From 72c9bf8c9f75d9dcad8c37a878f39f51d3e20d35 Mon Sep 17 00:00:00 2001 From: Danny Malter Date: Tue, 7 Jan 2025 15:55:38 -0600 Subject: [PATCH] update --- .DS_Store | Bin 10244 -> 10244 bytes NFL Big Data Bowl 2025/BDB25.ipynb | 172202 ----------------- _posts/2025-01-07-Sunday_Afternoon_Fever.md | 2 +- 3 files changed, 1 insertion(+), 172203 deletions(-) delete mode 100644 NFL Big Data Bowl 2025/BDB25.ipynb diff --git a/.DS_Store b/.DS_Store index ea4c8657a4fd38e6ab92ca80cf417f0771bc3f50..6b991a398086c9bd705770d99dd1df59c1438607 100644 GIT binary patch delta 25 dcmZn(XbITRAUt`Fp!??I!fl*j#uV8CE&z!h3G@H} delta 25 fcmZn(XbITRAUt`3kniTR!VR22hWq9zvISfKh;Iqx diff --git a/NFL Big Data Bowl 2025/BDB25.ipynb b/NFL Big Data Bowl 2025/BDB25.ipynb deleted file mode 100644 index b1bca17..0000000 --- a/NFL Big Data Bowl 2025/BDB25.ipynb +++ /dev/null @@ -1,172202 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d20d6aad-60ca-4e28-915d-50d95c9f7d9d", - "metadata": {}, - "source": [ - "# **DISCO**: **DIS**guising **CO**verages on Defense" - ] - }, - { - "cell_type": "code", - "execution_count": 214, - "id": "87cc41fa", - "metadata": { - "hide_input": true, - "jupyter": { - "source_hidden": true - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 214, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Click below to toggle on/off code.\n", - "from IPython.display import HTML\n", - "\n", - "HTML('''\n", - "
''')" - ] - }, - { - "cell_type": "markdown", - "id": "c039edb1-1436-4afe-b950-02c0efcf1972", - "metadata": {}, - "source": [ - "# Introduction" - ] - }, - { - "cell_type": "markdown", - "id": "47d841a9-c6c0-4c60-8970-ed7b410cd682", - "metadata": {}, - "source": [ - "Football is a sport where game theory and deception are components to almost every single play. In addition to great talent, football fans love to watch how head coaches, coordinators, and players are constantly trying to outsmart their opponents. One of many opportunities for teams to leverage deception is with defensive pre-snap alignments, when a defense’s goal is to confuse the offense as to what pass coverage they will run. Perhaps they are disguising a coverage, or they want the offense to *believe* a disguised coverage is coming when it is not. Thanks to the NFL and Next Gen Stats, we developed and propose a metric that evaluates the frequency each team’s defense disguises their coverage in the moments before the ball is snapped: *DISCO*\n", - "\n", - "#### ***DISCO***: ***DIS***guised ***CO***verage %:\n", - "- The percent of snaps a team’s defense transforms into a pass coverage that is observed less than 10% of cases (among all NFL snaps in a season) following their presented pre-snap alignment.\n", - "- Note: This metric can also be broken down by pre-snap alignment type, as we will explore below." - ] - }, - { - "cell_type": "markdown", - "id": "6037084d-2b10-4b61-8c32-8677fbcad24e", - "metadata": {}, - "source": [ - "# Approach for Developing DISCO" - ] - }, - { - "cell_type": "markdown", - "id": "edfacc69-e8a5-4d2c-820e-925fd0a9d4cf", - "metadata": {}, - "source": [ - "Our approach involves leveraging an unsupervised machine learning model, [K-Medoids](https://medium.com/@prasanNH/exploring-the-world-of-clustering-k-means-vs-k-medoids-f648ea738508), to separate thousands of pre-snap alignments into clusters, and then to analyze the resulting clusters to learn about what they entail post-snap. Because typically the behaviors of defensive backs pre-snap provide the best indications into what coverage the defense may run, we found that linemen and linebacker data added noise to our model that did not enhance our output and analysis.\n", - "\n", - "##### We focus our model on the following metrics:\n", - "\n", - "-\tSS horizontal distance from ball \n", - "-\tSS vertical distance from ball \n", - "-\tFS horizontal distance from ball\n", - "-\tFS vertical distance from ball\n", - "-\tFor each CB\n", - " - Horizontal distance from ball\n", - " - Vertical distance from ball\n", - " - Horizontal distance from nearest receiver\n", - " - Vertical distance from nearest receiver\n", - " - Body orientation \n", - "-\tFor offensive plays using a pre-snap motion\n", - "- Defender motion relative to receiver motion\n", - "- Direction of DB motion (if applicable)\n", - "\n", - "##### Potentially Relevant Metrics Not Included\n", - "- Verbal Signals\n", - "- Hand Gestures/Pointing\n", - "- Direction player is leaning\n", - "- Direction a player is looking (independent of body orientation)\n", - "- Game situation (e.g. - down, yards, score, previous play(s)\n", - "- Team Historical Alignments\n", - "\n", - "\n", - "We capture the location and orientation metrics at 2 seconds before the snap, and motion metrics throughout the pre-snap period. We found that defenses are more likely to reveal their alignment as time passes from the huddle break, yet we need to use a time early enough that the metric can be relevant to a QB/offense who has a moment to process what is seen.\n", - "\n", - "\n", - "The function of the K-Medoids model is to identify data points among a cluster of similar data (data within proximity) while differentiating data points that are far apart. For example, just as a QB can often spot the difference between a Cover 1 and Cover 3 based on the distance outside-backs are from receivers and perhaps their body orientation, even if a similar location of a middle deep safety, the cluster algorithm can recognize those alignments as distinct. The purpose of identifying unique clusters in an unsupervised model is not to predict the defenses coverage, but to further explore post-snap coverages as a function of pre-snap alignments. " - ] - }, - { - "cell_type": "markdown", - "id": "93a619f0-329c-4c4e-9933-fbdb3f389537", - "metadata": {}, - "source": [ - "# Import Packages" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "fb8ef696-8164-45b8-82a4-c94697198804", - "metadata": { - "hide_input": true, - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "#import packages\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib\n", - "import seaborn as sn\n", - "#from plotnine import *\n", - "\n", - "#import ggplot\n", - "#from ggplot import *\n", - "import io\n", - "import re as re\n", - "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n", - "#import xlsxwriter\n", - "#from pandas import ExcelWriterter\n", - "import matplotlib.pyplot as plt\n", - "pd.options.display.max_columns = 100\n", - "import datetime\n", - "import os" - ] - }, - { - "cell_type": "markdown", - "id": "31df57a9-c5ad-4293-afaa-ed26ef6cc3df", - "metadata": {}, - "source": [ - "# Read in Data" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "714b3637-86f1-46d3-b7a8-c98676273e92", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "#read in data and parse dates, adjust other data types \n", - "import os\n", - "os.chdir('/Users/drewmalter/Documents/Personal/NFL/nfl_big_data_bowl_25/data')\n", - "#df_plays = pd.read_csv('plays.csv', parse_dates = ['GameDate'])\n", - "df_plays = pd.read_csv('plays.csv')\n", - "df_games = pd.read_csv('games.csv')\n", - "df_players = pd.read_csv('players.csv')\n", - "df_player_play = pd.read_csv('player_play.csv')\n", - "df_week1 = pd.read_csv('tracking_week_1.csv', parse_dates = ['time'])\n", - "df_week2 = pd.read_csv('tracking_week_2.csv', parse_dates = ['time'])\n", - "df_week3 = pd.read_csv('tracking_week_3.csv', parse_dates = ['time'])\n", - "df_week4 = pd.read_csv('tracking_week_4.csv', parse_dates = ['time'])\n", - "df_week5 = pd.read_csv('tracking_week_5.csv', parse_dates = ['time'])\n", - "df_week6 = pd.read_csv('tracking_week_6.csv', parse_dates = ['time'])\n", - "df_week7 = pd.read_csv('tracking_week_7.csv', parse_dates = ['time'])\n", - "df_week8 = pd.read_csv('tracking_week_8.csv', parse_dates = ['time'])\n", - "df_week9 = pd.read_csv('tracking_week_9.csv', parse_dates = ['time'])\n", - "#df_week1.head()" - ] - }, - { - "cell_type": "markdown", - "id": "99cc439b-dcc8-41dc-aecd-cb818dba3c3e", - "metadata": {}, - "source": [ - "# Prep Play Game Data" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "d9a3f5c7-c4a1-431d-a110-d7f39f44dacb", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "#Data Prep\n", - "\n", - "#Concat all weekly df's into one\n", - "df_weeks = pd.concat([df_week1, df_week2, df_week3, df_week4, df_week5, df_week6, df_week7, df_week8, df_week9])\n", - "#df_weeks.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "7c22b0fd-6705-4fb3-a4d3-9576133bd86d", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "#filter for only plays with an offensive formation\n", - "df_plays_filtered = df_plays.loc[df_plays['offenseFormation'].notnull()]\n", - "\n", - "#filter out plays where the score is 3 possesions difference or more in the second half (teams are more predictable in this case)\n", - "##For improvement - model out scenarios where we should filter out - not just make this assumption\n", - "df_plays_filtered = df_plays_filtered.loc[(abs(df_plays_filtered['preSnapHomeScore'] - df_plays_filtered['preSnapVisitorScore']) < 17) \n", - "| (df_plays_filtered['quarter'] <3)]\n", - "\n", - "#filter out plays in the final 2 minutes of a half/game - same situation as above\n", - "df_plays_filtered['game_clock_numeric'] = df_plays_filtered.gameClock.str[:2].astype(float) + df_plays_filtered.gameClock.str[-2:].astype(float)/60\n", - "df_plays_filtered = df_plays_filtered.loc[(df_plays_filtered.game_clock_numeric >= 2) | (df_plays_filtered.quarter == 1) | (df_plays_filtered.quarter == 3)]\n", - "\n", - "#df_plays_filtered.shape\n", - "#df_plays_filtered.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "38087599-a6a7-462e-b391-c292bd74b266", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "#Join games to plays to get if offense or defense is home or visitor\n", - "df_plays_games = pd.merge(df_plays_filtered, df_games, on = 'gameId', how = 'left')\n", - "df_plays_games['possessionTeam_ishome'] = np.where(df_plays_games.homeTeamAbbr == df_plays_games.possessionTeam, 1, 0)\n", - "df_plays_games['offensive_outcome_prob_change'] = np.where((df_plays_games.possessionTeam_ishome == 1), df_plays_games.homeTeamWinProbabilityAdded, df_plays_games.visitorTeamWinProbilityAdded)\n", - "\n", - "#convert NaN to blank because we will be using the text to concat to other fields to make a 'scheme'\n", - "df_plays_games['is_playAction'] = np.where(df_plays_games.playAction == True, 'PLAY ACTION', '')\n", - "df_plays_games['is_Dropback'] = np.where(df_plays_games.isDropback == True, 'DROPBACK', '')\n", - "\n", - "df_plays_games['pff_runConceptPrimary'] = np.where((df_plays_games.pff_runConceptPrimary == 'UNDEFINED') | (df_plays_games.pff_runConceptPrimary.isnull()), '', df_plays_games.pff_runConceptPrimary)\n", - "df_plays_games['pff_runConceptSecondary'] = np.where(df_plays_games.pff_runConceptSecondary.isnull(), '', df_plays_games.pff_runConceptSecondary)\n", - "\n", - "#create offensive and defensive scheme variables\n", - "df_plays_games['offensive_scheme'] = df_plays_games.is_playAction.astype(str) + ' ' + df_plays_games.dropbackType.astype(str) #+ ' ' + df_plays_games.is_Dropback.astype(str)\n", - "df_plays_games['offensive_scheme'] = np.where(df_plays_games.rushLocationType.isnull(), df_plays_games['offensive_scheme'], 'RUN ' + df_plays_games.rushLocationType) #+ ' ' + df_plays_games.pff_runConceptPrimary + ' ' #+ df_plays_games.pff_runConceptSecondary\n", - "\n", - "df_plays_games['defensive_scheme'] = df_plays_games.pff_passCoverage.astype(str) + ' ' + df_plays_games.pff_manZone.astype(str)\n", - "\n", - "#df_plays_games.tail(2)\n", - "#df_plays_games.defensive_scheme.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "5ae50d67-1334-4843-830e-4566e15a147d", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "#Get number of route runners and initial pass rushers on each play to add to the schemes\n", - "df_player_play['is_route_runner'] = np.where(df_player_play.wasRunningRoute == 1, 1, 0)\n", - "df_player_play['is_initial_pass_rusher'] = np.where(df_player_play.wasInitialPassRusher == 1, 1, 0)\n", - "df_routes = df_player_play.loc[df_player_play['hadRushAttempt'] == 0].groupby(\n", - " ['gameId', 'playId'] \n", - ").agg(\n", - " {\n", - " 'is_route_runner': 'sum'\n", - " , 'is_initial_pass_rusher': 'sum'\n", - " \n", - " }\n", - ").reset_index().sort_values(by = 'playId', ascending = False)\n", - "df_routes = df_routes.rename(columns={'is_route_runner': 'route_runners'})\n", - "df_routes = df_routes.rename(columns={'is_route_runner': 'initial_pass_rushers'})\n", - "df_routes['pass_rushers'] = np.where(df_routes['is_initial_pass_rusher']>=5, '5+', '4')\n", - "#df_routes.sort_values(by = 'is_initial_pass_rusher', ascending = False)\n", - "#df_routes.