[main]: add 27/07

data.csv

@@ -1,28 +1,30 @@
-Date,Absolute Position,Relative Position,Number of Players,Number of Teams,Points on Scattergories,Ciaran,Jay,Sam,Drew,Theo,Tom,Ellora,Chloe,Jamie,Christine,Mide,Ellie
+Date,Absolute Position,Number of Players,Number of Teams,Points on Scattergories,Ciaran,Jay,Sam,Drew,Theo,Tom,Ellora,Chloe,Jamie,Christine,Mide,Ellie,Elliot,Kira
-17/03/2025,9,0.692,2,13,10,1,1,0,0,0,0,0,0,0,0,0,0
+17/03/2025,9,2,13,10,1,1,0,0,0,0,0,0,0,0,0,0,0,0
-24/03/2025,14,0.933,2,15,4,1,1,0,0,0,0,0,0,0,0,0,0
+24/03/2025,14,2,15,4,1,1,0,0,0,0,0,0,0,0,0,0,0,0
-31/03/2025,8,0.444,4,18,7,1,1,1,1,0,0,0,0,0,0,0,0
+31/03/2025,8,4,18,7,1,1,1,1,0,0,0,0,0,0,0,0,0,0
-07/04/2025,9,0.563,2,16,6,1,1,0,0,0,0,0,0,0,0,0,0
+07/04/2025,9,2,16,6,1,1,0,0,0,0,0,0,0,0,0,0,0,0
-28/04/2025,9,0.529,3,17,5,1,1,1,0,0,0,0,0,0,0,0,0
+28/04/2025,9,3,17,5,1,1,1,0,0,0,0,0,0,0,0,0,0,0
-26/05/2025,6,0.5,2,12,8,0,1,0,0,1,0,0,0,0,0,0,0
+26/05/2025,6,2,12,8,0,1,0,0,1,0,0,0,0,0,0,0,0,0
-02/06/2025,12,0.857,3,14,4,1,1,1,0,0,0,0,0,0,0,0,0
+02/06/2025,12,3,14,4,1,1,1,0,0,0,0,0,0,0,0,0,0,0
-16/06/2025,6,0.545,3,11,6,1,1,1,0,0,0,0,0,0,0,0,0
+16/06/2025,6,3,11,6,1,1,1,0,0,0,0,0,0,0,0,0,0,0
-30/06/2025,5,0.833,4,6,8,1,1,1,0,0,1,0,0,0,0,0,0
+30/06/2025,5,4,6,8,1,1,1,0,0,1,0,0,0,0,0,0,0,0
-14/07/2025,5,0.625,3,8,8,1,1,0,0,0,0,1,0,0,0,0,0
+14/07/2025,5,3,8,8,1,1,0,0,0,0,1,0,0,0,0,0,0,0
-21/07/2025,4,0.5,2,8,9,1,1,0,0,0,0,0,0,0,0,0,0
+21/07/2025,4,2,8,9,1,1,0,0,0,0,0,0,0,0,0,0,0,0
-28/07/2025,6,0.375,4,16,4,1,1,1,0,0,0,1,0,0,0,0,0
+28/07/2025,6,4,16,4,1,1,1,0,0,0,1,0,0,0,0,0,0,0
-04/08/2025,4,0.4,3,10,11,1,1,0,0,0,0,1,0,0,0,0,0
+04/08/2025,4,3,10,11,1,1,0,0,0,0,1,0,0,0,0,0,0,0
-15/09/2025,4,0.333,4,12,5,1,1,1,0,0,0,1,0,0,0,0,0
+15/09/2025,4,4,12,5,1,1,1,0,0,0,1,0,0,0,0,0,0,0
-22/09/2025,5,0.417,3,12,5,1,0,1,0,0,1,0,0,0,0,0,0
+22/09/2025,5,3,12,5,1,0,1,0,0,1,0,0,0,0,0,0,0,0
-29/09/2025,8,0.727,2,11,5,0,1,0,1,0,0,0,0,0,0,0,0
+29/09/2025,8,2,11,5,0,1,0,1,0,0,0,0,0,0,0,0,0,0
-24/11/2025,8,0.889,3,9,6,1,1,0,0,0,0,1,0,0,0,0,0
+24/11/2025,8,3,9,6,1,1,0,0,0,0,1,0,0,0,0,0,0,0
-05/01/2026,4,0.5,4,8,6,1,1,0,0,0,0,0,1,1,0,0,0
+05/01/2026,4,4,8,6,1,1,0,0,0,0,0,1,1,0,0,0,0,0
-26/01/2026,7,0.583,2,12,10,1,1,0,0,0,0,0,0,0,0,0,0
+26/01/2026,7,2,12,10,1,1,0,0,0,0,0,0,0,0,0,0,0,0
-02/02/2026,7,0.7,2,10,5,1,1,0,0,0,0,0,0,0,0,0,0
+02/02/2026,7,2,10,5,1,1,0,0,0,0,0,0,0,0,0,0,0,0
-16/02/2026,9,0.5,3,18,6,0,1,0,1,0,0,0,0,0,1,0,0
+16/02/2026,9,3,18,6,0,1,0,1,0,0,0,0,0,1,0,0,0,0
-23/02/2026,6,0.6,2,10,10,1,1,0,0,0,0,0,0,0,0,0,0
+23/02/2026,6,2,10,10,1,1,0,0,0,0,0,0,0,0,0,0,0,0
-09/03/2026,6,1,5,6,10,1,1,1,0,0,0,0,1,0,0,1,0
+09/03/2026,6,5,6,10,1,1,1,0,0,0,0,1,0,0,1,0,0,0
-30/03/2026,2,0.133,4,15,12,1,1,1,1,0,0,0,0,0,0,0,0
+30/03/2026,2,4,15,12,1,1,1,1,0,0,0,0,0,0,0,0,0,0
-27/04/2026,2,0.222,2,9,7,1,1,0,0,0,0,0,0,0,0,0,0
+27/04/2026,2,2,9,7,1,1,0,0,0,0,0,0,0,0,0,0,0,0
-11/05/2026,7,0.777,4,9,6,1,1,0,0,0,0,0,1,0,0,0,1
+11/05/2026,7,4,9,6,1,1,0,0,0,0,0,1,0,0,0,1,0,0
-18/05/2026,8,0.666,3,12,6,1,1,1,0,0,0,0,0,0,0,0,0
+18/05/2026,8,3,12,6,1,1,1,0,0,0,0,0,0,0,0,0,0,0
+08/06/2026,8,5,11,4,1,1,1,0,0,0,0,0,0,0,0,0,1,1
+27/07/2026,12,3,13,5,1,1,1,0,0,0,0,0,0,0,0,0,0,0

