Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,236 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import requests
+from io import StringIO
+from scipy import stats
+from scipy.stats import poisson, mode
+
+# ────────────────────────────────────────────────
+#  CONFIGURACIÓN DE PÁGINA
+# ────────────────────────────────────────────────
+st.set_page_config(
+    page_title="Análisis Equipo vs Rival + Momios",
+    layout="wide",
+    initial_sidebar_state="collapsed"
+)
+
+st.markdown("""
+<style>
+    .main > div { padding: 0.8rem 0.8rem; }
+    .stApp { font-size: 12px; }
+    h1 { font-size: 1.3rem !important; margin: 0.3rem 0 0.5rem; }
+    h2, h3 { font-size: 1.05rem !important; margin: 0.5rem 0 0.3rem; }
+    .stButton > button { padding: 0.4rem 0.8rem; font-size: 13px; }
+    .stDataFrame { font-size: 11.5px; }
+    header { visibility: hidden; }
+    .element-container { margin: 0.3rem 0; }
+    hr { margin: 0.5rem 0; }
+    .form-win { color: #28a745; font-weight: bold; }
+    .form-draw { color: #ffc107; font-weight: bold; }
+    .form-loss { color: #dc3545; font-weight: bold; }
+</style>
+""", unsafe_allow_html=True)
+
+plt.rcParams['figure.dpi'] = 90
+plt.rcParams['figure.figsize'] = (6.5, 3.2)
+plt.rcParams['font.size'] = 9
+
+DEFAULT_URL = "https://www.football-data.co.uk/mmz4281/2526/E0.csv"
+
+# ────────────────────────────────────────────────
+#  MÉTRICAS Y CARGA
+# ────────────────────────────────────────────────
+METRICS = {
+    "Goles anotados (FT)":          ("FTHG", "FTAG"),
+    "Goles recibidos (FT)":         ("FTAG", "FTHG"),
+    "Goles anotados (1T)":          ("HTHG", "HTAG"),
+    "Goles recibidos (1T)":         ("HTAG", "HTHG"),
+    "Corners a favor":              ("HC", "AC"),
+    "Corners en contra":            ("AC", "HC"),
+    "Tiros a favor":                ("HS", "AS"),
+    "Tiros en contra":              ("AS", "HS"),
+    "Tiros a puerta a favor":       ("HST", "AST"),
+    "Tiros a puerta en contra":     ("AST", "HST"),
+    "Faltas cometidas":             ("HF", "AF"),
+    "Faltas recibidas":             ("AF", "HF"),
+    "Tarjetas amarillas":           ("HY", "AY"),
+    "Tarjetas rojas":               ("HR", "AR")
+}
+
+@st.cache_data(show_spinner=False)
+def load_data(url):
+    try:
+        r = requests.get(url, timeout=12)
+        r.raise_for_status()
+        df = pd.read_csv(StringIO(r.text))
+        num_cols = ['FTHG','FTAG','HTHG','HTAG','HC','AC','HS','AS','HST','AST','HF','AF','HY','AY','HR','AR']
+        for col in num_cols:
+            if col in df.columns:
+                df[col] = pd.to_numeric(df[col], errors='coerce')
+        return df
+    except Exception as e:
+        st.error(f"Error al cargar datos: {str(e)}")
+        return None
+
+# Función modificada con INDICADORES DE COLOR (Emoji)
+def get_team_form(df, team, n=5):
+    team_matches = df[(df['HomeTeam'] == team) | (df['AwayTeam'] == team)].tail(n)
+    results = []
+    points = 0
+    for _, row in team_matches.iterrows():
+        is_home = row['HomeTeam'] == team
+        goals_for = row['FTHG'] if is_home else row['FTAG']
+        goals_ag = row['FTAG'] if is_home else row['FTHG']
+        res = row['FTR']
+        if (is_home and res == 'H') or (not is_home and res == 'A'):
+            results.append(f"🟢 G({goals_for}-{goals_ag})")
+            points += 3
+        elif res == 'D':
+            results.append(f"🟡 E({goals_for}-{goals_ag})")
+            points += 1
+        else:
+            results.append(f"🔴 P({goals_for}-{goals_ag})")
+    return results[::-1], points # Mostrar del más reciente al más antiguo
+
+def prob_to_decimal(p):
+    return round(1 / p, 2) if 0 < p < 1 else "—"
+
+# ────────────────────────────────────────────────
+#  LÓGICA DE INTERFAZ
+# ────────────────────────────────────────────────
+st.title("⚽ Análisis Equipo vs Rival + Momios Poisson")
+data_url = st.text_input("URL CSV", value=DEFAULT_URL, label_visibility="collapsed")
+
