AFL Statistics Series #7: Approximating the AFL Player Ratings or What Value Do All Of Those Statistics Add Anyway?

This is another main entry in my series on AFL statistics. In this one, I am going to look at how AFL Player Ratings relate to players’ statistics. This includes the more common statistics for actions such as kicks, marks, and handballs, but also the more recent statistics capturing the outcomes of those actions such as inside 50s and turnovers.

One aim is to get a sense of how much these statistics on average are valued within the AFL Player Ratings system (beyond what is already published), and therefore – to the extent that those Ratings try to capture a player’s contribution to his team’s overall net result – how important each of those statistics might be to a team’s winning or losing. But another aim is to also get a sense of how much further information we have got over time about a player’s contribution as more detailed statistics have been developed.

A big thanks here goes to Wheelo Ratings and its extensive collection of AFL player statistics. Most crucially it has each player’s average AFL Player Rating for the season, but also heaps of other measures without which the following analysis would not be possible.

What are the AFL Player Ratings again?

The AFL Player Ratings measure a player’s performance through ‘equity ratings’. These equity ratings place a value upon a player’s action – whether positive, negative, or zero – based on how much that action has improved the probability of his team being in a position to be the next to score.

For example:

·     When a player takes possession of the ball in the centre of the ground, his team is then somewhat more likely to score either a goal (6 points) or behind (1 point) than their opponents, and the Average Equity is 1.58.

·      When a player takes an uncontested mark 30 metres from goal his team is much more likely to score than their opponents – and also more likely to score a goal than a behind – making the Average Equity 4.33.

·       Therefore, the value created by a player kicking the ball from the centre of the ground to a teammate taking an uncontested mark 30 metres from goal is 2.75. (This change in value may be attributed entirely to the kicker or shared depending on whether the receiving teammate took the mark ‘on the lead’.)

Further details are in Dr Karl Jackson’s thesis “Assessing Player Performance in Australian Football Using Spatial Data”.

When watching a game, many Australian football observers would agree that not all possessions and disposals are created equal. They depend on where the ball is and whether it was won in a contest, along with how far it travelled and to which team the disposal went to. The ratings system was an attempt to quantify, through many points of data, how much each player was expected to be improving the scoring potential of his team.

Actions that are highly valued under this system are contested possessions and effective kicks, i.e. actions that ‘win’ the ball for the team and effectively progress it towards goal. Conversely, ‘clangers’ or unforced errors have a high negative value. Actions that gain only small amounts of ground for the team such as handballs and lateral kicks are more neutral. Successful shots at goal can add several points to a team’s expected score and player’s rating, particularly if they are kicked from an unlikely scoring location (the highest ever Player Rating for a game was by Lance Franklin when he kicked 13 goals). While behinds – in contrast to many other rating systems – can actually reduce a player’s estimated value, particularly if kicked from spots where players typically do not miss. 

Part 1: Approximating the Ratings using ‘basic’ statistics

First up, we are going to look at approximating AFL Player Ratings using the oldest and most ‘basic’ statistics for player actions. For the majority of Australian football history, the main statistic we have is just goals scored. Then from the mid-1960s onwards we have the basic counting statistics of actions a player took. These include kicks, marks, handballs, hit outs (sporadically), free kicks for and against, along with goals and behinds. Tackles we have from the late-1980s.

For many football followers just glancing over the ‘box scores’, these statistics still to a significant extent inform their view of how well a player has performed in a match. They may weight these things slightly differently in their heads – whether for example a player with 30 disposals had as much impact as a player with 17 disposals and 2 goals – but many will have their own sense of how ‘valuable’ each of these basic counting statistics are. But what if we regress them against the AFL Player Ratings?

Below is a regression of the average AFL Player Rating for each player in 2022 against his average kicks, marks, handballs, hit outs, free kick differential, tackles, goals, and behinds. (No intercept was included.)

Goals as expected have the most value, with a player’s average rating increasing by 2.8 points for each one goal increase in his average goals. However the average amount of points per scoring shot is a bit under four points, so given a goal is worth six points (some kicks out of bounds aside) this is probably in part capturing some other valuable actions undertaken in the forward line such as getting the ball inside 50, goal assists, and contested marks. The full effect of scoring a goal in this regression is actually a bit more again, because the player will also record a kick. I tried separating out scoring shots from other kicks, but the estimated value of the goal remained about the same – i.e. the coefficient for goals increased by about the amount of the coefficient for kicks (0.4-0.5).

