Return to Play (RTP) Performance Dip

Overview

This pillar analyzes the historical performance of rugby players in their first two games returning from a long-term injury. It identifies predictable drops in key metrics, providing an edge in player proposition markets.

What It Does

The model identifies players returning from significant time off, typically six weeks or more. It then compares their pre-injury performance baseline across key statistics with the average performance of similarly injured players in their initial return games. This process generates a projected 'performance dip' score for specific metrics like tackle efficiency and error rates.

Why It Matters

Narrative and fan excitement often inflate a star player's expected performance upon their return. This pillar provides a quantitative, data-driven counterpoint, highlighting the statistical reality that most players need time to regain match fitness and sharpness. This gap between public perception and likely outcome creates valuable prediction opportunities.

How It Works

First, the system flags a player returning from an injury classified as long-term. Second, it pulls their 10-game rolling average for key stats before the injury occurred. Third, it cross-references a historical database of player returns to calculate an expected percentage decrease in performance for the first two games back. Finally, it applies this dip factor to the player's baseline to generate a concrete statistical forecast.

Methodology

A 'long-term injury' is defined as any absence exceeding 6 weeks. The pre-injury baseline is a player's 10-game rolling average. The core calculation is the 'Dip Ratio', calculated as (Avg Performance in first 2 games post-injury) / (Avg Performance in 10 games pre-injury) for a cohort of players with similar positions and injury types. This ratio is then applied to the specific player's baseline stats.

Edge & Advantage

This pillar offers an edge by systematically trading against the emotional hype surrounding a player's return, using historical data to pinpoint likely underperformance.

Key Indicators

• First Game Back Error Rate

  high

  Compares the player's projected handling errors and penalties conceded to their pre-injury baseline.

• Minutes Restriction Likelihood

  medium

  Assesses the probability that the player will be substituted early and not complete the full match.

• Tackle Efficiency Post-Injury

  high

  Projects the player's tackle completion percentage versus their historical average, a key defensive metric.

Data Sources

• Opta Sports Data

  Provides granular player performance statistics for major rugby competitions, including tackles, carries, and errors.

• Official Club Injury Reports

  Team websites and press releases that confirm player injuries and expected return timelines.

• RugbyPass / Planet Rugby

  Specialist news outlets that provide context on player status, training participation, and recovery progress.

Example Questions This Pillar Answers

• → Will Owen Farrell's tackle completion rate be under 88.5% in his first game back for Saracens?
• → Will Antoine Dupont play fewer than 65.5 minutes against Ireland upon his return?
• → Will Siya Kolisi make more than 1.5 handling errors in his return match for Racing 92?

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