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"""
Baseball statistics application with txtai and Streamlit.
Install txtai and streamlit (>= 1.23) to run:
pip install txtai streamlit
"""
import datetime
import math
import os
import random
import altair as alt
import numpy as np
import pandas as pd
import streamlit as st
from txtai.embeddings import Embeddings
class Stats:
"""
Base stats class. Contains methods for loading, indexing and searching baseball stats.
"""
def __init__(self):
"""
Creates a new Stats instance.
"""
# Load columns
self.columns = self.loadcolumns()
# Load stats data
self.stats = self.load()
# Load names
self.names = self.loadnames()
# Build index
self.vectors, self.data, self.embeddings = self.index()
def loadcolumns(self):
"""
Returns a list of data columns.
Returns:
list of columns
"""
raise NotImplementedError
def load(self):
"""
Loads and returns raw stats.
Returns:
stats
"""
raise NotImplementedError
def metric(self):
"""
Primary metric column.
Returns:
metric column name
"""
raise NotImplementedError
def vector(self, row):
"""
Build a vector for input row.
Args:
row: input row
Returns:
row vector
"""
raise NotImplementedError
def loadnames(self):
"""
Loads a name - player id dictionary.
Returns:
{player name: player id}
"""
# Get unique names
names = {}
rows = self.stats.sort_values(by=self.metric(), ascending=False)[["nameFirst", "nameLast", "playerID"]].drop_duplicates().reset_index()
for x, row in rows.iterrows():
# Name key
key = f"{row['nameFirst']} {row['nameLast']}"
key += f" ({row['playerID']})" if key in names else ""
if key not in names:
# Scale scores of top n players
exponent = 2 if ((len(rows) - x) / len(rows)) >= 0.95 else 1
# score = num seasons ^ exponent
score = math.pow(len(self.stats[self.stats["playerID"] == row["playerID"]]), exponent)
# Save name key - values pair
names[key] = (row["playerID"], score)
return names
def index(self):
"""
Builds an embeddings index to stats data. Returns vectors, input data and embeddings index.
Returns:
vectors, data, embeddings
"""
# Build data dictionary
vectors = {f'{row["yearID"]}{row["playerID"]}': self.transform(row) for _, row in self.stats.iterrows()}
data = {f'{row["yearID"]}{row["playerID"]}': dict(row) for _, row in self.stats.iterrows()}
embeddings = Embeddings(
{
"transform": self.transform,
}
)
embeddings.index((uid, vectors[uid], None) for uid in vectors)
return vectors, data, embeddings
def metrics(self, name):
"""
Looks up a player's active years, best statistical year and key metrics.
Args:
name: player name
Returns:
active, best, metrics
"""
if name in self.names:
# Get player stats
stats = self.stats[self.stats["playerID"] == self.names[name][0]]
# Build key metrics
metrics = stats[["yearID", self.metric()]]
# Get best year, sort by primary metric
best = int(stats.sort_values(by=self.metric(), ascending=False)["yearID"].iloc[0])
# Get years active, best year, along with metric trends
return metrics["yearID"].tolist(), best, metrics
return range(1871, datetime.datetime.today().year), 1950, None
def search(self, name=None, year=None, row=None, limit=10):
"""
Runs an embeddings search. This method takes either a player-year or stats row as input.
Args:
name: player name to search
year: year to search
row: row of stats to search
limit: max results to return
Returns:
list of results
"""
if row:
query = self.vector(row)
else:
# Lookup player key and build vector id
name = self.names.get(name)
query = f"{year}{name[0] if name else name}"
query = self.vectors.get(query)
results, ids = [], set()
if query is not None:
for uid, _ in self.embeddings.search(query, limit * 5):
# Only add unique players
if uid[4:] not in ids:
result = self.data[uid].copy()
result["link"] = f'https://www.baseball-reference.com/players/{result["nameLast"].lower()[0]}/{result["bbrefID"]}.shtml'
result["yearID"] = str(result["yearID"])
results.append(result)
ids.add(uid[4:])
if len(ids) >= limit:
break
return results
def transform(self, row):
"""
Transforms a stats row into a vector.
Args:
row: stats row
Returns:
vector
"""
if isinstance(row, np.ndarray):
return row
return np.array([0.0 if not row[x] or np.isnan(row[x]) else row[x] for x in self.columns])
class Batting(Stats):
"""
Batting stats.
