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

    【Schema】
    【DB_ID】 ice_hockey_draft
【Schema】
# Table: SeasonStatus
[
(ELITEID:INTEGER, Examples: [3667, 3668, 3669]),
(SEASON:TEXT, Examples: [1997-1998, 1999-2000, 1998-1999]),
(TEAM:TEXT, Examples: [Rimouski Oceanic, Canada U20, Plymouth Whalers]),
(LEAGUE:TEXT, Examples: [QMJHL, WJC-20, OHL]),
(GAMETYPE:TEXT, Examples: [Regular Season, Playoffs]),
(GP:INTEGER, Examples: [58, 18, 7]),
(G:INTEGER, Examples: [44, 15, 1]),
(A:INTEGER, Examples: [71, 26, 1]),
(P:INTEGER, Examples: [115, 41, 2]),
(PIM:INTEGER, Examples: [117, 46, 4]),
(PLUSMINUS:INTEGER, Examples: [27, 4, 0])
]
# Table: PlayerInfo
[
(ELITEID:INTEGER, Primary Key, Examples: [9, 18, 27]),
(PlayerName:TEXT, Examples: [David Bornhammar, David Printz, Yared Hagos]),
(birthdate:TEXT, Examples: [1981-06-15, 1980-07-24, 1983-03-27]),
(birthyear:DATE),
(birthmonth:INTEGER, Examples: [6, 7, 3]),
(birthday:INTEGER, Examples: [15, 24, 27]),
(birthplace:TEXT, Examples: [Lidingo, SWE, Stockholm, SWE, Ostersund, SWE]),
(nation:TEXT, Examples: [Sweden, Austria, Slovakia]),
(height:INTEGER, Examples: [73, 76, 72]),
(weight:INTEGER, Examples: [198, 220, 218]),
(position_info:TEXT, Examples: [D, C, LW/C]),
(shoots:TEXT, Examples: [L, R, -]),
(draftyear:INTEGER, Examples: [1999, 2001, 2002]),
(draftround:INTEGER, Examples: [7, 3, 5]),
(overall:INTEGER, Examples: [192, 225, 70]),
(overallby:TEXT, Examples: [Washington Capitals, Philadelphia Flyers, Dallas Stars]),
(CSS_rank:INTEGER, Examples: [192, 176, 24]),
(sum_7yr_GP:INTEGER, Examples: [0, 13, 6]),
(sum_7yr_TOI:INTEGER, Examples: [0, 84, 42]),
(GP_greater_than_0:TEXT, Examples: [no, yes])
]
# Table: weight_info
[
(weight_id:INTEGER, Primary Key, Examples: [154, 159, 161]),
(weight_in_kg:INTEGER, Examples: [70, 72, 73]),
(weight_in_lbs:INTEGER, Examples: [154, 159, 161])
]
# Table: height_info
[
(height_id:INTEGER, Primary Key, Examples: [65, 67, 68]),
(height_in_cm:INTEGER, Examples: [165, 170, 172]),
(height_in_inch:TEXT, Examples: [5'5", 5'7", 5'8"])
]
【Foreign keys】
SeasonStatus.ELITEID=PlayerInfo.ELITEID
PlayerInfo.height=height_info.height_id
PlayerInfo.weight=weight_info.weight_id

    【Question】
    Among all players drafted by the Toronto Maple Leafs, identify the percentage who are from Eastern Europe. players refers to PlayerName; drafted by the Toronto Maple Leafs refers to overallby = 'Toronto Maple Leafs'; percentage = MULTIPLY(DIVIDE(SUM(nation = 'Eastern Europe'), COUNT(ELITEID) WHERE overallby = 'Toronto Maple Leafs'), 100); from Eastern Europe refers to nation in ('Belarus', 'Bulgaria', 'Czech Republic', 'Hungary', 'Moldova', 'Poland', 'Romania', 'Slovakia', 'Ukraine'); countries in a continent can be identified by referring to https://worldpopulationreview.com/country-rankings/list-of-countries-by-continent;

    【Evidence】
    players refers to PlayerName; drafted by the Toronto Maple Leafs refers to overallby = 'Toronto Maple Leafs'; percentage = MULTIPLY(DIVIDE(SUM(nation = 'Eastern Europe'), COUNT(ELITEID) WHERE overallby = 'Toronto Maple Leafs'), 100); from Eastern Europe refers to nation in ('Belarus', 'Bulgaria', 'Czech Republic', 'Hungary', 'Moldova', 'Poland', 'Romania', 'Slovakia', 'Ukraine'); countries in a continent can be identified by referring to https://worldpopulationreview.com/country-rankings/list-of-countries-by-continent;

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

SQL: SELECT CAST(COUNT(CASE WHEN nation IN ('Belarus', 'Czech Rep.', 'Slovakia', 'Ukraine') THEN ELITEID ELSE NULL END) AS REAL) * 100 / COUNT(ELITEID) FROM PlayerInfo WHERE overallby = 'Toronto Maple Leafs'