The devolution of style of play in the NRL

One of the outcomes of the introduction of set restarts after Round 3 in 2020 was an increased time in play, which artificially inflated a lot of numbers and not necessarily in a good way. It claimed to speed up the game, but mostly just resulted in more “stuff” happening in the middle of the field.

Six more tackles from a ruck infringement usually just meant they restarted their set as they would from any other spot on the field – three to four one out runs before spreading the ball before an attacking kick.

This isn’t new information. It’s something I’ve written about extensively over the past six months and will probably continue to be a factor in 2021 with offside violations now incorporated into set restarts.

What might be new information is that the Round 3 changes in 2020 didn’t really create a new trend in play style, they just accelerated one that had already started a few seasons ago. We’re seeing more one out running, dummy half freedom decreasing, fewer offloads and consequently less second phase play. The only thing that hasn’t changed is the frequency of long runs.

This makes sense as coaches have become even more conservative, favouring sensible mistake free football over an expansive and aggressive attacking philosophy. Given the immense pressure and scrutiny they’re under it’s hardly surprising, but aesthetically it’s not very viewer friendly.

By looking at some key running and passing statistics over the past seven seasons (2014-2020), we can get an understanding of how the style of play in the NRL has changed.

Let’s take a closer look at some of these trends affecting the style of play. As usual, all statistics are taken from Fox Sports Stats

One Pass Run % (Hitups)

The first was to look at the change in style can be seen by the percentage of one pass runs (a standard hit up, from one pass out of the ruck) out of all runs made by a team. We can see below that it has been increasing over the past three seasons, to the highest rate since 2017.

The reason for looking at one pass runs/hit ups as a percentage of all runs is that we know there were more runs in 2020 due to more time in play. Logically that means more hit ups. By looking at the percentage of all runs, we get an idea of how often they’re occurring.

Next we’ll breakdown just one pass run percentage even further. The below chart shows every coach from 2017 who has survived more than three games, their average runs per match and the percentage of all runs that came from one pass. The data points in aqua are from 2020 and the lighter shaded ones represent prior seasons.

It highlights just how much more running was done in 2020, with ten coaches averaging more than 165 runs per game. The previous high from the six prior seasons was 162 per game, by Dean Pay’s Bulldogs in 2019.

The other thing to note is that since 2014, there had only been five coaches who had a one pass run percentage of higher than 50%. We moved from just five in the previous six seasons, to eight in 2020, having more than half their runs come from a basic hit up. Some successful coaches from last season were among those eight, with Brad Arthur’s and Ricky Stuart’s sides using around 51% of their runs on hit ups. Others included Dean Pay and Anthony Seibold, indicating it’s not a metric that necessarily correlates with winning.

One data point that stands out is in the bottom right corner, which is Ivan Cleary in 2020, who had just 40.8% of their 180+ runs per game come from a one pass run. The only coach to use fewer hit ups was Justin Holbrook at 40.3%. Cleary has been at the bottom of this list for a number seasons so it’s nothing new, with 38.5% in 2019, 42.9% in 2018 after 48.9% in 2017.

Dummy half runs

The number of dummy half runs declined again in 2020, and chart below shows just how few coaches let their #9 run with the ball out of the ruck, dropping below 5% for the first time since 2014.

Breaking it down, again we’re looking at all coaches, three or more games at the helm, this time from 2014-2020 and the percentage of runs from dummy half, with 2020 highlighted.

What stands out is that no coach from 2020 had a dummy half run percentage higher than 7%, with Craig Bellamy and Michael McGuire the highest last season topping the NRL at 6.8%. More than half the coaches had a rate of less than 5%.

Another way of looking at the decline in hooker freedom is that there were zero teams with a percentage of dummy half runs higher than 7% in 2020. In 2019 that was three. In 2018 it was seven. And going back to 2014 there were ELEVEN coaches who had dummy half run percentages of at least 7%, with two of them (Neil Henry and Steve Price) above 12%. Let that decline sink in for a minute.

We were sold a bill of goods on set restarts bringing the little man back. They never left, it was only observation bias that indicated they were back post Covid break. The real story is that the little men in the middle of the field are being held back by their coaches, not the rules.

Long run %

The one constant of the past seven seasons has been the percentage of long runs (8 metres or more). Starting in 2014 at 60.7%, we haven’t seen an NRL season with the percentage of long runs below 61%, with the last three seasons sporting very similar rates.

2020 followed this trend, with a scattering of coaches between 66% (Brad Arthur) and 56% (Peter Gentle). That’s not to say that longer runs were an indicator of success, as Paul Green and Paul McGregor were in the top five for 2020, both in the 63% range.

Not a lot to see here, the changes last season didn’t influence the overall frequency of long runs.

General play passing

This one is pretty damning for 2020 significantly changing the style of play. As shown earlier, not only did we see more one out running, but we’ve also seen a significant decline in the number of passes per run. The number of passes per run in 2020 is nearly 10% down on the peak of passing in 2018. Not to sound like a broken record, but we’ve had more running and less passing.

This coach chart is somewhat of an inverse of the one pass run chart, with Holbrook and Cleary on the other end of the scale. Expansive passing games weren’t a feature of 2020, with most coaches from last season sitting in the bottom half of this chart for passing rate.

The worst offender in 2020 was Canberra’s Ricky Stuart with 0.46 passes per run only beaten by … Canberra’s Ricky Stuart in 2019 with 0.45 passes per run. It’s clearly working though, given the Raiders results for the past two seasons. In fact, Stuart holds the three lowest passes per run rates since 2014, and four of the six lowest seasons during that time frame.

Note: One thing I’d like to point out is that “General Play Passes” in the Fox Sports Stats don’t appear to include dummy half passes, so you can probably add 1 to the average number of passes per run. It doesn’t change the layout of the chart though, just the number.

Offloads

The rate of offloads has also declined each season since 2017. It’s already a low number since there are relatively few offloads per game, but they’re becoming even rarer and are 10% down since the peak in 2017.

One of the biggest differences in offload rate was something I’d written about previously, with the Warriors changing their coach mid-season and being given some more freedom to offload the ball. Under Stephen Kearney the Warriors had just 0.041 offloads per run, whilst under Todd Payten that number nearly doubled to 0.072, second in the NRL only behind Brad Arthur at 0.075.

Anthony Seibold was the only other coach to approach 0.07 offloads per run, at 0.068.

There were fifteen coaches who had an offload per run rate of 0.07 or better in 2014, and that number has declined to just eight in 2020.

