Monday, October 24, 2022

AFL WOMEN'S Rankings: Round 9 SEASON 7


After the first round this season I noted that the proportion of possessions that were contested had increased, supporting a suggestion that moving the AFLW season to the winter months might result in a more contested style of play. 

That early trend held up throughout the season. Teams have averaged slightly more contested possessions per match this season compared with last season (101.4 to 99.6 in Season 6), but over ten less uncontested possessions (102.7 to 113.0). Average tackles have also gone up (63.4 compared with 58.6 in Season 6), as have average hit outs (26.6 to 23.0).

Part of this has been due to the introduction of ‘expansion’ teams Hawthorn and Sydney who have each averaged less than 80 uncontested possessions per game (see table below). However, several established teams have also played a more contested style of football this season. Adelaide, Carlton, Collingwood, North Melbourne, and St. Kilda are all averaging far less uncontested possessions this season, ranging from 10 less per game for the Crows to 36 less for the Blues. They are also averaging more than seven tackles and hit outs more per game this season (see table below).

There are a few teams going against the trend though. Melbourne, GWS, and Geelong are averaging a lot more uncontested possessions per game this season (see table above), and other teams like the Bulldogs are averaging similar figures. Most teams though have still seen an increase in average tackles and hit outs this season, which indicates more stoppages.

Have more contests, tackling and stoppages led to less scoring though? Only slightly – average points per game has decreased from 33.9 in Season 6 to 32.2 in Season 7. More points is not necessarily better, but at least this suggests that teams are still finding avenues to scoring at about the same rate as before even if the matches have become slightly more of a ‘slog’ this season.

Monday, October 17, 2022

AFL WOMEN'S Rankings: Round 8 SEASON 7


The Magpies and Cats become more defensive

Collingwood will make the finals again this season, and Geelong will almost certainly make it in what has been its best AFLW season to date. A notable point with both teams this season is how few points they have allowed.

Collingwood has allowed only 132 points at 16.5 points per game, and Geelong has had 165 points scored against them at 20.6 points per game (see table below). Both teams have averaged around six scoring shots against per game. Collingwood has had more than 21 points scored against them only once this season. (Alas for Fremantle, they were the prime example of both teams’ stinginess, scoring only one point against Geelong and three points against Collingwood.) 

To some extent this low scoring has come from these teams’ abilities to limit inside 50 entries, with Collingwood ranking second and Geelong close to third on inside 50 entries against. However, of more note has been their ability to limit scoring once the ball is in the opposition 50, with Collingwood allowing only 0.71 points per inside 50 against and Geelong 0.83 points (see table above), compared with the league average of 1.11 points per inside 50.

Geelong was actually relatively stingy last season as well, at least for a ‘bottom half’ club. They averaged about 31 points against, which was over a goal less than any other team that lost more games than they won. This season however they have got even better at limiting points per opposition inside 50 – and of course they are scoring more themselves.

Can the ‘great walls’ of Collingwood and Geelong hold up once they face the best sides in the finals? Geelong conceded 28 points to North Melbourne, while Collingwood conceded only 21 points – though eleven scoring shots – to the Adelaide Crows earlier this season. Noting the small amount of evidence from this, it suggests they may be able to restrict the top sides to a fair extent – although they in turn will probably find their scoring more restricted.

Tuesday, October 11, 2022

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.

Monday, October 10, 2022

AFL WOMEN'S Rankings: Round 7 SEASON 7

 

The Kangaroos are better than their record suggests

North Melbourne currently sits at seventh on the AFLW ladder. They have won fewer matches this season than Geelong and Richmond, and as many matches as the Gold Coast Suns and Western Bulldogs. The rankings currently have them as a clear fourth however, and they are generally regarded as one of the top sides.

The Roos have had a relatively tough fixture this season. Part of that is because they made the finals last season. However their fixture has been tougher than Collingwood’s, who also went out in the first week of the finals. North has played all of the top three teams Adelaide, Brisbane, and Melbourne, while Collingwood has only played Adelaide so far (they will play Brisbane in the final round). The Magpies have also played lower-ranked sides such as Sydney, GWS, and St. Kilda, while North’s only ‘easy’ opponent has been Sydney, who they beat by 66 points. The difficulty of the Roos’ fixture is commensurate with Grand Finalists Adelaide and Melbourne, rather than a team that finished fifth or sixth (see table below).

