You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 soccer_2016
【Schema】
# Table: Ball_by_Ball
[
(Match_Id:INTEGER, Primary Key, Examples: [335987, 335988, 335989]),
(Over_Id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Ball_Id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Innings_No:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Team_Batting:INTEGER, Examples: [1, 2, 3]),
(Team_Bowling:INTEGER, Examples: [2, 1, 4]),
(Striker_Batting_Position:INTEGER, Examples: [1, 2, 3]),
(Striker:INTEGER, Examples: [1, 6, 2]),
(Non_Striker:INTEGER, Examples: [2, 7, 1]),
(Bowler:INTEGER, Examples: [14, 106, 15])
]
# Table: Umpire
[
(Umpire_Id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Umpire_Name:TEXT, Examples: [Asad Rauf, MR Benson, Aleem Dar]),
(Umpire_Country:INTEGER, Examples: [6, 10, 4])
]
# Table: Venue
[
(Venue_Id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Venue_Name:TEXT, Examples: [M Chinnaswamy Stadium]),
(City_Id:INTEGER, Examples: [1, 2, 3])
]
# Table: Team
[
(Team_Id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Team_Name:TEXT, Examples: [Kolkata Knight Riders])
]
# Table: Out_Type
[
(Out_Id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Out_Name:TEXT, Examples: [caught, bowled, run out])
]
# Table: Bowling_Style
[
(Bowling_Id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Bowling_skill:TEXT, Examples: [Right-arm medium])
]
# Table: Outcome
[
(Outcome_Id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Outcome_Type:TEXT, Examples: [Result, No Result, Superover])
]
# Table: Extra_Runs
[
(Match_Id:INTEGER, Primary Key, Examples: [335987, 335988, 335989]),
(Over_Id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Ball_Id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Extra_Type_Id:INTEGER, Examples: [1, 2, 3]),
(Extra_Runs:INTEGER, Examples: [1, 4, 5]),
(Innings_No:INTEGER, Primary Key, Examples: [1, 2, 3])
]
# Table: City
[
(City_Id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(City_Name:TEXT, Examples: [Bangalore, Chandigarh, Delhi]),
(Country_id:INTEGER, Examples: [1, 2, 3])
]
# Table: Season
[
(Season_Id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Man_of_the_Series:INTEGER, Examples: [32, 53, 133]),
(Orange_Cap:INTEGER, Examples: [100, 18, 133]),
(Purple_Cap:INTEGER, Examples: [102, 61, 131]),
(Season_Year:INTEGER, Examples: [2008, 2009, 2010])
]
# Table: Extra_Type
[
(Extra_Id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Extra_Name:TEXT, Examples: [legbyes, wides, byes])
]
# Table: Rolee
[
(Role_Id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Role_Desc:TEXT, Examples: [Captain, Keeper, Player])
]
# Table: Match
[
(Match_Id:INTEGER, Primary Key, Examples: [335987, 335988, 335989]),
(Team_1:INTEGER, Examples: [2, 4, 6]),
(Team_2:INTEGER, Examples: [1, 3, 5]),
(Match_Date:DATE, Examples: [2008-04-18]),
(Season_Id:INTEGER, Examples: [1, 2, 3]),
(Venue_Id:INTEGER, Examples: [1, 2, 3]),
(Toss_Winner:INTEGER, Examples: [2, 3, 5]),
(Toss_Decide:INTEGER, Examples: [1, 2]),
(Win_Type:INTEGER, Examples: [1, 2, 4]),
(Win_Margin:INTEGER, Examples: [140, 33, 9]),
(Outcome_type:INTEGER, Examples: [1, 3, 2]),