All of this indicates just how the style of NRL play is becoming more predictable, with basic one out running from middle forwards now not only the bread and butter of the game, but most of the main course as well.

Rugby League Advanced Stats Glossary

This page contains a short rundown of some of the advanced statistics for rugby league that used on the Eye Test. The first three have links to longer articles that explain them in greater detail. All statistics (unless otherwise noted) are taken from the Fox Sports statistics page

Tackle % – Also referred to ask Tackle Rate. An advanced statistic for rugby league that is used to quantify how often a player makes a tackle whilst on field, normalised by the estimated number of “plays” defended during their time on field. After all, you can’t complete a tackle if your team has the ball. The idea behind this metric (and the following two) is to provide a way of quantifying the effort of middle forwards who don’t play big minutes and are often overlooked for players who put up bigger raw numbers in larger minutes. The average Tackle % for a middle forward is in the 25-26% range, indicating they complete a tackle on one out of every four plays their team defends. The 2020 leader for Tackle % was Jai Whitbread of the Gold Coast Titans at 35.93%.

Run % – Also referred to as Run Rate. An advanced statistic for rugby league that is used to quantify how often a player completes a run whilst on field, normalised by the estimated number of “plays” the players team had whilst they were on field. Like Tackle %, the idea is to show which players are completing a high rate of runs for their time on field, as middle forwards usually play fewer minutes than the rest of a team. The average Run % for middle forwards is usually in the 10-12% range, meaning they complete a run in one out of every ten plays. The leader for 2020 was Cronulla’s Andrew Fifita at 17.34%.

Involvement Rate – Combines Tackle % and Run % to give a holistic metric for player involvement during a rugby league game. Like the other two advanced statistics, this is normalised by the number of plays whilst on field. The average Involvement Rate for middle forwards is between 17-19%, meaning they either complete a run or tackle on nearly two out of every five plays whilst they are on field. Generally Tackle %, Run % and therefore Involvement Rate generally decline as minutes increase. The leader for 2020 was Jaimin Jolliffe from the Gold Coast at 21.93%

Net Points Responsible For (NPRF) – A metric for a players overall contribution to a teams performance. Each score by a player is valued as it is on the scoreboard (try – 4, goal – 2, field goal – 1), plus 4 points for every try assist and try contribution. Four points are removed for every try cause a player concedes as a way of quantifying their defensive contribution. This total is then divided by the number of games played to get a plus or minus total points per game that a player is “responsible” for. The NPRF leader for 2020 was Penrith’s Nathan Cleary at +9.72 per game.

Error Rate – The number of possessions a player has divided by the number of errors they have made. The end result is X number of possessions by a player for every error generated. The worst performing players each season usually have an error rate of <10 possessions for every error committed. Nene McDonald from the Sharks was the 2020 leader, committing an error every 5.33 possessions in his two games.

The Eye Test’s Most Adequate of 2020 – The rule change affecting time in play that’s rarely talked about

This article was originally posted as part of NRL Round 14 notes and trends, August 18, 2020.

One of the things I’ve noticed over the past few rounds is that the average time of ball in play has dropped slightly to the pre Rugby League 2.0 levels. This comes after a decent increase earlier in the season once the rules were changed. Focusing just on time in possession, the last NRL three rounds haven’t had more than 57 minutes of ball in play, the three lowest rounds this season and both before V’Landysball was introduced in Round 3.

This led me to investigating why, and I put together the below chart plotting time in possession (sourced from NRL.com) against points scored per game. The blue line represents average time in possession for the first 14 rounds of the 2019 and 2020 seasons, and the yellow bars represent the average points scored per game in each round (by both teams).  There’s a reference line on these bar charts as well to show the average for 2019 and 2020. For points its about the same – 38.8 in 2019 and 39.9 in 2020.

Initially I thought that the amount of points scored was reducing the time in possession, with more tries and conversion increasing the amount of time the ball was doing nothing. But if you look at the above chart, it’s not really apparent – Rounds 8 and 11 had average game scores below 40 points, but time in possession above 62 minutes, significantly higher than other rounds this season.

I should note at this point I’ve filtered out any golden point games to normalise minutes per game. A great example of why is Round 3 2020, where the average goes from 58.81 to 61.46 if you include the Panthers v Knights drawn match which had a whopping 80 minutes of time in possession. Another note is that Round 12 2019 had only four games played due to State of Origin, which is why it looks like an outlier.

It’s not due to tries either, see below for the chart that shows why. Round 7 2020 had 8.3 tries and nearly 58 minutes of time in possession, while Round 9 this year had 6.7 tries but 62.7 minutes of possession. Again, this makes sense with the previous chart as points are a factor of tries scored.

My next thought was maybe there are fewer penalty goals? There are fewer penalties being called, so it makes sense that there were fewer penalty goal attempts this season. Whether or not that’s a good or bad thing is another discussion, especially in those instances where a team is down 2 inside the opponents 20m zone and gets a set restart. But that’s another matter for another time. Below is penalties awarded plotted against time in possession.

This led me to look at penalty goal attempts against time in possession. The data checks out – 1.6 attempted penalty goals last year against 1.1 in 2020. And they’re being taken at a lower rate too. In 2019 penalty goal attempts comprised nearly 20% of all shots at goal. In 2020, that number has dropped to just 13.6%. So that’s the likely reason for the increase in time in possession, right? Less time standing around waiting for a kick at goal.

Hang on, let’s look at something a bit closer on that chart. Round 1 and 2 had time in possession of 58 and a half minutes and an even 57 minutes, respectively. That’s more time in possession than the last three rounds under one referee and with set restarts. There’s actually been six rounds since Round with less time in possession than Round 1.

Yet Round 1 and 2 had over 2 penalty attempts per game, far higher than the rest of 2020 and more than most rounds last year to the same point. How did those two rounds still have high time of ball in play yet more penalty goal attempts?

Maybe the time elapsed during a penalty goals is counted as time in possession? If that were the case, that wouldn’t explain Round 12 having 56.6 minutes in play with 1.4 penalty goal attempts per game, while Round 8 had almost 63 minutes in play with just 0.6 penalty goal attempts per game.

Maybe the game is just faster? In this “faster pace” era, everything is up, and more stuff is being done. So far this season we’ve seen an increase in time that the ball is in play. There’s an increase in runs and play the balls as well. Although not an increase in play the ball speed.

