \nAmerical football has always fascinated me, but unfortunately as a European, I might don't fully understand all the rules, so if you find any error in this notebook - you're more than welcome to correct me :)\n\n* **The goal of this notebook is to build a tool to visualize matches more nicely.**\n* **Visualized tracking data can allow us to understand data better and develop better solutions**\n* **You'll learn how to draw pitch in matplotlib**\n* **You'll learn how to create animations**\n* **You'll learn how to create ipywidgets, making your notebooks interactive**\n* **The Whole tool is implemented in sole matplotlib which I hope makes this notebook even more didactic**\n\n*I hope that code is self-explanatory but if anything is unclear - just let me know and I will do my best to help!*\n\n# I invite you to ebmark on this journey with me - let's visualize and animate tracking data together.","metadata":{}},{"cell_type":"markdown","source":"# Football field\n\n**From wiki:**\n> The rectangular field of play used for American football games measures 100 yards (91.44 m) long between the goal lines, and 160 feet (48.8 m) (53+1⁄3 yards) wide. The field is made of grass. In addition, there are end zones extending another 10 yards (9.144 m) past the goal lines to the \"end lines\", for a total length of 120 yards (109.7 m). When the \"football field\" is used as unit of measurement, it is usually understood to mean 100 yards (91.44 m), although technically the full length of the official field, including the end zones, is 120 yards (109.7 m). There is a goal centered on each end line, with a crossbar 10 feet (3.0 m) above the ground and goalposts 18 feet 6 inches (5.64 m) apart extending at least 35 feet (11 m) above the crossbar. Between the goal lines, additional lines span the width of the field at 5-yard intervals.\n\n**Visualizations in this notebook are based on the above informations from Wikipedia and the the image provided by competition's organizers**\n\n","metadata":{}},{"cell_type":"markdown","source":"# Let's draw football pitch in matplotlib","metadata":{}},{"cell_type":"code","source":"import matplotlib.patches as patches\nfrom matplotlib.patches import Arc\nfrom matplotlib import pyplot as plt\nimport matplotlib.patches as mpatches\n\n# Change size of the figure\nplt.rcParams['figure.figsize'] = [20, 16]\ndef drawPitch(width, height, color=\"w\"):\n fig = plt.figure()\n ax = plt.axes(xlim=(-10, width + 30), ylim=(-15, height + 5))\n plt.axis('off')\n\n # Grass around pitch\n rect = patches.Rectangle((-10, -5), width + 40, height + 10, linewidth=1, facecolor='#3f995b', capstyle='round')\n ax.add_patch(rect)\n ###################\n\n # Pitch boundaries\n rect = plt.Rectangle((0, 0), width + 20, height, ec=color, fc=\"None\", lw=2)\n ax.add_patch(rect)\n ###################\n\n # vertical lines - every 5 yards\n for i in range(21):\n plt.plot([10 + 5 * i, 10 + 5 * i], [0, height], c=\"w\", lw=2)\n ###################\n \n # distance markers - every 10 yards\n for yards in range(10, width, 10):\n yards_text = yards if yards <= width / 2 else width - yards\n # top markers\n plt.text(10 + yards - 2, height - 7.5, yards_text, size=20, c=\"w\", weight=\"bold\")\n # botoom markers\n plt.text(10 + yards - 2, 7.5, yards_text, size=20, c=\"w\", weight=\"bold\", rotation=180)\n ###################\n\n # yards markers - every yard\n # bottom markers\n for x in range(20):\n for j in range(1, 5):\n plt.plot([10 + x * 5 + j, 10 + x * 5 + j], [1, 3], color=\"w\", lw=3)\n\n # top markers\n for x in range(20):\n for j in range(1, 5):\n plt.plot([10 + x * 5 + j, 10 + x * 5 + j], [height - 1, height - 3], color=\"w\", lw=3)\n\n # middle bottom markers\n y = (height - 