(Match_Winner:INTEGER, Examples: [1, 3, 6]),
(Man_of_the_Match:INTEGER, Examples: [2, 19, 90])
]
# Table: Player_Match
[
(Match_Id:INTEGER, Primary Key, Examples: [335987, 335988, 335989]),
(Player_Id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Role_Id:INTEGER, Primary Key, Examples: [1, 3, 2]),
(Team_Id:INTEGER, Examples: [1, 2, 3])
]
# Table: Toss_Decision
[
(Toss_Id:INTEGER, Primary Key, Examples: [1, 2]),
(Toss_Name:TEXT, Examples: [field, bat])
]
# Table: Country
[
(Country_Id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Country_Name:TEXT, Examples: [India, South Africa, U.A.E])
]
# Table: Batsman_Scored
[
(Match_Id:INTEGER, Primary Key, Examples: [335987, 335988, 335989]),
(Over_Id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Ball_Id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Runs_Scored:INTEGER, Examples: [0, 1, 4]),
(Innings_No:INTEGER, Primary Key, Examples: [1, 2, 3])
]
# Table: Player
[
(Player_Id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Player_Name:TEXT, Examples: [SC Ganguly, BB McCullum, RT Ponting]),
(DOB:DATE, Examples: [1972-07-08]),
(Batting_hand:INTEGER, Examples: [1, 2]),
(Bowling_skill:INTEGER, Examples: [1, 2, 3]),
(Country_Name:INTEGER, Examples: [1, 4, 5])
]
# Table: Win_By
[
(Win_Id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Win_Type:TEXT, Examples: [runs, wickets, NO Result])
]
# Table: Wicket_Taken
[
(Match_Id:INTEGER, Primary Key, Examples: [335987, 335988, 335989]),
(Over_Id:INTEGER, Primary Key, Examples: [2, 3, 5]),
(Ball_Id:INTEGER, Primary Key, Examples: [1, 2, 5]),
(Player_Out:INTEGER, Examples: [6, 8, 9]),
(Kind_Out:INTEGER, Examples: [2, 1, 3]),
(Fielders:INTEGER, Examples: [83, 9, 3]),
(Innings_No:INTEGER, Primary Key, Examples: [2, 1, 3])
]
# Table: Batting_Style
[
(Batting_Id:INTEGER, Primary Key, Examples: [1, 2]),
(Batting_hand:TEXT, Examples: [Left-hand bat, Right-hand bat])
]
【Foreign keys】
Ball_by_Ball.Match_Id=Match.Match_Id
Umpire.Umpire_Country=Country.Country_Id
Venue.City_Id=City.City_Id
Extra_Runs.Extra_Type_Id=Extra_Type.Extra_Id
Match.Team_1=Team.Team_Id
Match.Team_2=Team.Team_Id
Match.Season_Id=Season.Season_Id
Match.Venue_Id=Venue.Venue_Id
Match.Toss_Winner=Team.Team_Id
Match.Toss_Decide=Toss_Decision.Toss_Id
Match.Win_Type=Win_By.Win_Id
Match.Outcome_type=Out_Type.Out_Id
Match.Match_Winner=Team.Team_Id
Match.Man_of_the_Match=Player.Player_Id
Player_Match.Match_Id=Match.Match_Id
Player_Match.Player_Id=Player.Player_Id
Player_Match.Team_Id=Team.Team_Id
Player_Match.Role_Id=Rolee.Role_Id
Country.Country_Id=Country.Country_Id
Batsman_Scored.Match_Id=Match.Match_Id
Player.Batting_hand=Batting_Style.Batting_Id
Player.Bowling_skill=Bowling_Style.Bowling_Id
Player.Country_Name=Country.Country_Id
Wicket_Taken.Match_Id=Match.Match_Id
Wicket_Taken.Player_Out=Player.Player_Id
Wicket_Taken.Kind_Out=Out_Type.Out_Id
Wicket_Taken.Fielders=Player.Player_Id

    【Question】
    How many players were born in 1970? born in 1970 refers to DOB like '1970%';

    【Evidence】
    born in 1970 refers to DOB like '1970%';

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT COUNT(Player_Name) FROM Player WHERE DOB LIKE '1970%'