"""
def loadcolumns(self):
return [
"birthMonth",
"yearID",
"age",
"height",
"weight",
"G",
"AB",
"R",
"H",
"1B",
"2B",
"3B",
"HR",
"RBI",
"SB",
"CS",
"BB",
"SO",
"IBB",
"HBP",
"SH",
"SF",
"GIDP",
"POS",
"AVG",
"OBP",
"TB",
"SLG",
"OPS",
"OPS+",
]
def load(self):
# Retrieve raw data from GitHub
players = pd.read_csv("https://raw.githubusercontent.com/chadwickbureau/baseballdatabank/master/core/People.csv")
batting = pd.read_csv("https://raw.githubusercontent.com/chadwickbureau/baseballdatabank/master/core/Batting.csv")
fielding = pd.read_csv("https://raw.githubusercontent.com/chadwickbureau/baseballdatabank/master/core/Fielding.csv")
# Merge player data in
batting = pd.merge(players, batting, how="inner", on=["playerID"])
# Require player to have at least 350 plate appearances.
batting = batting[((batting["AB"] + batting["BB"]) >= 350) & (batting["stint"] == 1)]
# Derive primary player positions
positions = self.positions(fielding)
# Calculated columns
batting["age"] = batting["yearID"] - batting["birthYear"]
batting["POS"] = batting.apply(lambda row: self.position(positions, row), axis=1)
batting["AVG"] = batting["H"] / batting["AB"]
batting["OBP"] = (batting["H"] + batting["BB"]) / (batting["AB"] + batting["BB"])
batting["1B"] = batting["H"] - batting["2B"] - batting["3B"] - batting["HR"]
batting["TB"] = batting["1B"] + 2 * batting["2B"] + 3 * batting["3B"] + 4 * batting["HR"]
batting["SLG"] = batting["TB"] / batting["AB"]
batting["OPS"] = batting["OBP"] + batting["SLG"]
batting["OPS+"] = 100 + (batting["OPS"] - batting["OPS"].mean()) * 100
return batting
def metric(self):
return "OPS+"
def vector(self, row):
row["TB"] = row["1B"] + 2 * row["2B"] + 3 * row["3B"] + 4 * row["HR"]
row["AVG"] = row["H"] / row["AB"]
row["OBP"] = (row["H"] + row["BB"]) / (row["AB"] + row["BB"])
row["SLG"] = row["TB"] / row["AB"]
row["OPS"] = row["OBP"] + row["SLG"]
row["OPS+"] = 100 + (row["OPS"] - self.stats["OPS"].mean()) * 100
return self.transform(row)
def positions(self, fielding):
"""
Derives primary positions for players.
Args:
fielding: fielding data
Returns:
{player id: (position, number of games)}
"""
positions = {}
for _, row in fielding.iterrows():
uid = f'{row["yearID"]}{row["playerID"]}'
position = row["POS"] if row["POS"] else 0
if position == "P":
position = 1
elif position == "C":
position = 2
elif position == "1B":
position = 3
elif position == "2B":
position = 4
elif position == "3B":
position = 5
elif position == "SS":
position = 6
elif position == "OF":
position = 7
# Save position if not set or player played more at this position
if uid not in positions or positions[uid][1] < row["G"]:
positions[uid] = (position, row["G"])
return positions
def position(self, positions, row):
"""
Looks up primary position for player row.
Arg:
positions: all player positions
row: player row
Returns:
primary player positions
"""
uid = f'{row["yearID"]}{row["playerID"]}'
return positions[uid][0] if uid in positions else 0
class Pitching(Stats):
"""
Pitching stats.
"""
def loadcolumns(self):
return [
"birthMonth",
"yearID",
"age",
"height",
"weight",
"W",
"L",
"G",
"GS",
"CG",
"SHO",
"SV",
"IPouts",
"H",
"ER",
"HR",
"BB",
"SO",
"BAOpp",
"ERA",
"IBB",
"WP",
"HBP",
"BK",
"BFP",
"GF",
"R",
"SH",
"SF",
"GIDP",
"WHIP",
"WADJ",
]
def load(self):
# Retrieve raw data from GitHub
players = pd.read_csv("https://raw.githubusercontent.com/chadwickbureau/baseballdatabank/master/core/People.csv")
pitching = pd.read_csv("https://raw.githubusercontent.com/chadwickbureau/baseballdatabank/master/core/Pitching.csv")
# Merge player data in
pitching = pd.merge(players, pitching, how="inner", on=["playerID"])
# Require player to have 20 appearances
pitching = pitching[(pitching["G"] >= 20) & (pitching["stint"] == 1)]
# Calculated columns
pitching["age"] = pitching["yearID"] - pitching["birthYear"]
pitching["WHIP"] = (pitching["BB"] + pitching["H"]) / (pitching["IPouts"] / 3)
pitching["WADJ"] = (pitching["W"] + pitching["SV"]) / (pitching["ERA"] + pitching["WHIP"])
return pitching
def metric(self):
return "WADJ"
def vector(self, row):
row["WHIP"] = (row["BB"] + row["H"]) / (row["IPouts"] / 3) if row["IPouts"] else None
row["WADJ"] = (row["W"] + row["SV"]) / (row["ERA"] + row["WHIP"]) if row["ERA"] and row["WHIP"] else None
return self.transform(row)
class Application:
"""
Main application.