18.5) / 2\n for x in range(20):\n for j in range(1, 5):\n plt.plot([10 + x * 5 + j, 10 + x * 5 + j], [y, y + 2], color=\"w\", lw=3)\n\n # middle top markers\n for x in range(20):\n for j in range(1, 5):\n plt.plot([10 + x * 5 + j, 10 + x * 5 + j], [height - y, height - y - 2], color=\"w\", lw=3)\n ###################\n\n # draw home end zone\n plt.text(2.5, (height - 10) / 2, \"HOME\", size=40, c=\"w\", weight=\"bold\", rotation=90)\n rect = plt.Rectangle((0, 0), 10, height, ec=color, fc=\"#0064dc\", lw=2)\n ax.add_patch(rect)\n\n # draw away end zone \n plt.text(112.5, (height - 10) / 2, \"AWAY\", size=40, c=\"w\", weight=\"bold\", rotation=-90)\n rect = plt.Rectangle((width + 10, 0), 10, height, ec=color, fc=\"#c80014\", lw=2)\n ax.add_patch(rect)\n ###################\n \n # draw extra spot point\n # left\n y = (height - 3) / 2\n plt.plot([10 + 2, 10 + 2], [y, y + 3], c=\"w\", lw=2)\n \n # right\n plt.plot([width + 10 - 2, width + 10 - 2], [y, y + 3], c=\"w\", lw=2)\n ###################\n \n # draw goalpost\n goal_width = 6 # yards\n y = (height - goal_width) / 2\n # left\n plt.plot([0, 0], [y, y + goal_width], \"-\", c=\"y\", lw=10, ms=20)\n # right\n plt.plot([width + 20, width + 20], [y, y + goal_width], \"-\", c=\"y\", lw=10, ms=20)\n \n return fig, ax\n\nfig, ax = drawPitch(100, 53.3)","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:22:16.496696Z","iopub.execute_input":"2022-01-03T03:22:16.497005Z","iopub.status.idle":"2022-01-03T03:22:17.487197Z","shell.execute_reply.started":"2022-01-03T03:22:16.496968Z","shell.execute_reply":"2022-01-03T03:22:17.486284Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tracking2018 = pd.read_csv('../input/nfl-big-data-bowl-2022/tracking2018.csv')\ntracking2019 = pd.read_csv('../input/nfl-big-data-bowl-2022/tracking2019.csv')\ntracking2020 = pd.read_csv('../input/nfl-big-data-bowl-2022/tracking2020.csv')","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:22:17.488549Z","iopub.execute_input":"2022-01-03T03:22:17.488800Z","iopub.status.idle":"2022-01-03T03:24:17.200419Z","shell.execute_reply.started":"2022-01-03T03:22:17.488770Z","shell.execute_reply":"2022-01-03T03:24:17.199395Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tracking2018.head()\ntracking2019.head()\ntracking2020.head()","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:17.201618Z","iopub.execute_input":"2022-01-03T03:24:17.201819Z","iopub.status.idle":"2022-01-03T03:24:17.225848Z","shell.execute_reply.started":"2022-01-03T03:24:17.201795Z","shell.execute_reply":"2022-01-03T03:24:17.225216Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tracking2018 = downcast(tracking2018)\ntracking2019 = downcast(tracking2019)\ntracking2020 = downcast(tracking2020)","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:17.226942Z","iopub.execute_input":"2022-01-03T03:24:17.227644Z","iopub.status.idle":"2022-01-03T03:24:26.306504Z","shell.execute_reply.started":"2022-01-03T03:24:17.227602Z","shell.execute_reply":"2022-01-03T03:24:26.305849Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Let's add some interactivity\nFrom provided dropdown widgets you can choose a demanded game.\n\n**Animation's creation can take a while (depending on play's length - sometimes even up to 60s, so don't give up after clicking start)**","metadata":{}},{"cell_type":"markdown","source":"#### 2018123000 and playId == 36","metadata":{}},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize=(12, 8))\ntracking2018.query('gameId == 2018123000 and playId == 36').groupby('team') \\\n .plot(x='x', y='y', ax=ax, style='.')