Post-Marathon Match Hangover

Overview

This pillar analyzes a tennis player's historical performance in the match immediately following a grueling, marathon contest. It provides a statistical edge by identifying players who are likely to underperform due to physical and mental fatigue.

What It Does

The model identifies prior 'marathon' matches, defined as 5-setters or matches exceeding 3.5 hours for men and 2.5 hours for women. It then aggregates the player's performance metrics, like win percentage and first set success rate, in the very next match they play. This historical data reveals a player's typical recovery and resilience patterns.

Why It Matters

The market often overreacts to a big win, ignoring the physical cost. This pillar provides a contrarian signal, highlighting when a recent victor is vulnerable and their opponent is undervalued, creating profitable trading opportunities.

How It Works

First, the system detects if a player's most recent match qualifies as a marathon. Second, it pulls that player's career data for all matches played immediately after such contests. Third, it calculates their post-marathon win rates, first set performance, and service game statistics. Finally, it compares these fatigue-adjusted stats to their career baseline to generate a 'Hangover Score'.

Methodology

A 'marathon match' is defined as any men's 5-set match, a men's best-of-3 match over 3.5 hours, or a women's match over 2.5 hours. The analysis window is the single subsequent match. Key metrics like Win %, First Set Win %, and Service Games Won % are averaged across all historical post-marathon instances and compared to the player's 52-week rolling average to quantify the performance drop-off.

Edge & Advantage

This pillar replaces emotional narratives about 'toughness' with a data-driven fatigue score, systematically finding mispriced odds on players after grueling wins.

Key Indicators

• Post-Marathon First Set Win Rate

  high

  The player's historical win percentage in the first set of matches immediately following a marathon.

• Early Match Unforced Errors

  high

  An increase in unforced errors in the first few games compared to the player's average, signaling fatigue.

• Service Performance Decline

  medium

  A drop in first serve percentage or an increase in double faults in post-marathon matches.

Data Sources

• ATP/WTA Tour Official Stats

  Provides official match results, durations, and player statistics.

• Tennis Abstract

  A comprehensive database of historical match data and advanced tennis metrics.

• Flashscore

  Source for real-time scores and historical match data including duration and set scores.

Example Questions This Pillar Answers

• → Will Carlos Alcaraz win his next match after a 5-hour semifinal?
• → Will Aryna Sabalenka win the first set in the tournament final after a long semifinal match?
• → Will the match between Player A and Player B go over 20.5 total games?

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