src/app.py

@@ -22,6 +22,9 @@ app = Flask(__name__)
 def get_data_frame(filename):
     df = pd.read_csv(filename)
     df["Date"] = pd.to_datetime(df["Date"], dayfirst=True)
+    # Relative Position is derived, not entered manually, so it can never
+    # drift out of sync with Absolute Position / Number of Teams.
+    df["Relative Position"] = df["Absolute Position"] / df["Number of Teams"]
     df = df.sort_values("Date").reset_index(drop=True)
     return df
 
@@ -50,7 +53,7 @@ def generate_position_trend(df):
     df["Relative Percentile"] = df["Relative Position"] * 100
     df["Rolling Avg (5)"] = df["Relative Percentile"].rolling(
         5, min_periods=1).mean()
-    df["Attendees"] = build_hovertext(df, constants.PLAYER_NAME_COLUMNS)
+    df["Attendees"] = build_hovertext(df, constants.get_player_columns(df))
 
     X = sm.add_constant(df["Date_ordinal"])
     model = sm.OLS(df["Relative Percentile"], X).fit()
@@ -146,7 +149,7 @@ def generate_player_impact(df):
     overall_percentile = df["Relative Position"].mean() * 100
 
     rows = []
-    for name in constants.PLAYER_NAME_COLUMNS:
+    for name in constants.get_player_columns(df):
         if name not in df.columns:
             continue
         attended = df[df[name] == 1]
@@ -245,7 +248,7 @@ def generate_scattergories_chart(df):
 
 def generate_player_participation(df):
     """Heatmap of which player attended which game."""
-    player_cols = [c for c in constants.PLAYER_NAME_COLUMNS if c in df.columns]
+    player_cols = [c for c in constants.get_player_columns(df) if c in df.columns]
     df_players = df[player_cols]
     fig = px.imshow(
         df_players.T,

src/constants.py

@@ -8,39 +8,27 @@ def ordinal(n):
     return f"{n}{suffix}"
 
 
-PLAYER_NAME_COLUMNS = [
+# Columns that describe the quiz night itself rather than an individual
-    "Ciaran",
+# player's attendance. Any dataframe column not in this set is treated as a
-    "Jay",
+# player name, so new players are picked up automatically from data.csv
-    "Sam",
+# without needing a code change.
-    "Drew",
+NON_PLAYER_COLUMNS = {
-    "Theo",
+    "Date",
-    "Tom",
+    "Absolute Position",
-    "Ellora",
+    "Relative Position",
-    "Chloe",
-    "Jamie",
-    "Christine",
-    "Mide",
-    "Ellie",
-]
-
-FEATURE_COLUMNS = {
     "Number of Players",
     "Number of Teams",
     "Points on Scattergories",
-    "Ciaran",
-    "Jay",
-    "Sam",
-    "Drew",
-    "Theo",
-    "Tom",
-    "Ellora",
-    "Chloe",
-    "Jamie",
-    "Christine",
-    "Mide",
-    "Ellie",
 }
 
+
+def get_player_columns(df):
+    """Return the list of player-name columns present in df, in CSV order."""
+    return [c for c in df.columns if c not in NON_PLAYER_COLUMNS]
+
+
+FEATURE_COLUMNS = NON_PLAYER_COLUMNS - {"Date", "Relative Position"}
+
 ATTENDANCE_COLORSCHEME = [
     [0, "#E4ECF6"], [1, "#1e3a8a"]
 ]

src/player_table.py

@@ -1,11 +1,12 @@
-from constants import PLAYER_NAME_COLUMNS, ordinal
+import constants
+from constants import ordinal
 
 
 def generate_player_table(df):
     header = [["Player", "Appearances", "Avg. Relative Percentile", "Spent"]]
 
     player_stats = []
-    for name in PLAYER_NAME_COLUMNS:
+    for name in constants.get_player_columns(df):
         if name in df.columns:
             attended = df[df[name] == 1]
             n = len(attended)

src/stats.py

@@ -14,7 +14,7 @@ def _max_team_streak(dates):
 
 
 def _max_player_streak(df):
-    names = [col for col in df.columns if col in set(constants.PLAYER_NAME_COLUMNS)]
+    names = constants.get_player_columns(df)
     max_streak, max_name = 1, names[0]
     for name in names:
         local_max = current = 0