But we do know from the first chart that the ball is in play more this season by about 8% compared to 2019 for the first 14 rounds. Runs are up nearly 10% compared to the same point in 2019. Passes are up 5%, line breaks are up 7% and tries are up 10% Everything is up! More stuff is good!

Kicks are also up 7.5% year on year, with long kicks up 17% and attacking kicks are up 7%. More stuff! But hang on – weighted kicks are down 20%. That’s strange. Forced dropouts are down 2.1%. Kicks dead are down 3%. Why would those kicks be down year on year if everything else, including other types of kicks, has increased?

The fact there’s not a corresponding increase in weighted kicks, kicks dead and dropouts, and a higher increase in attacking kicks than other statistics indicates something has changed. You might be slowly getting at where I’m leading with this and why its taken over 700 words.

To save you anymore of this shaggy dog story, here’s my crackpot theory – teams have gotten more efficient and accurate at aiming their attacking kicks just outside the goal area to avoid a seven tackle set. The rule change which came into effect in Round 1 that gives airborne attacking players the same level of protection as airborne defensive players is surely a driver for this, as Daniel Tupou was showing before succumbing to injury.

This explains the drop in weighted kicks but the large increase in attacking kicks. Fewer kicks reaching the in-goal area leads to fewer dropouts which can take up to 45 seconds each. By aiming them a bit shorter than the try line, at worst a team will give up possession less than 10 metres out or a scrum at the same point. This is a much better result than a seven-tackle set from the 20-metre line.

Why does this make such a difference in time in possession? A drop out usually takes 40-45 seconds off the clock, because the NRL has a rule saying you can take that long (another rule change with unintended consequences). In the first two rounds this season, there were 20 fewer forced dropouts than the first two rounds last season than in 2019. If you are generous and say each one takes 40 seconds, there’s 920 seconds saved across two games. Divide by 60 to get minutes and then divide again by the eight games per round and you get an extra 57 seconds saved on average per round purely from fewer dropouts.

This would account for some of the time in play change for Rounds 1 and 2 this year compared with last year. It also explains why Round 3 had only a slightly higher time in possession than Rounds 1 and 2 – the time savings from reduced penalties was cancelled out by having over five dropouts per game that round. The chart for average dropouts against time in possession is below.

These first two rounds this season serve as my exhibit A, albeit with a small sample size. There is similar average time in possession to post Round 3 (excluding the golden point draw), but there were still two referees and no set restarts. A comparable number of penalties were awarded as previous seasons yet more penalty goals attempted. The key is fewer dropouts in Rounds 1 and 2 compared to 2019, and below the average for 2020.

Need more proof that a reduction in forced dropouts might be part of the increase in the time of possession? Exhibit B – the last three rounds have had the three lowest time in possession averages this season, all under 57 minutes as noted in the first paragraph. In the last two of those rounds (13 and 14, factoring out Round 12 due to fewer games), dropouts are up 31% year on year and weighted kicks up 11%. As opposed to down 2% and 20% for the season so far. Goal attempts were down 3% over these rounds too, ruling that out as a cause as well. Why the change in kicking? Teams may be finding that their tactic of launching more bombs aimed outside the try line hasn’t been as successful and are adapting. Whatever the cause, there’s another link between time in possession and dropouts taken.

I’m not denying that there is an increase in time in possession due to the Round 3 rule changes, the reduction in penalties also plays a part. There’s an average of three fewer penalties per game this season, and with the NRL has claimed there were five penalties per game in the play the ball last season and each one costs about 22 seconds of play. If you multiple those 3 fewer penalties by 22 seconds, there’s another minute with the ball in play. Add in fewer penalty goals and there’s a bit more time gained. Yet there’s also a similar component of time being saved from fewer line dropouts.

The increase in time on possession hasn’t isn’t just a result of rule changes, but a larger and more complicated combination of change in playing style to suit for these rule changes. The consistent attribution of faster “pace” and more “stuff” being done given solely to set restarts and one referee is proving to be a false equivalence, but one that will get a lot more airtime to boost agendas. If you really wanted to speed up the game, you’d halve the clock on dropouts.

The Eye Test’s Most Adequate of 2020 – The statistical improvements that back Todd Payten’s Cowboys appointment

This article was originally posted as part of NRL Round 17 notes and trends, September 8, 2020.

The North Queensland Cowboys announced interim Warriors coach Todd Payten as their coach for 2021 on Friday, and using the Eye Testtm it’s easy to see why. The Warriors have improved on the field under his watch and are showing a lot more enthusiasm and commitment, even after a coach they supported was removed. Their fightback against the Eels showed a desire that Warriors sides haven’t shown late in a game for quite some time.

This led me to have a look at what changed under Payten and how the Warriors improved under his tenure by looking at the teams per game statistical averages under Kearney compared with Payten’s performances.

First up I’m going to qualify everything below with a small sample size disclaimer – we’ve got six games for Kearney and eleven for Payten to analyse. These aren’t representative, more they are indicative of their performances, but within those games there are hundreds if instances of runs and tackles which gives me some comfort. It’s not like I’m writing an article purely on the win/loss percentages of teams where one win or loss would throw out my premise completely. But I digress.

Below is a chart of the percentage change for a number of statistical categories for the Warriors in 2020, with te blue dots represent the percentage difference between Kearney and Payten, and the orange dots represent the difference between Payten and Kearney. The further the circle is to the right or left, the larger the difference. Whichever colour sits on the right-hand side shows which coach had an advantage in that area, and at first glance you can see by the number of organe points on the right hand side that Payten has outperformed Kearney in the majority of these statistics.

I wanted to note that I’ve chosen percentage change because on a per game basis it’s hard to get a scale that fits average total run metres in the thousands (1400m+) with average metres per run in single digits (8.5-9.0). Otherwise it would be impossible to see some of the changes. Another reason, which if you’re becoming familiar with my posts you’ll know, is that the exact number isn’t as important to me as the size or direction of the trend. I’m looking for how much things have changed under Payten.

WIth that out of the way, let’s delve in a bit deeper to the differences and start with the first line for points scored per game – the orange dot on the right shows Payten had an increase of nearly 50% in points scored per game, with the Warriors going from 12.2 under Kearney to 18.4 per game under Payten’s stewardship. Looking at from the other perspective, the Warriors scored 34% fewer points when coached by Kearney in 2020.

