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Add number of wins as black swiss tie-break
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‎docs/changelog.rst

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@@ -46,6 +46,39 @@ Output::
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**3. Add number of wins with black swiss tie-break**
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Tie breaks::
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TB1 = Buchholz cut 1
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TB2 = Buchholz
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TB3 = Sonneborn-Berger
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TB4 = Direct Encounter
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TB5 = Most number of wins
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TB6 = Most number of wins with black
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Output::
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Rank Name Rating RChg R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 Games Score Score% TB1 TB2 TB3 TB4 TB5 TB6
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1 Firouzja, Alireza 2770 11.31 93W1 35B1 7W1 4B= 6W= 8B= 33W1 31W1 2B0 9W1 3B= 11 8.0 72.73 0.0 0.0 0.00 0.0 0 0
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2 Caruana, Fabiano 2800 1.06 55W1 18B= 36W= 35B= 9W1 33B= 13W= 24B1 1W1 6B= 7W= 11 7.5 68.18 67.0 72.5 49.75 0.0 4 1
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3 Oparin, Grigoriy 2654 21.62 62W= 78B1 17W= 70B= 26W= 5B= 73W1 19B1 7W= 23B1 1W= 11 7.5 68.18 63.5 68.5 45.75 0.0 4 3
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4 Yu, Yangyi 2704 9.20 5W= 63B1 61W1 1W= 49B= 13B= 15W= 28B= 33W1 8B= 6W= 11 7.0 63.64 66.5 72.0 44.50 0.0 3 1
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5 Keymer, Vincent 2630 22.06 4B= 27W1 25B= 10W= 59B= 3W= 24B0 82W1 53B1 29W1 9B= 11 7.0 63.64 65.5 70.0 43.25 0.0 4 1
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6 Vachier-Lagrave, Maxime 2763 2.12 52B= 51W1 71B= 60W1 1B= 32W1 31B= 8W= 11B= 2W= 4B= 11 7.0 63.64 65.0 70.0 43.50 0.0 3 0
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7 Predke, Alexandr 2666 14.22 46B1 54W1 1B0 47W= 42B1 31W0 57B1 49W1 3B= 11W= 2B= 11 7.0 63.64 64.5 70.0 42.25 0.0 5 3
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8 Shirov, Alexei 2659 16.99 43B= 87W1 50B= 92W1 36B1 1W= 14B= 6B= 23W= 4W= 10B= 11 7.0 63.64 64.5 68.5 41.50 0.0 3 1
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9 Howell, David W L 2658 15.43 82B1 92W= 72B= 50W= 2B0 54W1 47B1 14W1 12W1 1B0 5W= 11 7.0 63.64 62.5 66.5 40.25 0.0 5 2
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10 Sargissian, Gabriel 2664 10.56 72W= 83B= 94W1 5B= 70W1 15B= 18W= 34B= 28W1 13B= 8W= 11 7.0 63.64 61.5 65.5 40.50 0.0 3 0
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11 Anton Guijarro, David 2658 13.25 37W= 70B= 62W1 17B0 98W1 91B1 19W= 21B1 6W= 7B= 13W= 11 7.0 63.64 61.0 65.0 39.25 0.0 4 2
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12 Korobov, Anton 2690 6.07 48B= 20W1 47B= 42W= 50B1 49W= 17B= 41W1 9B0 34W= 31B1 11 7.0 63.64 60.5 66.0 41.50 0.0 4 2
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13 Sevian, Samuel 2654 15.89 80B= 84W1 92B= 72W1 32B= 4W= 2B= 53W1 30B= 10W= 11B= 11 7.0 63.64 60.5 64.5 39.75 0.0 3 0
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14 Esipenko, Andrey 2720 3.08 58B= 42W= 81B= 63W1 71B= 56W1 8W= 9B0 64B1 31W1 15B= 11 7.0 63.64 60.0 64.5 40.00 0.0 4 1
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15 Deac, Bogdan-Daniel 2643 15.05 89B= 104W= 106B1 53W= 29B1 10W= 4B= 25W= 18B= 38B1 14W= 11 7.0 63.64 60.0 63.0 39.25 0.0 3 3
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16 Artemiev, Vladislav 2699 4.40 76B1 50W= 75B= 31W= 51B= 47W= 48B= 52W= 49B1 17W= 34B1 11 7.0 63.64 56.5 61.5 39.00 0.0 3 3
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...
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Version 0.9.0 [2022-06-25]
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""""""""""""""""""""""""""

‎src/pgnhelper/swiss.py

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@@ -211,7 +211,16 @@ def table(self) -> pd.DataFrame:
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tb_label.append('TB5')
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df_tb5 = df_tb5.reset_index(drop=True)
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df_final = df_tb5.copy()
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# 1.5 Apply most number of wins with black.
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df_tb6 = pgnhelper.tiebreak.num_wins(self.record, df_tb5, label='TB6', bwins=True)
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df_tb6 = df_tb6.sort_values(
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by=['Score', 'TB1', 'TB2', 'TB3', 'TB4', 'TB5', 'TB6'],
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ascending=[False, False, False, False, False, False, False]
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)
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tb_label.append('TB6')
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df_tb6 = df_tb6.reset_index(drop=True)
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df_final = df_tb6.copy()
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# 2. Build a swiss table dataframe.
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if self.israting:
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df_swiss[tb_label[2]] = df_tb3[tb_label[2]].round(2)
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df_swiss[tb_label[3]] = df_tb4[tb_label[3]].round(2)
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df_swiss[tb_label[4]] = df_tb5[tb_label[4]].round(2)
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df_swiss[tb_label[5]] = df_tb6[tb_label[5]].round(2)
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# 4. Insert rank column at first column.
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df_swiss.insert(loc=0, column='Rank', value=range(1, len(df_swiss) + 1))

‎src/pgnhelper/tiebreak.py

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@@ -27,19 +27,31 @@ def played_each_other(result_df, tie_df) -> bool:
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return True
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def num_wins(result_df: pd.DataFrame, ranking_df: pd.DataFrame, label='Wins') -> pd.DataFrame:
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def num_wins(result_df: pd.DataFrame, ranking_df: pd.DataFrame, label='Wins', bwins: bool=False) -> pd.DataFrame:
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"""Creates a dataframe with Win column.
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If a game has an armageddon tie-break, we will only count the number of wins
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based from the normal game only.
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Args:
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result_df: The result dataframe.
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ranking_df: Ranking of players based on score.
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label: The label or header of the resulting dataframe.
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bwins: If true then only count wins by black. If not count all wins.
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Returns:
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A dataframe of ranking with Wins column for tie-break.
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"""
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ret = ranking_df.copy()
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players = list(ret.Name)
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tb = {}
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for _, g in ret.groupby(['Score']):
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if len(g) > 1:
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for p in g.Name:
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df_w = result_df.loc[(result_df.White == p) & (result_df.Result == '1-0') & (result_df.Arm == 0)]
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if not bwins:
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df_w = result_df.loc[(result_df.White == p) & (result_df.Result == '1-0') & (result_df.Arm == 0)]
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else:
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df_w = pd.DataFrame()
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df_b = result_df.loc[(result_df.Black == p) & (result_df.Result == '0-1') & (result_df.Arm == 0)]
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num_wins = len(df_w) + len(df_b)
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tb.update({p: num_wins})

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