"""
def __init__(self):
"""
Creates a new application.
"""
# Batting stats
self.batting = Batting()
# Pitching stats
self.pitching = Pitching()
def run(self):
"""
Runs a Streamlit application.
"""
st.title("⚾ Baseball Statistics")
st.markdown(
"""
This application finds the best matching historical players using vector search with [txtai](https://github.com/neuml/txtai).
Raw data is from the [Baseball Databank](https://github.com/chadwickbureau/baseballdatabank) GitHub project.
"""
)
self.player()
def player(self):
"""
Player tab.
"""
st.markdown("Match by player-season. Each player search defaults to the best season sorted by OPS or Wins Adjusted.")
# Get parameters
params = self.params()
# Category and stats
category = self.category(params.get("category"))
stats = self.batting if category == "Batting" else self.pitching
# Player name
name = self.name(stats.names, params.get("name"))
# Player metrics
active, best, metrics = stats.metrics(name)
# Player year
year = self.year(active, params.get("year"), best)
# Display metrics chart
if len(active) > 1:
self.chart(category, metrics)
# Run search
results = stats.search(name, year)
# Display results
self.table(results, ["nameFirst", "nameLast", "teamID"] + stats.columns[1:] + ["link"])
# Save parameters
st.experimental_set_query_params(category=category, name=name, year=year)
def params(self):
"""
Get application parameters. This method combines URL parameters with session parameters.
Returns:
parameters
"""
# Get parameters
params = st.experimental_get_query_params()
params = {x: params[x][0] for x in params}
# Sync parameters with session state
if all(x in st.session_state for x in ["category", "name", "year"]):
# Copy session year if category and name are unchanged
params["year"] = str(st.session_state["year"]) if all(params.get(x) == st.session_state[x] for x in ["category", "name"]) else None
# Copy category and name from session state
params["category"] = st.session_state["category"]
params["name"] = st.session_state["name"]
return params
def category(self, category):
"""
Builds category input widget.
Args:
category: category parameter
Returns:
category component
"""
# List of stat categories
categories = ["Batting", "Pitching"]
# Get category parameter, default if not available or valid
default = categories.index(category) if category and category in categories else 0
# Radio box component
return st.radio("Stat", categories, index=default, horizontal=True, key="category")
def name(self, names, name):
"""
Builds name input widget.
Args:
names: list of all allowable names
Returns:
name component
"""
# Get name parameter, default to random weighted value if not valid
name = name if name and name in names else random.choices(list(names.keys()), weights=[names[x][1] for x in names])[0]
# Sort names for display
names = sorted(names)
# Select box component
return st.selectbox("Name", names, names.index(name), key="name")
def year(self, years, year, best):
"""
Builds year input widget.
Args:
years: active years for a player
year: year parameter
best: default to best year if year is invalid
Returns:
year component
"""
# Get year parameter, default if not available or valid
year = int(year) if year and year.isdigit() and int(year) in years else best
# Slider component
return int(st.select_slider("Year", years, year, key="year") if len(years) > 1 else years[0])
def chart(self, category, metrics):
"""
Displays a metric chart.
Args:
category: Batting or Pitching
metrics: player metrics to plot
"""
# Key metric
metric = self.batting.metric() if category == "Batting" else self.pitching.metric()
# Cast year to string
metrics["yearID"] = metrics["yearID"].astype(str)
# Metric over years
chart = (
alt.Chart(metrics)
.mark_line(interpolate="monotone", point=True, strokeWidth=2.5, opacity=0.75)
.encode(x=alt.X("yearID", title=""), y=alt.Y(metric, scale=alt.Scale(zero=False)))
)
# Create metric median rule line
rule = alt.Chart(metrics).mark_rule(color="gray", strokeDash=[3, 5], opacity=0.5).encode(y=f"median({metric})")
# Layered chart configuration
chart = (chart + rule).encode(y=alt.Y(title=metric)).properties(height=200).configure_axis(grid=False)
# Draw chart
st.altair_chart(chart + rule, theme="streamlit", use_container_width=True)
def table(self, results, columns):
"""
Displays a list of results as a table.
Args:
results: list of results
columns: column names
"""
if results:
st.dataframe(pd.DataFrame(results)[columns])
else:
st.write("Player-Year not found")
@st.cache_resource(show_spinner=False)
def create():
"""
Creates and caches a Streamlit application.
Returns:
Application
"""
return Application()
if __name__ == "__main__":
os.environ["TOKENIZERS_PARALLELISM"] = "false"
# Create and run application
app = create()
app.run()
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