Tries and line breaks were also up significantly under Payten, whilst a possession statistics like play the balls and total sets were down between 1-4%. From this group of stats, you can see that not only were Payten’s Warriors scoring more, they were doing so with less possession. That is countered by the fact they had slightly better field position, as play the balls inside the opponents half and opponents 20 metre area were up 2% and 1% respectively.

The next set of stats I wanted to focus on were runs, run metres and passing. Both teams averaged the sane number of runs (168 per game), which makes a fantastic baseline to use.

There was no change there from a quantity point of view but it’s very clear they’ve changed how they were running the ball, and its effectiveness. The first is that dummy half runs were up 44%, from 7.5 to 10.8 per game. One pass runs, your standard hit up, were down 13% under Payten, whilst general play passes were up 17%. The increase in passing wasn’t just mindlessly throwing the ball around either, as offloads increased dramatically after the coaching change, with nearly 70% more total offloads (6.7 to 11.6) and a triple figure percentage increase in effective offloads.

This would suggest that he has given his dummy halves more freedom, allowing them to skip out of the ruck and engage the line before spreading the ball wide, and the increase in passing stats shows they were playing a more expansive game compared to the safe conservative style under Kearney. He’s also unlocked their ability to promote the ball with offloads as well. Given these changes it is not surprising that they may have scored the try of the year against the Eels on the weekend.

Although as you’d expect, moving the ball around more often did create more errors, which increased by 17%, similar to the increase in passing but that’s just the cost of doing business to improve a teams attack.

On the run metres front, it’s a blanket increase of 2-3% under Payten, and the increases to Post Contact Metres could indicate players increasing their effort as they hit the line and trying to push through initial contact.

Finally, I wanted to look at how their kicking changed, which has seen a drop under Payten in total kicks and kick metres. This lines up with the above changes, showing the team passing the ball more and in better field position, reducing the need for long kicks (or kicking the ball at all) to end a set. Total kicks are down nearly 6% And when they were kicking, they are doing so more accurately and effectively – fewer kicks dead (down 45%) and more forced dropouts (up 63%).

Things have also improved defensively for the Warriors under Payten as well in a few key areas. Total run metres conceded are down 6.7%, post contact metres by opponents are down 9% and offloads have dropped 8.4%. Clearly Payten’s Warriors are putting more effort in defense, reducing opponents gains after contact, and wrapping up the ball carrier more effectively.

And Payten has achieved all of this with a similar line up to Kearney. If anything, you could argue he was dealing with a weaker hand – Ken Maumalo, David Fusitu’a, Agnatius Paasi and King Vuniyayawa all returned home mid-season. They were replaced with loan players like Jack Hetherington, George Jennings, and Daniel Alvaro, who have been fantastic additions but came without knowing the structures and combinations the departing players already understood. Despite these issues, their discipline has improved with 15% fewer penalties conceded per game.

The fact that he has been able to squeeze this extra performance out of a squad with significant challenges living away from their families is incredible. Next season he’ll inherit a stronger Cowboys squad that desperately needs a new direction and an injection of belief and creativity. Payten has shown in just 11 games so far this season that he’s just the man to deliver all three, and it’s encouraging to see North Queensland give him a chance instead of giving a problematic and divisive former coach another run around.

The Eye Test’s Most Adequate of 2020 – Set restarts: if you’re not cheating, you’re not trying

This article was originally posted as part of NRL Round 9 notes and trends, July 14, 2020.

In last weeks trends and notes post, I showed there was a negative correlation between set restarts and margin, which had been positive from Round 3 to Round 7. With another week of matches completed, I thought I’d dig a bit deeper into this to see if I could find out what was leading to this.

The reason I find this so interesting is that it doesn’t conform with traditional rugby league thinking. Possession is treasured, and statistics like run metres correlate with winning games (keep in mind correlation doesn’t equal causation). Yet by giving away more set restarts, you’re giving possession and therefore more metres to the opposition. Surely that would result in giving up more points?

Looking at net margin plotted against net set restarts after Round 9 shows a similar chart to last round. I’ve named the quadrants as well to make it easier to identify what the chart is showing and the bigger the dot the more set restarts conceded.  

As with last week it appears that “winning” the set restart count is inconsequential, with only two teams having any significant net margin despite coming out ahead with set restarts. The top left quadrant – “Conceding and winning” – is the one I want to focus on though, given the makeup of teams within that area.

Last week there was only one top four side in that top left quadrant, which was the Panthers. This week they’re joined by the other top four sides – Melbourne, Parramatta, and the Roosters – indicating that giving away set restarts is a genuine part of their strategy. And that is only counting the set restarts given, not intentional slowing down of the ruck that isn’t called.

The Panthers are a curious case. Penrith not only have the largest difference between set restarts conceded and awarded at -23, they have also conceded the most in the NRL at 43 and been awarded the fewest at 20.

I noted last week that the only time they’ve come out ahead in set restart differential (Round 6 against the Eels), they lost by 6. This continued in Round Nine, with Penrith conceding three more set restarts than Cronulla, yet still beating them by 32 points.

In addition to the Panthers, among the other top four sides, the Roosters have the second fewest restarts awarded (23), the Storm are third fewest (24), while the Eels sit seventh (27).

With the limitations on publicly available data it’s hard to see exactly why they’re benefiting from these restarts, but we can use what is available to craft some ideas. One theory is that the early set restarts that are in vogue help the defensive team get set, limit chances for the team with the ball to gain momentum and exploit any breaks in their line.

A way of quantifying this is could be by examining the amount of runs under and over 8 metres conceded against the raw number of set restarts. As a team concedes more set restarts, there is a small positive correlation with runs shorter than 8 metres conceded (top of chart below), and a small negative correlation with runs longer than 8 metres (bottom of chart below).

Given this correlation, you could assume that the more set restarts you concede, the more likely you are to give up shorter, ineffective runs than longer more damaging runs.

This makes sense – giving up a set restart on the first or second tackle on your opponent’s 20m line and contain them within their own half is preferable to allowing them to string together a number of longer runs and push into your territory for an attacking kick or set piece. It also enables teams to maintain a defensive structure and limit any gains from broken play.

Examining when set restarts are given and the outcome of the consequent set compared to the average set would show if this is successful or not, but again we’re limited by publicly available data.

On the other end of the scale, the Bulldogs position on this chart is just another sad indictment on their run under former coach Dean Pay. They play a very conservative brand of football, limiting defensive mistakes and (attempting to) maintain possession and complete sets. As much as he has been able to get his players to show up every week under trying circumstances, this style of play hasn’t yielded any results and the constant switching of combinations appears to be actively hurting their performances. And let’s not even talk about the Queensland sides.

