Qualifying vs Race Pace Regression

Overview

This pillar analyzes the difference between a Formula 1 car's single-lap qualifying speed and its sustained race pace. It identifies drivers who qualify well but are likely to fall back during the race due to issues like high tire degradation, providing a critical edge for race day predictions.

What It Does

It calculates a 'Pace Regression Score' by comparing a driver's starting grid position to their final finishing position over multiple races. The analysis also incorporates data from practice long runs, which simulate race conditions, and historical performance on tracks known for high tire wear. This builds a profile for each driver and car, highlighting their consistency or lack thereof.

Why It Matters

Qualifying results often create market hype that doesn't reflect the reality of a long race. This pillar provides a data-driven way to fade overvalued drivers and identify undervalued drivers who have strong race pace, leading to more accurate predictions for finishing positions and head-to-head matchups.

How It Works

First, we collect qualifying and final race positions for each driver across the season. We then calculate the net positions gained or lost for every race. This data is correlated with track-specific tire degradation levels and long-run pace data from practice sessions to create a predictive model for how a driver will perform relative to their starting position.

Methodology

The primary metric is the Pace Regression Score (PRS), calculated as: PRS = Qualifying Position - Finishing Position. A negative score indicates a driver who consistently moves up the field, while a high positive score indicates a driver who drops back. This score is then weighted based on track type (e.g., high-degradation circuits like Bahrain get a higher weight) and adjusted for non-performance factors like reliability issues or on-track incidents.

Edge & Advantage

This provides an edge by systematically identifying market overreactions to qualifying results, allowing you to position against drivers whose cars cannot maintain pace over a full race distance.

Key Indicators

• Pace Regression Score

  high

  The net positions gained or lost from starting grid position to final race result.

• FP2 Long Run Average

  high

  A driver's average lap time during race simulation runs in the second practice session.

• Track Degradation Index

  medium

  A rating of how abrasive and demanding a specific circuit is on tires.

Data Sources

• FIA Official Results

  Provides official qualifying and race classifications for all F1 events.

• F1-Tempo

  A community-driven source for detailed lap-by-lap pace and sector time analysis.

• Pirelli Motorsport

  The official tire supplier, offering insights into tire compound performance and track characteristics.

Example Questions This Pillar Answers

• → Will Lando Norris finish in the Top 5 of the Bahrain Grand Prix?
• → Which driver will have a better finishing position: George Russell or Lewis Hamilton?
• → Will Haas F1 Team score points in the upcoming race?

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