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "72c6ce60-978c-42ec-ad0c-7f1f3a6506ff", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "#Create a column for each route ran in the play\n", - "\n", - "#the players should be in order from QBs left to QBs right for consitancy in the column\n", - "df_weeks_snap_time = df_weeks.loc[df_weeks['event'] == 'ball_snap'].reset_index()\n", - "\n", - "#If direction of offense play direction is left, then reverse the sign of the y\n", - "df_weeks_snap_time.loc[df_weeks_snap_time['playDirection'] == 'left', 'y'] = -1 * df_weeks_snap_time.y\n", - "df_weeks_snap_time = df_weeks_snap_time[['gameId', 'playId', 'nflId', 'y']]\n", - "\n", - "\n", - "df_player_play_routes = df_player_play.loc[df_player_play['is_route_runner'] == 1]\n", - "df_player_play_routes = pd.merge(df_player_play_routes, df_weeks_snap_time, on = ['gameId', 'playId', 'nflId'], how = 'left')\n", - "\n", - "df_player_play_routes = df_player_play_routes.sort_values(by = ['gameId', 'playId', 'y'], ascending = [True, True, False])\n", - "\n", - "# Step 1: Create a sequential row index for each `id`\n", - "df_player_play_routes['player'] = df_player_play_routes.groupby(['gameId', 'playId']).cumcount() + 1\n", - "\n", - "# Step 2: Pivot table to create 22 new columns (2 columns per row number)\n", - "pivoted = df_player_play_routes.pivot(index=['gameId', 'playId'], columns='player', values=['routeRan'])\n", - "\n", - "# Step 3: Flatten MultiIndex columns\n", - "pivoted.columns = [f\"player{col[1]}_{col[0]}\" for col in pivoted.columns]\n", - "\n", - "# Step 4: Merge with other columns (retain non-pivoted columns)\n", - "non_pivoted = df_player_play_routes.drop_duplicates(subset=['gameId', 'playId']).set_index(['gameId', 'playId'])\n", - "non_pivoted = non_pivoted.drop(columns=['routeRan', 'player'])\n", - "df_player_play_routes = non_pivoted.join(pivoted).reset_index()\n", - "\n", - "\n", - "#join back to df routes\n", - "df_player_play_routes = df_player_play_routes[['gameId', 'playId', 'player1_routeRan', 'player2_routeRan', 'player3_routeRan'\n", - " , 'player4_routeRan', 'player5_routeRan']]\n", - "df_routes = pd.merge(df_routes, df_player_play_routes, on = ['gameId', 'playId'], how = 'left').fillna('')\n", - "#df_routes.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "d2911215-4e46-4b4f-962b-42362d025937", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "df_plays_games = pd.merge(df_plays_games, df_routes, on = ['gameId', 'playId'], how = 'left')\n", - "#df_plays_games.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "a68e1cf8-c1fa-412b-83a6-7ed6d1078f13", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "df_plays_games['defensive_scheme'] = np.where(df_plays_games.rushLocationType.notnull(), df_plays_games['defensive_scheme'], df_plays_games['defensive_scheme'] + ' ' + df_plays_games['is_initial_pass_rusher'].astype(str) + ' RUSH')\n", - "df_plays_games['offensive_scheme'] = np.where(df_plays_games.rushLocationType.notnull(), df_plays_games['offensive_scheme'], df_plays_games['offensive_scheme'] + ' ' + df_plays_games['route_runners'].astype(str) + ' OUT')\n", - "#df_plays_games.head() " - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "c360ceeb-2f1d-4d3c-9e34-9ca4aa87df0e", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "# df_pass_coverages = df_plays_games.groupby(\n", - "# ['pff_passCoverage', 'pff_manZone'\n", - "# ] \n", - "# ).agg(\n", - "# {\n", - "# 'playId': 'count'\n", - "# }\n", - "# ).reset_index()\n", - "# df_pass_coverages.sort_values(by = 'playId', ascending = False)" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "9ca661fd-2299-4a48-a7df-99f1096bb944", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "#group offensive shcemes into more broad categories for simplicity \n", - "df_plays_games['offensive_scheme_cat'] = df_plays_games['offensive_scheme']\n", - "df_plays_games['offensive_scheme_cat'] = df_plays_games['offensive_scheme_cat'].replace(' DESIGNED_', ' ', regex=True)\n", - "df_plays_games['offensive_scheme_cat'] = df_plays_games['offensive_scheme_cat'].replace(' SCRAMBLE_', ' ', regex=True)\n", - "df_plays_games['offensive_scheme_cat'] = df_plays_games['offensive_scheme_cat'].replace('UNKNOWN', '', regex=True)\n", - "df_plays_games['offensive_scheme_cat'] = df_plays_games['offensive_scheme_cat'].replace('nan', '', regex=True)\n", - "df_plays_games['offensive_scheme_cat'] = df_plays_games['offensive_scheme_cat'].replace('_', ' ', regex=True)\n", - "#df_plays_games['offensive_scheme_cat'] = df_plays_games['offensive_scheme_cat'].replace(' ', ' ', regex=True)\n", - "#df_plays_games['offensive_scheme_cat'] = df_plays_games['offensive_scheme_cat'].replace(' SCRAMBLE', 'SCRAMBLE', regex=True)\n", - "#df_plays_games['offensive_scheme_cat'] = df_plays_games['offensive_scheme_cat'].replace(' TRADITIONAL', 'TRADITIONAL', regex=True)\n", - "df_plays_games['offensive_scheme_cat'] = df_plays_games['offensive_scheme_cat'].str.lstrip()\n", - "#df_plays_games.offensive_scheme_cat.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "1cc12042-2f4e-42c2-b204-c23b04db68a4", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "#group pass coverages into more broad categories for simplicity \n", - "df_plays_games['pff_passCoverageCat'] = 'Other'\n", - "df_plays_games.loc[df_plays_games.pff_passCoverage.fillna('').str.contains('3'), 'pff_passCoverageCat'] = 'Cover 3'\n", - "df_plays_games.loc[df_plays_games.pff_passCoverage.fillna('').str.contains('2'), 'pff_passCoverageCat'] = 'Cover 2'\n", - "df_plays_games.loc[df_plays_games.pff_passCoverage.fillna('').str.contains('1'), 'pff_passCoverageCat'] = 'Cover 1'\n", - "df_plays_games.loc[df_plays_games.pff_passCoverage.fillna('').str.contains('6'), 'pff_passCoverageCat'] = 'Cover 6'\n", - "df_plays_games.loc[df_plays_games.pff_passCoverage.fillna('').str.contains('0'), 'pff_passCoverageCat'] = 'Cover 0'\n", - "df_plays_games.loc[df_plays_games.pff_passCoverage.fillna('').str.contains('Quarters'), 'pff_passCoverageCat'] = 'Quarters'" - ] - }, - { - "cell_type": "markdown", - "id": "30a6ba81-b43b-4d7b-9fa1-83abc29f19a6", - "metadata": {}, - "source": [ - "# Prep Player Tracking Data" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "0640b732-b1b9-4b90-a202-618e3e5b5787", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "#Get the timestamp of the snap\n", - "df_weeks['time'] = pd.to_datetime(df_weeks['time'], errors='coerce')\n", - "\n", - "df_snaps = df_weeks.loc[df_weeks['frameType'] == 'SNAP'].groupby(\n", - " ['gameId', 'playId'\n", - " ] \n", - ").agg(\n", - " {\n", - " 'time': 'first'\n", - " }\n", - ").reset_index()\n", - "\n", - "df_snaps = df_snaps.rename(columns={'time': 'time_of_snap'})\n", - "\n", - "\n", - "#df_snaps.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "bfb18425-f52a-46b6-81ff-a6c37e19d9d9", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "##filter df weeks to only records that are 5 seconds before the snap\n", - "df_weeks_snaps = pd.merge(df_weeks, df_snaps, on = ['gameId', 'playId'], how = 'left')\n", - "\n", - "df_weeks_snaptime = df_weeks_snaps.loc[(df_weeks_snaps['time_of_snap'] == df_weeks_snaps['time'])]\n", - "df_weeks_5secs = df_weeks_snaps.loc[(df_weeks_snaps['time_of_snap'] - df_weeks_snaps['time']).dt.total_seconds() == 2]\n", - "#df_weeks_5secs.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "fe921060-1046-4d26-9316-76f2c6772cd5", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "#Get the coordinates of the football on each play at snap time\n", - "#df_players_centers = df_players[['nflId', 'position']].loc[df_players['position'] == 'C']\n", - "\n", - "\n", - "#innter join centers to weeks to only get records of the center\n", - "#df_weeks_centers = pd.merge(df_weeks_snaptime, df_players_centers, on = 'nflId', how = 'inner')\n", - "\n", - "df_weeks_centers = df_weeks_snaptime[['gameId', 'playId', 'x', 'y', 'displayName']].loc[df_weeks_snaptime['displayName'] == 'football']\n", - "df_weeks_centers = df_weeks_centers[['gameId', 'playId', 'x', 'y']]\n", - "df_weeks_centers = df_weeks_centers.rename(columns={'x': 'x_center', 'y': 'y_center'})\n", - "\n", - "#if 2 centers on play pick first one\n", - "df_weeks_centers = df_weeks_centers.drop_duplicates(subset = ['gameId', 'playId'])\n", - "df_weeks_centers.head()\n", - "\n", - "\n", - "# #make sure only 1 center per play\n", - "\n", - "# df_centers_test = df_weeks_centers.groupby(\n", - "# ['gameId', 'playId'\n", - "# ] \n", - "# ).agg(\n", - "# {\n", - "# 'x_center': 'count'\n", - "# }\n", - "# ).reset_index()\n", - "# df_centers_test.sort_values(by = 'x_center', ascending = False).head(50)\n", - "\n", - "\n", - "#get coords of receivers at each play at snap time\n", - "df_players_wr = df_players[['nflId', 'position']].loc[df_players['position'] == 'WR']\n", - "\n", - "\n", - "#innter join centers to weeks to only get records of the center\n", - "df_weeks_wr = pd.merge(df_weeks_snaptime, df_players_wr, on = 'nflId', how = 'inner')\n", - "\n", - "df_weeks_wr = df_weeks_wr[['gameId', 'playId', 'nflId', 'x', 'y']]\n", - "df_weeks_wr = df_weeks_wr.rename(columns={'x': 'x_wr', 'y': 'y_wr'})\n", - "\n", - "\n", - "\n", - "#get coords of dbacks at each play at snap time\n", - "df_players_db = df_players[['nflId', 'position']].loc[(df_players['position'] == 'CB') | (df_players['position'] == 'SS') |(df_players['position'] == 'FS')]\n", - "\n", - "\n", - "#innter join centers to weeks to only get records of the center\n", - "df_players_db = pd.merge(df_weeks_snaptime, df_players_db, on = 'nflId', how = 'inner')\n", - "\n", - "df_players_db = df_players_db[['gameId', 'playId', 'nflId', 'x', 'y']]\n", - "df_players_db = df_players_db.rename(columns={'x': 'x_db', 'y': 'y_db'})\n", - "\n", - "\n", - "#get direction of cb \n", - "df_players_cb = df_players[['nflId', 'position']].loc[(df_players['position'] == 'CB')]\n", - "\n", - "\n", - "#innter join centers to weeks to only get records of the center\n", - "df_players_cb = pd.merge(df_weeks_snaptime, df_players_cb, on = 'nflId', how = 'inner')\n", - "\n", - "df_players_cb = df_players_cb[['gameId', 'playId', 'nflId', 'o', 'playDirection']]\n", - "df_players_cb = df_players_cb.rename(columns={'d': 'o_cb'})\n", - "df_players_cb.loc[(df_players_cb.playDirection == 'right'), 'o'] = 360 - df_players_cb['o']\n", - "#just care how far from 90 degrees they are\n", - "df_players_cb['o'] = abs(90 - df_players_cb.o)\n", - "df_players_cb = df_players_cb[['gameId', 'playId', 'nflId', 'o']]\n", - "\n", - "#join cb orientation to db df\n", - "df_players_db = pd.merge(df_players_db, df_players_cb, on = ['gameId', 'playId', 'nflId'], how = 'left')\n", - "\n", - "\n", - "#if 2 centers on play pick first one\n", - "df_weeks_centers = df_weeks_centers.drop_duplicates(subset = ['gameId', 'playId'])\n", - "#df_weeks_centers.head()\n", - "#df_players_db.head(50)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "66a40cc7-1f29-4304-b49a-e3646ca3150e", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "#get the x and y deltas of each player on defense from the center 5 seconds before the snap\n", - "\n", - "#also bring in the offensive alginment and formation because the defensive alignment will depend on it\n", - "\n", - "\n", - "#filter df weeks for defense\n", - "df_defensive_team = df_plays_games[['gameId', 'playId', 'defensiveTeam']]\n", - "df_defensive_team = df_defensive_team.rename(columns={'defensiveTeam': 'club'})\n", - "df_defensive_weeks5secs = pd.merge(df_weeks_5secs, df_defensive_team, on = ['gameId', 'playId', 'club'], how = 'inner')\n", - "\n", - "df_defensive_weeks5secs = pd.merge(df_defensive_weeks5secs, df_weeks_centers, on = ['gameId', 'playId'])\n", - "\n", - "df_defensive_weeks5secs['x_delta'] = df_defensive_weeks5secs['x'] - df_defensive_weeks5secs['x_center']\n", - "df_defensive_weeks5secs['y_delta'] = df_defensive_weeks5secs['y'] - df_defensive_weeks5secs['y_center']\n", - "\n", - "#If direction of offense play direction is left, then reverse the sign of the x delta and y delta\n", - "df_defensive_weeks5secs.loc[df_defensive_weeks5secs.playDirection == 'left', 'x_delta'] = -1 * df_defensive_weeks5secs.x_delta\n", - "df_defensive_weeks5secs.loc[df_defensive_weeks5secs.playDirection == 'left', 'y_delta'] = -1 * df_defensive_weeks5secs.y_delta\n", - "\n", - "df_defensive_weeks5secs.loc[df_defensive_weeks5secs.playDirection == 'left', 'x'] = -1 * df_defensive_weeks5secs.x\n", - "df_defensive_weeks5secs.loc[df_defensive_weeks5secs.playDirection == 'left', 'y'] = -1 * df_defensive_weeks5secs.y\n", - "\n", - "\n", - "#df_defensive_weeks5secs.head()\n", - "df_defensive_weeks5secs.loc[df_defensive_weeks5secs['playDirection'] == 'left'].head()\n", - "\n", - "\n", - "\n", - "\n", - "#bring in yardline, down, score, quarter, time, last play as they are all relevant to defensive alignment as well\n", - "df_plays_games_alignment_features = df_plays_games[['gameId', 'playId', 'preSnapHomeScore', 'preSnapVisitorScore', 'possessionTeam_ishome'\n", - " , 'quarter', 'down', 'yardsToGo', 'absoluteYardlineNumber'\n", - " , 'offenseFormation', 'receiverAlignment']]\n", - "\n", - "df_defensive_weeks5secs = pd.merge(df_defensive_weeks5secs, df_plays_games_alignment_features, on = ['gameId', 'playId'], how = 'left')\n", - "\n", - "\n", - "\n", - "#df_defensive_weeks5secs.