diff --git a/11tegen11 position map.ipynb b/11tegen11 position map.ipynb
index f3ab587..04153b2 100644
--- a/11tegen11 position map.ipynb
+++ b/11tegen11 position map.ipynb
@@ -8,7 +8,7 @@
"source": [
"#edit only this tab\n",
"#give the folder path of the match\n",
- "path = \"\"\"C:\\\\Users\\\\koushik.r\\\\Documents\\\\open-data-master\\\\data\\\\events\\\\\"\"\"\n",
+ "path = \"/home/kirugulige/Documents/Football-Analytics/open-data-master/data/events/\"\n",
"home_team = 'Espanyol'\n",
"away_team = 'Barcelona'"
]
@@ -30,25 +30,22 @@
"import json\n",
"import os\n",
"import codecs\n",
- "from pandas.io.json import json_normalize\n",
"import numpy as np\n",
- "#import seaborn as sns\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.patches import Arc, Rectangle, ConnectionPatch\n",
"from matplotlib.offsetbox import OffsetImage\n",
- "#import squarify\n",
+ "import StatsbombPitch as sb\n",
"from functools import reduce\n",
"\n",
"Xg_req = pd.DataFrame(data=None)\n",
- "for filename in (os.listdir(path)):\n",
- " #print(filename)\n",
- "#filename = '69275.json' # remove the comment line to work for this match\n",
+ "\n",
+ "filename = '69275.json' # remove the comment line to work for this match\n",
"with codecs.open(\"%s\" % path + filename,encoding='utf-8') as data_file: \n",
" data = json.load(data_file)\n",
" df = pd.DataFrame(data=None)\n",
" \n",
- " df = json_normalize(data, sep = \"_\")\n",
+ " df = pd.json_normalize(data, sep = \"_\")\n",
" \n",
" #df = df[(df['type_name'] == \"Shot\")]\n",
" #df = df.loc[:,['location','shot_body_part_id','shot_end_location','shot_one_on_one','shot_technique_id','shot_type_id','under_pressure','shot_outcome_id']]\n",
@@ -149,149 +146,149 @@
" \n",
" \n",
" | \n",
- " 50_50_outcome_id | \n",
- " 50_50_outcome_name | \n",
- " bad_behaviour_card_id | \n",
- " bad_behaviour_card_name | \n",
- " ball_receipt_outcome_id | \n",
- " ball_receipt_outcome_name | \n",
- " ball_recovery_recovery_failure | \n",
- " block_deflection | \n",
- " block_offensive | \n",
- " carry_end_location | \n",
- " ... | \n",
- " substitution_replacement_id | \n",
- " substitution_replacement_name | \n",
- " tactics_formation | \n",
- " tactics_lineup | \n",
- " team_id | \n",
- " team_name | \n",
+ " id | \n",
+ " index | \n",
+ " period | \n",
" timestamp | \n",
+ " minute | \n",
+ " second | \n",
+ " possession | \n",
+ " duration | \n",
" type_id | \n",
" type_name | \n",
- " under_pressure | \n",
+ " ... | \n",
+ " pass_cut_back | \n",
+ " substitution_outcome_id | \n",
+ " substitution_outcome_name | \n",
+ " substitution_replacement_id | \n",
+ " substitution_replacement_name | \n",
+ " injury_stoppage_in_chain | \n",
+ " foul_committed_offensive | \n",
+ " pass_deflected | \n",
+ " block_deflection | \n",
+ " goalkeeper_punched_out | \n",
"
\n",
" \n",
"
\n",
" \n",
" 4 | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
+ " d6406089-778b-49e0-a70a-ade4788cf911 | \n",
+ " 5 | \n",
+ " 1 | \n",
+ " 00:00:00.738 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " 0.606100 | \n",
+ " 30 | \n",
+ " Pass | \n",
" ... | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
- " 214 | \n",
- " Espanyol | \n",