North Melbourne’s three losses this year have come to those top three teams. They were particularly competitive against Brisbane on the weekend, losing by seven points but having 43 to 23 inside 50s. They lost to Melbourne by only two points, which perhaps understates the disparity in that they had seven less scoring shots, although they did have only three less inside 50s.

Geelong has not had to play any of the top three teams. Richmond has had to play two of them, and to their credit did beat the Lions. Gold Coast has had to play only one (Brisbane), and the Western Bulldogs also has played one (Melbourne), and both were beaten by over ten goals.

North Melbourne does have an easier matchup this week against Port Adelaide, but then have a couple of tougher matches against fellow finals contenders Collingwood and Richmond. Those matches may end up shaping whether, despite their tough fixture, the Roos can still finish in the top four this season.

Monday, October 3, 2022

AFL WOMEN'S Rankings: Round 6 SEASON 7


This AFLW season, the four remaining AFL teams entered a women’s team in the competition for the first time: Essendon, Hawthorn, Port Adelaide, and Sydney. Things didn’t go too well for them on the weekend, as three of these teams were thrashed by more established clubs: Essendon lost by 44 points to Brisbane, Sydney lost by 66 points to North Melbourne, and – possibly most bitterly – Port Adelaide lost by 60 points to its biggest rival Adelaide. Indeed it could have been much worse if the winning teams had been more accurate in front of goal, with the expansion teams combining for 22 points from just seven scoring shots, while their opponents combined for 192 points from 67 scoring shots.

Are the expansion clubs worse this season than they have ever been? There is a logical argument that it would become harder for later entrants to be competitive as the talent pool becomes less deep. However, this year’s crop of new teams is not necessarily that much weaker than the last set (Gold Coast, Richmond, St. Kilda, West Coast) back in 2020.

Essendon and Port Adelaide lost to probably the two strongest teams in the league on the weekend, and have been on the whole reasonably competitive, particularly the Bombers (see table below). In terms of percentage and ranking points (which adjusts for opponent strength) they have performed similarly to Gold Coast and St. Kilda a couple of years back, who were each able to pick up a few wins, with the Suns even sneaking into the finals.

Hawthorn and Sydney have been significantly less competitive. The Hawks have picked up a couple of close wins in the past two weeks against the Swans themselves and the similarly-placed Eagles, but other than that have generally been soundly beaten, with a percentage of under 50 (see table above). The Swans have not won a game at all, and have a percentage under 30.

However, casting our minds back, Hawthorn and Sydney have actually performed similarly to Richmond and West Coast back in 2020. Both had percentages in the 30s in their first season, and they had only one win between them. West Coast is still struggling, but Richmond in their fourth season have managed to turn themselves into a finals contender.

Overall, some expansion teams have been ‘more ready than others’. North Melbourne was strong from the start, even if they did miss out on the finals in favour of Geelong due to the league’s conference system at the time. Essendon this season are a decent side; they are far from the first side this season to be well beaten by the Lions, and have been competitive in all of their matches up to that point. Gold Coast and St. Kilda were also decent in their first season. Port Adelaide, like Geelong before them, has been well-beaten at times, but on the whole has performed in line with some of the lower established teams.

Only Richmond and West Coast in 2020, and Hawthorn and Sydney this year, have been routinely trounced in their first seasons (at least when not playing another bottom team). AFLW teams tend to rely heavily on the strength of their midfields, and those teams have been relatively inexperienced in those areas. Richmond had Monique Conti in its first season, but has only now got significant support around her. Hawthorn only has Tilly Lucas-Rodd in its main midfield rotation who has played before this season, while Sydney’s midfield is full of rookies. The Eagles have Emma Swanson in the centre and had Dana Hooker, but the latter has played less in the guts since her major injury.

All of them have high draft picks though who may develop into the next wave of midfield stars; Ellie McKenzie at the Tigers, Jasmine Fleming at the Hawks, Montana Ham at the Swans, and Ella Roberts at the Eagles. It will be interesting to see over the next few years if this equalisation method has brought these newer teams up to the level of the ‘foundation’ clubs.