\nplt.legend().remove();","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:26.307617Z","iopub.execute_input":"2022-01-03T03:24:26.308477Z","iopub.status.idle":"2022-01-03T03:24:27.195313Z","shell.execute_reply.started":"2022-01-03T03:24:26.308434Z","shell.execute_reply":"2022-01-03T03:24:27.194396Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### gameId == 2018091001 and playId == 4033","metadata":{}},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize=(12, 8))\ntracking2018.query('gameId == 2018091001 and playId == 4033').groupby('team') \\\n .plot(x='x', y='y', ax=ax, style='.')\nplt.legend().remove();","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:27.196331Z","iopub.execute_input":"2022-01-03T03:24:27.196540Z","iopub.status.idle":"2022-01-03T03:24:27.576866Z","shell.execute_reply.started":"2022-01-03T03:24:27.196508Z","shell.execute_reply":"2022-01-03T03:24:27.576044Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### gameId == 2018091609 and position == \"CB\"","metadata":{}},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize=(12, 8))\ntracking2018.query('gameId == 2018091609 and position == \"CB\"').groupby('team') \\\n .plot(x='x', y='y', ax=ax, style='.')\nplt.legend().remove();","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:27.578117Z","iopub.execute_input":"2022-01-03T03:24:27.578343Z","iopub.status.idle":"2022-01-03T03:24:28.432009Z","shell.execute_reply.started":"2022-01-03T03:24:27.578317Z","shell.execute_reply":"2022-01-03T03:24:28.431202Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### gameId == 2018091609 and position == \"LB\"","metadata":{}},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize=(12, 8))\ntracking2018.query('gameId == 2018091609 and position == \"LB\"').groupby('team') \\\n .plot(x='x', y='y', ax=ax, style='.')\nplt.legend().remove();","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:28.433277Z","iopub.execute_input":"2022-01-03T03:24:28.434099Z","iopub.status.idle":"2022-01-03T03:24:29.247096Z","shell.execute_reply.started":"2022-01-03T03:24:28.434043Z","shell.execute_reply":"2022-01-03T03:24:29.246512Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### gameId == 2018091609 and position == \"RB\"","metadata":{}},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize=(12, 8))\ntracking2018.query('gameId == 2018091609 and position == \"RB\"').groupby('team') \\\n .plot(x='x', y='y', ax=ax, style='.')\nplt.legend().remove();","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:29.248223Z","iopub.execute_input":"2022-01-03T03:24:29.248714Z","iopub.status.idle":"2022-01-03T03:24:30.061311Z","shell.execute_reply.started":"2022-01-03T03:24:29.248659Z","shell.execute_reply":"2022-01-03T03:24:30.060211Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Function to create football field\n","metadata":{}},{"cell_type":"code","source":" fig, ax = drawPitch(100, 53.3)","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:30.062870Z","iopub.execute_input":"2022-01-03T03:24:30.063232Z","iopub.status.idle":"2022-01-03T03:24:30.929178Z","shell.execute_reply.started":"2022-01-03T03:24:30.063190Z","shell.execute_reply":"2022-01-03T03:24:30.928179Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nfrom matplotlib.colors import rgb2hex\n%matplotlib inline\nimport seaborn as sns\n\n#(Credit for the below code goes to @ANZ check out his notebook as well)\ncmap = cm.get_cmap('GnBu',12) #colormap and number\ncol_def =[]\nfor i in range(cmap.N):\n rgb = cmap(i)[:3]\n col_def.append(rgb2hex(rgb))\n print(rgb2hex(rgb))","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:30.931013Z","iopub.execute_input":"2022-01-03T03:24:30.931513Z","iopub.status.idle":"2022-01-03T03:24:30.947409Z","shell.execute_reply.started":"2022-01-03T03:24:30.931471Z","shell.execute_reply":"2022-01-03T03:24:30.946472Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Function