|
|
import polars as pl
|
|
|
import numpy as np
|
|
|
import joblib
|
|
|
from shiny import App, reactive, render, ui
|
|
|
import matplotlib.pyplot as plt
|
|
|
import matplotlib.ticker as tkr
|
|
|
import seaborn as sns
|
|
|
import adjustText
|
|
|
sns.set_style('whitegrid')
|
|
|
|
|
|
|
|
|
import matplotlib
|
|
|
cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#FFFFFF','#FFB000','#FE6100'])
|
|
|
|
|
|
xwoba_model = joblib.load('joblib_model/xwoba_model.joblib')
|
|
|
|
|
|
x = np.arange(-30,90.5,.5)
|
|
|
y = np.arange(0,120.5,0.1)
|
|
|
|
|
|
xx, yy = np.meshgrid(x, y)
|
|
|
|
|
|
df = pl.DataFrame({'launch_angle': xx.ravel(), 'launch_speed': yy.ravel()})
|
|
|
|
|
|
df = df.with_columns(
|
|
|
pl.Series('xwoba', xwoba_model.predict_proba(df) @ [0, 0.883, 1.244, 1.569, 2.004])
|
|
|
)
|
|
|
|
|
|
app_ui = ui.page_sidebar(
|
|
|
ui.sidebar(
|
|
|
ui.markdown("""
|
|
|
### How to use this app
|
|
|
|
|
|
1. Click anywhere on the plot to select a point, or manually enter coordinates
|
|
|
2. The selected point's coordinates will update automatically
|
|
|
3. The xwOBA value will be calculated based on these coordinates
|
|
|
"""),
|
|
|
ui.hr(),
|
|
|
ui.input_numeric("x_select", "Launch Speed (mph)", value=110),
|
|
|
ui.input_numeric("y_select", "Launch Angle (°)", value=30),
|
|
|
|
|
|
|
|
|
),
|
|
|
ui.output_plot("plot",width='900px',height='900px', click=True)
|
|
|
)
|
|
|
|
|
|
|
|
|
def server(input, output, session):
|
|
|
|
|
|
x_coord = reactive.value(110)
|
|
|
y_coord = reactive.value(30)
|
|
|
|
|
|
@reactive.effect
|
|
|
@reactive.event(input.plot_click)
|
|
|
def _():
|
|
|
|
|
|
click_data = input.plot_click()
|
|
|
if click_data is not None:
|
|
|
x_coord.set(click_data["x"])
|
|
|
y_coord.set(click_data["y"])
|
|
|
|
|
|
ui.update_numeric("x_select", value=round(click_data["x"],1))
|
|
|
ui.update_numeric("y_select", value=round(click_data["y"],1))
|
|
|
|
|
|
@reactive.effect
|
|
|
@reactive.event(input.x_select, input.y_select)
|
|
|
def _():
|
|
|
|
|
|
x_coord.set(round(input.x_select(),1))
|
|
|
y_coord.set(round(input.y_select(),1))
|
|
|
|
|
|
|
|
|
@render.plot
|
|
|
def plot():
|
|
|
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
|
|
|
|
|
|
h = ax.hexbin(df['launch_speed'],
|
|
|
df['launch_angle'],
|
|
|
C=df['xwoba'],
|
|
|
gridsize=(40,25),
|
|
|
cmap=cmap_sum,
|
|
|
vmin=0.0,
|
|
|
vmax=2.0,)
|
|
|
bounds=[0.0,0.4,0.8,1.2,1.6,2.0]
|
|
|
fig.colorbar(h, ax=ax, label='xwOBA',format=tkr.FormatStrFormatter('%.3f'),shrink=0.5,
|
|
|
ticks=bounds)
|
|
|
|
|
|
|
|
|
|
|
|
ax.set_xlabel('Launch Speed')
|
|
|
ax.set_ylabel('Launch Angle')
|
|
|
ax.set_title('Exit Velocity vs Launch Angle\nExpected Weighted On Base Average (xwOBA)\nBy: @TJStats, Data:MLB')
|
|
|
|
|
|
ax.grid(False)
|
|
|
ax.axis('square')
|
|
|
ax.set_xlim(0, 120)
|
|
|
ax.set_ylim(-30, 90)
|
|
|
|
|
|
x_select = input.x_select()
|
|
|
y_select = input.y_select()
|
|
|
|
|
|
|
|
|
sns.scatterplot(x=[x_select],y=[y_select],color='#648FFF',s=50,ax=ax,edgecolor='k',zorder=100)
|
|
|
|
|
|
xwoba_value = (xwoba_model.predict_proba([[y_select,x_select]]) @ [0, 0.883, 1.244, 1.569, 2.004])[0]
|
|
|
|
|
|
texts = [ax.text(x_select+3, y_select+3, f'xwOBA: {xwoba_value:.3f}', color='black', fontsize=12, weight='bold',
|
|
|
zorder=1000, bbox=dict(facecolor='white', alpha=0.5, edgecolor='black'))]
|
|
|
|
|
|
|
|
|
|
|
|
adjustText.adjust_text(texts,
|
|
|
|
|
|
arrowprops=dict(arrowstyle='->', color='#DC267F'),avoid_self=True,
|
|
|
min_arrow_len =5)
|
|
|
|
|
|
|
|
|
ax.axhline(y=y_select, color='k', linestyle='--',linewidth=1,alpha=0.5)
|
|
|
ax.axvline(x=x_select, color='k', linestyle='--',linewidth=1,alpha=0.5)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
app = App(app_ui, server) |