+if "df" not in st.session_state or st.session_state.get("last_url") != data_url:
+    df = load_data(data_url)
+    if df is not None:
+        st.session_state.df = df
+        st.session_state.last_url = data_url
+    else: st.stop()
+
+df = st.session_state.df
+teams = sorted(set(df["HomeTeam"]).union(set(df["AwayTeam"])))
+
+col1, col2, col3, col4 = st.columns([2, 2, 2.8, 1.8])
+with col1:
+    equipo = st.selectbox("Equipo principal", teams, index=teams.index("Arsenal") if "Arsenal" in teams else 0)
+with col2:
+    rival = st.selectbox("Equipo rival", [t for t in teams if t != equipo])
+with col3:
+    analysis_mode = st.radio("Modo de análisis", ["Contexto Local vs Visitante", "Historial general"], horizontal=True)
+with col4:
+    period = st.radio("Período", ["Temporada completa", "Últimos 5 partidos"], horizontal=True)
+
+if st.button("Analizar →", type="primary", use_container_width=True):
+    is_context_mode = analysis_mode.startswith("Contexto")
+    series_team, series_rival = {}, {}
+    for name in METRICS:
+        if is_context_mode:
+            s_team = df[df["HomeTeam"] == equipo][METRICS[name][0]].dropna()
+            s_rival = df[df["AwayTeam"] == rival][METRICS[name][1]].dropna()
+        else:
+            s_team = pd.concat([df[df["HomeTeam"]==equipo][METRICS[name][0]], df[df["AwayTeam"]==equipo][METRICS[name][1]]]).dropna()
+            s_rival = pd.concat([df[df["HomeTeam"]==rival][METRICS[name][0]], df[df["AwayTeam"]==rival][METRICS[name][1]]]).dropna()
+        if period == "Últimos 5 partidos":
+            s_team, s_rival = s_team.tail(5), s_rival.tail(5)
+        series_team[name], series_rival[name] = s_team, s_rival
+
+    st.session_state.update({"series_team": series_team, "series_rival": series_rival, "equipo": equipo, "rival": rival, "mode": analysis_mode, "period": period})
+
+# ────────────────────────────────────────────────
+#  RESULTADOS
+# ────────────────────────────────────────────────
+if "series_team" in st.session_state:
+    eq, rv = st.session_state.equipo, st.session_state.rival
+    
+    st.markdown(f"### **{eq}** vs **{rv}**")
+    st.caption(f"_{st.session_state.mode} • {st.session_state.period}_")
+    
+    # 1. Historial Reciente con Colores Visuales
+    st.subheader("🕒 Historial Reciente (Últimos 5 partidos)")
+    form_eq, pts_eq = get_team_form(df, eq)
+    form_rv, pts_rv = get_team_form(df, rv)
+    
+    cf1, cf2 = st.columns(2)
+    with cf1:
+        st.markdown(f"**{eq}**")
+        st.write(" | ".join(form_eq))
+        st.caption(f"Puntos obtenidos: {pts_eq}/15")
+    with cf2:
+        st.markdown(f"**{rv}**")
+        st.write(" | ".join(form_rv))
+        st.caption(f"Puntos obtenidos: {pts_rv}/15")
+    st.divider()
+
+    # 2. Estadísticas Detalladas
+    st.subheader("📊 Estadísticas detalladas")
+    stats_rows = []
+    for metric in METRICS:
+        t, r = st.session_state.series_team[metric], st.session_state.series_rival[metric]
+        if len(t) > 0 and len(r) > 0:
+            stats_rows.append({
+                "Métrica": metric, f"{eq} μ": round(t.mean(),2), f"{eq} Med": round(t.median(),2),
+                f"{eq} Mo": round(float(mode(t, keepdims=True).mode[0]), 1),
+                f"{rv} μ": round(r.mean(),2), f"{rv} Med": round(r.median(),2),
+                f"{rv} Mo": round(float(mode(r, keepdims=True).mode[0]), 1),
+                "Δ": round(t.mean() - r.mean(), 2)
+            })
+    st.dataframe(pd.DataFrame(stats_rows).set_index("Métrica"), use_container_width=True, height=350)
+
+    # 3. Ataque vs Defensa
+    st.subheader("⚔️ Ataque vs Defensa")
+    cross_rows = []
+    pairs = [("Goles anotados (FT)", "Goles recibidos (FT)", "⚽ Goles"), ("Goles anotados (1T)", "Goles recibidos (1T)", "⏰ Goles 1T"), ("Tiros a puerta a favor", "Tiros a puerta en contra", "🥅 Tiros"), ("Corners a favor", "Corners en contra", "🚩 Corners")]
+    for atk, dfn, lbl in pairs:
+        t_a, r_d = st.session_state.series_team[atk].mean(), st.session_state.series_rival[dfn].mean()
