Graph Analytics Demo: NFL game 2013 (Ravens vs. Broncos)

In [1]:

    

import trustedanalytics as ta


    

In [2]:

    

ta.connect()


    

    




Connected.  This client instance connected to server http://localhost:9099/v1 (version=TheReneNumber) as user test_api_key_1 at 2016-08-04 09:31:08.772139.

In [3]:

    

# create vertex frame schema
#=======================
vschema= [("id",ta.int32),("number",ta.int32),("player_name",str),("age",ta.int32),("team",str),("position",str)]
v_csv = ta.CsvFile("/nflgamedata/NFLgame2013vertices_players_fv.csv",vschema,skip_header_lines=1)


    

In [4]:

    

#create edge frame schema
#====================
eschema= [("the_doer",str),("the_receiver",str),("play_type",str),("play_evaluation",str),("penalty_type",str),("penalty_on",str),("pass_location",str),("quarter",ta.int32),("min",ta.int32),("sec",ta.int32),("off",str),("def",str),("down",ta.int32),("yards_to_go",ta.int32),("yard_line",ta.int32),("yards_done",ta.int32),("injuries",str),("score_type",str),("off_score",ta.int32),("def_score",ta.int32)]
e_csv = ta.CsvFile("/nflgamedata/NFLgame2013_edges_full_fv.csv",eschema,skip_header_lines=1)


    

In [5]:

    

#load the verticses CSV file into a frame
#========================================
vertex_frame = ta.Frame(v_csv)
vertex_frame.inspect()


    

    




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Out[5]:





[#]  id  number  player_name  age  team              position
=============================================================
[0]   1      51  D.Smith       31  Baltimore Ravens  MLB
[1]   2      32  J.Ihedigbo    30  Baltimore Ravens  SS
[2]   3      21  L.Webb        28  Baltimore Ravens  LCB
[3]   4      55  T.Suggs       31  Baltimore Ravens  LB
[4]   5      24  C.Graham      28  Baltimore Ravens  cb
[5]   6      26  M.Elam        22  Baltimore Ravens  FS
[6]   7      22  J.Smith       25  Baltimore Ravens  RCB
[7]   8      97  A.Jones       27  Baltimore Ravens  DT
[8]   9      53  J.McClain     28  Baltimore Ravens  lb/mlb
[9]  10      92  H.Ngata       29  Baltimore Ravens  NT

In [6]:

    

#load the the edges CSV file into a frame
#========================================
edge_frame = ta.Frame(e_csv)
edge_frame.inspect()


    

    




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Out[6]:





[#]  the_doer   the_receiver  play_type  play_evaluation  penalty_type         
===============================================================================
[0]  M.Prater   BAL           kickoff    touchback
[1]  J.Flacco   R.Rice        pass       complete
[2]  R.Rice     D.Ihenacho    rush       rush
[3]  J.Flacco   B.Stokley     pass       complete         off pass interference
[4]  S.Koch     M.Cox         punt       k out of bounds
[5]  P.Manning  J.Thomas      pass       incomplete
[6]  K.Moreno   T.Suggs       rush       rush
[7]  K.Moreno   J.Bynes       rush       rush
[8]  P.Manning  W.Welker      pass       complete
[9]  P.Manning  E.Decker      pass       incomplete

[#]  penalty_on  pass_location  quarter  min  sec  off  def  down  yards_to_go
==============================================================================
[0]                                   1    0    0  DEN  BAL     0            0
[1]              short middle         1   60    0  BAL  DEN     1           10
[2]                                   1   59   14  BAL  DEN     2           11
[3]  J.Jones     short left           1   58   38  BAL  DEN     3            8
[4]                                   1   58   18  BAL  DEN     4            2
[5]              short right          1   58   10  DEN  BAL     1           10
[6]                                   1   58    5  DEN  BAL     2           10
[7]                                   1   58    5  DEN  BAL     2           10
[8]              short left           1   57   25  DEN  BAL     3            9
[9]              deep right           1   57    4  DEN  BAL     1           10

[#]  yard_line  yards_done  injuries  score_type  off_score  def_score
======================================================================
[0]         35          65                                0          0
[1]         80          -1                                0          0
[2]         81           3                                0          0
[3]         78          -6                                0          0
[4]         72          49                                0          0
[5]         77           0                                0          0
[6]         77           1                                0          0
[7]         77           1                                0          0
[8]         76          10                                0          0
[9]         66           0                                0          0

