Home Blog Uncategorized Teasers y Pleasers en apuestas: modelos de probabilidad prácticos para novatos

Teasers y Pleasers en apuestas: modelos de probabilidad prácticos para novatos

¡Espera… esto no es solo jerga! Si acabas de ver una oferta con “teaser 6/6” o alguien te mencionó un “pleaser”, puedes confundir las cosas fácilmente. Aquí tienes, desde el primer minuto, lo que realmente importa: cómo calcular la probabilidad implícita, cuánto te cuesta la casa (vig/overround) y cuándo un teaser puede transformar una apuesta de valor en pérdida segura.

¡Wow! Dos reglas rápidas antes de seguir: apuesta solo si entiendes la conversión de cuotas a probabilidad, y limita el tamaño de la apuesta a un 1–2% del bankroll por parlay/teaser. Si ignoras esto, la varianza te comerá.

![image](data:image/webp;base64,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)

Qué son, en una frase

Teaser: un tipo de apuesta combinada donde el apostador consigue mover márgenes (puntos/corners/goles) a su favor a cambio de cuotas inferiores. Pleaser: lo contrario; mueves márgenes en contra para obtener mejores cuotas (más pago) pero con mayor riesgo. Ambos son variantes de los parlays, con reglas de ajuste de línea.

La matemática básica detrás (convertir probabilidades y vig)

Primero lo útil: convertir cuota americana/decimal a probabilidad. ¿Recuerdas eso? Si tienes cuota decimal Q, probabilidad implícita p = 1 / Q. Fácil. Pero atención: el mercado incluye vigorish (vig), así que la suma de probabilidades excede 1.

Expansión: toma dos selecciones 50/50 (cuotas 2.00 decimal cada una). Un parlay verdadero (sin vig) paga 4.00; probabilidad real 0.25. Con vig, cada selección puede cotizar 1.91 (por ejemplo), el parlay paga 3.65 y la probabilidad implícita real baja. Eso cambia decisiones simples en apuestas combinadas.

Reflexión larga: por un lado parece una oportunidad si puedes mover la línea a tu favor (teaser); pero por otro lado, la reducción de la cuota compensa esa mejora en la línea. Si no cuantificas ambos efectos, confundes valor con apariencia de valor.

Cómo modelar un teaser: paso a paso práctico

1) Lista las selecciones y sus probabilidades reales estimadas (p1, p2, …).

2) Convierte las cuotas ofrecidas en probabilidades implícitas (qi = 1/Qi).

3) Calcula el efecto del movimiento de línea (por ejemplo, +6 puntos en fútbol americano): estima cómo cambia la probabilidad real de ganar cada selección tras el ajuste (p’i).

4) Calcula la probabilidad conjunta P = ∏ p’i (producto de las probabilidades ajustadas).

5) Compara la expectativa: EV = P * payout – (1 – P) * stake. Si EV > 0, hay valor; si EV ≤ 0, evita.

Ejemplo numérico — teaser de 6 puntos, 2 selecciones

Observa: línea original — Equipo A -3 (probabilidad estimada de ganar 0.55) y Equipo B -4 (0.52). La casa ofrece un teaser de +6 en cada línea (mover el spread a A +3 y B +2).

Estimación práctica: mover 6 puntos puede cambiar probabilidad A de 0.55 a 0.68 y B de 0.52 a 0.66 (esto depende de la distribución; aquí uso una aproximación basada en curva normal para spreads). Probabilidad conjunta P = 0.68 * 0.66 = 0.449.

Pago típico de un teaser de 2 selecciones — alrededor de 13/10 (decimal 2.3). EV = 0.449 * 2.3 – 1 = 0.0327 (positivo, pero pequeño). Resultado: valor marginal; si tu estimación es precisa, podría justificarse. Pero ojo: errores de estimación de ±0.05 en p’i hacen volar el EV a negativo.

Qué es un pleaser y por qué lo usan profesionales

Un pleaser te exige aceptar líneas peores (ej.: mover -3 a -9) para obtener cuota mayor. Es tentador cuando crees firmemente que un resultado extremo (mucha diferencia) es probable. En general, los pleasers aumentan payout pero reducen drásticamente la probabilidad conjunta.

Case: si tu evaluación sugiere que un equipo favorito cubrirá -9 con probabilidad 0.45 (tras mover la línea a ese punto), y la otra selección 0.48, conjunto ≈ 0.216. Si el payout es 5.5x, EV = 0.216*5.5 – 1 = 0.188. Atractivo, sí — pero recuerda: peor streaks pueden arruinar el bankroll si no controlas tamaños y límites.

Tabla comparativa: Teaser vs Pleaser vs Parlay simple

Característica Teaser Pleaser Parlay simple
Línea (spread) movida Hacia favor del apostador (+ puntos) En contra del apostador (- puntos) No se mueve
Cuota Reducida Aumentada Multiplicativa estándar
Riesgo principal Cuota baja + vig Alta varianza Correlación entre selecciones
Uso típico Proteger parlays, reducir riesgo Buscar payout alto con convicción Maximizar payout con varias selecciones

Herramientas y enfoques prácticos (mini-checklist de modelado)

  • Convierte cuotas a probabilidad sin olvidar el overround.
  • Usa distribuciones normales para spreads; ajusta según histórico equipo vs línea.
  • Simula 10,000 tiradas (Monte Carlo) si vas a usar teasers/pleasers con regularidad.
  • Aplica Kelly fraccional (0.5 Kelly) para gestionar tamaños en pleasers de alto payout.
  • Documenta cada apuesta: línea original, línea teaser/pleaser, estimación de p antes/después, resultado y desviación.

