Tag: footy

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Footy-Love

The new “6-6-6 rule” might have been a headline grabber in the early weeks of the 2019 AFL season, but what I was thinking more about leading into the 2019 season is more akin to a “6-4 6-4 6-4” scoreline.

That’s right, thinking about footy more like tennis.

Wait, what?

For a few years now I’ve noticed a few similarities between the team dynamic of football and individual dynamic of tennis. In both sports, teams/players exchange ‘plays’ and essentially the aim to transport the ball past their opponent/s to hit the scoreboard.

In Australian football these individual plays takes the form of possession chains, with teams trading successive sequences of possessions essentially up and down the ground until one is able to ‘pass’ the other’s defence and a score is recorded. In tennis, players trade successive shots up and back across the net until finally one is able ‘pass’ the other’s defence for a winner (or, enforce an error) to register a point.

I also like to think of the strategies and strengths of footy teams in the style of tennis player types. Here I think of a team’s ‘first serve’ as its first play out of the centre bounce when it has won the clearance. Like on the court, in football not only can this be a point scorer on its own, but it can heavily dictate the rest of the play until the next score. Then a team’s ‘shots’ are their possession chains, in which they are successively trying to gain territorial advantage (‘court position’) in order to eventually overwhelm the opposition and score. Here may be some analogies of teams’ strategies with regard to commonly accepted types of tennis players:

• The big first server – These teams have their biggest strength in the initial play from the restarts at the centre bounce, both in quantity (number of clearance wins relative to the opposition) and quality (how much damage they can do from them). Through their centre clearances they either serve a lot of ‘aces’ (centre clearance leading to direct scores), or they set up a lot of the rest of their play via putting their opposition heavily on the back foot through winning strong field position via a forward press.
• The aggressive baseliner – These teams are less likely to worry about damaging the opposition with their first serve (centre clearance), but are really successful at grinding down their opponent around the ground (‘around the court’). With each possession chain typically a little more damaging than the next one coming back the other way, they are able to score ‘winners’ from all over the ground. They typically prevail by being a little bit better for a little bit longer.
• The counter-puncher – These teams are strong in defence and are effective in soaking up what ‘shots’ their opponent is hitting their way. They wait until their opponent has found themselves out of position after unsuccessfully attempting a few ‘winners’, and are able to cause damage on the turnover. These teams generate a higher percentage of scores from defensive intercepts, where they exploit their out-of-position opposition to convert defence into attack and score quickly the other way.

First serve wins?

In recent years in the AFL, I think it’s fair to say that ‘court speed has slowed’ and as a result there are ‘longer rallies’ between scores. The consistent and persistent underlying conditions of play have been horde of midfielders surrounding the ball and one or two additional defenders outnumbering a couple of forwards a kick or so either side of the play.

One of my hypotheses heading into this season was that the “6-6-6 rule” (now to be known in this article as the ‘restart positions’ rule) was going to change the initial mode of play for some period of time following each restart. As restricted restart positions would, for the first time, significantly alter this status quo for at least some period after each restart, I was interested in both the first order and potential second order effects of these changes. I was expecting that the ‘first serve’ would become more significant as teams could either potentially score with more ‘aces’ or at least set up to be in more dangerous field position from the first possession chain following a centre clearance. I thought teams would be able to gain more ground from centre clearances (as there would be fewer players closing into the middle), both taking the opportunity to exploit the opposition backline and score directly as well as moving the ball as far away from their own equally-numbered defence, in turn finding scoring easier with quicker and cleaner entries.

Going into 2019, I was wondering to what extent the court would ‘speed up’ in the initial play following a centre bounce, counteracting the overall trend towards slower plays and longer rallies. I was interested in how far the needle would swing back towards favouring the ‘big first server’ and away from the ‘aggressive baseliner’ and/or ‘counter-puncher’.

The approach

There are some limitations in the publicly-available data in trying to address my hypothesis. The first is that play-by-play data is not available in any sense. Score source aggregates by game are available through some channels, but to my knowledge that requires manual collation. The other issue with that data is that it does not contain a temporal aspect so we cannot see the timeliness of the original starting position effects before players are once again able to more freely over the ground.

Instead, my approach was to look to score progression data from AFL Tables and infer the ability to score based on each phase following a centre bounce.

I defined a ‘phase’ as the time it takes to realise an event (goal, behind or siren) following a previous event (goal, behind, or start of the quarter). For each restart from the centre bounce (either at the beginning of a quarter or following a goal within a quarter), I considered what was the following event and how long it would take for that event to occur. Because the data is recorded in ‘count up’ format and doesn’t account for stoppages in play, I used a manual adjustment based on sight of the data (it turned out to be about 50 seconds) to line up the plays following a goal with those from the start of a quarter (as best as I could). This time more or less accounts for the time taken for television broadcasters to play their ads and the ball to be returned back to the middle (where there is a rare double-goal from an immediate free-kick this time is reduced accordingly).

