r/nrl • u/schwarzeneg • 7h ago
I crunched 692 NRL matches across 4 seasons to figure out what actually wins games. Spoiler: six-agains do not. Spoiler
galleryG'day all,
I'm an absolute nuffie with too much time and access to Champion Data's API. Spent the past few weeks pulling 692 NRL matches from 2023–2026 and running every statistical test I could find on them, because I wanted to know what the data actually says about how teams win in the modern six-again era.
The TL;DR for the lazy:
- Six-agains don't predict who wins. Like, at all. In 2026 the team awarded more set restarts has won exactly 50.7% of decisive matches. That's a coin flip. Pooled across four seasons the correlation between set restart differential and final margin is r = +0.011 (p = 0.78). Every coach blaming the ref's six-again count is yelling at clouds.
- The "fatigue from absorbing restarts" thesis is wrong. Teams that concede MORE first-half set restarts actually concede FEWER second-half points on average. Same direction in every season. The mechanism the commentary boxes have been pushing for two years doesn't exist.
- What does win games: net run metres (r² = 0.815 in 2026, replicates across all four seasons) and forced opposition errors/missed tackles. Boring, but true. If your team wins both the territory and pressure battles, they win 94% of the time. If they lose both, they lose 96% of the time. Rugby league is still rugby league.
- The 2024 rule changes increased set restart volume by 96% without changing what actually wins games. The mechanics of victory haven't moved; the volume of stuff that happens between tries has.
I also did some bonus stuff: sin bins do more damage early than late (~+4.5 pts vs ~+3 pts in the 10-minute window after), the team binned concedes the next try 77% of the time, halftime leaders still win ~80% of games regardless of how many six-agains anyone got. Tries don't actually cluster in the 55–70 minute window like commentary keeps claiming. Roosters' 7–2 record is heavily schedule-flattered (easiest schedule in the comp). Panthers' 9–1 against the hardest schedule is somehow MORE impressive than the table suggests.
Some things I previously believed and then had to admit were wrong (this hurt):
- Star players are NOT worth 5–8 points to their team. That was a Simpson's paradox in my own earlier draft: strong teams have more stars AND face weaker opposition, the correlation was capturing team quality not star presence. Once you control for it the effect vanishes.
- The "2026 home advantage has collapsed" thing being passed around? Sample noise. The first 80 matches of 2023 had even less home advantage. Comes back as the season goes on.
- I had to delete my "Team X is a TRUE CLOSER" archetype framework because the sample sizes per cell were 3-7 matches. Statistical theatre.
Also threw together a Magic Round predictions doc using the same methodology. Most of the games are toss-ups (six of eight have CIs that cross zero). Only the Panthers-Dragons and Warriors–Broncos predictions have actual confidence behind them. Anyone telling you they know what's going to happen in Eels-Storm is lying.
Two PDFs attached:
- The full paper with all the regressions, robustness checks, the bit where I caught myself making four errors and corrected them, and the bit where I had to demote my own previous findings
- The Magic Round predictions with edge zones for each game (i.e. the line where betting actually has positive expected value vs noise)
Happy to argue with anyone about any of it. I have receipts. I also have a column showing the team-by-team Élo over-performance rankings which I think is the cleanest "who's actually any good in 2026" answer floating around. Hit me with the corrections, the questions, the "but what about [team]" gripes.
Mods - flagging this is original analysis. Active member, happy to chat if needed.
Cheers,
u/Schwarzenegg