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "09caf5fb-737b-45b4-8de8-8c3bd4da589c", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "#get all plays where there was a man in motion\n", - "\n", - "df_plays_with_motion = df_weeks[['gameId', 'playId']].loc[df_weeks['event'] == 'man_in_motion'].drop_duplicates()\n", - "\n", - "#get the player Ids of who was in motion\n", - "df_players_in_motion = df_player_play[['gameId', 'playId', 'nflId']].loc[df_player_play['motionSinceLineset'] == True]\n", - "df_plays_with_motion = pd.merge(df_plays_with_motion, df_players_in_motion, on = ['gameId', 'playId'], how = 'inner')\n", - "\n", - "#get the players pre motion x y\n", - "df_weeks_players_in_motion = df_weeks.loc[df_weeks['event'] == 'man_in_motion']\n", - "df_weeks_players_in_motion = pd.merge(df_plays_with_motion, df_weeks_players_in_motion, on = ['gameId', 'playId', 'nflId'], how = 'inner')\n", - "df_weeks_players_in_motion = df_weeks_players_in_motion.rename(columns={'x': 'x_premotion'\n", - " , 'y': 'y_premotion', 'time': 'time_of_motion'})\n", - "df_weeks_players_in_motion = df_weeks_players_in_motion[['gameId', 'playId', 'nflId', 'x_premotion', 'y_premotion', 'time_of_motion']]\n", - "\n", - "#get the players at snap x and y\n", - "df_weeks_snap_time = df_weeks.loc[df_weeks['event'] == 'ball_snap']\n", - "df_weeks_players_in_motion_snap_time = pd.merge(df_weeks_snap_time, df_weeks_players_in_motion, on = ['gameId', 'playId', 'nflId'], how = 'inner')\n", - "df_weeks_players_in_motion_snap_time = df_weeks_players_in_motion_snap_time.rename(columns={'x': 'x_snaptime'\n", - " , 'y': 'y_snaptime', 'time': 'time_of_snap', 'club': 'offense'})\n", - "df_weeks_players_in_motion_snap_time = df_weeks_players_in_motion_snap_time[['gameId', 'playId', 'nflId', 'x_snaptime', 'y_snaptime'\n", - " , 'time_of_snap', 'offense']]\n", - "\n", - "df_weeks_motions = pd.merge(df_weeks_players_in_motion, df_weeks_players_in_motion_snap_time, on = ['gameId', 'playId', 'nflId'], how = 'inner')\n", - "\n", - "df_weeks_motions['x_motion_delta'] = df_weeks_motions['x_snaptime'] - df_weeks_motions['x_premotion']\n", - "df_weeks_motions['y_motion_delta'] = df_weeks_motions['y_snaptime'] - df_weeks_motions['y_premotion']\n", - "\n", - "#filter out plays with 2 players in motion so we will just look at plays with 1\n", - "df_weeks_motions = df_weeks_motions.loc[df_weeks_motions['playId'] != df_weeks_motions['playId'].shift(1)]\n", - "\n", - "#df_plays_with_motion.head()\n", - "#df_weeks_motions.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "63f3d37f-7542-412c-8990-404697281ffe", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "#get defender who was closest to the man in motion at the time of the motion to see if he followed the motion player\n", - "\n", - "df_weeks_at_motion = df_weeks.loc[df_weeks['event'] == 'man_in_motion']\n", - "df_defending_motion = pd.merge(df_weeks_at_motion, df_weeks_motions, on = ['gameId', 'playId'], how = 'left')\n", - "\n", - "#get distance between player and motion player\n", - "df_defending_motion['x_distance_from_player_in_motion'] = df_defending_motion['x'] - df_defending_motion['x_premotion']\n", - "df_defending_motion['y_distance_from_player_in_motion'] = df_defending_motion['y'] - df_defending_motion['y_premotion']\n", - "df_defending_motion['distance_from_player_in_motion'] = (np.sqrt(df_defending_motion.x_distance_from_player_in_motion ** 2 + df_defending_motion.y_distance_from_player_in_motion ** 2))\n", - "\n", - "df_defending_motion = df_defending_motion.loc[df_defending_motion.club != df_defending_motion.offense]\n", - "df_defending_motion = df_defending_motion.rename(columns={'nflId_x': 'nflId_def'})\n", - "\n", - "df_defending_player_motion = df_defending_motion.loc[df_defending_motion['distance_from_player_in_motion']>0].loc[df_defending_motion['displayName'] != 'football'].groupby(\n", - " ['gameId', 'playId'\n", - " ] \n", - ").agg(\n", - " {\n", - " 'distance_from_player_in_motion': 'min'\n", - " }\n", - ").reset_index()\n", - "\n", - "df_defending_player_motion = pd.merge(df_defending_motion, df_defending_player_motion, on = ['gameId', 'playId', 'distance_from_player_in_motion'], how = 'inner')\n", - "\n", - "#df_defending_player_motion.tail(5)" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "4705b963-b734-4c71-b9fe-2736a80735b6", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "#Get where this player closest to motion player was at the time of the snap \n", - "df_defending_player_motion_player = df_defending_player_motion[['gameId', 'playId', 'nflId_def']]\n", - "df_defending_player_motion_player = df_defending_player_motion_player.rename(columns={'nflId_def': 'nflId'})\n", - "df_defending_player_motion_snaptime_def = pd.merge(df_weeks_snap_time, df_defending_player_motion_player, on = ['gameId', 'playId', 'nflId'], how = 'inner')\n", - "df_defending_player_motion_snaptime_def = df_defending_player_motion_snaptime_def[['gameId', 'playId', 'nflId'\n", - " , 'x', 'y']]\n", - "df_defending_player_motion_snaptime_def = df_defending_player_motion_snaptime_def.rename(columns={'x': 'x_snaptime_def'\n", - " , 'y': 'y_snaptime_def', 'nflId': 'nflId_def'})\n", - "\n", - "\n", - "\n", - "df_defending_player_motion_snaptime_def = pd.merge(df_defending_player_motion, df_defending_player_motion_snaptime_def, on = ['gameId', 'playId', 'nflId_def'], how = 'inner')\n", - "df_defending_player_motion_snaptime_def['x_motion_delta_def'] = df_defending_player_motion_snaptime_def['x_snaptime_def'] - df_defending_player_motion_snaptime_def['x']\n", - "df_defending_player_motion_snaptime_def['y_motion_delta_def'] = df_defending_player_motion_snaptime_def['y_snaptime_def'] - df_defending_player_motion_snaptime_def['y']\n", - "\n", - "df_defending_player_motion_snaptime_def = df_defending_player_motion_snaptime_def[['gameId', 'playId', 'x_motion_delta'\n", - " , 'y_motion_delta', 'x_motion_delta_def', 'y_motion_delta_def']]\n", - "#df_defending_player_motion_snaptime_def.sort_values(by = 'y_motion_delta_def', ascending = False).head()" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "558e8fc1-46d3-4b19-850d-4630dde555cb", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/ss/6ynjwn914qz80pm0b4b3yq940000gn/T/ipykernel_1314/350048923.py:34: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_top_3['player'] = 'player' + df_top_3['rank'].astype(int).astype(str)\n" - ] - } - ], - "source": [ - "#get defender who was closest to each receiver at time of snap and their orientation if a cb\n", - "\n", - "df_defending_wr = pd.merge(df_players_db, df_weeks_wr, on = ['gameId', 'playId'], how = 'left')\n", - "\n", - "#get y distance between player and motion player\n", - "df_defending_wr['y_distance_from_wr'] = df_defending_wr['y_db'] - df_defending_wr['y_wr']\n", - "df_defending_wr['x_distance_from_wr'] = abs(df_defending_wr['x_db'] - df_defending_wr['x_wr'])\n", - "\n", - "# df_defending_wr = df_defending_wr.loc[df_defending_wr.club != df_defending_wr.offense]\n", - "# df_defending_wr = df_defending_wr.rename(columns={'nflId_x': 'nflId_def'})\n", - "\n", - "\n", - "df_defending_wr = df_defending_wr.loc[df_defending_wr['y_distance_from_wr']>0].groupby(\n", - " ['gameId', 'playId', 'nflId_x', 'o'\n", - " ] \n", - ").agg(\n", - " {\n", - " 'x_distance_from_wr': 'min'\n", - " ,'y_distance_from_wr': 'min'\n", - " # , 'o': 'min'\n", - " }\n", - ").reset_index().sort_values(by = 'x_distance_from_wr')\n", - "import pandas as pd\n", - "\n", - "import pandas as pd\n", - "\n", - "# Add a ranking column for each `gameId` and `playId` based on `x_distance_from_wr`\n", - "df_defending_wr['rank'] = df_defending_wr.groupby(['gameId', 'playId'])['x_distance_from_wr'].rank(method='first')\n", - "\n", - "# Filter to keep only the top 3 rows per `gameId` and `playId`\n", - "df_top_3 = df_defending_wr[df_defending_wr['rank'] <= 3]\n", - "\n", - "# Add a player rank prefix for renaming later\n", - "df_top_3['player'] = 'player' + df_top_3['rank'].astype(int).astype(str)\n", - "\n", - "# Pivot the data\n", - "df_pivoted = df_top_3.pivot(\n", - " index=['gameId', 'playId'], \n", - " columns='player', \n", - " values=['x_distance_from_wr', 'y_distance_from_wr', 'o']\n", - ")\n", - "\n", - "# Rename columns to desired format\n", - "df_pivoted.columns = [\n", - " f\"{col[1]}_{col[0]}\" for col in df_pivoted.columns\n", - "]\n", - "df_defending_wr = df_pivoted.reset_index()\n", - "\n", - "#df_defending_wr.loc[df_defending_wr['gameId']== 2022091200].loc[df_defending_wr['playId']== 64]\n", - "#df_defending_wr.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "id": "1c54df09-52e7-4695-9f20-7cb1acb931cc", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "# #same process for cb directions\n", - "# # Add a ranking column for each `gameId` and `playId` based on `x_distance_from_wr`\n", - "# df_players_cb['rank'] = df_players_cb.groupby(['gameId', 'playId'])['o'].rank(method='first')\n", - "\n", - "# # Filter to keep only the top 3 rows per `gameId` and `playId`\n", - "# df_top_3 = df_defending_wr[df_defending_wr['rank'] <= 3]\n", - "\n", - "# # Add a player rank prefix for renaming later\n", - "# df_top_3['player'] = 'player' + df_top_3['rank'].astype(int).astype(str)\n", - "\n", - "# # Pivot the data\n", - "# df_pivoted = df_top_3.pivot(\n", - "# index=['gameId', 'playId'], \n", - "# columns='player', \n", - "# values=['x_distance_from_wr', 'y_distance_from_wr']\n", - "# )" - ] - }, - { - "cell_type": "markdown", - "id": "42546137-d26b-4f7c-8e37-94c33312134e", - "metadata": {}, - "source": [ - "# Set up final table for clustering" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "id": "14aecb3f-b311-4b43-8730-814f750eef23", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "#join back to feature dataset\n", - "df_features = pd.merge(df_defensive_weeks5secs, df_defending_player_motion_snaptime_def, on = ['gameId', 'playId'], how = 'left')\n", - "df_features = pd.merge(df_features, df_defending_wr, on = ['gameId', 'playId'], how = 'left')\n", - "#df_features = pd.merge(df_features, df_players_cb, on = ['gameId', 'playId'], how = 'left')\n", - "#df_features = pd.merge(df_features, df_LBs, on = ['gameId', 'playId'], how = 'left')\n", - "#df_features = pd.merge(df_features, df_LBs_avg, on = ['gameId', 'playId'], how = 'left')\n", - "\n", - "#get players position\n", - "df_features = pd.merge(df_features, df_players[['nflId', 'position']], on = ['nflId'], how = 'left')\n", - "\n", - "#only care about positioning of safeties, corners and LBs\n", - "#For LBs we will just want to see how close to line they are and any distance travelled before snap to time a blitz\n", - "df_features_safeties = df_features.loc[(df_features.position == 'SS') | (df_features.position == 'FS') | (df_features.position == 'CB')]\n", - "\n", - "#sort by position order and for same positions sort by y delta alignment from center\n", - "df_features = df_features_safeties.sort_values(by = ['gameId', 'playId', 'x_delta'],ascending = [True, True, False])\n", - "\n", - "#df_features.head(11)" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "b3b8f020-e89b-421a-93b7-51ecfc05dd25", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "#create columns for each defensive player x y per play (22 new columns)\n", - "#Check - do plays with too many or too few playerso on field have non 11 rows per play?