Breaking Bad? Analysis of 2012 AFL game results in relation to Days Break.

Published by Higgo on

Geelong has been dealt with three six-day breaks to start the 2013 AFL season.

Will this affect them? Or more importantly, will this affect potential fantasy relevant Cats such as Jimmy Bartell(newly appointed DPP) and of greatest importance, can I glean a football punting edge by factoring in each team’s days break into my line betting formula? The only way to answer these questions is to undergo some statistical analysis.

By analysing all game results in 2012, I shall now explore the degree to which “Longer Break” affects game results.

Research Stat 1:

  • The average percentage of Longer Break winning teams per round was 58.4%

This was not a very impressive finding. I decided to dig a little deeper by removing the expansion teams and Melbourne. Without wishing to offend, these teams could ruin the data set due to the fact that they generally were not competitive and would lose even with the Longer Break advantage.

Research Stat 2:

  • After removing GWS, Gold Coast and Melbourne, the average percentage of Longer Break winning teams per round was 60.8%.

This still represents an inconclusive statistic. My next exploration was to consider this Longer Break affect over the course of the season. Perhaps, as the season progresses and teams become weary, the Longer Break affect may become more significant.

Below are the graphs to try and support this theory;


Both graphs do support my theory as the trend line for each data set has a positive gradient.

Research Stat 3:

  • Teams with a longer break than their opponent will be more advantaged during the later stages of the season.

While this seems like common sense, it is comforting to confirm the finding with actual game data.

Next, I thought it relevant to look only at teams going into games with a 6 day break. Again, it makes logical sense that the shorter the break, the greater the impact on team fatigue and performance.

Research Stat 4:

  • The percentage of games won in 2012 by teams playing against a team coming off a six day break was 52.6%

This was surprisingly disappointing! Not what I expected but on closer inspection I realised that the AFL more often than not fixtures a 6 day break team against a supposed weaker opponent …. A good will gesture? This fact accounts for the low 52.6% result.

In an act of desperation, my final idea was to only look at team’s scores in the final quarter in relation to days break. Surely the shorter break teams would be more likely to “run out of legs” in the final quarter.

Research Stat 5:

  • Teams with a Longer Break outscored their opponent on 51.3% of occasions in 2012.

And after removing GWS, Gold Coast and Melbourne …

Research Stat 6:

  • Teams with a Longer Break (excluding GWS, GC & MFC) outscored their opponent on 61.1% of occasions in 2012.

In conclusion:

  1. The effect of days break advantage on teams becomes more evident over the course of a season; however, this affect is minimal.
  2. The six day break disadvantage is inconclusive at this stage given the AFL tends to allocate these to a more fancied team.
  3. Longer Break team are more likely to outscore their opponent in the last quarter of a game.

As a result of the above investigation, I will not be including a Days Break factor into my 2013 line betting formula.

All data I used can be accessed here

I would welcome any feedback.


Kind regards,

Peter Higginbotham




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It may be putting too many variables into the equation but I wonder if an adjustment for interstate clashes would do anything to give a higher % result.


Excellent. Also considered looking at each game in relation to teams position on the ladder each round …. Both explorations worth completing …… Leave it with me.


So this is where the top end in the grey matter department come to chew the fat eh Higg? Top article mate, very interesting.

So if the Saints are playin North Melbourne at Etihad does it count as an away game or a homey? Might be one of the factors to put into your spread sheet thingo?


Is one season enough of a sample? I'd be looking at 3-4 for a more conclusive result. Individual seasons can be the stand out from "the norm" in both directions, for the good or the bad.

Interesting work though Higgo.


Fair point Ace. I remember back in '93 I think it was. Adelaide came into the league & there was a bye team every week. 1st part of the year the team coming off the bye would loose nearly every week. By the 2nd half of the year it evened out.
A theory to understand it was while teams were fresh there wasn't much advantage in a weeks rest. Also the fitness staff had to learn how to cope with a rest week during the season which hadn't been a factor in the modern game at that time.


Higgo, good analysis as always. Stats are the new sexy amigo.

Further to Steve's point I think you need a variable (perhaps just a binary 0,1) to reflect both travelling to an interstate fixture and returning from an interstate game. For example the Dons going to WA for a game is a trip and their next game at home after going to WA is also a trip game.

Any chance of following that through? And naturally testing for heteroskedasticity? As if you wouldn't!!


Higgo great article and something that hits close to my heart, i use similar analysis to supplement my day job with a steady stream of tax free money from the bookies. May i suggest a few more areas of exploration;

>Analyse the last 6 seasons to get a better trend for the long break
>compare the score coming off the longer break to the average score against the same team when getting a standard break.

i.e Team A def Team B by 39 points after an eight day beak but over the last 6 season averaged a 21 point victory against this team

compare the +/- differential for every team with the longer break and plot the trend, haven't done this myself but its something I've been considering if time permits ( i just can't see it adding sufficient value to my model to justify the time spent, plus over constraining systems can be as destructive as not having enough variables)


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