In [7]:

    

graph = ta.Graph()


    

In [8]:

    

#define vertex type and add vertices
#===================================
graph.define_vertex_type('players')
graph.vertices['players'].add_vertices(vertex_frame,'player_name',['number','age','team','position'])


    

    




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In [9]:

    

#define edge type and add edges
#==============================
graph.define_edge_type('play_details','players', 'players', directed=True)
graph.edges['play_details'].add_edges(edge_frame, 'the_doer', 'the_receiver',['play_type','play_evaluation','penalty_type','penalty_on','pass_location','min','sec','quarter','off','def','down','yards_to_go','yard_line','yards_done','injuries','score_type','off_score','def_score'])


    

    




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In [10]:

    

graph


    

    
Out[10]:





Graph <unnamed>
status = ACTIVE  (last_read_date = 2016-08-04T09:31:21.597000-07:00)
vertices = 
  players : [player_name, number, age, team, position], count = 104
edges = 
  play_details : [play_type, play_evaluation, penalty_type, penalty_on, pass_location, min, sec, quarter, off, def, down, yards_to_go, yard_line, yards_done, injuries, score_type, off_score, def_score], count = 219

In [11]:

    

import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline


    

In [12]:

    

#play types summary
#Bal-Revens: 
bal_edge_frame = graph.edges['play_details'].copy()
bal_edge_frame.filter(lambda row: row.off=='BAL')
bal_play_summ = bal_edge_frame.group_by('play_type',ta.agg.count)
#Den-Broncos
den_edge_frame = graph.edges['play_details'].copy()
den_edge_frame.filter(lambda row: row.off=='DEN')
den_play_summ = den_edge_frame.group_by('play_type',ta.agg.count)


    

    




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In [13]:

    

print("Denver-Broncos")
den_play_summ.inspect()


    

    




Denver-Broncos






    
Out[13]:





[#]  play_type   count
======================
[0]  kickoff         8
[1]  kneel           2
[2]  pass           45
[3]  noplay          6
[4]  punt            7
[5]  rush           26
[6]  extrapoint      7

In [14]:

    

print("Baltimore-Ravens")
bal_play_summ.inspect()


    

    




Baltimore-Ravens






    
Out[14]:





[#]  play_type   count
======================
[0]  kickoff         6
[1]  pass           69
[2]  noplay          3
[3]  punt           10
[4]  fieldgoal       2
[5]  rush           25
[6]  extrapoint      3

In [15]:

    

#Bal
bal_edge_frame.filter(lambda row: row.score_type !='')
bal_scores_summ= bal_edge_frame.group_by('score_type',ta.agg.count)
#Den
den_edge_frame.filter(lambda row: row.score_type!='')
den_scores_summ= den_edge_frame.group_by('score_type',ta.agg.count)


    

    




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In [16]:

    

print("Baltimore-Ravens")
bal_scores_summ.inspect()


    

    




Baltimore-Ravens






    
Out[16]:





[#]  score_type  count
======================
[0]  fieldgoal       2
[1]  extrapoint      3
[2]  touchdown       3

In [17]:

    

print("Denver-Broncos")
den_scores_summ.inspect()


    

    




Denver-Broncos






    
Out[17]:





[#]  score_type  count
======================
[0]  extrapoint      7
[1]  touchdown       7

In [18]:

    

# Get Den 4th down data 
edges_frame = graph.edges['play_details'].copy()
edges_frame.filter(lambda row: row.down== 4)
edges_frame.filter(lambda row: row.off=='DEN')
DEN_4th_down_play = edges_frame.group_by('play_type',ta.agg.count)
# Get Bal 4th down data
edges_frame2 = graph.edges['play_details'].copy()
edges_frame2.filter(lambda row: row.down== 4)
edges_frame2.filter(lambda row: row.off=='BAL')
BAL_4th_down_play = edges_frame2.group_by('play_type',ta.agg.count)

# plot the results
ind = np.arange(3)
width = 0.35       
fig, ax = plt.subplots()
rects1 = ax.bar(ind,(1,10,2), width, color='b')
rects2 = ax.bar(ind+width,(0,7,0), width, color='orange')
ax.set_ylabel('Number of plays')
ax.set_title('What did the coach make at the 4th down?')
ax.set_xticks(ind + width)
ax.set_xticklabels(('Go for it','Punt','FG'))
ax.legend((rects1[0], rects2[0]),('BAL-Ravens', 'DEN-Broncos'))
plt.show