I then aggregated all of these phases following centre bounces each season back to 2018 and looked at three metrics over game duration:

1. The ability to score in a phase (how frequently are scores recorded?)
2. The accuracy of scores in a phase (of those scores, how many are goals?)
3. The scoreboard impact per phase (what are the average points scored?)

The data returns only a mini-break from the usual

1. The ability to score

My first hypothesis was that teams would find it easier to generate shots at goal (resulting in scores) for some period following the centre bounce, while positions are constrained. The data shows that, to date in 2019, any micro story in the uptick from the new ‘restart positions’ rule has been swamped by macro trend ongoing downward pressure on scores. There is some evidence that the rule has retained the ease of scoring from the previous decade for the first minute or so following each centre bounce, but after that there is quite a stark reduction in the ability to score this season (refer 2019 pane).

Within the first minute of game play following the restart, this year’s scoring ability tends to match the long-term trend, which suggests to me that the new rule has temporarily offset the increasing defensive prowess and structures of teams for around 60 seconds. After this time, the average number of scores per phase drops away well under the long-term trend.

Looking across the seasons, it’s interesting to note how much easier it was to generate scores in 2008 than it was in 2009 (where almost a goal per team per game was lost). Of course, we are only looking at a sub-set of all game play in this chart (within the first four minutes of play following each centre bounce), rather than all game play, but these trends should reflect game play and score dynamics following a clean restart (rather than, say, a kick out from one end of the ground).

There is always some risk with binning continuous data that it creates a signal that doesn’t otherwise exist. I played around with displaying this data in a number of different ways (including using smoothing methods) however they tended to have issues fitting to the first few data points (where there can often be a secondary stoppage and rarely a shot within the first few seconds) as well as the overall trend. I hope a set of 15-second intervals both displays the data simply enough to understand but doesn’t create signal where there is none. I think in this case it’s clear to see that until approximately one minute of game play, the 2019 scoring ability matches the long-term trend, whereas after this point it is consistently below the overall average.

2. The accuracy of scores

My second hypothesis was that teams would be able to generate cleaner entries from centre clearances, create shots from better positions and therefore improving conversion from goal kicking. The overall trend has the goal kicking conversion rate a little lower (50.5%) in the first minute than in all post-restart phases (overall 52.7%). The (admittedly a little noisy) data does support this hypothesis, with the goal kicking conversion rate above the long-term trend (52.6%) once again for about the first 60 seconds following a restart. After this point, the conversion rate hasn’t had quite the same drop off as the ability to create scores, however there has been a slight drop off this season (overall 51.4%) in that area too.

If you are interested in an in-depth discussion on factors affecting the reduction in accuracy in the modern game, refer to the ‘Footballistics’ chapter titled Goal Kicking Accuracy which addressed a number of these.

3. The scoreboard impact

My overall hypothesis was that through cleaner entries there would be greater ability to impact the scoreboard in the early moments after a centre bounce. The trend of average points per phase over game duration is effectively a combination of the average scores per phase and goal kicking accuracy.

Overall, there does seem to be some positive impact on the scoreboard within sixty seconds of the centre bounce, particularly in comparison to the overall slow-down of scoreboard impact more generally. The new ‘restart position’ rule looks to have retained a similar potency to the long-term modern trend, while the more macro evolution of the AFL has seen the unconstrained score dynamics dry up much further.

The ace in the pack?

I must admit that during the preseason I had expected the ‘restart position’ rule may have had a bigger impact than what we’ve seen in the first six rounds and that it would have fallen out of this analysis. I thought that we may have seen similar to 2008-style scoring dynamic patterns for potentially 90-120 seconds following a centre bounce. Although this doesn’t seem to be the case, certainly one reason appears to be that any decent uptick in initial scoring conditions has been heavily diluted by the greater overall downward trend. I think I am confident that data shows the mode of football in the first minute post-centre bounce adheres to a different behaviour than the resulting game play from then on.

In summary, we are a quarter of the way through the 2019 AFL season and there is some evidence for a little extra dominance by teams on their ‘first serve’, however at the same times they ‘court has slowed’ significantly during the rallies. It may be slightly more advantageous to have a big first serve this year, but to be a successful side you would also need the ability to either wear your opponent down ‘from the baseline’ during gameplay or ‘counter-punch’ swiftly when the opportunity arises!

I have a few ideas for future analysis looking at this data set and analytical approach. It would be worth addressing the score dynamics at a team-level to see which clubs are stronger or weaker from the centre bounce or in general gameplay. Further, adding in phases following kickouts can add an extra dynamic and can potentially call out the counter-punch ability of sides. This way we could infer and categorise teams into different styles, perhaps equivalent to the tennis analogies I provided above.

I am very open to feedback on this or any of my analysis. If I have missed something, made an error, or if you have any suggestions or ideas, please feel free to comment below, shoot me a direct message or hit me up on Twitter.

All data provided for analysis on this page comes thanks to AFL Tables.