I’m not arguing that conceding another set of six to their opponent is the reason that the top four sides are sitting where they are. But there is something in the new ruleset for rugby league that is widening the gap between the haves and have nots.

At a surface level there’s a minor increase in win percentage if you lose a set restart count. Removing drawn games and even set restart counts, teams who had a negative set restart differential have won 56% of their games this season. In Round 9, six of the eight games were won by teams with a negative set restart differential. The only teams that won with a positive set restart differential on the weekend were Souths and St George Illawarra.

Conceding more set restarts to your opponent isn’t going to win or lose games for you, but strategically conceding them appears to be part of the game plan for the successful clubs in 2020.

The Eye Test’s Most Adequate of 2020 – Why volume statistics aren’t always your friend

This article was originally posted as part of NRL Round 15 notes and trends, August 25, 2020.

This week should have been a fantastic matchup of halves, with Shaun Johnson of the Sharks facing up against the Panthers and Nathan Cleary. Cleary has been one of the standouts this season, whilst Johnson is having a great season, but you wouldn’t necessarily know it from the way he’s often covered by the mainstream media. I’d pointed it out previously with a radar chart comparison in early August:

Johnson ended up missing the game due to some minor injuries and the birth of his child (congratulations Shaun!) and Cleary had another strong showing. Despite this we can still have a look at their statistical output for the season and use it as a test case for counting stats and raw volume statistics not telling the full story.

If you’ve been following for me for any length of time you know that I’ve put together some advanced statistics for rugby league, as using raw numbers mean players who spend the whole 80 minutes on the field usually dominate. If you’ve not read them, I’d recommend checking out my articles on Run %, Tackle % and Involvement Rate on the website, as they’re all incredibly useful in identifying high

But back to the topic at hand. It’s lazy analysis to only use counting stats without context but is more palatable to the wider viewing audience so I’m not going to deride them for dishing up what the consumer wants and easier to digest in small doses.

Source: Fox Sports Stats

Comparison of raw numbers – Cleary seems ahead. More passes, more runs, more kicks, more attacking kicks, more tries, more try contributions, more line breaks and more line engagements. Johnson is only ahead in try assists (20 to 14) and weighted kicks (17 to 10).

Per game stats will give a slightly better comparison, although it paints a better picture for Cleary who has only played 12 games compared to Johnson’s 14.

When players are compared on the usual pregame shows, it’s assumed that all players in a certain position play a similar game or similar role within a team, leading to scorching hot takes like this on social media from mid-June:

And if you look at the raw volume or counting statistics at that time without any context you’d probably agree – Johnson isn’t impacting the game as much as Cleary is.

But there’s one variable that’s not usually discussed (although the wonderful Jason Oliver pointed it out in his Round 15 preview on SportsTechDaily, another must read each week), is possessions. The amount of times a player gets his hands on the ball plays a massive part in his statistical output. The basketball adage of “you can’t rebound the ball out of the basket” can be applied here with a twist, you can’t do more in attack in rugby league without the ball in your hands.

For the season Cleary has 964 possessions, compared to just 733 for Johnson, a difference of 231 possessions. On a per game basis, that’s roughly 73 possessions for Cleary and 52 for Johnson. Cleary has his hands on the ball nearly 30% more than Johnson on a per game basis.

Knowing this, what if we looked at the same statistics again for Cleary and Johnson on a per possession basis. Would it show anything?

Source: Fox Sports Stats

Not initially, as those numbers are essentially meaningless – 0.016 line break assists or 0.187 line breaks per possession isn’t really meaningful. You can’t create 20% of a line break.

Instead we’ll normalise it to take out any bias that having more possesions per game creates. I’m going to pick a set number of possessions in between both players to even things out. The number doesn’t matter so much as long as we use the same number for both, and for this exercise I’m going to use 60 per game, since it’s a nice round number that falls between both Cleary and Johnson’s season average.

Source: Fox Sports Stats

Now we can see that they’re not performing that differently. Cleary has an edge with kicking, especially long kicks, whilst Johnson leads on weighted kicks and try assists. Yet for all the calls that Johnson needs to run the ball more, his runs, passes and line engagements are very similar to Cleary’s. Does he really need to “do more”?

That leads into the other part of player this analysis their positioning and their role. As mentioned above it’s assumed that all #6s and all #7s should play identically but this is rarely the case.

This was shown in a great article by Jack Snape from the ABC showing the locations NRL halves are receiving the ball. If you apply the Eye Testtm during Sharks games you’d know that Johnson sticks primarily to the right side of the field, while Cleary tends to operate on both sides. The above article shows this, with the locations of Cleary’s touches coming evenly across the field whilst Johnson’s are mostly on the right. You could then argue that if Johnson had a similar level of freedom as Cleary, he would probably increase his raw statistics.

The other part of the role is not just what side they’re playing on but how often they’re involved and relied upon for their team. Cleary takes about 16% of the Panthers total possessions, makes 36% of their passes, 75% of their kicks, 66% of attacking kicks and 44% of line engagements. Johnson on the other hand, takes 12.6% of Sharks possessions, 29% of their kicks, only 52% of their kicks and 45% of attacking kicks.

This again ties back to role. Similar to a sport like Formula One, the Panthers play with a clearly defined lead half in Cleary, with Jarome Luai supporting him. The Sharks more often than not play with their halves on a closer to equal footing, a 1a/1b type scenario, with each either sticking to their side of the field or sharing in the playmaking duties.

Asking Johnson to “do more” or run the ball more won’t necessarily help his game. More stuff isn’t always better. Johnson will turn 30 in a few weeks and it could be that he doesn’t have the same explosiveness and has developed more as a player is now picking his spots and interjecting himself at the right time. Just because he’s not shredding teams anymore with one of his explosive runs and making defenders look as if their feet are stuck in cement doesn’t mean he’s not impacting games.

Whatever the reason, the outcome of this specific comparison of Cleary and Johnson is that they’re both having amazing seasons for their team, and the difference in their statistical output is simply down to the role they play for their side, which can be accounted for by looking at a per possession basis.

Claiming one is better based on one number or a group of counting statistics won’t prove anything other than teams and players are different and may play different styles. Hopefully we’ll see some more analysis based on possessions than just counting stats moving forward.