    

    




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Out[18]:





<function matplotlib.pyplot.show>






    

In [19]:

    

# BAL histogram of yards_done
edge_frame_hist=graph.edges['play_details'].copy()
edge_frame_hist.filter(lambda row: row.off=='BAL')
hist_bal = edge_frame_hist.histogram("yards_done",num_bins=25)
# DEN histogram of yards_done
edge_frame_hist1=graph.edges['play_details'].copy()
edge_frame_hist1.filter(lambda row: row.off=='DEN')
hist_den = edge_frame_hist1.histogram("yards_done",num_bins=25)

# plot histograms by team
num_bins=25
ind = hist_bal.cutoffs[0:num_bins]
width1 = hist_bal.cutoffs[1]-hist_bal.cutoffs[0]
width2 = hist_den.cutoffs[1]-hist_den.cutoffs[0]
fig,(ax0,ax) = plt.subplots(2,1)
fig.set_size_inches(18.5, 10.5, forward=True)
rects1 = ax.bar(hist_bal.cutoffs[0:num_bins],hist_bal.density,width1,color='blue')
rects2 = ax0.bar(hist_den.cutoffs[0:num_bins],hist_den.density,width2,color='orange')
ax0.set_ylabel('yards_done density function')
ax.set_xlabel('yards_done')
ax0.set_title('"yards_done" by Denver & Baltimore histogram')
ax0.legend((rects1[0], rects2[0]),('BAL', 'DEN'))
ax0.set_ylim(0,0.45)
ax.set_ylim(0,0.45)
plt.show


    

    




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Out[19]:





<function matplotlib.pyplot.show>






    

In [20]:

    

# BAl-Ravens: Get score types, yard lines and the corresponding time
bal_edge_frame = graph.edges['play_details'].copy()
bal_edge_frame.filter(lambda row: row.off=='BAL')
panda_frame_yards0 = bal_edge_frame.download(columns=['yard_line'])
panda_frame_min0 = bal_edge_frame.download(columns=['min'])
panda_frame_sec0 = bal_edge_frame.download(columns=['sec'])
yards_list = panda_frame_yards0['yard_line'].tolist()
bal_min_list = panda_frame_min0['min'].tolist()
bal_sec_list= panda_frame_sec0['sec'].tolist()
bal_time_list= [x+y/60.0 for x, y in zip(bal_min_list, bal_sec_list)]

#plot the scores vs time:
bal_edge_frame.filter(lambda row: row.score_type !='')
bal_scores_summ= bal_edge_frame.group_by('score_type',ta.agg.count)
panda_frame_min_s = bal_edge_frame.download(columns=['min'])
panda_frame_sec_s = bal_edge_frame.download(columns=['sec'])
bal_min_list_s = panda_frame_min_s['min'].tolist()
bal_sec_list_s= panda_frame_sec_s['sec'].tolist()
bal_time_list_s= [x+y/60.0 for x, y in zip(bal_min_list_s, bal_sec_list_s)]

# Den-Broncos: Get score types, yard lines and the corresponding time
#========================================
den_edge_frame = graph.edges['play_details'].copy()
den_edge_frame.filter(lambda row: row.off=='DEN')
panda_frame_yards1 = den_edge_frame.download(columns=['yard_line'])
panda_frame_min1 = den_edge_frame.download(columns=['min'])
panda_frame_sec1 = den_edge_frame.download(columns=['sec'])
den_yards_list = panda_frame_yards1['yard_line'].tolist()
den_min_list = panda_frame_min1['min'].tolist()
den_sec_list= panda_frame_sec1['sec'].tolist()
den_time_list=[x+y/60.0 for x, y in zip(den_min_list, den_sec_list)]

#the scores vs time:
den_edge_frame.filter(lambda row: row.score_type!='')
den_scores_summ= den_edge_frame.group_by('score_type',ta.agg.count)
panda_frame_min_s1 = den_edge_frame.download(columns=['min'])
panda_frame_sec_s1 = den_edge_frame.download(columns=['sec'])
den_min_list_s = panda_frame_min_s1['min'].tolist()
den_sec_list_s= panda_frame_sec_s1['sec'].tolist()
den_time_list_s= [x+y/60.0 for x, y in zip(den_min_list_s,den_sec_list_s)]


    

    




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In [21]:

    

fig, ax = plt.subplots()
fig.set_size_inches(18.5, 10.5, forward=True)
plt.grid(True)
bal_stats=ax.plot(bal_time_list, yards_list,'bo', markersize=10)
den_stats=ax.plot(den_time_list, den_yards_list,'ro', markersize=10)
ax.plot(bal_time_list_s,[0]*len(bal_time_list_s),'b*',markerfacecolor='b', markersize=16)
ax.plot(den_time_list_s,[0]*len(den_time_list_s),'r*',markerfacecolor='r', markersize=16)
ax.set_ylabel('Yard lines')
ax.set_title('Ravens vs Broncos game')
ax.legend((bal_stats[0], den_stats[0]),('Ravens','Broncos'))
plt.show


    

    
Out[21]:





<function matplotlib.pyplot.show>






    

In [44]:

    

playtypes_graph = ta.Graph()


    

In [45]:

    

playtypes_graph.define_vertex_type('players')
playtypes_graph.vertices['players'].add_vertices(vertex_frame,'player_name',['number','age','team','position'])


    

    




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In [46]:

    

#play_type: pass
pass_edgeframe = edge_frame.copy(where = lambda row:row.play_type=='pass')
playtypes_graph.define_edge_type('pass','players', 'players', directed=True)
playtypes_graph.edges['pass'].add_edges(pass_edgeframe, 'the_doer', 'the_receiver',['play_type','play_evaluation','penalty_type','penalty_on','pass_location','min','sec','quarter','off','def','down','yards_to_go','yard_line','yards_done','injuries','score_type','off_score','def_score'])

#play_type: rush
rush_edgeframe = edge_frame.copy(where = lambda row:row.play_type=='rush')
playtypes_graph.define_edge_type('rush_by','players', 'players', directed=True)
playtypes_graph.edges['rush_by'].add_edges(rush_edgeframe, 'the_doer', 'the_receiver',['play_type','play_evaluation','penalty_type','penalty_on','pass_location','min','sec','quarter','off','def','down','yards_to_go','yard_line','yards_done','injuries','score_type','off_score','def_score'])

#play_type: punt
punt_edgeframe = edge_frame.copy(where = lambda row:row.play_type=='punt')
playtypes_graph.define_edge_type('punt','players', 'players', directed=True)
playtypes_graph.edges['punt'].add_edges(punt_edgeframe, 'the_doer', 'the_receiver',['play_type','play_evaluation','penalty_type','penalty_on','pass_location','min','sec','quarter','off','def','down','yards_to_go','yard_line','yards_done','injuries','score_type','off_score','def_score'])

#play_type: kickoff
kickoff_edgeframe = edge_frame.copy(where = lambda row:row.play_type=='kickoff')
playtypes_graph.define_edge_type('kickoff','players', 'players', directed=True)
playtypes_graph.edges['kickoff'].add_edges(kickoff_edgeframe, 'the_doer', 'the_receiver',['play_type','play_evaluation','penalty_type','penalty_on','pass_location','min','sec','quarter','off','def','down','yards_to_go','yard_line','yards_done','injuries','score_type','off_score','def_score'])

#play_type: extrapoint
extrapoint_edgeframe = edge_frame.copy(where = lambda row:row.play_type=='extrapoint')
playtypes_graph.define_edge_type('extrapoint','players', 'players', directed=True)
playtypes_graph.edges['extrapoint'].add_edges(extrapoint_edgeframe, 'the_doer', 'the_receiver',['play_type','play_evaluation','penalty_type','penalty_on','pass_location','min','sec','quarter','off','def','down','yards_to_go','yard_line','yards_done','injuries','score_type','off_score','def_score'])

#play_type: kneel
kneel_edgeframe = edge_frame.copy(where = lambda row:row.play_type=='kneel')
playtypes_graph.define_edge_type('kneel','players', 'players', directed=True)
playtypes_graph.edges['kneel'].add_edges(kneel_edgeframe, 'the_doer', 'the_receiver',['play_type','play_evaluation','penalty_type','penalty_on','pass_location','min','sec','quarter','off','def','down','yards_to_go','yard_line','yards_done','injuries','score_type','off_score','def_score'])

#play_type: noplay
noplay_edgeframe = edge_frame.copy(where = lambda row:row.play_type=='noplay')
playtypes_graph.define_edge_type('noplay','players', 'players', directed=True)
playtypes_graph.edges['noplay'].add_edges(noplay_edgeframe, 'the_doer', 'the_receiver',['play_type','play_evaluation','penalty_type','penalty_on','pass_location','min','sec','quarter','off','def','down','yards_to_go','yard_line','yards_done','injuries','score_type','off_score','def_score'])