\n", - "\n", - "# Step 1: Create a sequential row index for each `id`\n", - "df_features['player'] = df_features.groupby(['gameId', 'playId']).cumcount() + 1\n", - "\n", - "# Step 2: Pivot table to create 22 new columns (2 columns per row number)\n", - "pivoted = df_features.pivot(index=['gameId', 'playId'], columns='player', values=['x_delta', 'y_delta'])\n", - "\n", - "# Step 3: Flatten MultiIndex columns\n", - "pivoted.columns = [f\"player{col[1]}_{col[0]}\" for col in pivoted.columns]\n", - "\n", - "# Step 4: Merge with other columns (retain non-pivoted columns)\n", - "non_pivoted = df_features.drop_duplicates(subset=['gameId', 'playId']).set_index(['gameId', 'playId'])\n", - "non_pivoted = non_pivoted.drop(columns=['position', 'x_delta', 'y_delta', 'player'])\n", - "df_features = non_pivoted.join(pivoted).reset_index()\n", - "\n", - "#Add in deepest player distance from center of field y\n", - "df_features['player_4_alignment_from_center'] = abs((df_features['y_center'] - df_features['player4_y_delta']) - 26.65)\n", - "\n", - "#only care about cb orientation if within 5 years of line\n", - "#df_features['min_o'] = np.where()np.minimum(df_features['player1_o'], df_features['player2_o'], df_features['player3_o'])\n", - "df_features['player1_o'] = np.where(df_features['player1_x_distance_from_wr'] < 5, df_features['player1_o'], np.nan)\n", - "df_features['player2_o'] = np.where(df_features['player2_x_distance_from_wr'] < 5, df_features['player2_o'], np.nan)\n", - "\n", - "#df_features.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "7d944ac7-fe91-42be-8c43-0ecf2ee4c297", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "#add in previous play schemes and previous play results\n", - "#df_plays_games['prev_yardsGained'] = np.where(df_plays_games['IsStartOfPossession'] == 0, df_plays_games['yardsGained'].shift(1), np.nan)\n", - "\n", - "\n", - "df_plays_games_features = df_plays_games[['gameId', 'playId'\n", - " #, 'previous_offensive_scheme_cat' , 'previous_defensive_scheme_cat'\n", - " #, 'prev_yardsGained', 'defensive_scheme_cat'\n", - " , 'pff_passCoverageCat', 'pff_manZone', 'pass_rushers']]\n", - "\n", - "\n", - "\n", - "\n", - "#df_plays_games_features.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "0fcee56e-2a5b-41a8-a848-0ec8fb184b75", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "df_features = pd.merge(df_features, df_plays_games_features, on = ['gameId', 'playId'], how = 'left')\n", - "\n", - "#clean up home and vis score to off and def score\n", - "df_features['preSnapOffense_score'] = np.where(df_features['possessionTeam_ishome'] == 1, df_features['preSnapHomeScore'], df_features['preSnapVisitorScore'])\n", - "df_features['preSnapDefense_score'] = np.where(df_features['possessionTeam_ishome'] == 1, df_features['preSnapVisitorScore'], df_features['preSnapHomeScore'])\n", - "\n", - "\n", - "df_features = df_features.drop(columns=['nflId', 'displayName', 'playDirection', 'frameId', 'frameType', 'time', 'jerseyNumber'\n", - " , 's', 'a', 'dis', 'o', 'dir', 'event', 'preSnapHomeScore', 'preSnapVisitorScore'])\n", - "\n", - "#df_features.head()" - ] - }, - { - "cell_type": "markdown", - "id": "40fb2810-dd93-4328-931f-cd1b3496a89a", - "metadata": {}, - "source": [ - "# Cluster Model" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "1f8327ef-3146-4b67-bdc2-e58071ad93a7", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "#k means cluser for player alignment\n", - "#!pip install scikit-learn-extra\n", - "from sklearn.cluster import KMeans\n", - "from sklearn_extra.cluster import KMedoids\n", - "from sklearn.preprocessing import StandardScaler\n", - "\n", - "#df_features_32 = df_features.copy()#loc[df_features['receiverAlignment'] == '2x1']\n", - "\n", - "# Assuming `result` is the DataFrame from the previous step\n", - "# Select the columns for clustering\n", - "# Fill NaN values with 0 for all numerical columns\n", - "df_features.loc[:, df_features.select_dtypes(include=['number']).columns] = df_features.select_dtypes(include=['number']).fillna(0)\n", - "columns_for_clustering = (\n", - " [col for col in df_features.columns if col.startswith('player')] \n", - " #+ ['x_motion_delta_def', 'y_motion_delta_def']\n", - ")\n", - "\n", - "# Step 1: Standardize the data (important for K-Means)\n", - "scaler = StandardScaler()\n", - "#data_for_clustering = scaler.fit_transform(df_features[columns_for_clustering])\n", - "# Fill NAs in the selected columns with their median\n", - "selected_columns = ['player1_x_delta', 'player1_y_delta', 'player2_x_delta', 'player2_y_delta'\n", - " #,'player3_x_delta', 'player4_x_delta'\n", - " , 'player1_x_distance_from_wr', 'player1_y_distance_from_wr'\n", - " , 'player2_x_distance_from_wr', 'player2_y_distance_from_wr'\n", - " #, 'player3_x_distance_from_wr', 'player3_y_distance_from_wr'\n", - " , 'player1_o', 'player2_o'\n", - " ]\n", - "\n", - "df_features[selected_columns] = df_features[selected_columns].apply(lambda col: col.fillna(col.median()))\n", - "\n", - "# Standardize the data after filling NAs\n", - "data_for_clustering = scaler.fit_transform(df_features[selected_columns])\n", - "\n", - "# # Step 2: Apply K-Means clustering\n", - "# kmeans = KMeans(n_clusters=8, random_state=42) # Change n_clusters as needed\n", - "# df_features['cluster'] = kmeans.fit_predict(data_for_clustering)\n", - "\n", - "# Step 2: Apply K-Medoids clustering\n", - "kmedoids = KMedoids(n_clusters=7, random_state=42, metric=\"manhattan\") # Change n_clusters as needed\n", - "df_features['cluster'] = kmedoids.fit_predict(data_for_clustering)\n", - "\n", - "# Display the head of the DataFrame with clusters\n", - "#df_features.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "a831a920-6423-4818-a823-b1fa2c72f541", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "# import matplotlib.pyplot as plt\n", - "# from sklearn_extra.cluster import KMedoids\n", - "# from sklearn.preprocessing import StandardScaler\n", - "# import numpy as np\n", - "\n", - "# # Step 1: Standardize the data\n", - "# scaler = StandardScaler()\n", - "# data_for_clustering = scaler.fit_transform(df_features[columns_for_clustering])\n", - "\n", - "# # Step 2: Calculate within-cluster distances for different values of k\n", - "# sum_of_distances = []\n", - "# k_values = range(1, 11) # Test k from 1 to 10\n", - "\n", - "# for k in k_values:\n", - "# kmedoids = KMedoids(n_clusters=k, random_state=42, metric=\"manhattan\") # Use Manhattan distance\n", - "# kmedoids.fit(data_for_clustering)\n", - "# # Calculate the sum of distances of samples to their closest medoid\n", - "# total_distance = np.sum(kmedoids.transform(data_for_clustering).min(axis=1))\n", - "# sum_of_distances.append(total_distance)\n", - "\n", - "# # Step 3: Plot the elbow curve\n", - "# plt.figure(figsize=(8, 6))\n", - "# plt.plot(k_values, sum_of_distances, marker='o', linestyle='-')\n", - "# plt.title('Elbow Method for Optimal k (K-Medoids)')\n", - "# plt.xlabel('Number of Clusters (k)')\n", - "# plt.ylabel('Sum of Distances to Medoids')\n", - "# plt.xticks(k_values)\n", - "# plt.grid(True)\n", - "# #plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "ec8644e5-3298-44ec-a7c0-d5f697d0bdff", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "#df_features = df_features.loc[df_features['cluster'] != 4]\n", - "df_features['coverage_cluster_cat'] = np.where((df_features['cluster'] == 0) | (df_features['cluster'] == 5)\n", - " | (df_features['cluster'] == 6) | (df_features['cluster'] == 2), 1, \n", - " np.where((df_features['cluster'] == 1), 2\n", - " , np.where(df_features['cluster'] == 3, 3, 4)))\n", - "\n", - "#IF a defender followed a motion auto place in cluster 2\n", - "#df_features.loc[(df_features.y_motion_delta >=4) & (df_features.y_motion_delta - df_features.y_motion_delta <2), 'coverage_cluster_cat'] = 2\n", - "#df_features.loc[abs((df_features.y_motion_delta) >=7) & (df_features.y_motion_delta - df_features.y_motion_delta <2), 'coverage_cluster_cat'] = 2\n", - "df_features.loc[abs((df_features.y_motion_delta >=6)) & (df_features.y_motion_delta - df_features.y_motion_delta >3), 'coverage_cluster_cat'] = 1\n", - "\n", - "\n", - "\n", - "df_coverages = df_features.loc[df_features['pff_passCoverageCat'] != 'Other'].groupby(\n", - " ['coverage_cluster_cat', 'pff_passCoverageCat'] \n", - ").agg(\n", - " {\n", - " 'gameId': 'count'\n", - " }\n", - ").reset_index()\n", - "\n", - "# Step 1: Compute total counts within each cluster\n", - "df_coverages['cluster_total'] = df_coverages.groupby('coverage_cluster_cat')['gameId'].transform('sum')\n", - "\n", - "# Step 2: Compute the percentage\n", - "df_coverages['percentage'] = (df_coverages['gameId'] / df_coverages['cluster_total'])\n", - "\n", - "# Step 3: Drop the intermediate column if not needed\n", - "df_coverages = df_coverages.drop(columns=['cluster_total'])\n", - "#df_coverages.sort_values(by = ['coverage_cluster_cat', 'pff_passCoverageCat'], ascending = [True, True])" - ] - }, - { - "cell_type": "markdown", - "id": "e7911dd5-49fd-4d57-b513-ee533f08379e", - "metadata": {}, - "source": [ - "# Label DISCO Outputs" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "ca00748f-a416-439c-b5f7-d73288d92ec1", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "df_features['disco'] = 0\n", - "#df_features.loc[(df_features.coverage_cluster_cat == 1) & (df_features.pff_passCoverageCat == 'Cover 0'), 'disco'] = 1\n", - "df_features.loc[(df_features.coverage_cluster_cat == 1) & (df_features.pff_passCoverageCat == 'Cover 0'), 'disco'] = 1\n", - "df_features.loc[(df_features.coverage_cluster_cat == 1) & (df_features.pff_passCoverageCat == 'Cover 2'), 'disco'] = 1\n", - "df_features.loc[(df_features.coverage_cluster_cat == 1) & (df_features.pff_passCoverageCat == 'Cover 6'), 'disco'] = 1\n", - "#df_features.loc[(df_features.coverage_cluster_cat == 1) & (df_features.pff_passCoverageCat == 'Quarters'), 'disco'] = 1\n", - "\n", - "df_features.loc[(df_features.coverage_cluster_cat == 2) & (df_features.pff_passCoverageCat == 'Cover 0'), 'disco'] = 1\n", - "df_features.loc[(df_features.coverage_cluster_cat == 2) & (df_features.pff_passCoverageCat == 'Cover 6'), 'disco'] = 1\n", - "df_features.loc[(df_features.coverage_cluster_cat == 2) & (df_features.pff_passCoverageCat == 'Cover 2'), 'disco'] = 1\n", - "df_features.loc[(df_features.coverage_cluster_cat == 2) & (df_features.pff_passCoverageCat == 'Quarters'), 'disco'] = 1\n", - "\n", - "#df_features.loc[(df_features.coverage_cluster_cat == 3) & (df_features.pff_passCoverageCat == 'Cover 0'), 'disco'] = 1\n", - "#df_features.loc[(df_features.coverage_cluster_cat == 3) & (df_features.pff_passCoverageCat == 'Cover 6'), 'disco'] = 1\n", - "df_features.loc[(df_features.coverage_cluster_cat == 3) & (df_features.pff_passCoverageCat == 'Cover 0'), 'disco'] = 1\n", - "\n", - "df_features.loc[(df_features.coverage_cluster_cat == 4) & (df_features.pff_passCoverageCat == 'Cover 0'), 'disco'] = 1\n", - "\n", - "#If player follows motion man but it's not cover 1, then it's disco\n", - "df_features.loc[abs((df_features.y_motion_delta) >=7) & (df_features.y_motion_delta - df_features.y_motion_delta <2) & (df_features.pff_passCoverageCat != 'Cover 1'), 'disco'] = 1\n", - "\n", - "#If player doesn't motion man but it's is cover 1, then it's disco\n", - "df_features.loc[abs((df_features.y_motion_delta) >=7) & (df_features.y_motion_delta - df_features.y_motion_delta > 4) & (df_features.pff_passCoverageCat == 'Cover 1'), 'disco'] = 1\n", - "\n", - "\n", - "#If an LB has moved more than 2 yards closer to the line within 5 seconds of snap, or is within 1 yard of line, or is more than 2 yards closer than avg, \n", - "#it looks like a blitz\n", - "\n", - "#df_features.loc[df_features['disco'] == 0].head()" - ] - }, - { - "cell_type": "markdown", - "id": "86edb66a-23d6-4ba4-9951-2f093de5f0bf", - "metadata": {}, - "source": [ - "# Cluster Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6c54542a-27b3-47af-b53c-ae92da86bce1", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "f3dccff2-45fb-4351-a25e-44c297b8bd98", - "metadata": {}, - "source": [ - "Theoretically pre-snap alignments can be grouped into any number of clusters, however we found that 4 was the fewest distinct groupings to find meaningful and insightful differences, yet few enough to not overfit our clusters. As we see in the below chart, defenses can and have operated in any coverage from any pre-snap alignment cluster, however we do find unique patterns across clusters. (Note: We are just showing the most common pass coverages, and all types of coverage within the same category are grouped together. E.g. – Cover 3 Cloud Left, Cover 3 Cloud Right)" - ] - }, - { - "cell_type": "code", - "execution_count": 232, - "id": "2d50306b-e345-43fe-9af5-bede067caa7e", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
