The NRL 2021 Eye Test Advanced Statistic leaders

With the 2021 regular season has drawing to a close it means we can take a look at who were the Eye Test’s advanced statistical leaders for the season. If you’re not aware of these advanced stats, I’d recommend a look at the glossary page for a quick rundown, and there’s further reading on Tackle %, Run % and Involvement Rate as well.

Before we start, I want to explain the minimum thresholds for these advanced statistics. For the first three (Tackle %, Run % and Involvement Rate) the cut off is having played 25% of games which is six appearances this season (last season it was five from a 20 game season). In addition to six games, there’s also a minimum 200 minutes played threshold to remove any players who have played enough games but not enough minutes to generate a decent sample size on the field.

By combining the games and minutes it eliminates players who don’t appear enough for decent sample size like Manly’s rookie Kurt De Luis who played the required 6 games but only 125 minutes (20.8/game) and I’d like to see a bigger sample size. It also removes players who may have played 200 plus minutes but in 4 or 5 games and had a few big games skew their data.

In the end the cut offs are mostly arbitrary however I feel they do a good job of recognizing the type of player these statistics were created to showcase.

For Net Points Responsible For (NPRF) and Error Rate the 25% cut off remains, however there is no minute restriction. In fact, NPRF only has the six game requirement, as low minute players don’t tend to feature. For Error rate, the only other minimum qualification is committing at least six errors. Again this number is subjective but passes the eye test for most cases.

If you’re curious as to what makes a good Tackle %, Run % or Involvement Rate, the average rates are shown below. Generally, they favour interchange middle forwards, but you do occasionally see some hookers and backrowers in the charts, although not typically for a full season.

The leaders below are all significantly beating these averages, making them elite by these metrics. On to the 2021 leaders. 

Tackle %

Billy Magoulias of the Sharks takes top spot for Tackle Rate in 2021 at 34.12%, meaning he is completing a tackle on at least three out of every play the balls Cronulla faces whilst he is on field. Second place goes to Reuben Cotter of the Cowboys at 33.77%, who may have taken top spot if he hadn’t spent the majority of the season out with a severe foot injury.

Rounding out the top three is Eye Test Hall of Famer Daniel Alvaro from the Dragons, with his usual high output at 31.43%. Alvaro would have taken the 2020 crown but only played 154 minutes during the season.

Previous winners:

2020 – Jai Whibread, Gold Coast (39.88%)

2019 – Daniel Alvaro, Parramatta (34.17%)

2018 – Daniel Alvaro, Parramatta (37.43%)

2017 – Daniel Alvaro, Parramatta (38.04%)

2016 – Siliva Havili, St George Illawarra (34.66%)

2015 – Christian Welch, Melbourne (42.88%)

2014 – Tim Robinson, Cronulla (36.65%)

Run %

Panthers impact sub Spencer Leniu held on after blitzing this statistic at the start of the season, finishing with a run rate of 15.98%, and one of just four qualified players in the NRL who had a rate higher than 15%. A pair of South Sydney forwards picked up the second and third spots, with Mark Nicholls (15.71%) and Liam Knight (15.51%) following Leniu. The only other player above 15% was Manly’s Josh Aloai at 15.35%.

Previous winners:

2020 – Andrew Fifita, Cronulla (17.34%)

2019 – Corey Horsburgh, Canberra (16.09%)

2018 – Martin Taupau, Manly (16.29%)

2017 – Nathaniel Peteru, Gold Coast (18.77%)

2016 – Jeff Lima, Canberra (18.60%)

2015 – Paul Vaughan, Canberra (16.98%)

2014 – David Klemmer, Canterbury (18.49%)

Involvement Rate %

The number one player in the NRL this season for Involvement Rate was Canberra’s (by way of Belmore) Corey Horsburgh, who ended the season at 22.68%. This means he completed a tackle or run on more than one in five possessions during a game this season, one of just seventeen NRL players to have a rate higher than 20%.

Taking the runner up spot for a second time is Cotter, with an Involvement Rate of 22.37%. This place him ahead of third place in Tackle %, with Alvaro sitting at 22.30%. Last year’s winner, Jaimin Jolliffe followed up his impressive rookie season by playing 34 minutes a game in all 24 of the Titan’s contests and finished fifth for the season with a rate of 21.56%.

Previous winners:

2020 – Jaimin Jolliffe, Gold Coast (28.04%)

2019 – Daniel Alvaro, Parramatta (23.59%)

2018 – Daniel Alvaro, Parramatta (25.85%)

2017 – Daniel Alvaro, Parramatta (24.21%)

2016 – Siliva Havili, St George Illawarra (34.66%)

2015 – Christian Welch, Melbourne (25.27%)

2014 – Tim Robinson, Cronulla (24.07%)

Net Points Responsible For

No surprises here with Tom Trbojevic’s devastating season topping the ladder here. The Manly #1 was responsible for +196 net points for his side, averaging out at +13.1 per game in his 15 appearances. Considering the Sea Eagles were +252 themselves, you could argue that Trbojevic was responsible for about 78% of their difference.

Again no surprises that Nathan Cleary was second, who was having his own amazing season at +12.4 net points per game, but was overshadowed by just how dominant Trbojevic was. In another non-surprise, third place was taken by Souths’ five eighth Cody Walker, who ended up with 37 try assists for the season and a net +9.27 points added per game.

Trbojevic’s +13.1 smashes the previous best of +9.7 by Cleary in 2020 by over 3 points per game, and Cleary himself beat his own record by nearly +3 points per game as well. Walker’s season was no slouch either, and is fifth overall since 2014.

Previous winners:

2020 – Nathan Cleary, Penrith (+9.72)

2019 – Luke Keary, Sydney (+8.63)

2018 – Cody Walker, South Sydney (+6.50)

2017 – Cooper Cronk, Melbourne (+8.21)

2016 – Mitchell Pearce, Sydney (+9.67)

2015 – Johnathan Thurston, North Queensland (+7.62)

2014 – Johnathan Thurston, North Queensland (+7.82)

Error Rate

The award for worst hands this season has been won by Panthers outside back and future Canterbury Bulldog Brent Naden, who committed an error every 7.9 times he touched the ball.

Naden was followed by Newcastle winger Dominic Young, who committed an error every 8.3 possessions, with another Panther in Izack Tago rounding out the top three with an error rate of one every 8.5 times he handled the ball.

Roosters fans will not be shocked to see Matt Ikuvalu on this list, having committed 27 errors this season at a rate of one every 11.65 possessions, the worst of players with at least 200 touches this season. He barely beat out yet another Panther in Viliame Kikau who had a rate of one every 11.89 possessions.

Tigers rookie Zac Cini would have topped this list, as he committed nine errors at a rate of one every 6.2 touches, but he only played four games this season. That may be a reason why.

Previous winners:

2020 – Shane Wright, North Queensland (7.75 possessions/error)

2019 – Lindsay Collins, Sydney (7.89 possessions/error)

2018 – Will Matthews, Gold Coast (7.89 possessions/error)

2017 – Joe Wardle, Newcastle (9.33 possessions/error)

2016 – Corey Denniss, Newcastle (7.36 possessions/error)

2015 – Blake Ferguson, Sydney (8.57 possessions/error)

2014 – Kyle Feldt, North Queensland (7.06 possessions/error)

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 rugby league 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.