- " 00:00:00.738 | \n",
- " 30 | \n",
- " Pass | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
" NaN | \n",
"
\n",
" \n",
" 8 | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
+ " 73d8e43f-8e84-4b5d-88cc-9423278134d5 | \n",
+ " 9 | \n",
+ " 1 | \n",
+ " 00:00:02.511 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " 2 | \n",
+ " 1.776163 | \n",
+ " 30 | \n",
+ " Pass | \n",
" ... | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
- " 214 | \n",
- " Espanyol | \n",
- " 00:00:02.511 | \n",
- " 30 | \n",
- " Pass | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
" NaN | \n",
"
\n",
" \n",
" 16 | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
+ " 8e47d35d-ab38-4419-a991-efe7105dec56 | \n",
+ " 17 | \n",
+ " 1 | \n",
+ " 00:00:09.581 | \n",
+ " 0 | \n",
+ " 9 | \n",
+ " 2 | \n",
+ " 1.242900 | \n",
+ " 30 | \n",
+ " Pass | \n",
" ... | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
- " 214 | \n",
- " Espanyol | \n",
- " 00:00:09.581 | \n",
- " 30 | \n",
- " Pass | \n",
- " True | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
"
\n",
" \n",
" 20 | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
+ " a449bfa2-7bd9-4f94-8e13-595620c9bcee | \n",
+ " 21 | \n",
+ " 1 | \n",
+ " 00:00:18.784 | \n",
+ " 0 | \n",
+ " 18 | \n",
+ " 3 | \n",
+ " 1.298500 | \n",
+ " 30 | \n",
+ " Pass | \n",
" ... | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
- " 214 | \n",
- " Espanyol | \n",
- " 00:00:18.784 | \n",
- " 30 | \n",
- " Pass | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
" NaN | \n",
"
\n",
" \n",
" 24 | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
- " NaN | \n",
+ " 77f45a53-2eab-4ee5-bfbd-ad39d20a36cb | \n",
+ " 25 | \n",
+ " 1 | \n",
+ " 00:00:20.122 | \n",
+ " 0 | \n",
+ " 20 | \n",
+ " 3 | \n",
+ " 0.950262 | \n",
+ " 30 | \n",
+ " Pass | \n",
" ... | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
- " 214 | \n",
- " Espanyol | \n",
- " 00:00:20.122 | \n",
- " 30 | \n",
- " Pass | \n",
- " True | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
"
\n",
" \n",
"\n",
@@ -299,47 +296,47 @@
""
],
"text/plain": [
- " 50_50_outcome_id 50_50_outcome_name bad_behaviour_card_id \\\n",
- "4 NaN NaN NaN \n",
- "8 NaN NaN NaN \n",
- "16 NaN NaN NaN \n",
- "20 NaN NaN NaN \n",
- "24 NaN NaN NaN \n",
+ " id index period timestamp minute \\\n",
+ "4 d6406089-778b-49e0-a70a-ade4788cf911 5 1 00:00:00.738 0 \n",
+ "8 73d8e43f-8e84-4b5d-88cc-9423278134d5 9 1 00:00:02.511 0 \n",
+ "16 8e47d35d-ab38-4419-a991-efe7105dec56 17 1 00:00:09.581 0 \n",
+ "20 a449bfa2-7bd9-4f94-8e13-595620c9bcee 21 1 00:00:18.784 0 \n",
+ "24 77f45a53-2eab-4ee5-bfbd-ad39d20a36cb 25 1 00:00:20.122 0 \n",
"\n",
- " bad_behaviour_card_name ball_receipt_outcome_id ball_receipt_outcome_name \\\n",
- "4 NaN NaN NaN \n",
- "8 NaN NaN NaN \n",
- "16 NaN NaN NaN \n",
- "20 NaN NaN NaN \n",
- "24 NaN NaN NaN \n",
+ " second possession duration type_id type_name ... pass_cut_back \\\n",
+ "4 0 2 0.606100 30 Pass ... NaN \n",
+ "8 2 2 1.776163 30 Pass ... NaN \n",
+ "16 9 2 1.242900 30 Pass ... NaN \n",
+ "20 18 3 1.298500 30 Pass ... NaN \n",
+ "24 20 3 0.950262 30 Pass ... NaN \n",
"\n",