to create animation","metadata":{}},{"cell_type":"code","source":"games_ids = {}\ngames_tracking2018 = tracking2018.groupby(by=[\"gameId\"])\nfor game, data in games_tracking2018:\n games_ids[game] = list(set(data.playId.tolist()))","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:30.949150Z","iopub.execute_input":"2022-01-03T03:24:30.949452Z","iopub.status.idle":"2022-01-03T03:24:33.377767Z","shell.execute_reply.started":"2022-01-03T03:24:30.949415Z","shell.execute_reply":"2022-01-03T03:24:33.376964Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def extract_one_game(game_id, play_id, df):\n game = df[(df.gameId == game_id) & (df.playId == play_id)]\n home1 = {}\n away1 = {}\n balls1 = []\n \n players = game.sort_values(['frameId'], ascending=True).groupby('nflId')\n for id, dx in players:\n jerseyNumber = int(dx.jerseyNumber.iloc[0])\n if dx.team.iloc[0] == \"home\":\n home1[jerseyNumber] = list(zip(dx.x.tolist(), dx.y.tolist()))\n elif dx.team.iloc[0] == \"away\":\n away1[jerseyNumber] = list(zip(dx.x.tolist(), dx.y.tolist()))\n\n\n ball_df = game.sort_values(['frameId'], ascending=True) \n ball_df = ball_df[ball_df.team == \"football\"]\n balls1 = list(zip(ball_df.x.tolist(), ball_df.y.tolist()))\n return home1, away1, balls1","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:33.379401Z","iopub.execute_input":"2022-01-03T03:24:33.379694Z","iopub.status.idle":"2022-01-03T03:24:33.389673Z","shell.execute_reply.started":"2022-01-03T03:24:33.379656Z","shell.execute_reply":"2022-01-03T03:24:33.388822Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"games_ids = {}\ngames_tracking2019 = tracking2019.groupby(by=[\"gameId\"])\nfor game, data in games_tracking2019:\n games_ids[game] = list(set(data.playId.tolist()))","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:33.391043Z","iopub.execute_input":"2022-01-03T03:24:33.391365Z","iopub.status.idle":"2022-01-03T03:24:36.238114Z","shell.execute_reply.started":"2022-01-03T03:24:33.391335Z","shell.execute_reply":"2022-01-03T03:24:36.237162Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def extract_one_game(game_id, play_id, df):\n game = df[(df.gameId == game_id) & (df.playId == play_id)]\n home2 = {}\n away2 = {}\n balls2 = []\n \n players = game.sort_values(['frameId'], ascending=True).groupby('nflId')\n for id, dx in players:\n jerseyNumber = int(dx.jerseyNumber.iloc[0])\n if dx.team.iloc[0] == \"home\":\n home2[jerseyNumber] = list(zip(dx.x.tolist(), dx.y.tolist()))\n elif dx.team.iloc[0] == \"away\":\n away2[jerseyNumber] = list(zip(dx.x.tolist(), dx.y.tolist()))\n\n\n ball_df = game.sort_values(['frameId'], ascending=True) \n ball_df = ball_df[ball_df.team == \"football\"]\n balls2 = list(zip(ball_df.x.tolist(), ball_df.y.tolist()))\n return home2, away2, balls2","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:36.239774Z","iopub.execute_input":"2022-01-03T03:24:36.240022Z","iopub.status.idle":"2022-01-03T03:24:36.249311Z","shell.execute_reply.started":"2022-01-03T03:24:36.239992Z","shell.execute_reply":"2022-01-03T03:24:36.248363Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"games_ids = {}\ngames_tracking2020 = tracking2020.groupby(by=[\"gameId\"])\nfor game, data in games_tracking2020:\n games_ids[game] = list(set(data.playId.tolist()))","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:36.250912Z","iopub.execute_input":"2022-01-03T03:24:36.251305Z","iopub.status.idle":"2022-01-03T03:24:39.112441Z","shell.execute_reply.started":"2022-01-03T03:24:36.251263Z","shell.execute_reply":"2022-01-03T03:24:39.111616Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def extract_one_game(game_id, play_id, df):\n game = df[(df.gameId == game_id) & (df.playId == play_id)]\n home3 = {}\n away3 = {}\n balls3 = []\n \n players = game.sort_values(['frameId'], ascending=True).groupby('nflId')\n for id, dx in players:\n jerseyNumber = int(dx.jerseyNumber.iloc[0])\n if dx.team.iloc[0] == \"home\":\n home3[jerseyNumber] = list(zip(dx.x.tolist(), dx.y.tolist()))\n elif dx.team.iloc[0] == \"away\":\n away3[jerseyNumber] = list(zip(dx.x.tolist(), dx.y.tolist()))\n\n\n ball_df = game.sort_values(['frameId'], ascending=True) \n ball_df = ball_df[ball_df.team == \"football\"]\n balls3 = list(zip(ball_df.x.tolist(), ball_df.y.tolist()))\n return home3, away3, balls3","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:39.113560Z","iopub.execute_input":"2022-01-03T03:24:39.113772Z","iopub.status.idle":"2022-01-03T03:24:39.123436Z","shell.execute_reply.started":"2022-01-03T03:24:39.113748Z","shell.execute_reply":"2022-01-03T03:24:39.122350Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from matplotlib import animation\nfrom IPython.display import HTML\ndef animate_one_play(game_id, play_id, df):\n fig, ax = drawPitch(100, 53.3)\n \n home1, away1, balls1 = extract_one_game(game_id, play_id, df)\n\n team_left, = ax.plot([], [], 'o', markersize=20, markerfacecolor=\"r\", markeredgewidth=2, markeredgecolor=\"white\", zorder=7)\n team_right, = ax.plot([], [], 'o', markersize=20, markerfacecolor=\"b\", markeredgewidth=2, markeredgecolor=\"white\", zorder=7)\n ball, = ax.plot([], [], 'o', markersize=10, markerfacecolor=\"black\", markeredgewidth=2, markeredgecolor=\"white\", zorder=7)\n drawings = [team_left, team_right, ball]\n\n def init():\n team_left.set_data([], [])\n team_right.set_data([], [])\n ball.set_data([], [])\n return drawings\n\n def draw_teams(i):\n X = []\n Y = []\n for k, v in home1.items():\n x, y = v[i]\n X.append(x)\n Y.append(y)\n team_left.set_data(X, Y)\n \n X = []\n Y = []\n for k, v in away1.items():\n x, y = v[i]\n X.append(x)\n Y.append(y)\n team_right.set_data(X, Y)\n\n def animate(i):\n draw_teams(i)\n \n x, y = balls1[i]\n ball.set_data([x, y])\n return drawings\n \n # !May take a while!\n anim1 = animation.FuncAnimation(fig, animate, init_func=init,\n frames=len(balls1), interval=100, blit=True)\n\n return HTML(anim1.to_html5_video())","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:39.124766Z","iopub.execute_input":"2022-01-03T03:24:39.125004Z","iopub.status.idle":"2022-01-03T03:24:39.139566Z","shell.execute_reply.started":"2022-01-03T03:24:39.124977Z","shell.execute_reply":"2022-01-03T03:24:39.138706Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from matplotlib import animation\nfrom IPython.display import HTML\ndef animate_one_play(game_id, play_id, df):\n fig, ax = drawPitch(100, 53.3)\n \n home2, away2, balls2 = extract_one_game(game_id, play_id, df)\n\n team_left, = ax.plot([], [], 'o', markersize=20, markerfacecolor=\"r\", markeredgewidth=2, markeredgecolor=\"white\", zorder=7)\n team_right, = ax.plot([], [], 'o', markersize=20, markerfacecolor=\"b\", markeredgewidth=2, markeredgecolor=\"white\", zorder=7)\n ball, = ax.plot([], [], 'o', markersize=10, markerfacecolor=\"black\", markeredgewidth=2, markeredgecolor=\"white\", zorder=7)\n drawings = [team_left, team_right, ball]\n\n def init():\n team_left.set_data([], [])\n team_right.set_data([], [])\n ball.set_data([], [])\n return drawings\n\n def draw_teams(i):\n X = []\n Y = []\n for k, v in home2.items():\n x, y = v[i]\n X.append(x)\n Y.append(y)\n team_left.set_data(X, Y)\n \n X = []\n Y = []\n for k, v in away2.items():\n x, y = v[i]\n X.append(x)\n Y.append(y)\n team_right.set_data(X, Y)\n\n def animate(i):\n draw_teams(i)\n \n x, y = balls2[i]\n ball.set_data([x, y])\n return drawings\n \n # !May take a while!