Cover image – “Nathan Cleary” by NAPARAZZI is licensed under CC BY-SA 2.0

NRL 2020 – how the game changed statistically this season

The NRL 2020 regular season has drawn to a close and much like the Brisbane Broncos it is time to get to the bottom of it. Statistically that is.

There were some significant rule changes this season that have impacted the way teams are playing. The biggest ones were the move to one referee and the introduction of six again calls for ruck infringements, which I’ve gone over extensively before.

The other less talked about one was the change to provide attacking players the same protection under high kicks that defensive players have received. This change has certainly played a part in how teams are attacking at the end of sets, with a sizable increase in kicks aimed between 1-10 metres out from the try line instead of trying to place the ball in the in goal area. It has also resulted in some teams, most notably the Melbourne Storm, just running the ball on the last tackle to hand it over a few metres out, rather than have a mistimed kick result in a seven tackle set from the twenty metre line.

Just how much did those rule changes affect the way the game looked statistically? To check how much things have changed, we’re going to look at the percentage increase on a per game basis from the average of all 25 rounds in 2019 to 2020 from Round 3 to 20, for groups of publicly available statistics from Fox Sports.

If you read my breakdown of the Warriors statistical improvement under Todd Payten from earlier in the season, you’ll know why I’m just using the percentage change and not the raw number change. If not, then the reason is that it’s hard to show per game shifts in statistics with massively different ranges. You can show the change in run metres from 2800-2900 per game, but you would never see the change in average metres per run from 8.92 to 8.85. To deal with that I am purely looking at the percentage change, which for most statistics is in the single figures to low double figure range, which allows for a greater distinction of change.

Now we’ve defined what we’re looking at, what changed under V’Landysball in 2020? And by how much? Turns out quite a bit.

Time in play

The biggest change was time in play. With around 22% fewer penalties being called, the ball wasn’t sitting idle as long and time in play jumped by 6% (golden point games excluded from each season). You can see the round by round breakdown and three round rolling average (orange line) below.

The average time in play increaed by 6% from 54.15 minutes to 57.73 minutes. A lot can happen in three minutes in the NRL, although from the statistics below most of it seems to be middle forwards running the ball. The interesting thing was the decline in time in play late in the season. Rounds 3-12 had an increase in time in play of 9.6%. Yet Rounds 12-20 only had a 1.6% increase. Something to investigate…

Possession

Moving on, these rule changes had a flow on affect to practically every other single statistic in the game. More time in play means more possession. More possession means more runs. More runs mean more run metres. Which leads to more kicks at the end of sets. More tackles need to be made. And so forth.

It does mean that anyone averaging a “career high” this season that is less than a 4-6% increase is probably not having a career high if you adjust 2020 stats to be in line with 2019. That doesn’t mean they haven’t had a career season by effort, just that their numbers are slightly inflated and not completely comparable to previous season without adjustment.

% change in possession statistics, 2019 vs 2020

Looking at the above chart, the orange data points show the 2020 percentage change, and you can see that average sets per game are up nearly 8%, as are average play the balls (+7.47%) but average tackles per set is basically flat (down 0.4%). This is no surprise with the increase in early kicks seen this season.

Completion rate also increased slightly, up 2.4% to 78.3% per game from 76.5% last season, pointing to a slightly more conservative approach despite the increase in tries and points scored.

Penalties and set restarts

As you’d expect with the significant rule change this season, here’s where you see the most changes and it’s had a flow on effect to other parts of the game. There was also the removal of one referee.

Penalties declined by 22% with the introduction of set restarts. This led to a large number of them being called in the first half and a one third drop to the second half as seen below.

Consistency of set restart calls in the second half has been an issue for the second half of the season. We had a run of 6-7 rounds where games had zero set restarts in the second half, and then we had Round 19 where three of the four highest second halves for set restarts occurred. It’s something that needs to be tightened up for 2021.

Scoring and passing

% change in scoring and passing statistics, 2019 vs 2020

Thanks to some high scoring final rounds of the regular season, scoring increased almost in line with the increase in time in play or possession, increasing 5.78% per game to 41.74 points per game, up from 39.5 per game last season.

Tries were up 9.78%, goal attempts were up 1.8% while goal makes were down 2.1%. Part of this is probably linked to the decline in penalties with set restarts being introduced, as penalty goal attempts are down 32% this season on the back of penalties awarded declining by 22%. As penalty attempts were usually taken in positions where the goal kicker was likely to succeed, it makes sense that the overall percentage would decline.

Line breaks increased at a lower rate than tries, at just 4.72%, which makes sense after you read the kicking analysis.

It is interesting that the increase in possession and time in play hasn’t led to an increase in general passes, which were only up 1%. We’ll see why though in the analysis of runs and running metres. Offloads were down by 5%, again supporting a theory that coaches were playing a more conservative game. That is also shown with errors being basically flat on last year, from 21.7 to 21.5 per game.

Running and metres

As you’d expect with more time in play there’s been an increase in runs, up 4.6% and total run metres are up 3.8%. The interesting thing with run metres is how we’ve arrived at that increase. Post contact metres are up 3%, while pre contact metres are up 5.4%, leading to a decline in average metres per run from 8.92 to 8.85. It’s not a lot but when you consider there’s been over 50,000 runs this season it does add up.

The type of runs has seen a big change as well, with dummy half runs down just over 9% on last year, dropping from 17.6 to 16.0 per game. This has mostly moved to one pass runs, a standard hit up, which has increased 7.3% to 155 per game from 145 per game in 2019. These numbers would explain the higher increase in pre contact metres and a slightly lower metres per run, and the decline in passing and offloads as mentioned above.

Again, this points to a more conservative approach, which again leads into the next section. Set restarts are also having an effect here, as when the tackle count restarts, they continue to push the ball through the middle.

Kicking stats

% change in kicking statistics, 2019 vs 2020

One of the biggest changes this season was in kicking type. Overall kicks have increased by a similar amount to runs and run metres, up 3.9% to 18.2 per game. When you drill down into that, you can see that long kicks are up 11.5% and attacking kicks have increased by 5.7%, while weighted kicks are down by a huge 25%, which you can see by the orange data point on the left. I’ll quote my theory from the Round 14 Notes and Trends post as to why that is the case.

“Here’s my crackpot theory – teams have gotten more efficient and accurate at aiming their attacking kicks just outside the goal area to avoid a seven tackle set. The rule change which came into effect in Round 1 that gives airborne attacking players the same level of protection as airborne defensive players is surely a driver for this, as Daniel Tupou was showing before succumbing to injury.