    

    




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In [55]:

    

# Export Parquet graph to OrientDB
#=================================
playtypes_graph.export_to_orientdb("NFLgameDBeeee",True)


    

    




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Out[55]:





{u'db_uri': u'plocal:/home/wtaie/graphDBs_home/orientdb-community-2.1.12/databases/NFLgameDBeeee',
 u'exported_edges': {u'extrapoint': {u'exported_count': 10,
   u'failure_count': 0},
  u'kickoff': {u'exported_count': 14, u'failure_count': 0},
  u'kneel': {u'exported_count': 2, u'failure_count': 0},
  u'noplay': {u'exported_count': 9, u'failure_count': 0},
  u'pass': {u'exported_count': 114, u'failure_count': 0},
  u'punt': {u'exported_count': 17, u'failure_count': 0},
  u'rush_by': {u'exported_count': 51, u'failure_count': 0}},
 u'exported_vertices': {u'players': {u'exported_count': 104,
   u'failure_count': 0}}}

In [50]:

    

playtypes_graph.edges['pass'].inspect()


    

    
Out[50]:





[#]  _eid  _src_vid  _dest_vid  _label  play_type  play_evaluation  penalty_type
================================================================================
[0]   138        45        104  pass    pass       good
[1]   109        76         79  pass    pass       incomplete
[2]   121        76         79  pass    pass       complete
[3]   129        76         79  pass    pass       incomplete
[4]   171        76         79  pass    pass       incomplete
[5]   184        76         79  pass    pass       incomplete
[6]   192        76         79  pass    pass       k out of bounds
[7]   210        76         79  pass    pass       incomplete
[8]   147        76          4  pass    pass       sack
[9]   110        76         85  pass    pass       incomplete

[#]  penalty_on  pass_location  min  sec  quarter  off  def  down  yards_to_go
==============================================================================
[0]                              38    8        2  BAL  DEN     1            1
[1]              deep right      57    4        1  DEN  BAL     1           10
[2]              short right     49   33        1  DEN  BAL     2            9
[3]              short left      43   30        2  DEN  BAL     1           10
[4]              short left      21   46        3  DEN  BAL     1            2
[5]              short left      16   16        3  DEN  BAL     2            6
[6]  L.Vasquez   short right     14   27        4  DEN  BAL     2            9
[7]              short right      4   47        4  DEN  BAL     2            8
[8]              unknown         34    0        2  DEN  BAL     3           15
[9]              short left      56   24        1  DEN  BAL     3            3

[#]  yard_line  yards_done  injuries  score_type  off_score  def_score
======================================================================
[0]          1           1            touchdown           7          7
[1]         66           0                                0          0
[2]         44           5                                0          7
[3]         64           0                                0          7
[4]          2           0                               28         17
[5]         54           0                               35         17
[6]         55          27                               35         17
[7]         78           0                               42         27
[8]         54          -6                               14         14
[9]         59           0                                0          0

In [52]:

    

playtypes_graph.vertices['players']


    

    
Out[52]:





Frame  <unnamed>
row_count = 104
schema = [_vid:int64, _label:unicode, player_name:unicode, number:int32, age:int32, team:unicode, position:unicode]
status = ACTIVE  (last_read_date = 2016-08-04T10:23:10.489000-07:00)

In [ ]:

    




    

In [56]:

    

# Import graph from OrientDB to ATK in Parquet format
#===================================================
orientGraph = ta.Graph(ta.OrientDBGraph("NFLgameDBeeee"))


    

    




Done [=========================] 100.00% Time 00:00:08

In [57]:

    

orientGraph


    

    
Out[57]:





Graph <unnamed>
status = ACTIVE  (last_read_date = 2016-08-04T10:28:32.882000-07:00)
vertices = 
  players : [number, player_name, team, position, age], count = 104
edges = 
  kneel : [def, injuries, yards_done, down, off, score_type, penalty_on, sec, play_evaluation, play_type, pass_location, min, def_score, yard_line, penalty_type, yards_to_go, off_score, quarter], count = 2
  punt : [def, injuries, yards_done, down, off, score_type, penalty_on, sec, play_evaluation, play_type, pass_location, min, def_score, yard_line, penalty_type, yards_to_go, off_score, quarter], count = 17
  extrapoint : [def, injuries, yards_done, down, off, score_type, penalty_on, sec, play_evaluation, play_type, pass_location, min, def_score, yard_line, penalty_type, yards_to_go, off_score, quarter], count = 10
  kickoff : [def, injuries, yards_done, down, off, score_type, penalty_on, sec, play_evaluation, play_type, pass_location, min, def_score, yard_line, penalty_type, yards_to_go, off_score, quarter], count = 14
  pass : [def, injuries, yards_done, down, off, score_type, penalty_on, sec, play_evaluation, play_type, pass_location, min, def_score, yard_line, penalty_type, yards_to_go, off_score, quarter], count = 114
  noplay : [def, injuries, yards_done, down, off, score_type, penalty_on, sec, play_evaluation, play_type, pass_location, min, def_score, yard_line, penalty_type, yards_to_go, off_score, quarter], count = 9
  rush_by : [def, injuries, yards_done, down, off, score_type, penalty_on, sec, play_evaluation, play_type, pass_location, min, def_score, yard_line, penalty_type, yards_to_go, off_score, quarter], count = 51

In [26]:

    

ta.release()


    

    




Releasing cluster resources...
Done [=========================] 100.00% Time 00:00:01

In [27]:

    

# to open the OrientDB we just created, need to shutdown the rest-server
import pyorient as pyorient


    

In [28]:

    

client = pyorient.OrientDB("localhost", 2424)
session_id = client.connect( "root", "root" )


    

    




---------------------------------------------------------------------------
PyOrientConnectionException               Traceback (most recent call last)
<ipython-input-28-6e865c583b0a> in <module>()
      1 client = pyorient.OrientDB("localhost", 2424)
----> 2 session_id = client.connect( "root", "root" )

/usr/lib/python2.7/site-packages/pyorient/orient.pyc in connect(self, user, password, client_id, serialization_type)
    290 
    291         '''
--> 292         return self.get_message("ConnectMessage") \
    293             .prepare((user, password, client_id, serialization_type)).send().fetch_response()
    294 

/usr/lib/python2.7/site-packages/pyorient/orient.pyc in get_message(self, command)
    487                     token = self._auth_token
    488 
--> 489                 message_instance = _Message(self._connection)\
    490                     .set_session_token(token)
    491                 message_instance._push_callback = self._push_received

/usr/lib/python2.7/site-packages/pyorient/messages/connection.pyc in __init__(self, _orient_socket)
     15 
     16     def __init__(self, _orient_socket):
---> 17         super( ConnectMessage, self ).__init__(_orient_socket)
     18 
     19         self._user = ''

/usr/lib/python2.7/site-packages/pyorient/messages/base.pyc in __init__(self, sock)
     22         :type sock: OrientSocket
     23         """
---> 24         sock.get_connection()
     25         self._orientSocket = sock
     26         self._protocol = self._orientSocket.protocol

/usr/lib/python2.7/site-packages/pyorient/orient.pyc in get_connection(self)
     50     def get_connection(self):
     51         if not self.connected:
---> 52             self.connect()
     53 
     54         return self._socket

/usr/lib/python2.7/site-packages/pyorient/orient.pyc in connect(self)
     80         except socket.error as e:
     81             self.connected = False
---> 82             raise PyOrientConnectionException( "Socket Error: %s" % e, [] )
     83 
     84     def close(self):

PyOrientConnectionException: Socket Error: [Errno 111] Connection refused

In [ ]:

    

client.db_open( "NFLgameDB", "admin", "admin" )


    

In [ ]:

    

den_scores_time = client.query("SELECT min,sec,score_type,down,yard_line FROM play_details WHERE score_type in['touchdown','fieldgoal','extrapoint'] and off =='DEN' limit 220")
for time in den_scores_time:
    print("Denver-Broncos scored "+time.score_type +" in down # "+ str(time.down)+"  @  "+ str(time.min) +":"+ str(time.sec))


    

In [ ]:

    

bal_scores_time = client.query("SELECT min,sec,score_type,down FROM play_details WHERE score_type in['touchdown','fieldgoal','extrapoint'] and off =='BAL' limit 220")
for time in bal_scores_time:
    print("Baltimore-Ravens scored "+time.score_type +" in down # "+ str(time.down)+"  @  "+ str(time.min) +":"+ str(time.sec))


    

In [ ]:

    

client.db_close()