- " ball_recovery_recovery_failure block_deflection block_offensive \\\n",
- "4 NaN NaN NaN \n",
- "8 NaN NaN NaN \n",
- "16 NaN NaN NaN \n",
- "20 NaN NaN NaN \n",
- "24 NaN NaN NaN \n",
+ " substitution_outcome_id substitution_outcome_name \\\n",
+ "4 NaN NaN \n",
+ "8 NaN NaN \n",
+ "16 NaN NaN \n",
+ "20 NaN NaN \n",
+ "24 NaN NaN \n",
"\n",
- " carry_end_location ... substitution_replacement_id \\\n",
- "4 NaN ... NaN \n",
- "8 NaN ... NaN \n",
- "16 NaN ... NaN \n",
- "20 NaN ... NaN \n",
- "24 NaN ... NaN \n",
+ " substitution_replacement_id substitution_replacement_name \\\n",
+ "4 NaN NaN \n",
+ "8 NaN NaN \n",
+ "16 NaN NaN \n",
+ "20 NaN NaN \n",
+ "24 NaN NaN \n",
"\n",
- " substitution_replacement_name tactics_formation tactics_lineup team_id \\\n",
- "4 NaN NaN NaN 214 \n",
- "8 NaN NaN NaN 214 \n",
- "16 NaN NaN NaN 214 \n",
- "20 NaN NaN NaN 214 \n",
- "24 NaN NaN NaN 214 \n",
+ " injury_stoppage_in_chain foul_committed_offensive pass_deflected \\\n",
+ "4 NaN NaN NaN \n",
+ "8 NaN NaN NaN \n",
+ "16 NaN NaN NaN \n",
+ "20 NaN NaN NaN \n",
+ "24 NaN NaN NaN \n",
"\n",
- " team_name timestamp type_id type_name under_pressure \n",
- "4 Espanyol 00:00:00.738 30 Pass NaN \n",
- "8 Espanyol 00:00:02.511 30 Pass NaN \n",
- "16 Espanyol 00:00:09.581 30 Pass True \n",
- "20 Espanyol 00:00:18.784 30 Pass NaN \n",
- "24 Espanyol 00:00:20.122 30 Pass True \n",
+ " block_deflection goalkeeper_punched_out \n",
+ "4 NaN NaN \n",
+ "8 NaN NaN \n",
+ "16 NaN NaN \n",
+ "20 NaN NaN \n",
+ "24 NaN NaN \n",
"\n",
"[5 rows x 122 columns]"
]
@@ -355,63 +352,28 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "def draw_pitch(ax):\n",
- " # focus on only half of the pitch\n",
- " #Pitch Outline & Centre Line\n",
- " Pitch = Rectangle([0,0], width = 120, height = 80, fill = False)\n",
- " #Left, Right Penalty Area and midline\n",
- " LeftPenalty = Rectangle([0,22.3], width = 14.6, height = 35.3, fill = False)\n",
- " RightPenalty = Rectangle([105.4,22.3], width = 14.6, height = 35.3, fill = False)\n",
- " midline = ConnectionPatch([60,0], [60,80], \"data\", \"data\")\n",
- "\n",
- " #Left, Right 6-yard Box\n",
- " LeftSixYard = Rectangle([0,32], width = 4.9, height = 16, fill = False)\n",
- " RightSixYard = Rectangle([115.1,32], width = 4.9, height = 16, fill = False)\n",
- "\n",
- "\n",
- " #Prepare Circles\n",
- " centreCircle = plt.Circle((60,40),8.1,color=\"white\", fill = False)\n",
- " centreSpot = plt.Circle((60,40),0.71,color=\"white\")\n",
- " #Penalty spots and Arcs around penalty boxes\n",
- " leftPenSpot = plt.Circle((9.7,40),0.71,color=\"white\")\n",
- " rightPenSpot = plt.Circle((110.3,40),0.71,color=\"white\")\n",
- " leftArc = Arc((9.7,40),height=16.2,width=16.2,angle=0,theta1=310,theta2=50,color=\"white\")\n",
- " rightArc = Arc((110.3,40),height=16.2,width=16.2,angle=0,theta1=130,theta2=230,color=\"white\")\n",
- " \n",
- " element = [Pitch, LeftPenalty, RightPenalty, midline, LeftSixYard, RightSixYard, centreCircle, \n",
- " centreSpot, rightPenSpot, leftPenSpot, leftArc, rightArc]\n",
- " for i in element:\n",
- " ax.add_patch(i)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
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+ "