This explains the drop in weighted kicks but the large increase in attacking kicks. Fewer kicks reaching the in-goal area leads to fewer dropouts which can take up to 45 seconds each. By aiming them a bit shorter than the try line, at worst a team will give up possession less than 10 metres out or a scrum at the same point. This is a much better result than a seven-tackle set from the 20 metre line.”

The Melbourne Storm were one of the teams driving this drop in weighted kicks, as I noted earlier in the season. They were quite happy to run out the ball on the final tackle and ensure their defense was set well inside their opponents 20 metre zone.

There’s been a decline in forced dropouts as well, down from 3.33 to 3.28 (-1.5%), and fewer kicks going dead (-6.9%) which also supports this trend. The reduction in dropouts taken has also led to part of the increase time in play, it’s not purely the cause of set restarts.

I mentioned before that line breaks hadn’t increased at the same rate as tries, and one of my theories is that there are more tries being scored from attacking kicks, which aren’t awarded any line breaks.

Defensive stats

% change in defensive statistics, 2019 vs2020

Not a lot has changed in the few defensive statistics publicly available, with tackles up just 3.2%, and missed tackles climbing by 2.5%. Tackle efficiency, which is a cautious stat to be using in the first place, barely changed, sitting at 92.41% last year and 92.46% this year, which is why you can’t see the orange data point.

When you bring all these small changes together, it shows a change in the way the game has been played. We’ve seen more ball in play thanks to the change to set restarts for ruck infringements. This has led to an increase in hit ups through the middle of the field, and a further neutering of dummy half running. The law of unintended consequences led to conservative one out running with little ball distribution becoming a larger part of the game.

This has been offset by the dramatic change in kicking profiles, with teams favouring attacking kicks within the 10 metre zone. This has come at the expense of short weighted kicks that are aimed to sit up in the in-goal area and draw repeated sets of six. Coaches have become even more risk adverse, happier to hand over the ball a few metres out instead of potentially giving up a seven-tackle set from the twenty metre line.

Given the gravity of the changes made this season, hopefully we’ll see a more nuanced approach to rule amendments in 2021.

Final set restart update

Hopefully, this is the last time I have to write something about set restarts for at least a month (*notes Grand Final date*). After Round 20, we had one of the lowest numbers of total infringements called since Round 4, with a penalty or set restart being called approximately every 22 play the balls.

On the positive side, there was a bit more consistently among whistleblowers this round, even with the wacky rule changes that were being used. I would have bet my house that Andrew Gee would have given at least 15 once they let him call them for offside.

And that restraint has ensured that Gee didn’t finish the season with an average of 10 or more set restarts called per game. He did manage to call four more per game than Chris Sutton though. There’s always next season Andrew.

Final Error Rate update

I’ve been posting Error Rate updates throughout the season, and with the regular season finished it’s a good time to reflect on 2020 and see who had the worst hands in the NRL this year.

I had planned to put more than a two-game minimum to qualify for this list, but with Nene McDonald making six in just two games, I stopped at a minimum of three errors required. McDonald’s rate of an error every five times he touched the ball and three every 80 minutes is horrific.

Only slightly less horrific is North Queensland’s Shane Wright at one every 7.8 possessions and the Tigers Asu Kepaoa at 8.3 possessions per error.

Not that it seems to be causing the Roosters many problems, but Josh Morris the highest profile name on this leaders list, with an error every 9.88 possessions. Of 28 NRL players who have made at least 20 errors, only one of them has fewer possessions. That would be another Tigers back, Tommy Talau, who has 20 errors in 206 possessions for a rate of 10.3.

Final NPRF update

And finally, as this is the last (regular) post for the season we’ll finish on a high with the full season look at Net Points Responsible For (NPRF).

Nathan Cleary hangs on to first place at +9.72 net points per game responsible for. Luke Keary and Shaun Johnson round out the top three, but the big story is Cody Walker charging into fourth spot after his amazing game against the Roosters on Friday.

Jarome Luai has also had a fantastic month to close the season and takes fifth spot at +6.0 NPRF per game, equal with Cameron Smith and Jahrome Hughes. You can see the impact AJ Brimson has had for the Gold Coast as well, averaging 4.0 NPRF per game.

Here’s the bottom 20 for the season with a minimum of five games played.

Brisbane’s Jesse Arthars holds the worst NPRF per game this season, giving up 5.33 points per outing. Manly’s Albert Hopoate (-4.80) and the Bulldogs Christian Crichton (-4.50) make up the remainder of the bottom three.

Eels fans won’t be surprised to see Blake Ferguson sitting on this list either, especially after his defensive lapses against the Tigers, at a stone cold -3.16 per game.

NRLW Advanced Stats – 2018 and 2019 seasons

If you’re a regular reader of the Eye Testtm, you’ll be (somewhat) familiar with the advanced statistics I use to analyse players throughout and across each NRL season. And now, they’re also available for previous NRLW seasons.

What do these statistics show? The issue is that generally middle forwards don’t play big minutes or put up big numbers and go unnoticed besides the odd comment about how much of an impact they make. To do so I created three advanced statistics for rugby league – Tackle %, Run % and Involvement Rate.

  1. Tackle % estimates the percentage of opponent plays whilst on field where a player completed a tackle.
  2. Run % estimates the percentage of team plays where a player completed a run during their time on field.
  3. Involvement Rate combines them and estimates the percentage of total plays a player completed a run or tackle whilst on the field.

If you want to read more about them, I’ve linked the explanations of them from the site.

Now I’ve explained them, let’s see how NRLW positions compare for these statistics to their NRL equivalents and look back at some top performers within each statistic from the past two NRL seasons. I would like to note that we’re dealing with some very small sample sizes, and even with a lenient minute restriction of 40 minutes I would still take these as indicative rather than representative of performances. Also, “current team” may also mean “previous team” for someone not playing NRLW currently, which is one of the issues looking at multiple seasons of data at once. Anyway, lets move on to the analysis.

Tackle %

There’s not a huge difference between the NRL and NRLW for Tackle %. Hookers and Locks are making tackles at the same rate, around 25-25%, indicating they complete a tackle on one in four defensive plays. Props and interchange players still sit over 20%, but around 4% lower than their NRL counterparts. This could indicate that a lot of the basic hit up work could be centered less on the middle of the field.

Positions on either edge of the field are similar as well, other than Second Row being down over 2% and Five Eight being up almost 2%, which given that they defend in the same spot could cancel each other out. Fullback is slightly higher as well at 4% for NRLW compared to 2.8% for NRL.

