Home-Field Advantage — Technical Report
Methods, data, and findings behind the NFL home-field analysis (2000–2023)
Course: DTSC-2301 Data Science Foundations (UNC Charlotte)
Tools: Python, pandas, matplotlib, seaborn
Data: nflverse game-level data (games.csv)
Companion piece: Home-Field Advantage case study →
Abstract
This report quantifies NFL home-field advantage over the 2000–2023 regular seasons and tests whether the effect has changed across that period. Using 6,175 regular-season games from the nflverse dataset, home teams won 56.26% of contests with a mean scoring margin of +2.23 points. The advantage is real but appears to have weakened: the 2000–2008 home win rate of 57.03% fell to 54.84% across 2015–2023, a decline of 2.19 percentage points. The analysis is descriptive — it characterizes the trend without claiming a single cause.
1. Problem definition
Research question. To what extent does home-field advantage influence NFL game outcomes, and how has that effect changed from 2000 to 2023?
This is an exploratory study aimed at describing patterns in historical data rather than building a predictive model. The motivating claim — that NFL teams play better at home — is widely repeated by fans, broadcasters, and bettors; the goal here is to check it against the data and measure how stable the effect has been over time.
2. Data and sample design
Source. nflverse games.csv (one row per game), retrieved February 18, 2026.
Fields used. season, game_type, home_score, away_score, game_id.
Filtering. The sample was narrowed in three steps:
| Step | Games |
|---|---|
| Raw file (1999–2025) | 7,276 |
| Restrict to 2000–2023 | 6,447 |
| Regular season only | 6,175 |
The final sample spans 24 seasons, averaging 257.3 games per season (range 248–272), and contains 14 ties (0.23%).
Out of scope. The dataset omits factors that plausibly drive home advantage — travel burden, crowd size and noise, injuries, and full team-strength controls — so the analysis can describe what happened without isolating why.
3. Cleaning and preparation
The preparation steps and their rationale:
- Window (2000–2023). Enough seasons to study a trend while keeping the focus on the modern game; older and post-2023 seasons are excluded.
- Regular season only. Postseason structure varies and is labeled
WC/DIV/CON/SB(notPOST) in this dataset, which made the postseason export unreliable; final claims are therefore restricted to the regular season. - Numeric scores.
home_scoreandaway_scorewere coerced to numeric and rows missing either score removed. No regular-season rows were dropped, but the check guards against invalid win/loss calculations. - Ties handled separately.
home_winwas set to 1 (home win), 0 (home loss), orNaN(tie), with a separatehome_tieflag, so ties are never miscounted as losses. - Deduplication.
game_idvalues were verified unique in the final sample.
4. Methodology
Three views were used to characterize the effect:
- Home win rate by season — the share of regular-season games won by the home team each year.
- Five-year rolling average of home win rate. A 5-year window was chosen deliberately: 2- or 3-year windows leave too much noise and can make ordinary fluctuation look like a trend break, while a very long window hides genuine shifts. Five years smooths short-term noise while still exposing major moves such as 2020.
- Home scoring-margin distribution — because a binary win/loss obscures how much home teams
win by, the distribution of
home_score − away_scoreadds magnitude to the picture.
5. Results
Overall effect. Across 6,175 regular-season games, home teams won 56.26% of the time, with a mean home margin of +2.23 points (mean home score 23.17; mean away score 20.94).
Trend over time. The advantage has narrowed:
| Period | Home win rate |
|---|---|
| 2000–2008 | 57.03% |
| 2015–2023 | 54.84% |
| Change | −2.19 pts |
Extremes. The lowest season in the sample is 2020 at 49.80% — the only season home teams did not win a majority — and the highest is 2003 at 61.33%. Home win rate rebounds somewhat after 2020 but remains below most early-2000s seasons.
6. Interpretation and limitations
The defensible conclusion is narrow: home-field advantage still exists across this period but appears weaker than it was in the early 2000s. The data supports a trend, not a cause. It would be misleading to claim the advantage is gone, that crowd noise is the sole driver, or that this analysis establishes causation.
Limitations. No controls for team, quarterback, or coaching quality; no direct attendance or crowd-noise variable; neutral-site and international games are not modeled separately. A small percentage shift still matters across thousands of games — but honest analysis means stating clearly what the data can and cannot show.
Next steps. Add attendance data to test crowd-related hypotheses directly, separate neutral-site/international games, introduce team-strength controls, and compare periods with a formal statistical test rather than period averages.
References and transparency
Data. nflverse. nfldata: games.csv (GitHub repository dataset file). Retrieved February 18,
2026, from https://github.com/nflverse/nfldata/raw/master/data/games.csv
Code. github.com/hamptonabbott/home-field-advantage
AI transparency. ChatGPT was used as a support tool to polish written explanations for clarity and as a teaching aid during web development (explaining concepts, troubleshooting). It was not used to design, generate, or compose the core analytical, technical, or conceptual work; all primary ideas, decisions, and outcomes are the author’s own.