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🥇_leaderboards.py
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🥇_leaderboards.py
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import streamlit as st
import pandas as pd
import plotly.express as px
from PIL import Image
from supabase import create_client
favicon = Image.open("assets/abstract_square_300dpi.png")
st.set_page_config(
layout="wide",
initial_sidebar_state="expanded",
page_icon=favicon,
page_title="abstract",
)
st.sidebar.image(Image.open("assets/abstract_05x.png"))
# Initialize connection.
# Uses st.experimental_singleton to only run once.
@st.experimental_singleton
def init_connection():
url = st.secrets["supabase_url"]
key = st.secrets["supabase_key"]
return create_client(url, key)
supabase = init_connection()
# # Perform query.
# @st.cache(allow_output_mutation=True)
# def run_query(table):
# rows = supabase.table(table).execute()
# res = pd.DataFrame(rows.data)
# st.write(len(res))
# return res
@st.cache(ttl=60 * 5)
def load_all_stats(season, variant):
""" """
table = "d1_" + variant + "_" + str(season)
rows = supabase.table(table).select("*").execute()
res = pd.DataFrame(rows.data)
return res
def load_season_stats(season, variant, school, minimum, class_year):
df = load_all_stats(season, variant)
if school != "all":
df = df.loc[df.school == school]
# if position != "all":
# df = df.loc[df.position == position]
if minimum > 0:
if variant == "batting":
df = df.loc[df.PA >= minimum]
else:
df = df.loc[df.IP >= minimum]
if class_year != "all":
if "other" == class_year:
class_year == "N/A"
df = df.loc[df.Yr == class_year]
if variant == "batting":
df = df[
[
"name",
"Yr",
"school",
"PA",
"H",
"2B",
"3B",
"HR",
"RBI",
"R",
"BB",
"IBB",
"HBP",
"SF",
"SH",
"K",
"GP",
"AB",
"1B",
"wOBA",
"OPS",
"OBP",
"SLG",
"BA",
"ISO",
"BABIP",
"HR/PA",
"K/PA",
"BB/PA",
"wRAA",
"wRC",
]
]
else:
df = df[
[
"name",
"Yr",
"school",
"BF",
"IP",
"FIP",
"ERA",
"WHIP",
"SO",
"BB",
"H",
"HR-A",
"ER",
"R",
"OPS-against",
"OBP-against",
"SLG-against",
"BA-against",
"K/PA",
"K/9",
"BB/PA",
"BB/9",
"HR-A/PA",
"BABIP-against",
"Pitches/PA",
"IP/App",
"App",
"HB",
"GO",
"FO",
"W",
"L",
"SV",
]
]
return df
# IMPORTANT: Cache the conversion to prevent computation on every rerun
@st.cache(persist=True)
def convert_df(df):
return df.to_csv(index=False).encode("utf-8")
def load_school_options():
rows = supabase.table("schools").select("*").eq("division", 1).execute()
df = pd.DataFrame(rows.data)
options = list(df.ncaa_name)
options.insert(0, "all")
return options
def create_dist(df, metric, season, minimum, min_type):
fig = px.histogram(
df,
x=metric,
marginal="box",
hover_name="name",
template="seaborn",
color_discrete_sequence=['indianred']
)
fig.update_layout(
title=str(season)
+ " "
+ metric
+ " distribution, min "
+ str(minimum)
+ " "
+ min_type,
title_yanchor="top",
title_x=0.5,
xaxis_title=metric,