CoverageCluster-1 %Cluster-2 %Cluster-3 %Cluster-4 %
Cover 04%7%2%1%
Cover 114%53%19%20%
Cover 29%8%19%23%
Cover 350%30%28%26%
Cover 67%1%15%16%
Quarters15%2%17%15%
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_coverages_clusters = df_coverages.pivot(index=['pff_passCoverageCat'], columns='coverage_cluster_cat', values=['percentage']).reset_index()\n", - "df_coverages_clusters.columns = [''.join(map(str, col)) if isinstance(col, tuple) else col for col in df_coverages_clusters.columns]\n", - "\n", - "# Rename columns\n", - "df_coverages_clusters = df_coverages_clusters.rename(columns={\n", - " 'pff_passCoverageCat': 'Coverage',\n", - " 'percentage1': 'Cluster-1 %',\n", - " 'percentage2': 'Cluster-2 %',\n", - " 'percentage3': 'Cluster-3 %',\n", - " 'percentage4': 'Cluster-4 %'\n", - "})\n", - "\n", - "# Round the percentage columns to the nearest percent and format as percentage\n", - "for col in ['Cluster-1 %', 'Cluster-2 %', 'Cluster-3 %', 'Cluster-4 %']:\n", - " df_coverages_clusters[col] = df_coverages_clusters[col].apply(lambda x: f\"{round(x * 100)}%\")\n", - "\n", - "# Use Pandas styling to display the DataFrame with formatting and hide the index\n", - "df_coverages_clusters_styled = df_coverages_clusters.style.format({\n", - " 'Coverage': lambda x: x, # Leave 'Coverage' column as is\n", - "}).set_table_styles([\n", - " {'selector': 'th', 'props': [('text-align', 'center'), ('background-color', '#013369'),\n", - " ('font-weight', 'bold'), ('color', 'white')]}, # Make column label text white\n", - " {'selector': 'td', 'props': [('text-align', 'center'), ('font-size', '14px')]}, # Adjust font size here\n", - " {'selector': 'tr:nth-child(even)', 'props': [('background-color', '#f2f2f2')]}, # Lighter grey for even rows\n", - " {'selector': 'tr:nth-child(odd)', 'props': [('background-color', 'white')]}, # Default grey for odd rows\n", - "], overwrite=False).set_properties(**{'border': 'none', 'padding': '5px'}) # Remove the borders\n", - "\n", - "# Hide the index\n", - "df_coverages_clusters_styled = df_coverages_clusters_styled.hide(axis='index')\n", - "\n", - "# Display the styled DataFrame\n", - "from IPython.display import display\n", - "display(df_coverages_clusters_styled)" - ] - }, - { - "cell_type": "markdown", - "id": "c8cc3583-2e4c-4857-a516-1d4b59f57e3f", - "metadata": {}, - "source": [ - "Now that we have a sense of the frequency defenses operate different pass coverages from each cluster, we can explore how several of our model input metrics average out by Cluster.\n", - "\n", - "Note: \n", - "- Metrics are adjusted as if all play directions are left to right\n", - "- 0 degrees is facing directly at the line of scrimmage to the left.\n", - "\n", - "\n", - "There are several takeaways but a few to note are:\n", - "\n", - "- Clusters 1 and 2 tend to have the farthest DB aligned close to the center of the field.\n", - "- Clusters 3 and 4 on average see the 2 farthest DB’s aligned 5-6 yards off the center on each side. \n", - "- Cluster 2’s greatest distinction is having the combination of DB’s closest to the WR’s while the farthest DB is aligned in the center.\n", - "- Clusters 3 and 4 have average separation between DB’s and WR’s greater than 5 yards.\n", - "- The average orientation of DB’s is lowest for Cluster 1.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "id": "8c4ea72c-dbca-4a16-a58e-322e7e0acf9c", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Metric (Yards)Cluster 1Cluster 2Cluster 3Cluster 4
Farthest DB X Dist. From Football12.5212.9713.5313.82
Farthest DB Y Dist. From Football0.590.29-6.785.98
2nd Farthest DB X Dist. From Football8.425.7511.0511.02
2nd Fathest DB Y Dist. From Ball1.08-4.865.93-6.20
Nearest DB X Dist. From WR15.832.353.262.84
Nearest DB Y Dist. From WR16.104.736.085.54
Nearest DB X Dist. From WR23.493.345.525.00
Nearest DB Y Dist. From WR22.353.544.945.31
Nearest DB to WR Orientation Degrees From LOS8.6329.2920.4126.57
2nd Nearest DB to WR Orientation Degrees From LOS0.8315.149.208.65
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# List of fields to compute mean for\n", - "fields_to_summarize = [\n", - " 'player1_x_delta', 'player1_y_delta', 'player2_x_delta', 'player2_y_delta',\n", - " 'player1_x_distance_from_wr', 'player1_y_distance_from_wr',\n", - " 'player2_x_distance_from_wr', 'player2_y_distance_from_wr',\n", - " 'player1_o', 'player2_o'\n", - "]\n", - "\n", - "# Compute the mean for each metric grouped by coverage_cluster_cat\n", - "mean_summary = df_features.groupby('coverage_cluster_cat')[fields_to_summarize].mean()\n", - "\n", - "# Transpose the DataFrame without including coverage_cluster_cat explicitly\n", - "mean_summary_transposed = mean_summary.T\n", - "mean_summary_transposed.reset_index(inplace=True)\n", - "\n", - "# Rename 'index' column to 'Metric'\n", - "mean_summary_transposed.rename(columns={'index': 'Metric (Yards)'}, inplace=True)\n", - "\n", - "# Update metric names\n", - "metric_names = {\n", - " 'player1_x_delta': 'Farthest DB X Dist. From Football',\n", - " 'player1_y_delta': 'Farthest DB Y Dist. From Football',\n", - " 'player2_x_delta': '2nd Farthest DB X Dist. From Football',\n", - " 'player2_y_delta': '2nd Fathest DB Y Dist. From Ball',\n", - " 'player1_x_distance_from_wr': 'Nearest DB X Dist. From WR1',\n", - " 'player1_y_distance_from_wr': 'Nearest DB Y Dist. From WR1',\n", - " 'player2_x_distance_from_wr': 'Nearest DB X Dist. From WR2',\n", - " 'player2_y_distance_from_wr': 'Nearest DB Y Dist. From WR2',\n", - " 'player1_o': 'Nearest DB to WR Orientation Degrees From LOS',\n", - " 'player2_o': '2nd Nearest DB to WR Orientation Degrees From LOS'\n", - "}\n", - "\n", - "mean_summary_transposed['Metric (Yards)'] = mean_summary_transposed['Metric (Yards)'].map(metric_names)\n", - "\n", - "# Rename cluster columns\n", - "cluster_column_map = {1: 'Cluster 1', 2: 'Cluster 2', 3: 'Cluster 3', 4: 'Cluster 4'}\n", - "mean_summary_transposed.rename(columns=cluster_column_map, inplace=True)\n", - "\n", - "# Apply formatting and hide the index\n", - "styled_df = mean_summary_transposed.style.format(precision=2).set_table_styles([\n", - " {'selector': 'th', 'props': [('text-align', 'center'), ('background-color', '#013369'),\n", - " ('font-weight', 'bold'), ('color', 'white')]}, # Header styles\n", - " {'selector': 'td', 'props': [('text-align', 'center'), ('font-size', '14px')]}, # Cell styles\n", - " {'selector': 'tr:nth-child(even)', 'props': [('background-color', '#f2f2f2')]}, # Lighter grey for even rows\n", - " {'selector': 'tr:nth-child(odd)', 'props': [('background-color', 'white')]}, # Default white for odd rows\n", - "]).set_properties(**{'border': 'none', 'padding': '5px'}) # Cell padding and border adjustments\n", - "\n", - "# Hide the index\n", - "styled_df = styled_df.hide(axis=\"index\")\n", - "\n", - "# Display the styled DataFrame\n", - "from IPython.display import display\n", - "display(styled_df)" - ] - }, - { - "cell_type": "code", - "execution_count": 243, - "id": "4c03705a-8a75-4bbb-8a6c-c43468bd8da3", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "# #Show Cluster 1 (Looks like a Cover 3 most likely coverage)\n", - "# test = df_weeks.loc[df_weeks['gameId'] == 2022103002]\n", - "# test.loc[test['playId'] == 2500]" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "id": "1043903c-3257-4022-b0e5-7de72f94c34e", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "###Map out an example of cluster1\n", - "df_disco_cluster2 = df_features[['gameId', 'playId', 'coverage_cluster_cat', 'disco', 'pff_passCoverageCat', 'player1_o', 'player2_o']].loc[(df_features.coverage_cluster_cat == 1) & (df_features.disco == 0)]\n", - "#df_disco_cluster2 = df_features[['gameId', 'playId', 'coverage_cluster_cat', 'disco', 'pff_passCoverageCat', 'y_motion_delta', 'y_motion_delta_def']].loc[(df_features.pff_passCoverageCat == 'Cover 1') & (df_features.disco == 0)]\n", - "#df_plays_games_cluster2 = pd.merge(df_plays_games, df_disco_cluster2, on = ['gameId', 'playId'], how = 'inner')\n", - "df_disco_cluster2 = df_disco_cluster2.sort_values(by = 'pff_passCoverageCat')\n", - "df_pick = pd.merge(df_plays_games, df_disco_cluster2, on = ['gameId', 'playId'], how = 'inner')\n", - "#df_pick.loc[df_pick['passResult'] == 'C'].sort_values(by = 'expectedPointsAdded', ascending = False).head(20)" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "id": "af2f917a-901c-4bb2-8c76-2e9cc76ffb6c", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "df_weeks_game1 = df_weeks.loc[df_weeks['gameId'] == 2022100911].reset_index()\n", - "df_weeks_game1 = df_weeks_game1.loc[df_weeks_game1['playId'] == 1566]\n", - "\n", - "df_weeks_game2 = df_weeks.loc[df_weeks['gameId'] == 2022091804].reset_index()\n", - "df_weeks_game2 = df_weeks_game2.loc[df_weeks_game2['playId'] == 3409]\n", - "\n", - "df_weeks_game3 = df_weeks.loc[df_weeks['gameId'] == 2022100904].reset_index()\n", - "df_weeks_game3 = df_weeks_game3.loc[df_weeks_game3['playId'] == 3001]\n", - "\n", - "df_weeks_game4 = df_weeks.loc[df_weeks['gameId'] == 2022092900].reset_index()\n", - "df_weeks_game4 = df_weeks_game4.loc[df_weeks_game4['playId'] == 2598]\n", - "\n", - "df_weeks_game5 = df_weeks.loc[df_weeks['gameId'] == 2022102307].reset_index()\n", - "df_weeks_game5 = df_weeks_game5.loc[df_weeks_game5['playId'] == 2733]\n", - "\n", - "#df_weeks_game2.tail()" - ] - }, - { - "cell_type": "markdown", - "id": "fd367630-e6b2-4c2e-a023-a1e68a37b190", - "metadata": {}, - "source": [ - "# Visualizing the Clusters" - ] - }, - { - "cell_type": "markdown", - "id": "84e71c47-32d6-43fe-8b81-f18263079589", - "metadata": {}, - "source": [ - "With a basic understanding of the differentiators of each cluster, we can now look at a sample play from each of the 4 alignment clusters, plus one additional example of a disguised coverage from a (lack of) motion." - ] - }, - { - "cell_type": "markdown", - "id": "9c292e45-83f3-4880-81b8-909eb89fe95f", - "metadata": {}, - "source": [ - "# Cluster 1: Jazz\n", - "\n", - "Like Jazz, this pre-snap alignment allows for a smooth transition into its most common coverage, Cover 3. In this play, Kyler Murray sees a potential Cover 3 on 3rd and short and looks to hit Marquise Brown over the middle just across the first down marker. After Brown bounces off the would-be tackler, he gives the Cardinals more than they bargained for with a touchdown.\n", - "\n", - "Press the play button to watch (time is 2x), or watch the play manually by adjusting the time toggle. " - ] - }, - { - "cell_type": "code", - "execution_count": 251, - "id": "f54187fa-315d-43a9-9c34-e7b082c6f6c5", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "hovertemplate": "club=PHI