\n anim2 = animation.FuncAnimation(fig, animate, init_func=init,\n frames=len(balls2), interval=100, blit=True)\n\n return HTML(anim2.to_html5_video())","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:39.140996Z","iopub.execute_input":"2022-01-03T03:24:39.141248Z","iopub.status.idle":"2022-01-03T03:24:39.155898Z","shell.execute_reply.started":"2022-01-03T03:24:39.141214Z","shell.execute_reply":"2022-01-03T03:24:39.155064Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from matplotlib import animation\nfrom IPython.display import HTML\ndef animate_one_play(game_id, play_id, df):\n fig, ax = drawPitch(100, 53.3)\n \n home3, away3, balls3 = extract_one_game(game_id, play_id, df)\n\n team_left, = ax.plot([], [], 'o', markersize=20, markerfacecolor=\"r\", markeredgewidth=2, markeredgecolor=\"white\", zorder=7)\n team_right, = ax.plot([], [], 'o', markersize=20, markerfacecolor=\"b\", markeredgewidth=2, markeredgecolor=\"white\", zorder=7)\n ball, = ax.plot([], [], 'o', markersize=10, markerfacecolor=\"black\", markeredgewidth=2, markeredgecolor=\"white\", zorder=7)\n drawings = [team_left, team_right, ball]\n\n def init():\n team_left.set_data([], [])\n team_right.set_data([], [])\n ball.set_data([], [])\n return drawings\n\n def draw_teams(i):\n X = []\n Y = []\n for k, v in home3.items():\n x, y = v[i]\n X.append(x)\n Y.append(y)\n team_left.set_data(X, Y)\n \n X = []\n Y = []\n for k, v in away3.items():\n x, y = v[i]\n X.append(x)\n Y.append(y)\n team_right.set_data(X, Y)\n\n def animate(i):\n draw_teams(i)\n \n x, y = balls3[i]\n ball.set_data([x, y])\n return drawings\n \n # !May take a while!\n anim3= animation.FuncAnimation(fig, animate, init_func=init,\n frames=len(balls3), interval=100, blit=True)\n\n return HTML(anim3.to_html5_video())","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:39.157338Z","iopub.execute_input":"2022-01-03T03:24:39.157590Z","iopub.status.idle":"2022-01-03T03:24:39.173567Z","shell.execute_reply.started":"2022-01-03T03:24:39.157564Z","shell.execute_reply":"2022-01-03T03:24:39.172982Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"animate_one_play(2018123000, 36, tracking2018)","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:39.174596Z","iopub.execute_input":"2022-01-03T03:24:39.175322Z","iopub.status.idle":"2022-01-03T03:24:53.758218Z","shell.execute_reply.started":"2022-01-03T03:24:39.175276Z","shell.execute_reply":"2022-01-03T03:24:53.757284Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Some visualizations about Scouting data","metadata":{}},{"cell_type":"markdown","source":"

Some visualizations about Scouting data

\n \n
","metadata":{}},{"cell_type":"code","source":"df_scouting= pd.read_csv('../input/nfl-big-data-bowl-2022/PFFScoutingData.csv')\ndf_scouting.head()","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:53.759818Z","iopub.execute_input":"2022-01-03T03:24:53.760086Z","iopub.status.idle":"2022-01-03T03:24:53.877967Z","shell.execute_reply.started":"2022-01-03T03:24:53.760038Z","shell.execute_reply":"2022-01-03T03:24:53.877049Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_scouting.info()","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:53.879313Z","iopub.execute_input":"2022-01-03T03:24:53.879605Z","iopub.status.idle":"2022-01-03T03:24:53.919021Z","shell.execute_reply.started":"2022-01-03T03:24:53.879566Z","shell.execute_reply":"2022-01-03T03:24:53.918009Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### The number of kicktypes and how frequently each one is implemented in the game","metadata":{}},{"cell_type":"code","source":"fig, ax = plt.subplots()\ndf_scouting['kickType'].value_counts().plot(ax=ax, kind='bar')","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:53.920356Z","iopub.execute_input":"2022-01-03T03:24:53.920634Z","iopub.status.idle":"2022-01-03T03:24:54.233213Z","shell.execute_reply.started":"2022-01-03T03:24:53.920601Z","shell.execute_reply":"2022-01-03T03:24:54.232221Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## I thought of implementing the pairplots here for data as they end up giving us very insightful observations into the dataset as a whole","metadata":{}},{"cell_type":"markdown","source":"### A pairplot of the scouting data with the hue as 'kicktype'","metadata":{}},{"cell_type":"code","source":"sns.pairplot(df_scouting, hue='kickDirectionActual')","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:34:24.383671Z","iopub.execute_input":"2022-01-03T03:34:24.383986Z","iopub.status.idle":"2022-01-03T03:34:47.158589Z","shell.execute_reply.started":"2022-01-03T03:34:24.383955Z","shell.execute_reply":"2022-01-03T03:34:47.157702Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.pairplot(df_scouting, hue='kickDirectionIntended')","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:34:02.739249Z","iopub.execute_input":"2022-01-03T03:34:02.740472Z","iopub.status.idle":"2022-01-03T03:34:24.382199Z","shell.execute_reply.started":"2022-01-03T03:34:02.740415Z","shell.execute_reply":"2022-01-03T03:34:24.381178Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.pairplot(df_scouting, hue='kickType')","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:24:54.234310Z","iopub.execute_input":"2022-01-03T03:24:54.234525Z","iopub.status.idle":"2022-01-03T03:25:19.406953Z","shell.execute_reply.started":"2022-01-03T03:24:54.234501Z","shell.execute_reply":"2022-01-03T03:25:19.406133Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Violin Plots and Box Plots","metadata":{}},{"cell_type":"markdown","source":"## Here are some plots for comparing various parameters available in the datasets provided, such as kickLength, kickReturnYardage, hangTime, kickType, yardsToGo, etc.","metadata":{}},{"cell_type":"code","source":"import seaborn as sns\nsns.set_theme(style=\"whitegrid\")\nax = sns.violinplot(x=plays['quarter'])","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:30:02.511514Z","iopub.execute_input":"2022-01-03T03:30:02.511901Z","iopub.status.idle":"2022-01-03T03:30:02.896388Z","shell.execute_reply.started":"2022-01-03T03:30:02.511853Z","shell.execute_reply":"2022-01-03T03:30:02.895498Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.set_theme(style=\"whitegrid\")\nax = sns.violinplot(x=plays['yardsToGo'],y=plays['specialTeamsPlayType'])","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:30:02.897559Z","iopub.execute_input":"2022-01-03T03:30:02.897788Z","iopub.status.idle":"2022-01-03T03:30:03.261463Z","shell.execute_reply.started":"2022-01-03T03:30:02.897762Z","shell.execute_reply":"2022-01-03T03:30:03.260635Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.set_theme(style=\"whitegrid\")\nax = sns.violinplot(x=plays['kickLength'],y=plays['kickReturnYardage'])","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:30:03.262971Z","iopub.execute_input":"2022-01-03T03:30:03.263741Z","iopub.status.idle":"2022-01-03T03:30:07.174011Z","shell.execute_reply.started":"2022-01-03T03:30:03.263698Z","shell.execute_reply":"2022-01-03T03:30:07.173185Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.set_theme(style=\"whitegrid\")\nax = sns.violinplot(x=df_scouting['hangTime'],y=df_scouting['kickContactType'])","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:30:07.175457Z","iopub.execute_input":"2022-01-03T03:30:07.175870Z","iopub.status.idle":"2022-01-03T03:30:07.878457Z","shell.execute_reply.started":"2022-01-03T03:30:07.175841Z","shell.execute_reply":"2022-01-03T03:30:07.877580Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import