yaxis_title="# of players",
)
return fig
col1, col2, col3, col4, col5, col6 = st.columns(6)
season = col1.selectbox(
"Season", options=reversed(range(2013, 2023)), index=0, key="season_select"
)
variant = col5.selectbox(
"Stats Type", options=["batting", "pitching"], index=0, key="variant_select"
)
class_year = col4.selectbox(
"Class Year",
options=["all", "Fr", "So", "Jr", "Sr", "other"],
index=0,
key="class_year_select",
)
if variant == "batting":
position = col3.selectbox(
"Position",
options=["all", "INF", "OF", "C", "P", "DH"],
index=0,
key="position_select",
)
minimum = col6.number_input("Min. PA", min_value=0, value=100, step=1)
else:
position = col3.selectbox(
"Position", options=["all", "P", "notP"], index=0, key="position_select"
)
minimum = col6.number_input("Min. IP", min_value=0, value=10, step=1)
school = col2.selectbox(
"School", options=load_school_options(), index=0, key="school_select"
)
stats = load_season_stats(season, variant, school, minimum, class_year)
if variant == "batting":
counting_stats = stats[
[
"name",
"Yr",
"school",
"PA",
"H",
"2B",
"3B",
"HR",
"K",
"BB",
"R",
"RBI",
"IBB",
"SF",
"SH",
"HBP",
]
].sort_values(by="H", ascending=False)
counting_slice = [
"PA",
"H",
"2B",
"3B",
"HR",
"BB",
"K",
"IBB",
"HBP",
"RBI",
"R",
"SF",
"SH",
]
rate_stats = stats[
[
"name",
"Yr",
"school",
"PA",
"wOBA",
"wRC",
"wRAA",
"OPS",
"OBP",
"SLG",
"BA",
"ISO",
"BABIP",
"K/PA",
"BB/PA",
"HR/PA",
]
].sort_values(by="wOBA", ascending=False)
rate_slice = [
"PA",
"wOBA",
"wRC",
"wRAA",
"OPS",
"OBP",
"SLG",
"BA",
"BABIP",
"ISO",
"K/PA",
"BB/PA",
"HR/PA",
]
col1, col2, col3 = st.columns([3, 2, 10])
col1.markdown("## Counting Stats")
counting_stats_csv = convert_df(counting_stats)
col2.markdown("")
col2.download_button(
label="download as csv",
data=counting_stats_csv,
file_name=str(season)
+ "_"
+ variant
+ "_counting_stat_leaders_batting"
+ "_"
+ str(position)
+ "_"
+ str(school)
+ "_minPA_"
+ str(minimum)
+ ".csv",
mime="text/csv",
)
col3.write("")
st.dataframe(
counting_stats.style.highlight_max(
axis=0,
props="color:white; font-weight:bold; background-color:#aa3a3d;",
subset=counting_slice,
)
)
col1, col2, col3 = st.columns([3, 2, 10])
col1.markdown("## Rate Stats")
rate_stats_csv = convert_df(rate_stats)
col2.markdown("")
col2.download_button(
label="download as csv",
data=rate_stats_csv,
file_name=str(season)
+ "_"
+ variant
+ "_rate_stat_leaders_batting"
+ "_"
+ str(position)
+ "_"
+ str(school)
+ "_minPA_"
+ str(minimum)
+ ".csv",
mime="text/csv",
)
col3.write('')
st.dataframe(
rate_stats.style.format(
{
"PA": "{:.0f}",
"wOBA": "{:.3f}",
"wRC": "{:.1f}",
"wRAA": "{:.1f}",
"OPS": "{:.3f}",
"OBP": "{:.3f}",
"SLG": "{:.3f}",
"BA": "{:.3f}",
"BABIP": "{:.3f}",
"ISO": "{:.3f}",
"K/PA": "{:.3f}",
"BB/PA": "{:.3f}",
"HR/PA": "{:.3f}",
},
na_rep="",
subset=rate_slice,
).highlight_max(
axis=0,
props="color:white; font-weight:bold; background-color:#aa3a3d;",