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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Example of Cluster 1\n", - "\n", - "import pandas as pd\n", - "import plotly.express as px\n", - "import plotly.graph_objects as go\n", - "\n", - "# Assuming df_weeks_game3 is your DataFrame\n", - "df_weeks_game1 = df_weeks_game1.loc[df_weeks_game1['time'].notnull()]\n", - "df_weeks_game1['time_str'] = df_weeks_game1['time'].astype(str) # Convert time to string for Plotly\n", - "\n", - "# Create scatter plot with animation\n", - "fig = px.scatter(\n", - " df_weeks_game1,\n", - " x='x',\n", - " y='y',\n", - " animation_frame='time_str',\n", - " color='club',\n", - " size=[1] * len(df_weeks_game1), # Set dot sizes\n", - " size_max=12,\n", - " text='jerseyNumber', # Add jersey numbers as text labels\n", - " range_x=[0, 120],\n", - " range_y=[0, 53.3],\n", - " title='Eagles at Cardinals: Cluster 1 to Cover 3',\n", - " color_discrete_map={'football': '#814d0f', 'PHI': '#58aab2', 'ARI': '#f43535'} # Set colors for dots\n", - ")\n", - "# Center the title and customize the font\n", - "fig.update_layout(\n", - " title={\n", - " 'x': 0.5, # Center title horizontally\n", - " 'xanchor': 'center', # Ensure it aligns correctly\n", - " 'yanchor': 'top', # Top alignment for title\n", - " 'font': {\n", - " 'family': 'Arial, sans-serif', # Change font family\n", - " 'size': 16, # Set font size\n", - " 'color': 'black' # Set font color\n", - " }\n", - " }\n", - ")\n", - "# Customize layout to resemble a football field\n", - "fig.update_layout(\n", - " xaxis_title=\"\",\n", - " yaxis_title=\"\",\n", - " plot_bgcolor=\"#3f9b0b\", # Set field color\n", - " xaxis=dict(\n", - " showticklabels=False, # Remove x-axis labels\n", - " gridcolor=\"white\", # Vertical grid lines for yard markers\n", - " tickvals=list(range(0, 121, 10)), # Tick marks every 10 yards\n", - " ticktext=[f\"{i}\" for i in range(0, 121, 10)],\n", - " ),\n", - " yaxis=dict(\n", - " showgrid=False, # Disable horizontal grid lines\n", - " showticklabels=False # No ticks on the y-axis\n", - " ),\n", - " height=500, # Adjust height\n", - " width=700, # Adjust width\n", - " legend_title_text=\"Team\", # Set legend title\n", - ")\n", - "\n", - "# Add darker green end zones\n", - "fig.add_shape(\n", - " type=\"rect\",\n", - " x0=0,\n", - " x1=10,\n", - " y0=0,\n", - " y1=53.3,\n", - " fillcolor=\"#f43535\", # endzone\n", - " opacity=1,\n", - " layer=\"below\",\n", - " line_width=0,\n", - ")\n", - "fig.add_shape(\n", - " type=\"rect\",\n", - " x0=110,\n", - " x1=120,\n", - " y0=0,\n", - " y1=53.3,\n", - " fillcolor=\"#f43535\", # endzone\n", - " opacity=1,\n", - " layer=\"below\",\n", - " line_width=0,\n", - ")\n", - "\n", - "# Add vertical yard markers\n", - "for x in range(10, 111, 10):\n", - " fig.add_shape(\n", - " type=\"line\",\n", - " x0=x,\n", - " x1=x,\n", - " y0=0,\n", - " y1=53.3,\n", - " line=dict(color=\"white\", width=2),\n", - " layer=\"below\"\n", - " )\n", - "\n", - "# Add a yellow line at x = 52\n", - "fig.add_shape(\n", - " type=\"line\",\n", - " x0=32,\n", - " x1=32,\n", - " y0=0,\n", - " y1=53.3,\n", - " line=dict(color=\"yellow\", width=3), # Yellow color\n", - " layer=\"below\"\n", - ")\n", - "\n", - "\n", - "# Add dotted white horizontal lines\n", - "fig.add_shape(\n", - " type=\"line\",\n", - " x0=0,\n", - " x1=120,\n", - " y0=22.8,\n", - " y1=22.8,\n", - " line=dict(color=\"white\", width=2, dash=\"dot\"), # Dotted white line\n", - " layer=\"below\"\n", - ")\n", - "\n", - "# Add dotted white horizontal lines\n", - "fig.add_shape(\n", - " type=\"line\",\n", - " x0=0,\n", - " x1=120,\n", - " y0=30.5,\n", - " y1=30.5,\n", - " line=dict(color=\"white\", width=2, dash=\"dot\"), # Dotted white line\n", - " layer=\"below\"\n", - ")\n", - "# Add yard marker annotations\n", - "yard_markers = {\n", - " 20: \"10\", 30: \"20\", 40: \"30\", 50: \"40\",\n", - " 60: \"50\", 70: \"40\", 80: \"30\", 90: \"20\", 100: \"10\"\n", - "}\n", - "for x, text in yard_markers.items():\n", - " fig.add_annotation(\n", - " x=x,\n", - " y=11, # Fixed y coordinate for text placement\n", - 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"\n", - "# Rename the animation label above the slider\n", - "fig.update_layout(\n", - " sliders=[{\n", - " 'currentvalue': {\n", - " 'prefix': 'Time: ', # Change \"time_str:\" to \"time:\"\n", - " 'font': {'size': 14, 'color': 'black'}\n", - " }\n", - " }]\n", - ")\n", - "# Ensure dots are on top by adjusting the z-order of the scatter plot trace\n", - "fig.update_traces(marker=dict(size=10, opacity=0.8, line=dict(width=2, color=\"DarkSlateGrey\")), selector=dict(mode=\"markers\"), z=101)\n", - "# Show the plot\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "id": "796976ea-5ecb-4286-ab7e-6bcdc8ff2b1c", - "metadata": {}, - "source": [ - "# Cluster 2: Rock n’ Roll\n", - "Just as a rock song can depend on a great guitar solo, this alignment depends on a solo DB. 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")\n", - "# Center the title and customize the font\n", - "fig.update_layout(\n", - " title={\n", - " 'x': 0.5, # Center title horizontally\n", - " 'xanchor': 'center', # Ensure it aligns correctly\n", - " 'yanchor': 'top', # Top alignment for title\n", - " 'font': {\n", - " 'family': 'Arial, sans-serif', # Change font family\n", - " 'size': 16, # Set font size\n", - " 'color': 'black' # Set font color\n", - " }\n", - " }\n", - ")\n", - "# Customize layout to resemble a football field\n", - "fig.update_layout(\n", - " xaxis_title=\"\",\n", - " yaxis_title=\"\",\n", - " plot_bgcolor=\"#3f9b0b\", # Set field color\n", - " xaxis=dict(\n", - " showticklabels=False, # Remove x-axis labels\n", - " gridcolor=\"white\", # Vertical grid lines for yard markers\n", - " tickvals=list(range(0, 121, 10)), # Tick marks every 10 yards\n", - " ticktext=[f\"{i}\" for i in range(0, 121, 10)],\n", - " ),\n", - " yaxis=dict(\n", - " showgrid=False, # Disable horizontal grid lines\n", - " showticklabels=False # No ticks on the y-axis\n", - " ),\n", - " height=500, # Adjust height\n", - " width=700, # Adjust width\n", - " legend_title_text=\"Team\", # Set legend title\n", - ")\n", - "\n", - "# Add darker green end zones\n", - "fig.add_shape(\n", - " type=\"rect\",\n", - " x0=0,\n", - " x1=10,\n", - " y0=0,\n", - " y1=53.3,\n", - " fillcolor=\"#D3BC8D\", # endzone\n", - " opacity=1,\n", - " layer=\"below\",\n", - " line_width=0,\n", - ")\n", - "fig.add_shape(\n", - " type=\"rect\",\n", - " x0=110,\n", - " x1=120,\n", - " y0=0,\n", - " y1=53.3,\n", - " fillcolor=\"#D3BC8D\", # endzone\n", - " opacity=1,\n", - " layer=\"below\",\n", - " line_width=0,\n", - ")\n", - "\n", - "# Add vertical yard markers\n", - "for x in range(10, 111, 10):\n", - " fig.add_shape(\n", - " type=\"line\",\n", - " x0=x,\n", - " x1=x,\n", - " y0=0,\n", - " y1=53.3,\n", - " line=dict(color=\"white\", width=2),\n", - " layer=\"below\"\n", - " )\n", - "\n", - "# Add a yellow line at x = 52\n", - "fig.add_shape(\n", - " type=\"line\",\n", - " x0=33,\n", - " x1=33,\n", - " y0=0,\n", - " y1=53.3,\n", - " line=dict(color=\"yellow\", width=3), # Yellow color\n", - " layer=\"below\"\n", - ")\n", - "\n", - "\n", - "# Add dotted white horizontal lines\n", - "fig.add_shape(\n", - " type=\"line\",\n", - " x0=0,\n", - " x1=120,\n", - " y0=22.8,\n", - " y1=22.8,\n", - " line=dict(color=\"white\", width=2, dash=\"dot\"), # Dotted white line\n", - " layer=\"below\"\n", - ")\n", - "\n", - "# Add dotted white horizontal lines\n", - "fig.add_shape(\n", - " type=\"line\",\n", - " x0=0,\n", - " x1=120,\n", - " y0=30.5,\n", - " y1=30.5,\n", - " line=dict(color=\"white\", width=2, dash=\"dot\"), # Dotted white line\n", - " layer=\"below\"\n", - ")\n", - "# Add yard marker annotations\n", - "yard_markers = {\n", - " 20: \"10\", 30: \"20\", 40: \"30\", 50: \"40\",\n", - " 60: \"50\", 70: \"40\", 80: \"30\", 90: \"20\", 100: \"10\"\n", - "}\n", - "for x, text in yard_markers.items():\n", - " fig.add_annotation(\n", - " x=x,\n", - " y=11, # Fixed y coordinate for text placement\n", - " text=text,\n", - " showarrow=False,\n", - " font=dict(color=\"white\", size=15), # White font\n", - " align=\"center\"\n", - " )\n", - "\n", - "\n", - "for x, text in yard_markers.items():\n", - " fig.add_annotation(\n", - " x=x,\n", - " y=42.2, # Fixed y coordinate for text placement\n", - " text=text,\n", - " showarrow=False,\n", - " font=dict(color=\"white\", size=15), # White font\n", - " align=\"center\",\n", - " textangle=180\n", - " )\n", - "# Add the NFL logo image to the center of the field\n", - "fig.update_layout(\n", - " images=[\n", - " dict(\n", - " source=\"https://upload.wikimedia.org/wikipedia/en/a/a2/National_Football_League_logo.svg\", \n", - " x=0.5, # Center of the field (normalized coordinate: 0 to 1)\n", - " y=0.5, # Center of the field (normalized coordinate: 0 to 1)\n", - " xref=\"paper\", # Reference x-axis in normalized coordinates\n", - " yref=\"paper\", # Reference y-axis in normalized coordinates\n", - " sizex=0.2, # Width of the image (relative to the plot)\n", - 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Compared to Clusters 1 and 2, several coverages are within the norm from this alignment. In this play, Bears DB Kindle Vindor is pressed on his receiver, perhaps disguising a man coverage, but plays his zone to perfection as he grabs the interception. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "id": "6e7136a7-287f-40fe-8f89-6ef2187326b3", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "hovertemplate": "club=CHI
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")\n", - "\n", - "# Center the title and customize the font\n", - "fig.update_layout(\n", - " title={\n", - " 'x': 0.5, # Center title horizontally\n", - " 'xanchor': 'center', # Ensure it aligns correctly\n", - " 'yanchor': 'top', # Top alignment for title\n", - " 'font': {\n", - " 'family': 'Arial, sans-serif', # Change font family\n", - " 'size': 16, # Set font size\n", - " 'color': 'black' # Set font color\n", - " }\n", - " }\n", - ")\n", - "\n", - "# Customize layout to resemble a football field\n", - "fig.update_layout(\n", - " xaxis_title=\"\",\n", - " yaxis_title=\"\",\n", - " plot_bgcolor=\"#3f9b0b\", # Set field color\n", - " xaxis=dict(\n", - " showticklabels=False, # Remove x-axis labels\n", - " gridcolor=\"white\", # Vertical grid lines for yard markers\n", - " tickvals=list(range(0, 121, 10)), # Tick marks every 10 yards\n", - " ticktext=[f\"{i}\" for i in range(0, 121, 10)],\n", - " ),\n", - " yaxis=dict(\n", - " showgrid=False, # Disable horizontal grid lines\n", - " showticklabels=False # No ticks on the y-axis\n", - " ),\n", - " height=500, # Adjust height\n", - " width=700, # Adjust width\n", - " legend_title_text=\"Team\", # Set legend title\n", - ")\n", - "\n", - "# Add darker green end zones\n", - "fig.add_shape(\n", - " type=\"rect\",\n", - " x0=0,\n", - " x1=10,\n", - " y0=0,\n", - " y1=53.3,\n", - " fillcolor=\"#9f19ce\", # endzone\n", - " opacity=1,\n", - " layer=\"below\",\n", - " line_width=0,\n", - ")\n", - "fig.add_shape(\n", - " type=\"rect\",\n", - " x0=110,\n", - " x1=120,\n", - " y0=0,\n", - " y1=53.3,\n", - " fillcolor=\"#9f19ce\", # endzone\n", - " opacity=1,\n", - " layer=\"below\",\n", - " line_width=0,\n", - ")\n", - "\n", - "# Add vertical yard markers\n", - "for x in range(10, 111, 10):\n", - " fig.add_shape(\n", - " type=\"line\",\n", - " x0=x,\n", - " x1=x,\n", - " y0=0,\n", - " y1=53.3,\n", - " line=dict(color=\"white\", width=2),\n", - " layer=\"below\"\n", - " )\n", - "\n", - "# Add a yellow line at x = 52\n", - "fig.add_shape(\n", - " type=\"line\",\n", - " x0=78,\n", - " x1=78,\n", - " y0=0,\n", - " y1=53.3,\n", - " line=dict(color=\"yellow\", width=3), # Yellow color\n", - " layer=\"below\"\n", - ")\n", - "\n", - "\n", - "# Add dotted white horizontal lines\n", - "fig.add_shape(\n", - " type=\"line\",\n", - " x0=0,\n", - " x1=120,\n", - " y0=22.8,\n", - " y1=22.8,\n", - " line=dict(color=\"white\", width=2, dash=\"dot\"), # Dotted white line\n", - " layer=\"below\"\n", - ")\n", - "\n", - "# Add dotted white horizontal lines\n", - "fig.add_shape(\n", - " type=\"line\",\n", - " x0=0,\n", - " x1=120,\n", - " y0=30.5,\n", - " y1=30.5,\n", - " line=dict(color=\"white\", width=2, dash=\"dot\"), # Dotted white line\n", - " layer=\"below\"\n", - ")\n", - "# Add yard marker annotations\n", - "yard_markers = {\n", - " 20: \"10\", 30: \"20\", 40: \"30\", 50: \"40\",\n", - " 60: \"50\", 70: \"40\", 80: \"30\", 90: \"20\", 100: \"10\"\n", - "}\n", - "for x, text in yard_markers.items():\n", - " fig.add_annotation(\n", - " x=x,\n", - " y=11, # Fixed y coordinate for text placement\n", - " text=text,\n", - " showarrow=False,\n", - " font=dict(color=\"white\", size=15), # White font\n", - " align=\"center\"\n", - " )\n", - "\n", - "\n", - "for x, text in yard_markers.items():\n", - " fig.add_annotation(\n", - " x=x,\n", - " y=42.2, # Fixed y coordinate for text placement\n", - " text=text,\n", - " showarrow=False,\n", - " font=dict(color=\"white\", size=15), # White font\n", - " align=\"center\",\n", - " textangle=180\n", - " )\n", - "# Add the NFL logo image to the center of the field\n", - "fig.update_layout(\n", - " images=[\n", - " dict(\n", - " source=\"https://upload.wikimedia.org/wikipedia/en/a/a2/National_Football_League_logo.svg\", # Replace with the URL of the NFL logo\n", - " x=0.5, # Center of the field (normalized coordinate: 0 to 1)\n", - " y=0.5, # Center of the field (normalized coordinate: 0 to 1)\n", - " xref=\"paper\", # Reference x-axis in normalized coordinates\n", - " yref=\"paper\", # Reference y-axis in normalized coordinates\n", - " sizex=0.2, # Width of the image (relative to the plot)\n", - 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"\n", - "# Rename the animation label above the slider\n", - "fig.update_layout(\n", - " sliders=[{\n", - " 'currentvalue': {\n", - " 'prefix': 'Time: ', # Change \"time_str:\" to \"time:\"\n", - " 'font': {'size': 14, 'color': 'black'}\n", - " }\n", - " }]\n", - ")\n", - "\n", - "# Ensure dots are on top by adjusting the z-order of the scatter plot trace\n", - "fig.update_traces(marker=dict(size=10, opacity=0.8, line=dict(width=2, color=\"DarkSlateGrey\")), selector=dict(mode=\"markers\"), z=101)\n", - "# Show the plot\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "id": "aa0fd8b6-4a1f-4f30-ae2a-1d38069ab7b1", - "metadata": {}, - "source": [ - "# Cluster 4: Electronic\n", - "While electronic music is versatile to a variety of different tones, beats, and rhythms, this alignment follows suit by being the most versatile to coverage variety. 