Add approximate value calculation

src/config.py

@@ -1,2 +1,3 @@
 LEAGUE_NAME = "LOFG"
 DEFAULT_ICON = "🏉"
+LEAGUE_NUMBER_TEAMS = 12

src/pages/1_Keepers.py

@@ -2,7 +2,7 @@ import os
 import pandas as pd
 import streamlit as st
 
-from config import DEFAULT_ICON, LEAGUE_NAME
+from config import DEFAULT_ICON, LEAGUE_NAME, LEAGUE_NUMBER_TEAMS
 from streamlit_filter import filter_dataframe
 
 
@@ -21,6 +21,11 @@ def load_adp() -> pd.DataFrame:
     return df
 
 
+def add_opinionated_keeper_value(df: pd.DataFrame):
+    for score_fmt in ["1qb", "2qb"]:
+        df[f"value_{score_fmt}"] = (df["keeper_cost"] - 0.5) * LEAGUE_NUMBER_TEAMS - df[f"ecr_{score_fmt}"]
+
+
 @st.cache_data
 def load_data():
     data = pd.read_csv(os.path.join(os.path.dirname(__file__), KEEPER_DATA_URL), index_col=0)
@@ -36,7 +41,7 @@ def load_data():
     # Merge ADP
     df_adp = load_adp()
     data = data.merge(df_adp, how="left", left_on="fantasypros_id", right_on="fp_id", suffixes=("", "_adp"))
-
+    add_opinionated_keeper_value(data)
     return data, teams_list
 
 
@@ -54,7 +59,9 @@ def filtered_keeper_dataframe(data: pd.DataFrame, teams_list: list[str]):
         "years_eligible",
         "eligible",
         "ecr_1qb",
+        "value_1qb",
         "ecr_2qb",
+        "value_2qb",
         "ecr_pos",
     ]