subset=rate_slice,
),
width=1000,
)
metric = st.selectbox(
"select metric",
options=[
"PA",
"H",
"2B",
"3B",
"HR",
"BB",
"K",
"IBB",
"HBP",
"RBI",
"R",
"SF",
"SH",
"wOBA",
"wRC",
"wRAA",
"OPS",
"OBP",
"SLG",
"BA",
"BABIP",
"ISO",
"K/PA",
"BB/PA",
"HR/PA",
],
index=13,
key="metric_select",
)
st.plotly_chart(
create_dist(stats, metric, season, minimum, "PA"), use_container_width=True
)
else:
rate_stats = stats[
[
"name",
"Yr",
"school",
"IP",
"BF",
"ERA",
"FIP",
"WHIP",
"K/PA",
"BB/PA",
"OPS-against",
"OBP-against",
"SLG-against",
"BA-against",
"BABIP-against",
"Pitches/PA",
"HR-A/PA",
"IP/App",
]
]
rate_slice = [
"IP",
"BF",
"ERA",
"FIP",
"WHIP",
"K/PA",
"BB/PA",
"OPS-against",
"OBP-against",
"SLG-against",
"BA-against",
"BABIP-against",
"Pitches/PA",
"HR-A/PA",
"IP/App",
]
counting_stats = stats[
[
"name",
"Yr",
"school",
"IP",
"BF",
"App",
"H",
"SO",
"BB",
"ER",
"R",
"HR-A",
"HB",
"GO",
"FO",
"W",
"L",
"SV",
]
]
counting_slice = [
"IP",
"BF",
"App",
"H",
"SO",
"BB",
"ER",
"R",
"HR-A",
"HB",
"GO",
"FO",
"W",
"L",
"SV",
]
col1, col2, col3 = st.columns([3, 2, 10])
col1.markdown("## Counting Stats")
counting_stats_csv = convert_df(counting_stats)
col2.markdown("")
col2.download_button(
label="download as csv",
data=counting_stats_csv,
file_name=str(season)
+ "_"
+ variant
+ "_counting_stat_leaders_pitching"
+ "_"
+ str(position)
+ "_"
+ str(school)
+ "_minIP_"
+ str(minimum)
+ ".csv",
mime="text/csv",
)
col3.write('')
st.dataframe(
counting_stats.style.highlight_max(
axis=0,
props="color:white; font-weight:bold; background-color:#aa3a3d;",
subset=counting_slice,
).format({"IP": "{:.1f}"}),
width=1000,
)
col1, col2, col3 = st.columns([3, 2, 10])
col1.markdown("## Rate Stats")
rate_stats_csv = convert_df(rate_stats)
col2.markdown("")
col2.download_button(
label="download as csv",
data=rate_stats_csv,
file_name=str(season)
+ "_"
+ variant
+ "_rate_stat_leaders_pitching"
+ "_"
+ str(position)
+ "_"
+ str(school)
+ "_minPA_"
+ str(minimum)
+ ".csv",
mime="text/csv",
)
col3.write('')
st.dataframe(
rate_stats.style.format(
{
"IP": "{:.1f}",
"BF": "{:.0f}",
"ERA": "{:.2f}",
"FIP": "{:.3f}",
"WHIP": "{:.3f}",
"K/PA": "{:.3f}",
"BB/PA": "{:.3f}",
"OPS-against": "{:.3f}",
"OBP-against": "{:.3f}",
"BABIP-against": "{:.3f}",
"SLG-against": "{:.3f}",
"BA-against": "{:.3f}",
"Pitches/PA": "{:.3f}",
"HR-A/PA": "{:.3f}",
"IP/App": "{:.3f}",
},
na_rep="",
subset=rate_slice,
)
)
metric = st.selectbox(
"select metric",
options=[
"IP",
"BF",
"App",
"H",
"SO",
"BB",
"ER",
"R",
"HR-A",
"HB",
"GO",
"FO",
"W",
"L",
"SV",
"ERA",
"FIP",
"WHIP",
"K/PA",
"BB/PA",
"OPS-against",
"OBP-against",
"SLG-against",
"BA-against",
"BABIP-against",
"Pitches/PA",
"HR-A/PA",
"IP/App",
],
index=17,
key="metric_select",
)
st.plotly_chart(
create_dist(stats, metric, season, minimum, "IP"), use_container_width=True
)
st.write("")
st.info(
"Data from stats.ncaa.org. Linear Weights courtesy of Robert Frey."
)