+        r_a, t_d = st.session_state.series_rival[atk].mean(), st.session_state.series_team[dfn].mean()
+        cross_rows.append({"": lbl, "Eq": eq, "Atq": round(t_a,2), "vs": "→", "Riv": rv, "Def": round(r_d,2), "Δ": round(t_a - r_d,2)})
+        cross_rows.append({"": "", "Eq": rv, "Atq": round(r_a,2), "vs": "→", "Riv": eq, "Def": round(t_d,2), "Δ": round(r_a - t_d,2)})
+    st.dataframe(pd.DataFrame(cross_rows), use_container_width=True, height=250)
+
+    # 4. Momios Poisson Ampliados + Ajuste
+    st.subheader("💰 Momios estimados (Poisson)")
+    l_eq, l_rv = st.session_state.series_team["Goles anotados (FT)"].mean(), st.session_state.series_rival["Goles anotados (FT)"].mean()
+    l_eq_ht, l_rv_ht = st.session_state.series_team["Goles anotados (1T)"].mean(), st.session_state.series_rival["Goles anotados (1T)"].mean()
+    lam_yellow = (st.session_state.series_team["Tarjetas amarillas"].mean() + st.session_state.series_rival["Tarjetas amarillas"].mean()) / 2
+    lam_red = (st.session_state.series_team["Tarjetas rojas"].mean() + st.session_state.series_rival["Tarjetas rojas"].mean()) / 2
+
+    p_h, p_d, p_a = 0, 0, 0
+    for h in range(12):
+        for a in range(12):
+            prob = poisson.pmf(h, l_eq) * poisson.pmf(a, l_rv)
+            if h > a: p_h += prob
+            elif h == a: p_d += prob
+            else: p_a += prob
+    
+    def get_adj(pts):
+        if pts >= 10: return "🔥 Valor (Racha)"
+        if pts <= 4: return "⚠️ Riesgo (Baja)"
+        return "Normal"
+
+    momios = [
+        {"Mercado": f"1 ({eq})", "Prob%": f"{p_h*100:.1f}", "Momio": prob_to_decimal(p_h), "Ajuste": get_adj(pts_eq)},
+        {"Mercado": "X (Empate)", "Prob%": f"{p_d*100:.1f}", "Momio": prob_to_decimal(p_d), "Ajuste": "Estable"},
+        {"Mercado": f"2 ({rv})", "Prob%": f"{p_a*100:.1f}", "Momio": prob_to_decimal(p_a), "Ajuste": get_adj(pts_rv)},
+        {"Mercado": "Ambos anotan", "Prob%": f"{(1 - poisson.pmf(0,l_eq)*poisson.pmf(0,l_rv))*100:.1f}", "Momio": prob_to_decimal(1 - poisson.pmf(0,l_eq)*poisson.pmf(0,l_rv)), "Ajuste": "-"},
+        {"Mercado": "+2.5 goles", "Prob%": f"{(1 - poisson.cdf(2, l_eq + l_rv))*100:.1f}", "Momio": prob_to_decimal(1 - poisson.cdf(2, l_eq + l_rv)), "Ajuste": "-"},
+        {"Mercado": "-2.5 goles", "Prob%": f"{poisson.cdf(2, l_eq + l_rv)*100:.1f}", "Momio": prob_to_decimal(poisson.cdf(2, l_eq + l_rv)), "Ajuste": "-"},
+        {"Mercado": "+1.5 goles", "Prob%": f"{(1 - poisson.cdf(1, l_eq + l_rv))*100:.1f}", "Momio": prob_to_decimal(1 - poisson.cdf(1, l_eq + l_rv)), "Ajuste": "-"},
+        {"Mercado": "+3.5 goles", "Prob%": f"{(1 - poisson.cdf(3, l_eq + l_rv))*100:.1f}", "Momio": prob_to_decimal(1 - poisson.cdf(3, l_eq + l_rv)), "Ajuste": "-"},
+        {"Mercado": "+1.5 goles 1T", "Prob%": f"{(1 - poisson.cdf(1, l_eq_ht + l_rv_ht))*100:.1f}", "Momio": prob_to_decimal(1 - poisson.cdf(1, l_eq_ht + l_rv_ht)), "Ajuste": "-"},
+        {"Mercado": "+4.5 amarillas", "Prob%": f"{(1 - poisson.cdf(4, lam_yellow))*100:.1f}", "Momio": prob_to_decimal(1 - poisson.cdf(4, lam_yellow)), "Ajuste": "-"},
+        {"Mercado": "+0.5 rojas", "Prob%": f"{(1 - poisson.pmf(0, lam_red))*100:.1f}", "Momio": prob_to_decimal(1 - poisson.pmf(0, lam_red)), "Ajuste": "-"},
+    ]
+    st.dataframe(pd.DataFrame(momios), use_container_width=True, height=400)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    # 5. Gráfico evolutivo
+    st.subheader("📈 Evolución por partido")
+    metric_viz = st.selectbox("Métrica", options=list(METRICS.keys()), label_visibility="collapsed")
+    fig, ax = plt.subplots()
+    t, r = st.session_state.series_team[metric_viz], st.session_state.series_rival[metric_viz]
+    if len(t) > 0: ax.plot(t.values, "o-", label=eq, ms=5); ax.axhline(t.mean(), color="blue", ls="--", alpha=0.3)
+    if len(r) > 0: ax.plot(r.values, "s-", label=rv, ms=5); ax.axhline(r.mean(), color="orange", ls="--", alpha=0.3)
+    ax.legend(); ax.grid(alpha=0.2); ax.set_title(metric_viz)
+    st.pyplot(fig)