Comparativa de herramientas / enfoques

Herramienta Ventaja Limitación Recomendado para
Simulación Monte Carlo (Excel/python) Estima EV real y distribución de resultados Requiere datos y tiempo Apostadores cuantitativos
Modelos de regresión para spreads Predicciones basadas en variables explicativas Sobreajuste si pocos partidos Analistas con histórico amplio
Regla empírica / heurística Rápido y simple Poco preciso en mercados con vig variable Novatos que quieren entender el mercado

Dónde encaja un operador confiable

Si exploras mercados y herramientas en plataformas serias, busca operadores con licencia (por ejemplo MGA para muchos operadores internacionales) y opciones claras de KYC y límites de depósito. Para empezar a probar teasers en un entorno adaptado al mercado ecuatoriano, resulta útil revisar las ofertas y reglas específicas de la casa antes de colocar dinero; por ejemplo, en plataformas que listan ajustes de línea y payouts detallados puedes comparar escenarios sin arriesgar fondos — visit site ofrece información sobre promociones y sus reglas aplicables al mercado regional.

Quick Checklist — antes de tocar el botón

  • ¿Has convertido las cuotas a probabilidades implícitas?
  • ¿Has modelado el efecto del movimiento de línea en la probabilidad?
  • ¿El EV estimado es positivo después de incluir vig?
  • ¿La apuesta respeta tu bankroll y tu regla de máximo 2% por unidad?
  • ¿Has leído los términos del teaser/pleaser (límites, contribución, reglas específicas)?

Errores comunes y cómo evitarlos

Espera, aquí viene lo que suele arruinar a los novatos:

  • Confundir apariencia de ventaja con valor real. Evítalo calculando EV.
  • No ajustar por vig. Solución: recalcula probabilidades prorrateando el overround.
  • Subestimar correlación entre selecciones (por ejemplo, dos líneas que dependen del mismo marcador). Usa análisis de correlación.
  • Usar pleasers por “sensación” sin modelo. Regla: si no puedes justificar probabilísticamente, no juegues.
  • Apostar tamaños grandes tras una racha ganadora (sesgo del jugador). Mantén disciplina de bankroll.

Mini-FAQ

¿Un teaser siempre reduce el riesgo?

Observa: no necesariamente. Expandir la línea te ayuda, pero la reducción de cuota y la estructura de pago pueden dejar el EV neutro o negativo. Reflexiona: sin modelar, pensar que “mover la línea = mejor” es una falacia de confirmación.

¿Cómo calculo la probabilidad real al mover puntos?

La forma práctica: usa la función de distribución normal con media igual al margen esperado y desviación estándar histórica para convertir un spread en probabilidad de cubrir; simula si puedes.

¿Debo usar Kelly para pleasers?

Si tienes una estimación razonable de p y payout, Kelly fraccional (25–50%) es útil para limitar riesgo. Si tu estimación es incierta, reduce la fracción.

Dos mini-casos prácticos (hipotéticos)

Case A — Teaser conservador: 2 selecciones de NBA, mover 4.5 puntos en cada línea. Tu modelo sube p1 de 0.60 a 0.73 y p2 de 0.57 a 0.70. P conjunta ≈ 0.511. Payout teaser = 2.0 aprox. EV = 0.511*2 – 1 = 0.022 positivo: tomar si la muestra histórica y la desviación estándar respaldan esos saltos.

Case B — Pleaser agresivo: 3 selecciones NFL, mover líneas 7 puntos en contra. p’s estimadas: 0.40, 0.38, 0.35; P≈0.053. Payout ofrecido 12x. EV≈0.636 – 1 = -0.364 (negativo). Resultado: a pesar del pago, el pleaser es matemáticamente desfavorable aquí.

Reflexión final sobre los casos: los números mandan. Por un lado, la emoción de “esto paga mucho” es seductora; por otro lado, la matemática te devuelve la verdad.

18+ | Apuesta con responsabilidad. Gestiona tu bankroll, usa límites de depósito y herramientas de autoexclusión cuando sea necesario. Si sientes pérdida de control, busca ayuda local o líneas de soporte. En Ecuador, revisa condiciones de KYC y la licencia del operador antes de jugar.

Fuentes

  • https://www.mga.org.mt/licences/
  • https://en.wikipedia.org/wiki/Point_spread
  • https://www.sportingtech.com/insights/bookmakers-margin

Sobre el autor: Carlos Méndez, iGaming expert. Trabajo con modelos de apuestas y gestión de riesgo desde 2012; he desarrollado simuladores de parlays y teasers para analistas y apostadores recreativos.

Add comment

Sign up to receive the latest updates and news

All copyrights reserved © 2022 - 4myAds Concepts