Behinds on average decrease a player’s average rating by 1.9 point. As I understand it the negative impact of behinds in the Player Ratings is on average lower in magnitude than the positive impact of goals as a team remains reasonably likely to score next after a behind since the ball is still close to their goal.

The coefficient for kicks is only slightly more than that of handballs in this regression, even though kicks on average gain substantially more territory. Players that record high amounts of handballs are more likely to be involved in stoppages and contests – think of players like Clayton Oliver, Lachie Neale, Patrick Cripps, and Rory Laird – and therefore they tend to record more contested possessions which are valued highly in the Player Ratings system. Before contested possessions were recorded handballs were possibly our strongest indicator of which players, like Greg Williams and Terry Wallace, were winning the ball ‘in the coalface’. Once contested possessions enter the equation – see next section – then the coefficients for kicks and handballs become more reflective of purely the territory they gain.

As for marks, most marks are uncontested – i.e. ‘chipping’ the ball around – and this type of action is not highly valued in the Player Ratings system. Their value in the basic statistics regression probably captures the valuable things that defensive and ‘outside’ players do such as kicking long and/or efficiently and intercepting the ball, and as we shall see they drop out of the regressions as we add more ‘advanced’ statistics.

Free kicks enter the regression as the differential between free kicks for and against. The coefficient for free kicks against was close to zero in other versions of this regression, probably because players that give away more free kicks are those that tend to be involved in more contests and put more pressure on the opposition.

We can multiply a player’s average for each of these statistics by the coefficients to get an estimated average rating for each player. Compared with the Player Ratings, the players that have a high percentage of possessions that are contested (e.g. Tom Liberatore) tend to have a lower estimated average than their actual average Player Rating, while players that have a lot of kicks but a high percentage of possessions that re uncontested such as Jake Lloyd or Andrew Gaff have higher estimated averages. The lack of defensive statistics outside of tackles also means that spoiling and intercepting key defenders such as Darcy Moore, Harris Andrews, Jeremy McGovern are ‘undervalued’. All of this suggests that the ‘basic’ statistics we had from the 1960s to the 1980s left out some important information in assessing a player’s contribution.

Part 2: Approximating the Ratings using ‘intermediate’ statistics

Next we are going to include some of the ‘intermediate’ statistics that became available from the 1990s. This included the famous distinction between contested and uncontested possessions, which told us which teams and players were winning more of the ‘hard’ ball when possession was in dispute, with contested possessions further broken down into clearances (first effective disposal out of stoppages and centre bounces) and contested marks. We also got an indication of which players were costing their team possession through ‘clangers’ (giving the ball directly to the opposition, along with conceding free kicks). Inside 50s and rebound 50s told us a bit more about how far and where on the ground a player was moving the ball, while goal assists told us more about who was involved in scoring a goal for his team. Crucially, the ‘one percenters’ statistic, which captures spoils and smothers, finally gave us a lot more information with which to value the contribution of defenders.

These statistics are still used a lot by fans and commentators, as they are relatively simple and well-understood and do more to gauge the ‘impact’ of a player than just looking at how many times they got the ball. Many fans recognize that players can rack up a lot of ‘easy’ possessions but still not have much of an influence upon the result, or dispose of the ball poorly.

Below is a regression of the average AFL Player Rating for each player in 2022 against selected ‘basic’ and ‘intermediate’ statistics.

The coefficients of kicks and handballs are reduced with the inclusion of statistics such as inside and rebound 50s that can now capture a fair chunk regarding a player’s ability to progress the ball, and contested possessions which capture a player’s ability to ‘win’ possession. Kicks now have a coefficient of between three and four more times that of handballs, which is probably more reflective of the relative territory they gain.

Contested possessions are valued highly in the Player Ratings system and have a relatively high coefficient, with their inclusion reducing the coefficients for handballs and tackles. I tried separating out clearances, contested marks, and other contested possessions (ground ball gets and knock-ons), but they all had about the same coefficient, so I have just included all contested possessions outside of free kicks together.  

An uncontested possession (infamously) has a value of zero if the AFL Player Ratings system. Uncontested possessions and marks were dropped from the final regression, with versions that did include them having small negative coefficients.      

Clangers are one of the outcomes that are most hurtful to a player’s rating, since the player has ended their team’s chain of possession and started a chain of possession for their opposition. A player’s average rating drops by about one point for each one point increase in average (non-free kick) clangers. This is an even greater impact in magnitude than contested possessions, where the ball is still in dispute rather than in the team’s possession.

