00:00:00

Description

Editorial

Ranking Shift After Point Updates

MEDIUM20 pts

A sports analytics platform tracks team rating points and refreshes world standings after each competition window.

Table: TeamPoints

team_id: unique integer identifier for each national team
name: unique team name (country label)
points: current score used for global ranking

Table: PointsChange

team_id: unique integer team identifier
points_change: signed change applied to that team's points

Ranking policy:

Teams are ranked by points in descending order.
If two teams have the same points, the team with lexicographically smaller name ranks higher.

Task:

Compute each team's rank before updates.
Apply points_change to compute updated points.
Compute each team's rank after updates.
Return rank_diff = old_rank - new_rank for each team.

Output requirements:

Return columns: team_id, name, rank_diff
Return one row per team appearing in TeamPoints
Result can be returned in any order

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

TeamPoints:
| team_id | name     | points |
|---------|----------|--------|
| 1       | atlas    | 2110   |
| 2       | bora     | 1780   |
| 3       | celta    | 1770   |
| 4       | delta    | 1400   |

PointsChange:
| team_id | points_change |
|---------|---------------|
| 1       | -25           |
| 2       | -10           |
| 3       | 5             |
| 4       | 450           |

Output

[
  {"team_id":1,"name":"atlas","rank_diff":0},
  {"team_id":2,"name":"bora","rank_diff":-2},
  {"team_id":3,"name":"celta","rank_diff":0},
  {"team_id":4,"name":"delta","rank_diff":2}
]

Explanation

Delta climbs from rank 4 to rank 2 after a large gain, while Bora drops from rank 2 to rank 4 after losing points.

Example

Input

TeamPoints:
| team_id | name   | points |
|---------|--------|--------|
| 101     | zara   | 95     |
| 102     | alex   | 90     |
| 103     | bella  | 90     |

PointsChange:
| team_id | points_change |
|---------|---------------|
| 101     | -5            |
| 102     | 0             |
| 103     | 0             |

Output

[
  {"team_id":101,"name":"zara","rank_diff":-2},
  {"team_id":102,"name":"alex","rank_diff":1},
  {"team_id":103,"name":"bella","rank_diff":1}
]

Explanation

All teams end at 90 points. Lexicographic order (alex < bella < zara) decides final ranking.

Example

Input

TeamPoints: []
PointsChange: []

Output

[]

Explanation

No teams means no ranking-difference rows.

Accepted0/0·0% Acceptance

Constraints

0 <= number of rows in TeamPoints <= 700
number of rows in PointsChange equals number of rows in TeamPoints
1 <= team_id <= 1000000000
team_id is unique in TeamPoints and in PointsChange
name is unique in TeamPoints
-100000 <= points <= 100000
-20000 <= points_change <= 20000
Every team_id in TeamPoints appears exactly once in PointsChange
Rank uses ordering: points DESC, name ASC
Return columns team_id, name, rank_diff
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with columns team_id, name, rank_diff

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

TeamPoints =

[{"team_id":3,"name":"algeron","points":1431},{"team_id":1,"name":"senica","points":2132},{"team_id":2,"name":"novaland","points":1402},{"team_id":4,"name":"croven","points":1817}]

PointsChange =

[{"team_id":3,"points_change":399},{"team_id":1,"points_change":-22},{"team_id":2,"points_change":0},{"team_id":4,"points_change":13}]

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101

00:00:00

Description

Editorial

Ranking Shift After Point Updates

MEDIUM20 pts

A sports analytics platform tracks team rating points and refreshes world standings after each competition window.

Table: TeamPoints

team_id: unique integer identifier for each national team
name: unique team name (country label)
points: current score used for global ranking

Table: PointsChange

team_id: unique integer team identifier
points_change: signed change applied to that team's points

Ranking policy:

Teams are ranked by points in descending order.
If two teams have the same points, the team with lexicographically smaller name ranks higher.

Task:

Compute each team's rank before updates.
Apply points_change to compute updated points.
Compute each team's rank after updates.
Return rank_diff = old_rank - new_rank for each team.

Output requirements:

Return columns: team_id, name, rank_diff
Return one row per team appearing in TeamPoints
Result can be returned in any order

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

TeamPoints:
| team_id | name     | points |
|---------|----------|--------|
| 1       | atlas    | 2110   |
| 2       | bora     | 1780   |
| 3       | celta    | 1770   |
| 4       | delta    | 1400   |

PointsChange:
| team_id | points_change |
|---------|---------------|
| 1       | -25           |
| 2       | -10           |
| 3       | 5             |
| 4       | 450           |

Output

[
  {"team_id":1,"name":"atlas","rank_diff":0},
  {"team_id":2,"name":"bora","rank_diff":-2},
  {"team_id":3,"name":"celta","rank_diff":0},
  {"team_id":4,"name":"delta","rank_diff":2}
]

Explanation

Delta climbs from rank 4 to rank 2 after a large gain, while Bora drops from rank 2 to rank 4 after losing points.

Example

Input

TeamPoints:
| team_id | name   | points |
|---------|--------|--------|
| 101     | zara   | 95     |
| 102     | alex   | 90     |
| 103     | bella  | 90     |

PointsChange:
| team_id | points_change |
|---------|---------------|
| 101     | -5            |
| 102     | 0             |
| 103     | 0             |

Output

[
  {"team_id":101,"name":"zara","rank_diff":-2},
  {"team_id":102,"name":"alex","rank_diff":1},
  {"team_id":103,"name":"bella","rank_diff":1}
]

Explanation

All teams end at 90 points. Lexicographic order (alex < bella < zara) decides final ranking.

Example

Input

TeamPoints: []
PointsChange: []

Output

[]

Explanation

No teams means no ranking-difference rows.

Accepted0/0·0% Acceptance

Constraints

0 <= number of rows in TeamPoints <= 700
number of rows in PointsChange equals number of rows in TeamPoints
1 <= team_id <= 1000000000
team_id is unique in TeamPoints and in PointsChange
name is unique in TeamPoints
-100000 <= points <= 100000
-20000 <= points_change <= 20000
Every team_id in TeamPoints appears exactly once in PointsChange
Rank uses ordering: points DESC, name ASC
Return columns team_id, name, rank_diff
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with columns team_id, name, rank_diff

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

TeamPoints =

[{"team_id":3,"name":"algeron","points":1431},{"team_id":1,"name":"senica","points":2132},{"team_id":2,"name":"novaland","points":1402},{"team_id":4,"name":"croven","points":1817}]

PointsChange =

[{"team_id":3,"points_change":399},{"team_id":1,"points_change":-22},{"team_id":2,"points_change":0},{"team_id":4,"points_change":13}]

Ranking Shift After Point Updates

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Ranking Shift After Point Updates

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