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")\n", - "# Center the title and customize the font\n", - "fig.update_layout(\n", - " title={\n", - " 'x': 0.5, # Center title horizontally\n", - " 'xanchor': 'center', # Ensure it aligns correctly\n", - " 'yanchor': 'top', # Top alignment for title\n", - " 'font': {\n", - " 'family': 'Arial, sans-serif', # Change font family\n", - " 'size': 16, # Set font size\n", - " 'color': 'black' # Set font color\n", - " }\n", - " }\n", - ")\n", - "# Customize layout to resemble a football field\n", - "fig.update_layout(\n", - " xaxis_title=\"\",\n", - " yaxis_title=\"\",\n", - " plot_bgcolor=\"#3f9b0b\", # Set field color\n", - " xaxis=dict(\n", - " showticklabels=False, # Remove x-axis labels\n", - " gridcolor=\"white\", # Vertical grid lines for yard markers\n", - " tickvals=list(range(0, 121, 10)), # Tick marks every 10 yards\n", - " ticktext=[f\"{i}\" for i in range(0, 121, 10)],\n", - " ),\n", - " yaxis=dict(\n", - " showgrid=False, # Disable horizontal grid lines\n", - " showticklabels=False # No ticks on the y-axis\n", - " ),\n", - " height=500, # Adjust height\n", - " width=700, # Adjust width\n", - " legend_title_text=\"Team\", # Set legend title\n", - ")\n", - "\n", - "# Add darker green end zones\n", - "fig.add_shape(\n", - " type=\"rect\",\n", - " x0=0,\n", - " x1=10,\n", - " y0=0,\n", - " y1=53.3,\n", - " fillcolor=\"#FB4F14\", # endzone\n", - " opacity=1,\n", - " layer=\"below\",\n", - " line_width=0,\n", - ")\n", - "fig.add_shape(\n", - " type=\"rect\",\n", - " x0=110,\n", - " x1=120,\n", - " y0=0,\n", - " y1=53.3,\n", - " fillcolor=\"#FB4F14\", # endzone\n", - " opacity=1,\n", - " layer=\"below\",\n", - " line_width=0,\n", - ")\n", - "\n", - "# Add vertical yard markers\n", - "for x in range(10, 111, 10):\n", - " fig.add_shape(\n", - " type=\"line\",\n", - " x0=x,\n", - " x1=x,\n", - " y0=0,\n", - " y1=53.3,\n", - " line=dict(color=\"white\", width=2),\n", - " layer=\"below\"\n", - " )\n", - "\n", - "# Add a yellow line at x = 52\n", - "fig.add_shape(\n", - " type=\"line\",\n", - " x0=69,\n", - " x1=69,\n", - " y0=0,\n", - " y1=53.3,\n", - " line=dict(color=\"yellow\", width=3), # Yellow color\n", - " layer=\"below\"\n", - ")\n", - "\n", - "# Add dotted white horizontal lines\n", - "fig.add_shape(\n", - " type=\"line\",\n", - " x0=0,\n", - " x1=120,\n", - " y0=22.8,\n", - " y1=22.8,\n", - " line=dict(color=\"white\", width=2, dash=\"dot\"), # Dotted white line\n", - " layer=\"below\"\n", - ")\n", - "\n", - "# Add dotted white horizontal lines\n", - "fig.add_shape(\n", - " type=\"line\",\n", - " x0=0,\n", - " x1=120,\n", - " y0=30.5,\n", - " y1=30.5,\n", - " line=dict(color=\"white\", width=2, dash=\"dot\"), # Dotted white line\n", - " layer=\"below\"\n", - ")\n", - "# Add yard marker annotations\n", - "yard_markers = {\n", - " 20: \"10\", 30: \"20\", 40: \"30\", 50: \"40\",\n", - " 60: \"50\", 70: \"40\", 80: \"30\", 90: \"20\", 100: \"10\"\n", - "}\n", - "for x, text in yard_markers.items():\n", - " fig.add_annotation(\n", - " x=x,\n", - " y=11, # Fixed y coordinate for text placement\n", - " text=text,\n", - " showarrow=False,\n", - " font=dict(color=\"white\", size=15), # White font\n", - " align=\"center\"\n", - " )\n", - "\n", - "\n", - "for x, text in yard_markers.items():\n", - " fig.add_annotation(\n", - " x=x,\n", - " y=42.2, # Fixed y coordinate for text placement\n", - " text=text,\n", - " showarrow=False,\n", - " font=dict(color=\"white\", size=15), # White font\n", - " align=\"center\",\n", - " textangle=180\n", - " )\n", - "# Add the NFL logo image to the center of the field\n", - "fig.update_layout(\n", - " images=[\n", - " dict(\n", - " source=\"https://upload.wikimedia.org/wikipedia/en/a/a2/National_Football_League_logo.svg\", # Replace with the URL of the NFL logo\n", - " x=0.5, # Center of the field (normalized coordinate: 0 to 1)\n", - " y=0.5, # Center of the field (normalized coordinate: 0 to 1)\n", - " xref=\"paper\", # Reference x-axis in normalized coordinates\n", - " yref=\"paper\", # Reference y-axis in normalized coordinates\n", - " sizex=0.2, # Width of the image (relative to the plot)\n", - 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In this case, the Jets motion a receiver across the field, and the Broncos DB’s stay put, disguising their eventual man coverage in the Cluster 4 alignment. As soon as the ball is snapped, defenders find their man and the 3rd down play results in an incomplete pass. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "id": "bcd2ca4b-5a13-4d71-b261-e32dd392170c", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "hovertemplate": "club=NYJ
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Example of Motion \n", - "\n", - "import pandas as pd\n", - "import plotly.express as px\n", - "import plotly.graph_objects as go\n", - "\n", - "# Assuming df_weeks_game5 is your DataFrame\n", - "df_weeks_game5 = df_weeks_game5.loc[df_weeks_game5['time'].notnull()]\n", - "df_weeks_game5['time_str'] = df_weeks_game5['time'].astype(str) # Convert time to string for Plotly\n", - "\n", - "# Create scatter plot with animation\n", - "fig = px.scatter(\n", - " df_weeks_game5,\n", - " x='x',\n", - " y='y',\n", - " animation_frame='time_str',\n", - " color='club',\n", - " size=[1] * len(df_weeks_game5), # Set dot sizes\n", - " size_max=12,\n", - " text='jerseyNumber', # Add jersey numbers as text labels\n", - " range_x=[0, 120],\n", - " range_y=[0, 53.3],\n", - " title='Jets at Broncos: Cluster 4 to Cover 1',\n", - " color_discrete_map={'football': '#814d0f', 'NYJ': '#6ca893', 'DEN': '#FB4F14'} # Set colors for dots\n", - ")\n", - "# Center the title and customize the font\n", - "fig.update_layout(\n", - " title={\n", - " 'x': 0.5, # Center title horizontally\n", - " 'xanchor': 'center', # Ensure it aligns correctly\n", - " 'yanchor': 'top', # Top alignment for title\n", - " 'font': {\n", - " 'family': 'Arial, sans-serif', # Change font family\n", - " 'size': 16, # Set font size\n", - " 'color': 'black' # Set font color\n", - " }\n", - " }\n", - ")\n", - "# Customize layout to resemble a football field\n", - "fig.update_layout(\n", - " xaxis_title=\"\",\n", - " yaxis_title=\"\",\n", - " plot_bgcolor=\"#3f9b0b\", # Set field color\n", - " xaxis=dict(\n", - " showticklabels=False, # Remove x-axis labels\n", - " gridcolor=\"white\", # Vertical grid lines for yard markers\n", - " tickvals=list(range(0, 121, 10)), # Tick marks every 10 yards\n", - " ticktext=[f\"{i}\" for i in range(0, 121, 10)],\n", - " ),\n", - " yaxis=dict(\n", - " showgrid=False, # Disable horizontal grid lines\n", - " showticklabels=False # No ticks on the y-axis\n", - " ),\n", - " height=500, # Adjust height\n", - " width=700, # Adjust width\n", - " legend_title_text=\"Team\", # Set legend title\n", - ")\n", - "\n", - "# Add darker green end zones\n", - "fig.add_shape(\n", - " type=\"rect\",\n", - " x0=0,\n", - " x1=10,\n", - " y0=0,\n", - " y1=53.3,\n", - " fillcolor=\"#FB4F14\", # endzone\n", - " opacity=1,\n", - " layer=\"below\",\n", - " line_width=0,\n", - ")\n", - "fig.add_shape(\n", - " type=\"rect\",\n", - " x0=110,\n", - " x1=120,\n", - " y0=0,\n", - " y1=53.3,\n", - " fillcolor=\"#FB4F14\", # endzone\n", - " opacity=1,\n", - " layer=\"below\",\n", - " line_width=0,\n", - ")\n", - "\n", - "# Add vertical yard markers\n", - "for x in range(10, 111, 10):\n", - " fig.add_shape(\n", - " type=\"line\",\n", - " x0=x,\n", - " x1=x,\n", - " y0=0,\n", - " y1=53.3,\n", - " line=dict(color=\"white\", width=2),\n", - " layer=\"below\"\n", - " )\n", - "\n", - "# Add a yellow line at x = 52\n", - "fig.add_shape(\n", - " type=\"line\",\n", - " x0=52,\n", - " x1=52,\n", - " y0=0,\n", - " y1=53.3,\n", - " line=dict(color=\"yellow\", width=3), # Yellow color\n", - " layer=\"below\"\n", - ")\n", - "\n", - "# Add dotted white horizontal lines\n", - "fig.add_shape(\n", - " type=\"line\",\n", - " x0=0,\n", - " x1=120,\n", - " y0=22.8,\n", - " y1=22.8,\n", - " line=dict(color=\"white\", width=2, dash=\"dot\"), # Dotted white line\n", - " layer=\"below\"\n", - ")\n", - "\n", - "# Add dotted white horizontal lines\n", - "fig.add_shape(\n", - " type=\"line\",\n", - " x0=0,\n", - " x1=120,\n", - " y0=30.5,\n", - " y1=30.5,\n", - " line=dict(color=\"white\", width=2, dash=\"dot\"), # Dotted white line\n", - " layer=\"below\"\n", - ")\n", - "# Add yard marker annotations\n", - "yard_markers = {\n", - " 20: \"10\", 30: \"20\", 40: \"30\", 50: \"40\",\n", - " 60: \"50\", 70: \"40\", 80: \"30\", 90: \"20\", 100: \"10\"\n", - "}\n", - "for x, text in yard_markers.items():\n", - " fig.add_annotation(\n", - " x=x,\n", - " y=11, # Fixed y coordinate for text placement\n", - " text=text,\n", - " showarrow=False,\n", - " font=dict(color=\"white\", size=15), # White font\n", - " align=\"center\"\n", - " )\n", - "\n", - "\n", - "for x, text in yard_markers.items():\n", - " fig.add_annotation(\n", - " x=x,\n", - " y=42.2, # Fixed y coordinate for text placement\n", - " text=text,\n", - " showarrow=False,\n", - " font=dict(color=\"white\", size=15), # White font\n", - " align=\"center\",\n", - " textangle=180\n", - " )\n", - "# Add the NFL logo image to the center of the field\n", - "fig.update_layout(\n", - " images=[\n", - " dict(\n", - " source=\"https://upload.wikimedia.org/wikipedia/en/a/a2/National_Football_League_logo.svg\", # Replace with the URL of the NFL logo\n", - " x=0.5, # Center of the field (normalized coordinate: 0 to 1)\n", - " y=0.5, # Center of the field (normalized coordinate: 0 to 1)\n", - " xref=\"paper\", # Reference x-axis in normalized coordinates\n", - " yref=\"paper\", # Reference y-axis in normalized coordinates\n", - " sizex=0.2, # Width of the image (relative to the plot)\n", - " sizey=0.2, # Height of the image (relative to the plot)\n", - " xanchor=\"center\", # Align image to its center on x\n", - " yanchor=\"middle\", # Align image to its center on y\n", - " layer=\"below\" # Place the image below the data\n", - " )\n", - " ]\n", - ")\n", - "\n", - "# Customize Play/Pause buttons\n", - "fig.update_layout(\n", - " updatemenus=[{\n", - " 'buttons': [\n", - " {\n", - " 'args': [None, {'frame': {'duration': 50, 'redraw': True}, 'fromcurrent': True}],\n", - " 'label': '▶', # Play icon\n", - " 'method': 'animate'\n", - " },\n", - " {\n", - " 'args': [[None], {'frame': {'duration': 0, 'redraw': True}, 'mode': 'immediate', 'transition': {'duration': 0}}],\n", - " 'label': '⏸', # Pause icon\n", - " 'method': 'animate'\n", - " }\n", - " ],\n", - " 'direction': 'left',\n", - " 'pad': {'r': 10, 't': 87},\n", - " 'showactive': False,\n", - " 'type': 'buttons',\n", - " 'x': 0.1,\n", - " 'xanchor': 'right',\n", - " 'y': 0,\n", - " 'yanchor': 'top'\n", - " }]\n", - ")\n", - "\n", - "# Rename the animation label above the slider\n", - "fig.update_layout(\n", - " sliders=[{\n", - " 'currentvalue': {\n", - " 'prefix': 'Time: ', # Change \"time_str:\" to \"time:\"\n", - " 'font': {'size': 14, 'color': 'black'}\n", - " }\n", - " }]\n", - ")\n", - "# Ensure dots are on top by adjusting the z-order of the scatter plot trace\n", - "fig.update_traces(marker=dict(size=10, opacity=0.8, line=dict(width=2, color=\"DarkSlateGrey\")), selector=dict(mode=\"markers\"), z=101)\n", - "# Show the plot\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c2b4be22-ea27-44b9-a269-344a33da3e9f", - "metadata": {}, - "source": [ - "# Frequency of DISCO in the NFL" - ] - }, - { - "cell_type": "markdown", - "id": "8e4c9629-6080-4dff-b147-962f4e4e396c", - "metadata": {}, - "source": [ - "For the DISCO metric, we define a disguise as a transformation from a given cluster to a pass coverage that has less than a 10% outcome, such as going into a Quarters coverage