An inside 50 is on average worth more than a rebound 50 under the AFL Player Rating system. For the same action that moves the ball forward, the gain in ‘equity’ is higher if the ball is in the team’s forward half compared with it being in their back half. In other words the marginal increase in probability of the team scoring next from moving the ball from (for example) 140 to 110 metres out from goal is lower than from moving it from 60 to 30 metres out from goal.

Goal assists have a coefficient of one, which I was slightly surprised by, but I guess captures that the Player Ratings system highly rewards effective disposals up forward.

As expected, the inclusion of one percenters helps to better capture the contribution of defenders. As we will see in the next section however the coefficient is probably also in part capturing the value of intercept possessions.

We can again multiply a player’s average for each of these statistics by the coefficient to get an estimated average rating for each player, and this now actually does pretty well in matching a player’s average AFL player rating. One type of player that is still ‘underestimated’ compared with the AFL Player Ratings is the ‘striker’ midfielder-forward type that wins the hard ball and is a penetrating kick into the forward line. This includes players like Jordan de Goey and Shai Bolton this season, and Dustin Martin and Jake Stringer in previous seasons, and also to some extent attacking midfielders like Marcus Bontempelli.

Part 3: Approximating the Ratings using ‘advanced’ statistics

Finally, we will include the more detailed statistics that have only become publically available in recent years. This includes statistics like metres gained, intercepts and turnovers, score involvements, hit outs to advantage, and pressure acts.

These types of statistics provide useful additional information about a player’s impact (e.g. how often a ruck actually hits the ball out to a teammate rather than just hits it), but they are generally not available that widely, or for a long historical period. It will be interesting to see then how much information they do seem to add in explaining a player’s contribution over the more familiar ‘1990s’ statistics. 

Below is a regression of the average AFL Player Rating for each player in 2022 against a selected range of ‘full’ statistics.

Note that some of these new statistics will in full or part encompass older statistics – turnovers include some clangers, score involvements include goals, behinds, and goal assists, and pressure acts include tackles. However, as with goals and kicks I found the overall values of scores, tackles, and clangers were not much different whether they were separated out or not – e.g. the coefficient of tackles increased if they were separated out from other pressure acts.  

Average metres gained now essentially takes over from kicks and handballs, and even inside and rebound 50s, as the most ‘useful’ measure of a player’s ability to gain territory. Inside and rebound 50s still have value in terms of indicating where the player is on the field, and therefore how much they are increasing the team’s probability of scoring when disposing of the ball (although the coefficient for rebound 50s is now closer to zero).

Hit outs to advantage has displaced ‘raw’ hit outs in the final regression as the more significant explanation of a player’s average rating. However as hit out to advantage percentages do not vary hugely across players these are highly correlated, and ‘raw’ hit outs would still tell us a fair bit about a ruck’s ability to win the ball for his team.

Turnovers have a negative coefficient that is lower in magnitude than that of clangers. Many turnovers occur when a player kicks the ball to a contest. These may often be considered ‘ineffective’ kicks, but they still before the contest was decided gave the team a chance of retaining the ball, and are therefore on average less hurtful than straight out giving the ball to the opponent.

The inclusion of intercepts has slightly reduced the coefficient on one percenters, with the main contributions of defenders now being ‘split’ across the two variables.

Once again we multiply a player’s average for each of these statistics by the coefficient to get an estimated average rating for each player. As it turns out, this actually leads to fairly similar results to those from the ‘intermediate’ statistics. That is, the same types of players – such as ‘striker’ midfielder-forwards – tend to remain ‘undervalued’ as was the case under the ‘intermediate’ statistics regression.

This suggests that the main advance in capturing the types of contributions a player makes was when we went from ‘basic’ to ‘intermediate’ statistics in the 1990s, while the jump from ‘intermediate’ to ‘advanced’ statistics is actually more a delineation of these contributions. For example, metres gained is possibly just a more detailed, refined measure of who is kicking the ball long that was already largely captured by inside 50s and rebound 50s, while tackles already captured to a fair extent a player’s overall number of pressure acts, and one percenters a defender’s ability to stop or intercept the opposition’s attack. This could be why a player rating system like the PAV system, which is largely built on those ‘1990s statistics’, is able to approximate the AFL Player Ratings reasonably well.

That for the most part concludes this series of posts. If/when I return with another post it will hopefully be to finally put forward a ‘player rating’ system of my own.