Who are the top NRLW players for Tackle % then? Below are the top 15 players by Tackle % for seasons 2018 & 2019 who played at least 40 total minutes.

Kate Haren formely of the Dragons leads the way with a Tackle % of 43% from her two games, indicating that she made a tackle on two out of every five defensive plays whilst on the field. Aliti Namoce who last plaeyd for Roosters placed second at 31.45% and Talesha Quinn who also last played for he Dragons came in third at 31.32%.

Rebecca Young from the Roosters was the only other player to have a tackle rate above 30%. As noted above, the average Tackle % for middle forwards is about 20-25% so each of these players are tackling well above average for their position.

One of the interesting differences here compared to the NRL is seeing Second Rowers at the top of this list, which is usually just middle forwards for the NRL. In addition to Namoce, Lorina Papali’I (29.38%) and Holli Wheeler (25.93%) also make the top 15 whilst playing in the second row. Again, the average for second rowers is 16.2%, which puts Namoce’s tackle rate almost twice as high the average second rower.

Run %

Looking at the average Run % across positions for the NRLW against the NRL, there’s not as much variance as there was for Tackle %. Differences fall between 0.5% to 1% for most positions, and the only significant change is at lock, where NRL players make a run on 10% of plays, whilst NRLW locks make a run on 7%.

So, who has the highest run rate among all NRLW players who played at least 40 minutes across 2018 & 2019?

Ngatotokotoru Arakua takes first place with a Run % of 20.92%, meaning she completes a run on at least two out of every five plays the Dragons used the ball. Second place is Chloe Caldwell from the Roosters at 20.45%, the only other player above 20%. Another former Rooster in Elianna Walton picks up third place with a run rate of 17.99%.

With the average Run % for middle forwards sitting in the 10-12% range, anyone over 15% is putting in an elite amount of work.

It’s also worth noting that new Dragons signing Isabelle Kelly at centre was extremely close to making this top 15, sitting only 0.15% outside with a run rate of 12.04% which is exceptional for a centre.

Involvement Rate

Given that Involvement Rate is a combination of Tackle % and Run %, it would make sense that any changes we saw in the previous two statistics would be reflected here as well.

Involvement Rates for middle forwards are slightly down as was exhibited with Tackle % rates. NRLW Prop forwards suffer the biggest drop at 2% compared to their NRL counterparts, otherwise things are relatively consistent.

The top 15 players in the NRL for Involvement Rate from the 2018 and 2019 seasons who played at least 40 minutes are shown below.

Kate Haren takes top spot here, as she did with tackle %, with an Involvement Rate of 26.37%, meaning she completed a run or a tackle on one in every four plays whilst she was on the field. Ngatotokotoru Arakua came in second with an Involvement Rate of 21.97% while Chloe Caldwell rounded out the top three with an Involvement Rate of 21.42%.

Brisbane halfback Tarryn Aiken is also worth mentioning, sitting in 19th place with an involvement Rate of 17.25% in a list that is dominated by middle forwards. This is mainly due to her running game, as she sits just outside the leaders in Run % with a run rate of 10.23%.

NRL Round 20 advanced stats – Involvement Rate

Involvement Rate is an advanced statistic for rugby league that I created to identify players who have a high workload but don’t play a lot of minutes. If you’re new to the site and want to understand how it works, I would recommend reading this post on Involvement Rate.

With that out of the way, here’s the all minutes leaders for Round 20

It’s the single digit crew again after these three took over the Run % chart. The Raiders Jarret Subloo sits first (49.23%) ahead of Jake Friend (33.97%) and Daniel Alvaro (28.66%).

The highest double figure minutes player was Aaron Woods at 27.77%.

Next, we’ll look at those players who spent 40 minutes or more on field

Alex Twal takes first place in Involvement Rate after taking first in Tackle % as well. This time his Involvement Rate was 26.48% meaning he made a tackle or run in over a quarter of all plays whilst on field for the Tigers against the Eels.

James Tamou from Penrith placed next with a rate of 24.61% and Christian Welch was next with an Involvement Rate of 22.61%. Nathan Brown was the only 80 minute player on the leader board this week with an Involvement Rate of 19.11% in his full game.

There’s that Darren Schonig again, popping up at the bottom of the leader board with 18.61% in 44 minutes. Always a good sign when a high workload player can maintain that in extended minutes.

Finally, we have the leaders for Involvement Rate this season.

No changes here with Jaimin Jolliffe snaring first place for the season with an Involvement Rate of 21.93%. A huge effort for the rookie and things continue to look up for the Titans in 2021. Despite not playing, he managed to hold off Jai Whitbread who again snuck into the top three after not qualifying with enough minutes beforehand, with a rate of 21.79%.

Third place went to the Warriors Jazz Tevaga at 21.39%, however he was only 0.02% ahead of the Dragons Blake Lawrie and 0.03% ahead of Melbourne’s Christian Welch.

NRL Round 20 advanced stats – Run %

For those new to the site, I’d recommend reading this post on Run % which details how it is calculated and how to use it.

Here’s the leading players in the NRL after Round 20 without a minute restriction

Canberra rookie Jarrett Subloo sneaks into first with two runs in one minute for a Run % of 93.02% and a lesson in small sample sizes. Eye TestTM Hall of Famer Daniel Alvaro placed second with 23.81% in his active eight minutes on field as the Eels ran down the Tigers. Jake Friend completed the single digit minute trifecta with a Run % of 22.22% in his three minutes. Again, how good are small sample sizes?

Moving on, let’s look at the 40 minute plus players for this round.

Another raider nabs first here with Dunamis Lui capping off his strong 2020 campaign with a Run % of 18.43% in the Raiders win over Cronulla. Second place went to the Warriors Lachlan Burr at 17.74% with James Tamou placing third at 17.67%.

Two Roosters had the only 80 minute games in the leader board this week, both backs with James Tedesco and Daniel Tupou sporting the same Run % (15.00%).

To finish up we’ll take a look at the 2020 season leaders for Run %.

Unlike with Tackle %, there was no last minute change in the order. Cronulla’s Andrew Fitifa takes the crown here with a Run % of 17.34% for the season. Second place was Melbourne’s Nelson Asofa-Solomona at 16.50% and Parramatta’s Kane Evans in third place with a run rate of 16.35%.

No other player managed a run rate above 15% for the season, showing just how far ahead these three were from the rest of the league.