seaborn as sns\nsns.set_theme(style=\"whitegrid\")\nax = sns.boxplot(x=df_scouting[\"hangTime\"], y=df_scouting[\"kickType\"])\n","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:30:07.879578Z","iopub.execute_input":"2022-01-03T03:30:07.879813Z","iopub.status.idle":"2022-01-03T03:30:08.816905Z","shell.execute_reply.started":"2022-01-03T03:30:07.879790Z","shell.execute_reply":"2022-01-03T03:30:08.816083Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import seaborn as sns\nsns.set_theme(style=\"whitegrid\")\nax = sns.boxplot(x=df_scouting[\"hangTime\"], y=df_scouting[\"operationTime\"])","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:30:08.818051Z","iopub.execute_input":"2022-01-03T03:30:08.818291Z","iopub.status.idle":"2022-01-03T03:30:21.595197Z","shell.execute_reply.started":"2022-01-03T03:30:08.818265Z","shell.execute_reply":"2022-01-03T03:30:21.594359Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import seaborn as sns\nsns.set_theme(style=\"whitegrid\")\nax = sns.boxplot(x=df_scouting[\"hangTime\"], y=df_scouting[\"snapTime\"])","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:30:21.596564Z","iopub.execute_input":"2022-01-03T03:30:21.596789Z","iopub.status.idle":"2022-01-03T03:30:34.832807Z","shell.execute_reply.started":"2022-01-03T03:30:21.596763Z","shell.execute_reply":"2022-01-03T03:30:34.831900Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import seaborn as sns\nsns.set_theme(style=\"whitegrid\")\nax = sns.boxplot(x=df_scouting[\"snapTime\"], y=df_scouting[\"operationTime\"])","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:30:34.834175Z","iopub.execute_input":"2022-01-03T03:30:34.834801Z","iopub.status.idle":"2022-01-03T03:30:37.527703Z","shell.execute_reply.started":"2022-01-03T03:30:34.834757Z","shell.execute_reply":"2022-01-03T03:30:37.526809Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.set_theme(style=\"whitegrid\")\nax = sns.boxplot(x=players[\"height\"], y=players[\"Position\"])","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:30:37.528980Z","iopub.execute_input":"2022-01-03T03:30:37.529259Z","iopub.status.idle":"2022-01-03T03:30:38.303373Z","shell.execute_reply.started":"2022-01-03T03:30:37.529230Z","shell.execute_reply":"2022-01-03T03:30:38.302701Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.set_theme(style=\"whitegrid\")\nax = sns.boxplot(x=players[\"weight\"], y=players[\"Position\"])","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:30:38.304309Z","iopub.execute_input":"2022-01-03T03:30:38.304642Z","iopub.status.idle":"2022-01-03T03:30:39.039138Z","shell.execute_reply.started":"2022-01-03T03:30:38.304612Z","shell.execute_reply":"2022-01-03T03:30:39.038293Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.set_theme(style=\"whitegrid\")\nax = sns.boxplot(x=players[\"weight\"], y=players[\"height\"])","metadata":{"execution":{"iopub.status.busy":"2022-01-03T03:30:39.040303Z","iopub.execute_input":"2022-01-03T03:30:39.041279Z","iopub.status.idle":"2022-01-03T03:30:47.763711Z","shell.execute_reply.started":"2022-01-03T03:30:39.041227Z","shell.execute_reply":"2022-01-03T03:30:47.762841Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
","metadata":{}},{"cell_type":"markdown","source":"## UPVOTE THE NOTEBOOK IF YOU LIKE IT \n![image.png](attachment:d3fec2bf-19d5-4eb3-a86f-9d81e7071451.png)![image.png](attachment:df4ff67b-7bc5-425b-a5df-bcf90938f577.png)","metadata":{},"attachments":{"d3fec2bf-19d5-4eb3-a86f-9d81e7071451.png":{"image/png":"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"},"df4ff67b-7bc5-425b-a5df-bcf90938f577.png":{"image/png":"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"}}}]}