from Cluster 2. A disguise can also be identified as dealing with a pre-snap motion opposite to coverage norms, such as a defender following closely a man in motion to eventually drop into coverage once the ball is snapped. The below chart displays the frequency of snaps a team’s defense disguises coverage (weeks 1-9 in NFL 2022-23 season), as well as a breakdown of frequencies relative to each cluster for that team. While the Vikings used DISCO more frequently than any other team, the Jaguars used a Cluster 2 disguise at the highest rate. With further analysis, it may also be determined how teams prefer to move into specific coverages, depending on their personnel and other factors." - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "id": "c55b3b52-93cd-437b-9eb5-a2901634a37e", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "####\n", - "#Strategic ALignment Transformation % (disco)\n", - "##disco - Presnap Expected Points\n", - "\n", - "#Fix graphs so numbers are behind players and logo is behind players\n" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "id": "d1b8460e-3957-4726-9577-e8771e028ef2", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "df_team_disco = df_features.groupby(\n", - " ['club'] \n", - ").agg(\n", - " {\n", - " 'disco': 'mean'\n", - " }\n", - ").reset_index().sort_values(by = 'disco', ascending = False)\n", - "#df_team_disco = df_team_disco.pivot(index=['club'], columns='coverage_cluster_cat', values=['disco'])\n", - "\n", - "\n", - "df_team_disco_clusters = df_features.groupby(\n", - " ['club', 'coverage_cluster_cat'] \n", - ").agg(\n", - " {\n", - " 'disco': 'mean'\n", - " }\n", - ").reset_index().sort_values(by = 'disco', ascending = False)\n", - "df_team_disco_clusters = df_team_disco_clusters.pivot(index=['club'], columns='coverage_cluster_cat', values=['disco']).reset_index()\n", - "df_team_disco_clusters.columns = [''.join(map(str, col)) if isinstance(col, tuple) else col for col in df_team_disco_clusters.columns]\n", - "df_team_disco = pd.merge(df_team_disco, df_team_disco_clusters, on = 'club', how = 'inner')\n", - "df_team_disco = df_team_disco[['club', 'disco', 'disco1', 'disco2', 'disco3', 'disco4']]\n", - "#df_team_disco" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "id": "9b039f4d-65d8-48ee-ba31-e41d4f4d8f04", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
TeamDISCO %DISCO Cluster 1 %DISCO Cluster 2 %DISCO Cluster 3 %DISCO Cluster 4 %
25%40%22%4%2%
24%34%22%6%2%
23%32%21%13%4%
22%32%17%9%0%
21%27%18%7%9%
21%25%23%7%4%
21%31%17%9%7%
19%21%15%21%12%
19%24%13%6%3%
18%26%33%0%2%
18%23%26%0%2%
18%31%29%7%10%
17%26%11%4%2%
17%25%24%6%2%
16%27%18%2%5%
15%19%20%4%3%
15%20%12%11%4%
14%14%19%9%0%
14%13%32%4%1%
13%15%23%4%5%
13%17%12%0%3%
13%19%13%0%5%
12%15%23%0%0%
12%15%27%0%5%
12%16%12%3%0%
12%15%14%4%5%
11%24%3%1%5%
11%13%18%2%0%
11%15%17%1%5%
10%16%29%4%0%
10%15%15%1%7%
6%5%11%4%2%
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Create a DataFrame with all 32 NFL teams and their logo URLs\n", - "logos = pd.DataFrame({\n", - " 'club': [\n", - " 'ARI', 'ATL', 'BAL', 'BUF', 'CAR', 'CHI', 'CIN', 'CLE', 'DAL', 'DEN', 'DET', \n", - " 'GB', 'HOU', 'IND', 'JAX', 'KC', 'LAC', 'LA', 'LV', 'MIA', 'MIN', 'NE', \n", - " 'NO', 'NYG', 'NYJ', 'PHI', 'PIT', 'SF', 'SEA', 'TB', 'TEN', 'WAS'\n", - " ],\n", - " 'logo_url': [\n", - " 'https://upload.wikimedia.org/wikipedia/en/7/72/Arizona_Cardinals_logo.svg', # ARI\n", - " 'https://upload.wikimedia.org/wikipedia/en/c/c5/Atlanta_Falcons_logo.svg', # ATL\n", - " 'https://upload.wikimedia.org/wikipedia/en/1/16/Baltimore_Ravens_logo.svg', # BAL\n", - " 'https://upload.wikimedia.org/wikipedia/en/7/77/Buffalo_Bills_logo.svg', # BUF\n", - " 'https://upload.wikimedia.org/wikipedia/en/1/1c/Carolina_Panthers_logo.svg', # CAR\n", - " 'https://upload.wikimedia.org/wikipedia/commons/5/5c/Chicago_Bears_logo.svg', # CHI\n", - " 'https://upload.wikimedia.org/wikipedia/commons/8/81/Cincinnati_Bengals_logo.svg', # CIN\n", - " 'https://upload.wikimedia.org/wikipedia/en/d/d9/Cleveland_Browns_logo.svg', # CLE\n", - " 'https://upload.wikimedia.org/wikipedia/commons/1/15/Dallas_Cowboys.svg', # DAL\n", - " 'https://upload.wikimedia.org/wikipedia/en/4/44/Denver_Broncos_logo.svg', # DEN\n", - " 'https://upload.wikimedia.org/wikipedia/en/7/71/Detroit_Lions_logo.svg', # DET\n", - " 'https://upload.wikimedia.org/wikipedia/commons/5/50/Green_Bay_Packers_logo.svg', # GB\n", - " 'https://upload.wikimedia.org/wikipedia/en/2/28/Houston_Texans_logo.svg', # HOU\n", - " 'https://upload.wikimedia.org/wikipedia/commons/0/00/Indianapolis_Colts_logo.svg', # IND\n", - " 'https://upload.wikimedia.org/wikipedia/en/7/74/Jacksonville_Jaguars_logo.svg', # JAX\n", - " 'https://upload.wikimedia.org/wikipedia/en/e/e1/Kansas_City_Chiefs_logo.svg', # KC\n", - " 'https://upload.wikimedia.org/wikipedia/commons/thumb/a/a6/Los_Angeles_Chargers_logo.svg/200px-Los_Angeles_Chargers_logo.svg.png', # LAC\n", - " 'https://upload.wikimedia.org/wikipedia/en/8/8a/Los_Angeles_Rams_logo.svg', # LA\n", - " 'https://upload.wikimedia.org/wikipedia/en/thumb/4/48/Las_Vegas_Raiders_logo.svg/1280px-Las_Vegas_Raiders_logo.svg.png', # LV\n", - " 'https://upload.wikimedia.org/wikipedia/en/3/37/Miami_Dolphins_logo.svg', # MIA\n", - " 'https://upload.wikimedia.org/wikipedia/en/4/48/Minnesota_Vikings_logo.svg', # MIN\n", - " 'https://upload.wikimedia.org/wikipedia/en/b/b9/New_England_Patriots_logo.svg', # NE\n", - " 'https://upload.wikimedia.org/wikipedia/commons/5/50/New_Orleans_Saints_logo.svg', # NO\n", - " 'https://upload.wikimedia.org/wikipedia/commons/6/60/New_York_Giants_logo.svg', # NYG\n", - " 'https://upload.wikimedia.org/wikipedia/en/6/6b/New_York_Jets_logo.svg', # NYJ\n", - " 'https://upload.wikimedia.org/wikipedia/en/8/8e/Philadelphia_Eagles_logo.svg', # PHI\n", - " 'https://upload.wikimedia.org/wikipedia/commons/d/de/Pittsburgh_Steelers_logo.svg', # PIT\n", - " 'https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/San_Francisco_49ers_logo.svg/2560px-San_Francisco_49ers_logo.svg.png', # SF\n", - " 'https://upload.wikimedia.org/wikipedia/en/8/8e/Seattle_Seahawks_logo.svg', # SEA\n", - " 'https://upload.wikimedia.org/wikipedia/en/a/a2/Tampa_Bay_Buccaneers_logo.svg', # TB\n", - " 'https://upload.wikimedia.org/wikipedia/en/c/c1/Tennessee_Titans_logo.svg', # TEN\n", - " 'https://upload.wikimedia.org/wikipedia/commons/thumb/0/0c/Washington_Commanders_logo.svg/2560px-Washington_Commanders_logo.svg.png', # WAS\n", - " ]\n", - "})\n", - "\n", - "\n", - "# Merge df_team_disco with logos DataFrame\n", - "df_team_disco1 = pd.merge(df_team_disco, logos, on='club', how='left')\n", - "\n", - "# Rename columns\n", - "df_team_disco1 = df_team_disco1.rename(columns={\n", - " 'club': 'Team', \n", - " 'disco': 'DISCO %', \n", - " 'disco1': 'DISCO Cluster 1 %', \n", - " 'disco2': 'DISCO Cluster 2 %', \n", - " 'disco3': 'DISCO Cluster 3 %',\n", - " 'disco4': 'DISCO Cluster 4 %'\n", - "})\n", - "\n", - "# Merge the logo and team into one column\n", - "df_team_disco1['Team'] = ''\n", - "df_team_disco1['Team'] = df_team_disco1.apply(\n", - " lambda row: f'{row[\"Team\"]}',\n", - " axis=1\n", - ")\n", - "\n", - "# Drop the original 'logo_url' and 'Team' columns\n", - "df_team_disco1 = df_team_disco1.drop(columns=['logo_url'])\n", - "\n", - "# Format disco columns as percentages with rounding to the nearest whole number\n", - "df_team_disco1['DISCO %'] = df_team_disco1['DISCO %'].apply(lambda x: f\"{round(x*100)}%\")\n", - "df_team_disco1['DISCO Cluster 1 %'] = df_team_disco1['DISCO Cluster 1 %'].apply(lambda x: f\"{round(x*100)}%\")\n", - "df_team_disco1['DISCO Cluster 2 %'] = df_team_disco1['DISCO Cluster 2 %'].apply(lambda x: f\"{round(x*100)}%\")\n", - "df_team_disco1['DISCO Cluster 3 %'] = df_team_disco1['DISCO Cluster 3 %'].apply(lambda x: f\"{round(x*100)}%\")\n", - "df_team_disco1['DISCO Cluster 4 %'] = df_team_disco1['DISCO Cluster 4 %'].apply(lambda x: f\"{round(x*100)}%\")\n", - "\n", - "# Reorder columns so the new merged column is first\n", - "df_team_disco1 = df_team_disco1[['Team', 'DISCO %', 'DISCO Cluster 1 %', 'DISCO Cluster 2 %', 'DISCO Cluster 3 %', 'DISCO Cluster 4 %']]\n", - "\n", - "# Use Pandas styling to display the merged column and format the DataFrame\n", - "df_team_disco_styled = df_team_disco1.style.format({\n", - " 'Logo and Team': lambda x: x # Keep the merged column as is\n", - "}).hide(axis='index') # Hide the default index\n", - "\n", - "# Set table styles to ensure correct alignment and appearance\n", - "df_team_disco_styled.set_table_styles([\n", - " {'selector': 'th', 'props': [('text-align', 'center'), ('background-color', '#f4f4f4'), ('font-weight', 'bold')]},\n", - " {'selector': 'td', 'props': [('text-align', 'center')]},\n", - "])#.set_properties(**{'border': '1px solid black', 'padding': '5px'})\n", - "\n", - "# Use Pandas styling to display the logos and format the DataFrame\n", - "def render_logo(url):\n", - " return f''\n", - "\n", - "\n", - "# Set table styles to ensure correct alignment, appearance, and alternating row colors\n", - "df_team_disco_styled.set_table_styles([\n", - " {'selector': 'th', 'props': [('text-align', 'center'), ('background-color', '#013369'), \n", - " ('font-weight', 'bold'), ('color', 'white')]}, # Make column label text white\n", - " {'selector': 'td', 'props': [('text-align', 'center'), ('font-size', '16px')]}, # Adjust font size here\n", - " {'selector': 'tr:nth-child(aeven)', 'props': [('background-color', '#f9f9f9')]}, # Lighter grey for even rows\n", - " {'selector': 'tr:nth-child(odd)', 'props': [('background-color', 'white')]}, # Default grey for odd rows\n", - "], overwrite=False).set_properties(**{'border': 'none', 'padding': '7px'}) # Remove the borders\n", - "\n", - "# Display the styled DataFrame with the merged column\n", - "from IPython.display import display\n", - "display(df_team_disco_styled)" - ] - }, - { - "cell_type": "markdown", - "id": "d650cb77-6893-473e-8d64-85e3e4764e45", - "metadata": {}, - "source": [ - "# Conclusion and Future Enhancements" - ] - }, - { - "cell_type": "markdown", - "id": "62fdabb9-1987-4120-9455-4e0d62268cbb", - "metadata": {}, - "source": [ - "#### It's noteworthy that the DISCO metric is not an evaluation of defensive strategy, rather an analysis of defensive strategy. Being unpredictable sometimes means playing the hand right in front of your opponent’s eyes. One intention for use of DISCO would be as a supplement to scouting and film, guiding teams to focus on specific defensive behaviors of their opponents. Another is for common NFL fans to leverage as they watch games, as it can be fun to predict what teams will do on each play. \n", - "\n", - "#### For future enhancements to DISCO, we would want to potentially include additional variables that were not used, such as game situation, team historical behavior, and movement from the linebackers and offensive line. It might also be an improvement to leverage a supervised machine learning algorithm that includes such information. Also, using more than 4 clusters could give us an opportunity to have more precise results. Lastly, looking into blitz disguises (and fake blitzes) would improve DISCO." - ] - }, - { - "cell_type": "markdown", - "id": "e342f4ef-3edd-4861-b8a4-7f98b69b74ab", - "metadata": {}, - "source": [ - "# Acknowledgments:\n", - "\n", - "Thank you to the NFL analytics community and the hosts of the Big Data Bowl competition for engaging with fans and data enthusiasts in such a cool way! Also, thank you to the data engineers and architects who developed Next Gen Stats, providing easily accessible and useable data to the public. \n", - "\n", - "\n", - "# Appendix: \n", - "All code is available is github here:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "02874d17-40f5-48de-b000-976f429a905a", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "hide_input": true, - "kernelspec": { - "display_name": "Python [conda env:base] *", - "language": "python", - "name": "conda-base-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/_posts/2025-01-07-Sunday_Afternoon_Fever.md b/_posts/2025-01-07-Sunday_Afternoon_Fever.md index 9b18ae3..3d40aa3 100644 --- a/_posts/2025-01-07-Sunday_Afternoon_Fever.md +++ b/_posts/2025-01-07-Sunday_Afternoon_Fever.md @@ -8,7 +8,7 @@ tags: [ NFL] featured: false --- -#### A metric and analysis of **DIS**guised **CO**verages on Defense +#### DISCO: A metric and analysis of **DIS**guised **CO**verages on Defense # Introduction